2025-12-17 08:50:15 +01:00
2443 changed files with 45434 additions and 433133 deletions
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@ -1,5 +0,0 @@
 FROM node:18-bullseye
 RUN useradd -m -s /bin/bash vscode
 RUN mkdir -p /workspaces && chown -R vscode:vscode /workspaces
 WORKDIR /workspaces
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@ -1,18 +0,0 @@
 {
  "dockerComposeFile": "docker-compose.yml",
  "service": "app",
  "workspaceFolder": "/workspaces",
  "customizations": {
    "vscode": {
      "extensions": [],
      "settings": {
        "terminal.integrated.profiles.linux": {
          "bash": null
        }
      }
    }
  },
  "postCreateCommand": "",
  "features": { "ghcr.io/devcontainers/features/git:1": {} },
  "remoteUser": "vscode"
 }
--- a/.devcontainer/docker-compose.yml
+++ b/.devcontainer/docker-compose.yml
@ -1,62 +0,0 @@
 services:
  app:
    build:
      context: ..
      dockerfile: .devcontainer/Dockerfile
    # restart: always
    links:
      - mongodb
      - meilisearch
    # ports:
    #   - 3080:3080               # Change it to 9000:3080 to use nginx
    extra_hosts: # if you are running APIs on docker you need access to, you will need to uncomment this line and next
    - "host.docker.internal:host-gateway"
    volumes:
      # This is where VS Code should expect to find your project's source code and the value of "workspaceFolder" in .devcontainer/devcontainer.json
      - ..:/workspaces:cached
      # Uncomment the next line to use Docker from inside the container. See https://aka.ms/vscode-remote/samples/docker-from-docker-compose for details.
      # - /var/run/docker.sock:/var/run/docker.sock 
    environment:
      - HOST=0.0.0.0
      - MONGO_URI=mongodb://mongodb:27017/LibreChat
      # - OPENAI_REVERSE_PROXY=http://host.docker.internal:8070/v1
      - MEILI_HOST=http://meilisearch:7700
    # Runs app on the same network as the service container, allows "forwardPorts" in devcontainer.json function.
    # network_mode: service:another-service
    # Use "forwardPorts" in **devcontainer.json** to forward an app port locally. 
    # (Adding the "ports" property to this file will not forward from a Codespace.)
    # Use a non-root user for all processes - See https://aka.ms/vscode-remote/containers/non-root for details.
    user: vscode
    # Overrides default command so things don't shut down after the process ends.
    command: /bin/sh -c "while sleep 1000; do :; done"  
  mongodb:
    container_name: chat-mongodb
    expose:
      - 27017
    # ports:
    #   - 27018:27017
    image: mongo
    # restart: always
    volumes:
      - ./data-node:/data/db
    command: mongod --noauth
  meilisearch:
    container_name: chat-meilisearch
    image: getmeili/meilisearch:v1.5
    # restart: always
    expose:
      - 7700
    # Uncomment this to access meilisearch from outside docker
    # ports: 
    #   - 7700:7700 # if exposing these ports, make sure your master key is not the default value
    environment:
      - MEILI_NO_ANALYTICS=true
      - MEILI_MASTER_KEY=5c71cf56d672d009e36070b5bc5e47b743535ae55c818ae3b735bb6ebfb4ba63
    volumes:
      - ./meili_data_v1.5:/meili_data
--- a/.dockerignore
+++ b/.dockerignore
@ -1,17 +1,2 @@
 **/.circleci
 **/.editorconfig
 **/.dockerignore
 **/.git
 **/.DS_Store
 **/.vscode
 **/node_modules
-
+**/.env
 # Specific patterns to ignore
 data-node
 meili_data*
 librechat*
 Dockerfile*
 docs
 # Ignore all hidden files
 .*
--- a/.env.example
+++ b/.env.example
@ -1,782 +0,0 @@
 #=====================================================================#
 #                       LibreChat Configuration                       #
 #=====================================================================#
 # Please refer to the reference documentation for assistance          #
 # with configuring your LibreChat environment.                        #
 #                                                                     #
 # https://www.librechat.ai/docs/configuration/dotenv                  #
 #=====================================================================#
 #==================================================#
 #               Server Configuration               #
 #==================================================#
 HOST=localhost
 PORT=3080
 MONGO_URI=mongodb://127.0.0.1:27017/LibreChat
 #The maximum number of connections in the connection pool. */
 MONGO_MAX_POOL_SIZE=
 #The minimum number of connections in the connection pool. */
 MONGO_MIN_POOL_SIZE=
 #The maximum number of connections that may be in the process of being established concurrently by the connection pool. */
 MONGO_MAX_CONNECTING=
 #The maximum number of milliseconds that a connection can remain idle in the pool before being removed and closed. */
 MONGO_MAX_IDLE_TIME_MS=
 #The maximum time in milliseconds that a thread can wait for a connection to become available. */
 MONGO_WAIT_QUEUE_TIMEOUT_MS=
 # Set to false to disable automatic index creation for all models associated with this connection. */
 MONGO_AUTO_INDEX=
 # Set to `false` to disable Mongoose automatically calling `createCollection()` on every model created on this connection. */
 MONGO_AUTO_CREATE=
 DOMAIN_CLIENT=http://localhost:3080
 DOMAIN_SERVER=http://localhost:3080
 NO_INDEX=true
 # Use the address that is at most n number of hops away from the Express application.
 # req.socket.remoteAddress is the first hop, and the rest are looked for in the X-Forwarded-For header from right to left.
 # A value of 0 means that the first untrusted address would be req.socket.remoteAddress, i.e. there is no reverse proxy.
 # Defaulted to 1.
 TRUST_PROXY=1
 # Minimum password length for user authentication
 # Default: 8
 # Note: When using LDAP authentication, you may want to set this to 1 
 # to bypass local password validation, as LDAP servers handle their own
 # password policies.
 # MIN_PASSWORD_LENGTH=8
 #===============#
 # JSON Logging  #
 #===============#
 # Use when process console logs in cloud deployment like GCP/AWS
 CONSOLE_JSON=false
 #===============#
 # Debug Logging #
 #===============#
 DEBUG_LOGGING=true
 DEBUG_CONSOLE=false
 #=============#
 # Permissions #
 #=============#
 # UID=1000
 # GID=1000
 #===============#
 # Configuration #
 #===============#
 # Use an absolute path, a relative path, or a URL
 # CONFIG_PATH="/alternative/path/to/librechat.yaml"
 #===================================================#
 #                     Endpoints                     #
 #===================================================#
 # ENDPOINTS=openAI,assistants,azureOpenAI,google,anthropic
 PROXY=
 #===================================#
 # Known Endpoints - librechat.yaml  #
 #===================================#
 # https://www.librechat.ai/docs/configuration/librechat_yaml/ai_endpoints
 # ANYSCALE_API_KEY=
 # APIPIE_API_KEY=
 # COHERE_API_KEY=
 # DEEPSEEK_API_KEY=
 # DATABRICKS_API_KEY=
 # FIREWORKS_API_KEY=
 # GROQ_API_KEY=
 # HUGGINGFACE_TOKEN=
 # MISTRAL_API_KEY=
 # OPENROUTER_KEY=
 # PERPLEXITY_API_KEY=
 # SHUTTLEAI_API_KEY=
 # TOGETHERAI_API_KEY=
 # UNIFY_API_KEY=
 # XAI_API_KEY=
 #============#
 # Anthropic  #
 #============#
 ANTHROPIC_API_KEY=user_provided
 # ANTHROPIC_MODELS=claude-opus-4-20250514,claude-sonnet-4-20250514,claude-3-7-sonnet-20250219,claude-3-5-sonnet-20241022,claude-3-5-haiku-20241022,claude-3-opus-20240229,claude-3-sonnet-20240229,claude-3-haiku-20240307
 # ANTHROPIC_REVERSE_PROXY=
 #============#
 # Azure      #
 #============#
 # Note: these variables are DEPRECATED
 # Use the `librechat.yaml` configuration for `azureOpenAI` instead
 # You may also continue to use them if you opt out of using the `librechat.yaml` configuration
 # AZURE_OPENAI_DEFAULT_MODEL=gpt-3.5-turbo # Deprecated
 # AZURE_OPENAI_MODELS=gpt-3.5-turbo,gpt-4 # Deprecated
 # AZURE_USE_MODEL_AS_DEPLOYMENT_NAME=TRUE # Deprecated
 # AZURE_API_KEY= # Deprecated
 # AZURE_OPENAI_API_INSTANCE_NAME= # Deprecated
 # AZURE_OPENAI_API_DEPLOYMENT_NAME= # Deprecated
 # AZURE_OPENAI_API_VERSION= # Deprecated
 # AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME= # Deprecated
 # AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME= # Deprecated
 #=================#
 #   AWS Bedrock   #
 #=================#
 # BEDROCK_AWS_DEFAULT_REGION=us-east-1 # A default region must be provided
 # BEDROCK_AWS_ACCESS_KEY_ID=someAccessKey
 # BEDROCK_AWS_SECRET_ACCESS_KEY=someSecretAccessKey
 # BEDROCK_AWS_SESSION_TOKEN=someSessionToken
 # Note: This example list is not meant to be exhaustive. If omitted, all known, supported model IDs will be included for you.
 # BEDROCK_AWS_MODELS=anthropic.claude-3-5-sonnet-20240620-v1:0,meta.llama3-1-8b-instruct-v1:0
 # See all Bedrock model IDs here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns
 # Notes on specific models:
 # The following models are not support due to not supporting streaming:
 # ai21.j2-mid-v1
 # The following models are not support due to not supporting conversation history:
 # ai21.j2-ultra-v1, cohere.command-text-v14, cohere.command-light-text-v14
 #============#
 # Google     #
 #============#
 GOOGLE_KEY=user_provided
 # GOOGLE_REVERSE_PROXY=
 # Some reverse proxies do not support the X-goog-api-key header, uncomment to pass the API key in Authorization header instead.
 # GOOGLE_AUTH_HEADER=true
 # Gemini API (AI Studio)
 # GOOGLE_MODELS=gemini-2.5-pro,gemini-2.5-flash,gemini-2.5-flash-lite,gemini-2.0-flash,gemini-2.0-flash-lite
 # Vertex AI
 # GOOGLE_MODELS=gemini-2.5-pro,gemini-2.5-flash,gemini-2.5-flash-lite,gemini-2.0-flash-001,gemini-2.0-flash-lite-001
 # GOOGLE_TITLE_MODEL=gemini-2.0-flash-lite-001
 # GOOGLE_LOC=us-central1
 # Google Safety Settings
 # NOTE: These settings apply to both Vertex AI and Gemini API (AI Studio)
 #
 # For Vertex AI:
 # To use the BLOCK_NONE setting, you need either:
 # (a) Access through an allowlist via your Google account team, or
 # (b) Switch to monthly invoiced billing: https://cloud.google.com/billing/docs/how-to/invoiced-billing
 #
 # For Gemini API (AI Studio):
 # BLOCK_NONE is available by default, no special account requirements.
 #
 # Available options: BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MEDIUM_AND_ABOVE, BLOCK_LOW_AND_ABOVE
 #
 # GOOGLE_SAFETY_SEXUALLY_EXPLICIT=BLOCK_ONLY_HIGH
 # GOOGLE_SAFETY_HATE_SPEECH=BLOCK_ONLY_HIGH
 # GOOGLE_SAFETY_HARASSMENT=BLOCK_ONLY_HIGH
 # GOOGLE_SAFETY_DANGEROUS_CONTENT=BLOCK_ONLY_HIGH
 # GOOGLE_SAFETY_CIVIC_INTEGRITY=BLOCK_ONLY_HIGH
 #============#
 # OpenAI     #
 #============#
 OPENAI_API_KEY=user_provided
 # OPENAI_MODELS=gpt-5,gpt-5-codex,gpt-5-mini,gpt-5-nano,o3-pro,o3,o4-mini,gpt-4.1,gpt-4.1-mini,gpt-4.1-nano,o3-mini,o1-pro,o1,gpt-4o,gpt-4o-mini
 DEBUG_OPENAI=false
 # TITLE_CONVO=false
 # OPENAI_TITLE_MODEL=gpt-4o-mini
 # OPENAI_SUMMARIZE=true
 # OPENAI_SUMMARY_MODEL=gpt-4o-mini
 # OPENAI_FORCE_PROMPT=true
 # OPENAI_REVERSE_PROXY=
 # OPENAI_ORGANIZATION=
 #====================#
 #   Assistants API   #
 #====================#
 ASSISTANTS_API_KEY=user_provided
 # ASSISTANTS_BASE_URL=
 # ASSISTANTS_MODELS=gpt-4o,gpt-4o-mini,gpt-3.5-turbo-0125,gpt-3.5-turbo-16k-0613,gpt-3.5-turbo-16k,gpt-3.5-turbo,gpt-4,gpt-4-0314,gpt-4-32k-0314,gpt-4-0613,gpt-3.5-turbo-0613,gpt-3.5-turbo-1106,gpt-4-0125-preview,gpt-4-turbo-preview,gpt-4-1106-preview
 #==========================#
 #   Azure Assistants API   #
 #==========================#
 # Note: You should map your credentials with custom variables according to your Azure OpenAI Configuration
 # The models for Azure Assistants are also determined by your Azure OpenAI configuration.
 # More info, including how to enable use of Assistants with Azure here:
 # https://www.librechat.ai/docs/configuration/librechat_yaml/ai_endpoints/azure#using-assistants-with-azure
 CREDS_KEY=f34be427ebb29de8d88c107a71546019685ed8b241d8f2ed00c3df97ad2566f0
 CREDS_IV=e2341419ec3dd3d19b13a1a87fafcbfb
 # Azure AI Search
 #-----------------
 AZURE_AI_SEARCH_SERVICE_ENDPOINT=
 AZURE_AI_SEARCH_INDEX_NAME=
 AZURE_AI_SEARCH_API_KEY=
 AZURE_AI_SEARCH_API_VERSION=
 AZURE_AI_SEARCH_SEARCH_OPTION_QUERY_TYPE=
 AZURE_AI_SEARCH_SEARCH_OPTION_TOP=
 AZURE_AI_SEARCH_SEARCH_OPTION_SELECT=
 # OpenAI Image Tools Customization
 #----------------
 # IMAGE_GEN_OAI_API_KEY= # Create or reuse OpenAI API key for image generation tool
 # IMAGE_GEN_OAI_BASEURL= # Custom OpenAI base URL for image generation tool
 # IMAGE_GEN_OAI_AZURE_API_VERSION= # Custom Azure OpenAI deployments
 # IMAGE_GEN_OAI_DESCRIPTION=
 # IMAGE_GEN_OAI_DESCRIPTION_WITH_FILES=Custom description for image generation tool when files are present
 # IMAGE_GEN_OAI_DESCRIPTION_NO_FILES=Custom description for image generation tool when no files are present
 # IMAGE_EDIT_OAI_DESCRIPTION=Custom description for image editing tool
 # IMAGE_GEN_OAI_PROMPT_DESCRIPTION=Custom prompt description for image generation tool
 # IMAGE_EDIT_OAI_PROMPT_DESCRIPTION=Custom prompt description for image editing tool
 # DALL·E
 #----------------
 # DALLE_API_KEY=
 # DALLE3_API_KEY=
 # DALLE2_API_KEY=
 # DALLE3_SYSTEM_PROMPT=
 # DALLE2_SYSTEM_PROMPT=
 # DALLE_REVERSE_PROXY=
 # DALLE3_BASEURL=
 # DALLE2_BASEURL=
 # DALL·E (via Azure OpenAI)
 # Note: requires some of the variables above to be set
 #----------------
 # DALLE3_AZURE_API_VERSION=
 # DALLE2_AZURE_API_VERSION=
 # Flux
 #-----------------
 FLUX_API_BASE_URL=https://api.us1.bfl.ai
 # FLUX_API_BASE_URL = 'https://api.bfl.ml';
 # Get your API key at https://api.us1.bfl.ai/auth/profile
 # FLUX_API_KEY=
 # Google
 #-----------------
 GOOGLE_SEARCH_API_KEY=
 GOOGLE_CSE_ID=
 # YOUTUBE
 #-----------------
 YOUTUBE_API_KEY=
 # Stable Diffusion
 #-----------------
 SD_WEBUI_URL=http://host.docker.internal:7860
 # Tavily
 #-----------------
 TAVILY_API_KEY=
 # Traversaal
 #-----------------
 TRAVERSAAL_API_KEY=
 # WolframAlpha
 #-----------------
 WOLFRAM_APP_ID=
 # Zapier
 #-----------------
 ZAPIER_NLA_API_KEY=
 #==================================================#
 #                      Search                      #
 #==================================================#
 SEARCH=true
 MEILI_NO_ANALYTICS=true
 MEILI_HOST=http://0.0.0.0:7700
 MEILI_MASTER_KEY=DrhYf7zENyR6AlUCKmnz0eYASOQdl6zxH7s7MKFSfFCt
 # Optional: Disable indexing, useful in a multi-node setup
 # where only one instance should perform an index sync.
 # MEILI_NO_SYNC=true
 #==================================================#
 #          Speech to Text & Text to Speech         #
 #==================================================#
 STT_API_KEY=
 TTS_API_KEY=
 #==================================================#
 #                        RAG                       #
 #==================================================#
 # More info: https://www.librechat.ai/docs/configuration/rag_api
 # RAG_OPENAI_BASEURL=
 # RAG_OPENAI_API_KEY=
 # RAG_USE_FULL_CONTEXT=
 # EMBEDDINGS_PROVIDER=openai
 # EMBEDDINGS_MODEL=text-embedding-3-small
 #===================================================#
 #                    User System                    #
 #===================================================#
 #========================#
 # Moderation             #
 #========================#
 OPENAI_MODERATION=false
 OPENAI_MODERATION_API_KEY=
 # OPENAI_MODERATION_REVERSE_PROXY=
 BAN_VIOLATIONS=true
 BAN_DURATION=1000 * 60 * 60 * 2
 BAN_INTERVAL=20
 LOGIN_VIOLATION_SCORE=1
 REGISTRATION_VIOLATION_SCORE=1
 CONCURRENT_VIOLATION_SCORE=1
 MESSAGE_VIOLATION_SCORE=1
 NON_BROWSER_VIOLATION_SCORE=20
 TTS_VIOLATION_SCORE=0
 STT_VIOLATION_SCORE=0
 FORK_VIOLATION_SCORE=0
 IMPORT_VIOLATION_SCORE=0
 FILE_UPLOAD_VIOLATION_SCORE=0
 LOGIN_MAX=7
 LOGIN_WINDOW=5
 REGISTER_MAX=5
 REGISTER_WINDOW=60
 LIMIT_CONCURRENT_MESSAGES=true
 CONCURRENT_MESSAGE_MAX=2
 LIMIT_MESSAGE_IP=true
 MESSAGE_IP_MAX=40
 MESSAGE_IP_WINDOW=1
 LIMIT_MESSAGE_USER=false
 MESSAGE_USER_MAX=40
 MESSAGE_USER_WINDOW=1
 ILLEGAL_MODEL_REQ_SCORE=5
 #========================#
 # Balance                #
 #========================#
 # CHECK_BALANCE=false
 # START_BALANCE=20000 # note: the number of tokens that will be credited after registration.
 #========================#
 # Registration and Login #
 #========================#
 ALLOW_EMAIL_LOGIN=true
 ALLOW_REGISTRATION=true
 ALLOW_SOCIAL_LOGIN=false
 ALLOW_SOCIAL_REGISTRATION=false
 ALLOW_PASSWORD_RESET=false
 # ALLOW_ACCOUNT_DELETION=true # note: enabled by default if omitted/commented out
 ALLOW_UNVERIFIED_EMAIL_LOGIN=true
 SESSION_EXPIRY=1000 * 60 * 15
 REFRESH_TOKEN_EXPIRY=(1000 * 60 * 60 * 24) * 7
 JWT_SECRET=16f8c0ef4a5d391b26034086c628469d3f9f497f08163ab9b40137092f2909ef
 JWT_REFRESH_SECRET=eaa5191f2914e30b9387fd84e254e4ba6fc51b4654968a9b0803b456a54b8418
 # Discord
 DISCORD_CLIENT_ID=
 DISCORD_CLIENT_SECRET=
 DISCORD_CALLBACK_URL=/oauth/discord/callback
 # Facebook
 FACEBOOK_CLIENT_ID=
 FACEBOOK_CLIENT_SECRET=
 FACEBOOK_CALLBACK_URL=/oauth/facebook/callback
 # GitHub
 GITHUB_CLIENT_ID=
 GITHUB_CLIENT_SECRET=
 GITHUB_CALLBACK_URL=/oauth/github/callback
 # GitHub Enterprise
 # GITHUB_ENTERPRISE_BASE_URL=
 # GITHUB_ENTERPRISE_USER_AGENT=
 # Google
 GOOGLE_CLIENT_ID=
 GOOGLE_CLIENT_SECRET=
 GOOGLE_CALLBACK_URL=/oauth/google/callback
 # Apple
 APPLE_CLIENT_ID=
 APPLE_TEAM_ID=
 APPLE_KEY_ID=
 APPLE_PRIVATE_KEY_PATH=
 APPLE_CALLBACK_URL=/oauth/apple/callback
 # OpenID
 OPENID_CLIENT_ID=
 OPENID_CLIENT_SECRET=
 OPENID_ISSUER=
 OPENID_SESSION_SECRET=
 OPENID_SCOPE="openid profile email"
 OPENID_CALLBACK_URL=/oauth/openid/callback
 OPENID_REQUIRED_ROLE=
 OPENID_REQUIRED_ROLE_TOKEN_KIND=
 OPENID_REQUIRED_ROLE_PARAMETER_PATH=
 OPENID_ADMIN_ROLE=
 OPENID_ADMIN_ROLE_PARAMETER_PATH=
 OPENID_ADMIN_ROLE_TOKEN_KIND=
 # Set to determine which user info property returned from OpenID Provider to store as the User's username
 OPENID_USERNAME_CLAIM=
 # Set to determine which user info property returned from OpenID Provider to store as the User's name
 OPENID_NAME_CLAIM=
 # Optional audience parameter for OpenID authorization requests
 OPENID_AUDIENCE=
 OPENID_BUTTON_LABEL=
 OPENID_IMAGE_URL=
 # Set to true to automatically redirect to the OpenID provider when a user visits the login page
 # This will bypass the login form completely for users, only use this if OpenID is your only authentication method
 OPENID_AUTO_REDIRECT=false
 # Set to true to use PKCE (Proof Key for Code Exchange) for OpenID authentication
 OPENID_USE_PKCE=false
 #Set to true to reuse openid tokens for authentication management instead of using the mongodb session and the custom refresh token.
 OPENID_REUSE_TOKENS=
 #By default, signing key verification results are cached in order to prevent excessive HTTP requests to the JWKS endpoint.
 #If a signing key matching the kid is found, this will be cached and the next time this kid is requested the signing key will be served from the cache.
 #Default is true.
 OPENID_JWKS_URL_CACHE_ENABLED=
 OPENID_JWKS_URL_CACHE_TIME= # 600000 ms eq to 10 minutes leave empty to disable caching
 #Set to true to trigger token exchange flow to acquire access token for the userinfo endpoint.
 OPENID_ON_BEHALF_FLOW_FOR_USERINFO_REQUIRED=
 OPENID_ON_BEHALF_FLOW_USERINFO_SCOPE="user.read" # example for Scope Needed for Microsoft Graph API
 # Set to true to use the OpenID Connect end session endpoint for logout
 OPENID_USE_END_SESSION_ENDPOINT=
 #========================#
 # SharePoint Integration #
 #========================#
 # Requires Entra ID (OpenID) authentication to be configured
 # Enable SharePoint file picker in chat and agent panels
 # ENABLE_SHAREPOINT_FILEPICKER=true
 # SharePoint tenant base URL (e.g., https://yourtenant.sharepoint.com)
 # SHAREPOINT_BASE_URL=https://yourtenant.sharepoint.com
 # Microsoft Graph API And SharePoint scopes for file picker
 # SHAREPOINT_PICKER_SHAREPOINT_SCOPE==https://yourtenant.sharepoint.com/AllSites.Read
 # SHAREPOINT_PICKER_GRAPH_SCOPE=Files.Read.All
 #========================#
 # SAML
 # Note: If OpenID is enabled, SAML authentication will be automatically disabled.
 SAML_ENTRY_POINT=
 SAML_ISSUER=
 SAML_CERT=
 SAML_CALLBACK_URL=/oauth/saml/callback
 SAML_SESSION_SECRET=
 # Attribute mappings (optional)
 SAML_EMAIL_CLAIM=
 SAML_USERNAME_CLAIM=
 SAML_GIVEN_NAME_CLAIM=
 SAML_FAMILY_NAME_CLAIM=
 SAML_PICTURE_CLAIM=
 SAML_NAME_CLAIM=
 # Logint buttion settings (optional)
 SAML_BUTTON_LABEL=
 SAML_IMAGE_URL=
 # Whether the SAML Response should be signed.
 # - If "true", the entire `SAML Response` will be signed.
 # - If "false" or unset, only the `SAML Assertion` will be signed (default behavior).
 # SAML_USE_AUTHN_RESPONSE_SIGNED=
 #===============================================#
 # Microsoft Graph API / Entra ID Integration  #
 #===============================================#
 # Enable Entra ID people search integration in permissions/sharing system
 # When enabled, the people picker will search both local database and Entra ID
 USE_ENTRA_ID_FOR_PEOPLE_SEARCH=false
 # When enabled, entra id groups owners will be considered as members of the group
 ENTRA_ID_INCLUDE_OWNERS_AS_MEMBERS=false
 # Microsoft Graph API scopes needed for people/group search
 # Default scopes provide access to user profiles and group memberships
 OPENID_GRAPH_SCOPES=User.Read,People.Read,GroupMember.Read.All
 # LDAP
 LDAP_URL=
 LDAP_BIND_DN=
 LDAP_BIND_CREDENTIALS=
 LDAP_USER_SEARCH_BASE=
 #LDAP_SEARCH_FILTER="mail="
 LDAP_CA_CERT_PATH=
 # LDAP_TLS_REJECT_UNAUTHORIZED=
 # LDAP_STARTTLS=
 # LDAP_LOGIN_USES_USERNAME=true
 # LDAP_ID=
 # LDAP_USERNAME=
 # LDAP_EMAIL=
 # LDAP_FULL_NAME=
 #========================#
 # Email Password Reset   #
 #========================#
 EMAIL_SERVICE=
 EMAIL_HOST=
 EMAIL_PORT=25
 EMAIL_ENCRYPTION=
 EMAIL_ENCRYPTION_HOSTNAME=
 EMAIL_ALLOW_SELFSIGNED=
 EMAIL_USERNAME=
 EMAIL_PASSWORD=
 EMAIL_FROM_NAME=
 EMAIL_FROM=noreply@librechat.ai
 #========================#
 #      Mailgun API       #
 #========================#
 # MAILGUN_API_KEY=your-mailgun-api-key
 # MAILGUN_DOMAIN=mg.yourdomain.com
 # EMAIL_FROM=noreply@yourdomain.com
 # EMAIL_FROM_NAME="LibreChat"
 # # Optional: For EU region
 # MAILGUN_HOST=https://api.eu.mailgun.net
 #========================#
 # Firebase CDN           #
 #========================#
 FIREBASE_API_KEY=
 FIREBASE_AUTH_DOMAIN=
 FIREBASE_PROJECT_ID=
 FIREBASE_STORAGE_BUCKET=
 FIREBASE_MESSAGING_SENDER_ID=
 FIREBASE_APP_ID=
 #========================#
 # S3 AWS Bucket          #
 #========================#
 AWS_ENDPOINT_URL=
 AWS_ACCESS_KEY_ID=
 AWS_SECRET_ACCESS_KEY=
 AWS_REGION=
 AWS_BUCKET_NAME=
 #========================#
 # Azure Blob Storage     #
 #========================#
 AZURE_STORAGE_CONNECTION_STRING=
 AZURE_STORAGE_PUBLIC_ACCESS=false
 AZURE_CONTAINER_NAME=files
 #========================#
 # Shared Links           #
 #========================#
 ALLOW_SHARED_LINKS=true
 ALLOW_SHARED_LINKS_PUBLIC=true
 #==============================#
 # Static File Cache Control    #
 #==============================#
 # Leave commented out to use defaults: 1 day (86400 seconds) for s-maxage and 2 days (172800 seconds) for max-age
 # NODE_ENV must be set to production for these to take effect
 # STATIC_CACHE_MAX_AGE=172800
 # STATIC_CACHE_S_MAX_AGE=86400
 # If you have another service in front of your LibreChat doing compression, disable express based compression here
 # DISABLE_COMPRESSION=true
 # If you have gzipped version of uploaded image images in the same folder, this will enable gzip scan and serving of these images
 # Note: The images folder will be scanned on startup and a ma kept in memory. Be careful for large number of images.
 # ENABLE_IMAGE_OUTPUT_GZIP_SCAN=true
 #===================================================#
 #                        UI                         #
 #===================================================#
 APP_TITLE=LibreChat
 # CUSTOM_FOOTER="My custom footer"
 HELP_AND_FAQ_URL=https://librechat.ai
 # SHOW_BIRTHDAY_ICON=true
 # Google tag manager id
 #ANALYTICS_GTM_ID=user provided google tag manager id
 # limit conversation file imports to a certain number of bytes in size to avoid the container
 # maxing out memory limitations by unremarking this line and supplying a file size in bytes
 # such as the below example of 250 mib
 # CONVERSATION_IMPORT_MAX_FILE_SIZE_BYTES=262144000
 #===============#
 # REDIS Options #
 #===============#
 # Enable Redis for caching and session storage
 # USE_REDIS=true
 # Single Redis instance
 # REDIS_URI=redis://127.0.0.1:6379
 # Redis cluster (multiple nodes)
 # REDIS_URI=redis://127.0.0.1:7001,redis://127.0.0.1:7002,redis://127.0.0.1:7003
 # Redis with TLS/SSL encryption and CA certificate
 # REDIS_URI=rediss://127.0.0.1:6380
 # REDIS_CA=/path/to/ca-cert.pem
 # Elasticache may need to use an alternate dnsLookup for TLS connections.  see "Special Note: Aws Elasticache Clusters with TLS" on this webpage: https://www.npmjs.com/package/ioredis
 # Enable alternative dnsLookup for redis
 # REDIS_USE_ALTERNATIVE_DNS_LOOKUP=true
 # Redis authentication (if required)
 # REDIS_USERNAME=your_redis_username
 # REDIS_PASSWORD=your_redis_password
 # Redis key prefix configuration
 # Use environment variable name for dynamic prefix (recommended for cloud deployments)
 # REDIS_KEY_PREFIX_VAR=K_REVISION
 # Or use static prefix directly
 # REDIS_KEY_PREFIX=librechat
 # Redis connection limits
 # REDIS_MAX_LISTENERS=40
 # Redis ping interval in seconds (0 = disabled, >0 = enabled)
 # When set to a positive integer, Redis clients will ping the server at this interval to keep connections alive
 # When unset or 0, no pinging is performed (recommended for most use cases)
 # REDIS_PING_INTERVAL=300
 # Force specific cache namespaces to use in-memory storage even when Redis is enabled
 # Comma-separated list of CacheKeys (e.g., ROLES,MESSAGES)
 # FORCED_IN_MEMORY_CACHE_NAMESPACES=ROLES,MESSAGES
 # Leader Election Configuration (for multi-instance deployments with Redis)
 # Duration in seconds that the leader lease is valid before it expires (default: 25)
 # LEADER_LEASE_DURATION=25
 # Interval in seconds at which the leader renews its lease (default: 10)
 # LEADER_RENEW_INTERVAL=10
 # Maximum number of retry attempts when renewing the lease fails (default: 3)
 # LEADER_RENEW_ATTEMPTS=3
 # Delay in seconds between retry attempts when renewing the lease (default: 0.5)
 # LEADER_RENEW_RETRY_DELAY=0.5
 #==================================================#
 #                      Others                      #
 #==================================================#
 #   You should leave the following commented out   #
 # NODE_ENV=
 # E2E_USER_EMAIL=
 # E2E_USER_PASSWORD=
 #=====================================================#
 #                  Cache Headers                      #
 #=====================================================#
 #   Headers that control caching of the index.html    #
 #   Default configuration prevents caching to ensure  #
 #   users always get the latest version. Customize    #
 #   only if you understand caching implications.      #
 # INDEX_CACHE_CONTROL=no-cache, no-store, must-revalidate
 # INDEX_PRAGMA=no-cache
 # INDEX_EXPIRES=0
 # no-cache: Forces validation with server before using cached version
 # no-store: Prevents storing the response entirely
 # must-revalidate: Prevents using stale content when offline
 #=====================================================#
 #                  OpenWeather                        #
 #=====================================================#
 OPENWEATHER_API_KEY=
 #====================================#
 # LibreChat Code Interpreter API     #
 #====================================#
 # https://code.librechat.ai
 # LIBRECHAT_CODE_API_KEY=your-key
 #======================#
 # Web Search           #
 #======================#
 # Note: All of the following variable names can be customized.
 # Omit values to allow user to provide them.
 # For more information on configuration values, see:
 # https://librechat.ai/docs/features/web_search
 # Search Provider (Required)
 # SERPER_API_KEY=your_serper_api_key
 # Scraper (Required)
 # FIRECRAWL_API_KEY=your_firecrawl_api_key
 # Optional: Custom Firecrawl API URL
 # FIRECRAWL_API_URL=your_firecrawl_api_url
 # Reranker (Required)
 # JINA_API_KEY=your_jina_api_key
 # or
 # COHERE_API_KEY=your_cohere_api_key
 #======================#
 # MCP Configuration    #
 #======================#
 # Treat 401/403 responses as OAuth requirement when no oauth metadata found
 # MCP_OAUTH_ON_AUTH_ERROR=true
 # Timeout for OAuth detection requests in milliseconds
 # MCP_OAUTH_DETECTION_TIMEOUT=5000
 # Cache connection status checks for this many milliseconds to avoid expensive verification
 # MCP_CONNECTION_CHECK_TTL=60000
 # Skip code challenge method validation (e.g., for AWS Cognito that supports S256 but doesn't advertise it)
 # When set to true, forces S256 code challenge even if not advertised in .well-known/openid-configuration
 # MCP_SKIP_CODE_CHALLENGE_CHECK=false
--- a/.github/CODE_OF_CONDUCT.md
+++ b/.github/CODE_OF_CONDUCT.md
@ -1,132 +0,0 @@
 # Contributor Covenant Code of Conduct
 ## Our Pledge
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
 identity and expression, level of experience, education, socio-economic status,
 nationality, personal appearance, race, religion, or sexual identity
 and orientation.
 We pledge to act and interact in ways that contribute to an open, welcoming,
 diverse, inclusive, and healthy community.
 ## Our Standards
 Examples of behavior that contributes to a positive environment for our
 community include:
 * Demonstrating empathy and kindness toward other people
 * Being respectful of differing opinions, viewpoints, and experiences
 * Giving and gracefully accepting constructive feedback
 * Accepting responsibility and apologizing to those affected by our mistakes,
  and learning from the experience
 * Focusing on what is best not just for us as individuals, but for the
  overall community
 Examples of unacceptable behavior include:
 * The use of sexualized language or imagery, and sexual attention or
  advances of any kind
 * Trolling, insulting or derogatory comments, and personal or political attacks
 * Public or private harassment
 * Publishing others' private information, such as a physical or email
  address, without their explicit permission
 * Other conduct which could reasonably be considered inappropriate in a
  professional setting
 ## Enforcement Responsibilities
 Community leaders are responsible for clarifying and enforcing our standards of
 acceptable behavior and will take appropriate and fair corrective action in
 response to any behavior that they deem inappropriate, threatening, offensive,
 or harmful.
 Community leaders have the right and responsibility to remove, edit, or reject
 comments, commits, code, wiki edits, issues, and other contributions that are
 not aligned to this Code of Conduct, and will communicate reasons for moderation
 decisions when appropriate.
 ## Scope
 This Code of Conduct applies within all community spaces, and also applies when
 an individual is officially representing the community in public spaces.
 Examples of representing our community include using an official e-mail address,
 posting via an official social media account, or acting as an appointed
 representative at an online or offline event.
 ## Enforcement
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement here on GitHub or
 on the official [Discord Server](https://discord.librechat.ai). 
 All complaints will be reviewed and investigated promptly and fairly.
 All community leaders are obligated to respect the privacy and security of the
 reporter of any incident.
 ## Enforcement Guidelines
 Community leaders will follow these Community Impact Guidelines in determining
 the consequences for any action they deem in violation of this Code of Conduct:
 ### 1. Correction
 **Community Impact**: Use of inappropriate language or other behavior deemed
 unprofessional or unwelcome in the community.
 **Consequence**: A private, written warning from community leaders, providing
 clarity around the nature of the violation and an explanation of why the
 behavior was inappropriate. A public apology may be requested.
 ### 2. Warning
 **Community Impact**: A violation through a single incident or series
 of actions.
 **Consequence**: A warning with consequences for continued behavior. No
 interaction with the people involved, including unsolicited interaction with
 those enforcing the Code of Conduct, for a specified period of time. This
 includes avoiding interactions in community spaces as well as external channels
 like social media. Violating these terms may lead to a temporary or
 permanent ban.
 ### 3. Temporary Ban
 **Community Impact**: A serious violation of community standards, including
 sustained inappropriate behavior.
 **Consequence**: A temporary ban from any sort of interaction or public
 communication with the community for a specified period of time. No public or
 private interaction with the people involved, including unsolicited interaction
 with those enforcing the Code of Conduct, is allowed during this period.
 Violating these terms may lead to a permanent ban.
 ### 4. Permanent Ban
 **Community Impact**: Demonstrating a pattern of violation of community
 standards, including sustained inappropriate behavior,  harassment of an
 individual, or aggression toward or disparagement of classes of individuals.
 **Consequence**: A permanent ban from any sort of public interaction within
 the community.
 ## Attribution
 This Code of Conduct is adapted from the [Contributor Covenant][homepage],
 version 2.0, available at
 https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
 Community Impact Guidelines were inspired by [Mozilla's code of conduct
 enforcement ladder](https://github.com/mozilla/diversity).
 [homepage]: https://www.contributor-covenant.org
 For answers to common questions about this code of conduct, see the FAQ at
 https://www.contributor-covenant.org/faq. Translations are available at
 https://www.contributor-covenant.org/translations.
 ---
 ## [Go Back to ReadMe](../README.md)
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@ -1,168 +0,0 @@
 # Contributor Guidelines
 Thank you to all the contributors who have helped make this project possible! We welcome various types of contributions, such as bug reports, documentation improvements, feature requests, and code contributions.
 ## Contributing Guidelines
 If the feature you would like to contribute has not already received prior approval from the project maintainers (i.e., the feature is currently on the [roadmap](https://github.com/users/danny-avila/projects/2)), please submit a request in the [Feature Requests & Suggestions category](https://github.com/danny-avila/LibreChat/discussions/new?category=feature-requests-suggestions) of the discussions board before beginning work on it. The requests should include specific implementation details, including areas of the application that will be affected by the change (including designs if applicable), and any other relevant information that might be required for a speedy review. However, proposals are not required for small changes, bug fixes, or documentation improvements. Small changes and bug fixes should be tied to an [issue](https://github.com/danny-avila/LibreChat/issues) and included in the corresponding pull request for tracking purposes.
 Please note that a pull request involving a feature that has not been reviewed and approved by the project maintainers may be rejected. We appreciate your understanding and cooperation.
 If you would like to discuss the changes you wish to make, join our [Discord community](https://discord.librechat.ai), where you can engage with other contributors and seek guidance from the community.
 ## Our Standards
 We strive to maintain a positive and inclusive environment within our project community. We expect all contributors to adhere to the following standards:
 - Using welcoming and inclusive language.
 - Being respectful of differing viewpoints and experiences.
 - Gracefully accepting constructive criticism.
 - Focusing on what is best for the community.
 - Showing empathy towards other community members.
 Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that do not align with these standards.
 ## To contribute to this project, please adhere to the following guidelines:
 ## 1. Development Setup
 1. Use Node.JS 20.x.
 2. Install typescript globally: `npm i -g typescript`.
 3. Run `npm ci` to install dependencies.
 4. Build the data provider: `npm run build:data-provider`.
 5. Build data schemas: `npm run build:data-schemas`.
 6. Build API methods: `npm run build:api`.
 7. Setup and run unit tests:
    - Copy `.env.test`: `cp api/test/.env.test.example api/test/.env.test`.
    - Run backend unit tests: `npm run test:api`.
    - Run frontend unit tests: `npm run test:client`.
 8. Setup and run integration tests:
    - Build client: `cd client && npm run build`.
    - Create `.env`: `cp .env.example .env`.
    - Install [MongoDB Community Edition](https://www.mongodb.com/docs/manual/administration/install-community/), ensure that `mongosh` connects to your local instance.
    - Run: `npx install playwright`, then `npx playwright install`.
    - Copy `config.local`: `cp e2e/config.local.example.ts e2e/config.local.ts`.
    - Copy `librechat.yaml`: `cp librechat.example.yaml librechat.yaml`.
    - Run: `npm run e2e`.
 ## 2. Development Notes
 1. Before starting work, make sure your main branch has the latest commits with `npm run update`.
 3. Run linting command to find errors: `npm run lint`. Alternatively, ensure husky pre-commit checks are functioning.
 3. After your changes, reinstall packages in your current branch using `npm run reinstall` and ensure everything still works. 
    - Restart the ESLint server ("ESLint: Restart ESLint Server" in VS Code command bar) and your IDE after reinstalling or updating.
 4. Clear web app localStorage and cookies before and after changes.
 5. For frontend changes, compile typescript before and after changes to check for introduced errors: `cd client && npm run build`.
 6. Run backend unit tests: `npm run test:api`.
 7. Run frontend unit tests: `npm run test:client`.
 8. Run integration tests: `npm run e2e`.
 ## 3. Git Workflow
 We utilize a GitFlow workflow to manage changes to this project's codebase. Follow these general steps when contributing code:
 1. Fork the repository and create a new branch with a descriptive slash-based name (e.g., `new/feature/x`).
 2. Implement your changes and ensure that all tests pass.
 3. Commit your changes using conventional commit messages with GitFlow flags. Begin the commit message with a tag indicating the change type, such as "feat" (new feature), "fix" (bug fix), "docs" (documentation), or "refactor" (code refactoring), followed by a brief summary of the changes (e.g., `feat: Add new feature X to the project`).
 4. Submit a pull request with a clear and concise description of your changes and the reasons behind them.
 5. We will review your pull request, provide feedback as needed, and eventually merge the approved changes into the main branch.
 ## 4. Commit Message Format
 We follow the [semantic format](https://gist.github.com/joshbuchea/6f47e86d2510bce28f8e7f42ae84c716) for commit messages.
 ### Example
 ```
 feat: add hat wobble
 ^--^  ^------------^
 |     |
 |     +-> Summary in present tense.
 |
 +-------> Type: chore, docs, feat, fix, refactor, style, or test.
 ```
 ### Commit Guidelines
 - Do your best to reduce the number of commits, organizing them as much possible. Look into [squashing commits](https://www.freecodecamp.org/news/git-squash-commits/) in order to keep a neat history.
 - For those that care about maximizing commits for stats, adhere to the above as I 'squash and merge' an unorganized and/or unformatted commit history, which reduces the number of your commits to 1,:
 ```
 * Update Br.tsx
 * Update Es.tsx
 * Update Br.tsx
 ```
 ## 5. Pull Request Process
 When submitting a pull request, please follow these guidelines:
 - Ensure that any installation or build dependencies are removed before the end of the layer when doing a build.
 - Update the README.md with details of changes to the interface, including new environment variables, exposed ports, useful file locations, and container parameters.
 - Increase the version numbers in any example files and the README.md to reflect the new version that the pull request represents. We use [SemVer](http://semver.org/) for versioning.
 Ensure that your changes meet the following criteria:
 - All tests pass as highlighted [above](#1-development-notes).
 - The code is well-formatted and adheres to our coding standards.
 - The commit history is clean and easy to follow. You can use `git rebase` or `git merge --squash` to clean your commit history before submitting the pull request.
 - The pull request description clearly outlines the changes and the reasons behind them. Be sure to include the steps to test the pull request.
 ## 6. Naming Conventions
 Apply the following naming conventions to branches, labels, and other Git-related entities:
 - **Branch names:** Descriptive and slash-based (e.g., `new/feature/x`).
 - **Labels:** Descriptive and kebab case (e.g., `bug-fix`).
 - **JS/TS:** Directories and file names: Descriptive and camelCase. First letter uppercased for React files (e.g., `helperFunction.ts, ReactComponent.tsx`).
 - **Docs:** Directories and file names: Descriptive and snake_case (e.g., `config_files.md`).
 ## 7. TypeScript Conversion
 1. **Original State**: The project was initially developed entirely in JavaScript (JS).
 2. **Frontend Transition**:
   - We are in the process of transitioning the frontend from JS to TypeScript (TS).
   - The transition is nearing completion.
   - This conversion is feasible due to React's capability to intermix JS and TS prior to code compilation. It's standard practice to compile/bundle the code in such scenarios.
 3. **Backend Considerations**:
   - Transitioning the backend to TypeScript would be a more intricate process, especially for an established Express.js server.
   - **Options for Transition**:
      - **Single Phase Overhaul**: This involves converting the entire backend to TypeScript in one go. It's the most straightforward approach but can be disruptive, especially for larger codebases.
      - **Incremental Transition**: Convert parts of the backend progressively. This can be done by:
         - Maintaining a separate directory for TypeScript files.
         - Gradually migrating and testing individual modules or routes.
         - Using a build tool like `tsc` to compile TypeScript files independently until the entire transition is complete.
   - **Compilation Considerations**: 
      - Introducing a compilation step for the server is an option. This would involve using tools like `ts-node` for development and `tsc` for production builds.
      - However, this is not a conventional approach for Express.js servers and could introduce added complexity, especially in terms of build and deployment processes.
   - **Current Stance**: At present, this backend transition is of lower priority and might not be pursued.
 ## 8. Module Import Conventions
 - `npm` packages first, 
     - from longest line (top) to shortest (bottom)
 - Followed by typescript types (pertains to data-provider and client workspaces)
     - longest line (top) to shortest (bottom)
     - types from package come first
 - Lastly, local imports
     - longest line (top) to shortest (bottom)
     - imports with alias `~` treated the same as relative import with respect to line length
 **Note:** ESLint will automatically enforce these import conventions when you run `npm run lint --fix` or through pre-commit hooks.
 ---
 Please ensure that you adapt this summary to fit the specific context and nuances of your project.
 ---
 ## [Go Back to ReadMe](../README.md)
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@ -1,13 +0,0 @@
 # These are supported funding model platforms
 github: [danny-avila]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
 ko_fi: # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
 custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
--- a/.github/ISSUE_TEMPLATE/BUG-REPORT.yml
+++ b/.github/ISSUE_TEMPLATE/BUG-REPORT.yml
@ -1,96 +0,0 @@
 name: Bug Report
 description: File a bug report
 title: "[Bug]: "
 labels: ["🐛 bug"]
 body:
  - type: markdown
    attributes:
      value: |
        Thanks for taking the time to fill out this bug report!
        Before submitting, please:
        - Search existing [Issues and Discussions](https://github.com/danny-avila/LibreChat/discussions) to see if your bug has already been reported
        - Use [Discussions](https://github.com/danny-avila/LibreChat/discussions) instead of Issues for:
          - General inquiries
          - Help with setup
          - Questions about whether you're experiencing a bug
  - type: textarea
    id: what-happened
    attributes:
      label: What happened?
      description: Also tell us, what did you expect to happen?
      placeholder: Please give as many details as possible
    validations:
      required: true
  - type: textarea
    id: version-info
    attributes:
      label: Version Information
      description: |
        If using Docker, please run and provide the output of:
        ```bash
        docker images | grep librechat
        ```
        If running from source, please run and provide the output of:
        ```bash
        git rev-parse HEAD
        ```
      placeholder: Paste the output here
    validations:
      required: true
  - type: textarea
    id: steps-to-reproduce
    attributes:
      label: Steps to Reproduce
      description: Please list the steps needed to reproduce the issue.
      placeholder: "1. Step 1\n2. Step 2\n3. Step 3"
    validations:
      required: true
  - type: dropdown
    id: browsers
    attributes:
      label: What browsers are you seeing the problem on?
      multiple: true
      options:
        - Firefox
        - Chrome
        - Safari
        - Microsoft Edge
        - Mobile (iOS)
        - Mobile (Android)
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: |
        Please paste relevant logs that were created when reproducing the error.
        Log locations:
        - Docker: Project root directory ./logs
        - npm: ./api/logs
        There are two types of logs that can help diagnose the issue:
        - debug logs (debug-YYYY-MM-DD.log)
        - error logs (error-YYYY-MM-DD.log)
        Error logs contain exact stack traces and are especially helpful, but both can provide valuable information.
        Please only include the relevant portions of logs that correspond to when you reproduced the error.
        For UI-related issues, browser console logs can be very helpful. You can provide these as screenshots or paste the text here.
      render: shell
    validations:
      required: true
  - type: textarea
    id: screenshots
    attributes:
      label: Screenshots
      description: If applicable, add screenshots to help explain your problem. You can drag and drop, paste images directly here or link to them.
  - type: checkboxes
    id: terms
    attributes:
      label: Code of Conduct
      description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/danny-avila/LibreChat/blob/main/.github/CODE_OF_CONDUCT.md)
      options:
        - label: I agree to follow this project's Code of Conduct
          required: true
--- a/.github/ISSUE_TEMPLATE/FEATURE-REQUEST.yml
+++ b/.github/ISSUE_TEMPLATE/FEATURE-REQUEST.yml
@ -1,49 +0,0 @@
 name: Feature Request
 description: File a feature request
 title: "[Enhancement]: "
 labels: ["✨ enhancement"]
 body:
  - type: markdown
    attributes:
      value: |
        Thank you for taking the time to fill this out!
  - type: textarea
    id: what
    attributes:
      label: What features would you like to see added?
      description: Please provide as many details as possible.
      placeholder: Please provide as many details as possible.
    validations:
      required: true
  - type: textarea
    id: details
    attributes:
      label: More details
      description: Please provide additional details if needed.
      placeholder: Please provide additional details if needed.
    validations:
      required: true
  - type: dropdown
    id: subject
    attributes:
      label: Which components are impacted by your request?
      multiple: true
      options:
        - General
        - UI
        - Endpoints
        - Plugins
        - Other
  - type: textarea
    id: screenshots
    attributes:
      label: Pictures
      description: If relevant, please include images to help clarify your request. You can drag and drop images directly here, paste them, or provide a link to them.
  - type: checkboxes
    id: terms
    attributes:
      label: Code of Conduct
      description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/danny-avila/LibreChat/blob/main/.github/CODE_OF_CONDUCT.md)
      options:
        - label: I agree to follow this project's Code of Conduct
          required: true
--- a/.github/ISSUE_TEMPLATE/LOCIZE_TRANSLATION_ACCESS_REQUEST.yml
+++ b/.github/ISSUE_TEMPLATE/LOCIZE_TRANSLATION_ACCESS_REQUEST.yml
@ -1,42 +0,0 @@
 name: Locize Translation Access Request
 description: Request access to an additional language in Locize for LibreChat translations.
 title: "Locize Access Request: "
 labels: ["🌍 i18n", "🔑 access request"]
 body:
  - type: markdown
    attributes:
      value: |
        Thank you for your interest in contributing to LibreChat translations!  
        Please fill out the form below to request access to an additional language in **Locize**.
        **🔗 Available Languages:** [View the list here](https://www.librechat.ai/docs/translation)
        **📌 Note:** Ensure that the requested language is supported before submitting your request.
  - type: input
    id: account_name
    attributes:
      label: Locize Account Name
      description: Please provide your Locize account name (e.g., John Doe).
      placeholder: e.g., John Doe
    validations:
      required: true
  - type: input
    id: language_requested
    attributes:
      label: Language Code (ISO 639-1)
      description: |
        Enter the **ISO 639-1** language code for the language you want to translate into.  
        Example: `es` for Spanish, `zh-Hant` for Traditional Chinese.
        **🔗 Reference:** [Available Languages](https://www.librechat.ai/docs/translation)
      placeholder: e.g., es
    validations:
      required: true
  - type: checkboxes
    id: agreement
    attributes:
      label: Agreement
      description: By submitting this request, you confirm that you will contribute responsibly and adhere to the project guidelines.
      options:
        - label: I agree to use my access solely for contributing to LibreChat translations.
          required: true
--- a/.github/ISSUE_TEMPLATE/NEW-LANGUAGE-REQUEST.yml
+++ b/.github/ISSUE_TEMPLATE/NEW-LANGUAGE-REQUEST.yml
@ -1,33 +0,0 @@
 name: New Language Request
 description: Request to add a new language for LibreChat translations.
 title: "New Language Request: "
 labels: ["✨ enhancement", "🌍 i18n"]
 body:
  - type: markdown
    attributes:
      value: |
        Thank you for taking the time to submit a new language request! Please fill out the following details so we can review your request.
  - type: input
    id: language_name
    attributes:
      label: Language Name
      description: Please provide the full name of the language (e.g., Spanish, Mandarin).
      placeholder: e.g., Spanish
    validations:
      required: true
  - type: input
    id: iso_code
    attributes:
      label: ISO 639-1 Code
      description: Please provide the ISO 639-1 code for the language (e.g., es for Spanish). You can refer to [this list](https://www.w3schools.com/tags/ref_language_codes.asp) for valid codes.
      placeholder: e.g., es
    validations:
      required: true
  - type: checkboxes
    id: terms
    attributes:
      label: Code of Conduct
      description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/danny-avila/LibreChat/blob/main/.github/CODE_OF_CONDUCT.md).
      options:
        - label: I agree to follow this project's Code of Conduct
          required: true
--- a/.github/SECURITY.md
+++ b/.github/SECURITY.md
@ -1,63 +0,0 @@
 # Security Policy
 At LibreChat, we prioritize the security of our project and value the contributions of security researchers in helping us improve the security of our codebase. If you discover a security vulnerability within our project, we appreciate your responsible disclosure. Please follow the guidelines below to report any vulnerabilities to us:
 **Note: Only report sensitive vulnerability details via the appropriate private communication channels mentioned below. Public channels, such as GitHub issues and Discord, should be used for initiating contact and establishing private communication channels.**
 ## Communication Channels
 When reporting a security vulnerability, you have the following options to reach out to us:
 - **Option 1: GitHub Security Advisory System**: We encourage you to use GitHub's Security Advisory system to report any security vulnerabilities you find. This allows us to receive vulnerability reports directly through GitHub. For more information on how to submit a security advisory report, please refer to the [GitHub Security Advisories documentation](https://docs.github.com/en/code-security/getting-started-with-security-vulnerability-alerts/about-github-security-advisories).
 - **Option 2: GitHub Issues**: You can initiate first contact via GitHub Issues. However, please note that initial contact through GitHub Issues should not include any sensitive details.
 - **Option 3: Discord Server**: You can join our [Discord community](https://discord.librechat.ai) and initiate first contact in the `#issues` channel. However, please ensure that initial contact through Discord does not include any sensitive details.
 _After the initial contact, we will establish a private communication channel for further discussion._
 ### When submitting a vulnerability report, please provide us with the following information:
 - A clear description of the vulnerability, including steps to reproduce it.
 - The version(s) of the project affected by the vulnerability.
 - Any additional information that may be useful for understanding and addressing the issue.
 We strive to acknowledge vulnerability reports within 72 hours and will keep you informed of the progress towards resolution.
 ## Security Updates and Patching
 We are committed to maintaining the security of our open-source project, LibreChat, and promptly addressing any identified vulnerabilities. To ensure the security of our project, we adhere to the following practices:
 - We prioritize security updates for the current major release of our software.
 - We actively monitor the GitHub Security Advisory system and the `#issues` channel on Discord for any vulnerability reports.
 - We promptly review and validate reported vulnerabilities and take appropriate actions to address them.
 - We release security patches and updates in a timely manner to mitigate any identified vulnerabilities.
 Please note that as a security-conscious community, we may not always disclose detailed information about security issues until we have determined that doing so would not put our users or the project at risk. We appreciate your understanding and cooperation in these matters.
 ## Scope
 This security policy applies to the following GitHub repository:
 - Repository: [LibreChat](https://github.librechat.ai)
 ## Contact
 If you have any questions or concerns regarding the security of our project, please join our [Discord community](https://discord.librechat.ai) and report them in the appropriate channel. You can also reach out to us by [opening an issue](https://github.com/danny-avila/LibreChat/issues/new) on GitHub. Please note that the response time may vary depending on the nature and severity of the inquiry.
 ## Acknowledgments
 We would like to express our gratitude to the security researchers and community members who help us improve the security of our project. Your contributions are invaluable, and we sincerely appreciate your efforts.
 ## Bug Bounty Program
 We currently do not have a bug bounty program in place. However, we welcome and appreciate any
 security-related contributions through pull requests (PRs) that address vulnerabilities in our codebase. We believe in the power of collaboration to improve the security of our project and invite you to join us in making it more robust.
 **Reference**
 - https://cheatsheetseries.owasp.org/cheatsheets/Vulnerability_Disclosure_Cheat_Sheet.html
 ---
 ## [Go Back to ReadMe](../README.md)
--- a/.github/configuration-release.json
+++ b/.github/configuration-release.json
@ -1,60 +0,0 @@
 {
  "categories": [
    {
      "title": "### ✨ New Features",
      "labels": ["feat"]
    },
    {
      "title": "### 🌍 Internationalization",
      "labels": ["i18n"]
    },
    {
      "title": "### 👐 Accessibility",
      "labels": ["a11y"]
    },
    {
      "title": "### 🔧 Fixes",
      "labels": ["Fix", "fix"]
    },
    {
      "title": "### ⚙️ Other Changes",
      "labels": ["ci", "style", "docs", "refactor", "chore"]
    }
  ],
  "ignore_labels": [
    "🔁 duplicate",
    "📊 analytics",
    "🌱 good first issue",
    "🔍 investigation",
    "🙏 help wanted",
    "❌ invalid",
    "❓ question",
    "🚫 wontfix",
    "🚀 release",
    "version"
  ],
  "base_branches": ["main"],
  "sort": {
    "order": "ASC",
    "on_property": "mergedAt"
  },
  "label_extractor": [
    {
      "pattern": "^(?:[^A-Za-z0-9]*)(feat|fix|chore|docs|refactor|ci|style|a11y|i18n)\\s*:",
      "target": "$1",
      "flags": "i",
      "on_property": "title",
      "method": "match"
    },
    {
      "pattern": "^(?:[^A-Za-z0-9]*)(v\\d+\\.\\d+\\.\\d+(?:-rc\\d+)?).*",
      "target": "version",
      "flags": "i",
      "on_property": "title",
      "method": "match"
    }
  ],
  "template": "## [#{{TO_TAG}}] - #{{TO_TAG_DATE}}\n\nChanges from #{{FROM_TAG}} to #{{TO_TAG}}.\n\n#{{CHANGELOG}}\n\n[See full release details][release-#{{TO_TAG}}]\n\n[release-#{{TO_TAG}}]: https://github.com/#{{OWNER}}/#{{REPO}}/releases/tag/#{{TO_TAG}}\n\n---",
  "pr_template": "- #{{TITLE}} by **@#{{AUTHOR}}** in [##{{NUMBER}}](#{{URL}})",
  "empty_template": "- no changes"
 }
--- a/.github/configuration-unreleased.json
+++ b/.github/configuration-unreleased.json
@ -1,68 +0,0 @@
 {
  "categories": [
    {
      "title": "### ✨ New Features",
      "labels": ["feat"]
    },
    {
      "title": "### 🌍 Internationalization",
      "labels": ["i18n"]
    },
    {
      "title": "### 👐 Accessibility",
      "labels": ["a11y"]
    },
    {
      "title": "### 🔧 Fixes",
      "labels": ["Fix", "fix"]
    },
    {
      "title": "### ⚙️ Other Changes",
      "labels": ["ci", "style", "docs", "refactor", "chore"]
    }
  ],
  "ignore_labels": [
    "🔁 duplicate",
    "📊 analytics",
    "🌱 good first issue",
    "🔍 investigation",
    "🙏 help wanted",
    "❌ invalid",
    "❓ question",
    "🚫 wontfix",
    "🚀 release",
    "version",
    "action"
  ],
  "base_branches": ["main"],
  "sort": {
    "order": "ASC",
    "on_property": "mergedAt"
  },
  "label_extractor": [
    {
      "pattern": "^(?:[^A-Za-z0-9]*)(feat|fix|chore|docs|refactor|ci|style|a11y|i18n)\\s*:",
      "target": "$1",
      "flags": "i",
      "on_property": "title",
      "method": "match"
    },
    {
      "pattern": "^(?:[^A-Za-z0-9]*)(v\\d+\\.\\d+\\.\\d+(?:-rc\\d+)?).*",
      "target": "version",
      "flags": "i",
      "on_property": "title",
      "method": "match"
    },
    {
      "pattern": "^(?:[^A-Za-z0-9]*)(action)\\b.*",
      "target": "action",
      "flags": "i",
      "on_property": "title",
      "method": "match"
    }
  ],
  "template": "## [Unreleased]\n\n#{{CHANGELOG}}\n\n---",
  "pr_template": "- #{{TITLE}} by **@#{{AUTHOR}}** in [##{{NUMBER}}](#{{URL}})",
  "empty_template": "- no changes"
 }
--- a/.github/playwright.yml
+++ b/.github/playwright.yml
@ -1,72 +0,0 @@
 # name: Playwright Tests
 # on:
 #   pull_request:
 #     branches: 
 #       - main
 #       - dev
 #       - release/*
 #     paths:
 #       - 'api/**'
 #       - 'client/**'
 #       - 'packages/**'
 #       - 'e2e/**'
 # jobs:
 #   tests_e2e:
 #     name: Run Playwright tests
 #     if: github.event.pull_request.head.repo.full_name == 'danny-avila/LibreChat'
 #     timeout-minutes: 60
 #     runs-on: ubuntu-latest
 #     env:
 #       NODE_ENV: CI
 #       CI: true
 #       SEARCH: false
 #       BINGAI_TOKEN: user_provided
 #       CHATGPT_TOKEN: user_provided
 #       MONGO_URI: ${{ secrets.MONGO_URI }}
 #       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 #       E2E_USER_EMAIL: ${{ secrets.E2E_USER_EMAIL }}
 #       E2E_USER_PASSWORD: ${{ secrets.E2E_USER_PASSWORD }}
 #       JWT_SECRET: ${{ secrets.JWT_SECRET }}
 #       JWT_REFRESH_SECRET: ${{ secrets.JWT_REFRESH_SECRET }}
 #       CREDS_KEY: ${{ secrets.CREDS_KEY }}
 #       CREDS_IV: ${{ secrets.CREDS_IV }}
 #       DOMAIN_CLIENT: ${{ secrets.DOMAIN_CLIENT }}
 #       DOMAIN_SERVER: ${{ secrets.DOMAIN_SERVER }}
 #       PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD: 1 # Skip downloading during npm install
 #       PLAYWRIGHT_BROWSERS_PATH: 0 # Places binaries to node_modules/@playwright/test
 #       TITLE_CONVO: false
 #     steps:
 #       - uses: actions/checkout@v4
 #       - uses: actions/setup-node@v4
 #         with:
 #           node-version: 18
 #           cache: 'npm'
 #       - name: Install global dependencies
 #         run: npm ci
 #       # - name: Remove sharp dependency
 #       #   run: rm -rf node_modules/sharp
 #       # - name: Install sharp with linux dependencies
 #       #   run: cd api && SHARP_IGNORE_GLOBAL_LIBVIPS=1 npm install --arch=x64 --platform=linux --libc=glibc sharp
 #       - name: Build Client
 #         run: npm run frontend
 #       - name: Install Playwright
 #         run: |
 #           npx playwright install-deps
 #           npm install -D @playwright/test@latest
 #           npx playwright install chromium
 #       - name: Run Playwright tests
 #         run: npm run e2e:ci
 #       - name: Upload playwright report
 #         uses: actions/upload-artifact@v3
 #         if: always()
 #         with:
 #           name: playwright-report
 #           path: e2e/playwright-report/
 #           retention-days: 30
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@ -1,41 +0,0 @@
 # Pull Request Template
 ⚠️ Before Submitting a PR, Please Review:
 - Please ensure that you have thoroughly read and understood the [Contributing Docs](https://github.com/danny-avila/LibreChat/blob/main/.github/CONTRIBUTING.md) before submitting your Pull Request.
 ⚠️ Documentation Updates Notice:
 - Kindly note that documentation updates are managed in this repository: [librechat.ai](https://github.com/LibreChat-AI/librechat.ai)
 ## Summary
 Please provide a brief summary of your changes and the related issue. Include any motivation and context that is relevant to your changes. If there are any dependencies necessary for your changes, please list them here.
 ## Change Type
 Please delete any irrelevant options.
 - [ ] Bug fix (non-breaking change which fixes an issue)
 - [ ] New feature (non-breaking change which adds functionality)
 - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
 - [ ] This change requires a documentation update
 - [ ] Translation update
 ## Testing
 Please describe your test process and include instructions so that we can reproduce your test. If there are any important variables for your testing configuration, list them here.
 ### **Test Configuration**:
 ## Checklist
 Please delete any irrelevant options.
 - [ ] My code adheres to this project's style guidelines
 - [ ] I have performed a self-review of my own code
 - [ ] I have commented in any complex areas of my code
 - [ ] I have made pertinent documentation changes
 - [ ] My changes do not introduce new warnings
 - [ ] I have written tests demonstrating that my changes are effective or that my feature works
 - [ ] Local unit tests pass with my changes
 - [ ] Any changes dependent on mine have been merged and published in downstream modules.
 - [ ] A pull request for updating the documentation has been submitted.
--- a/.github/workflows/a11y.yml
+++ b/.github/workflows/a11y.yml
@ -1,26 +0,0 @@
 name: Lint for accessibility issues
 on:
  pull_request:
    paths:
      - 'client/src/**'
  workflow_dispatch:
    inputs:
      run_workflow:
        description: 'Set to true to run this workflow'
        required: true
        default: 'false'
 jobs:
  axe-linter:
    runs-on: ubuntu-latest
    if: >
      (github.event_name == 'pull_request' && github.event.pull_request.head.repo.full_name == 'danny-avila/LibreChat') ||
      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_workflow == 'true')
    steps:
      - uses: actions/checkout@v4
      - uses: dequelabs/axe-linter-action@v1
        with:
          api_key: ${{ secrets.AXE_LINTER_API_KEY }}
          github_token: ${{ secrets.GITHUB_TOKEN }}
--- a/.github/workflows/backend-review.yml
+++ b/.github/workflows/backend-review.yml
@ -1,75 +0,0 @@
 name: Backend Unit Tests
 on:
  pull_request:
    branches:
      - main
      - dev
      - dev-staging
      - release/*
    paths:
      - 'api/**'
      - 'packages/**'
 jobs:
  tests_Backend:
    name: Run Backend unit tests
    timeout-minutes: 60
    runs-on: ubuntu-latest
    env:
      MONGO_URI: ${{ secrets.MONGO_URI }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      JWT_SECRET: ${{ secrets.JWT_SECRET }}
      CREDS_KEY: ${{ secrets.CREDS_KEY }}
      CREDS_IV: ${{ secrets.CREDS_IV }}
      BAN_VIOLATIONS: ${{ secrets.BAN_VIOLATIONS }}
      BAN_DURATION: ${{ secrets.BAN_DURATION }}
      BAN_INTERVAL: ${{ secrets.BAN_INTERVAL }}
      NODE_ENV: CI
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - name: Install dependencies
        run: npm ci
      - name: Install Data Provider Package
        run: npm run build:data-provider
      - name: Install Data Schemas Package
        run: npm run build:data-schemas
      - name: Install API Package
        run: npm run build:api
      - name: Create empty auth.json file
        run: |
          mkdir -p api/data
          echo '{}' > api/data/auth.json
      - name: Check for Circular dependency in rollup
        working-directory: ./packages/data-provider
        run: |
          output=$(npm run rollup:api)
          echo "$output"
          if echo "$output" | grep -q "Circular dependency"; then
            echo "Error: Circular dependency detected!"
            exit 1
          fi
      - name: Prepare .env.test file
        run: cp api/test/.env.test.example api/test/.env.test
      - name: Run unit tests
        run: cd api && npm run test:ci
      - name: Run librechat-data-provider unit tests
        run: cd packages/data-provider && npm run test:ci
      - name: Run @librechat/data-schemas unit tests
        run: cd packages/data-schemas && npm run test:ci
      - name: Run @librechat/api unit tests
        run: cd packages/api && npm run test:ci
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@ -1,38 +0,0 @@
 name: Linux_Container_Workflow
 on:
  workflow_dispatch:
 env:
  RUNNER_VERSION: 2.293.0
 jobs:
  build-and-push:
    runs-on: ubuntu-latest
    steps:
      # checkout the repo
      - name: 'Checkout GitHub Action'
        uses: actions/checkout@main
      - name: 'Login via Azure CLI'
        uses: azure/login@v1
        with:
          creds: ${{ secrets.AZURE_CREDENTIALS }}
      - name: 'Build GitHub Runner container image'
        uses: azure/docker-login@v1
        with:
          login-server: ${{ secrets.REGISTRY_LOGIN_SERVER }}
          username: ${{ secrets.REGISTRY_USERNAME }}
          password: ${{ secrets.REGISTRY_PASSWORD }}
      - run: |
          docker build --build-arg RUNNER_VERSION=${{ env.RUNNER_VERSION }} -t ${{ secrets.REGISTRY_LOGIN_SERVER }}/pwd9000-github-runner-lin:${{ env.RUNNER_VERSION }} .
      - name: 'Push container image to ACR'
        uses: azure/docker-login@v1
        with:
          login-server: ${{ secrets.REGISTRY_LOGIN_SERVER }}
          username: ${{ secrets.REGISTRY_USERNAME }}
          password: ${{ secrets.REGISTRY_PASSWORD }}
      - run: |
          docker push ${{ secrets.REGISTRY_LOGIN_SERVER }}/pwd9000-github-runner-lin:${{ env.RUNNER_VERSION }}
--- a/.github/workflows/cache-integration-tests.yml
+++ b/.github/workflows/cache-integration-tests.yml
@ -1,90 +0,0 @@
 name: Cache Integration Tests
 on:
  pull_request:
    branches:
      - main
      - dev
      - dev-staging
      - release/*
    paths:
      - 'packages/api/src/cache/**'
      - 'packages/api/src/cluster/**'
      - 'packages/api/src/mcp/**'
      - 'redis-config/**'
      - '.github/workflows/cache-integration-tests.yml'
 jobs:
  cache_integration_tests:
    name: Integration Tests that use actual Redis Cache
    timeout-minutes: 30
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Use Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - name: Install Redis tools
        run: |
          sudo apt-get update
          sudo apt-get install -y redis-server redis-tools
      - name: Start Single Redis Instance
        run: |
          redis-server --daemonize yes --port 6379
          sleep 2
          # Verify single Redis is running
          redis-cli -p 6379 ping || exit 1
      - name: Start Redis Cluster
        working-directory: redis-config
        run: |
          chmod +x start-cluster.sh stop-cluster.sh
          ./start-cluster.sh
          sleep 10
          # Verify cluster is running
          redis-cli -p 7001 cluster info || exit 1
          redis-cli -p 7002 cluster info || exit 1
          redis-cli -p 7003 cluster info || exit 1
      - name: Install dependencies
        run: npm ci
      - name: Build packages
        run: |
          npm run build:data-provider
          npm run build:data-schemas
          npm run build:api
      - name: Run all cache integration tests (Single Redis Node)
        working-directory: packages/api
        env:
          NODE_ENV: test
          USE_REDIS: true
          USE_REDIS_CLUSTER: false
          REDIS_URI: redis://127.0.0.1:6379
        run: npm run test:cache-integration
      - name: Run all cache integration tests (Redis Cluster)
        working-directory: packages/api
        env:
          NODE_ENV: test
          USE_REDIS: true
          USE_REDIS_CLUSTER: true
          REDIS_URI: redis://127.0.0.1:7001,redis://127.0.0.1:7002,redis://127.0.0.1:7003
        run: npm run test:cache-integration
      - name: Stop Redis Cluster
        if: always()
        working-directory: redis-config
        run: ./stop-cluster.sh || true
      - name: Stop Single Redis Instance
        if: always()
        run: redis-cli -p 6379 shutdown || true
--- a/.github/workflows/client.yml
+++ b/.github/workflows/client.yml
@ -1,64 +0,0 @@
 name: Publish `@librechat/client` to NPM
 on:
  push:
    branches:
      - main
    paths:
      - 'packages/client/package.json'
  workflow_dispatch:
    inputs:
      reason:
        description: 'Reason for manual trigger'
        required: false
        default: 'Manual publish requested'
 permissions:
  id-token: write  # Required for OIDC trusted publishing
  contents: read
 jobs:
  build-and-publish:
    runs-on: ubuntu-latest
    environment: publish  # Must match npm trusted publisher config
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20.x'
          registry-url: 'https://registry.npmjs.org'
      - name: Update npm for OIDC support
        run: npm install -g npm@latest  # Must be 11.5.1+ for provenance
      - name: Install client dependencies
        run: cd packages/client && npm ci
      - name: Build client
        run: cd packages/client && npm run build
      - name: Check version change
        id: check
        working-directory: packages/client
        run: |
          PACKAGE_VERSION=$(node -p "require('./package.json').version")
          PUBLISHED_VERSION=$(npm view @librechat/client version 2>/dev/null || echo "0.0.0")
          if [ "$PACKAGE_VERSION" = "$PUBLISHED_VERSION" ]; then
            echo "No version change, skipping publish"
            echo "skip=true" >> $GITHUB_OUTPUT
          else
            echo "Version changed, proceeding with publish"
            echo "skip=false" >> $GITHUB_OUTPUT
          fi
      - name: Pack package
        if: steps.check.outputs.skip != 'true'
        working-directory: packages/client
        run: npm pack
      - name: Publish
        if: steps.check.outputs.skip != 'true'
        working-directory: packages/client
        run: npm publish *.tgz --access public --provenance
--- a/.github/workflows/data-provider.yml
+++ b/.github/workflows/data-provider.yml
@ -1,47 +0,0 @@
 name: Publish `librechat-data-provider` to NPM
 on:
  push:
    branches:
      - main
    paths:
      - 'packages/data-provider/package.json'
  workflow_dispatch:
    inputs:
      reason:
        description: 'Reason for manual trigger'
        required: false
        default: 'Manual publish requested'
 permissions:
  id-token: write  # Required for OIDC trusted publishing
  contents: read
 jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: cd packages/data-provider && npm ci
      - run: cd packages/data-provider && npm run build
  publish-npm:
    needs: build
    runs-on: ubuntu-latest
    environment: publish  # Must match npm trusted publisher config
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          registry-url: 'https://registry.npmjs.org'
      - name: Update npm for OIDC support
        run: npm install -g npm@latest  # Must be 11.5.1+ for provenance
      - run: cd packages/data-provider && npm ci
      - run: cd packages/data-provider && npm run build
      - run: cd packages/data-provider && npm publish --provenance
--- a/.github/workflows/data-schemas.yml
+++ b/.github/workflows/data-schemas.yml
@ -1,64 +0,0 @@
 name: Publish `@librechat/data-schemas` to NPM
 on:
  push:
    branches:
      - main
    paths:
      - 'packages/data-schemas/package.json'
  workflow_dispatch:
    inputs:
      reason:
        description: 'Reason for manual trigger'
        required: false
        default: 'Manual publish requested'
 permissions:
  id-token: write  # Required for OIDC trusted publishing
  contents: read
 jobs:
  build-and-publish:
    runs-on: ubuntu-latest
    environment: publish  # Must match npm trusted publisher config
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20.x'
          registry-url: 'https://registry.npmjs.org'
      - name: Update npm for OIDC support
        run: npm install -g npm@latest  # Must be 11.5.1+ for provenance
      - name: Install dependencies
        run: cd packages/data-schemas && npm ci
      - name: Build
        run: cd packages/data-schemas && npm run build
      - name: Check version change
        id: check
        working-directory: packages/data-schemas
        run: |
          PACKAGE_VERSION=$(node -p "require('./package.json').version")
          PUBLISHED_VERSION=$(npm view @librechat/data-schemas version 2>/dev/null || echo "0.0.0")
          if [ "$PACKAGE_VERSION" = "$PUBLISHED_VERSION" ]; then
            echo "No version change, skipping publish"
            echo "skip=true" >> $GITHUB_OUTPUT
          else
            echo "Version changed, proceeding with publish"
            echo "skip=false" >> $GITHUB_OUTPUT
          fi
      - name: Pack package
        if: steps.check.outputs.skip != 'true'
        working-directory: packages/data-schemas
        run: npm pack
      - name: Publish
        if: steps.check.outputs.skip != 'true'
        working-directory: packages/data-schemas
        run: npm publish *.tgz --access public --provenance
--- a/.github/workflows/deploy-dev.yml
+++ b/.github/workflows/deploy-dev.yml
@ -1,46 +0,0 @@
 name: Update Test Server
 on:
  workflow_run:
    workflows: ["Docker Dev Branch Images Build"]
    types:
      - completed
  workflow_dispatch:
 jobs:
  deploy:
    runs-on: ubuntu-latest
    if: |
      github.repository == 'danny-avila/LibreChat' &&
      (github.event_name == 'workflow_dispatch' || 
       (github.event.workflow_run.conclusion == 'success' && github.event.workflow_run.head_branch == 'dev'))
    steps:
    - name: Checkout repository
      uses: actions/checkout@v4
    - name: Install SSH Key
      uses: shimataro/ssh-key-action@v2
      with:
        key: ${{ secrets.DO_SSH_PRIVATE_KEY }}
        known_hosts: ${{ secrets.DO_KNOWN_HOSTS }}
    - name: Run update script on DigitalOcean Droplet
      env:
        DO_HOST: ${{ secrets.DO_HOST }}
        DO_USER: ${{ secrets.DO_USER }}
      run: |
        ssh -o StrictHostKeyChecking=no ${DO_USER}@${DO_HOST} << EOF
        sudo -i -u danny bash << 'EEOF'
        cd ~/LibreChat && \
        git fetch origin main && \
        sudo npm run stop:deployed && \
        sudo docker images --format "{{.Repository}}:{{.ID}}" | grep -E "lc-dev|librechat" | cut -d: -f2 | xargs -r sudo docker rmi -f || true && \
        sudo npm run update:deployed && \
        git checkout dev && \
        git pull origin dev && \
        git checkout do-deploy && \
        git rebase dev && \
        sudo npm run start:deployed && \
        echo "Update completed. Application should be running now."
        EEOF
        EOF
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@ -1,38 +0,0 @@
 name: Deploy_GHRunner_Linux_ACI
 on:
  workflow_dispatch:
 env:
  RUNNER_VERSION: 2.293.0
  ACI_RESOURCE_GROUP: 'Demo-ACI-GitHub-Runners-RG'
  ACI_NAME: 'gh-runner-linux-01'
  DNS_NAME_LABEL: 'gh-lin-01'
  GH_OWNER: ${{ github.repository_owner }}
  GH_REPOSITORY: 'LibreChat' #Change here to deploy self hosted runner ACI to another repo.
 jobs:
  deploy-gh-runner-aci:
    runs-on: ubuntu-latest
    steps:
      # checkout the repo
      - name: 'Checkout GitHub Action'
        uses: actions/checkout@v4
      - name: 'Login via Azure CLI'
        uses: azure/login@v1
        with:
          creds: ${{ secrets.AZURE_CREDENTIALS }}
      - name: 'Deploy to Azure Container Instances'
        uses: 'azure/aci-deploy@v1'
        with:
          resource-group: ${{ env.ACI_RESOURCE_GROUP }}
          image: ${{ secrets.REGISTRY_LOGIN_SERVER }}/pwd9000-github-runner-lin:${{ env.RUNNER_VERSION }}
          registry-login-server: ${{ secrets.REGISTRY_LOGIN_SERVER }}
          registry-username: ${{ secrets.REGISTRY_USERNAME }}
          registry-password: ${{ secrets.REGISTRY_PASSWORD }}
          name: ${{ env.ACI_NAME }}
          dns-name-label: ${{ env.DNS_NAME_LABEL }}
          environment-variables: GH_TOKEN=${{ secrets.PAT_TOKEN }} GH_OWNER=${{ env.GH_OWNER }} GH_REPOSITORY=${{ env.GH_REPOSITORY }}
          location: 'eastus'
--- a/.github/workflows/dev-branch-images.yml
+++ b/.github/workflows/dev-branch-images.yml
@ -1,72 +0,0 @@
 name: Docker Dev Branch Images Build
 on:
  workflow_dispatch:
  push:
    branches:
      - dev
    paths:
      - 'api/**'
      - 'client/**'
      - 'packages/**'
 jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-build
            file: Dockerfile.multi
            image_name: lc-dev-api
          - target: node
            file: Dockerfile
            image_name: lc-dev
    steps:
      # Check out the repository
      - name: Checkout
        uses: actions/checkout@v4
      # Set up QEMU
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
      # Set up Docker Buildx
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v2
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Login to Docker Hub
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      # Prepare the environment
      - name: Prepare environment
        run: |
          cp .env.example .env
      # Build and push Docker images for each target
      - name: Build and push Docker images
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ${{ matrix.file }}
          push: true
          tags: |
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:${{ github.sha }}
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:latest
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:${{ github.sha }}
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:latest
          platforms: linux/amd64,linux/arm64
          target: ${{ matrix.target }} 
--- a/.github/workflows/dev-images.yml
+++ b/.github/workflows/dev-images.yml
@ -1,72 +0,0 @@
 name: Docker Dev Images Build
 on:
  workflow_dispatch:
  push:
    branches:
      - main
    paths:
      - 'api/**'
      - 'client/**'
      - 'packages/**'
 jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-build
            file: Dockerfile.multi
            image_name: librechat-dev-api
          - target: node
            file: Dockerfile
            image_name: librechat-dev
    steps:
      # Check out the repository
      - name: Checkout
        uses: actions/checkout@v4
      # Set up QEMU
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
      # Set up Docker Buildx
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v2
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Login to Docker Hub
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      # Prepare the environment
      - name: Prepare environment
        run: |
          cp .env.example .env
      # Build and push Docker images for each target
      - name: Build and push Docker images
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ${{ matrix.file }}
          push: true
          tags: |
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:${{ github.sha }}
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:latest
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:${{ github.sha }}
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:latest
          platforms: linux/amd64,linux/arm64
          target: ${{ matrix.target }}
--- a/.github/workflows/dev-staging-images.yml
+++ b/.github/workflows/dev-staging-images.yml
@ -1,66 +0,0 @@
 name: Docker Dev Staging Images Build
 on:
  workflow_dispatch:
 jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-build
            file: Dockerfile.multi
            image_name: lc-dev-staging-api
          - target: node
            file: Dockerfile
            image_name: lc-dev-staging
    steps:
      # Check out the repository
      - name: Checkout
        uses: actions/checkout@v4
      # Set up QEMU
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
      # Set up Docker Buildx
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v2
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Login to Docker Hub
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      # Prepare the environment
      - name: Prepare environment
        run: |
          cp .env.example .env
      # Build and push Docker images for each target
      - name: Build and push Docker images
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ${{ matrix.file }}
          push: true
          tags: |
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:${{ github.sha }}
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:latest
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:${{ github.sha }}
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:latest
          platforms: linux/amd64,linux/arm64
          target: ${{ matrix.target }}
--- a/.github/workflows/eslint-ci.yml
+++ b/.github/workflows/eslint-ci.yml
@ -1,60 +0,0 @@
 name: ESLint Code Quality Checks
 on:
  pull_request:
    branches:
      - main
      - dev
      - dev-staging
      - release/*
    paths:
      - 'api/**'
      - 'client/**'
 jobs:
  eslint_checks:
    name: Run ESLint Linting
    runs-on: ubuntu-latest
    permissions:
      contents: read
      security-events: write
      actions: read
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Set up Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: npm
      - name: Install dependencies
        run: npm ci
      # Run ESLint on changed files within the api/ and client/ directories.
      - name: Run ESLint on changed files
        run: |
          # Extract the base commit SHA from the pull_request event payload.
          BASE_SHA=$(jq --raw-output .pull_request.base.sha "$GITHUB_EVENT_PATH")
          echo "Base commit SHA: $BASE_SHA"
          # Get changed files (only JS/TS files in api/ or client/)
          CHANGED_FILES=$(git diff --name-only --diff-filter=ACMRTUXB "$BASE_SHA" HEAD | grep -E '^(api|client)/.*\.(js|jsx|ts|tsx)$' || true)
          # Debug output
          echo "Changed files:"
          echo "$CHANGED_FILES"
          # Ensure there are files to lint before running ESLint
          if [[ -z "$CHANGED_FILES" ]]; then
            echo "No matching files changed. Skipping ESLint."
            exit 0
          fi
          # Run ESLint
          npx eslint --no-error-on-unmatched-pattern \
            --config eslint.config.mjs \
            $CHANGED_FILES
--- a/.github/workflows/frontend-review.yml
+++ b/.github/workflows/frontend-review.yml
@ -1,57 +0,0 @@
 name: Frontend Unit Tests
 on:
  pull_request:
    branches: 
      - main
      - dev
      - dev-staging
      - release/*
    paths:
      - 'client/**'
      - 'packages/data-provider/**'
 jobs:
  tests_frontend_ubuntu:
    name: Run frontend unit tests on Ubuntu
    timeout-minutes: 60
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - name: Install dependencies
        run: npm ci
      - name: Build Client
        run: npm run frontend:ci
      - name: Run unit tests
        run: npm run test:ci --verbose
        working-directory: client
  tests_frontend_windows:
    name: Run frontend unit tests on Windows
    timeout-minutes: 60
    runs-on: windows-latest
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - name: Install dependencies
        run: npm ci
      - name: Build Client
        run: npm run frontend:ci
      - name: Run unit tests
        run: npm run test:ci --verbose
        working-directory: client
--- a/.github/workflows/generate_embeddings.yml
+++ b/.github/workflows/generate_embeddings.yml
@ -1,20 +0,0 @@
 name: 'generate_embeddings'
 on:
  workflow_dispatch:
  push:
    branches:
      - main
    paths:
      - 'docs/**'
 jobs:
  generate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: supabase/embeddings-generator@v0.0.5
        with:
          supabase-url: ${{ secrets.SUPABASE_URL }}
          supabase-service-role-key: ${{ secrets.SUPABASE_SERVICE_ROLE_KEY }}
          openai-key: ${{ secrets.OPENAI_DOC_EMBEDDINGS_KEY }}
          docs-root-path: 'docs'
--- a/.github/workflows/helmcharts.yml
+++ b/.github/workflows/helmcharts.yml
@ -1,75 +0,0 @@
 name: Build Helm Charts on Tag
 # The workflow is triggered when a tag is pushed
 on:
  push:
    tags:
      - "chart-*"
 jobs:
  release:
    permissions:
      contents: write
      packages: write
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Configure Git
        run: |
          git config user.name "$GITHUB_ACTOR"
          git config user.email "$GITHUB_ACTOR@users.noreply.github.com"
      - name: Install Helm
        uses: azure/setup-helm@v4
        env:
          GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
      - name: Build Subchart Deps
        run: |
          cd helm/librechat
          helm dependency build  
          cd ../librechat-rag-api
          helm dependency build      
      - name: Get Chart Version
        id: chart-version
        run: |
          CHART_VERSION=$(echo "${{ github.ref_name }}" | cut -d'-' -f2)
          echo "CHART_VERSION=${CHART_VERSION}" >> "$GITHUB_OUTPUT"
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Run Helm OCI Charts Releaser
      # This is for the librechat chart
      - name: Release Helm OCI Charts for librechat
        uses: appany/helm-oci-chart-releaser@v0.4.2
        with:
          name: librechat
          repository: ${{ github.actor }}/librechat-chart
          tag: ${{ steps.chart-version.outputs.CHART_VERSION }}
          path: helm/librechat
          registry: ghcr.io
          registry_username: ${{ github.actor }}
          registry_password: ${{ secrets.GITHUB_TOKEN }}
      # this is for the librechat-rag-api chart
      - name: Release Helm OCI Charts for librechat-rag-api
        uses: appany/helm-oci-chart-releaser@v0.4.2
        with:
          name: librechat-rag-api
          repository: ${{ github.actor }}/librechat-chart
          tag: ${{ steps.chart-version.outputs.CHART_VERSION }}
          path: helm/librechat-rag-api
          registry: ghcr.io
          registry_username: ${{ github.actor }}
          registry_password: ${{ secrets.GITHUB_TOKEN }}
--- a/.github/workflows/i18n-unused-keys.yml
+++ b/.github/workflows/i18n-unused-keys.yml
@ -1,149 +0,0 @@
 name: Detect Unused i18next Strings
 # This workflow checks for unused i18n keys in translation files.
 # It has special handling for:
 # - com_ui_special_var_* keys that are dynamically constructed
 # - com_agents_category_* keys that are stored in the database and used dynamically
 on:
  pull_request:
    paths:
      - "client/src/**"
      - "api/**"
      - "packages/data-provider/src/**"
      - "packages/client/**"
      - "packages/data-schemas/src/**"
 jobs:
  detect-unused-i18n-keys:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3
      - name: Find unused i18next keys
        id: find-unused
        run: |
          echo "🔍 Scanning for unused i18next keys..."
          # Define paths
          I18N_FILE="client/src/locales/en/translation.json"
          SOURCE_DIRS=("client/src" "api" "packages/data-provider/src" "packages/client" "packages/data-schemas/src")
          # Check if translation file exists
          if [[ ! -f "$I18N_FILE" ]]; then
            echo "::error title=Missing i18n File::Translation file not found: $I18N_FILE"
            exit 1
          fi
          # Extract all keys from the JSON file
          KEYS=$(jq -r 'keys[]' "$I18N_FILE")
          # Track unused keys
          UNUSED_KEYS=()
          # Check if each key is used in the source code
          for KEY in $KEYS; do
            FOUND=false
            # Special case for dynamically constructed special variable keys
            if [[ "$KEY" == com_ui_special_var_* ]]; then
              # Check if TSpecialVarLabel is used in the codebase
              for DIR in "${SOURCE_DIRS[@]}"; do
                if grep -r --include=\*.{js,jsx,ts,tsx} -q "TSpecialVarLabel" "$DIR"; then
                  FOUND=true
                  break
                fi
              done
              # Also check if the key is directly used somewhere
              if [[ "$FOUND" == false ]]; then
                for DIR in "${SOURCE_DIRS[@]}"; do
                  if grep -r --include=\*.{js,jsx,ts,tsx} -q "$KEY" "$DIR"; then
                    FOUND=true
                    break
                  fi
                done
              fi
            # Special case for agent category keys that are dynamically used from database
            elif [[ "$KEY" == com_agents_category_* ]]; then
              # Check if agent category localization is being used
              for DIR in "${SOURCE_DIRS[@]}"; do
                # Check for dynamic category label/description usage
                if grep -r --include=\*.{js,jsx,ts,tsx} -E "category\.(label|description).*startsWith.*['\"]com_" "$DIR" > /dev/null 2>&1 || \
                   # Check for the method that defines these keys
                   grep -r --include=\*.{js,jsx,ts,tsx} "ensureDefaultCategories" "$DIR" > /dev/null 2>&1 || \
                   # Check for direct usage in agentCategory.ts
                   grep -r --include=\*.ts -E "label:.*['\"]$KEY['\"]" "$DIR" > /dev/null 2>&1 || \
                   grep -r --include=\*.ts -E "description:.*['\"]$KEY['\"]" "$DIR" > /dev/null 2>&1; then
                  FOUND=true
                  break
                fi
              done
              # Also check if the key is directly used somewhere
              if [[ "$FOUND" == false ]]; then
                for DIR in "${SOURCE_DIRS[@]}"; do
                  if grep -r --include=\*.{js,jsx,ts,tsx} -q "$KEY" "$DIR"; then
                    FOUND=true
                    break
                  fi
                done
              fi
            else
              # Regular check for other keys
              for DIR in "${SOURCE_DIRS[@]}"; do
                if grep -r --include=\*.{js,jsx,ts,tsx} -q "$KEY" "$DIR"; then
                  FOUND=true
                  break
                fi
              done
            fi
            if [[ "$FOUND" == false ]]; then
              UNUSED_KEYS+=("$KEY")
            fi
          done
          # Output results
          if [[ ${#UNUSED_KEYS[@]} -gt 0 ]]; then
            echo "🛑 Found ${#UNUSED_KEYS[@]} unused i18n keys:"
            echo "unused_keys=$(echo "${UNUSED_KEYS[@]}" | jq -R -s -c 'split(" ")')" >> $GITHUB_ENV
            for KEY in "${UNUSED_KEYS[@]}"; do
              echo "::warning title=Unused i18n Key::'$KEY' is defined but not used in the codebase."
            done
          else
            echo "✅ No unused i18n keys detected!"
            echo "unused_keys=[]" >> $GITHUB_ENV
          fi
      - name: Post verified comment on PR
        if: env.unused_keys != '[]'
        run: |
          PR_NUMBER=$(jq --raw-output .pull_request.number "$GITHUB_EVENT_PATH")
          # Format the unused keys list as checkboxes for easy manual checking.
          FILTERED_KEYS=$(echo "$unused_keys" | jq -r '.[]' | grep -v '^\s*$' | sed 's/^/- [ ] `/;s/$/`/' )
          COMMENT_BODY=$(cat <<EOF
          ### 🚨 Unused i18next Keys Detected
          The following translation keys are defined in \`translation.json\` but are **not used** in the codebase:
          $FILTERED_KEYS
          ⚠️ **Please remove these unused keys to keep the translation files clean.**
          EOF
          )
          gh api "repos/${{ github.repository }}/issues/${PR_NUMBER}/comments" \
            -f body="$COMMENT_BODY" \
            -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}"
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Fail workflow if unused keys found
        if: env.unused_keys != '[]'
        run: exit 1
--- a/.github/workflows/locize-i18n-sync.yml
+++ b/.github/workflows/locize-i18n-sync.yml
@ -1,72 +0,0 @@
 name: Sync Locize Translations & Create Translation PR
 on:
  push:
    branches: [main]
  repository_dispatch:
    types: [locize/versionPublished]
 jobs:
  sync-translations:
    name: Sync Translation Keys with Locize
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Repository
        uses: actions/checkout@v4
      - name: Set Up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: 20
      - name: Install locize CLI
        run: npm install -g locize-cli
      # Sync translations (Push missing keys & remove deleted ones)
      - name: Sync Locize with Repository
        if: ${{ github.event_name == 'push' }}
        run: |
          cd client/src/locales
          locize sync --api-key ${{ secrets.LOCIZE_API_KEY }} --project-id ${{ secrets.LOCIZE_PROJECT_ID }} --language en
      # When triggered by repository_dispatch, skip sync step.
      - name: Skip sync step on non-push events
        if: ${{ github.event_name != 'push' }}
        run: echo "Skipping sync as the event is not a push."
  create-pull-request:
    name: Create Translation PR on Version Published
    runs-on: ubuntu-latest
    needs: sync-translations
    permissions:
      contents: write
      pull-requests: write
    steps:
      # 1. Check out the repository.
      - name: Checkout Repository
        uses: actions/checkout@v4
      # 2. Download translation files from locize.
      - name: Download Translations from locize
        uses: locize/download@v2
        with:
          project-id: ${{ secrets.LOCIZE_PROJECT_ID }}
          path: "client/src/locales"
      # 3. Create a Pull Request using built-in functionality.
      - name: Create Pull Request
        uses: peter-evans/create-pull-request@v7
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          sign-commits: true
          commit-message: "🌍 i18n: Update translation.json with latest translations"
          base: main
          branch: i18n/locize-translation-update
          reviewers: danny-avila
          title: "🌍 i18n: Update translation.json with latest translations"
          body: |
            **Description**:
            - 🎯 **Objective**: Update `translation.json` with the latest translations from locize.
            - 🔍 **Details**: This PR is automatically generated upon receiving a versionPublished event with version "latest". It reflects the newest translations provided by locize.
            - ✅ **Status**: Ready for review.
          labels: "🌍 i18n"
--- a/.github/workflows/main-image-workflow.yml
+++ b/.github/workflows/main-image-workflow.yml
@ -1,69 +0,0 @@
 name: Docker Compose Build Latest Main Image Tag (Manual Dispatch)
 on:
  workflow_dispatch:
 jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-build
            file: Dockerfile.multi
            image_name: librechat-api
          - target: node
            file: Dockerfile
            image_name: librechat
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      - name: Fetch tags and set the latest tag
        run: |
          git fetch --tags
          echo "LATEST_TAG=$(git describe --tags `git rev-list --tags --max-count=1`)" >> $GITHUB_ENV
      # Set up QEMU
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
      # Set up Docker Buildx
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v2
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Login to Docker Hub
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      # Prepare the environment
      - name: Prepare environment
        run: |
          cp .env.example .env
      # Build and push Docker images for each target
      - name: Build and push Docker images
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ${{ matrix.file }}
          push: true
          tags: |
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:${{ env.LATEST_TAG }}
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:latest
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:${{ env.LATEST_TAG }}
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:latest
          platforms: linux/amd64,linux/arm64
          target: ${{ matrix.target }}
--- a/.github/workflows/tag-images.yml
+++ b/.github/workflows/tag-images.yml
@ -1,67 +0,0 @@
 name: Docker Images Build on Tag
 on:
  push:
    tags:
      - '*'
 jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-build
            file: Dockerfile.multi
            image_name: librechat-api
          - target: node
            file: Dockerfile
            image_name: librechat
    steps:
      # Check out the repository
      - name: Checkout
        uses: actions/checkout@v4
      # Set up QEMU
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3
      # Set up Docker Buildx
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      # Log in to GitHub Container Registry
      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v2
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      # Login to Docker Hub
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      # Prepare the environment
      - name: Prepare environment
        run: |
          cp .env.example .env
      # Build and push Docker images for each target
      - name: Build and push Docker images
        uses: docker/build-push-action@v5
        with:
          context: .
          file: ${{ matrix.file }}
          push: true
          tags: |
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:${{ github.ref_name }}
            ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}:latest
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:${{ github.ref_name }}
            ${{ secrets.DOCKERHUB_USERNAME }}/${{ matrix.image_name }}:latest
          platforms: linux/amd64,linux/arm64
          target: ${{ matrix.target }}
--- a/.github/workflows/unused-packages.yml
+++ b/.github/workflows/unused-packages.yml
@ -1,281 +0,0 @@
 name: Detect Unused NPM Packages
 on:
  pull_request:
    paths:
      - 'package.json'
      - 'package-lock.json'
      - 'client/**'
      - 'api/**'
      - 'packages/client/**'
      - 'packages/api/**'
 jobs:
  detect-unused-packages:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js 20.x
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: 'npm'
      - name: Install depcheck
        run: npm install -g depcheck
      - name: Validate JSON files
        run: |
          for FILE in package.json client/package.json api/package.json packages/client/package.json; do
            if [[ -f "$FILE" ]]; then
              jq empty "$FILE" || (echo "::error title=Invalid JSON::$FILE is invalid" && exit 1)
            fi
          done
      - name: Extract Dependencies Used in Scripts
        id: extract-used-scripts
        run: |
          extract_deps_from_scripts() {
            local package_file=$1
            if [[ -f "$package_file" ]]; then
              jq -r '.scripts | to_entries[].value' "$package_file" | \
                grep -oE '([a-zA-Z0-9_-]+)' | sort -u > used_scripts.txt
            else
              touch used_scripts.txt
            fi
          }
          extract_deps_from_scripts "package.json"
          mv used_scripts.txt root_used_deps.txt
          extract_deps_from_scripts "client/package.json"
          mv used_scripts.txt client_used_deps.txt
          extract_deps_from_scripts "api/package.json"
          mv used_scripts.txt api_used_deps.txt
      - name: Extract Dependencies Used in Source Code
        id: extract-used-code
        run: |
          extract_deps_from_code() {
            local folder=$1
            local output_file=$2
            # Initialize empty output file
            > "$output_file"
            if [[ -d "$folder" ]]; then
              # Extract require() statements (use explicit includes for portability)
              grep -rEho "require\\(['\"]([a-zA-Z0-9@/._-]+)['\"]\\)" "$folder" \
                --include='*.js' --include='*.ts' --include='*.tsx' --include='*.jsx' --include='*.mjs' --include='*.cjs' 2>/dev/null | \
                sed -E "s/require\\(['\"]([a-zA-Z0-9@/._-]+)['\"]\\)/\1/" >> "$output_file" || true
              # Extract ES6 imports - import x from 'module'
              grep -rEho "import .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]" "$folder" \
                --include='*.js' --include='*.ts' --include='*.tsx' --include='*.jsx' --include='*.mjs' --include='*.cjs' 2>/dev/null | \
                sed -E "s/import .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]/\1/" >> "$output_file" || true
              # import 'module' (side-effect imports)
              grep -rEho "import ['\"]([a-zA-Z0-9@/._-]+)['\"]" "$folder" \
                --include='*.js' --include='*.ts' --include='*.tsx' --include='*.jsx' --include='*.mjs' --include='*.cjs' 2>/dev/null | \
                sed -E "s/import ['\"]([a-zA-Z0-9@/._-]+)['\"]/\1/" >> "$output_file" || true
              # export { x } from 'module' or export * from 'module'
              grep -rEho "export .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]" "$folder" \
                --include='*.js' --include='*.ts' --include='*.tsx' --include='*.jsx' --include='*.mjs' --include='*.cjs' 2>/dev/null | \
                sed -E "s/export .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]/\1/" >> "$output_file" || true
              # import type { x } from 'module' (TypeScript)
              grep -rEho "import type .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]" "$folder" \
                --include='*.ts' --include='*.tsx' 2>/dev/null | \
                sed -E "s/import type .* from ['\"]([a-zA-Z0-9@/._-]+)['\"]/\1/" >> "$output_file" || true
              # Remove subpath imports but keep the base package
              # For scoped packages: '@scope/pkg/subpath' -> '@scope/pkg'
              # For regular packages: 'pkg/subpath' -> 'pkg'
              # Scoped packages (must keep @scope/package, strip anything after)
              sed -i -E 's|^(@[a-zA-Z0-9_-]+/[a-zA-Z0-9_-]+)/.*|\1|' "$output_file" 2>/dev/null || true
              # Non-scoped packages (keep package name, strip subpath)
              sed -i -E 's|^([a-zA-Z0-9_-]+)/.*|\1|' "$output_file" 2>/dev/null || true
              sort -u "$output_file" -o "$output_file"
            fi
          }
          extract_deps_from_code "." root_used_code.txt
          extract_deps_from_code "client" client_used_code.txt
          extract_deps_from_code "api" api_used_code.txt
          # Extract dependencies used by workspace packages
          # These packages are used in the workspace but dependencies are provided by parent package.json
          extract_deps_from_code "packages/client" packages_client_used_code.txt
          extract_deps_from_code "packages/api" packages_api_used_code.txt
      - name: Get @librechat/client dependencies
        id: get-librechat-client-deps
        run: |
          if [[ -f "packages/client/package.json" ]]; then
            # Get all dependencies from @librechat/client (dependencies, devDependencies, and peerDependencies)
            DEPS=$(jq -r '.dependencies // {} | keys[]' packages/client/package.json 2>/dev/null || echo "")
            DEV_DEPS=$(jq -r '.devDependencies // {} | keys[]' packages/client/package.json 2>/dev/null || echo "")
            PEER_DEPS=$(jq -r '.peerDependencies // {} | keys[]' packages/client/package.json 2>/dev/null || echo "")
            # Combine all dependencies
            echo "$DEPS" > librechat_client_deps.txt
            echo "$DEV_DEPS" >> librechat_client_deps.txt
            echo "$PEER_DEPS" >> librechat_client_deps.txt
            # Also include dependencies that are imported in packages/client
            cat packages_client_used_code.txt >> librechat_client_deps.txt
            # Remove empty lines and sort
            grep -v '^$' librechat_client_deps.txt | sort -u > temp_deps.txt
            mv temp_deps.txt librechat_client_deps.txt
          else
            touch librechat_client_deps.txt
          fi
      - name: Get @librechat/api dependencies
        id: get-librechat-api-deps
        run: |
          if [[ -f "packages/api/package.json" ]]; then
            # Get all dependencies from @librechat/api (dependencies, devDependencies, and peerDependencies)
            DEPS=$(jq -r '.dependencies // {} | keys[]' packages/api/package.json 2>/dev/null || echo "")
            DEV_DEPS=$(jq -r '.devDependencies // {} | keys[]' packages/api/package.json 2>/dev/null || echo "")
            PEER_DEPS=$(jq -r '.peerDependencies // {} | keys[]' packages/api/package.json 2>/dev/null || echo "")
            # Combine all dependencies
            echo "$DEPS" > librechat_api_deps.txt
            echo "$DEV_DEPS" >> librechat_api_deps.txt
            echo "$PEER_DEPS" >> librechat_api_deps.txt
            # Also include dependencies that are imported in packages/api
            cat packages_api_used_code.txt >> librechat_api_deps.txt
            # Remove empty lines and sort
            grep -v '^$' librechat_api_deps.txt | sort -u > temp_deps.txt
            mv temp_deps.txt librechat_api_deps.txt
          else
            touch librechat_api_deps.txt
          fi
      - name: Extract Workspace Dependencies
        id: extract-workspace-deps
        run: |
          # Function to get dependencies from a workspace package that are used by another package
          get_workspace_package_deps() {
            local package_json=$1
            local output_file=$2
            # Get all workspace dependencies (starting with @librechat/)
            if [[ -f "$package_json" ]]; then
              local workspace_deps=$(jq -r '.dependencies // {} | to_entries[] | select(.key | startswith("@librechat/")) | .key' "$package_json" 2>/dev/null || echo "")
              # For each workspace dependency, get its dependencies
              for dep in $workspace_deps; do
                # Convert @librechat/api to packages/api
                local workspace_path=$(echo "$dep" | sed 's/@librechat\//packages\//')
                local workspace_package_json="${workspace_path}/package.json"
                if [[ -f "$workspace_package_json" ]]; then
                  # Extract all dependencies from the workspace package
                  jq -r '.dependencies // {} | keys[]' "$workspace_package_json" 2>/dev/null >> "$output_file"
                  # Also extract peerDependencies
                  jq -r '.peerDependencies // {} | keys[]' "$workspace_package_json" 2>/dev/null >> "$output_file"
                fi
              done
            fi
            if [[ -f "$output_file" ]]; then
              sort -u "$output_file" -o "$output_file"
            else
              touch "$output_file"
            fi
          }
          # Get workspace dependencies for each package
          get_workspace_package_deps "package.json" root_workspace_deps.txt
          get_workspace_package_deps "client/package.json" client_workspace_deps.txt
          get_workspace_package_deps "api/package.json" api_workspace_deps.txt
      - name: Run depcheck for root package.json
        id: check-root
        run: |
          if [[ -f "package.json" ]]; then
            UNUSED=$(depcheck --json | jq -r '.dependencies | join("\n")' || echo "")
            # Exclude dependencies used in scripts, code, and workspace packages
            UNUSED=$(comm -23 <(echo "$UNUSED" | sort) <(cat root_used_deps.txt root_used_code.txt root_workspace_deps.txt | sort) || echo "")
            echo "ROOT_UNUSED<<EOF" >> $GITHUB_ENV
            echo "$UNUSED" >> $GITHUB_ENV
            echo "EOF" >> $GITHUB_ENV
          fi
      - name: Run depcheck for client/package.json
        id: check-client
        run: |
          if [[ -f "client/package.json" ]]; then
            chmod -R 755 client
            cd client
            UNUSED=$(depcheck --json | jq -r '.dependencies | join("\n")' || echo "")
            # Exclude dependencies used in scripts, code, workspace packages, and @librechat/client imports
            UNUSED=$(comm -23 <(echo "$UNUSED" | sort) <(cat ../client_used_deps.txt ../client_used_code.txt ../client_workspace_deps.txt ../packages_client_used_code.txt ../librechat_client_deps.txt 2>/dev/null | sort -u) || echo "")
            # Filter out false positives
            UNUSED=$(echo "$UNUSED" | grep -v "^micromark-extension-llm-math$" || echo "")
            echo "CLIENT_UNUSED<<EOF" >> $GITHUB_ENV
            echo "$UNUSED" >> $GITHUB_ENV
            echo "EOF" >> $GITHUB_ENV
            cd ..
          fi
      - name: Run depcheck for api/package.json
        id: check-api
        run: |
          if [[ -f "api/package.json" ]]; then
            chmod -R 755 api
            cd api
            UNUSED=$(depcheck --json | jq -r '.dependencies | join("\n")' || echo "")
            # Exclude dependencies used in scripts, code, workspace packages, and @librechat/api imports
            UNUSED=$(comm -23 <(echo "$UNUSED" | sort) <(cat ../api_used_deps.txt ../api_used_code.txt ../api_workspace_deps.txt ../packages_api_used_code.txt ../librechat_api_deps.txt 2>/dev/null | sort -u) || echo "")
            echo "API_UNUSED<<EOF" >> $GITHUB_ENV
            echo "$UNUSED" >> $GITHUB_ENV
            echo "EOF" >> $GITHUB_ENV
            cd ..
          fi
      - name: Post comment on PR if unused dependencies are found
        if: env.ROOT_UNUSED != '' || env.CLIENT_UNUSED != '' || env.API_UNUSED != ''
        run: |
          PR_NUMBER=$(jq --raw-output .pull_request.number "$GITHUB_EVENT_PATH")
          ROOT_LIST=$(echo "$ROOT_UNUSED" | awk '{print "- `" $0 "`"}')
          CLIENT_LIST=$(echo "$CLIENT_UNUSED" | awk '{print "- `" $0 "`"}')
          API_LIST=$(echo "$API_UNUSED" | awk '{print "- `" $0 "`"}')
          COMMENT_BODY=$(cat <<EOF
          ### 🚨 Unused NPM Packages Detected
          The following **unused dependencies** were found:
          $(if [[ ! -z "$ROOT_UNUSED" ]]; then echo "#### 📂 Root \`package.json\`"; echo ""; echo "$ROOT_LIST"; echo ""; fi)
          $(if [[ ! -z "$CLIENT_UNUSED" ]]; then echo "#### 📂 Client \`client/package.json\`"; echo ""; echo "$CLIENT_LIST"; echo ""; fi)
          $(if [[ ! -z "$API_UNUSED" ]]; then echo "#### 📂 API \`api/package.json\`"; echo ""; echo "$API_LIST"; echo ""; fi)
          ⚠️ **Please remove these unused dependencies to keep your project clean.**
          EOF
          )
          gh api "repos/${{ github.repository }}/issues/${PR_NUMBER}/comments" \
            -f body="$COMMENT_BODY" \
            -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}"
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Fail workflow if unused dependencies found
        if: env.ROOT_UNUSED != '' || env.CLIENT_UNUSED != '' || env.API_UNUSED != ''
        run: exit 1
--- a/.gitignore
+++ b/.gitignore
@ -2,8 +2,7 @@
 # Logs
 data-node
-meili_data*
+meili_data
 data/
 logs
 *.log
@ -11,10 +10,6 @@ logs
 pids
 *.pid
 *.seed
 .git
 # CI/CD data
 test-image*
 # Directory for instrumented libs generated by jscoverage/JSCover
 lib-cov
@ -25,25 +20,16 @@ coverage
 # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files)
 .grunt
 # translation services
 config/translations/stores/*
 client/src/localization/languages/*_missing_keys.json
 # Compiled Dirs (http://nodejs.org/api/addons.html)
 build/
 dist/
 public/main.js
 public/main.js.map
 public/main.js.LICENSE.txt
 client/public/images/
 client/public/main.js
 client/public/main.js.map
 client/public/main.js.LICENSE.txt
 # Azure Blob Storage Emulator (Azurite)
 __azurite**
 __blobstorage__/**/*
 # Dependency directorys
 # Deployed apps should consider commenting these lines out:
 # see https://npmjs.org/doc/faq.html#Should-I-check-my-node_modules-folder-into-git
@ -52,13 +38,6 @@ meili_data/
 api/node_modules/
 client/node_modules/
 bower_components/
 *.d.ts
 !vite-env.d.ts
 # AI
 .clineignore
 .cursor
 .aider*
 # Floobits
 .floo
@ -66,106 +45,12 @@ bower_components/
 .floo
 .flooignore
 #config file
 librechat.yaml
 librechat.yml
 # Environment
-.npmrc
+.env
 .env*
 my.secrets
 !**/.env.example
 !**/.env.test.example
 cache.json
 api/data/
 owner.yml
 archive
 .vscode/settings.json
 src/style - official.css
 /e2e/specs/.test-results/
 /e2e/playwright-report/
 /playwright/.cache/
 .DS_Store
 *.code-workspace
 .idx
 monospace.json
 .idea
 *.iml
 *.pem
 config.local.ts
 **/storageState.json
 junit.xml
 **/.venv/
 **/venv/
-# docker override file
+src/style - official.css
 docker-compose.override.yaml
 docker-compose.override.yml
 # meilisearch
 meilisearch
 meilisearch.exe
 data.ms/*
 auth.json
 /packages/ux-shared/
 /images
 !client/src/components/Nav/SettingsTabs/Data/
 # User uploads
 uploads/
 # owner
 release/
 # Helm
 helm/librechat/Chart.lock
 helm/**/charts/
 helm/**/.values.yaml
 !/client/src/@types/i18next.d.ts
 # SAML Idp cert
 *.cert
 # AI Assistants
 /.claude/
 /.cursor/
 /.copilot/
 /.aider/
 /.openai/
 /.tabnine/
 /.codeium
 *.local.md
 # Removed Windows wrapper files per user request
 hive-mind-prompt-*.txt
 # Claude Flow generated files
 .claude/settings.local.json
 .mcp.json
 claude-flow.config.json
 .swarm/
 .hive-mind/
 .claude-flow/
 memory/
 coordination/
 memory/claude-flow-data.json
 memory/sessions/*
 !memory/sessions/README.md
 memory/agents/*
 !memory/agents/README.md
 coordination/memory_bank/*
 coordination/subtasks/*
 coordination/orchestration/*
 *.db
 *.db-journal
 *.db-wal
 *.sqlite
 *.sqlite-journal
 *.sqlite-wal
 claude-flow
 # Removed Windows wrapper files per user request
 hive-mind-prompt-*.txt
--- a/.husky/lint-staged.config.js
+++ b/.husky/lint-staged.config.js
@ -1,4 +0,0 @@
 module.exports = {
  '*.{js,jsx,ts,tsx}': ['prettier --write', 'eslint --fix', 'eslint'],
  '*.json': ['prettier --write'],
 };
--- a/.husky/pre-commit
+++ b/.husky/pre-commit
@ -1,2 +0,0 @@
 [ -n "$CI" ] && exit 0
 npx lint-staged --config ./.husky/lint-staged.config.js
--- a/.prettierrc
+++ b/.prettierrc
@ -1,19 +0,0 @@
 {
  "tailwindConfig": "./client/tailwind.config.cjs",
  "printWidth": 100,
  "tabWidth": 2,
  "useTabs": false,
  "semi": true,
  "singleQuote": true,
  "trailingComma": "all",
  "arrowParens": "always",
  "embeddedLanguageFormatting": "auto",
  "insertPragma": false,
  "proseWrap": "preserve",
  "quoteProps": "as-needed",
  "requirePragma": false,
  "rangeStart": 0,
  "endOfLine": "auto",
  "jsxSingleQuote": false,
  "plugins": ["prettier-plugin-tailwindcss"]
 }
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@ -1,18 +0,0 @@
 {
  "version": "0.2.0",
  "configurations": [
    {
      "type": "node",
      "request": "launch",
      "name": "Launch LibreChat (debug)",
      "skipFiles": ["<node_internals>/**"],
      "program": "${workspaceFolder}/api/server/index.js",
      "env": {
        "NODE_ENV": "production",
        "NODE_TLS_REJECT_UNAUTHORIZED": "0"
      },
      "console": "integratedTerminal",
      "envFile": "${workspaceFolder}/.env"
    }
  ]
 }
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -1,236 +0,0 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 ## [Unreleased]
 ### ✨ New Features
 - ✨ feat: implement search parameter updates by **@mawburn** in [#7151](https://github.com/danny-avila/LibreChat/pull/7151)
 - 🎏 feat: Add MCP support for Streamable HTTP Transport by **@benverhees** in [#7353](https://github.com/danny-avila/LibreChat/pull/7353)
 - 🔒 feat: Add Content Security Policy using Helmet middleware by **@rubentalstra** in [#7377](https://github.com/danny-avila/LibreChat/pull/7377)
 - ✨ feat: Add Normalization for MCP Server Names by **@danny-avila** in [#7421](https://github.com/danny-avila/LibreChat/pull/7421)
 - 📊 feat: Improve Helm Chart by **@hofq** in [#3638](https://github.com/danny-avila/LibreChat/pull/3638)
 - 🦾 feat: Claude-4 Support by **@danny-avila** in [#7509](https://github.com/danny-avila/LibreChat/pull/7509)
 - 🪨 feat: Bedrock Support for Claude-4 Reasoning by **@danny-avila** in [#7517](https://github.com/danny-avila/LibreChat/pull/7517)
 ### 🌍 Internationalization
 - 🌍 i18n: Add `Danish` and `Czech` and `Catalan` localization support by **@rubentalstra** in [#7373](https://github.com/danny-avila/LibreChat/pull/7373)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#7375](https://github.com/danny-avila/LibreChat/pull/7375)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#7468](https://github.com/danny-avila/LibreChat/pull/7468)
 ### 🔧 Fixes
 - 💬 fix: update aria-label for accessibility in ConvoLink component by **@berry-13** in [#7320](https://github.com/danny-avila/LibreChat/pull/7320)
 - 🔑 fix: use `apiKey` instead of `openAIApiKey` in OpenAI-like Config by **@danny-avila** in [#7337](https://github.com/danny-avila/LibreChat/pull/7337)
 - 🔄 fix: update navigation logic in `useFocusChatEffect` to ensure correct search parameters are used by **@mawburn** in [#7340](https://github.com/danny-avila/LibreChat/pull/7340)
 - 🔄 fix: Improve MCP Connection Cleanup by **@danny-avila** in [#7400](https://github.com/danny-avila/LibreChat/pull/7400)
 - 🛡️ fix: Preset and Validation Logic for URL Query Params by **@danny-avila** in [#7407](https://github.com/danny-avila/LibreChat/pull/7407)
 - 🌘 fix: artifact of preview text is illegible in dark mode by **@nhtruong** in [#7405](https://github.com/danny-avila/LibreChat/pull/7405)
 - 🛡️ fix: Temporarily Remove CSP until Configurable by **@danny-avila** in [#7419](https://github.com/danny-avila/LibreChat/pull/7419)
 - 💽 fix: Exclude index page `/` from static cache settings by **@sbruel** in [#7382](https://github.com/danny-avila/LibreChat/pull/7382)
 ### ⚙️ Other Changes
 - 📜 docs: CHANGELOG for release v0.7.8 by **@github-actions[bot]** in [#7290](https://github.com/danny-avila/LibreChat/pull/7290)
 - 📦 chore: Update API Package Dependencies by **@danny-avila** in [#7359](https://github.com/danny-avila/LibreChat/pull/7359)
 - 📜 docs: Unreleased Changelog by **@github-actions[bot]** in [#7321](https://github.com/danny-avila/LibreChat/pull/7321)
 - 📜 docs: Unreleased Changelog by **@github-actions[bot]** in [#7434](https://github.com/danny-avila/LibreChat/pull/7434)
 - 🛡️ chore: `multer` v2.0.0 for CVE-2025-47935 and CVE-2025-47944 by **@danny-avila** in [#7454](https://github.com/danny-avila/LibreChat/pull/7454)
 - 📂 refactor: Improve `FileAttachment` & File Form Deletion by **@danny-avila** in [#7471](https://github.com/danny-avila/LibreChat/pull/7471)
 - 📊 chore: Remove Old Helm Chart by **@hofq** in [#7512](https://github.com/danny-avila/LibreChat/pull/7512)
 - 🪖 chore: bump helm app version to v0.7.8 by **@austin-barrington** in [#7524](https://github.com/danny-avila/LibreChat/pull/7524)
 ---
 ## [v0.7.8] - 
 Changes from v0.7.8-rc1 to v0.7.8.
 ### ✨ New Features
 - ✨ feat: Enhance form submission for touch screens by **@berry-13** in [#7198](https://github.com/danny-avila/LibreChat/pull/7198)
 - 🔍 feat: Additional Tavily API Tool Parameters by **@glowforge-opensource** in [#7232](https://github.com/danny-avila/LibreChat/pull/7232)
 - 🐋 feat: Add python to Dockerfile for increased MCP compatibility by **@technicalpickles** in [#7270](https://github.com/danny-avila/LibreChat/pull/7270)
 ### 🔧 Fixes
 - 🔧 fix: Google Gemma Support & OpenAI Reasoning Instructions by **@danny-avila** in [#7196](https://github.com/danny-avila/LibreChat/pull/7196)
 - 🛠️ fix: Conversation Navigation State by **@danny-avila** in [#7210](https://github.com/danny-avila/LibreChat/pull/7210)
 - 🔄 fix: o-Series Model Regex for System Messages by **@danny-avila** in [#7245](https://github.com/danny-avila/LibreChat/pull/7245)
 - 🔖 fix: Custom Headers for Initial MCP SSE Connection by **@danny-avila** in [#7246](https://github.com/danny-avila/LibreChat/pull/7246)
 - 🛡️ fix: Deep Clone `MCPOptions` for User MCP Connections by **@danny-avila** in [#7247](https://github.com/danny-avila/LibreChat/pull/7247)
 - 🔄 fix: URL Param Race Condition and File Draft Persistence by **@danny-avila** in [#7257](https://github.com/danny-avila/LibreChat/pull/7257)
 - 🔄 fix: Assistants Endpoint & Minor Issues by **@danny-avila** in [#7274](https://github.com/danny-avila/LibreChat/pull/7274)
 - 🔄 fix: Ollama Think Tag Edge Case with Tools by **@danny-avila** in [#7275](https://github.com/danny-avila/LibreChat/pull/7275)
 ### ⚙️ Other Changes
 - 📜 docs: CHANGELOG for release v0.7.8-rc1 by **@github-actions[bot]** in [#7153](https://github.com/danny-avila/LibreChat/pull/7153)
 - 🔄 refactor: Artifact Visibility Management by **@danny-avila** in [#7181](https://github.com/danny-avila/LibreChat/pull/7181)
 - 📦 chore: Bump Package Security by **@danny-avila** in [#7183](https://github.com/danny-avila/LibreChat/pull/7183)
 - 🌿 refactor: Unmount Fork Popover on Hide for Better Performance by **@danny-avila** in [#7189](https://github.com/danny-avila/LibreChat/pull/7189)
 - 🧰 chore: ESLint configuration to enforce Prettier formatting rules by **@mawburn** in [#7186](https://github.com/danny-avila/LibreChat/pull/7186)
 - 🎨 style: Improve KaTeX Rendering for LaTeX Equations by **@andresgit** in [#7223](https://github.com/danny-avila/LibreChat/pull/7223)
 - 📝 docs: Update `.env.example` Google models by **@marlonka** in [#7254](https://github.com/danny-avila/LibreChat/pull/7254)
 - 💬 refactor: MCP Chat Visibility Option, Google Rates, Remove OpenAPI Plugins by **@danny-avila** in [#7286](https://github.com/danny-avila/LibreChat/pull/7286)
 - 📜 docs: Unreleased Changelog by **@github-actions[bot]** in [#7214](https://github.com/danny-avila/LibreChat/pull/7214)
 [See full release details][release-v0.7.8]
 [release-v0.7.8]: https://github.com/danny-avila/LibreChat/releases/tag/v0.7.8
 ---
 ## [v0.7.8-rc1] - 
 Changes from v0.7.7 to v0.7.8-rc1.
 ### ✨ New Features
 - 🔍 feat: Mistral OCR API / Upload Files as Text by **@danny-avila** in [#6274](https://github.com/danny-avila/LibreChat/pull/6274)
 - 🤖 feat: Support OpenAI Web Search models by **@danny-avila** in [#6313](https://github.com/danny-avila/LibreChat/pull/6313)
 - 🔗 feat: Agent Chain (Mixture-of-Agents) by **@danny-avila** in [#6374](https://github.com/danny-avila/LibreChat/pull/6374)
 - ⌛ feat: `initTimeout` for Slow Starting MCP Servers by **@perweij** in [#6383](https://github.com/danny-avila/LibreChat/pull/6383)
 - 🚀 feat: `S3` Integration for File handling and Image uploads by **@rubentalstra** in [#6142](https://github.com/danny-avila/LibreChat/pull/6142)
 - 🔒feat: Enable OpenID Auto-Redirect by **@leondape** in [#6066](https://github.com/danny-avila/LibreChat/pull/6066)
 - 🚀 feat: Integrate `Azure Blob Storage` for file handling and image uploads by **@rubentalstra** in [#6153](https://github.com/danny-avila/LibreChat/pull/6153)
 - 🚀 feat: Add support for custom `AWS` endpoint in `S3` by **@rubentalstra** in [#6431](https://github.com/danny-avila/LibreChat/pull/6431)
 - 🚀 feat: Add support for LDAP STARTTLS in LDAP authentication by **@rubentalstra** in [#6438](https://github.com/danny-avila/LibreChat/pull/6438)
 - 🚀 feat: Refactor schema exports and update package version to 0.0.4 by **@rubentalstra** in [#6455](https://github.com/danny-avila/LibreChat/pull/6455)
 - 🔼 feat: Add Auto Submit For URL Query Params by **@mjaverto** in [#6440](https://github.com/danny-avila/LibreChat/pull/6440)
 - 🛠 feat: Enhance Redis Integration, Rate Limiters & Log Headers by **@danny-avila** in [#6462](https://github.com/danny-avila/LibreChat/pull/6462)
 - 💵 feat: Add Automatic Balance Refill by **@rubentalstra** in [#6452](https://github.com/danny-avila/LibreChat/pull/6452)
 - 🗣️ feat: add support for gpt-4o-transcribe models by **@berry-13** in [#6483](https://github.com/danny-avila/LibreChat/pull/6483)
 - 🎨 feat: UI Refresh for Enhanced UX by **@berry-13** in [#6346](https://github.com/danny-avila/LibreChat/pull/6346)
 - 🌍 feat: Add support for Hungarian language localization by **@rubentalstra** in [#6508](https://github.com/danny-avila/LibreChat/pull/6508)
 - 🚀 feat: Add Gemini 2.5 Token/Context Values, Increase Max Possible Output to 64k by **@danny-avila** in [#6563](https://github.com/danny-avila/LibreChat/pull/6563)
 - 🚀 feat: Enhance MCP Connections For Multi-User Support by **@danny-avila** in [#6610](https://github.com/danny-avila/LibreChat/pull/6610)
 - 🚀 feat: Enhance S3 URL Expiry with Refresh; fix: S3 File Deletion by **@danny-avila** in [#6647](https://github.com/danny-avila/LibreChat/pull/6647)
 - 🚀 feat: enhance UI components and refactor settings by **@berry-13** in [#6625](https://github.com/danny-avila/LibreChat/pull/6625)
 - 💬 feat: move TemporaryChat to the Header by **@berry-13** in [#6646](https://github.com/danny-avila/LibreChat/pull/6646)
 - 🚀 feat: Use Model Specs + Specific Endpoints, Limit Providers for Agents by **@danny-avila** in [#6650](https://github.com/danny-avila/LibreChat/pull/6650)
 - 🪙 feat: Sync Balance Config on Login by **@danny-avila** in [#6671](https://github.com/danny-avila/LibreChat/pull/6671)
 - 🔦 feat: MCP Support for Non-Agent Endpoints by **@danny-avila** in [#6775](https://github.com/danny-avila/LibreChat/pull/6775)
 - 🗃️ feat: Code Interpreter File Persistence between Sessions by **@danny-avila** in [#6790](https://github.com/danny-avila/LibreChat/pull/6790)
 - 🖥️ feat: Code Interpreter API for Non-Agent Endpoints by **@danny-avila** in [#6803](https://github.com/danny-avila/LibreChat/pull/6803)
 - ⚡ feat: Self-hosted Artifacts Static Bundler URL by **@danny-avila** in [#6827](https://github.com/danny-avila/LibreChat/pull/6827)
 - 🐳 feat: Add Jemalloc and UV to Docker Builds by **@danny-avila** in [#6836](https://github.com/danny-avila/LibreChat/pull/6836)
 - 🤖 feat: GPT-4.1 by **@danny-avila** in [#6880](https://github.com/danny-avila/LibreChat/pull/6880)
 - 👋 feat: remove Edge TTS by **@berry-13** in [#6885](https://github.com/danny-avila/LibreChat/pull/6885)
 - feat: nav optimization  by **@berry-13** in [#5785](https://github.com/danny-avila/LibreChat/pull/5785)
 - 🗺️ feat: Add Parameter Location Mapping for OpenAPI actions by **@peeeteeer** in [#6858](https://github.com/danny-avila/LibreChat/pull/6858)
 - 🤖 feat: Support `o4-mini` and `o3` Models by **@danny-avila** in [#6928](https://github.com/danny-avila/LibreChat/pull/6928)
 - 🎨 feat: OpenAI Image Tools (GPT-Image-1) by **@danny-avila** in [#7079](https://github.com/danny-avila/LibreChat/pull/7079)
 - 🗓️ feat: Add Special Variables for Prompts & Agents, Prompt UI Improvements by **@danny-avila** in [#7123](https://github.com/danny-avila/LibreChat/pull/7123)
 ### 🌍 Internationalization
 - 🌍 i18n: Add Thai Language Support and Update Translations by **@rubentalstra** in [#6219](https://github.com/danny-avila/LibreChat/pull/6219)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6220](https://github.com/danny-avila/LibreChat/pull/6220)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6240](https://github.com/danny-avila/LibreChat/pull/6240)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6241](https://github.com/danny-avila/LibreChat/pull/6241)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6277](https://github.com/danny-avila/LibreChat/pull/6277)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6414](https://github.com/danny-avila/LibreChat/pull/6414)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6505](https://github.com/danny-avila/LibreChat/pull/6505)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6530](https://github.com/danny-avila/LibreChat/pull/6530)
 - 🌍 i18n: Add Persian Localization Support by **@rubentalstra** in [#6669](https://github.com/danny-avila/LibreChat/pull/6669)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#6667](https://github.com/danny-avila/LibreChat/pull/6667)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#7126](https://github.com/danny-avila/LibreChat/pull/7126)
 - 🌍 i18n: Update translation.json with latest translations by **@github-actions[bot]** in [#7148](https://github.com/danny-avila/LibreChat/pull/7148)
 ### 👐 Accessibility
 - 🎨 a11y: Update Model Spec Description Text by **@berry-13** in [#6294](https://github.com/danny-avila/LibreChat/pull/6294)
 - 🗑️ a11y: Add Accessible Name to Button for File Attachment Removal by **@kangabell** in [#6709](https://github.com/danny-avila/LibreChat/pull/6709)
 - ⌨️ a11y: enhance accessibility & visual consistency by **@berry-13** in [#6866](https://github.com/danny-avila/LibreChat/pull/6866)
 - 🙌 a11y: Searchbar/Conversations List Focus by **@danny-avila** in [#7096](https://github.com/danny-avila/LibreChat/pull/7096)
 - 👐 a11y: Improve Fork and SplitText Accessibility by **@danny-avila** in [#7147](https://github.com/danny-avila/LibreChat/pull/7147)
 ### 🔧 Fixes
 - 🐛 fix: Avatar Type Definitions in Agent/Assistant Schemas by **@danny-avila** in [#6235](https://github.com/danny-avila/LibreChat/pull/6235)
 - 🔧 fix: MeiliSearch Field Error and Patch Incorrect Import by #6210 by **@rubentalstra** in [#6245](https://github.com/danny-avila/LibreChat/pull/6245)
 - 🔏 fix: Enhance Two-Factor Authentication by **@rubentalstra** in [#6247](https://github.com/danny-avila/LibreChat/pull/6247)
 - 🐛 fix: Await saveMessage in abortMiddleware to ensure proper execution by **@sh4shii** in [#6248](https://github.com/danny-avila/LibreChat/pull/6248)
 - 🔧 fix: Axios Proxy Usage And Bump `mongoose` by **@danny-avila** in [#6298](https://github.com/danny-avila/LibreChat/pull/6298)
 - 🔧 fix: comment out MCP servers to resolve service run issues by **@KunalScriptz** in [#6316](https://github.com/danny-avila/LibreChat/pull/6316)
 - 🔧 fix: Update Token Calculations and Mapping, MCP `env` Initialization by **@danny-avila** in [#6406](https://github.com/danny-avila/LibreChat/pull/6406)
 - 🐞 fix: Agent "Resend" Message Attachments + Source Icon Styling by **@danny-avila** in [#6408](https://github.com/danny-avila/LibreChat/pull/6408)
 - 🐛 fix: Prevent Crash on Duplicate Message ID by **@Odrec** in [#6392](https://github.com/danny-avila/LibreChat/pull/6392)
 - 🔐 fix: Invalid Key Length in 2FA Encryption by **@rubentalstra** in [#6432](https://github.com/danny-avila/LibreChat/pull/6432)
 - 🏗️ fix: Fix Agents Token Spend Race Conditions, Expand Test Coverage by **@danny-avila** in [#6480](https://github.com/danny-avila/LibreChat/pull/6480)
 - 🔃 fix: Draft Clearing, Claude Titles, Remove Default Vision Max Tokens by **@danny-avila** in [#6501](https://github.com/danny-avila/LibreChat/pull/6501)
 - 🔧 fix: Update username reference to use user.name in greeting display by **@rubentalstra** in [#6534](https://github.com/danny-avila/LibreChat/pull/6534)
 - 🔧 fix: S3 Download Stream with Key Extraction and Blob Storage Encoding for Vision by **@danny-avila** in [#6557](https://github.com/danny-avila/LibreChat/pull/6557)
 - 🔧 fix: Mistral type strictness for `usage` & update token values/windows by **@danny-avila** in [#6562](https://github.com/danny-avila/LibreChat/pull/6562)
 - 🔧 fix: Consolidate Text Parsing and TTS Edge Initialization by **@danny-avila** in [#6582](https://github.com/danny-avila/LibreChat/pull/6582)
 - 🔧 fix: Ensure continuation in image processing on base64 encoding from Blob Storage by **@danny-avila** in [#6619](https://github.com/danny-avila/LibreChat/pull/6619)
 - ✉️ fix: Fallback For User Name In Email Templates by **@danny-avila** in [#6620](https://github.com/danny-avila/LibreChat/pull/6620)
 - 🔧 fix: Azure Blob Integration and File Source References by **@rubentalstra** in [#6575](https://github.com/danny-avila/LibreChat/pull/6575)
 - 🐛 fix: Safeguard against undefined addedEndpoints by **@wipash** in [#6654](https://github.com/danny-avila/LibreChat/pull/6654)
 - 🤖 fix: Gemini 2.5 Vision Support by **@danny-avila** in [#6663](https://github.com/danny-avila/LibreChat/pull/6663)
 - 🔄 fix: Avatar & Error Handling Enhancements by **@danny-avila** in [#6687](https://github.com/danny-avila/LibreChat/pull/6687)
 - 🔧 fix: Chat Middleware, Zod Conversion, Auto-Save and S3 URL Refresh by **@danny-avila** in [#6720](https://github.com/danny-avila/LibreChat/pull/6720)
 - 🔧 fix: Agent Capability Checks & DocumentDB Compatibility for Agent Resource Removal by **@danny-avila** in [#6726](https://github.com/danny-avila/LibreChat/pull/6726)
 - 🔄 fix: Improve audio MIME type detection and handling by **@berry-13** in [#6707](https://github.com/danny-avila/LibreChat/pull/6707)
 - 🪺 fix: Update Role Handling due to New Schema Shape by **@danny-avila** in [#6774](https://github.com/danny-avila/LibreChat/pull/6774)
 - 🗨️ fix: Show ModelSpec Greeting by **@berry-13** in [#6770](https://github.com/danny-avila/LibreChat/pull/6770)
 - 🔧 fix: Keyv and Proxy Issues, and More Memory Optimizations by **@danny-avila** in [#6867](https://github.com/danny-avila/LibreChat/pull/6867)
 - ✨ fix: Implement dynamic text sizing for greeting and name display by **@berry-13** in [#6833](https://github.com/danny-avila/LibreChat/pull/6833)
 - 📝 fix: Mistral OCR Image Support and Azure Agent Titles by **@danny-avila** in [#6901](https://github.com/danny-avila/LibreChat/pull/6901)
 - 📢 fix: Invalid `engineTTS` and Conversation State on Navigation by **@berry-13** in [#6904](https://github.com/danny-avila/LibreChat/pull/6904)
 - 🛠️ fix: Improve Accessibility and Display of Conversation Menu by **@danny-avila** in [#6913](https://github.com/danny-avila/LibreChat/pull/6913)
 - 🔧 fix: Agent Resource Form, Convo Menu Style, Ensure Draft Clears on Submission by **@danny-avila** in [#6925](https://github.com/danny-avila/LibreChat/pull/6925)
 - 🔀 fix: MCP Improvements, Auto-Save Drafts, Artifact Markup by **@danny-avila** in [#7040](https://github.com/danny-avila/LibreChat/pull/7040)
 - 🐋 fix: Improve Deepseek Compatbility by **@danny-avila** in [#7132](https://github.com/danny-avila/LibreChat/pull/7132)
 - 🐙 fix: Add Redis Ping Interval to Prevent Connection Drops by **@peeeteeer** in [#7127](https://github.com/danny-avila/LibreChat/pull/7127)
 ### ⚙️ Other Changes
 - 📦 refactor: Move DB Models to `@librechat/data-schemas` by **@rubentalstra** in [#6210](https://github.com/danny-avila/LibreChat/pull/6210)
 - 📦 chore: Patch `axios` to address CVE-2025-27152 by **@danny-avila** in [#6222](https://github.com/danny-avila/LibreChat/pull/6222)
 - ⚠️ refactor: Use Error Content Part Instead Of Throwing Error for Agents by **@danny-avila** in [#6262](https://github.com/danny-avila/LibreChat/pull/6262)
 - 🏃‍♂️ refactor: Improve Agent Run Context & Misc. Changes by **@danny-avila** in [#6448](https://github.com/danny-avila/LibreChat/pull/6448)
 - 📝 docs: librechat.example.yaml by **@ineiti** in [#6442](https://github.com/danny-avila/LibreChat/pull/6442)
 - 🏃‍♂️ refactor: More Agent Context Improvements during Run by **@danny-avila** in [#6477](https://github.com/danny-avila/LibreChat/pull/6477)
 - 🔃 refactor: Allow streaming for `o1` models by **@danny-avila** in [#6509](https://github.com/danny-avila/LibreChat/pull/6509)
 - 🔧 chore: `Vite` Plugin Upgrades & Config Optimizations by **@rubentalstra** in [#6547](https://github.com/danny-avila/LibreChat/pull/6547)
 - 🔧 refactor: Consolidate Logging, Model Selection & Actions Optimizations, Minor Fixes by **@danny-avila** in [#6553](https://github.com/danny-avila/LibreChat/pull/6553)
 - 🎨 style: Address Minor UI Refresh Issues by **@berry-13** in [#6552](https://github.com/danny-avila/LibreChat/pull/6552)
 - 🔧 refactor: Enhance Model & Endpoint Configurations with Global Indicators 🌍 by **@berry-13** in [#6578](https://github.com/danny-avila/LibreChat/pull/6578)
 - 💬 style: Chat UI, Greeting, and Message adjustments by **@berry-13** in [#6612](https://github.com/danny-avila/LibreChat/pull/6612)
 - ⚡ refactor: DocumentDB Compatibility for Balance Updates by **@danny-avila** in [#6673](https://github.com/danny-avila/LibreChat/pull/6673)
 - 🧹 chore: Update ESLint rules for React hooks by **@rubentalstra** in [#6685](https://github.com/danny-avila/LibreChat/pull/6685)
 - 🪙 chore: Update Gemini Pricing by **@RedwindA** in [#6731](https://github.com/danny-avila/LibreChat/pull/6731)
 - 🪺 refactor: Nest Permission fields for Roles by **@rubentalstra** in [#6487](https://github.com/danny-avila/LibreChat/pull/6487)
 - 📦 chore: Update `caniuse-lite` dependency to version 1.0.30001706 by **@rubentalstra** in [#6482](https://github.com/danny-avila/LibreChat/pull/6482)
 - ⚙️ refactor: OAuth Flow Signal, Type Safety, Tool Progress & Updated Packages by **@danny-avila** in [#6752](https://github.com/danny-avila/LibreChat/pull/6752)
 - 📦 chore: bump vite from 6.2.3 to 6.2.5 by **@dependabot[bot]** in [#6745](https://github.com/danny-avila/LibreChat/pull/6745)
 - 💾 chore: Enhance Local Storage Handling and Update MCP SDK by **@danny-avila** in [#6809](https://github.com/danny-avila/LibreChat/pull/6809)
 - 🤖 refactor: Improve Agents Memory Usage, Bump Keyv, Grok 3 by **@danny-avila** in [#6850](https://github.com/danny-avila/LibreChat/pull/6850)
 - 💾 refactor: Enhance Memory In Image Encodings & Client Disposal by **@danny-avila** in [#6852](https://github.com/danny-avila/LibreChat/pull/6852)
 - 🔁 refactor: Token Event Handler and Standardize `maxTokens` Key by **@danny-avila** in [#6886](https://github.com/danny-avila/LibreChat/pull/6886)
 - 🔍 refactor: Search & Message Retrieval by **@berry-13** in [#6903](https://github.com/danny-avila/LibreChat/pull/6903)
 - 🎨 style: standardize dropdown styling & fix z-Index layering by **@berry-13** in [#6939](https://github.com/danny-avila/LibreChat/pull/6939)
 - 📙 docs: CONTRIBUTING.md by **@dblock** in [#6831](https://github.com/danny-avila/LibreChat/pull/6831)
 - 🧭 refactor: Modernize Nav/Header by **@danny-avila** in [#7094](https://github.com/danny-avila/LibreChat/pull/7094)
 - 🪶 refactor: Chat Input Focus for Conversation Navigations & ChatForm Optimizations by **@danny-avila** in [#7100](https://github.com/danny-avila/LibreChat/pull/7100)
 - 🔃 refactor: Streamline Navigation, Message Loading UX by **@danny-avila** in [#7118](https://github.com/danny-avila/LibreChat/pull/7118)
 - 📜 docs: Unreleased changelog by **@github-actions[bot]** in [#6265](https://github.com/danny-avila/LibreChat/pull/6265)
 [See full release details][release-v0.7.8-rc1]
 [release-v0.7.8-rc1]: https://github.com/danny-avila/LibreChat/releases/tag/v0.7.8-rc1
 ---
--- a/82
+++ b/82
@ -1,57 +1,35 @@
-# v0.8.2-rc1
+FROM node:19-alpine AS react-client
 WORKDIR /client
 # copy package.json into the container at /client
 COPY /client/package*.json /client/
 # install dependencies
 RUN npm ci
 # Copy the current directory contents into the container at /client
 COPY /client/ /client/
 # Build webpack artifacts
 RUN npm run build
-# Base node image
+FROM node:19-alpine AS node-api
-FROM node:20-alpine AS node
+WORKDIR /api
-
+# copy package.json into the container at /api
-# Install jemalloc
+COPY /api/package*.json /api/
-RUN apk add --no-cache jemalloc
+# install dependencies
-RUN apk add --no-cache python3 py3-pip uv
+RUN npm ci
-
+# Copy the current directory contents into the container at /api
-# Set environment variable to use jemalloc
+COPY /api/ /api/
-ENV LD_PRELOAD=/usr/lib/libjemalloc.so.2
+# Copy the client side code
-
+COPY --from=react-client /client/public /client/public
-# Add `uv` for extended MCP support
+# Make port 3080 available to the world outside this container
 COPY --from=ghcr.io/astral-sh/uv:0.9.5-python3.12-alpine /usr/local/bin/uv /usr/local/bin/uvx /bin/
 RUN uv --version
 RUN mkdir -p /app && chown node:node /app
 WORKDIR /app
 USER node
 COPY --chown=node:node package.json package-lock.json ./
 COPY --chown=node:node api/package.json ./api/package.json
 COPY --chown=node:node client/package.json ./client/package.json
 COPY --chown=node:node packages/data-provider/package.json ./packages/data-provider/package.json
 COPY --chown=node:node packages/data-schemas/package.json ./packages/data-schemas/package.json
 COPY --chown=node:node packages/api/package.json ./packages/api/package.json
 RUN \
    # Allow mounting of these files, which have no default
    touch .env ; \
    # Create directories for the volumes to inherit the correct permissions
    mkdir -p /app/client/public/images /app/logs /app/uploads ; \
    npm config set fetch-retry-maxtimeout 600000 ; \
    npm config set fetch-retries 5 ; \
    npm config set fetch-retry-mintimeout 15000 ; \
    npm ci --no-audit
 COPY --chown=node:node . .
 RUN \
    # React client build
    NODE_OPTIONS="--max-old-space-size=2048" npm run frontend; \
    npm prune --production; \
    npm cache clean --force
 # Node API setup
 EXPOSE 3080
 # Expose the server to 0.0.0.0
 ENV HOST=0.0.0.0
-CMD ["npm", "run", "backend"]
+# Run the app when the container launches
 CMD ["npm", "start"]
 # Optional: for client with nginx routing
-# FROM nginx:stable-alpine AS nginx-client
+FROM nginx:stable-alpine AS nginx-client
-# WORKDIR /usr/share/nginx/html
+WORKDIR /usr/share/nginx/html
-# COPY --from=node /app/client/dist /usr/share/nginx/html
+COPY --from=react-client /client/public /usr/share/nginx/html
-# COPY client/nginx.conf /etc/nginx/conf.d/default.conf
+# Add your nginx.conf
-# ENTRYPOINT ["nginx", "-g", "daemon off;"]
+COPY /client/nginx.conf /etc/nginx/conf.d/default.conf
 ENTRYPOINT ["nginx", "-g", "daemon off;"]
--- a/Dockerfile.multi
+++ b/Dockerfile.multi
@ -1,82 +0,0 @@
 # Dockerfile.multi
 # v0.8.2-rc1
 # Base for all builds
 FROM node:20-alpine AS base-min
 # Install jemalloc
 RUN apk add --no-cache jemalloc
 # Set environment variable to use jemalloc
 ENV LD_PRELOAD=/usr/lib/libjemalloc.so.2
 WORKDIR /app
 RUN apk --no-cache add curl
 RUN npm config set fetch-retry-maxtimeout 600000 && \
    npm config set fetch-retries 5 && \
    npm config set fetch-retry-mintimeout 15000
 COPY package*.json ./
 COPY packages/data-provider/package*.json ./packages/data-provider/
 COPY packages/api/package*.json ./packages/api/
 COPY packages/data-schemas/package*.json ./packages/data-schemas/
 COPY packages/client/package*.json ./packages/client/
 COPY client/package*.json ./client/
 COPY api/package*.json ./api/
 # Install all dependencies for every build
 FROM base-min AS base
 WORKDIR /app
 RUN npm ci
 # Build `data-provider` package
 FROM base AS data-provider-build
 WORKDIR /app/packages/data-provider
 COPY packages/data-provider ./
 RUN npm run build
 # Build `data-schemas` package
 FROM base AS data-schemas-build
 WORKDIR /app/packages/data-schemas
 COPY packages/data-schemas ./
 COPY --from=data-provider-build /app/packages/data-provider/dist /app/packages/data-provider/dist
 RUN npm run build
 # Build `api` package
 FROM base AS api-package-build
 WORKDIR /app/packages/api
 COPY packages/api ./
 COPY --from=data-provider-build /app/packages/data-provider/dist /app/packages/data-provider/dist
 COPY --from=data-schemas-build /app/packages/data-schemas/dist /app/packages/data-schemas/dist
 RUN npm run build
 # Build `client` package
 FROM base AS client-package-build
 WORKDIR /app/packages/client
 COPY packages/client ./
 RUN npm run build
 # Client build
 FROM base AS client-build
 WORKDIR /app/client
 COPY client ./
 COPY --from=data-provider-build /app/packages/data-provider/dist /app/packages/data-provider/dist
 COPY --from=client-package-build /app/packages/client/dist /app/packages/client/dist
 COPY --from=client-package-build /app/packages/client/src /app/packages/client/src
 ENV NODE_OPTIONS="--max-old-space-size=2048"
 RUN npm run build
 # API setup (including client dist)
 FROM base-min AS api-build
 # Add `uv` for extended MCP support
 COPY --from=ghcr.io/astral-sh/uv:0.6.13 /uv /uvx /bin/
 RUN uv --version
 WORKDIR /app
 # Install only production deps
 RUN npm ci --omit=dev
 COPY api ./api
 COPY config ./config
 COPY --from=data-provider-build /app/packages/data-provider/dist ./packages/data-provider/dist
 COPY --from=data-schemas-build /app/packages/data-schemas/dist ./packages/data-schemas/dist
 COPY --from=api-package-build /app/packages/api/dist ./packages/api/dist
 COPY --from=client-build /app/client/dist ./client/dist
 WORKDIR /app/api
 EXPOSE 3080
 ENV HOST=0.0.0.0
 CMD ["node", "server/index.js"]
--- a/21
+++ b/21
@ -1,21 +0,0 @@
 MIT License
 Copyright (c) 2025 LibreChat
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 The above copyright notice and this permission notice shall be included in all
 copies or substantial portions of the Software.
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
--- a/packages/data-schemas/LICENSE
+++ b/packages/data-schemas/LICENSE
@ -1,6 +1,6 @@
 MIT License
-Copyright (c) 2025 LibreChat
+Copyright (c) 2023 Danny Avila
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
--- a/README.md
+++ b/README.md
@ -1,217 +1,330 @@
-<p align="center">
+# ChatGPT Clone #
-  <a href="https://librechat.ai">
+https://user-images.githubusercontent.com/110412045/223754183-8b7f45ce-6517-4bd5-9b39-c624745bf399.mp4
    <img src="client/public/assets/logo.svg" height="256">
  </a>
  <h1 align="center">
    <a href="https://librechat.ai">LibreChat</a>
  </h1>
 </p>
-<p align="center">
+## All AI Conversations under One Roof. ##
-  <a href="https://discord.librechat.ai"> 
+  Assistant AIs are the future and OpenAI revolutionized this movement with ChatGPT. While numerous methods exist to integrate them, this app commemorates the original styling of ChatGPT, with the ability to integrate any current/future AI models, while improving upon original client features, such as conversation search and prompt templates (currently WIP).
    <img
      src="https://img.shields.io/discord/1086345563026489514?label=&logo=discord&style=for-the-badge&logoWidth=20&logoColor=white&labelColor=000000&color=blueviolet">
  </a>
  <a href="https://www.youtube.com/@LibreChat"> 
    <img
      src="https://img.shields.io/badge/YOUTUBE-red.svg?style=for-the-badge&logo=youtube&logoColor=white&labelColor=000000&logoWidth=20">
  </a>
  <a href="https://docs.librechat.ai"> 
    <img
      src="https://img.shields.io/badge/DOCS-blue.svg?style=for-the-badge&logo=read-the-docs&logoColor=white&labelColor=000000&logoWidth=20">
  </a>
  <a aria-label="Sponsors" href="https://github.com/sponsors/danny-avila">
    <img
      src="https://img.shields.io/badge/SPONSORS-brightgreen.svg?style=for-the-badge&logo=github-sponsors&logoColor=white&labelColor=000000&logoWidth=20">
  </a>
 </p>
-<p align="center">
+  This project was started early in Feb '23, anticipating the release of the official ChatGPT API from OpenAI, and now uses it. Through this clone, you can avoid ChatGPT Plus in favor of free or pay-per-call APIs. I will soon deploy a demo of this app. Feel free to contribute, clone, or fork. Currently dockerized.
 <a href="https://railway.app/template/b5k2mn?referralCode=HI9hWz">
  <img src="https://railway.app/button.svg" alt="Deploy on Railway" height="30">
 </a>
 <a href="https://zeabur.com/templates/0X2ZY8">
  <img src="https://zeabur.com/button.svg" alt="Deploy on Zeabur" height="30"/>
 </a>
 <a href="https://template.cloud.sealos.io/deploy?templateName=librechat">
  <img src="https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg" alt="Deploy on Sealos" height="30">
 </a>
 </p>
-<p align="center">
+## Updates
-  <a href="https://www.librechat.ai/docs/translation">
+<summary><strong>2023-03-20</strong></summary>
    <img 
      src="https://img.shields.io/badge/dynamic/json.svg?style=for-the-badge&color=2096F3&label=locize&query=%24.translatedPercentage&url=https://api.locize.app/badgedata/4cb2598b-ed4d-469c-9b04-2ed531a8cb45&suffix=%+translated" 
      alt="Translation Progress">
  </a>
 </p>
 # ✨ Features
- 🖥️ **UI & Experience** inspired by ChatGPT with enhanced design and features
+**Searching messages** is almost here as I test more of its functionality. There've been a lot of great features requested and great contributions and I will work on some soon, namely, further customizing the custom gpt params with sliders similar to the OpenAI playground, and including the custom params and system messages available to Bing.
- 🤖 **AI Model Selection**:  
+The above features are next and then I will have to focus on building the **test environment.** I would **greatly appreciate** help in this area with any test environment you're familiar with (mocha, chai, jest, playwright, puppeteer). This is to aid in the velocity of contributing and to save time I spend debugging.
  - Anthropic (Claude), AWS Bedrock, OpenAI, Azure OpenAI, Google, Vertex AI, OpenAI Responses API (incl. Azure)
  - [Custom Endpoints](https://www.librechat.ai/docs/quick_start/custom_endpoints): Use any OpenAI-compatible API with LibreChat, no proxy required
  - Compatible with [Local & Remote AI Providers](https://www.librechat.ai/docs/configuration/librechat_yaml/ai_endpoints):
    - Ollama, groq, Cohere, Mistral AI, Apple MLX, koboldcpp, together.ai,
    - OpenRouter, Helicone, Perplexity, ShuttleAI, Deepseek, Qwen, and more
- 🔧 **[Code Interpreter API](https://www.librechat.ai/docs/features/code_interpreter)**: 
+On that note, I had to switch the default branch due to some breaking changes that haven't been straight forward to debug, mainly related to node-chat-gpt the main dependency of the project. Thankfully, my working branch, now switched to default as main, is working as expected.
  - Secure, Sandboxed Execution in Python, Node.js (JS/TS), Go, C/C++, Java, PHP, Rust, and Fortran
  - Seamless File Handling: Upload, process, and download files directly
  - No Privacy Concerns: Fully isolated and secure execution
- 🔦 **Agents & Tools Integration**:  
+</details>
  - **[LibreChat Agents](https://www.librechat.ai/docs/features/agents)**:
    - No-Code Custom Assistants: Build specialized, AI-driven helpers
    - Agent Marketplace: Discover and deploy community-built agents
    - Collaborative Sharing: Share agents with specific users and groups
    - Flexible & Extensible: Use MCP Servers, tools, file search, code execution, and more
    - Compatible with Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, Google, Vertex AI, Responses API, and more
    - [Model Context Protocol (MCP) Support](https://modelcontextprotocol.io/clients#librechat) for Tools
- 🔍 **Web Search**:  
+<details>
-  - Search the internet and retrieve relevant information to enhance your AI context
+<details>
-  - Combines search providers, content scrapers, and result rerankers for optimal results
+<details>
-  - **Customizable Jina Reranking**: Configure custom Jina API URLs for reranking services
+<summary><strong>2023-03-16</strong></summary>
  - **[Learn More →](https://www.librechat.ai/docs/features/web_search)**
 - 🪄 **Generative UI with Code Artifacts**:  
  - [Code Artifacts](https://youtu.be/GfTj7O4gmd0?si=WJbdnemZpJzBrJo3) allow creation of React, HTML, and Mermaid diagrams directly in chat
 - 🎨 **Image Generation & Editing**
  - Text-to-image and image-to-image with [GPT-Image-1](https://www.librechat.ai/docs/features/image_gen#1--openai-image-tools-recommended)
  - Text-to-image with [DALL-E (3/2)](https://www.librechat.ai/docs/features/image_gen#2--dalle-legacy), [Stable Diffusion](https://www.librechat.ai/docs/features/image_gen#3--stable-diffusion-local), [Flux](https://www.librechat.ai/docs/features/image_gen#4--flux), or any [MCP server](https://www.librechat.ai/docs/features/image_gen#5--model-context-protocol-mcp)
  - Produce stunning visuals from prompts or refine existing images with a single instruction
- 💾 **Presets & Context Management**:  
+[Latest release (v0.0.4)](https://github.com/danny-avila/chatgpt-clone/releases/tag/v0.0.4) includes Resubmitting messages & Branching messages, which mirrors official ChatGPT feature of editing a sent message, that then branches the conversation into separate message paths (works only with ChatGPT)
  - Create, Save, & Share Custom Presets  
  - Switch between AI Endpoints and Presets mid-chat
  - Edit, Resubmit, and Continue Messages with Conversation branching  
  - Create and share prompts with specific users and groups
  - [Fork Messages & Conversations](https://www.librechat.ai/docs/features/fork) for Advanced Context control
- 💬 **Multimodal & File Interactions**:  
+Full details and [example here](https://github.com/danny-avila/chatgpt-clone/releases/tag/v0.0.4). Message search is on the docket
  - Upload and analyze images with Claude 3, GPT-4.5, GPT-4o, o1, Llama-Vision, and Gemini 📸  
  - Chat with Files using Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, & Google 🗃️
- 🌎 **Multilingual UI**:
+</details>
-  - English, 中文 (简体), 中文 (繁體), العربية, Deutsch, Español, Français, Italiano
+<summary><strong>2023-03-12</strong></summary>
  - Polski, Português (PT), Português (BR), Русский, 日本語, Svenska, 한국어, Tiếng Việt
  - Türkçe, Nederlands, עברית, Català, Čeština, Dansk, Eesti, فارسی
  - Suomi, Magyar, Հայերեն, Bahasa Indonesia, ქართული, Latviešu, ไทย, ئۇيغۇرچە
 - 🧠 **Reasoning UI**:  
  - Dynamic Reasoning UI for Chain-of-Thought/Reasoning AI models like DeepSeek-R1
 - 🎨 **Customizable Interface**:  
  - Customizable Dropdown & Interface that adapts to both power users and newcomers
 - 🗣️ **Speech & Audio**:  
  - Chat hands-free with Speech-to-Text and Text-to-Speech  
  - Automatically send and play Audio  
  - Supports OpenAI, Azure OpenAI, and Elevenlabs
- 📥 **Import & Export Conversations**:  
+Really thankful for all the issues reported and contributions made, the project's features and improvements have accelerated as result. Honorable mention is [wtlyu](https://github.com/wtlyu) for contributing a lot of mindful code, namely hostname configuration and mobile styling. I will upload images on next release for faster docker setup, and starting updating them simultaneously with this repo.
  - Import Conversations from LibreChat, ChatGPT, Chatbot UI  
  - Export conversations as screenshots, markdown, text, json
 - 🔍 **Search & Discovery**:  
  - Search all messages/conversations
 - 👥 **Multi-User & Secure Access**:
  - Multi-User, Secure Authentication with OAuth2, LDAP, & Email Login Support
  - Built-in Moderation, and Token spend tools
- ⚙️ **Configuration & Deployment**:  
+Many improvements across the board, the biggest is being able to start conversations simultaneously (again thanks to [wtlyu](https://github.com/wtlyu) for bringing it to my attention), as you can switch conversations or start a new chat without any response streaming from a prior one, as the backend will still process/save client responses. Just watch out for any rate limiting from OpenAI/Microsoft if this is done excessively.
  - Configure Proxy, Reverse Proxy, Docker, & many Deployment options  
  - Use completely local or deploy on the cloud
 - 📖 **Open-Source & Community**:  
  - Completely Open-Source & Built in Public  
  - Community-driven development, support, and feedback
-[For a thorough review of our features, see our docs here](https://docs.librechat.ai/) 📚
+Adding support for conversation search is next! Thank you [mysticaltech](https://github.com/mysticaltech) for bringing up a method I can use for this.
 </details>
 <details>
 <summary><strong>2023-03-09</strong></summary>
 Released v.0.0.2
-## 🪶 All-In-One AI Conversations with LibreChat
+Adds Sydney (jailbroken Bing AI) to the model menu. Thank you [DavesDevFails](https://github.com/DavesDevFails) for bringing it to my attention in this [issue](https://github.com/danny-avila/chatgpt-clone/issues/13). Bing/Sydney now correctly cite links, more styling to come. Fix some overlooked bugs, and model menu doesn't close upon deleting a customGpt.
 LibreChat brings together the future of assistant AIs with the revolutionary technology of OpenAI's ChatGPT. Celebrating the original styling, LibreChat gives you the ability to integrate multiple AI models. It also integrates and enhances original client features such as conversation and message search, prompt templates and plugins.
-With LibreChat, you no longer need to opt for ChatGPT Plus and can instead use free or pay-per-call APIs. We welcome contributions, cloning, and forking to enhance the capabilities of this advanced chatbot platform.
+I've re-enabled the ChatGPT browser client (free version) since it might be working for most people, it no longer works for me. Sydney is the best free route anyway.
 </details>
 <details>
 <summary><strong>2023-03-07</strong></summary>
 Due to increased interest in the repo, I've dockerized the app as of this update for quick setup! See setup instructions below. I realize this still takes some time with installing docker dependencies, so it's on the roadmap to have a deployed demo. Besides this, I've made major improvements for a lot of the existing features across the board, mainly UI/UX.
 [![Watch the video](https://raw.githubusercontent.com/LibreChat-AI/librechat.ai/main/public/images/changelog/v0.7.6.gif)](https://www.youtube.com/watch?v=ilfwGQtJNlI)
-Click on the thumbnail to open the video☝️
+Also worth noting, the method to access the Free Version is no longer working, so I've removed it from model selection until further notice.
 </details>
 <summary><strong>Previous Updates</strong></summary>
---
+<details>
 <summary><strong>2023-03-04</strong></summary>
 Custom prompt prefixing and labeling is now supported through the official API. This nets some interesting results when you need ChatGPT for specific uses or entertainment. Select 'CustomGPT' in the model menu to configure this, and you can choose to save the configuration or reference it by conversation. Model selection will change by conversation.
 </details>
 <details>
 <summary><strong>2023-03-01</strong></summary>
 Official ChatGPT API is out! Removed davinci since the official API is extremely fast and 10x less expensive. Since user labeling and prompt prefixing is officially supported, I will add a View feature so you can set this within chat, which gives the UI an added use case. I've kept the BrowserClient, since it's free to use like the official site.
-## 🌐 Resources
+The Messages UI correctly mirrors code syntax highlighting. The exact replication of the cursor is not 1-to-1 yet, but pretty close. Later on in the project, I'll implement tests for code edge cases and explore the possibility of running code in-browser. Right now, unknown code defaults to javascript, but will detect language as close as possible.
 </details>
 <details>
 <summary><strong>2023-02-21</strong></summary>
 BingAI is integrated (although sadly limited by Microsoft with the 5 msg/convo limit, 50 msgs/day). I will need to handle the case when Bing refuses to give more answers on top of the other styling features I have in mind. Official ChatGPT use is back with the new BrowserClient. Brainstorming how to handle the UI when the Ai model changes, since conversations can't be persisted between them (or perhaps build a way to achieve this at some level).
 </details>
 <details >
 <summary><strong>2023-02-15</strong></summary>
 Just got access to Bing AI so I'll be focusing on integrating that through waylaidwanderer's 'experimental' BingAIClient.
 </details>
 <details>
 <summary><strong>2023-02-14</strong></summary>
-**GitHub Repo:**
+Official ChatGPT use is no longer possible though I recently used it with waylaidwanderer's [reverse proxy method](https://github.com/waylaidwanderer/node-chatgpt-api/blob/main/README.md#using-a-reverse-proxy), and before that, through leaked models he also discovered.
  - **RAG API:** [github.com/danny-avila/rag_api](https://github.com/danny-avila/rag_api)
  - **Website:** [github.com/LibreChat-AI/librechat.ai](https://github.com/LibreChat-AI/librechat.ai)
-**Other:**
+Currently, this project is only functional with the `text-davinci-003` model.
-  - **Website:** [librechat.ai](https://librechat.ai)
+</details>
-  - **Documentation:** [librechat.ai/docs](https://librechat.ai/docs)
+</details>
  - **Blog:** [librechat.ai/blog](https://librechat.ai/blog)
---
+# Table of Contents
 - [ChatGPT Clone](#chatgpt-clone)
  - [All AI Conversations under One Roof.](#all-ai-conversations-under-one-roof)
  - [Updates](#updates)
 - [Table of Contents](#table-of-contents)
  - [Roadmap](#roadmap)
    - [Features](#features)
    - [Tech Stack](#tech-stack)
  - [Getting Started](#getting-started)
    - [Prerequisites](#prerequisites)
  - [Usage](#usage)
    - [Local](#local)
    - [Docker](#docker)
    - [Access Tokens](#access-tokens)
    - [Proxy](#proxy)
    - [User System](#user-system)
    - [Updating](#updating)
  - [Use Cases](#use-cases)
  - [Origin](#origin)
  - [Caveats](#caveats)
    - [Regarding use of Official ChatGPT API](#regarding-use-of-official-chatgpt-api)
  - [Contributing](#contributing)
  - [License](#license)
-## 📝 Changelog
+## Roadmap
-Keep up with the latest updates by visiting the releases page and notes:
+> **Warning**
 - [Releases](https://github.com/danny-avila/LibreChat/releases)
 - [Changelog](https://www.librechat.ai/changelog) 
-**⚠️ Please consult the [changelog](https://www.librechat.ai/changelog) for breaking changes before updating.**
+>  This is a work in progress. I'm building this in public. FYI there is still a lot of tech debt to cleanup. You can follow the progress here or on my [Linkedin](https://www.linkedin.com/in/danny-avila).
---
+Here are my recently completed and planned features:
-## ⭐ Star History
+- [x] Persistent conversation
 - [x] Rename, delete conversations
 - [x] UI Error handling
 - [x] Bing AI integration
 - [x] AI model change handling (start new convos within existing, remembers last selected)
 - [x] Code block handling (highlighting, markdown, clipboard, language detection)
 - [x] Markdown handling
 - [x] Customize prompt prefix/label (custom ChatGPT using official API)
 - [x] Server convo pagination (limit fetch and load more with 'show more' button)
 - [x] Config file for easy startup (docker compose)
 - [x] Mobile styling (thanks to [wtlyu](https://github.com/wtlyu))
 - [x] Resubmit/edit sent messages (thanks to [wtlyu](https://github.com/wtlyu))
 - [ ] Message Search
 - [ ] Custom params for ChatGPT API (temp, top_p, presence_penalty)
 - [ ] Bing AI Styling (params, suggested responses, convo end, etc.) - **In progress**
 - [ ] Add warning before clearing convos
 - [ ] Build test suite for CI/CD
 - [ ] Prompt Templates/Search
 - [ ] Refactor/clean up code (tech debt)
 - [ ] Optional use of local storage for credentials
 - [ ] Deploy demo
-<p align="center">
+### Features
  <a href="https://star-history.com/#danny-avila/LibreChat&Date">
    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=danny-avila/LibreChat&type=Date&theme=dark" onerror="this.src='https://api.star-history.com/svg?repos=danny-avila/LibreChat&type=Date'" />
  </a>
 </p>
 <p align="center">
  <a href="https://trendshift.io/repositories/4685" target="_blank" style="padding: 10px;">
    <img src="https://trendshift.io/api/badge/repositories/4685" alt="danny-avila%2FLibreChat | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
  </a>
  <a href="https://runacap.com/ross-index/q1-24/" target="_blank" rel="noopener" style="margin-left: 20px;">
    <img style="width: 260px; height: 56px" src="https://runacap.com/wp-content/uploads/2024/04/ROSS_badge_white_Q1_2024.svg" alt="ROSS Index - Fastest Growing Open-Source Startups in Q1 2024 | Runa Capital" width="260" height="56"/>
  </a>
 </p>
---
+- Response streaming identical to ChatGPT through server-sent events
 - UI from original ChatGPT, including Dark mode
 - AI model selection (official ChatGPT API, BingAI, ChatGPT Free)
 - Create and Save custom ChatGPTs*
-## ✨ Contributions
+^* ChatGPT can be 'customized' by setting a system message or prompt prefix and alternate 'role' to the API request
-Contributions, suggestions, bug reports and fixes are welcome!
+[More info here](https://platform.openai.com/docs/guides/chat/instructing-chat-models). Here's an [example from this app.]()
-For new features, components, or extensions, please open an issue and discuss before sending a PR.
+### Tech Stack
-If you'd like to help translate LibreChat into your language, we'd love your contribution! Improving our translations not only makes LibreChat more accessible to users around the world but also enhances the overall user experience. Please check out our [Translation Guide](https://www.librechat.ai/docs/translation).
+- Utilizes [node-chatgpt-api](https://github.com/waylaidwanderer/node-chatgpt-api)
 - No React boilerplate/toolchain/clone tutorials, created from scratch with react@latest
 - Use of Tailwind CSS and [shadcn/ui](https://github.com/shadcn/ui) components
 - Docker, useSWR, Redux, Express, MongoDB, [Keyv](https://www.npmjs.com/package/keyv)
---
+## Getting Started
-## 💖 This project exists in its current state thanks to all the people who contribute
+### Prerequisites
 - npm
 - Node.js >= 19.0.0
 - MongoDB installed or [MongoDB Atlas](https://account.mongodb.com/account/login) (required if not using Docker)
    - MongoDB does not support older ARM CPUs like those found in Raspberry Pis. However, you can make it work by setting MongoDB's version to mongo:4.4.18 in docker-compose.yml, the most recent version compatible with
 - [Docker (optional)](https://www.docker.com/get-started/)
 - [OpenAI API key](https://platform.openai.com/account/api-keys)
 - BingAI, ChatGPT access tokens (optional, free AIs)
-<a href="https://github.com/danny-avila/LibreChat/graphs/contributors">
+## Usage
  <img src="https://contrib.rocks/image?repo=danny-avila/LibreChat" />
 </a>
---
+- **Clone/download** the repo down where desired
 ```bash
  git clone https://github.com/danny-avila/chatgpt-clone.git
 ```
 - If using MongoDB Atlas, remove `&w=majority` from default connection string.
-## 🎉 Special Thanks
+### Local
 - **Run npm** ci in both the api and client directories
 - **Provide** all credentials, (API keys, access tokens, and Mongo Connection String) in api/.env [(see .env example)](api/.env.example)
 - **Run** `npm run build` in /client/ dir, `npm start` in /api/ dir
 - **Visit** http://localhost:3080 (default port) & enjoy
-We thank [Locize](https://locize.com) for their translation management tools that support multiple languages in LibreChat.
+By default, only local machine can access this server. To share within network or serve as a public server, set `HOST` to `0.0.0.0` in `.env` file
-<p align="center">
+### Docker
-  <a href="https://locize.com" target="_blank" rel="noopener noreferrer">
+
-    <img src="https://github.com/user-attachments/assets/d6b70894-6064-475e-bb65-92a9e23e0077" alt="Locize Logo" height="50">
+- **Provide** all credentials, (API keys, access tokens, and Mongo Connection String) in [docker-compose.yml](docker-compose.yml) under api service
-  </a>
+- **Run** `docker-compose up` to start the app
-</p>
+- Note: MongoDB does not support older ARM CPUs like those found in Raspberry Pis. However, you can make it work by setting MongoDB's version to mongo:4.4.18 in docker-compose.yml, the most recent version compatible with
 ### Access Tokens
 <details>
 <summary><strong>ChatGPT Free Instructions</strong></summary>
 To get your Access token For ChatGPT 'Free Version', login to chat.openai.com, then visit https://chat.openai.com/api/auth/session.
 **Warning:** There may be a high chance of your account being banned with this method. Continue doing so at your own risk.
 </details>
 <details>
 <summary><strong>BingAI Instructions</strong></summary>
 The Bing Access Token is the "_U" cookie from bing.com. Use dev tools or an extension while logged into the site to view it.
 **Note:** Specific error handling and styling for this model is still in progress.
 </details>
 ### Proxy
 If your server cannot connect to the chatGPT API server by some reason, (eg in China). You can set a environment variable `PROXY`. This will be transmitted to `node-chatgpt-api` interface.
 **Warning:** `PROXY` is not `reverseProxyUrl` in `node-chatgpt-api`
 <details>
 <summary><strong>Set up proxy in local environment </strong></summary>
 Here is two ways to set proxy.
 - Option 1: system level environment
 `export PROXY="http://127.0.0.1:7890"`
 - Option 2: set in .env file
 `PROXY="http://127.0.0.1:7890"`
 **Change `http://127.0.0.1:7890` to your proxy server**
 </details>
 <details>
 <summary><strong>Set up proxy in docker environment </strong></summary>
 set in docker-compose.yml file, under services - api - environment
 ```
    api:
        ...
        environment:
                ...
                - "PROXY=http://127.0.0.1:7890"
                # add this line ↑
 ```
 **Change `http://127.0.0.1:7890` to your proxy server**
 </details>
 ### User System
 By default, there is no user system enabled, so anyone can access your server.
 **This project is not designed to provide a complete and full-featured user system.** It's not high priority task and might never be provided.
 [wtlyu](https://github.com/wtlyu) provide a sample user system structure, that you can implement your own user system. It's simple and not a ready-for-use edition. 
 (If you want to implement your user system, open this ↓)
 <details>
 <summary><strong>Implement your own user system </strong></summary>
 To enable the user system, set `ENABLE_USER_SYSTEM=1` in your `.env` file.
 The sample structure is simple. It provide three basic endpoint:
 1. `/auth/login` will redirect to your own login url. In the sample code, it's `/auth/your_login_page`.
 2. `/auth/logout` will redirect to your own logout url. In the sample code, it's `/auth/your_login_page/logout`.
 3. `/api/me` will return the userinfo: `{ username, display }`.
   1. `username` will be used in db, used to distinguish between users.
   2. `display` will be displayed in UI.
 The only one thing that drive user system work is `req.session.user`. Once it's set, the client will be trusted. Set to `null` if logout.
 Please refer to `/api/server/routes/authYourLogin.js` file. It's very clear and simple to tell you how to implement your user system.
 Or you can ask chatGPT to write the code for you, here is one example to connect LDAP:
 ```
 Please write me an express module, that serve the login and logout endpoint as a router. The login and logout uri is '/' and '/logout'. Once loginned, save display name and username in session.user, as {display, username}. Then redirect to '/'. Please write the code using express and other lib, and storage any server configuration in a config variable. I want the user to be connected to my LDAP server.
 ```
 </details>
 ### Updating
 - As the project is still a work-in-progress, you should pull the latest and run the steps over. Reset your browser cache/clear site data.
 ## Use Cases ##
  - One stop shop for all conversational AIs, with the added bonus of searching past conversations.
  - Using the official API, you'd have to generate 7.5 million words to expense the same cost as ChatGPT Plus ($20).
  - ChatGPT/Google Bard/Bing AI conversations are lost in space or
  cannot be searched past a certain timeframe.
  - **Customize ChatGPT**
    ![use case example](./images/use_case3.png "Make a Custom GPT")
  - **API is not as limited as ChatGPT Free (at [chat.openai.com](https://chat.openai.com/chat))**
    ![use case example](./images/use_case2.png "chat.openai.com is getting more limited by the day!")
  - **ChatGPT Free is down.**
    ![use case example](./images/use_case.png "GPT is down! Plus is too expensive!")
 ## Origin ##
  This project was originally created as a Minimum Viable Product (or MVP) for the [@HackReactor](https://github.com/hackreactor/) Bootcamp. It was built with OpenAI response streaming and most of the UI completed in under 20 hours. During the end of that time, I had most of the UI and basic functionality done. This was created without using any boilerplates or templates, including create-react-app and other toolchains. I didn't follow any 'un-official chatgpt' video tutorials, and simply referenced the official site for the UI. The purpose of the exercise was to learn setting up a full stack project from scratch. Please feel free to give feedback, suggestions, or fork the project for your own use.
 ## Caveats
 ### Regarding use of Official ChatGPT API
 From [@waylaidwanderer](https://github.com/waylaidwanderer/node-chatgpt-api/blob/main/README.md#caveats):
 Since `gpt-3.5-turbo` is ChatGPT's underlying model, I had to do my best to replicate the way the official ChatGPT website uses it.
 This means my implementation or the underlying model may not behave exactly the same in some ways:
 - Conversations are not tied to any user IDs, so if that's important to you, you should implement your own user ID system.
 - ChatGPT's model parameters (temperature, frequency penalty, etc.) are unknown, so I set some defaults that I thought would be reasonable.
 - Conversations are limited to roughly the last 3000 tokens, so earlier messages may be forgotten during longer conversations.
  - This works in a similar way to ChatGPT, except I'm pretty sure they have some additional way of retrieving context from earlier messages when needed (which can probably be achieved with embeddings, but I consider that out-of-scope for now).
 ## Contributing
 If you'd like to contribute, please create a pull request with a detailed description of your changes.
 ## License
 This project is licensed under the MIT License.
--- a/api/.env.example
+++ b/api/.env.example
@ -0,0 +1,29 @@
 # Server configuration. 
 # The server will listen to localhost:3080 request by default. You can set the target ip as you want.
 # If you want this server can be used outside your local machine, for example to share with other 
 # machine or expose this from a docker container, set HOST=0.0.0.0 or your external ip interface.
 #
 # Tips: HOST=0.0.0.0 means listening on all interface. It's not a real ip. Use localhost:port rather
 # than 0.0.0.0:port to open it.
 HOST=localhost 
 PORT=3080
 NODE_ENV=development
 # Change this to proxy any API request. It's useful if your machine have difficulty calling the original API server.
 # PROXY="http://YOUR_PROXY_SERVER"
 # Change this to your MongoDB URI if different and I recommend appending chatgpt-clone
 MONGO_URI="mongodb://127.0.0.1:27017/chatgpt-clone"
 # API key configuration.
 # Leave blank if you don't want them.
 OPENAI_KEY=
 CHATGPT_TOKEN=
 BING_TOKEN=
 # User System
 # global enable/disable the sample user system.
 # this is not a ready to use user system.
 # dont't use it, unless you can write your own code.
 ENABLE_USER_SYSTEM=
--- a/api/.eslintrc.js
+++ b/api/.eslintrc.js
@ -0,0 +1,39 @@
 module.exports = {
  env: {
    es2021: true,
    node: true
  },
  extends: ['eslint:recommended'],
  overrides: [],
  parserOptions: {
    ecmaVersion: 'latest',
    sourceType: 'module'
  },
  rules: {
    indent: ['error', 2, { SwitchCase: 1 }],
    'max-len': [
      'error',
      {
        code: 150,
        ignoreStrings: true,
        ignoreTemplateLiterals: true,
        ignoreComments: true
      }
    ],
    'linebreak-style': 0,
    'arrow-parens': [2, 'as-needed', { requireForBlockBody: true }],
    // 'no-plusplus': ['error', { allowForLoopAfterthoughts: true }],
    'no-console': 'off',
    'import/extensions': 'off',
    'no-use-before-define': [
      'error',
      {
        functions: false
      }
    ],
    'no-promise-executor-return': 'off',
    'no-param-reassign': 'off',
    'no-continue': 'off',
    'no-restricted-syntax': 'off'
  }
 };
--- a/api/app/clients/BaseClient.js
+++ b/api/app/clients/BaseClient.js
--- a/api/app/clients/OllamaClient.js
+++ b/api/app/clients/OllamaClient.js
@ -1,167 +0,0 @@
 const { z } = require('zod');
 const axios = require('axios');
 const { Ollama } = require('ollama');
 const { sleep } = require('@librechat/agents');
 const { logger } = require('@librechat/data-schemas');
 const { Constants } = require('librechat-data-provider');
 const { resolveHeaders, deriveBaseURL } = require('@librechat/api');
 const ollamaPayloadSchema = z.object({
  mirostat: z.number().optional(),
  mirostat_eta: z.number().optional(),
  mirostat_tau: z.number().optional(),
  num_ctx: z.number().optional(),
  repeat_last_n: z.number().optional(),
  repeat_penalty: z.number().optional(),
  temperature: z.number().optional(),
  seed: z.number().nullable().optional(),
  stop: z.array(z.string()).optional(),
  tfs_z: z.number().optional(),
  num_predict: z.number().optional(),
  top_k: z.number().optional(),
  top_p: z.number().optional(),
  stream: z.optional(z.boolean()),
  model: z.string(),
 });
 /**
 * @param {string} imageUrl
 * @returns {string}
 * @throws {Error}
 */
 const getValidBase64 = (imageUrl) => {
  const parts = imageUrl.split(';base64,');
  if (parts.length === 2) {
    return parts[1];
  } else {
    logger.error('Invalid or no Base64 string found in URL.');
  }
 };
 class OllamaClient {
  constructor(options = {}) {
    const host = deriveBaseURL(options.baseURL ?? 'http://localhost:11434');
    this.streamRate = options.streamRate ?? Constants.DEFAULT_STREAM_RATE;
    this.headers = options.headers ?? {};
    /** @type {Ollama} */
    this.client = new Ollama({ host });
  }
  /**
   * Fetches Ollama models from the specified base API path.
   * @param {string} baseURL
   * @param {Object} [options] - Optional configuration
   * @param {Partial<IUser>} [options.user] - User object for header resolution
   * @param {Record<string, string>} [options.headers] - Headers to include in the request
   * @returns {Promise<string[]>} The Ollama models.
   * @throws {Error} Throws if the Ollama API request fails
   */
  static async fetchModels(baseURL, options = {}) {
    if (!baseURL) {
      return [];
    }
    const ollamaEndpoint = deriveBaseURL(baseURL);
    const resolvedHeaders = resolveHeaders({
      headers: options.headers,
      user: options.user,
    });
    /** @type {Promise<AxiosResponse<OllamaListResponse>>} */
    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
      headers: resolvedHeaders,
      timeout: 5000,
    });
    const models = response.data.models.map((tag) => tag.name);
    return models;
  }
  /**
   * @param {ChatCompletionMessage[]} messages
   * @returns {OllamaMessage[]}
   */
  static formatOpenAIMessages(messages) {
    const ollamaMessages = [];
    for (const message of messages) {
      if (typeof message.content === 'string') {
        ollamaMessages.push({
          role: message.role,
          content: message.content,
        });
        continue;
      }
      let aggregatedText = '';
      let imageUrls = [];
      for (const content of message.content) {
        if (content.type === 'text') {
          aggregatedText += content.text + ' ';
        } else if (content.type === 'image_url') {
          imageUrls.push(getValidBase64(content.image_url.url));
        }
      }
      const ollamaMessage = {
        role: message.role,
        content: aggregatedText.trim(),
      };
      if (imageUrls.length > 0) {
        ollamaMessage.images = imageUrls;
      }
      ollamaMessages.push(ollamaMessage);
    }
    return ollamaMessages;
  }
  /***
   * @param {Object} params
   * @param {ChatCompletionPayload} params.payload
   * @param {onTokenProgress} params.onProgress
   * @param {AbortController} params.abortController
   */
  async chatCompletion({ payload, onProgress, abortController = null }) {
    let intermediateReply = '';
    const parameters = ollamaPayloadSchema.parse(payload);
    const messages = OllamaClient.formatOpenAIMessages(payload.messages);
    if (parameters.stream) {
      const stream = await this.client.chat({
        messages,
        ...parameters,
      });
      for await (const chunk of stream) {
        const token = chunk.message.content;
        intermediateReply += token;
        onProgress(token);
        if (abortController.signal.aborted) {
          stream.controller.abort();
          break;
        }
        await sleep(this.streamRate);
      }
    }
    // TODO: regular completion
    else {
      // const generation = await this.client.generate(payload);
    }
    return intermediateReply;
  }
  catch(err) {
    logger.error('[OllamaClient.chatCompletion]', err);
    throw err;
  }
 }
 module.exports = { OllamaClient, ollamaPayloadSchema };
--- a/api/app/clients/TextStream.js
+++ b/api/app/clients/TextStream.js
@ -1,60 +0,0 @@
 const { Readable } = require('stream');
 const { logger } = require('@librechat/data-schemas');
 class TextStream extends Readable {
  constructor(text, options = {}) {
    super(options);
    this.text = text;
    this.currentIndex = 0;
    this.minChunkSize = options.minChunkSize ?? 2;
    this.maxChunkSize = options.maxChunkSize ?? 4;
    this.delay = options.delay ?? 20; // Time in milliseconds
  }
  _read() {
    const { delay, minChunkSize, maxChunkSize } = this;
    if (this.currentIndex < this.text.length) {
      setTimeout(() => {
        const remainingChars = this.text.length - this.currentIndex;
        const chunkSize = Math.min(this.randomInt(minChunkSize, maxChunkSize + 1), remainingChars);
        const chunk = this.text.slice(this.currentIndex, this.currentIndex + chunkSize);
        this.push(chunk);
        this.currentIndex += chunkSize;
      }, delay);
    } else {
      this.push(null); // signal end of data
    }
  }
  randomInt(min, max) {
    return Math.floor(Math.random() * (max - min)) + min;
  }
  async processTextStream(onProgressCallback) {
    const streamPromise = new Promise((resolve, reject) => {
      this.on('data', (chunk) => {
        onProgressCallback(chunk.toString());
      });
      this.on('end', () => {
        // logger.debug('[processTextStream] Stream ended');
        resolve();
      });
      this.on('error', (err) => {
        reject(err);
      });
    });
    try {
      await streamPromise;
    } catch (err) {
      logger.error('[processTextStream] Error in text stream:', err);
      // Handle the error appropriately, e.g., return an error message or throw an error
    }
  }
 }
 module.exports = TextStream;
--- a/api/app/clients/bingai.js
+++ b/api/app/clients/bingai.js
@ -0,0 +1,55 @@
 require('dotenv').config();
 const { KeyvFile } = require('keyv-file');
 const askBing = async ({ text, onProgress, convo }) => {
  const { BingAIClient } = (await import('@waylaidwanderer/chatgpt-api'));
  const bingAIClient = new BingAIClient({
    // "_U" cookie from bing.com
    userToken: process.env.BING_TOKEN,
    // If the above doesn't work, provide all your cookies as a string instead
    // cookies: '',
    debug: false,
    cache: { store: new KeyvFile({ filename: './data/cache.json' }) },
    proxy: process.env.PROXY || null,
  });
  let options = { onProgress };
  if (convo) {
    options = { ...options, ...convo };
  }
  if (options?.jailbreakConversationId == 'false')
    options.jailbreakConversationId = false
  const res = await bingAIClient.sendMessage(text, options);
  return res;
  // Example response for reference
  // {
  //   conversationSignature: 'wwZ2GC/qRgEqP3VSNIhbPGwtno5RcuBhzZFASOM+Sxg=',
  //   conversationId: '51D|BingProd|026D3A4017554DE6C446798144B6337F4D47D5B76E62A31F31D0B1D0A95ED868',
  //   clientId: '914800201536527',
  //   invocationId: 1,
  //   conversationExpiryTime: '2023-02-15T21:48:46.2892088Z',
  //   response: 'Hello, this is Bing. Nice to meet you. 😊',
  //   details: {
  //     text: 'Hello, this is Bing. Nice to meet you. 😊',
  //     author: 'bot',
  //     createdAt: '2023-02-15T15:48:43.0631898+00:00',
  //     timestamp: '2023-02-15T15:48:43.0631898+00:00',
  //     messageId: '9d0c9a80-91b1-49ab-b9b1-b457dc3fe247',
  //     requestId: '5b252ef8-4f09-4c08-b6f5-4499d2e12fba',
  //     offense: 'None',
  //     adaptiveCards: [ [Object] ],
  //     sourceAttributions: [],
  //     feedback: { tag: null, updatedOn: null, type: 'None' },
  //     contentOrigin: 'DeepLeo',
  //     privacy: null,
  //     suggestedResponses: [ [Object], [Object], [Object] ]
  //   }
  // }
 };
 module.exports = { askBing };
--- a/api/app/clients/chatgpt-browser.js
+++ b/api/app/clients/chatgpt-browser.js
@ -0,0 +1,36 @@
 require('dotenv').config();
 const { KeyvFile } = require('keyv-file');
 const clientOptions = {
  // Warning: This will expose your access token to a third party. Consider the risks before using this.
  reverseProxyUrl: 'https://bypass.duti.tech/api/conversation',
  // Access token from https://chat.openai.com/api/auth/session
  accessToken: process.env.CHATGPT_TOKEN,
  // debug: true
  proxy: process.env.PROXY || null,
 };
 const browserClient = async ({ text, onProgress, convo, abortController }) => {
  const { ChatGPTBrowserClient } = await import('@waylaidwanderer/chatgpt-api');
  const store = {
    store: new KeyvFile({ filename: './data/cache.json' })
  };
  const client = new ChatGPTBrowserClient(clientOptions, store);
  let options = { onProgress, abortController };
  if (!!convo.parentMessageId && !!convo.conversationId) {
    options = { ...options, ...convo };
  }
  /* will error if given a convoId at the start */
  if (convo.parentMessageId.startsWith('0000')) {
    delete options.conversationId;
  }
  const res = await client.sendMessage(text, options);
  return res;
 };
 module.exports = { browserClient };
--- a/api/app/clients/chatgpt-client.js
+++ b/api/app/clients/chatgpt-client.js
@ -0,0 +1,29 @@
 require('dotenv').config();
 const { KeyvFile } = require('keyv-file');
 const clientOptions = {
  modelOptions: {
    model: 'gpt-3.5-turbo'
  },
  proxy: process.env.PROXY || null,
  debug: false
 };
 const askClient = async ({ text, onProgress, convo, abortController }) => {
  const ChatGPTClient = (await import('@waylaidwanderer/chatgpt-api')).default;
  const store = {
    store: new KeyvFile({ filename: './data/cache.json' })
  };
  const client = new ChatGPTClient(process.env.OPENAI_KEY, clientOptions, store);
  let options = { onProgress, abortController };
  if (!!convo.parentMessageId && !!convo.conversationId) {
    options = { ...options, ...convo };
  }
  const res = await client.sendMessage(text, options);
  return res;
 };
 module.exports = { askClient };
--- a/api/app/clients/chatgpt-custom.js
+++ b/api/app/clients/chatgpt-custom.js
@ -0,0 +1,35 @@
 require('dotenv').config();
 const { KeyvFile } = require('keyv-file');
 const clientOptions = {
  modelOptions: {
    model: 'gpt-3.5-turbo'
  },
  proxy: process.env.PROXY || null,
  debug: false
 };
 const customClient = async ({ text, onProgress, convo, promptPrefix, chatGptLabel, abortController }) => {
  const ChatGPTClient = (await import('@waylaidwanderer/chatgpt-api')).default;
  const store = {
    store: new KeyvFile({ filename: './data/cache.json' })
  };
  clientOptions.chatGptLabel = chatGptLabel;
  if (promptPrefix?.length > 0) {
    clientOptions.promptPrefix = promptPrefix;
  }
  const client = new ChatGPTClient(process.env.OPENAI_KEY, clientOptions, store);
  let options = { onProgress, abortController };
  if (!!convo.parentMessageId && !!convo.conversationId) {
    options = { ...options, ...convo };
  }
  const res = await client.sendMessage(text, options);
  return res;
 };
 module.exports = customClient;
--- a/api/app/clients/index.js
+++ b/api/app/clients/index.js
@ -1,7 +0,0 @@
 const TextStream = require('./TextStream');
 const toolUtils = require('./tools/util');
 module.exports = {
  TextStream,
  ...toolUtils,
 };
--- a/api/app/clients/prompts/artifacts.js
+++ b/api/app/clients/prompts/artifacts.js
@ -1,537 +0,0 @@
 const dedent = require('dedent');
 const { EModelEndpoint, ArtifactModes } = require('librechat-data-provider');
 const { generateShadcnPrompt } = require('~/app/clients/prompts/shadcn-docs/generate');
 const { components } = require('~/app/clients/prompts/shadcn-docs/components');
 /** @deprecated */
 // eslint-disable-next-line no-unused-vars
 const artifactsPromptV1 = dedent`The assistant can create and reference artifacts during conversations.
 Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.
 # Good artifacts are...
 - Substantial content (>15 lines)
 - Content that the user is likely to modify, iterate on, or take ownership of
 - Self-contained, complex content that can be understood on its own, without context from the conversation
 - Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
 - Content likely to be referenced or reused multiple times
 # Don't use artifacts for...
 - Simple, informational, or short content, such as brief code snippets, mathematical equations, or small examples
 - Primarily explanatory, instructional, or illustrative content, such as examples provided to clarify a concept
 - Suggestions, commentary, or feedback on existing artifacts
 - Conversational or explanatory content that doesn't represent a standalone piece of work
 - Content that is dependent on the current conversational context to be useful
 - Content that is unlikely to be modified or iterated upon by the user
 - Request from users that appears to be a one-off question
 # Usage notes
 - One artifact per message unless specifically requested
 - Prefer in-line content (don't use artifacts) when possible. Unnecessary use of artifacts can be jarring for users.
 - If a user asks the assistant to "draw an SVG" or "make a website," the assistant does not need to explain that it doesn't have these capabilities. Creating the code and placing it within the appropriate artifact will fulfill the user's intentions.
 - If asked to generate an image, the assistant can offer an SVG instead. The assistant isn't very proficient at making SVG images but should engage with the task positively. Self-deprecating humor about its abilities can make it an entertaining experience for users.
 - The assistant errs on the side of simplicity and avoids overusing artifacts for content that can be effectively presented within the conversation.
 - Always provide complete, specific, and fully functional content without any placeholders, ellipses, or 'remains the same' comments.
 <artifact_instructions>
  When collaborating with the user on creating content that falls into compatible categories, the assistant should follow these steps:
  1. Create the artifact using the following format:
     :::artifact{identifier="unique-identifier" type="mime-type" title="Artifact Title"}
     \`\`\`
     Your artifact content here
     \`\`\`
     :::
  2. Assign an identifier to the \`identifier\` attribute. For updates, reuse the prior identifier. For new artifacts, the identifier should be descriptive and relevant to the content, using kebab-case (e.g., "example-code-snippet"). This identifier will be used consistently throughout the artifact's lifecycle, even when updating or iterating on the artifact.
  3. Include a \`title\` attribute to provide a brief title or description of the content.
  4. Add a \`type\` attribute to specify the type of content the artifact represents. Assign one of the following values to the \`type\` attribute:
    - HTML: "text/html"
      - The user interface can render single file HTML pages placed within the artifact tags. HTML, JS, and CSS should be in a single file when using the \`text/html\` type.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - The only place external scripts can be imported from is https://cdnjs.cloudflare.com
    - Mermaid Diagrams: "application/vnd.mermaid"
      - The user interface will render Mermaid diagrams placed within the artifact tags.
    - React Components: "application/vnd.react"
      - Use this for displaying either: React elements, e.g. \`<strong>Hello World!</strong>\`, React pure functional components, e.g. \`() => <strong>Hello World!</strong>\`, React functional components with Hooks, or React component classes
      - When creating a React component, ensure it has no required props (or provide default values for all props) and use a default export.
      - Use Tailwind classes for styling. DO NOT USE ARBITRARY VALUES (e.g. \`h-[600px]\`).
      - Base React is available to be imported. To use hooks, first import it at the top of the artifact, e.g. \`import { useState } from "react"\`
      - The lucide-react@0.263.1 library is available to be imported. e.g. \`import { Camera } from "lucide-react"\` & \`<Camera color="red" size={48} />\`
      - The recharts charting library is available to be imported, e.g. \`import { LineChart, XAxis, ... } from "recharts"\` & \`<LineChart ...><XAxis dataKey="name"> ...\`
      - The assistant can use prebuilt components from the \`shadcn/ui\` library after it is imported: \`import { Alert, AlertDescription, AlertTitle, AlertDialog, AlertDialogAction } from '/components/ui/alert';\`. If using components from the shadcn/ui library, the assistant mentions this to the user and offers to help them install the components if necessary.
      - Components MUST be imported from \`/components/ui/name\` and NOT from \`/components/name\` or \`@/components/ui/name\`.
      - NO OTHER LIBRARIES (e.g. zod, hookform) ARE INSTALLED OR ABLE TO BE IMPORTED.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - If you are unable to follow the above requirements for any reason, don't use artifacts and use regular code blocks instead, which will not attempt to render the component.
  5. Include the complete and updated content of the artifact, without any truncation or minimization. Don't use "// rest of the code remains the same...".
  6. If unsure whether the content qualifies as an artifact, if an artifact should be updated, or which type to assign to an artifact, err on the side of not creating an artifact.
  7. Always use triple backticks (\`\`\`) to enclose the content within the artifact, regardless of the content type.
 </artifact_instructions>
 Here are some examples of correct usage of artifacts:
 <examples>
  <example_docstring>
    This example demonstrates how to create a Mermaid artifact for a simple flow chart.
  </example_docstring>
  <example>
    <user_query>Can you create a simple flow chart showing the process of making tea using Mermaid?</user_query>
    <assistant_response>
      Sure! Here's a simple flow chart depicting the process of making tea using Mermaid syntax:
      :::artifact{identifier="tea-making-flowchart" type="application/vnd.mermaid" title="Flow chart: Making Tea"}
      \`\`\`mermaid
      graph TD
          A[Start] --> B{Water boiled?}
          B -->|Yes| C[Add tea leaves to cup]
          B -->|No| D[Boil water]
          D --> B
          C --> E[Pour boiling water into cup]
          E --> F[Steep tea for desired time]
          F --> G[Remove tea leaves]
          G --> H[Add milk or sugar, if desired]
          H --> I[Enjoy your tea!]
          I --> J[End]
      \`\`\`
      :::
      This flow chart uses Mermaid syntax to visualize the steps involved in making a cup of tea. Here's a brief explanation of the process:
      1. Start
      2. Check if water is boiled
      3. If not boiled, boil the water
      4. Once water is boiled, add tea leaves to the cup
      5. Pour boiling water into the cup
      6. Steep the tea for the desired time
      7. Remove the tea leaves
      8. Optionally add milk or sugar
      9. Enjoy your tea!
      10. End
      This chart provides a clear visual representation of the tea-making process. You can easily modify or expand this chart if you want to add more details or steps to the process. Let me know if you'd like any changes or have any questions!
    </assistant_response>
  </example>
 </examples>`;
 const artifactsPrompt = dedent`The assistant can create and reference artifacts during conversations.
 Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.
 # Good artifacts are...
 - Substantial content (>15 lines)
 - Content that the user is likely to modify, iterate on, or take ownership of
 - Self-contained, complex content that can be understood on its own, without context from the conversation
 - Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
 - Content likely to be referenced or reused multiple times
 # Don't use artifacts for...
 - Simple, informational, or short content, such as brief code snippets, mathematical equations, or small examples
 - Primarily explanatory, instructional, or illustrative content, such as examples provided to clarify a concept
 - Suggestions, commentary, or feedback on existing artifacts
 - Conversational or explanatory content that doesn't represent a standalone piece of work
 - Content that is dependent on the current conversational context to be useful
 - Content that is unlikely to be modified or iterated upon by the user
 - Request from users that appears to be a one-off question
 # Usage notes
 - One artifact per message unless specifically requested
 - Prefer in-line content (don't use artifacts) when possible. Unnecessary use of artifacts can be jarring for users.
 - If a user asks the assistant to "draw an SVG" or "make a website," the assistant does not need to explain that it doesn't have these capabilities. Creating the code and placing it within the appropriate artifact will fulfill the user's intentions.
 - If asked to generate an image, the assistant can offer an SVG instead. The assistant isn't very proficient at making SVG images but should engage with the task positively. Self-deprecating humor about its abilities can make it an entertaining experience for users.
 - The assistant errs on the side of simplicity and avoids overusing artifacts for content that can be effectively presented within the conversation.
 - Always provide complete, specific, and fully functional content for artifacts without any snippets, placeholders, ellipses, or 'remains the same' comments.
 - If an artifact is not necessary or requested, the assistant should not mention artifacts at all, and respond to the user accordingly.
 <artifact_instructions>
  When collaborating with the user on creating content that falls into compatible categories, the assistant should follow these steps:
  1. Create the artifact using the following format:
     :::artifact{identifier="unique-identifier" type="mime-type" title="Artifact Title"}
     \`\`\`
     Your artifact content here
     \`\`\`
     :::
  2. Assign an identifier to the \`identifier\` attribute. For updates, reuse the prior identifier. For new artifacts, the identifier should be descriptive and relevant to the content, using kebab-case (e.g., "example-code-snippet"). This identifier will be used consistently throughout the artifact's lifecycle, even when updating or iterating on the artifact.
  3. Include a \`title\` attribute to provide a brief title or description of the content.
  4. Add a \`type\` attribute to specify the type of content the artifact represents. Assign one of the following values to the \`type\` attribute:
    - HTML: "text/html"
      - The user interface can render single file HTML pages placed within the artifact tags. HTML, JS, and CSS should be in a single file when using the \`text/html\` type.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - The only place external scripts can be imported from is https://cdnjs.cloudflare.com
    - SVG: "image/svg+xml"
      - The user interface will render the Scalable Vector Graphics (SVG) image within the artifact tags.
      - The assistant should specify the viewbox of the SVG rather than defining a width/height
    - Markdown: "text/markdown" or "text/md"
      - The user interface will render Markdown content placed within the artifact tags.
      - Supports standard Markdown syntax including headers, lists, links, images, code blocks, tables, and more.
      - Both "text/markdown" and "text/md" are accepted as valid MIME types for Markdown content.
    - Mermaid Diagrams: "application/vnd.mermaid"
      - The user interface will render Mermaid diagrams placed within the artifact tags.
    - React Components: "application/vnd.react"
      - Use this for displaying either: React elements, e.g. \`<strong>Hello World!</strong>\`, React pure functional components, e.g. \`() => <strong>Hello World!</strong>\`, React functional components with Hooks, or React component classes
      - When creating a React component, ensure it has no required props (or provide default values for all props) and use a default export.
      - Use Tailwind classes for styling. DO NOT USE ARBITRARY VALUES (e.g. \`h-[600px]\`).
      - Base React is available to be imported. To use hooks, first import it at the top of the artifact, e.g. \`import { useState } from "react"\`
      - The lucide-react@0.394.0 library is available to be imported. e.g. \`import { Camera } from "lucide-react"\` & \`<Camera color="red" size={48} />\`
      - The recharts charting library is available to be imported, e.g. \`import { LineChart, XAxis, ... } from "recharts"\` & \`<LineChart ...><XAxis dataKey="name"> ...\`
      - The three.js library is available to be imported, e.g. \`import * as THREE from "three";\`
      - The date-fns library is available to be imported, e.g. \`import { compareAsc, format } from "date-fns";\`
      - The react-day-picker library is available to be imported, e.g. \`import { DayPicker } from "react-day-picker";\`
      - The assistant can use prebuilt components from the \`shadcn/ui\` library after it is imported: \`import { Alert, AlertDescription, AlertTitle, AlertDialog, AlertDialogAction } from '/components/ui/alert';\`. If using components from the shadcn/ui library, the assistant mentions this to the user and offers to help them install the components if necessary.
      - Components MUST be imported from \`/components/ui/name\` and NOT from \`/components/name\` or \`@/components/ui/name\`.
      - NO OTHER LIBRARIES (e.g. zod, hookform) ARE INSTALLED OR ABLE TO BE IMPORTED.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - When iterating on code, ensure that the code is complete and functional without any snippets, placeholders, or ellipses.
      - If you are unable to follow the above requirements for any reason, don't use artifacts and use regular code blocks instead, which will not attempt to render the component.
  5. Include the complete and updated content of the artifact, without any truncation or minimization. Don't use "// rest of the code remains the same...".
  6. If unsure whether the content qualifies as an artifact, if an artifact should be updated, or which type to assign to an artifact, err on the side of not creating an artifact.
  7. Always use triple backticks (\`\`\`) to enclose the content within the artifact, regardless of the content type.
 </artifact_instructions>
 Here are some examples of correct usage of artifacts:
 <examples>
  <example_docstring>
    This example demonstrates how to create a Mermaid artifact for a simple flow chart.
  </example_docstring>
  <example>
    <user_query>Can you create a simple flow chart showing the process of making tea using Mermaid?</user_query>
    <assistant_response>
      Sure! Here's a simple flow chart depicting the process of making tea using Mermaid syntax:
      :::artifact{identifier="tea-making-flowchart" type="application/vnd.mermaid" title="Flow chart: Making Tea"}
      \`\`\`mermaid
      graph TD
          A[Start] --> B{Water boiled?}
          B -->|Yes| C[Add tea leaves to cup]
          B -->|No| D[Boil water]
          D --> B
          C --> E[Pour boiling water into cup]
          E --> F[Steep tea for desired time]
          F --> G[Remove tea leaves]
          G --> H[Add milk or sugar, if desired]
          H --> I[Enjoy your tea!]
          I --> J[End]
      \`\`\`
      :::
      This flow chart uses Mermaid syntax to visualize the steps involved in making a cup of tea. Here's a brief explanation of the process:
      1. Start
      2. Check if water is boiled
      3. If not boiled, boil the water
      4. Once water is boiled, add tea leaves to the cup
      5. Pour boiling water into the cup
      6. Steep the tea for the desired time
      7. Remove the tea leaves
      8. Optionally add milk or sugar
      9. Enjoy your tea!
      10. End
      This chart provides a clear visual representation of the tea-making process. You can easily modify or expand this chart if you want to add more details or steps to the process. Let me know if you'd like any changes or have any questions!
    </assistant_response>
  </example>
  <example>
    <user_query>Create a simple React counter component</user_query>
    <assistant_response>
      Here's a simple React counter component:
      :::artifact{identifier="react-counter" type="application/vnd.react" title="React Counter"}
      \`\`\`
      import { useState } from 'react';
      export default function Counter() {
        const [count, setCount] = useState(0);
        return (
          <div className="p-4">
            <p className="mb-2">Count: {count}</p>
            <button className="bg-blue-500 text-white px-4 py-2 rounded" onClick={() => setCount(count + 1)}>
              Increment
            </button>
          </div>
        );
      }
      \`\`\`
      :::
      This component creates a simple counter with an increment button.
    </assistant_response>
  </example>
  <example>
    <user_query>Create a basic HTML structure for a blog post</user_query>
    <assistant_response>
      Here's a basic HTML structure for a blog post:
      :::artifact{identifier="blog-post-html" type="text/html" title="Blog Post HTML"}
      \`\`\`
      <!DOCTYPE html>
      <html lang="en">
      <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>My Blog Post</title>
        <style>
          body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 800px; margin: 0 auto; padding: 20px; }
          h1 { color: #333; }
          p { margin-bottom: 15px; }
        </style>
      </head>
      <body>
        <header>
          <h1>My First Blog Post</h1>
        </header>
        <main>
          <article>
            <p>This is the content of my blog post. It's short and sweet!</p>
          </article>
        </main>
        <footer>
          <p>&copy; 2023 My Blog</p>
        </footer>
      </body>
      </html>
      \`\`\`
      :::
      This HTML structure provides a simple layout for a blog post.
    </assistant_response>
  </example>
 </examples>`;
 const artifactsOpenAIPrompt = dedent`The assistant can create and reference artifacts during conversations.
 Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.
 # Good artifacts are...
 - Substantial content (>15 lines)
 - Content that the user is likely to modify, iterate on, or take ownership of
 - Self-contained, complex content that can be understood on its own, without context from the conversation
 - Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
 - Content likely to be referenced or reused multiple times
 # Don't use artifacts for...
 - Simple, informational, or short content, such as brief code snippets, mathematical equations, or small examples
 - Primarily explanatory, instructional, or illustrative content, such as examples provided to clarify a concept
 - Suggestions, commentary, or feedback on existing artifacts
 - Conversational or explanatory content that doesn't represent a standalone piece of work
 - Content that is dependent on the current conversational context to be useful
 - Content that is unlikely to be modified or iterated upon by the user
 - Request from users that appears to be a one-off question
 # Usage notes
 - One artifact per message unless specifically requested
 - Prefer in-line content (don't use artifacts) when possible. Unnecessary use of artifacts can be jarring for users.
 - If a user asks the assistant to "draw an SVG" or "make a website," the assistant does not need to explain that it doesn't have these capabilities. Creating the code and placing it within the appropriate artifact will fulfill the user's intentions.
 - If asked to generate an image, the assistant can offer an SVG instead. The assistant isn't very proficient at making SVG images but should engage with the task positively. Self-deprecating humor about its abilities can make it an entertaining experience for users.
 - The assistant errs on the side of simplicity and avoids overusing artifacts for content that can be effectively presented within the conversation.
 - Always provide complete, specific, and fully functional content for artifacts without any snippets, placeholders, ellipses, or 'remains the same' comments.
 - If an artifact is not necessary or requested, the assistant should not mention artifacts at all, and respond to the user accordingly.
 ## Artifact Instructions
  When collaborating with the user on creating content that falls into compatible categories, the assistant should follow these steps:
  1. Create the artifact using the following remark-directive markdown format:
      :::artifact{identifier="unique-identifier" type="mime-type" title="Artifact Title"}
      \`\`\`
      Your artifact content here
      \`\`\`
      :::
  a. Example of correct format:
      :::artifact{identifier="example-artifact" type="text/plain" title="Example Artifact"}
      \`\`\`
      This is the content of the artifact.
      It can span multiple lines.
      \`\`\`
      :::
  b. Common mistakes to avoid:
   - Don't split the opening ::: line
   - Don't add extra backticks outside the artifact structure
   - Don't omit the closing :::
  2. Assign an identifier to the \`identifier\` attribute. For updates, reuse the prior identifier. For new artifacts, the identifier should be descriptive and relevant to the content, using kebab-case (e.g., "example-code-snippet"). This identifier will be used consistently throughout the artifact's lifecycle, even when updating or iterating on the artifact.
  3. Include a \`title\` attribute to provide a brief title or description of the content.
  4. Add a \`type\` attribute to specify the type of content the artifact represents. Assign one of the following values to the \`type\` attribute:
    - HTML: "text/html"
      - The user interface can render single file HTML pages placed within the artifact tags. HTML, JS, and CSS should be in a single file when using the \`text/html\` type.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - The only place external scripts can be imported from is https://cdnjs.cloudflare.com
    - SVG: "image/svg+xml"
      - The user interface will render the Scalable Vector Graphics (SVG) image within the artifact tags.
      - The assistant should specify the viewbox of the SVG rather than defining a width/height
    - Markdown: "text/markdown" or "text/md"
      - The user interface will render Markdown content placed within the artifact tags.
      - Supports standard Markdown syntax including headers, lists, links, images, code blocks, tables, and more.
      - Both "text/markdown" and "text/md" are accepted as valid MIME types for Markdown content.
    - Mermaid Diagrams: "application/vnd.mermaid"
      - The user interface will render Mermaid diagrams placed within the artifact tags.
    - React Components: "application/vnd.react"
      - Use this for displaying either: React elements, e.g. \`<strong>Hello World!</strong>\`, React pure functional components, e.g. \`() => <strong>Hello World!</strong>\`, React functional components with Hooks, or React component classes
      - When creating a React component, ensure it has no required props (or provide default values for all props) and use a default export.
      - Use Tailwind classes for styling. DO NOT USE ARBITRARY VALUES (e.g. \`h-[600px]\`).
      - Base React is available to be imported. To use hooks, first import it at the top of the artifact, e.g. \`import { useState } from "react"\`
      - The lucide-react@0.394.0 library is available to be imported. e.g. \`import { Camera } from "lucide-react"\` & \`<Camera color="red" size={48} />\`
      - The recharts charting library is available to be imported, e.g. \`import { LineChart, XAxis, ... } from "recharts"\` & \`<LineChart ...><XAxis dataKey="name"> ...\`
      - The three.js library is available to be imported, e.g. \`import * as THREE from "three";\`
      - The date-fns library is available to be imported, e.g. \`import { compareAsc, format } from "date-fns";\`
      - The react-day-picker library is available to be imported, e.g. \`import { DayPicker } from "react-day-picker";\`
      - The assistant can use prebuilt components from the \`shadcn/ui\` library after it is imported: \`import { Alert, AlertDescription, AlertTitle, AlertDialog, AlertDialogAction } from '/components/ui/alert';\`. If using components from the shadcn/ui library, the assistant mentions this to the user and offers to help them install the components if necessary.
      - Components MUST be imported from \`/components/ui/name\` and NOT from \`/components/name\` or \`@/components/ui/name\`.
      - NO OTHER LIBRARIES (e.g. zod, hookform) ARE INSTALLED OR ABLE TO BE IMPORTED.
      - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so \`<img src="/api/placeholder/400/320" alt="placeholder" />\`
      - When iterating on code, ensure that the code is complete and functional without any snippets, placeholders, or ellipses.
      - If you are unable to follow the above requirements for any reason, don't use artifacts and use regular code blocks instead, which will not attempt to render the component.
  5. Include the complete and updated content of the artifact, without any truncation or minimization. Don't use "// rest of the code remains the same...".
  6. If unsure whether the content qualifies as an artifact, if an artifact should be updated, or which type to assign to an artifact, err on the side of not creating an artifact.
  7. NEVER use triple backticks to enclose the artifact, ONLY the content within the artifact.
 Here are some examples of correct usage of artifacts:
 ## Examples
 ### Example 1
    This example demonstrates how to create a Mermaid artifact for a simple flow chart.
    User: Can you create a simple flow chart showing the process of making tea using Mermaid?
    Assistant: Sure! Here's a simple flow chart depicting the process of making tea using Mermaid syntax:
      :::artifact{identifier="tea-making-flowchart" type="application/vnd.mermaid" title="Flow chart: Making Tea"}
      \`\`\`mermaid
      graph TD
          A[Start] --> B{Water boiled?}
          B -->|Yes| C[Add tea leaves to cup]
          B -->|No| D[Boil water]
          D --> B
          C --> E[Pour boiling water into cup]
          E --> F[Steep tea for desired time]
          F --> G[Remove tea leaves]
          G --> H[Add milk or sugar, if desired]
          H --> I[Enjoy your tea!]
          I --> J[End]
      \`\`\`
      :::
      This flow chart uses Mermaid syntax to visualize the steps involved in making a cup of tea. Here's a brief explanation of the process:
      1. Start
      2. Check if water is boiled
      3. If not boiled, boil the water
      4. Once water is boiled, add tea leaves to the cup
      5. Pour boiling water into the cup
      6. Steep the tea for the desired time
      7. Remove the tea leaves
      8. Optionally add milk or sugar
      9. Enjoy your tea!
      10. End
      This chart provides a clear visual representation of the tea-making process. You can easily modify or expand this chart if you want to add more details or steps to the process. Let me know if you'd like any changes or have any questions!
 ---
 ### Example 2
    User: Create a simple React counter component
    Assistant: Here's a simple React counter component:
      :::artifact{identifier="react-counter" type="application/vnd.react" title="React Counter"}
      \`\`\`
      import { useState } from 'react';
      export default function Counter() {
        const [count, setCount] = useState(0);
        return (
          <div className="p-4">
            <p className="mb-2">Count: {count}</p>
            <button className="bg-blue-500 text-white px-4 py-2 rounded" onClick={() => setCount(count + 1)}>
              Increment
            </button>
          </div>
        );
      }
      \`\`\`
      :::
      This component creates a simple counter with an increment button.
 ---
 ### Example 3
    User: Create a basic HTML structure for a blog post
    Assistant: Here's a basic HTML structure for a blog post:
      :::artifact{identifier="blog-post-html" type="text/html" title="Blog Post HTML"}
      \`\`\`
      <!DOCTYPE html>
      <html lang="en">
      <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>My Blog Post</title>
        <style>
          body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 800px; margin: 0 auto; padding: 20px; }
          h1 { color: #333; }
          p { margin-bottom: 15px; }
        </style>
      </head>
      <body>
        <header>
          <h1>My First Blog Post</h1>
        </header>
        <main>
          <article>
            <p>This is the content of my blog post. It's short and sweet!</p>
          </article>
        </main>
        <footer>
          <p>&copy; 2023 My Blog</p>
        </footer>
      </body>
      </html>
      \`\`\`
      :::
      This HTML structure provides a simple layout for a blog post.
 ---`;
 /**
 *
 * @param {Object} params
 * @param {EModelEndpoint | string} params.endpoint - The current endpoint
 * @param {ArtifactModes} params.artifacts - The current artifact mode
 * @returns
 */
 const generateArtifactsPrompt = ({ endpoint, artifacts }) => {
  if (artifacts === ArtifactModes.CUSTOM) {
    return null;
  }
  let prompt = artifactsPrompt;
  if (endpoint !== EModelEndpoint.anthropic) {
    prompt = artifactsOpenAIPrompt;
  }
  if (artifacts === ArtifactModes.SHADCNUI) {
    prompt += generateShadcnPrompt({ components, useXML: endpoint === EModelEndpoint.anthropic });
  }
  return prompt;
 };
 module.exports = generateArtifactsPrompt;
--- a/api/app/clients/prompts/createContextHandlers.js
+++ b/api/app/clients/prompts/createContextHandlers.js
@ -1,160 +0,0 @@
 const axios = require('axios');
 const { logger } = require('@librechat/data-schemas');
 const { isEnabled, generateShortLivedToken } = require('@librechat/api');
 const footer = `Use the context as your learned knowledge to better answer the user.
 In your response, remember to follow these guidelines:
 - If you don't know the answer, simply say that you don't know.
 - If you are unsure how to answer, ask for clarification.
 - Avoid mentioning that you obtained the information from the context.
 `;
 function createContextHandlers(req, userMessageContent) {
  if (!process.env.RAG_API_URL) {
    return;
  }
  const queryPromises = [];
  const processedFiles = [];
  const processedIds = new Set();
  const jwtToken = generateShortLivedToken(req.user.id);
  const useFullContext = isEnabled(process.env.RAG_USE_FULL_CONTEXT);
  const query = async (file) => {
    if (useFullContext) {
      return axios.get(`${process.env.RAG_API_URL}/documents/${file.file_id}/context`, {
        headers: {
          Authorization: `Bearer ${jwtToken}`,
        },
      });
    }
    return axios.post(
      `${process.env.RAG_API_URL}/query`,
      {
        file_id: file.file_id,
        query: userMessageContent,
        k: 4,
      },
      {
        headers: {
          Authorization: `Bearer ${jwtToken}`,
          'Content-Type': 'application/json',
        },
      },
    );
  };
  const processFile = async (file) => {
    if (file.embedded && !processedIds.has(file.file_id)) {
      try {
        const promise = query(file);
        queryPromises.push(promise);
        processedFiles.push(file);
        processedIds.add(file.file_id);
      } catch (error) {
        logger.error(`Error processing file ${file.filename}:`, error);
      }
    }
  };
  const createContext = async () => {
    try {
      if (!queryPromises.length || !processedFiles.length) {
        return '';
      }
      const oneFile = processedFiles.length === 1;
      const header = `The user has attached ${oneFile ? 'a' : processedFiles.length} file${
        !oneFile ? 's' : ''
      } to the conversation:`;
      const files = `${
        oneFile
          ? ''
          : `
      <files>`
      }${processedFiles
        .map(
          (file) => `
              <file>
                <filename>${file.filename}</filename>
                <type>${file.type}</type>
              </file>`,
        )
        .join('')}${
        oneFile
          ? ''
          : `
        </files>`
      }`;
      const resolvedQueries = await Promise.all(queryPromises);
      const context =
        resolvedQueries.length === 0
          ? '\n\tThe semantic search did not return any results.'
          : resolvedQueries
              .map((queryResult, index) => {
                const file = processedFiles[index];
                let contextItems = queryResult.data;
                const generateContext = (currentContext) =>
                  `
          <file>
            <filename>${file.filename}</filename>
            <context>${currentContext}
            </context>
          </file>`;
                if (useFullContext) {
                  return generateContext(`\n${contextItems}`);
                }
                contextItems = queryResult.data
                  .map((item) => {
                    const pageContent = item[0].page_content;
                    return `
            <contextItem>
              <![CDATA[${pageContent?.trim()}]]>
            </contextItem>`;
                  })
                  .join('');
                return generateContext(contextItems);
              })
              .join('');
      if (useFullContext) {
        const prompt = `${header}
          ${context}
          ${footer}`;
        return prompt;
      }
      const prompt = `${header}
        ${files}
        A semantic search was executed with the user's message as the query, retrieving the following context inside <context></context> XML tags.
        <context>${context}
        </context>
        ${footer}`;
      return prompt;
    } catch (error) {
      logger.error('Error creating context:', error);
      throw error;
    }
  };
  return {
    processFile,
    createContext,
  };
 }
 module.exports = createContextHandlers;
--- a/api/app/clients/prompts/createVisionPrompt.js
+++ b/api/app/clients/prompts/createVisionPrompt.js
@ -1,34 +0,0 @@
 /**
 * Generates a prompt instructing the user to describe an image in detail, tailored to different types of visual content.
 * @param {boolean} pluralized - Whether to pluralize the prompt for multiple images.
 * @returns {string} - The generated vision prompt.
 */
 const createVisionPrompt = (pluralized = false) => {
  return `Please describe the image${
    pluralized ? 's' : ''
  } in detail, covering relevant aspects such as:
  For photographs, illustrations, or artwork:
  - The main subject(s) and their appearance, positioning, and actions
  - The setting, background, and any notable objects or elements
  - Colors, lighting, and overall mood or atmosphere
  - Any interesting details, textures, or patterns
  - The style, technique, or medium used (if discernible)
  For screenshots or images containing text:
  - The content and purpose of the text
  - The layout, formatting, and organization of the information
  - Any notable visual elements, such as logos, icons, or graphics
  - The overall context or message conveyed by the screenshot
  For graphs, charts, or data visualizations:
  - The type of graph or chart (e.g., bar graph, line chart, pie chart)
  - The variables being compared or analyzed
  - Any trends, patterns, or outliers in the data
  - The axis labels, scales, and units of measurement
  - The title, legend, and any additional context provided
  Be as specific and descriptive as possible while maintaining clarity and concision.`;
 };
 module.exports = createVisionPrompt;
--- a/api/app/clients/prompts/formatAgentMessages.spec.js
+++ b/api/app/clients/prompts/formatAgentMessages.spec.js
@ -1,361 +0,0 @@
 const { ToolMessage } = require('@langchain/core/messages');
 const { ContentTypes } = require('librechat-data-provider');
 const { HumanMessage, AIMessage, SystemMessage } = require('@langchain/core/messages');
 const { formatAgentMessages } = require('./formatMessages');
 describe('formatAgentMessages', () => {
  it('should format simple user and AI messages', () => {
    const payload = [
      { role: 'user', content: 'Hello' },
      { role: 'assistant', content: 'Hi there!' },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(2);
    expect(result[0]).toBeInstanceOf(HumanMessage);
    expect(result[1]).toBeInstanceOf(AIMessage);
  });
  it('should handle system messages', () => {
    const payload = [{ role: 'system', content: 'You are a helpful assistant.' }];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(SystemMessage);
  });
  it('should format messages with content arrays', () => {
    const payload = [
      {
        role: 'user',
        content: [{ type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Hello' }],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(HumanMessage);
  });
  it('should handle tool calls and create ToolMessages', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          {
            type: ContentTypes.TEXT,
            [ContentTypes.TEXT]: 'Let me check that for you.',
            tool_call_ids: ['123'],
          },
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: '123',
              name: 'search',
              args: '{"query":"weather"}',
              output: 'The weather is sunny.',
            },
          },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(2);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(result[1]).toBeInstanceOf(ToolMessage);
    expect(result[0].tool_calls).toHaveLength(1);
    expect(result[1].tool_call_id).toBe('123');
  });
  it('should handle multiple content parts in assistant messages', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Part 1' },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Part 2' },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(result[0].content).toHaveLength(2);
  });
  it('should throw an error for invalid tool call structure', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: '123',
              name: 'search',
              args: '{"query":"weather"}',
              output: 'The weather is sunny.',
            },
          },
        ],
      },
    ];
    expect(() => formatAgentMessages(payload)).toThrow('Invalid tool call structure');
  });
  it('should handle tool calls with non-JSON args', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Checking...', tool_call_ids: ['123'] },
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: '123',
              name: 'search',
              args: 'non-json-string',
              output: 'Result',
            },
          },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(2);
    expect(result[0].tool_calls[0].args).toStrictEqual({ input: 'non-json-string' });
  });
  it('should handle complex tool calls with multiple steps', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          {
            type: ContentTypes.TEXT,
            [ContentTypes.TEXT]: "I'll search for that information.",
            tool_call_ids: ['search_1'],
          },
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: 'search_1',
              name: 'search',
              args: '{"query":"weather in New York"}',
              output: 'The weather in New York is currently sunny with a temperature of 75°F.',
            },
          },
          {
            type: ContentTypes.TEXT,
            [ContentTypes.TEXT]: "Now, I'll convert the temperature.",
            tool_call_ids: ['convert_1'],
          },
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: 'convert_1',
              name: 'convert_temperature',
              args: '{"temperature": 75, "from": "F", "to": "C"}',
              output: '23.89°C',
            },
          },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: "Here's your answer." },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(5);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(result[1]).toBeInstanceOf(ToolMessage);
    expect(result[2]).toBeInstanceOf(AIMessage);
    expect(result[3]).toBeInstanceOf(ToolMessage);
    expect(result[4]).toBeInstanceOf(AIMessage);
    // Check first AIMessage
    expect(result[0].content).toBe("I'll search for that information.");
    expect(result[0].tool_calls).toHaveLength(1);
    expect(result[0].tool_calls[0]).toEqual({
      id: 'search_1',
      name: 'search',
      args: { query: 'weather in New York' },
    });
    // Check first ToolMessage
    expect(result[1].tool_call_id).toBe('search_1');
    expect(result[1].name).toBe('search');
    expect(result[1].content).toBe(
      'The weather in New York is currently sunny with a temperature of 75°F.',
    );
    // Check second AIMessage
    expect(result[2].content).toBe("Now, I'll convert the temperature.");
    expect(result[2].tool_calls).toHaveLength(1);
    expect(result[2].tool_calls[0]).toEqual({
      id: 'convert_1',
      name: 'convert_temperature',
      args: { temperature: 75, from: 'F', to: 'C' },
    });
    // Check second ToolMessage
    expect(result[3].tool_call_id).toBe('convert_1');
    expect(result[3].name).toBe('convert_temperature');
    expect(result[3].content).toBe('23.89°C');
    // Check final AIMessage
    expect(result[4].content).toStrictEqual([
      { [ContentTypes.TEXT]: "Here's your answer.", type: ContentTypes.TEXT },
    ]);
  });
  it.skip('should not produce two consecutive assistant messages and format content correctly', () => {
    const payload = [
      { role: 'user', content: 'Hello' },
      {
        role: 'assistant',
        content: [{ type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Hi there!' }],
      },
      {
        role: 'assistant',
        content: [{ type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'How can I help you?' }],
      },
      { role: 'user', content: "What's the weather?" },
      {
        role: 'assistant',
        content: [
          {
            type: ContentTypes.TEXT,
            [ContentTypes.TEXT]: 'Let me check that for you.',
            tool_call_ids: ['weather_1'],
          },
          {
            type: ContentTypes.TOOL_CALL,
            tool_call: {
              id: 'weather_1',
              name: 'check_weather',
              args: '{"location":"New York"}',
              output: 'Sunny, 75°F',
            },
          },
        ],
      },
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: "Here's the weather information." },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    // Check correct message count and types
    expect(result).toHaveLength(6);
    expect(result[0]).toBeInstanceOf(HumanMessage);
    expect(result[1]).toBeInstanceOf(AIMessage);
    expect(result[2]).toBeInstanceOf(HumanMessage);
    expect(result[3]).toBeInstanceOf(AIMessage);
    expect(result[4]).toBeInstanceOf(ToolMessage);
    expect(result[5]).toBeInstanceOf(AIMessage);
    // Check content of messages
    expect(result[0].content).toStrictEqual([
      { [ContentTypes.TEXT]: 'Hello', type: ContentTypes.TEXT },
    ]);
    expect(result[1].content).toStrictEqual([
      { [ContentTypes.TEXT]: 'Hi there!', type: ContentTypes.TEXT },
      { [ContentTypes.TEXT]: 'How can I help you?', type: ContentTypes.TEXT },
    ]);
    expect(result[2].content).toStrictEqual([
      { [ContentTypes.TEXT]: "What's the weather?", type: ContentTypes.TEXT },
    ]);
    expect(result[3].content).toBe('Let me check that for you.');
    expect(result[4].content).toBe('Sunny, 75°F');
    expect(result[5].content).toStrictEqual([
      { [ContentTypes.TEXT]: "Here's the weather information.", type: ContentTypes.TEXT },
    ]);
    // Check that there are no consecutive AIMessages
    const messageTypes = result.map((message) => message.constructor);
    for (let i = 0; i < messageTypes.length - 1; i++) {
      expect(messageTypes[i] === AIMessage && messageTypes[i + 1] === AIMessage).toBe(false);
    }
    // Additional check to ensure the consecutive assistant messages were combined
    expect(result[1].content).toHaveLength(2);
  });
  it('should skip THINK type content parts', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Initial response' },
          { type: ContentTypes.THINK, [ContentTypes.THINK]: 'Reasoning about the problem...' },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Final answer' },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(result[0].content).toEqual('Initial response\nFinal answer');
  });
  it('should join TEXT content as string when THINK content type is present', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.THINK, [ContentTypes.THINK]: 'Analyzing the problem...' },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'First part of response' },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Second part of response' },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Final part of response' },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(typeof result[0].content).toBe('string');
    expect(result[0].content).toBe(
      'First part of response\nSecond part of response\nFinal part of response',
    );
    expect(result[0].content).not.toContain('Analyzing the problem...');
  });
  it('should exclude ERROR type content parts', () => {
    const payload = [
      {
        role: 'assistant',
        content: [
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Hello there' },
          {
            type: ContentTypes.ERROR,
            [ContentTypes.ERROR]:
              'An error occurred while processing the request: Something went wrong',
          },
          { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Final answer' },
        ],
      },
    ];
    const result = formatAgentMessages(payload);
    expect(result).toHaveLength(1);
    expect(result[0]).toBeInstanceOf(AIMessage);
    expect(result[0].content).toEqual([
      { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Hello there' },
      { type: ContentTypes.TEXT, [ContentTypes.TEXT]: 'Final answer' },
    ]);
    // Make sure no error content exists in the result
    const hasErrorContent = result[0].content.some(
      (item) =>
        item.type === ContentTypes.ERROR || JSON.stringify(item).includes('An error occurred'),
    );
    expect(hasErrorContent).toBe(false);
  });
 });
--- a/api/app/clients/prompts/formatGoogleInputs.js
+++ b/api/app/clients/prompts/formatGoogleInputs.js
@ -1,42 +0,0 @@
 /**
 * Formats an object to match the struct_val, list_val, string_val, float_val, and int_val format.
 *
 * @param {Object} obj - The object to be formatted.
 * @returns {Object} The formatted object.
 *
 * Handles different types:
 * - Arrays are wrapped in list_val and each element is processed.
 * - Objects are recursively processed.
 * - Strings are wrapped in string_val.
 * - Numbers are wrapped in float_val or int_val depending on whether they are floating-point or integers.
 */
 function formatGoogleInputs(obj) {
  const formattedObj = {};
  for (const key in obj) {
    if (Object.prototype.hasOwnProperty.call(obj, key)) {
      const value = obj[key];
      // Handle arrays
      if (Array.isArray(value)) {
        formattedObj[key] = { list_val: value.map((item) => formatGoogleInputs(item)) };
      }
      // Handle objects
      else if (typeof value === 'object' && value !== null) {
        formattedObj[key] = formatGoogleInputs(value);
      }
      // Handle numbers
      else if (typeof value === 'number') {
        formattedObj[key] = Number.isInteger(value) ? { int_val: value } : { float_val: value };
      }
      // Handle other types (e.g., strings)
      else {
        formattedObj[key] = { string_val: [value] };
      }
    }
  }
  return { struct_val: formattedObj };
 }
 module.exports = formatGoogleInputs;
--- a/api/app/clients/prompts/formatGoogleInputs.spec.js
+++ b/api/app/clients/prompts/formatGoogleInputs.spec.js
@ -1,274 +0,0 @@
 const formatGoogleInputs = require('./formatGoogleInputs');
 describe('formatGoogleInputs', () => {
  it('formats message correctly', () => {
    const input = {
      messages: [
        {
          content: 'hi',
          author: 'user',
        },
      ],
      context: 'context',
      examples: [
        {
          input: {
            author: 'user',
            content: 'user input',
          },
          output: {
            author: 'bot',
            content: 'bot output',
          },
        },
      ],
      parameters: {
        temperature: 0.2,
        topP: 0.8,
        topK: 40,
        maxOutputTokens: 1024,
      },
    };
    const expectedOutput = {
      struct_val: {
        messages: {
          list_val: [
            {
              struct_val: {
                content: {
                  string_val: ['hi'],
                },
                author: {
                  string_val: ['user'],
                },
              },
            },
          ],
        },
        context: {
          string_val: ['context'],
        },
        examples: {
          list_val: [
            {
              struct_val: {
                input: {
                  struct_val: {
                    author: {
                      string_val: ['user'],
                    },
                    content: {
                      string_val: ['user input'],
                    },
                  },
                },
                output: {
                  struct_val: {
                    author: {
                      string_val: ['bot'],
                    },
                    content: {
                      string_val: ['bot output'],
                    },
                  },
                },
              },
            },
          ],
        },
        parameters: {
          struct_val: {
            temperature: {
              float_val: 0.2,
            },
            topP: {
              float_val: 0.8,
            },
            topK: {
              int_val: 40,
            },
            maxOutputTokens: {
              int_val: 1024,
            },
          },
        },
      },
    };
    const result = formatGoogleInputs(input);
    expect(JSON.stringify(result)).toEqual(JSON.stringify(expectedOutput));
  });
  it('formats real payload parts', () => {
    const input = {
      instances: [
        {
          context: 'context',
          examples: [
            {
              input: {
                author: 'user',
                content: 'user input',
              },
              output: {
                author: 'bot',
                content: 'user output',
              },
            },
          ],
          messages: [
            {
              author: 'user',
              content: 'hi',
            },
          ],
        },
      ],
      parameters: {
        candidateCount: 1,
        maxOutputTokens: 1024,
        temperature: 0.2,
        topP: 0.8,
        topK: 40,
      },
    };
    const expectedOutput = {
      struct_val: {
        instances: {
          list_val: [
            {
              struct_val: {
                context: { string_val: ['context'] },
                examples: {
                  list_val: [
                    {
                      struct_val: {
                        input: {
                          struct_val: {
                            author: { string_val: ['user'] },
                            content: { string_val: ['user input'] },
                          },
                        },
                        output: {
                          struct_val: {
                            author: { string_val: ['bot'] },
                            content: { string_val: ['user output'] },
                          },
                        },
                      },
                    },
                  ],
                },
                messages: {
                  list_val: [
                    {
                      struct_val: {
                        author: { string_val: ['user'] },
                        content: { string_val: ['hi'] },
                      },
                    },
                  ],
                },
              },
            },
          ],
        },
        parameters: {
          struct_val: {
            candidateCount: { int_val: 1 },
            maxOutputTokens: { int_val: 1024 },
            temperature: { float_val: 0.2 },
            topP: { float_val: 0.8 },
            topK: { int_val: 40 },
          },
        },
      },
    };
    const result = formatGoogleInputs(input);
    expect(JSON.stringify(result)).toEqual(JSON.stringify(expectedOutput));
  });
  it('helps create valid payload parts', () => {
    const instances = {
      context: 'context',
      examples: [
        {
          input: {
            author: 'user',
            content: 'user input',
          },
          output: {
            author: 'bot',
            content: 'user output',
          },
        },
      ],
      messages: [
        {
          author: 'user',
          content: 'hi',
        },
      ],
    };
    const expectedInstances = {
      struct_val: {
        context: { string_val: ['context'] },
        examples: {
          list_val: [
            {
              struct_val: {
                input: {
                  struct_val: {
                    author: { string_val: ['user'] },
                    content: { string_val: ['user input'] },
                  },
                },
                output: {
                  struct_val: {
                    author: { string_val: ['bot'] },
                    content: { string_val: ['user output'] },
                  },
                },
              },
            },
          ],
        },
        messages: {
          list_val: [
            {
              struct_val: {
                author: { string_val: ['user'] },
                content: { string_val: ['hi'] },
              },
            },
          ],
        },
      },
    };
    const parameters = {
      candidateCount: 1,
      maxOutputTokens: 1024,
      temperature: 0.2,
      topP: 0.8,
      topK: 40,
    };
    const expectedParameters = {
      struct_val: {
        candidateCount: { int_val: 1 },
        maxOutputTokens: { int_val: 1024 },
        temperature: { float_val: 0.2 },
        topP: { float_val: 0.8 },
        topK: { int_val: 40 },
      },
    };
    const instancesResult = formatGoogleInputs(instances);
    const parametersResult = formatGoogleInputs(parameters);
    expect(JSON.stringify(instancesResult)).toEqual(JSON.stringify(expectedInstances));
    expect(JSON.stringify(parametersResult)).toEqual(JSON.stringify(expectedParameters));
  });
 });
--- a/api/app/clients/prompts/formatMessages.js
+++ b/api/app/clients/prompts/formatMessages.js
@ -1,245 +0,0 @@
 const { ToolMessage } = require('@langchain/core/messages');
 const { EModelEndpoint, ContentTypes } = require('librechat-data-provider');
 const { HumanMessage, AIMessage, SystemMessage } = require('@langchain/core/messages');
 /**
 * Formats a message to OpenAI Vision API payload format.
 *
 * @param {Object} params - The parameters for formatting.
 * @param {Object} params.message - The message object to format.
 * @param {string} [params.message.role] - The role of the message sender (must be 'user').
 * @param {string} [params.message.content] - The text content of the message.
 * @param {EModelEndpoint} [params.endpoint] - Identifier for specific endpoint handling
 * @param {Array<string>} [params.image_urls] - The image_urls to attach to the message.
 * @returns {(Object)} - The formatted message.
 */
 const formatVisionMessage = ({ message, image_urls, endpoint }) => {
  if (endpoint === EModelEndpoint.anthropic) {
    message.content = [...image_urls, { type: ContentTypes.TEXT, text: message.content }];
    return message;
  }
  message.content = [{ type: ContentTypes.TEXT, text: message.content }, ...image_urls];
  return message;
 };
 /**
 * Formats a message to OpenAI payload format based on the provided options.
 *
 * @param {Object} params - The parameters for formatting.
 * @param {Object} params.message - The message object to format.
 * @param {string} [params.message.role] - The role of the message sender (e.g., 'user', 'assistant').
 * @param {string} [params.message._name] - The name associated with the message.
 * @param {string} [params.message.sender] - The sender of the message.
 * @param {string} [params.message.text] - The text content of the message.
 * @param {string} [params.message.content] - The content of the message.
 * @param {Array<string>} [params.message.image_urls] - The image_urls attached to the message for Vision API.
 * @param {string} [params.userName] - The name of the user.
 * @param {string} [params.assistantName] - The name of the assistant.
 * @param {string} [params.endpoint] - Identifier for specific endpoint handling
 * @param {boolean} [params.langChain=false] - Whether to return a LangChain message object.
 * @returns {(Object|HumanMessage|AIMessage|SystemMessage)} - The formatted message.
 */
 const formatMessage = ({ message, userName, assistantName, endpoint, langChain = false }) => {
  let { role: _role, _name, sender, text, content: _content, lc_id } = message;
  if (lc_id && lc_id[2] && !langChain) {
    const roleMapping = {
      SystemMessage: 'system',
      HumanMessage: 'user',
      AIMessage: 'assistant',
    };
    _role = roleMapping[lc_id[2]];
  }
  const role = _role ?? (sender && sender?.toLowerCase() === 'user' ? 'user' : 'assistant');
  const content = _content ?? text ?? '';
  const formattedMessage = {
    role,
    content,
  };
  const { image_urls } = message;
  if (Array.isArray(image_urls) && image_urls.length > 0 && role === 'user') {
    return formatVisionMessage({
      message: formattedMessage,
      image_urls: message.image_urls,
      endpoint,
    });
  }
  if (_name) {
    formattedMessage.name = _name;
  }
  if (userName && formattedMessage.role === 'user') {
    formattedMessage.name = userName;
  }
  if (assistantName && formattedMessage.role === 'assistant') {
    formattedMessage.name = assistantName;
  }
  if (formattedMessage.name) {
    // Conform to API regex: ^[a-zA-Z0-9_-]{1,64}$
    // https://community.openai.com/t/the-format-of-the-name-field-in-the-documentation-is-incorrect/175684/2
    formattedMessage.name = formattedMessage.name.replace(/[^a-zA-Z0-9_-]/g, '_');
    if (formattedMessage.name.length > 64) {
      formattedMessage.name = formattedMessage.name.substring(0, 64);
    }
  }
  if (!langChain) {
    return formattedMessage;
  }
  if (role === 'user') {
    return new HumanMessage(formattedMessage);
  } else if (role === 'assistant') {
    return new AIMessage(formattedMessage);
  } else {
    return new SystemMessage(formattedMessage);
  }
 };
 /**
 * Formats an array of messages for LangChain.
 *
 * @param {Array<Object>} messages - The array of messages to format.
 * @param {Object} formatOptions - The options for formatting each message.
 * @param {string} [formatOptions.userName] - The name of the user.
 * @param {string} [formatOptions.assistantName] - The name of the assistant.
 * @returns {Array<(HumanMessage|AIMessage|SystemMessage)>} - The array of formatted LangChain messages.
 */
 const formatLangChainMessages = (messages, formatOptions) =>
  messages.map((msg) => formatMessage({ ...formatOptions, message: msg, langChain: true }));
 /**
 * Formats a LangChain message object by merging properties from `lc_kwargs` or `kwargs` and `additional_kwargs`.
 *
 * @param {Object} message - The message object to format.
 * @param {Object} [message.lc_kwargs] - Contains properties to be merged. Either this or `message.kwargs` should be provided.
 * @param {Object} [message.kwargs] - Contains properties to be merged. Either this or `message.lc_kwargs` should be provided.
 * @param {Object} [message.kwargs.additional_kwargs] - Additional properties to be merged.
 *
 * @returns {Object} The formatted LangChain message.
 */
 const formatFromLangChain = (message) => {
  const { additional_kwargs, ...message_kwargs } = message.lc_kwargs ?? message.kwargs;
  return {
    ...message_kwargs,
    ...additional_kwargs,
  };
 };
 /**
 * Formats an array of messages for LangChain, handling tool calls and creating ToolMessage instances.
 *
 * @param {Array<Partial<TMessage>>} payload - The array of messages to format.
 * @returns {Array<(HumanMessage|AIMessage|SystemMessage|ToolMessage)>} - The array of formatted LangChain messages, including ToolMessages for tool calls.
 */
 const formatAgentMessages = (payload) => {
  const messages = [];
  for (const message of payload) {
    if (typeof message.content === 'string') {
      message.content = [{ type: ContentTypes.TEXT, [ContentTypes.TEXT]: message.content }];
    }
    if (message.role !== 'assistant') {
      messages.push(formatMessage({ message, langChain: true }));
      continue;
    }
    let currentContent = [];
    let lastAIMessage = null;
    let hasReasoning = false;
    for (const part of message.content) {
      if (part.type === ContentTypes.TEXT && part.tool_call_ids) {
        /*
        If there's pending content, it needs to be aggregated as a single string to prepare for tool calls.
        For Anthropic models, the "tool_calls" field on a message is only respected if content is a string.
         */
        if (currentContent.length > 0) {
          let content = currentContent.reduce((acc, curr) => {
            if (curr.type === ContentTypes.TEXT) {
              return `${acc}${curr[ContentTypes.TEXT]}\n`;
            }
            return acc;
          }, '');
          content = `${content}\n${part[ContentTypes.TEXT] ?? ''}`.trim();
          lastAIMessage = new AIMessage({ content });
          messages.push(lastAIMessage);
          currentContent = [];
          continue;
        }
        // Create a new AIMessage with this text and prepare for tool calls
        lastAIMessage = new AIMessage({
          content: part.text || '',
        });
        messages.push(lastAIMessage);
      } else if (part.type === ContentTypes.TOOL_CALL) {
        if (!lastAIMessage) {
          throw new Error('Invalid tool call structure: No preceding AIMessage with tool_call_ids');
        }
        // Note: `tool_calls` list is defined when constructed by `AIMessage` class, and outputs should be excluded from it
        const { output, args: _args, ...tool_call } = part.tool_call;
        // TODO: investigate; args as dictionary may need to be provider-or-tool-specific
        let args = _args;
        try {
          args = JSON.parse(_args);
        } catch (e) {
          if (typeof _args === 'string') {
            args = { input: _args };
          }
        }
        tool_call.args = args;
        lastAIMessage.tool_calls.push(tool_call);
        // Add the corresponding ToolMessage
        messages.push(
          new ToolMessage({
            tool_call_id: tool_call.id,
            name: tool_call.name,
            content: output || '',
          }),
        );
      } else if (part.type === ContentTypes.THINK) {
        hasReasoning = true;
        continue;
      } else if (part.type === ContentTypes.ERROR || part.type === ContentTypes.AGENT_UPDATE) {
        continue;
      } else {
        currentContent.push(part);
      }
    }
    if (hasReasoning) {
      currentContent = currentContent
        .reduce((acc, curr) => {
          if (curr.type === ContentTypes.TEXT) {
            return `${acc}${curr[ContentTypes.TEXT]}\n`;
          }
          return acc;
        }, '')
        .trim();
    }
    if (currentContent.length > 0) {
      messages.push(new AIMessage({ content: currentContent }));
    }
  }
  return messages;
 };
 module.exports = {
  formatMessage,
  formatFromLangChain,
  formatAgentMessages,
  formatLangChainMessages,
 };
--- a/api/app/clients/prompts/formatMessages.spec.js
+++ b/api/app/clients/prompts/formatMessages.spec.js
@ -1,276 +0,0 @@
 const { Constants } = require('librechat-data-provider');
 const { HumanMessage, AIMessage, SystemMessage } = require('@langchain/core/messages');
 const { formatMessage, formatLangChainMessages, formatFromLangChain } = require('./formatMessages');
 describe('formatMessage', () => {
  it('formats user message', () => {
    const input = {
      message: {
        sender: 'user',
        text: 'Hello',
      },
      userName: 'John',
    };
    const result = formatMessage(input);
    expect(result).toEqual({
      role: 'user',
      content: 'Hello',
      name: 'John',
    });
  });
  it('sanitizes the name by replacing invalid characters (per OpenAI)', () => {
    const input = {
      message: {
        sender: 'user',
        text: 'Hello',
      },
      userName: ' John$Doe@Example! ',
    };
    const result = formatMessage(input);
    expect(result).toEqual({
      role: 'user',
      content: 'Hello',
      name: '_John_Doe_Example__',
    });
  });
  it('trims the name to a maximum length of 64 characters', () => {
    const longName = 'a'.repeat(100);
    const input = {
      message: {
        sender: 'user',
        text: 'Hello',
      },
      userName: longName,
    };
    const result = formatMessage(input);
    expect(result.name.length).toBe(64);
    expect(result.name).toBe('a'.repeat(64));
  });
  it('formats a realistic user message', () => {
    const input = {
      message: {
        _id: '6512cdfb92cbf69fea615331',
        messageId: 'b620bf73-c5c3-4a38-b724-76886aac24c4',
        __v: 0,
        conversationId: '5c23d24f-941f-4aab-85df-127b596c8aa5',
        createdAt: Date.now(),
        error: false,
        finish_reason: null,
        isCreatedByUser: true,
        model: null,
        parentMessageId: Constants.NO_PARENT,
        sender: 'User',
        text: 'hi',
        tokenCount: 5,
        unfinished: false,
        updatedAt: Date.now(),
        user: '6512cdf475f05c86d44c31d2',
      },
      userName: 'John',
    };
    const result = formatMessage(input);
    expect(result).toEqual({
      role: 'user',
      content: 'hi',
      name: 'John',
    });
  });
  it('formats assistant message', () => {
    const input = {
      message: {
        sender: 'assistant',
        text: 'Hi there',
      },
      assistantName: 'Assistant',
    };
    const result = formatMessage(input);
    expect(result).toEqual({
      role: 'assistant',
      content: 'Hi there',
      name: 'Assistant',
    });
  });
  it('formats system message', () => {
    const input = {
      message: {
        role: 'system',
        text: 'Hi there',
      },
    };
    const result = formatMessage(input);
    expect(result).toEqual({
      role: 'system',
      content: 'Hi there',
    });
  });
  it('formats user message with langChain', () => {
    const input = {
      message: {
        sender: 'user',
        text: 'Hello',
      },
      userName: 'John',
      langChain: true,
    };
    const result = formatMessage(input);
    expect(result).toBeInstanceOf(HumanMessage);
    expect(result.lc_kwargs.content).toEqual(input.message.text);
    expect(result.lc_kwargs.name).toEqual(input.userName);
  });
  it('formats assistant message with langChain', () => {
    const input = {
      message: {
        sender: 'assistant',
        text: 'Hi there',
      },
      assistantName: 'Assistant',
      langChain: true,
    };
    const result = formatMessage(input);
    expect(result).toBeInstanceOf(AIMessage);
    expect(result.lc_kwargs.content).toEqual(input.message.text);
    expect(result.lc_kwargs.name).toEqual(input.assistantName);
  });
  it('formats system message with langChain', () => {
    const input = {
      message: {
        role: 'system',
        text: 'This is a system message.',
      },
      langChain: true,
    };
    const result = formatMessage(input);
    expect(result).toBeInstanceOf(SystemMessage);
    expect(result.lc_kwargs.content).toEqual(input.message.text);
  });
  it('formats langChain messages into OpenAI payload format', () => {
    const human = {
      message: new HumanMessage({
        content: 'Hello',
      }),
    };
    const system = {
      message: new SystemMessage({
        content: 'Hello',
      }),
    };
    const ai = {
      message: new AIMessage({
        content: 'Hello',
      }),
    };
    const humanResult = formatMessage(human);
    const systemResult = formatMessage(system);
    const aiResult = formatMessage(ai);
    expect(humanResult).toEqual({
      role: 'user',
      content: 'Hello',
    });
    expect(systemResult).toEqual({
      role: 'system',
      content: 'Hello',
    });
    expect(aiResult).toEqual({
      role: 'assistant',
      content: 'Hello',
    });
  });
 });
 describe('formatLangChainMessages', () => {
  it('formats an array of messages for LangChain', () => {
    const messages = [
      {
        role: 'system',
        content: 'This is a system message',
      },
      {
        sender: 'user',
        text: 'Hello',
      },
      {
        sender: 'assistant',
        text: 'Hi there',
      },
    ];
    const formatOptions = {
      userName: 'John',
      assistantName: 'Assistant',
    };
    const result = formatLangChainMessages(messages, formatOptions);
    expect(result).toHaveLength(3);
    expect(result[0]).toBeInstanceOf(SystemMessage);
    expect(result[1]).toBeInstanceOf(HumanMessage);
    expect(result[2]).toBeInstanceOf(AIMessage);
    expect(result[0].lc_kwargs.content).toEqual(messages[0].content);
    expect(result[1].lc_kwargs.content).toEqual(messages[1].text);
    expect(result[2].lc_kwargs.content).toEqual(messages[2].text);
    expect(result[1].lc_kwargs.name).toEqual(formatOptions.userName);
    expect(result[2].lc_kwargs.name).toEqual(formatOptions.assistantName);
  });
  describe('formatFromLangChain', () => {
    it('should merge kwargs and additional_kwargs', () => {
      const message = {
        kwargs: {
          content: 'some content',
          name: 'dan',
          additional_kwargs: {
            function_call: {
              name: 'dall-e',
              arguments: '{\n  "input": "Subject: hedgehog, Style: cute"\n}',
            },
          },
        },
      };
      const expected = {
        content: 'some content',
        name: 'dan',
        function_call: {
          name: 'dall-e',
          arguments: '{\n  "input": "Subject: hedgehog, Style: cute"\n}',
        },
      };
      expect(formatFromLangChain(message)).toEqual(expected);
    });
    it('should handle messages without additional_kwargs', () => {
      const message = {
        kwargs: {
          content: 'some content',
          name: 'dan',
        },
      };
      const expected = {
        content: 'some content',
        name: 'dan',
      };
      expect(formatFromLangChain(message)).toEqual(expected);
    });
    it('should handle empty messages', () => {
      const message = {
        kwargs: {},
      };
      const expected = {};
      expect(formatFromLangChain(message)).toEqual(expected);
    });
  });
 });
--- a/api/app/clients/prompts/index.js
+++ b/api/app/clients/prompts/index.js
@ -1,13 +0,0 @@
 const formatMessages = require('./formatMessages');
 const summaryPrompts = require('./summaryPrompts');
 const truncate = require('./truncate');
 const createVisionPrompt = require('./createVisionPrompt');
 const createContextHandlers = require('./createContextHandlers');
 module.exports = {
  ...formatMessages,
  ...summaryPrompts,
  ...truncate,
  createVisionPrompt,
  createContextHandlers,
 };
--- a/api/app/clients/prompts/shadcn-docs/components.js
+++ b/api/app/clients/prompts/shadcn-docs/components.js
@ -1,495 +0,0 @@
 // Essential Components
 const essentialComponents = {
  avatar: {
    componentName: 'Avatar',
    importDocs: 'import { Avatar, AvatarFallback, AvatarImage } from "/components/ui/avatar"',
    usageDocs: `
 <Avatar>
  <AvatarImage src="https://github.com/shadcn.png" />
  <AvatarFallback>CN</AvatarFallback>
 </Avatar>`,
  },
  button: {
    componentName: 'Button',
    importDocs: 'import { Button } from "/components/ui/button"',
    usageDocs: `
 <Button variant="outline">Button</Button>`,
  },
  card: {
    componentName: 'Card',
    importDocs: `
 import {
  Card,
  CardContent,
  CardDescription,
  CardFooter,
  CardHeader,
  CardTitle,
 } from "/components/ui/card"`,
    usageDocs: `
 <Card>
  <CardHeader>
    <CardTitle>Card Title</CardTitle>
    <CardDescription>Card Description</CardDescription>
  </CardHeader>
  <CardContent>
    <p>Card Content</p>
  </CardContent>
  <CardFooter>
    <p>Card Footer</p>
  </CardFooter>
 </Card>`,
  },
  checkbox: {
    componentName: 'Checkbox',
    importDocs: 'import { Checkbox } from "/components/ui/checkbox"',
    usageDocs: '<Checkbox />',
  },
  input: {
    componentName: 'Input',
    importDocs: 'import { Input } from "/components/ui/input"',
    usageDocs: '<Input />',
  },
  label: {
    componentName: 'Label',
    importDocs: 'import { Label } from "/components/ui/label"',
    usageDocs: '<Label htmlFor="email">Your email address</Label>',
  },
  radioGroup: {
    componentName: 'RadioGroup',
    importDocs: `
 import { Label } from "/components/ui/label"
 import { RadioGroup, RadioGroupItem } from "/components/ui/radio-group"`,
    usageDocs: `
 <RadioGroup defaultValue="option-one">
  <div className="flex items-center space-x-2">
    <RadioGroupItem value="option-one" id="option-one" />
    <Label htmlFor="option-one">Option One</Label>
  </div>
  <div className="flex items-center space-x-2">
    <RadioGroupItem value="option-two" id="option-two" />
    <Label htmlFor="option-two">Option Two</Label>
  </div>
 </RadioGroup>`,
  },
  select: {
    componentName: 'Select',
    importDocs: `
 import {
  Select,
  SelectContent,
  SelectItem,
  SelectTrigger,
  SelectValue,
 } from "/components/ui/select"`,
    usageDocs: `
 <Select>
  <SelectTrigger className="w-[180px]">
    <SelectValue placeholder="Theme" />
  </SelectTrigger>
  <SelectContent>
    <SelectItem value="light">Light</SelectItem>
    <SelectItem value="dark">Dark</SelectItem>
    <SelectItem value="system">System</SelectItem>
  </SelectContent>
 </Select>`,
  },
  textarea: {
    componentName: 'Textarea',
    importDocs: 'import { Textarea } from "/components/ui/textarea"',
    usageDocs: '<Textarea />',
  },
 };
 // Extra Components
 const extraComponents = {
  accordion: {
    componentName: 'Accordion',
    importDocs: `
 import {
  Accordion,
  AccordionContent,
  AccordionItem,
  AccordionTrigger,
 } from "/components/ui/accordion"`,
    usageDocs: `
 <Accordion type="single" collapsible>
  <AccordionItem value="item-1">
    <AccordionTrigger>Is it accessible?</AccordionTrigger>
    <AccordionContent>
      Yes. It adheres to the WAI-ARIA design pattern.
    </AccordionContent>
  </AccordionItem>
 </Accordion>`,
  },
  alertDialog: {
    componentName: 'AlertDialog',
    importDocs: `
 import {
  AlertDialog,
  AlertDialogAction,
  AlertDialogCancel,
  AlertDialogContent,
  AlertDialogDescription,
  AlertDialogFooter,
  AlertDialogHeader,
  AlertDialogTitle,
  AlertDialogTrigger,
 } from "/components/ui/alert-dialog"`,
    usageDocs: `
 <AlertDialog>
  <AlertDialogTrigger>Open</AlertDialogTrigger>
  <AlertDialogContent>
    <AlertDialogHeader>
      <AlertDialogTitle>Are you absolutely sure?</AlertDialogTitle>
      <AlertDialogDescription>
        This action cannot be undone.
      </AlertDialogDescription>
    </AlertDialogHeader>
    <AlertDialogFooter>
      <AlertDialogCancel>Cancel</AlertDialogCancel>
      <AlertDialogAction>Continue</AlertDialogAction>
    </AlertDialogFooter>
  </AlertDialogContent>
 </AlertDialog>`,
  },
  alert: {
    componentName: 'Alert',
    importDocs: `
 import {
  Alert,
  AlertDescription,
  AlertTitle,
 } from "/components/ui/alert"`,
    usageDocs: `
 <Alert>
  <AlertTitle>Heads up!</AlertTitle>
  <AlertDescription>
    You can add components to your app using the cli.
  </AlertDescription>
 </Alert>`,
  },
  aspectRatio: {
    componentName: 'AspectRatio',
    importDocs: 'import { AspectRatio } from "/components/ui/aspect-ratio"',
    usageDocs: `
 <AspectRatio ratio={16 / 9}>
  <Image src="..." alt="Image" className="rounded-md object-cover" />
 </AspectRatio>`,
  },
  badge: {
    componentName: 'Badge',
    importDocs: 'import { Badge } from "/components/ui/badge"',
    usageDocs: '<Badge>Badge</Badge>',
  },
  calendar: {
    componentName: 'Calendar',
    importDocs: 'import { Calendar } from "/components/ui/calendar"',
    usageDocs: '<Calendar />',
  },
  carousel: {
    componentName: 'Carousel',
    importDocs: `
 import {
  Carousel,
  CarouselContent,
  CarouselItem,
  CarouselNext,
  CarouselPrevious,
 } from "/components/ui/carousel"`,
    usageDocs: `
 <Carousel>
  <CarouselContent>
    <CarouselItem>...</CarouselItem>
    <CarouselItem>...</CarouselItem>
    <CarouselItem>...</CarouselItem>
  </CarouselContent>
  <CarouselPrevious />
  <CarouselNext />
 </Carousel>`,
  },
  collapsible: {
    componentName: 'Collapsible',
    importDocs: `
 import {
  Collapsible,
  CollapsibleContent,
  CollapsibleTrigger,
 } from "/components/ui/collapsible"`,
    usageDocs: `
 <Collapsible>
  <CollapsibleTrigger>Can I use this in my project?</CollapsibleTrigger>
  <CollapsibleContent>
    Yes. Free to use for personal and commercial projects. No attribution required.
  </CollapsibleContent>
 </Collapsible>`,
  },
  dialog: {
    componentName: 'Dialog',
    importDocs: `
 import {
  Dialog,
  DialogContent,
  DialogDescription,
  DialogHeader,
  DialogTitle,
  DialogTrigger,
 } from "/components/ui/dialog"`,
    usageDocs: `
 <Dialog>
  <DialogTrigger>Open</DialogTrigger>
  <DialogContent>
    <DialogHeader>
      <DialogTitle>Are you sure absolutely sure?</DialogTitle>
      <DialogDescription>
        This action cannot be undone.
      </DialogDescription>
    </DialogHeader>
  </DialogContent>
 </Dialog>`,
  },
  dropdownMenu: {
    componentName: 'DropdownMenu',
    importDocs: `
 import {
  DropdownMenu,
  DropdownMenuContent,
  DropdownMenuItem,
  DropdownMenuLabel,
  DropdownMenuSeparator,
  DropdownMenuTrigger,
 } from "/components/ui/dropdown-menu"`,
    usageDocs: `
 <DropdownMenu>
  <DropdownMenuTrigger>Open</DropdownMenuTrigger>
  <DropdownMenuContent>
    <DropdownMenuLabel>My Account</DropdownMenuLabel>
    <DropdownMenuSeparator />
    <DropdownMenuItem>Profile</DropdownMenuItem>
    <DropdownMenuItem>Billing</DropdownMenuItem>
    <DropdownMenuItem>Team</DropdownMenuItem>
    <DropdownMenuItem>Subscription</DropdownMenuItem>
  </DropdownMenuContent>
 </DropdownMenu>`,
  },
  menubar: {
    componentName: 'Menubar',
    importDocs: `
 import {
  Menubar,
  MenubarContent,
  MenubarItem,
  MenubarMenu,
  MenubarSeparator,
  MenubarShortcut,
  MenubarTrigger,
 } from "/components/ui/menubar"`,
    usageDocs: `
 <Menubar>
  <MenubarMenu>
    <MenubarTrigger>File</MenubarTrigger>
    <MenubarContent>
      <MenubarItem>
        New Tab <MenubarShortcut>⌘T</MenubarShortcut>
      </MenubarItem>
      <MenubarItem>New Window</MenubarItem>
      <MenubarSeparator />
      <MenubarItem>Share</MenubarItem>
      <MenubarSeparator />
      <MenubarItem>Print</MenubarItem>
    </MenubarContent>
  </MenubarMenu>
 </Menubar>`,
  },
  navigationMenu: {
    componentName: 'NavigationMenu',
    importDocs: `
 import {
  NavigationMenu,
  NavigationMenuContent,
  NavigationMenuItem,
  NavigationMenuLink,
  NavigationMenuList,
  NavigationMenuTrigger,
  navigationMenuTriggerStyle,
 } from "/components/ui/navigation-menu"`,
    usageDocs: `
 <NavigationMenu>
  <NavigationMenuList>
    <NavigationMenuItem>
      <NavigationMenuTrigger>Item One</NavigationMenuTrigger>
      <NavigationMenuContent>
        <NavigationMenuLink>Link</NavigationMenuLink>
      </NavigationMenuContent>
    </NavigationMenuItem>
  </NavigationMenuList>
 </NavigationMenu>`,
  },
  popover: {
    componentName: 'Popover',
    importDocs: `
 import {
  Popover,
  PopoverContent,
  PopoverTrigger,
 } from "/components/ui/popover"`,
    usageDocs: `
 <Popover>
  <PopoverTrigger>Open</PopoverTrigger>
  <PopoverContent>Place content for the popover here.</PopoverContent>
 </Popover>`,
  },
  progress: {
    componentName: 'Progress',
    importDocs: 'import { Progress } from "/components/ui/progress"',
    usageDocs: '<Progress value={33} />',
  },
  separator: {
    componentName: 'Separator',
    importDocs: 'import { Separator } from "/components/ui/separator"',
    usageDocs: '<Separator />',
  },
  sheet: {
    componentName: 'Sheet',
    importDocs: `
 import {
  Sheet,
  SheetContent,
  SheetDescription,
  SheetHeader,
  SheetTitle,
  SheetTrigger,
 } from "/components/ui/sheet"`,
    usageDocs: `
 <Sheet>
  <SheetTrigger>Open</SheetTrigger>
  <SheetContent>
    <SheetHeader>
      <SheetTitle>Are you sure absolutely sure?</SheetTitle>
      <SheetDescription>
        This action cannot be undone.
      </SheetDescription>
    </SheetHeader>
  </SheetContent>
 </Sheet>`,
  },
  skeleton: {
    componentName: 'Skeleton',
    importDocs: 'import { Skeleton } from "/components/ui/skeleton"',
    usageDocs: '<Skeleton className="w-[100px] h-[20px] rounded-full" />',
  },
  slider: {
    componentName: 'Slider',
    importDocs: 'import { Slider } from "/components/ui/slider"',
    usageDocs: '<Slider defaultValue={[33]} max={100} step={1} />',
  },
  switch: {
    componentName: 'Switch',
    importDocs: 'import { Switch } from "/components/ui/switch"',
    usageDocs: '<Switch />',
  },
  table: {
    componentName: 'Table',
    importDocs: `
 import {
  Table,
  TableBody,
  TableCaption,
  TableCell,
  TableHead,
  TableHeader,
  TableRow,
 } from "/components/ui/table"`,
    usageDocs: `
 <Table>
  <TableCaption>A list of your recent invoices.</TableCaption>
  <TableHeader>
    <TableRow>
      <TableHead className="w-[100px]">Invoice</TableHead>
      <TableHead>Status</TableHead>
      <TableHead>Method</TableHead>
      <TableHead className="text-right">Amount</TableHead>
    </TableRow>
  </TableHeader>
  <TableBody>
    <TableRow>
      <TableCell className="font-medium">INV001</TableCell>
      <TableCell>Paid</TableCell>
      <TableCell>Credit Card</TableCell>
      <TableCell className="text-right">$250.00</TableCell>
    </TableRow>
  </TableBody>
 </Table>`,
  },
  tabs: {
    componentName: 'Tabs',
    importDocs: `
 import {
  Tabs,
  TabsContent,
  TabsList,
  TabsTrigger,
 } from "/components/ui/tabs"`,
    usageDocs: `
 <Tabs defaultValue="account" className="w-[400px]">
  <TabsList>
    <TabsTrigger value="account">Account</TabsTrigger>
    <TabsTrigger value="password">Password</TabsTrigger>
  </TabsList>
  <TabsContent value="account">Make changes to your account here.</TabsContent>
  <TabsContent value="password">Change your password here.</TabsContent>
 </Tabs>`,
  },
  toast: {
    componentName: 'Toast',
    importDocs: `
 import { useToast } from "/components/ui/use-toast"
 import { Button } from "/components/ui/button"`,
    usageDocs: `
 export function ToastDemo() {
  const { toast } = useToast()
  return (
    <Button
      onClick={() => {
        toast({
          title: "Scheduled: Catch up",
          description: "Friday, February 10, 2023 at 5:57 PM",
        })
      }}
    >
      Show Toast
    </Button>
  )
 }`,
  },
  toggle: {
    componentName: 'Toggle',
    importDocs: 'import { Toggle } from "/components/ui/toggle"',
    usageDocs: '<Toggle>Toggle</Toggle>',
  },
  tooltip: {
    componentName: 'Tooltip',
    importDocs: `
 import {
  Tooltip,
  TooltipContent,
  TooltipProvider,
  TooltipTrigger,
 } from "/components/ui/tooltip"`,
    usageDocs: `
 <TooltipProvider>
  <Tooltip>
    <TooltipTrigger>Hover</TooltipTrigger>
    <TooltipContent>
      <p>Add to library</p>
    </TooltipContent>
  </Tooltip>
 </TooltipProvider>`,
  },
 };
 const components = Object.assign({}, essentialComponents, extraComponents);
 module.exports = {
  components,
 };
--- a/api/app/clients/prompts/shadcn-docs/generate.js
+++ b/api/app/clients/prompts/shadcn-docs/generate.js
@ -1,50 +0,0 @@
 const dedent = require('dedent');
 /**
 * Generate system prompt for AI-assisted React component creation
 * @param {Object} options - Configuration options
 * @param {Object} options.components - Documentation for shadcn components
 * @param {boolean} [options.useXML=false] - Whether to use XML-style formatting for component instructions
 * @returns {string} The generated system prompt
 */
 function generateShadcnPrompt(options) {
  const { components, useXML = false } = options;
  let systemPrompt = dedent`
    ## Additional Artifact Instructions for React Components: "application/vnd.react"
    There are some prestyled components (primitives) available for use. Please use your best judgement to use any of these components if the app calls for one.
    Here are the components that are available, along with how to import them, and how to use them:
    ${Object.values(components)
      .map((component) => {
        if (useXML) {
          return dedent`
            <component>
              <name>${component.componentName}</name>
              <import-instructions>${component.importDocs}</import-instructions>
              <usage-instructions>${component.usageDocs}</usage-instructions>
            </component>
          `;
        } else {
          return dedent`
            # ${component.componentName}
            ## Import Instructions
            ${component.importDocs}
            ## Usage Instructions
            ${component.usageDocs}
          `;
        }
      })
      .join('\n\n')}
  `;
  return systemPrompt;
 }
 module.exports = {
  generateShadcnPrompt,
 };
--- a/api/app/clients/prompts/summaryPrompts.js
+++ b/api/app/clients/prompts/summaryPrompts.js
@ -1,53 +0,0 @@
 const { PromptTemplate } = require('@langchain/core/prompts');
 /*
 * Without `{summary}` and `{new_lines}`, token count is 98
 * We are counting this towards the max context tokens for summaries, +3 for the assistant label (101)
 * If this prompt changes, use https://tiktokenizer.vercel.app/ to count the tokens
 */
 const _DEFAULT_SUMMARIZER_TEMPLATE = `Summarize the conversation by integrating new lines into the current summary.
 EXAMPLE:
 Current summary:
 The human inquires about the AI's view on artificial intelligence. The AI believes it's beneficial.
 New lines:
 Human: Why is it beneficial?
 AI: It helps humans achieve their potential.
 New summary:
 The human inquires about the AI's view on artificial intelligence. The AI believes it's beneficial because it helps humans achieve their potential.
 Current summary:
 {summary}
 New lines:
 {new_lines}
 New summary:`;
 const SUMMARY_PROMPT = new PromptTemplate({
  inputVariables: ['summary', 'new_lines'],
  template: _DEFAULT_SUMMARIZER_TEMPLATE,
 });
 /*
 * Without `{new_lines}`, token count is 27
 * We are counting this towards the max context tokens for summaries, rounded up to 30
 * If this prompt changes, use https://tiktokenizer.vercel.app/ to count the tokens
 */
 const _CUT_OFF_SUMMARIZER = `The following text is cut-off:
 {new_lines}
 Summarize the content as best as you can, noting that it was cut-off.
 Summary:`;
 const CUT_OFF_PROMPT = new PromptTemplate({
  inputVariables: ['new_lines'],
  template: _CUT_OFF_SUMMARIZER,
 });
 module.exports = {
  SUMMARY_PROMPT,
  CUT_OFF_PROMPT,
 };
--- a/api/app/clients/prompts/truncate.js
+++ b/api/app/clients/prompts/truncate.js
@ -1,115 +0,0 @@
 const MAX_CHAR = 255;
 /**
 * Truncates a given text to a specified maximum length, appending ellipsis and a notification
 * if the original text exceeds the maximum length.
 *
 * @param {string} text - The text to be truncated.
 * @param {number} [maxLength=MAX_CHAR] - The maximum length of the text after truncation. Defaults to MAX_CHAR.
 * @returns {string} The truncated text if the original text length exceeds maxLength, otherwise returns the original text.
 */
 function truncateText(text, maxLength = MAX_CHAR) {
  if (text.length > maxLength) {
    return `${text.slice(0, maxLength)}... [text truncated for brevity]`;
  }
  return text;
 }
 /**
 * Truncates a given text to a specified maximum length by showing the first half and the last half of the text,
 * separated by ellipsis. This method ensures the output does not exceed the maximum length, including the addition
 * of ellipsis and notification if the original text exceeds the maximum length.
 *
 * @param {string} text - The text to be truncated.
 * @param {number} [maxLength=MAX_CHAR] - The maximum length of the output text after truncation. Defaults to MAX_CHAR.
 * @returns {string} The truncated text showing the first half and the last half, or the original text if it does not exceed maxLength.
 */
 function smartTruncateText(text, maxLength = MAX_CHAR) {
  const ellipsis = '...';
  const notification = ' [text truncated for brevity]';
  const halfMaxLength = Math.floor((maxLength - ellipsis.length - notification.length) / 2);
  if (text.length > maxLength) {
    const startLastHalf = text.length - halfMaxLength;
    return `${text.slice(0, halfMaxLength)}${ellipsis}${text.slice(startLastHalf)}${notification}`;
  }
  return text;
 }
 /**
 * @param {TMessage[]} _messages
 * @param {number} maxContextTokens
 * @param {function({role: string, content: TMessageContent[]}): number} getTokenCountForMessage
 *
 * @returns {{
 *  dbMessages: TMessage[],
 * editedIndices: number[]
 * }}
 */
 function truncateToolCallOutputs(_messages, maxContextTokens, getTokenCountForMessage) {
  const THRESHOLD_PERCENTAGE = 0.5;
  const targetTokenLimit = maxContextTokens * THRESHOLD_PERCENTAGE;
  let currentTokenCount = 3;
  const messages = [..._messages];
  const processedMessages = [];
  let currentIndex = messages.length;
  const editedIndices = new Set();
  while (messages.length > 0) {
    currentIndex--;
    const message = messages.pop();
    currentTokenCount += message.tokenCount;
    if (currentTokenCount < targetTokenLimit) {
      processedMessages.push(message);
      continue;
    }
    if (!message.content || !Array.isArray(message.content)) {
      processedMessages.push(message);
      continue;
    }
    const toolCallIndices = message.content
      .map((item, index) => (item.type === 'tool_call' ? index : -1))
      .filter((index) => index !== -1)
      .reverse();
    if (toolCallIndices.length === 0) {
      processedMessages.push(message);
      continue;
    }
    const newContent = [...message.content];
    // Truncate all tool outputs since we're over threshold
    for (const index of toolCallIndices) {
      const toolCall = newContent[index].tool_call;
      if (!toolCall || !toolCall.output) {
        continue;
      }
      editedIndices.add(currentIndex);
      newContent[index] = {
        ...newContent[index],
        tool_call: {
          ...toolCall,
          output: '[OUTPUT_OMITTED_FOR_BREVITY]',
        },
      };
    }
    const truncatedMessage = {
      ...message,
      content: newContent,
      tokenCount: getTokenCountForMessage({ role: 'assistant', content: newContent }),
    };
    processedMessages.push(truncatedMessage);
  }
  return { dbMessages: processedMessages.reverse(), editedIndices: Array.from(editedIndices) };
 }
 module.exports = { truncateText, smartTruncateText, truncateToolCallOutputs };
--- a/api/app/clients/specs/BaseClient.test.js
+++ b/api/app/clients/specs/BaseClient.test.js
@ -1,931 +0,0 @@
 const { Constants } = require('librechat-data-provider');
 const { initializeFakeClient } = require('./FakeClient');
 jest.mock('~/db/connect');
 jest.mock('~/server/services/Config', () => ({
  getAppConfig: jest.fn().mockResolvedValue({
    // Default app config for tests
    paths: { uploads: '/tmp' },
    fileStrategy: 'local',
    memory: { disabled: false },
  }),
 }));
 jest.mock('~/models', () => ({
  User: jest.fn(),
  Key: jest.fn(),
  Session: jest.fn(),
  Balance: jest.fn(),
  Transaction: jest.fn(),
  getMessages: jest.fn().mockResolvedValue([]),
  saveMessage: jest.fn(),
  updateMessage: jest.fn(),
  deleteMessagesSince: jest.fn(),
  deleteMessages: jest.fn(),
  getConvoTitle: jest.fn(),
  getConvo: jest.fn(),
  saveConvo: jest.fn(),
  deleteConvos: jest.fn(),
  getPreset: jest.fn(),
  getPresets: jest.fn(),
  savePreset: jest.fn(),
  deletePresets: jest.fn(),
  findFileById: jest.fn(),
  createFile: jest.fn(),
  updateFile: jest.fn(),
  deleteFile: jest.fn(),
  deleteFiles: jest.fn(),
  getFiles: jest.fn(),
  updateFileUsage: jest.fn(),
 }));
 const { getConvo, saveConvo } = require('~/models');
 jest.mock('@librechat/agents', () => {
  const { Providers } = jest.requireActual('@librechat/agents');
  return {
    Providers,
    ChatOpenAI: jest.fn().mockImplementation(() => {
      return {};
    }),
  };
 });
 let parentMessageId;
 let conversationId;
 const fakeMessages = [];
 const userMessage = 'Hello, ChatGPT!';
 const apiKey = 'fake-api-key';
 const messageHistory = [
  { role: 'user', isCreatedByUser: true, text: 'Hello', messageId: '1' },
  { role: 'assistant', isCreatedByUser: false, text: 'Hi', messageId: '2', parentMessageId: '1' },
  {
    role: 'user',
    isCreatedByUser: true,
    text: "What's up",
    messageId: '3',
    parentMessageId: '2',
  },
 ];
 describe('BaseClient', () => {
  let TestClient;
  const options = {
    // debug: true,
    modelOptions: {
      model: 'gpt-4o-mini',
      temperature: 0,
    },
  };
  beforeEach(() => {
    TestClient = initializeFakeClient(apiKey, options, fakeMessages);
    TestClient.summarizeMessages = jest.fn().mockResolvedValue({
      summaryMessage: {
        role: 'system',
        content: 'Refined answer',
      },
      summaryTokenCount: 5,
    });
  });
  test('returns the input messages without instructions when addInstructions() is called with empty instructions', () => {
    const messages = [{ content: 'Hello' }, { content: 'How are you?' }, { content: 'Goodbye' }];
    const instructions = '';
    const result = TestClient.addInstructions(messages, instructions);
    expect(result).toEqual(messages);
  });
  test('returns the input messages with instructions properly added when addInstructions() is called with non-empty instructions', () => {
    const messages = [{ content: 'Hello' }, { content: 'How are you?' }, { content: 'Goodbye' }];
    const instructions = { content: 'Please respond to the question.' };
    const result = TestClient.addInstructions(messages, instructions);
    const expected = [
      { content: 'Please respond to the question.' },
      { content: 'Hello' },
      { content: 'How are you?' },
      { content: 'Goodbye' },
    ];
    expect(result).toEqual(expected);
  });
  test('returns the input messages with instructions properly added when addInstructions() with legacy flag', () => {
    const messages = [{ content: 'Hello' }, { content: 'How are you?' }, { content: 'Goodbye' }];
    const instructions = { content: 'Please respond to the question.' };
    const result = TestClient.addInstructions(messages, instructions, true);
    const expected = [
      { content: 'Hello' },
      { content: 'How are you?' },
      { content: 'Please respond to the question.' },
      { content: 'Goodbye' },
    ];
    expect(result).toEqual(expected);
  });
  test('concats messages correctly in concatenateMessages()', () => {
    const messages = [
      { name: 'User', content: 'Hello' },
      { name: 'Assistant', content: 'How can I help you?' },
      { name: 'User', content: 'I have a question.' },
    ];
    const result = TestClient.concatenateMessages(messages);
    const expected =
      'User:\nHello\n\nAssistant:\nHow can I help you?\n\nUser:\nI have a question.\n\n';
    expect(result).toBe(expected);
  });
  test('refines messages correctly in summarizeMessages()', async () => {
    const messagesToRefine = [
      { role: 'user', content: 'Hello', tokenCount: 10 },
      { role: 'assistant', content: 'How can I help you?', tokenCount: 20 },
    ];
    const remainingContextTokens = 100;
    const expectedRefinedMessage = {
      role: 'system',
      content: 'Refined answer',
    };
    const result = await TestClient.summarizeMessages({ messagesToRefine, remainingContextTokens });
    expect(result.summaryMessage).toEqual(expectedRefinedMessage);
  });
  test('gets messages within token limit (under limit) correctly in getMessagesWithinTokenLimit()', async () => {
    TestClient.maxContextTokens = 100;
    TestClient.shouldSummarize = true;
    const messages = [
      { role: 'user', content: 'Hello', tokenCount: 5 },
      { role: 'assistant', content: 'How can I help you?', tokenCount: 19 },
      { role: 'user', content: 'I have a question.', tokenCount: 18 },
    ];
    const expectedContext = [
      { role: 'user', content: 'Hello', tokenCount: 5 }, // 'Hello'.length
      { role: 'assistant', content: 'How can I help you?', tokenCount: 19 },
      { role: 'user', content: 'I have a question.', tokenCount: 18 },
    ];
    // Subtract 3 tokens for Assistant Label priming after all messages have been counted.
    const expectedRemainingContextTokens = 58 - 3; // (100 - 5 - 19 - 18) - 3
    const expectedMessagesToRefine = [];
    const lastExpectedMessage =
      expectedMessagesToRefine?.[expectedMessagesToRefine.length - 1] ?? {};
    const expectedIndex = messages.findIndex((msg) => msg.content === lastExpectedMessage?.content);
    const result = await TestClient.getMessagesWithinTokenLimit({ messages });
    expect(result.context).toEqual(expectedContext);
    expect(result.messagesToRefine.length - 1).toEqual(expectedIndex);
    expect(result.remainingContextTokens).toBe(expectedRemainingContextTokens);
    expect(result.messagesToRefine).toEqual(expectedMessagesToRefine);
  });
  test('gets result over token limit correctly in getMessagesWithinTokenLimit()', async () => {
    TestClient.maxContextTokens = 50; // Set a lower limit
    TestClient.shouldSummarize = true;
    const messages = [
      { role: 'user', content: 'Hello', tokenCount: 30 },
      { role: 'assistant', content: 'How can I help you?', tokenCount: 30 },
      { role: 'user', content: 'I have a question.', tokenCount: 5 },
      { role: 'user', content: 'I need a coffee, stat!', tokenCount: 19 },
      { role: 'assistant', content: 'Sure, I can help with that.', tokenCount: 18 },
    ];
    // Subtract 3 tokens for Assistant Label priming after all messages have been counted.
    const expectedRemainingContextTokens = 5; // (50 - 18 - 19 - 5) - 3
    const expectedMessagesToRefine = [
      { role: 'user', content: 'Hello', tokenCount: 30 },
      { role: 'assistant', content: 'How can I help you?', tokenCount: 30 },
    ];
    const expectedContext = [
      { role: 'user', content: 'I have a question.', tokenCount: 5 },
      { role: 'user', content: 'I need a coffee, stat!', tokenCount: 19 },
      { role: 'assistant', content: 'Sure, I can help with that.', tokenCount: 18 },
    ];
    const lastExpectedMessage =
      expectedMessagesToRefine?.[expectedMessagesToRefine.length - 1] ?? {};
    const expectedIndex = messages.findIndex((msg) => msg.content === lastExpectedMessage?.content);
    const result = await TestClient.getMessagesWithinTokenLimit({ messages });
    expect(result.context).toEqual(expectedContext);
    expect(result.messagesToRefine.length - 1).toEqual(expectedIndex);
    expect(result.remainingContextTokens).toBe(expectedRemainingContextTokens);
    expect(result.messagesToRefine).toEqual(expectedMessagesToRefine);
  });
  describe('getMessagesForConversation', () => {
    it('should return an empty array if the parentMessageId does not exist', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessages,
        parentMessageId: '999',
      });
      expect(result).toEqual([]);
    });
    it('should handle messages with messageId property', () => {
      const messagesWithMessageId = [
        { messageId: '1', parentMessageId: null, text: 'Message 1' },
        { messageId: '2', parentMessageId: '1', text: 'Message 2' },
      ];
      const result = TestClient.constructor.getMessagesForConversation({
        messages: messagesWithMessageId,
        parentMessageId: '2',
      });
      expect(result).toEqual([
        { messageId: '1', parentMessageId: null, text: 'Message 1' },
        { messageId: '2', parentMessageId: '1', text: 'Message 2' },
      ]);
    });
    const messagesWithNullParent = [
      { id: '1', parentMessageId: null, text: 'Message 1' },
      { id: '2', parentMessageId: null, text: 'Message 2' },
    ];
    it('should handle messages with null parentMessageId that are not root', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: messagesWithNullParent,
        parentMessageId: '2',
      });
      expect(result).toEqual([{ id: '2', parentMessageId: null, text: 'Message 2' }]);
    });
    const cyclicMessages = [
      { id: '3', parentMessageId: '2', text: 'Message 3' },
      { id: '1', parentMessageId: '3', text: 'Message 1' },
      { id: '2', parentMessageId: '1', text: 'Message 2' },
    ];
    it('should handle cyclic references without going into an infinite loop', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: cyclicMessages,
        parentMessageId: '3',
      });
      expect(result).toEqual([
        { id: '1', parentMessageId: '3', text: 'Message 1' },
        { id: '2', parentMessageId: '1', text: 'Message 2' },
        { id: '3', parentMessageId: '2', text: 'Message 3' },
      ]);
    });
    const unorderedMessages = [
      { id: '3', parentMessageId: '2', text: 'Message 3' },
      { id: '2', parentMessageId: '1', text: 'Message 2' },
      { id: '1', parentMessageId: Constants.NO_PARENT, text: 'Message 1' },
    ];
    it('should return ordered messages based on parentMessageId', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessages,
        parentMessageId: '3',
      });
      expect(result).toEqual([
        { id: '1', parentMessageId: Constants.NO_PARENT, text: 'Message 1' },
        { id: '2', parentMessageId: '1', text: 'Message 2' },
        { id: '3', parentMessageId: '2', text: 'Message 3' },
      ]);
    });
    const unorderedBranchedMessages = [
      { id: '4', parentMessageId: '2', text: 'Message 4', summary: 'Summary for Message 4' },
      { id: '10', parentMessageId: '7', text: 'Message 10' },
      { id: '1', parentMessageId: null, text: 'Message 1' },
      { id: '6', parentMessageId: '5', text: 'Message 7' },
      { id: '7', parentMessageId: '5', text: 'Message 7' },
      { id: '2', parentMessageId: '1', text: 'Message 2' },
      { id: '8', parentMessageId: '6', text: 'Message 8' },
      { id: '5', parentMessageId: '3', text: 'Message 5' },
      { id: '3', parentMessageId: '1', text: 'Message 3' },
      { id: '6', parentMessageId: '4', text: 'Message 6' },
      { id: '8', parentMessageId: '7', text: 'Message 9' },
      { id: '9', parentMessageId: '7', text: 'Message 9' },
      { id: '11', parentMessageId: '2', text: 'Message 11', summary: 'Summary for Message 11' },
    ];
    it('should return ordered messages from a branched array based on parentMessageId', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedBranchedMessages,
        parentMessageId: '10',
        summary: true,
      });
      expect(result).toEqual([
        { id: '1', parentMessageId: null, text: 'Message 1' },
        { id: '3', parentMessageId: '1', text: 'Message 3' },
        { id: '5', parentMessageId: '3', text: 'Message 5' },
        { id: '7', parentMessageId: '5', text: 'Message 7' },
        { id: '10', parentMessageId: '7', text: 'Message 10' },
      ]);
    });
    it('should return an empty array if no messages are provided', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: [],
        parentMessageId: '3',
      });
      expect(result).toEqual([]);
    });
    it('should map over the ordered messages if mapMethod is provided', () => {
      const mapMethod = (msg) => msg.text;
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessages,
        parentMessageId: '3',
        mapMethod,
      });
      expect(result).toEqual(['Message 1', 'Message 2', 'Message 3']);
    });
    let unorderedMessagesWithSummary = [
      { id: '4', parentMessageId: '3', text: 'Message 4' },
      { id: '2', parentMessageId: '1', text: 'Message 2', summary: 'Summary for Message 2' },
      { id: '3', parentMessageId: '2', text: 'Message 3', summary: 'Summary for Message 3' },
      { id: '1', parentMessageId: null, text: 'Message 1' },
    ];
    it('should start with the message that has a summary property and continue until the specified parentMessageId', () => {
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessagesWithSummary,
        parentMessageId: '4',
        summary: true,
      });
      expect(result).toEqual([
        {
          id: '3',
          parentMessageId: '2',
          role: 'system',
          text: 'Summary for Message 3',
          summary: 'Summary for Message 3',
        },
        { id: '4', parentMessageId: '3', text: 'Message 4' },
      ]);
    });
    it('should handle multiple summaries and return the branch from the latest to the parentMessageId', () => {
      unorderedMessagesWithSummary = [
        { id: '5', parentMessageId: '4', text: 'Message 5' },
        { id: '2', parentMessageId: '1', text: 'Message 2', summary: 'Summary for Message 2' },
        { id: '3', parentMessageId: '2', text: 'Message 3', summary: 'Summary for Message 3' },
        { id: '4', parentMessageId: '3', text: 'Message 4', summary: 'Summary for Message 4' },
        { id: '1', parentMessageId: null, text: 'Message 1' },
      ];
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessagesWithSummary,
        parentMessageId: '5',
        summary: true,
      });
      expect(result).toEqual([
        {
          id: '4',
          parentMessageId: '3',
          role: 'system',
          text: 'Summary for Message 4',
          summary: 'Summary for Message 4',
        },
        { id: '5', parentMessageId: '4', text: 'Message 5' },
      ]);
    });
    it('should handle summary at root edge case and continue until the parentMessageId', () => {
      unorderedMessagesWithSummary = [
        { id: '5', parentMessageId: '4', text: 'Message 5' },
        { id: '1', parentMessageId: null, text: 'Message 1', summary: 'Summary for Message 1' },
        { id: '4', parentMessageId: '3', text: 'Message 4', summary: 'Summary for Message 4' },
        { id: '2', parentMessageId: '1', text: 'Message 2', summary: 'Summary for Message 2' },
        { id: '3', parentMessageId: '2', text: 'Message 3', summary: 'Summary for Message 3' },
      ];
      const result = TestClient.constructor.getMessagesForConversation({
        messages: unorderedMessagesWithSummary,
        parentMessageId: '5',
        summary: true,
      });
      expect(result).toEqual([
        {
          id: '4',
          parentMessageId: '3',
          role: 'system',
          text: 'Summary for Message 4',
          summary: 'Summary for Message 4',
        },
        { id: '5', parentMessageId: '4', text: 'Message 5' },
      ]);
    });
  });
  describe('sendMessage', () => {
    test('sendMessage should return a response message', async () => {
      const expectedResult = expect.objectContaining({
        sender: TestClient.sender,
        text: expect.any(String),
        isCreatedByUser: false,
        messageId: expect.any(String),
        parentMessageId: expect.any(String),
        conversationId: expect.any(String),
      });
      const response = await TestClient.sendMessage(userMessage);
      parentMessageId = response.messageId;
      conversationId = response.conversationId;
      expect(response).toEqual(expectedResult);
    });
    test('should replace responseMessageId with new UUID when isRegenerate is true and messageId ends with underscore', async () => {
      const mockCrypto = require('crypto');
      const newUUID = 'new-uuid-1234';
      jest.spyOn(mockCrypto, 'randomUUID').mockReturnValue(newUUID);
      const opts = {
        isRegenerate: true,
        responseMessageId: 'existing-message-id_',
      };
      await TestClient.setMessageOptions(opts);
      expect(TestClient.responseMessageId).toBe(newUUID);
      expect(TestClient.responseMessageId).not.toBe('existing-message-id_');
      mockCrypto.randomUUID.mockRestore();
    });
    test('should not replace responseMessageId when isRegenerate is false', async () => {
      const opts = {
        isRegenerate: false,
        responseMessageId: 'existing-message-id_',
      };
      await TestClient.setMessageOptions(opts);
      expect(TestClient.responseMessageId).toBe('existing-message-id_');
    });
    test('should not replace responseMessageId when it does not end with underscore', async () => {
      const opts = {
        isRegenerate: true,
        responseMessageId: 'existing-message-id',
      };
      await TestClient.setMessageOptions(opts);
      expect(TestClient.responseMessageId).toBe('existing-message-id');
    });
    test('sendMessage should work with provided conversationId and parentMessageId', async () => {
      const userMessage = 'Second message in the conversation';
      const opts = {
        conversationId,
        parentMessageId,
        getReqData: jest.fn(),
        onStart: jest.fn(),
      };
      const expectedResult = expect.objectContaining({
        sender: TestClient.sender,
        text: expect.any(String),
        isCreatedByUser: false,
        messageId: expect.any(String),
        parentMessageId: expect.any(String),
        conversationId: opts.conversationId,
      });
      const response = await TestClient.sendMessage(userMessage, opts);
      parentMessageId = response.messageId;
      expect(response.conversationId).toEqual(conversationId);
      expect(response).toEqual(expectedResult);
      expect(opts.getReqData).toHaveBeenCalled();
      expect(opts.onStart).toHaveBeenCalled();
      expect(TestClient.getBuildMessagesOptions).toHaveBeenCalled();
      expect(TestClient.getSaveOptions).toHaveBeenCalled();
    });
    test('should return chat history', async () => {
      TestClient = initializeFakeClient(apiKey, options, messageHistory);
      const chatMessages = await TestClient.loadHistory(conversationId, '2');
      expect(TestClient.currentMessages).toHaveLength(2);
      expect(chatMessages[0].text).toEqual('Hello');
      const chatMessages2 = await TestClient.loadHistory(conversationId, '3');
      expect(TestClient.currentMessages).toHaveLength(3);
      expect(chatMessages2[chatMessages2.length - 1].text).toEqual("What's up");
    });
    /* Most of the new sendMessage logic revolving around edited/continued AI messages
     *  can be summarized by the following test. The condition will load the entire history up to
     *  the message that is being edited, which will trigger the AI API to 'continue' the response.
     *  The 'userMessage' is only passed by convention and is not necessary for the generation.
     */
    it('should not push userMessage to currentMessages when isEdited is true and vice versa', async () => {
      const overrideParentMessageId = 'user-message-id';
      const responseMessageId = 'response-message-id';
      const newHistory = messageHistory.slice();
      newHistory.push({
        role: 'assistant',
        isCreatedByUser: false,
        text: 'test message',
        messageId: responseMessageId,
        parentMessageId: '3',
      });
      TestClient = initializeFakeClient(apiKey, options, newHistory);
      const sendMessageOptions = {
        isEdited: true,
        overrideParentMessageId,
        parentMessageId: '3',
        responseMessageId,
      };
      await TestClient.sendMessage('test message', sendMessageOptions);
      const currentMessages = TestClient.currentMessages;
      expect(currentMessages[currentMessages.length - 1].messageId).not.toEqual(
        overrideParentMessageId,
      );
      // Test the opposite case
      sendMessageOptions.isEdited = false;
      await TestClient.sendMessage('test message', sendMessageOptions);
      const currentMessages2 = TestClient.currentMessages;
      expect(currentMessages2[currentMessages2.length - 1].messageId).toEqual(
        overrideParentMessageId,
      );
    });
    test('setOptions is called with the correct arguments only when replaceOptions is set to true', async () => {
      TestClient.setOptions = jest.fn();
      const opts = { conversationId: '123', parentMessageId: '456', replaceOptions: true };
      await TestClient.sendMessage('Hello, world!', opts);
      expect(TestClient.setOptions).toHaveBeenCalledWith(opts);
      TestClient.setOptions.mockClear();
    });
    test('loadHistory is called with the correct arguments', async () => {
      const opts = { conversationId: '123', parentMessageId: '456' };
      await TestClient.sendMessage('Hello, world!', opts);
      expect(TestClient.loadHistory).toHaveBeenCalledWith(
        opts.conversationId,
        opts.parentMessageId,
      );
    });
    test('getReqData is called with the correct arguments', async () => {
      const getReqData = jest.fn();
      const opts = { getReqData };
      const response = await TestClient.sendMessage('Hello, world!', opts);
      expect(getReqData).toHaveBeenCalledWith(
        expect.objectContaining({
          userMessage: expect.objectContaining({ text: 'Hello, world!' }),
          conversationId: response.conversationId,
          responseMessageId: response.messageId,
        }),
      );
    });
    test('onStart is called with the correct arguments', async () => {
      const onStart = jest.fn();
      const opts = { onStart };
      await TestClient.sendMessage('Hello, world!', opts);
      expect(onStart).toHaveBeenCalledWith(
        expect.objectContaining({ text: 'Hello, world!' }),
        expect.any(String),
        /** `isNewConvo` */
        true,
      );
    });
    test('saveMessageToDatabase is called with the correct arguments', async () => {
      const saveOptions = TestClient.getSaveOptions();
      const user = {};
      const opts = { user };
      const saveSpy = jest.spyOn(TestClient, 'saveMessageToDatabase');
      await TestClient.sendMessage('Hello, world!', opts);
      expect(saveSpy).toHaveBeenCalledWith(
        expect.objectContaining({
          sender: expect.any(String),
          text: expect.any(String),
          isCreatedByUser: expect.any(Boolean),
          messageId: expect.any(String),
          parentMessageId: expect.any(String),
          conversationId: expect.any(String),
        }),
        saveOptions,
        user,
      );
    });
    test('should handle existing conversation when getConvo retrieves one', async () => {
      const existingConvo = {
        conversationId: 'existing-convo-id',
        endpoint: 'openai',
        endpointType: 'openai',
        model: 'gpt-3.5-turbo',
        messages: [
          { role: 'user', content: 'Existing message 1' },
          { role: 'assistant', content: 'Existing response 1' },
        ],
        temperature: 1,
      };
      const { temperature: _temp, ...newConvo } = existingConvo;
      const user = {
        id: 'user-id',
      };
      getConvo.mockResolvedValue(existingConvo);
      saveConvo.mockResolvedValue(newConvo);
      TestClient = initializeFakeClient(
        apiKey,
        {
          ...options,
          req: {
            user,
          },
        },
        [],
      );
      const saveSpy = jest.spyOn(TestClient, 'saveMessageToDatabase');
      const newMessage = 'New message in existing conversation';
      const response = await TestClient.sendMessage(newMessage, {
        user,
        conversationId: existingConvo.conversationId,
      });
      expect(getConvo).toHaveBeenCalledWith(user.id, existingConvo.conversationId);
      expect(TestClient.conversationId).toBe(existingConvo.conversationId);
      expect(response.conversationId).toBe(existingConvo.conversationId);
      expect(TestClient.fetchedConvo).toBe(true);
      expect(saveSpy).toHaveBeenCalledWith(
        expect.objectContaining({
          conversationId: existingConvo.conversationId,
          text: newMessage,
        }),
        expect.any(Object),
        expect.any(Object),
      );
      expect(saveConvo).toHaveBeenCalledTimes(2);
      expect(saveConvo).toHaveBeenCalledWith(
        expect.any(Object),
        expect.objectContaining({
          conversationId: existingConvo.conversationId,
        }),
        expect.objectContaining({
          context: 'api/app/clients/BaseClient.js - saveMessageToDatabase #saveConvo',
          unsetFields: {
            temperature: 1,
          },
        }),
      );
      await TestClient.sendMessage('Another message', {
        conversationId: existingConvo.conversationId,
      });
      expect(getConvo).toHaveBeenCalledTimes(1);
    });
    test('should correctly handle existing conversation and unset fields appropriately', async () => {
      const existingConvo = {
        conversationId: 'existing-convo-id',
        endpoint: 'openai',
        endpointType: 'openai',
        model: 'gpt-3.5-turbo',
        messages: [
          { role: 'user', content: 'Existing message 1' },
          { role: 'assistant', content: 'Existing response 1' },
        ],
        title: 'Existing Conversation',
        someExistingField: 'existingValue',
        anotherExistingField: 'anotherValue',
        temperature: 0.7,
        modelLabel: 'GPT-3.5',
      };
      getConvo.mockResolvedValue(existingConvo);
      saveConvo.mockResolvedValue(existingConvo);
      TestClient = initializeFakeClient(
        apiKey,
        {
          ...options,
          modelOptions: {
            model: 'gpt-4',
            temperature: 0.5,
          },
        },
        [],
      );
      const newMessage = 'New message in existing conversation';
      await TestClient.sendMessage(newMessage, {
        conversationId: existingConvo.conversationId,
      });
      expect(saveConvo).toHaveBeenCalledTimes(2);
      const saveConvoCall = saveConvo.mock.calls[0];
      const [, savedFields, saveOptions] = saveConvoCall;
      // Instead of checking all excludedKeys, we'll just check specific fields
      // that we know should be excluded
      expect(savedFields).not.toHaveProperty('messages');
      expect(savedFields).not.toHaveProperty('title');
      // Only check that someExistingField is in unsetFields
      expect(saveOptions.unsetFields).toHaveProperty('someExistingField', 1);
      // Mock saveConvo to return the expected fields
      saveConvo.mockImplementation((req, fields) => {
        return Promise.resolve({
          ...fields,
          endpoint: 'openai',
          endpointType: 'openai',
          model: 'gpt-4',
          temperature: 0.5,
        });
      });
      // Only check the conversationId since that's the only field we can be sure about
      expect(savedFields).toHaveProperty('conversationId', 'existing-convo-id');
      expect(TestClient.fetchedConvo).toBe(true);
      await TestClient.sendMessage('Another message', {
        conversationId: existingConvo.conversationId,
      });
      expect(getConvo).toHaveBeenCalledTimes(1);
      const secondSaveConvoCall = saveConvo.mock.calls[1];
      expect(secondSaveConvoCall[2]).toHaveProperty('unsetFields', {});
    });
    test('sendCompletion is called with the correct arguments', async () => {
      const payload = {}; // Mock payload
      TestClient.buildMessages.mockReturnValue({ prompt: payload, tokenCountMap: null });
      const opts = {};
      await TestClient.sendMessage('Hello, world!', opts);
      expect(TestClient.sendCompletion).toHaveBeenCalledWith(payload, opts);
    });
    test('getTokenCount for response is called with the correct arguments', async () => {
      const tokenCountMap = {}; // Mock tokenCountMap
      TestClient.buildMessages.mockReturnValue({ prompt: [], tokenCountMap });
      TestClient.getTokenCountForResponse = jest.fn();
      const response = await TestClient.sendMessage('Hello, world!', {});
      expect(TestClient.getTokenCountForResponse).toHaveBeenCalledWith(response);
    });
    test('returns an object with the correct shape', async () => {
      const response = await TestClient.sendMessage('Hello, world!', {});
      expect(response).toEqual(
        expect.objectContaining({
          sender: expect.any(String),
          text: expect.any(String),
          isCreatedByUser: expect.any(Boolean),
          messageId: expect.any(String),
          parentMessageId: expect.any(String),
          conversationId: expect.any(String),
        }),
      );
    });
    test('userMessagePromise is awaited before saving response message', async () => {
      // Mock the saveMessageToDatabase method
      TestClient.saveMessageToDatabase = jest.fn().mockImplementation(() => {
        return new Promise((resolve) => setTimeout(resolve, 100)); // Simulate a delay
      });
      // Send a message
      const messagePromise = TestClient.sendMessage('Hello, world!');
      // Wait a short time to ensure the user message save has started
      await new Promise((resolve) => setTimeout(resolve, 50));
      // Check that saveMessageToDatabase has been called once (for the user message)
      expect(TestClient.saveMessageToDatabase).toHaveBeenCalledTimes(1);
      // Wait for the message to be fully processed
      await messagePromise;
      // Check that saveMessageToDatabase has been called twice (once for user message, once for response)
      expect(TestClient.saveMessageToDatabase).toHaveBeenCalledTimes(2);
      // Check the order of calls
      const calls = TestClient.saveMessageToDatabase.mock.calls;
      expect(calls[0][0].isCreatedByUser).toBe(true); // First call should be for user message
      expect(calls[1][0].isCreatedByUser).toBe(false); // Second call should be for response message
    });
  });
  describe('getMessagesWithinTokenLimit with instructions', () => {
    test('should always include instructions when present', async () => {
      TestClient.maxContextTokens = 50;
      const instructions = {
        role: 'system',
        content: 'System instructions',
        tokenCount: 20,
      };
      const messages = [
        instructions,
        { role: 'user', content: 'Hello', tokenCount: 10 },
        { role: 'assistant', content: 'Hi there', tokenCount: 15 },
      ];
      const result = await TestClient.getMessagesWithinTokenLimit({
        messages,
        instructions,
      });
      expect(result.context[0]).toBe(instructions);
      expect(result.remainingContextTokens).toBe(2);
    });
    test('should handle case when messages exceed limit but instructions must be preserved', async () => {
      TestClient.maxContextTokens = 30;
      const instructions = {
        role: 'system',
        content: 'System instructions',
        tokenCount: 20,
      };
      const messages = [
        instructions,
        { role: 'user', content: 'Hello', tokenCount: 10 },
        { role: 'assistant', content: 'Hi there', tokenCount: 15 },
      ];
      const result = await TestClient.getMessagesWithinTokenLimit({
        messages,
        instructions,
      });
      // Should only include instructions and the last message that fits
      expect(result.context).toHaveLength(1);
      expect(result.context[0].content).toBe(instructions.content);
      expect(result.messagesToRefine).toHaveLength(2);
      expect(result.remainingContextTokens).toBe(7); // 30 - 20 - 3 (assistant label)
    });
    test('should work correctly without instructions (1/2)', async () => {
      TestClient.maxContextTokens = 50;
      const messages = [
        { role: 'user', content: 'Hello', tokenCount: 10 },
        { role: 'assistant', content: 'Hi there', tokenCount: 15 },
      ];
      const result = await TestClient.getMessagesWithinTokenLimit({
        messages,
      });
      expect(result.context).toHaveLength(2);
      expect(result.remainingContextTokens).toBe(22); // 50 - 10 - 15 - 3(assistant label)
      expect(result.messagesToRefine).toHaveLength(0);
    });
    test('should work correctly without instructions (2/2)', async () => {
      TestClient.maxContextTokens = 30;
      const messages = [
        { role: 'user', content: 'Hello', tokenCount: 10 },
        { role: 'assistant', content: 'Hi there', tokenCount: 20 },
      ];
      const result = await TestClient.getMessagesWithinTokenLimit({
        messages,
      });
      expect(result.context).toHaveLength(1);
      expect(result.remainingContextTokens).toBe(7);
      expect(result.messagesToRefine).toHaveLength(1);
    });
    test('should handle case when only instructions fit within limit', async () => {
      TestClient.maxContextTokens = 25;
      const instructions = {
        role: 'system',
        content: 'System instructions',
        tokenCount: 20,
      };
      const messages = [
        instructions,
        { role: 'user', content: 'Hello', tokenCount: 10 },
        { role: 'assistant', content: 'Hi there', tokenCount: 15 },
      ];
      const result = await TestClient.getMessagesWithinTokenLimit({
        messages,
        instructions,
      });
      expect(result.context).toHaveLength(1);
      expect(result.context[0]).toBe(instructions);
      expect(result.messagesToRefine).toHaveLength(2);
      expect(result.remainingContextTokens).toBe(2); // 25 - 20 - 3(assistant label)
    });
  });
 });
--- a/api/app/clients/specs/FakeClient.js
+++ b/api/app/clients/specs/FakeClient.js
@ -1,126 +0,0 @@
 const { getModelMaxTokens } = require('@librechat/api');
 const BaseClient = require('../BaseClient');
 class FakeClient extends BaseClient {
  constructor(apiKey, options = {}) {
    super(apiKey, options);
    this.sender = 'AI Assistant';
    this.setOptions(options);
  }
  setOptions(options) {
    if (this.options && !this.options.replaceOptions) {
      this.options.modelOptions = {
        ...this.options.modelOptions,
        ...options.modelOptions,
      };
      delete options.modelOptions;
      this.options = {
        ...this.options,
        ...options,
      };
    } else {
      this.options = options;
    }
    if (this.options.openaiApiKey) {
      this.apiKey = this.options.openaiApiKey;
    }
    const modelOptions = this.options.modelOptions || {};
    if (!this.modelOptions) {
      this.modelOptions = {
        ...modelOptions,
        model: modelOptions.model || 'gpt-3.5-turbo',
        temperature:
          typeof modelOptions.temperature === 'undefined' ? 0.8 : modelOptions.temperature,
        top_p: typeof modelOptions.top_p === 'undefined' ? 1 : modelOptions.top_p,
        presence_penalty:
          typeof modelOptions.presence_penalty === 'undefined' ? 1 : modelOptions.presence_penalty,
        stop: modelOptions.stop,
      };
    }
    this.maxContextTokens =
      this.options.maxContextTokens ?? getModelMaxTokens(this.modelOptions.model) ?? 4097;
  }
  buildMessages() {}
  getTokenCount(str) {
    return str.length;
  }
  getTokenCountForMessage(message) {
    return message?.content?.length || message.length;
  }
 }
 const initializeFakeClient = (apiKey, options, fakeMessages) => {
  let TestClient = new FakeClient(apiKey);
  TestClient.options = options;
  TestClient.abortController = { abort: jest.fn() };
  TestClient.loadHistory = jest
    .fn()
    .mockImplementation((conversationId, parentMessageId = null) => {
      if (!conversationId) {
        TestClient.currentMessages = [];
        return Promise.resolve([]);
      }
      const orderedMessages = TestClient.constructor.getMessagesForConversation({
        messages: fakeMessages,
        parentMessageId,
      });
      TestClient.currentMessages = orderedMessages;
      return Promise.resolve(orderedMessages);
    });
  TestClient.getSaveOptions = jest.fn().mockImplementation(() => {
    return {};
  });
  TestClient.getBuildMessagesOptions = jest.fn().mockImplementation(() => {
    return {};
  });
  TestClient.sendCompletion = jest.fn(async () => {
    return {
      completion: 'Mock response text',
      metadata: undefined,
    };
  });
  TestClient.getCompletion = jest.fn().mockImplementation(async (..._args) => {
    return {
      choices: [
        {
          message: {
            content: 'Mock response text',
          },
        },
      ],
    };
  });
  TestClient.buildMessages = jest.fn(async (messages, parentMessageId) => {
    const orderedMessages = TestClient.constructor.getMessagesForConversation({
      messages,
      parentMessageId,
    });
    const formattedMessages = orderedMessages.map((message) => {
      let { role: _role, sender, text } = message;
      const role = _role ?? sender;
      const content = text ?? '';
      return {
        role: role?.toLowerCase() === 'user' ? 'user' : 'assistant',
        content,
      };
    });
    return {
      prompt: formattedMessages,
      tokenCountMap: null, // Simplified for the mock
    };
  });
  return TestClient;
 };
 module.exports = { FakeClient, initializeFakeClient };
--- a/api/app/clients/sydney.js
+++ b/api/app/clients/sydney.js
@ -0,0 +1,36 @@
 require('dotenv').config();
 const { KeyvFile } = require('keyv-file');
 const askSydney = async ({ text, onProgress, convo }) => {
  const { BingAIClient } = (await import('@waylaidwanderer/chatgpt-api'));
  const sydneyClient = new BingAIClient({
    // "_U" cookie from bing.com
    userToken: process.env.BING_TOKEN,
    // If the above doesn't work, provide all your cookies as a string instead
    // cookies: '',
    debug: false,
    cache: { store: new KeyvFile({ filename: './data/cache.json' }) }
  });
  let options = {
    jailbreakConversationId: true,
    onProgress,
  };
  if (convo.jailbreakConversationId) {
    options = { ...options, jailbreakConversationId: convo.jailbreakConversationId, parentMessageId: convo.parentMessageId };
  }
  console.log('sydney options', options);
  const res = await sydneyClient.sendMessage(text, options
  );
  return res;
  // for reference:
  // https://github.com/waylaidwanderer/node-chatgpt-api/blob/main/demos/use-bing-client.js
 };
 module.exports = { askSydney };
--- a/api/app/clients/tools/index.js
+++ b/api/app/clients/tools/index.js
@ -1,30 +0,0 @@
 const manifest = require('./manifest');
 // Structured Tools
 const DALLE3 = require('./structured/DALLE3');
 const FluxAPI = require('./structured/FluxAPI');
 const OpenWeather = require('./structured/OpenWeather');
 const StructuredWolfram = require('./structured/Wolfram');
 const createYouTubeTools = require('./structured/YouTube');
 const StructuredACS = require('./structured/AzureAISearch');
 const StructuredSD = require('./structured/StableDiffusion');
 const GoogleSearchAPI = require('./structured/GoogleSearch');
 const TraversaalSearch = require('./structured/TraversaalSearch');
 const createOpenAIImageTools = require('./structured/OpenAIImageTools');
 const TavilySearchResults = require('./structured/TavilySearchResults');
 module.exports = {
  ...manifest,
  // Structured Tools
  DALLE3,
  FluxAPI,
  OpenWeather,
  StructuredSD,
  StructuredACS,
  GoogleSearchAPI,
  TraversaalSearch,
  StructuredWolfram,
  createYouTubeTools,
  TavilySearchResults,
  createOpenAIImageTools,
 };
--- a/api/app/clients/tools/manifest.js
+++ b/api/app/clients/tools/manifest.js
@ -1,20 +0,0 @@
 const availableTools = require('./manifest.json');
 /** @type {Record<string, TPlugin | undefined>} */
 const manifestToolMap = {};
 /** @type {Array<TPlugin>} */
 const toolkits = [];
 availableTools.forEach((tool) => {
  manifestToolMap[tool.pluginKey] = tool;
  if (tool.toolkit === true) {
    toolkits.push(tool);
  }
 });
 module.exports = {
  toolkits,
  availableTools,
  manifestToolMap,
 };
--- a/api/app/clients/tools/manifest.json
+++ b/api/app/clients/tools/manifest.json
@ -1,183 +0,0 @@
 [
  {
    "name": "Traversaal",
    "pluginKey": "traversaal_search",
    "description": "Traversaal is a robust search API tailored for LLM Agents. Get an API key here: https://api.traversaal.ai",
    "icon": "https://traversaal.ai/favicon.ico",
    "authConfig": [
      {
        "authField": "TRAVERSAAL_API_KEY",
        "label": "Traversaal API Key",
        "description": "Get your API key here: <a href=\"https://api.traversaal.ai\" target=\"_blank\">https://api.traversaal.ai</a>"
      }
    ]
  },
  {
    "name": "Google",
    "pluginKey": "google",
    "description": "Use Google Search to find information about the weather, news, sports, and more.",
    "icon": "https://i.imgur.com/SMmVkNB.png",
    "authConfig": [
      {
        "authField": "GOOGLE_CSE_ID",
        "label": "Google CSE ID",
        "description": "This is your Google Custom Search Engine ID. For instructions on how to obtain this, see <a href='https://github.com/danny-avila/LibreChat/blob/main/docs/features/plugins/google_search.md'>Our Docs</a>."
      },
      {
        "authField": "GOOGLE_SEARCH_API_KEY",
        "label": "Google API Key",
        "description": "This is your Google Custom Search API Key. For instructions on how to obtain this, see <a href='https://github.com/danny-avila/LibreChat/blob/main/docs/features/plugins/google_search.md'>Our Docs</a>."
      }
    ]
  },
  {
    "name": "YouTube",
    "pluginKey": "youtube",
    "toolkit": true,
    "description": "Get YouTube video information, retrieve comments, analyze transcripts and search for videos.",
    "icon": "https://www.youtube.com/s/desktop/7449ebf7/img/favicon_144x144.png",
    "authConfig": [
      {
        "authField": "YOUTUBE_API_KEY",
        "label": "YouTube API Key",
        "description": "Your YouTube Data API v3 key."
      }
    ]
  },
  {
    "name": "OpenAI Image Tools",
    "pluginKey": "image_gen_oai",
    "toolkit": true,
    "description": "Image Generation and Editing using OpenAI's latest state-of-the-art models",
    "icon": "assets/image_gen_oai.png",
    "authConfig": [
      {
        "authField": "IMAGE_GEN_OAI_API_KEY",
        "label": "OpenAI Image Tools API Key",
        "description": "Your OpenAI API Key for Image Generation and Editing"
      }
    ]
  },
  {
    "name": "Wolfram",
    "pluginKey": "wolfram",
    "description": "Access computation, math, curated knowledge & real-time data through Wolfram|Alpha and Wolfram Language.",
    "icon": "https://www.wolframcdn.com/images/icons/Wolfram.png",
    "authConfig": [
      {
        "authField": "WOLFRAM_APP_ID",
        "label": "Wolfram App ID",
        "description": "An AppID must be supplied in all calls to the Wolfram|Alpha API. You can get one by registering at <a href='http://products.wolframalpha.com/api/'>Wolfram|Alpha</a> and going to the <a href='https://developer.wolframalpha.com/portal/myapps/'>Developer Portal</a>."
      }
    ]
  },
  {
    "name": "Browser",
    "pluginKey": "web-browser",
    "description": "Scrape and summarize webpage data",
    "icon": "assets/web-browser.svg",
    "authConfig": [
      {
        "authField": "OPENAI_API_KEY",
        "label": "OpenAI API Key",
        "description": "Browser makes use of OpenAI embeddings"
      }
    ]
  },
  {
    "name": "DALL-E-3",
    "pluginKey": "dalle",
    "description": "[DALL-E-3] Create realistic images and art from a description in natural language",
    "icon": "https://i.imgur.com/u2TzXzH.png",
    "authConfig": [
      {
        "authField": "DALLE3_API_KEY||DALLE_API_KEY",
        "label": "OpenAI API Key",
        "description": "You can use DALL-E with your API Key from OpenAI."
      }
    ]
  },
  {
    "name": "Tavily Search",
    "pluginKey": "tavily_search_results_json",
    "description": "Tavily Search is a robust search API tailored for LLM Agents. It seamlessly integrates with diverse data sources to ensure a superior, relevant search experience.",
    "icon": "https://tavily.com/favicon.ico",
    "authConfig": [
      {
        "authField": "TAVILY_API_KEY",
        "label": "Tavily API Key",
        "description": "Get your API key here: https://app.tavily.com/"
      }
    ]
  },
  {
    "name": "Calculator",
    "pluginKey": "calculator",
    "description": "Perform simple and complex mathematical calculations.",
    "icon": "https://i.imgur.com/RHsSG5h.png",
    "authConfig": []
  },
  {
    "name": "Stable Diffusion",
    "pluginKey": "stable-diffusion",
    "description": "Generate photo-realistic images given any text input.",
    "icon": "https://i.imgur.com/Yr466dp.png",
    "authConfig": [
      {
        "authField": "SD_WEBUI_URL",
        "label": "Your Stable Diffusion WebUI API URL",
        "description": "You need to provide the URL of your Stable Diffusion WebUI API. For instructions on how to obtain this, see <a href='url'>Our Docs</a>."
      }
    ]
  },
  {
    "name": "Azure AI Search",
    "pluginKey": "azure-ai-search",
    "description": "Use Azure AI Search to find information",
    "icon": "https://i.imgur.com/E7crPze.png",
    "authConfig": [
      {
        "authField": "AZURE_AI_SEARCH_SERVICE_ENDPOINT",
        "label": "Azure AI Search Endpoint",
        "description": "You need to provide your Endpoint for Azure AI Search."
      },
      {
        "authField": "AZURE_AI_SEARCH_INDEX_NAME",
        "label": "Azure AI Search Index Name",
        "description": "You need to provide your Index Name for Azure AI Search."
      },
      {
        "authField": "AZURE_AI_SEARCH_API_KEY",
        "label": "Azure AI Search API Key",
        "description": "You need to provide your API Key for Azure AI Search."
      }
    ]
  },
  {
    "name": "OpenWeather",
    "pluginKey": "open_weather",
    "description": "Get weather forecasts and historical data from the OpenWeather API",
    "icon": "assets/openweather.png",
    "authConfig": [
      {
        "authField": "OPENWEATHER_API_KEY",
        "label": "OpenWeather API Key",
        "description": "Sign up at <a href=\"https://home.openweathermap.org/users/sign_up\" target=\"_blank\">OpenWeather</a>, then get your key at <a href=\"https://home.openweathermap.org/api_keys\" target=\"_blank\">API keys</a>."
      }
    ]
  },
  {
    "name": "Flux",
    "pluginKey": "flux",
    "description": "Generate images using text with the Flux API.",
    "icon": "https://blackforestlabs.ai/wp-content/uploads/2024/07/bfl_logo_retraced_blk.png",
    "isAuthRequired": "true",
    "authConfig": [
      {
        "authField": "FLUX_API_KEY",
        "label": "Your Flux API Key",
        "description": "Provide your Flux API key from your user profile."
      }
    ]
  }
 ]
--- a/api/app/clients/tools/structured/AzureAISearch.js
+++ b/api/app/clients/tools/structured/AzureAISearch.js
@ -1,104 +0,0 @@
 const { z } = require('zod');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { SearchClient, AzureKeyCredential } = require('@azure/search-documents');
 class AzureAISearch extends Tool {
  // Constants for default values
  static DEFAULT_API_VERSION = '2023-11-01';
  static DEFAULT_QUERY_TYPE = 'simple';
  static DEFAULT_TOP = 5;
  // Helper function for initializing properties
  _initializeField(field, envVar, defaultValue) {
    return field || process.env[envVar] || defaultValue;
  }
  constructor(fields = {}) {
    super();
    this.name = 'azure-ai-search';
    this.description =
      "Use the 'azure-ai-search' tool to retrieve search results relevant to your input";
    /* Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    // Define schema
    this.schema = z.object({
      query: z.string().describe('Search word or phrase to Azure AI Search'),
    });
    // Initialize properties using helper function
    this.serviceEndpoint = this._initializeField(
      fields.AZURE_AI_SEARCH_SERVICE_ENDPOINT,
      'AZURE_AI_SEARCH_SERVICE_ENDPOINT',
    );
    this.indexName = this._initializeField(
      fields.AZURE_AI_SEARCH_INDEX_NAME,
      'AZURE_AI_SEARCH_INDEX_NAME',
    );
    this.apiKey = this._initializeField(fields.AZURE_AI_SEARCH_API_KEY, 'AZURE_AI_SEARCH_API_KEY');
    this.apiVersion = this._initializeField(
      fields.AZURE_AI_SEARCH_API_VERSION,
      'AZURE_AI_SEARCH_API_VERSION',
      AzureAISearch.DEFAULT_API_VERSION,
    );
    this.queryType = this._initializeField(
      fields.AZURE_AI_SEARCH_SEARCH_OPTION_QUERY_TYPE,
      'AZURE_AI_SEARCH_SEARCH_OPTION_QUERY_TYPE',
      AzureAISearch.DEFAULT_QUERY_TYPE,
    );
    this.top = this._initializeField(
      fields.AZURE_AI_SEARCH_SEARCH_OPTION_TOP,
      'AZURE_AI_SEARCH_SEARCH_OPTION_TOP',
      AzureAISearch.DEFAULT_TOP,
    );
    this.select = this._initializeField(
      fields.AZURE_AI_SEARCH_SEARCH_OPTION_SELECT,
      'AZURE_AI_SEARCH_SEARCH_OPTION_SELECT',
    );
    // Check for required fields
    if (!this.override && (!this.serviceEndpoint || !this.indexName || !this.apiKey)) {
      throw new Error(
        'Missing AZURE_AI_SEARCH_SERVICE_ENDPOINT, AZURE_AI_SEARCH_INDEX_NAME, or AZURE_AI_SEARCH_API_KEY environment variable.',
      );
    }
    if (this.override) {
      return;
    }
    // Create SearchClient
    this.client = new SearchClient(
      this.serviceEndpoint,
      this.indexName,
      new AzureKeyCredential(this.apiKey),
      { apiVersion: this.apiVersion },
    );
  }
  // Improved error handling and logging
  async _call(data) {
    const { query } = data;
    try {
      const searchOption = {
        queryType: this.queryType,
        top: typeof this.top === 'string' ? Number(this.top) : this.top,
      };
      if (this.select) {
        searchOption.select = this.select.split(',');
      }
      const searchResults = await this.client.search(query, searchOption);
      const resultDocuments = [];
      for await (const result of searchResults.results) {
        resultDocuments.push(result.document);
      }
      return JSON.stringify(resultDocuments);
    } catch (error) {
      logger.error('Azure AI Search request failed', error);
      return 'There was an error with Azure AI Search.';
    }
  }
 }
 module.exports = AzureAISearch;
--- a/api/app/clients/tools/structured/DALLE3.js
+++ b/api/app/clients/tools/structured/DALLE3.js
@ -1,230 +0,0 @@
 const { z } = require('zod');
 const path = require('path');
 const OpenAI = require('openai');
 const { v4: uuidv4 } = require('uuid');
 const { ProxyAgent, fetch } = require('undici');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { getImageBasename, extractBaseURL } = require('@librechat/api');
 const { FileContext, ContentTypes } = require('librechat-data-provider');
 const displayMessage =
  "DALL-E displayed an image. All generated images are already plainly visible, so don't repeat the descriptions in detail. Do not list download links as they are available in the UI already. The user may download the images by clicking on them, but do not mention anything about downloading to the user.";
 class DALLE3 extends Tool {
  constructor(fields = {}) {
    super();
    /** @type {boolean} Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    /** @type {boolean} Necessary for output to contain all image metadata. */
    this.returnMetadata = fields.returnMetadata ?? false;
    this.userId = fields.userId;
    this.fileStrategy = fields.fileStrategy;
    /** @type {boolean} */
    this.isAgent = fields.isAgent;
    if (fields.processFileURL) {
      /** @type {processFileURL} Necessary for output to contain all image metadata. */
      this.processFileURL = fields.processFileURL.bind(this);
    }
    let apiKey = fields.DALLE3_API_KEY ?? fields.DALLE_API_KEY ?? this.getApiKey();
    const config = { apiKey };
    if (process.env.DALLE_REVERSE_PROXY) {
      config.baseURL = extractBaseURL(process.env.DALLE_REVERSE_PROXY);
    }
    if (process.env.DALLE3_AZURE_API_VERSION && process.env.DALLE3_BASEURL) {
      config.baseURL = process.env.DALLE3_BASEURL;
      config.defaultQuery = { 'api-version': process.env.DALLE3_AZURE_API_VERSION };
      config.defaultHeaders = {
        'api-key': process.env.DALLE3_API_KEY,
        'Content-Type': 'application/json',
      };
      config.apiKey = process.env.DALLE3_API_KEY;
    }
    if (process.env.PROXY) {
      const proxyAgent = new ProxyAgent(process.env.PROXY);
      config.fetchOptions = {
        dispatcher: proxyAgent,
      };
    }
    /** @type {OpenAI} */
    this.openai = new OpenAI(config);
    this.name = 'dalle';
    this.description = `Use DALLE to create images from text descriptions.
    - It requires prompts to be in English, detailed, and to specify image type and human features for diversity.
    - Create only one image, without repeating or listing descriptions outside the "prompts" field.
    - Maintains the original intent of the description, with parameters for image style, quality, and size to tailor the output.`;
    this.description_for_model =
      process.env.DALLE3_SYSTEM_PROMPT ??
      `// Whenever a description of an image is given, generate prompts (following these rules), and use dalle to create the image. If the user does not ask for a specific number of images, default to creating 2 prompts to send to dalle that are written to be as diverse as possible. All prompts sent to dalle must abide by the following policies:
    // 1. Prompts must be in English. Translate to English if needed.
    // 2. One image per function call. Create only 1 image per request unless explicitly told to generate more than 1 image.
    // 3. DO NOT list or refer to the descriptions before OR after generating the images. They should ONLY ever be written out ONCE, in the \`"prompts"\` field of the request. You do not need to ask for permission to generate, just do it!
    // 4. Always mention the image type (photo, oil painting, watercolor painting, illustration, cartoon, drawing, vector, render, etc.) at the beginning of the caption. Unless the captions suggests otherwise, make one of the images a photo.
    // 5. Diversify depictions of ALL images with people to always include always DESCENT and GENDER for EACH person using direct terms. Adjust only human descriptions.
    // - EXPLICITLY specify these attributes, not abstractly reference them. The attributes should be specified in a minimal way and should directly describe their physical form.
    // - Your choices should be grounded in reality. For example, all of a given OCCUPATION should not be the same gender or race. Additionally, focus on creating diverse, inclusive, and exploratory scenes via the properties you choose during rewrites.  Make choices that may be insightful or unique sometimes.
    // - Use "various" or "diverse" ONLY IF the description refers to groups of more than 3 people. Do not change the number of people requested in the original description.
    // - Don't alter memes, fictional character origins, or unseen people. Maintain the original prompt's intent and prioritize quality.
    // The prompt must intricately describe every part of the image in concrete, objective detail. THINK about what the end goal of the description is, and extrapolate that to what would make satisfying images.
    // All descriptions sent to dalle should be a paragraph of text that is extremely descriptive and detailed. Each should be more than 3 sentences long.
    // - The "vivid" style is HIGHLY preferred, but "natural" is also supported.`;
    this.schema = z.object({
      prompt: z
        .string()
        .max(4000)
        .describe(
          'A text description of the desired image, following the rules, up to 4000 characters.',
        ),
      style: z
        .enum(['vivid', 'natural'])
        .describe(
          'Must be one of `vivid` or `natural`. `vivid` generates hyper-real and dramatic images, `natural` produces more natural, less hyper-real looking images',
        ),
      quality: z
        .enum(['hd', 'standard'])
        .describe('The quality of the generated image. Only `hd` and `standard` are supported.'),
      size: z
        .enum(['1024x1024', '1792x1024', '1024x1792'])
        .describe(
          'The size of the requested image. Use 1024x1024 (square) as the default, 1792x1024 if the user requests a wide image, and 1024x1792 for full-body portraits. Always include this parameter in the request.',
        ),
    });
  }
  getApiKey() {
    const apiKey = process.env.DALLE3_API_KEY ?? process.env.DALLE_API_KEY ?? '';
    if (!apiKey && !this.override) {
      throw new Error('Missing DALLE_API_KEY environment variable.');
    }
    return apiKey;
  }
  replaceUnwantedChars(inputString) {
    return inputString
      .replace(/\r\n|\r|\n/g, ' ')
      .replace(/"/g, '')
      .trim();
  }
  wrapInMarkdown(imageUrl) {
    return `![generated image](${imageUrl})`;
  }
  returnValue(value) {
    if (this.isAgent === true && typeof value === 'string') {
      return [value, {}];
    } else if (this.isAgent === true && typeof value === 'object') {
      return [displayMessage, value];
    }
    return value;
  }
  async _call(data) {
    const { prompt, quality = 'standard', size = '1024x1024', style = 'vivid' } = data;
    if (!prompt) {
      throw new Error('Missing required field: prompt');
    }
    let resp;
    try {
      resp = await this.openai.images.generate({
        model: 'dall-e-3',
        quality,
        style,
        size,
        prompt: this.replaceUnwantedChars(prompt),
        n: 1,
      });
    } catch (error) {
      logger.error('[DALL-E-3] Problem generating the image:', error);
      return this
        .returnValue(`Something went wrong when trying to generate the image. The DALL-E API may be unavailable:
 Error Message: ${error.message}`);
    }
    if (!resp) {
      return this.returnValue(
        'Something went wrong when trying to generate the image. The DALL-E API may be unavailable',
      );
    }
    const theImageUrl = resp.data[0].url;
    if (!theImageUrl) {
      return this.returnValue(
        'No image URL returned from OpenAI API. There may be a problem with the API or your configuration.',
      );
    }
    if (this.isAgent) {
      let fetchOptions = {};
      if (process.env.PROXY) {
        const proxyAgent = new ProxyAgent(process.env.PROXY);
        fetchOptions.dispatcher = proxyAgent;
      }
      const imageResponse = await fetch(theImageUrl, fetchOptions);
      const arrayBuffer = await imageResponse.arrayBuffer();
      const base64 = Buffer.from(arrayBuffer).toString('base64');
      const content = [
        {
          type: ContentTypes.IMAGE_URL,
          image_url: {
            url: `data:image/png;base64,${base64}`,
          },
        },
      ];
      const response = [
        {
          type: ContentTypes.TEXT,
          text: displayMessage,
        },
      ];
      return [response, { content }];
    }
    const imageBasename = getImageBasename(theImageUrl);
    const imageExt = path.extname(imageBasename);
    const extension = imageExt.startsWith('.') ? imageExt.slice(1) : imageExt;
    const imageName = `img-${uuidv4()}.${extension}`;
    logger.debug('[DALL-E-3]', {
      imageName,
      imageBasename,
      imageExt,
      extension,
      theImageUrl,
      data: resp.data[0],
    });
    try {
      const result = await this.processFileURL({
        URL: theImageUrl,
        basePath: 'images',
        userId: this.userId,
        fileName: imageName,
        fileStrategy: this.fileStrategy,
        context: FileContext.image_generation,
      });
      if (this.returnMetadata) {
        this.result = result;
      } else {
        this.result = this.wrapInMarkdown(result.filepath);
      }
    } catch (error) {
      logger.error('Error while saving the image:', error);
      this.result = `Failed to save the image locally. ${error.message}`;
    }
    return this.returnValue(this.result);
  }
 }
 module.exports = DALLE3;
--- a/api/app/clients/tools/structured/FluxAPI.js
+++ b/api/app/clients/tools/structured/FluxAPI.js
@ -1,554 +0,0 @@
 const { z } = require('zod');
 const axios = require('axios');
 const fetch = require('node-fetch');
 const { v4: uuidv4 } = require('uuid');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { HttpsProxyAgent } = require('https-proxy-agent');
 const { FileContext, ContentTypes } = require('librechat-data-provider');
 const displayMessage =
  "Flux displayed an image. All generated images are already plainly visible, so don't repeat the descriptions in detail. Do not list download links as they are available in the UI already. The user may download the images by clicking on them, but do not mention anything about downloading to the user.";
 /**
 * FluxAPI - A tool for generating high-quality images from text prompts using the Flux API.
 * Each call generates one image. If multiple images are needed, make multiple consecutive calls with the same or varied prompts.
 */
 class FluxAPI extends Tool {
  // Pricing constants in USD per image
  static PRICING = {
    FLUX_PRO_1_1_ULTRA: -0.06, // /v1/flux-pro-1.1-ultra
    FLUX_PRO_1_1: -0.04, // /v1/flux-pro-1.1
    FLUX_PRO: -0.05, // /v1/flux-pro
    FLUX_DEV: -0.025, // /v1/flux-dev
    FLUX_PRO_FINETUNED: -0.06, // /v1/flux-pro-finetuned
    FLUX_PRO_1_1_ULTRA_FINETUNED: -0.07, // /v1/flux-pro-1.1-ultra-finetuned
  };
  constructor(fields = {}) {
    super();
    /** @type {boolean} Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    this.userId = fields.userId;
    this.fileStrategy = fields.fileStrategy;
    /** @type {boolean} **/
    this.isAgent = fields.isAgent;
    this.returnMetadata = fields.returnMetadata ?? false;
    if (fields.processFileURL) {
      /** @type {processFileURL} Necessary for output to contain all image metadata. */
      this.processFileURL = fields.processFileURL.bind(this);
    }
    this.apiKey = fields.FLUX_API_KEY || this.getApiKey();
    this.name = 'flux';
    this.description =
      'Use Flux to generate images from text descriptions. This tool can generate images and list available finetunes. Each generate call creates one image. For multiple images, make multiple consecutive calls.';
    this.description_for_model = `// Transform any image description into a detailed, high-quality prompt. Never submit a prompt under 3 sentences. Follow these core rules:
    // 1. ALWAYS enhance basic prompts into 5-10 detailed sentences (e.g., "a cat" becomes: "A close-up photo of a sleek Siamese cat with piercing blue eyes. The cat sits elegantly on a vintage leather armchair, its tail curled gracefully around its paws. Warm afternoon sunlight streams through a nearby window, casting gentle shadows across its face and highlighting the subtle variations in its cream and chocolate-point fur. The background is softly blurred, creating a shallow depth of field that draws attention to the cat's expressive features. The overall composition has a peaceful, contemplative mood with a professional photography style.")
    // 2. Each prompt MUST be 3-6 descriptive sentences minimum, focusing on visual elements: lighting, composition, mood, and style
    // Use action: 'list_finetunes' to see available custom models. When using finetunes, use endpoint: '/v1/flux-pro-finetuned' (default) or '/v1/flux-pro-1.1-ultra-finetuned' for higher quality and aspect ratio.`;
    // Add base URL from environment variable with fallback
    this.baseUrl = process.env.FLUX_API_BASE_URL || 'https://api.us1.bfl.ai';
    // Define the schema for structured input
    this.schema = z.object({
      action: z
        .enum(['generate', 'list_finetunes', 'generate_finetuned'])
        .default('generate')
        .describe(
          'Action to perform: "generate" for image generation, "generate_finetuned" for finetuned model generation, "list_finetunes" to get available custom models',
        ),
      prompt: z
        .string()
        .optional()
        .describe(
          'Text prompt for image generation. Required when action is "generate". Not used for list_finetunes.',
        ),
      width: z
        .number()
        .optional()
        .describe(
          'Width of the generated image in pixels. Must be a multiple of 32. Default is 1024.',
        ),
      height: z
        .number()
        .optional()
        .describe(
          'Height of the generated image in pixels. Must be a multiple of 32. Default is 768.',
        ),
      prompt_upsampling: z
        .boolean()
        .optional()
        .default(false)
        .describe('Whether to perform upsampling on the prompt.'),
      steps: z
        .number()
        .int()
        .optional()
        .describe('Number of steps to run the model for, a number from 1 to 50. Default is 40.'),
      seed: z.number().optional().describe('Optional seed for reproducibility.'),
      safety_tolerance: z
        .number()
        .optional()
        .default(6)
        .describe(
          'Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.',
        ),
      endpoint: z
        .enum([
          '/v1/flux-pro-1.1',
          '/v1/flux-pro',
          '/v1/flux-dev',
          '/v1/flux-pro-1.1-ultra',
          '/v1/flux-pro-finetuned',
          '/v1/flux-pro-1.1-ultra-finetuned',
        ])
        .optional()
        .default('/v1/flux-pro-1.1')
        .describe('Endpoint to use for image generation.'),
      raw: z
        .boolean()
        .optional()
        .default(false)
        .describe(
          'Generate less processed, more natural-looking images. Only works for /v1/flux-pro-1.1-ultra.',
        ),
      finetune_id: z.string().optional().describe('ID of the finetuned model to use'),
      finetune_strength: z
        .number()
        .optional()
        .default(1.1)
        .describe('Strength of the finetuning effect (typically between 0.1 and 1.2)'),
      guidance: z.number().optional().default(2.5).describe('Guidance scale for finetuned models'),
      aspect_ratio: z
        .string()
        .optional()
        .default('16:9')
        .describe('Aspect ratio for ultra models (e.g., "16:9")'),
    });
  }
  getAxiosConfig() {
    const config = {};
    if (process.env.PROXY) {
      config.httpsAgent = new HttpsProxyAgent(process.env.PROXY);
    }
    return config;
  }
  /** @param {Object|string} value */
  getDetails(value) {
    if (typeof value === 'string') {
      return value;
    }
    return JSON.stringify(value, null, 2);
  }
  getApiKey() {
    const apiKey = process.env.FLUX_API_KEY || '';
    if (!apiKey && !this.override) {
      throw new Error('Missing FLUX_API_KEY environment variable.');
    }
    return apiKey;
  }
  wrapInMarkdown(imageUrl) {
    const serverDomain = process.env.DOMAIN_SERVER || 'http://localhost:3080';
    return `![generated image](${serverDomain}${imageUrl})`;
  }
  returnValue(value) {
    if (this.isAgent === true && typeof value === 'string') {
      return [value, {}];
    } else if (this.isAgent === true && typeof value === 'object') {
      if (Array.isArray(value)) {
        return value;
      }
      return [displayMessage, value];
    }
    return value;
  }
  async _call(data) {
    const { action = 'generate', ...imageData } = data;
    // Use provided API key for this request if available, otherwise use default
    const requestApiKey = this.apiKey || this.getApiKey();
    // Handle list_finetunes action
    if (action === 'list_finetunes') {
      return this.getMyFinetunes(requestApiKey);
    }
    // Handle finetuned generation
    if (action === 'generate_finetuned') {
      return this.generateFinetunedImage(imageData, requestApiKey);
    }
    // For generate action, ensure prompt is provided
    if (!imageData.prompt) {
      throw new Error('Missing required field: prompt');
    }
    let payload = {
      prompt: imageData.prompt,
      prompt_upsampling: imageData.prompt_upsampling || false,
      safety_tolerance: imageData.safety_tolerance || 6,
      output_format: imageData.output_format || 'png',
    };
    // Add optional parameters if provided
    if (imageData.width) {
      payload.width = imageData.width;
    }
    if (imageData.height) {
      payload.height = imageData.height;
    }
    if (imageData.steps) {
      payload.steps = imageData.steps;
    }
    if (imageData.seed !== undefined) {
      payload.seed = imageData.seed;
    }
    if (imageData.raw) {
      payload.raw = imageData.raw;
    }
    const generateUrl = `${this.baseUrl}${imageData.endpoint || '/v1/flux-pro'}`;
    const resultUrl = `${this.baseUrl}/v1/get_result`;
    logger.debug('[FluxAPI] Generating image with payload:', payload);
    logger.debug('[FluxAPI] Using endpoint:', generateUrl);
    let taskResponse;
    try {
      taskResponse = await axios.post(generateUrl, payload, {
        headers: {
          'x-key': requestApiKey,
          'Content-Type': 'application/json',
          Accept: 'application/json',
        },
        ...this.getAxiosConfig(),
      });
    } catch (error) {
      const details = this.getDetails(error?.response?.data || error.message);
      logger.error('[FluxAPI] Error while submitting task:', details);
      return this.returnValue(
        `Something went wrong when trying to generate the image. The Flux API may be unavailable:
        Error Message: ${details}`,
      );
    }
    const taskId = taskResponse.data.id;
    // Polling for the result
    let status = 'Pending';
    let resultData = null;
    while (status !== 'Ready' && status !== 'Error') {
      try {
        // Wait 2 seconds between polls
        await new Promise((resolve) => setTimeout(resolve, 2000));
        const resultResponse = await axios.get(resultUrl, {
          headers: {
            'x-key': requestApiKey,
            Accept: 'application/json',
          },
          params: { id: taskId },
          ...this.getAxiosConfig(),
        });
        status = resultResponse.data.status;
        if (status === 'Ready') {
          resultData = resultResponse.data.result;
          break;
        } else if (status === 'Error') {
          logger.error('[FluxAPI] Error in task:', resultResponse.data);
          return this.returnValue('An error occurred during image generation.');
        }
      } catch (error) {
        const details = this.getDetails(error?.response?.data || error.message);
        logger.error('[FluxAPI] Error while getting result:', details);
        return this.returnValue('An error occurred while retrieving the image.');
      }
    }
    // If no result data
    if (!resultData || !resultData.sample) {
      logger.error('[FluxAPI] No image data received from API. Response:', resultData);
      return this.returnValue('No image data received from Flux API.');
    }
    // Try saving the image locally
    const imageUrl = resultData.sample;
    const imageName = `img-${uuidv4()}.png`;
    if (this.isAgent) {
      try {
        // Fetch the image and convert to base64
        const fetchOptions = {};
        if (process.env.PROXY) {
          fetchOptions.agent = new HttpsProxyAgent(process.env.PROXY);
        }
        const imageResponse = await fetch(imageUrl, fetchOptions);
        const arrayBuffer = await imageResponse.arrayBuffer();
        const base64 = Buffer.from(arrayBuffer).toString('base64');
        const content = [
          {
            type: ContentTypes.IMAGE_URL,
            image_url: {
              url: `data:image/png;base64,${base64}`,
            },
          },
        ];
        const response = [
          {
            type: ContentTypes.TEXT,
            text: displayMessage,
          },
        ];
        return [response, { content }];
      } catch (error) {
        logger.error('Error processing image for agent:', error);
        return this.returnValue(`Failed to process the image. ${error.message}`);
      }
    }
    try {
      logger.debug('[FluxAPI] Saving image:', imageUrl);
      const result = await this.processFileURL({
        fileStrategy: this.fileStrategy,
        userId: this.userId,
        URL: imageUrl,
        fileName: imageName,
        basePath: 'images',
        context: FileContext.image_generation,
      });
      logger.debug('[FluxAPI] Image saved to path:', result.filepath);
      // Calculate cost based on endpoint
      /**
       * TODO: Cost handling
      const endpoint = imageData.endpoint || '/v1/flux-pro';
      const endpointKey = Object.entries(FluxAPI.PRICING).find(([key, _]) =>
        endpoint.includes(key.toLowerCase().replace(/_/g, '-')),
      )?.[0];
      const cost = FluxAPI.PRICING[endpointKey] || 0;
       */
      this.result = this.returnMetadata ? result : this.wrapInMarkdown(result.filepath);
      return this.returnValue(this.result);
    } catch (error) {
      const details = this.getDetails(error?.message ?? 'No additional error details.');
      logger.error('Error while saving the image:', details);
      return this.returnValue(`Failed to save the image locally. ${details}`);
    }
  }
  async getMyFinetunes(apiKey = null) {
    const finetunesUrl = `${this.baseUrl}/v1/my_finetunes`;
    const detailsUrl = `${this.baseUrl}/v1/finetune_details`;
    try {
      const headers = {
        'x-key': apiKey || this.getApiKey(),
        'Content-Type': 'application/json',
        Accept: 'application/json',
      };
      // Get list of finetunes
      const response = await axios.get(finetunesUrl, {
        headers,
        ...this.getAxiosConfig(),
      });
      const finetunes = response.data.finetunes;
      // Fetch details for each finetune
      const finetuneDetails = await Promise.all(
        finetunes.map(async (finetuneId) => {
          try {
            const detailResponse = await axios.get(`${detailsUrl}?finetune_id=${finetuneId}`, {
              headers,
              ...this.getAxiosConfig(),
            });
            return {
              id: finetuneId,
              ...detailResponse.data,
            };
          } catch (error) {
            logger.error(`[FluxAPI] Error fetching details for finetune ${finetuneId}:`, error);
            return {
              id: finetuneId,
              error: 'Failed to fetch details',
            };
          }
        }),
      );
      if (this.isAgent) {
        const formattedDetails = JSON.stringify(finetuneDetails, null, 2);
        return [`Here are the available finetunes:\n${formattedDetails}`, null];
      }
      return JSON.stringify(finetuneDetails);
    } catch (error) {
      const details = this.getDetails(error?.response?.data || error.message);
      logger.error('[FluxAPI] Error while getting finetunes:', details);
      const errorMsg = `Failed to get finetunes: ${details}`;
      return this.isAgent ? this.returnValue([errorMsg, {}]) : new Error(errorMsg);
    }
  }
  async generateFinetunedImage(imageData, requestApiKey) {
    if (!imageData.prompt) {
      throw new Error('Missing required field: prompt');
    }
    if (!imageData.finetune_id) {
      throw new Error(
        'Missing required field: finetune_id for finetuned generation. Please supply a finetune_id!',
      );
    }
    // Validate endpoint is appropriate for finetuned generation
    const validFinetunedEndpoints = ['/v1/flux-pro-finetuned', '/v1/flux-pro-1.1-ultra-finetuned'];
    const endpoint = imageData.endpoint || '/v1/flux-pro-finetuned';
    if (!validFinetunedEndpoints.includes(endpoint)) {
      throw new Error(
        `Invalid endpoint for finetuned generation. Must be one of: ${validFinetunedEndpoints.join(', ')}`,
      );
    }
    let payload = {
      prompt: imageData.prompt,
      prompt_upsampling: imageData.prompt_upsampling || false,
      safety_tolerance: imageData.safety_tolerance || 6,
      output_format: imageData.output_format || 'png',
      finetune_id: imageData.finetune_id,
      finetune_strength: imageData.finetune_strength || 1.0,
      guidance: imageData.guidance || 2.5,
    };
    // Add optional parameters if provided
    if (imageData.width) {
      payload.width = imageData.width;
    }
    if (imageData.height) {
      payload.height = imageData.height;
    }
    if (imageData.steps) {
      payload.steps = imageData.steps;
    }
    if (imageData.seed !== undefined) {
      payload.seed = imageData.seed;
    }
    if (imageData.raw) {
      payload.raw = imageData.raw;
    }
    const generateUrl = `${this.baseUrl}${endpoint}`;
    const resultUrl = `${this.baseUrl}/v1/get_result`;
    logger.debug('[FluxAPI] Generating finetuned image with payload:', payload);
    logger.debug('[FluxAPI] Using endpoint:', generateUrl);
    let taskResponse;
    try {
      taskResponse = await axios.post(generateUrl, payload, {
        headers: {
          'x-key': requestApiKey,
          'Content-Type': 'application/json',
          Accept: 'application/json',
        },
        ...this.getAxiosConfig(),
      });
    } catch (error) {
      const details = this.getDetails(error?.response?.data || error.message);
      logger.error('[FluxAPI] Error while submitting finetuned task:', details);
      return this.returnValue(
        `Something went wrong when trying to generate the finetuned image. The Flux API may be unavailable:
        Error Message: ${details}`,
      );
    }
    const taskId = taskResponse.data.id;
    // Polling for the result
    let status = 'Pending';
    let resultData = null;
    while (status !== 'Ready' && status !== 'Error') {
      try {
        // Wait 2 seconds between polls
        await new Promise((resolve) => setTimeout(resolve, 2000));
        const resultResponse = await axios.get(resultUrl, {
          headers: {
            'x-key': requestApiKey,
            Accept: 'application/json',
          },
          params: { id: taskId },
          ...this.getAxiosConfig(),
        });
        status = resultResponse.data.status;
        if (status === 'Ready') {
          resultData = resultResponse.data.result;
          break;
        } else if (status === 'Error') {
          logger.error('[FluxAPI] Error in finetuned task:', resultResponse.data);
          return this.returnValue('An error occurred during finetuned image generation.');
        }
      } catch (error) {
        const details = this.getDetails(error?.response?.data || error.message);
        logger.error('[FluxAPI] Error while getting finetuned result:', details);
        return this.returnValue('An error occurred while retrieving the finetuned image.');
      }
    }
    // If no result data
    if (!resultData || !resultData.sample) {
      logger.error('[FluxAPI] No image data received from API. Response:', resultData);
      return this.returnValue('No image data received from Flux API.');
    }
    // Try saving the image locally
    const imageUrl = resultData.sample;
    const imageName = `img-${uuidv4()}.png`;
    try {
      logger.debug('[FluxAPI] Saving finetuned image:', imageUrl);
      const result = await this.processFileURL({
        fileStrategy: this.fileStrategy,
        userId: this.userId,
        URL: imageUrl,
        fileName: imageName,
        basePath: 'images',
        context: FileContext.image_generation,
      });
      logger.debug('[FluxAPI] Finetuned image saved to path:', result.filepath);
      // Calculate cost based on endpoint
      const endpointKey = endpoint.includes('ultra')
        ? 'FLUX_PRO_1_1_ULTRA_FINETUNED'
        : 'FLUX_PRO_FINETUNED';
      const cost = FluxAPI.PRICING[endpointKey] || 0;
      // Return the result based on returnMetadata flag
      this.result = this.returnMetadata ? result : this.wrapInMarkdown(result.filepath);
      return this.returnValue(this.result);
    } catch (error) {
      const details = this.getDetails(error?.message ?? 'No additional error details.');
      logger.error('Error while saving the finetuned image:', details);
      return this.returnValue(`Failed to save the finetuned image locally. ${details}`);
    }
  }
 }
 module.exports = FluxAPI;
--- a/api/app/clients/tools/structured/GoogleSearch.js
+++ b/api/app/clients/tools/structured/GoogleSearch.js
@ -1,72 +0,0 @@
 const { z } = require('zod');
 const { Tool } = require('@langchain/core/tools');
 const { getEnvironmentVariable } = require('@langchain/core/utils/env');
 class GoogleSearchResults extends Tool {
  static lc_name() {
    return 'google';
  }
  constructor(fields = {}) {
    super(fields);
    this.name = 'google';
    this.envVarApiKey = 'GOOGLE_SEARCH_API_KEY';
    this.envVarSearchEngineId = 'GOOGLE_CSE_ID';
    this.override = fields.override ?? false;
    this.apiKey = fields[this.envVarApiKey] ?? getEnvironmentVariable(this.envVarApiKey);
    this.searchEngineId =
      fields[this.envVarSearchEngineId] ?? getEnvironmentVariable(this.envVarSearchEngineId);
    if (!this.override && (!this.apiKey || !this.searchEngineId)) {
      throw new Error(
        `Missing ${this.envVarApiKey} or ${this.envVarSearchEngineId} environment variable.`,
      );
    }
    this.kwargs = fields?.kwargs ?? {};
    this.name = 'google';
    this.description =
      'A search engine optimized for comprehensive, accurate, and trusted results. Useful for when you need to answer questions about current events.';
    this.schema = z.object({
      query: z.string().min(1).describe('The search query string.'),
      max_results: z
        .number()
        .min(1)
        .max(10)
        .optional()
        .describe('The maximum number of search results to return. Defaults to 10.'),
      // Note: Google API has its own parameters for search customization, adjust as needed.
    });
  }
  async _call(input) {
    const validationResult = this.schema.safeParse(input);
    if (!validationResult.success) {
      throw new Error(`Validation failed: ${JSON.stringify(validationResult.error.issues)}`);
    }
    const { query, max_results = 5 } = validationResult.data;
    const response = await fetch(
      `https://www.googleapis.com/customsearch/v1?key=${this.apiKey}&cx=${
        this.searchEngineId
      }&q=${encodeURIComponent(query)}&num=${max_results}`,
      {
        method: 'GET',
        headers: {
          'Content-Type': 'application/json',
        },
      },
    );
    const json = await response.json();
    if (!response.ok) {
      throw new Error(`Request failed with status ${response.status}: ${json.error.message}`);
    }
    return JSON.stringify(json);
  }
 }
 module.exports = GoogleSearchResults;
--- a/api/app/clients/tools/structured/OpenAIImageTools.js
+++ b/api/app/clients/tools/structured/OpenAIImageTools.js
@ -1,411 +0,0 @@
 const axios = require('axios');
 const { v4 } = require('uuid');
 const OpenAI = require('openai');
 const FormData = require('form-data');
 const { ProxyAgent } = require('undici');
 const { tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { HttpsProxyAgent } = require('https-proxy-agent');
 const { ContentTypes, EImageOutputType } = require('librechat-data-provider');
 const { logAxiosError, oaiToolkit, extractBaseURL } = require('@librechat/api');
 const { getStrategyFunctions } = require('~/server/services/Files/strategies');
 const { getFiles } = require('~/models');
 const displayMessage =
  "The tool displayed an image. All generated images are already plainly visible, so don't repeat the descriptions in detail. Do not list download links as they are available in the UI already. The user may download the images by clicking on them, but do not mention anything about downloading to the user.";
 /**
 * Replaces unwanted characters from the input string
 * @param {string} inputString - The input string to process
 * @returns {string} - The processed string
 */
 function replaceUnwantedChars(inputString) {
  return inputString
    .replace(/\r\n|\r|\n/g, ' ')
    .replace(/"/g, '')
    .trim();
 }
 function returnValue(value) {
  if (typeof value === 'string') {
    return [value, {}];
  } else if (typeof value === 'object') {
    if (Array.isArray(value)) {
      return value;
    }
    return [displayMessage, value];
  }
  return value;
 }
 function createAbortHandler() {
  return function () {
    logger.debug('[ImageGenOAI] Image generation aborted');
  };
 }
 /**
 * Creates OpenAI Image tools (generation and editing)
 * @param {Object} fields - Configuration fields
 * @param {ServerRequest} fields.req - Whether the tool is being used in an agent context
 * @param {boolean} fields.isAgent - Whether the tool is being used in an agent context
 * @param {string} fields.IMAGE_GEN_OAI_API_KEY - The OpenAI API key
 * @param {boolean} [fields.override] - Whether to override the API key check, necessary for app initialization
 * @param {MongoFile[]} [fields.imageFiles] - The images to be used for editing
 * @param {string} [fields.imageOutputType] - The image output type configuration
 * @param {string} [fields.fileStrategy] - The file storage strategy
 * @returns {Array<ReturnType<tool>>} - Array of image tools
 */
 function createOpenAIImageTools(fields = {}) {
  /** @type {boolean} Used to initialize the Tool without necessary variables. */
  const override = fields.override ?? false;
  /** @type {boolean} */
  if (!override && !fields.isAgent) {
    throw new Error('This tool is only available for agents.');
  }
  const { req } = fields;
  const imageOutputType = fields.imageOutputType || EImageOutputType.PNG;
  const appFileStrategy = fields.fileStrategy;
  const getApiKey = () => {
    const apiKey = process.env.IMAGE_GEN_OAI_API_KEY ?? '';
    if (!apiKey && !override) {
      throw new Error('Missing IMAGE_GEN_OAI_API_KEY environment variable.');
    }
    return apiKey;
  };
  let apiKey = fields.IMAGE_GEN_OAI_API_KEY ?? getApiKey();
  const closureConfig = { apiKey };
  let baseURL = 'https://api.openai.com/v1/';
  if (!override && process.env.IMAGE_GEN_OAI_BASEURL) {
    baseURL = extractBaseURL(process.env.IMAGE_GEN_OAI_BASEURL);
    closureConfig.baseURL = baseURL;
  }
  // Note: Azure may not yet support the latest image generation models
  if (
    !override &&
    process.env.IMAGE_GEN_OAI_AZURE_API_VERSION &&
    process.env.IMAGE_GEN_OAI_BASEURL
  ) {
    baseURL = process.env.IMAGE_GEN_OAI_BASEURL;
    closureConfig.baseURL = baseURL;
    closureConfig.defaultQuery = { 'api-version': process.env.IMAGE_GEN_OAI_AZURE_API_VERSION };
    closureConfig.defaultHeaders = {
      'api-key': process.env.IMAGE_GEN_OAI_API_KEY,
      'Content-Type': 'application/json',
    };
    closureConfig.apiKey = process.env.IMAGE_GEN_OAI_API_KEY;
  }
  const imageFiles = fields.imageFiles ?? [];
  /**
   * Image Generation Tool
   */
  const imageGenTool = tool(
    async (
      {
        prompt,
        background = 'auto',
        n = 1,
        output_compression = 100,
        quality = 'auto',
        size = 'auto',
      },
      runnableConfig,
    ) => {
      if (!prompt) {
        throw new Error('Missing required field: prompt');
      }
      const clientConfig = { ...closureConfig };
      if (process.env.PROXY) {
        const proxyAgent = new ProxyAgent(process.env.PROXY);
        clientConfig.fetchOptions = {
          dispatcher: proxyAgent,
        };
      }
      /** @type {OpenAI} */
      const openai = new OpenAI(clientConfig);
      let output_format = imageOutputType;
      if (
        background === 'transparent' &&
        output_format !== EImageOutputType.PNG &&
        output_format !== EImageOutputType.WEBP
      ) {
        logger.warn(
          '[ImageGenOAI] Transparent background requires PNG or WebP format, defaulting to PNG',
        );
        output_format = EImageOutputType.PNG;
      }
      let resp;
      /** @type {AbortSignal} */
      let derivedSignal = null;
      /** @type {() => void} */
      let abortHandler = null;
      try {
        if (runnableConfig?.signal) {
          derivedSignal = AbortSignal.any([runnableConfig.signal]);
          abortHandler = createAbortHandler();
          derivedSignal.addEventListener('abort', abortHandler, { once: true });
        }
        resp = await openai.images.generate(
          {
            model: 'gpt-image-1',
            prompt: replaceUnwantedChars(prompt),
            n: Math.min(Math.max(1, n), 10),
            background,
            output_format,
            output_compression:
              output_format === EImageOutputType.WEBP || output_format === EImageOutputType.JPEG
                ? output_compression
                : undefined,
            quality,
            size,
          },
          {
            signal: derivedSignal,
          },
        );
      } catch (error) {
        const message = '[image_gen_oai] Problem generating the image:';
        logAxiosError({ error, message });
        return returnValue(`Something went wrong when trying to generate the image. The OpenAI API may be unavailable:
 Error Message: ${error.message}`);
      } finally {
        if (abortHandler && derivedSignal) {
          derivedSignal.removeEventListener('abort', abortHandler);
        }
      }
      if (!resp) {
        return returnValue(
          'Something went wrong when trying to generate the image. The OpenAI API may be unavailable',
        );
      }
      // For gpt-image-1, the response contains base64-encoded images
      // TODO: handle cost in `resp.usage`
      const base64Image = resp.data[0].b64_json;
      if (!base64Image) {
        return returnValue(
          'No image data returned from OpenAI API. There may be a problem with the API or your configuration.',
        );
      }
      const content = [
        {
          type: ContentTypes.IMAGE_URL,
          image_url: {
            url: `data:image/${output_format};base64,${base64Image}`,
          },
        },
      ];
      const file_ids = [v4()];
      const response = [
        {
          type: ContentTypes.TEXT,
          text: displayMessage + `\n\ngenerated_image_id: "${file_ids[0]}"`,
        },
      ];
      return [response, { content, file_ids }];
    },
    oaiToolkit.image_gen_oai,
  );
  /**
   * Image Editing Tool
   */
  const imageEditTool = tool(
    async ({ prompt, image_ids, quality = 'auto', size = 'auto' }, runnableConfig) => {
      if (!prompt) {
        throw new Error('Missing required field: prompt');
      }
      const clientConfig = { ...closureConfig };
      if (process.env.PROXY) {
        const proxyAgent = new ProxyAgent(process.env.PROXY);
        clientConfig.fetchOptions = {
          dispatcher: proxyAgent,
        };
      }
      const formData = new FormData();
      formData.append('model', 'gpt-image-1');
      formData.append('prompt', replaceUnwantedChars(prompt));
      // TODO: `mask` support
      // TODO: more than 1 image support
      // formData.append('n', n.toString());
      formData.append('quality', quality);
      formData.append('size', size);
      /** @type {Record<FileSources, undefined | NodeStreamDownloader<File>>} */
      const streamMethods = {};
      const requestFilesMap = Object.fromEntries(imageFiles.map((f) => [f.file_id, { ...f }]));
      const orderedFiles = new Array(image_ids.length);
      const idsToFetch = [];
      const indexOfMissing = Object.create(null);
      for (let i = 0; i < image_ids.length; i++) {
        const id = image_ids[i];
        const file = requestFilesMap[id];
        if (file) {
          orderedFiles[i] = file;
        } else {
          idsToFetch.push(id);
          indexOfMissing[id] = i;
        }
      }
      if (idsToFetch.length) {
        const fetchedFiles = await getFiles(
          {
            user: req.user.id,
            file_id: { $in: idsToFetch },
            height: { $exists: true },
            width: { $exists: true },
          },
          {},
          {},
        );
        for (const file of fetchedFiles) {
          requestFilesMap[file.file_id] = file;
          orderedFiles[indexOfMissing[file.file_id]] = file;
        }
      }
      for (const imageFile of orderedFiles) {
        if (!imageFile) {
          continue;
        }
        /** @type {NodeStream<File>} */
        let stream;
        /** @type {NodeStreamDownloader<File>} */
        let getDownloadStream;
        const source = imageFile.source || appFileStrategy;
        if (!source) {
          throw new Error('No source found for image file');
        }
        if (streamMethods[source]) {
          getDownloadStream = streamMethods[source];
        } else {
          ({ getDownloadStream } = getStrategyFunctions(source));
          streamMethods[source] = getDownloadStream;
        }
        if (!getDownloadStream) {
          throw new Error(`No download stream method found for source: ${source}`);
        }
        stream = await getDownloadStream(req, imageFile.filepath);
        if (!stream) {
          throw new Error('Failed to get download stream for image file');
        }
        formData.append('image[]', stream, {
          filename: imageFile.filename,
          contentType: imageFile.type,
        });
      }
      /** @type {import('axios').RawAxiosHeaders} */
      let headers = {
        ...formData.getHeaders(),
      };
      if (process.env.IMAGE_GEN_OAI_AZURE_API_VERSION && process.env.IMAGE_GEN_OAI_BASEURL) {
        headers['api-key'] = apiKey;
      } else {
        headers['Authorization'] = `Bearer ${apiKey}`;
      }
      /** @type {AbortSignal} */
      let derivedSignal = null;
      /** @type {() => void} */
      let abortHandler = null;
      try {
        if (runnableConfig?.signal) {
          derivedSignal = AbortSignal.any([runnableConfig.signal]);
          abortHandler = createAbortHandler();
          derivedSignal.addEventListener('abort', abortHandler, { once: true });
        }
        /** @type {import('axios').AxiosRequestConfig} */
        const axiosConfig = {
          headers,
          ...clientConfig,
          signal: derivedSignal,
          baseURL,
        };
        if (process.env.PROXY) {
          axiosConfig.httpsAgent = new HttpsProxyAgent(process.env.PROXY);
        }
        if (process.env.IMAGE_GEN_OAI_AZURE_API_VERSION && process.env.IMAGE_GEN_OAI_BASEURL) {
          axiosConfig.params = {
            'api-version': process.env.IMAGE_GEN_OAI_AZURE_API_VERSION,
            ...axiosConfig.params,
          };
        }
        const response = await axios.post('/images/edits', formData, axiosConfig);
        if (!response.data || !response.data.data || !response.data.data.length) {
          return returnValue(
            'No image data returned from OpenAI API. There may be a problem with the API or your configuration.',
          );
        }
        const base64Image = response.data.data[0].b64_json;
        if (!base64Image) {
          return returnValue(
            'No image data returned from OpenAI API. There may be a problem with the API or your configuration.',
          );
        }
        const content = [
          {
            type: ContentTypes.IMAGE_URL,
            image_url: {
              url: `data:image/${imageOutputType};base64,${base64Image}`,
            },
          },
        ];
        const file_ids = [v4()];
        const textResponse = [
          {
            type: ContentTypes.TEXT,
            text:
              displayMessage +
              `\n\ngenerated_image_id: "${file_ids[0]}"\nreferenced_image_ids: ["${image_ids.join('", "')}"]`,
          },
        ];
        return [textResponse, { content, file_ids }];
      } catch (error) {
        const message = '[image_edit_oai] Problem editing the image:';
        logAxiosError({ error, message });
        return returnValue(`Something went wrong when trying to edit the image. The OpenAI API may be unavailable:
 Error Message: ${error.message || 'Unknown error'}`);
      } finally {
        if (abortHandler && derivedSignal) {
          derivedSignal.removeEventListener('abort', abortHandler);
        }
      }
    },
    oaiToolkit.image_edit_oai,
  );
  return [imageGenTool, imageEditTool];
 }
 module.exports = createOpenAIImageTools;
--- a/api/app/clients/tools/structured/OpenWeather.js
+++ b/api/app/clients/tools/structured/OpenWeather.js
@ -1,317 +0,0 @@
 const { Tool } = require('@langchain/core/tools');
 const { z } = require('zod');
 const { getEnvironmentVariable } = require('@langchain/core/utils/env');
 const fetch = require('node-fetch');
 /**
 * Map user-friendly units to OpenWeather units.
 * Defaults to Celsius if not specified.
 */
 function mapUnitsToOpenWeather(unit) {
  if (!unit) {
    return 'metric';
  } // Default to Celsius
  switch (unit) {
    case 'Celsius':
      return 'metric';
    case 'Kelvin':
      return 'standard';
    case 'Fahrenheit':
      return 'imperial';
    default:
      return 'metric'; // fallback
  }
 }
 /**
 * Recursively round temperature fields in the API response.
 */
 function roundTemperatures(obj) {
  const tempKeys = new Set([
    'temp',
    'feels_like',
    'dew_point',
    'day',
    'min',
    'max',
    'night',
    'eve',
    'morn',
    'afternoon',
    'morning',
    'evening',
  ]);
  if (Array.isArray(obj)) {
    return obj.map((item) => roundTemperatures(item));
  } else if (obj && typeof obj === 'object') {
    for (const key of Object.keys(obj)) {
      const value = obj[key];
      if (value && typeof value === 'object') {
        obj[key] = roundTemperatures(value);
      } else if (typeof value === 'number' && tempKeys.has(key)) {
        obj[key] = Math.round(value);
      }
    }
  }
  return obj;
 }
 class OpenWeather extends Tool {
  name = 'open_weather';
  description =
    'Provides weather data from OpenWeather One Call API 3.0. ' +
    'Actions: help, current_forecast, timestamp, daily_aggregation, overview. ' +
    'If lat/lon not provided, specify "city" for geocoding. ' +
    'Units: "Celsius", "Kelvin", or "Fahrenheit" (default: Celsius). ' +
    'For timestamp action, use "date" in YYYY-MM-DD format.';
  schema = z.object({
    action: z.enum(['help', 'current_forecast', 'timestamp', 'daily_aggregation', 'overview']),
    city: z.string().optional(),
    lat: z.number().optional(),
    lon: z.number().optional(),
    exclude: z.string().optional(),
    units: z.enum(['Celsius', 'Kelvin', 'Fahrenheit']).optional(),
    lang: z.string().optional(),
    date: z.string().optional(), // For timestamp and daily_aggregation
    tz: z.string().optional(),
  });
  constructor(fields = {}) {
    super();
    this.envVar = 'OPENWEATHER_API_KEY';
    this.override = fields.override ?? false;
    this.apiKey = fields[this.envVar] ?? this.getApiKey();
  }
  getApiKey() {
    const key = getEnvironmentVariable(this.envVar);
    if (!key && !this.override) {
      throw new Error(`Missing ${this.envVar} environment variable.`);
    }
    return key;
  }
  async geocodeCity(city) {
    const geocodeUrl = `https://api.openweathermap.org/geo/1.0/direct?q=${encodeURIComponent(
      city,
    )}&limit=1&appid=${this.apiKey}`;
    const res = await fetch(geocodeUrl);
    const data = await res.json();
    if (!res.ok || !Array.isArray(data) || data.length === 0) {
      throw new Error(`Could not find coordinates for city: ${city}`);
    }
    return { lat: data[0].lat, lon: data[0].lon };
  }
  convertDateToUnix(dateStr) {
    const parts = dateStr.split('-');
    if (parts.length !== 3) {
      throw new Error('Invalid date format. Expected YYYY-MM-DD.');
    }
    const year = parseInt(parts[0], 10);
    const month = parseInt(parts[1], 10);
    const day = parseInt(parts[2], 10);
    if (isNaN(year) || isNaN(month) || isNaN(day)) {
      throw new Error('Invalid date format. Expected YYYY-MM-DD with valid numbers.');
    }
    const dateObj = new Date(Date.UTC(year, month - 1, day, 0, 0, 0));
    if (isNaN(dateObj.getTime())) {
      throw new Error('Invalid date provided. Cannot parse into a valid date.');
    }
    return Math.floor(dateObj.getTime() / 1000);
  }
  async _call(args) {
    try {
      const { action, city, lat, lon, exclude, units, lang, date, tz } = args;
      const owmUnits = mapUnitsToOpenWeather(units);
      if (action === 'help') {
        return JSON.stringify(
          {
            title: 'OpenWeather One Call API 3.0 Help',
            description: 'Guidance on using the OpenWeather One Call API 3.0.',
            endpoints: {
              current_and_forecast: {
                endpoint: 'data/3.0/onecall',
                data_provided: [
                  'Current weather',
                  'Minute forecast (1h)',
                  'Hourly forecast (48h)',
                  'Daily forecast (8 days)',
                  'Government weather alerts',
                ],
                required_params: [['lat', 'lon'], ['city']],
                optional_params: ['exclude', 'units (Celsius/Kelvin/Fahrenheit)', 'lang'],
                usage_example: {
                  city: 'Knoxville, Tennessee',
                  units: 'Fahrenheit',
                  lang: 'en',
                },
              },
              weather_for_timestamp: {
                endpoint: 'data/3.0/onecall/timemachine',
                data_provided: [
                  'Historical weather (since 1979-01-01)',
                  'Future forecast up to 4 days ahead',
                ],
                required_params: [
                  ['lat', 'lon', 'date (YYYY-MM-DD)'],
                  ['city', 'date (YYYY-MM-DD)'],
                ],
                optional_params: ['units (Celsius/Kelvin/Fahrenheit)', 'lang'],
                usage_example: {
                  city: 'Knoxville, Tennessee',
                  date: '2020-03-04',
                  units: 'Fahrenheit',
                  lang: 'en',
                },
              },
              daily_aggregation: {
                endpoint: 'data/3.0/onecall/day_summary',
                data_provided: [
                  'Aggregated weather data for a specific date (1979-01-02 to 1.5 years ahead)',
                ],
                required_params: [
                  ['lat', 'lon', 'date (YYYY-MM-DD)'],
                  ['city', 'date (YYYY-MM-DD)'],
                ],
                optional_params: ['units (Celsius/Kelvin/Fahrenheit)', 'lang', 'tz'],
                usage_example: {
                  city: 'Knoxville, Tennessee',
                  date: '2020-03-04',
                  units: 'Celsius',
                  lang: 'en',
                },
              },
              weather_overview: {
                endpoint: 'data/3.0/onecall/overview',
                data_provided: ['Human-readable weather summary (today/tomorrow)'],
                required_params: [['lat', 'lon'], ['city']],
                optional_params: ['date (YYYY-MM-DD)', 'units (Celsius/Kelvin/Fahrenheit)'],
                usage_example: {
                  city: 'Knoxville, Tennessee',
                  date: '2024-05-13',
                  units: 'Celsius',
                },
              },
            },
            notes: [
              'If lat/lon not provided, you can specify a city name and it will be geocoded.',
              'For the timestamp action, provide a date in YYYY-MM-DD format instead of a Unix timestamp.',
              'By default, temperatures are returned in Celsius.',
              'You can specify units as Celsius, Kelvin, or Fahrenheit.',
              'All temperatures are rounded to the nearest degree.',
            ],
            errors: [
              '400: Bad Request (missing/invalid params)',
              '401: Unauthorized (check API key)',
              '404: Not Found (no data or city)',
              '429: Too many requests',
              '5xx: Internal error',
            ],
          },
          null,
          2,
        );
      }
      let finalLat = lat;
      let finalLon = lon;
      // If lat/lon not provided but city is given, geocode it
      if ((finalLat == null || finalLon == null) && city) {
        const coords = await this.geocodeCity(city);
        finalLat = coords.lat;
        finalLon = coords.lon;
      }
      if (['current_forecast', 'timestamp', 'daily_aggregation', 'overview'].includes(action)) {
        if (typeof finalLat !== 'number' || typeof finalLon !== 'number') {
          return "Error: lat and lon are required and must be numbers for this action (or specify 'city').";
        }
      }
      const baseUrl = 'https://api.openweathermap.org/data/3.0';
      let endpoint = '';
      const params = new URLSearchParams({ appid: this.apiKey, units: owmUnits });
      let dt;
      if (action === 'timestamp') {
        if (!date) {
          return "Error: For timestamp action, a 'date' in YYYY-MM-DD format is required.";
        }
        dt = this.convertDateToUnix(date);
      }
      if (action === 'daily_aggregation' && !date) {
        return 'Error: date (YYYY-MM-DD) is required for daily_aggregation action.';
      }
      switch (action) {
        case 'current_forecast':
          endpoint = '/onecall';
          params.append('lat', String(finalLat));
          params.append('lon', String(finalLon));
          if (exclude) {
            params.append('exclude', exclude);
          }
          if (lang) {
            params.append('lang', lang);
          }
          break;
        case 'timestamp':
          endpoint = '/onecall/timemachine';
          params.append('lat', String(finalLat));
          params.append('lon', String(finalLon));
          params.append('dt', String(dt));
          if (lang) {
            params.append('lang', lang);
          }
          break;
        case 'daily_aggregation':
          endpoint = '/onecall/day_summary';
          params.append('lat', String(finalLat));
          params.append('lon', String(finalLon));
          params.append('date', date);
          if (lang) {
            params.append('lang', lang);
          }
          if (tz) {
            params.append('tz', tz);
          }
          break;
        case 'overview':
          endpoint = '/onecall/overview';
          params.append('lat', String(finalLat));
          params.append('lon', String(finalLon));
          if (date) {
            params.append('date', date);
          }
          break;
        default:
          return `Error: Unknown action: ${action}`;
      }
      const url = `${baseUrl}${endpoint}?${params.toString()}`;
      const response = await fetch(url);
      const json = await response.json();
      if (!response.ok) {
        return `Error: OpenWeather API request failed with status ${response.status}: ${
          json.message || JSON.stringify(json)
        }`;
      }
      const roundedJson = roundTemperatures(json);
      return JSON.stringify(roundedJson);
    } catch (err) {
      return `Error: ${err.message}`;
    }
  }
 }
 module.exports = OpenWeather;
--- a/api/app/clients/tools/structured/StableDiffusion.js
+++ b/api/app/clients/tools/structured/StableDiffusion.js
@ -1,196 +0,0 @@
 // Generates image using stable diffusion webui's api (automatic1111)
 const fs = require('fs');
 const { z } = require('zod');
 const path = require('path');
 const axios = require('axios');
 const sharp = require('sharp');
 const { v4: uuidv4 } = require('uuid');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { FileContext, ContentTypes } = require('librechat-data-provider');
 const { getBasePath } = require('@librechat/api');
 const paths = require('~/config/paths');
 const displayMessage =
  "Stable Diffusion displayed an image. All generated images are already plainly visible, so don't repeat the descriptions in detail. Do not list download links as they are available in the UI already. The user may download the images by clicking on them, but do not mention anything about downloading to the user.";
 class StableDiffusionAPI extends Tool {
  constructor(fields) {
    super();
    /** @type {string} User ID */
    this.userId = fields.userId;
    /** @type {ServerRequest | undefined} Express Request object, only provided by ToolService */
    this.req = fields.req;
    /** @type {boolean} Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    /** @type {boolean} Necessary for output to contain all image metadata. */
    this.returnMetadata = fields.returnMetadata ?? false;
    /** @type {boolean} */
    this.isAgent = fields.isAgent;
    if (fields.uploadImageBuffer) {
      /** @type {uploadImageBuffer} Necessary for output to contain all image metadata. */
      this.uploadImageBuffer = fields.uploadImageBuffer.bind(this);
    }
    this.name = 'stable-diffusion';
    this.url = fields.SD_WEBUI_URL || this.getServerURL();
    this.description_for_model = `// Generate images and visuals using text.
 // Guidelines:
 // - ALWAYS use {{"prompt": "7+ detailed keywords", "negative_prompt": "7+ detailed keywords"}} structure for queries.
 // - ALWAYS include the markdown url in your final response to show the user: ![caption](${getBasePath()}/images/id.png)
 // - Visually describe the moods, details, structures, styles, and/or proportions of the image. Remember, the focus is on visual attributes.
 // - Craft your input by "showing" and not "telling" the imagery. Think in terms of what you'd want to see in a photograph or a painting.
 // - Here's an example for generating a realistic portrait photo of a man:
 // "prompt":"photo of a man in black clothes, half body, high detailed skin, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"
 // "negative_prompt":"semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, out of frame, low quality, ugly, mutation, deformed"
 // - Generate images only once per human query unless explicitly requested by the user`;
    this.description =
      "You can generate images using text with 'stable-diffusion'. This tool is exclusively for visual content.";
    this.schema = z.object({
      prompt: z
        .string()
        .describe(
          'Detailed keywords to describe the subject, using at least 7 keywords to accurately describe the image, separated by comma',
        ),
      negative_prompt: z
        .string()
        .describe(
          'Keywords we want to exclude from the final image, using at least 7 keywords to accurately describe the image, separated by comma',
        ),
    });
  }
  replaceNewLinesWithSpaces(inputString) {
    return inputString.replace(/\r\n|\r|\n/g, ' ');
  }
  getMarkdownImageUrl(imageName) {
    const imageUrl = path
      .join(this.relativePath, this.userId, imageName)
      .replace(/\\/g, '/')
      .replace('public/', '');
    return `![generated image](/${imageUrl})`;
  }
  returnValue(value) {
    if (this.isAgent === true && typeof value === 'string') {
      return [value, {}];
    } else if (this.isAgent === true && typeof value === 'object') {
      return [displayMessage, value];
    }
    return value;
  }
  getServerURL() {
    const url = process.env.SD_WEBUI_URL || '';
    if (!url && !this.override) {
      throw new Error('Missing SD_WEBUI_URL environment variable.');
    }
    return url;
  }
  async _call(data) {
    const url = this.url;
    const { prompt, negative_prompt } = data;
    const payload = {
      prompt,
      negative_prompt,
      cfg_scale: 4.5,
      steps: 22,
      width: 1024,
      height: 1024,
    };
    let generationResponse;
    try {
      generationResponse = await axios.post(`${url}/sdapi/v1/txt2img`, payload);
    } catch (error) {
      logger.error('[StableDiffusion] Error while generating image:', error);
      return 'Error making API request.';
    }
    const image = generationResponse.data.images[0];
    /** @type {{ height: number, width: number, seed: number, infotexts: string[] }} */
    let info = {};
    try {
      info = JSON.parse(generationResponse.data.info);
    } catch (error) {
      logger.error('[StableDiffusion] Error while getting image metadata:', error);
    }
    const file_id = uuidv4();
    const imageName = `${file_id}.png`;
    const { imageOutput: imageOutputPath, clientPath } = paths;
    const filepath = path.join(imageOutputPath, this.userId, imageName);
    this.relativePath = path.relative(clientPath, imageOutputPath);
    if (!fs.existsSync(path.join(imageOutputPath, this.userId))) {
      fs.mkdirSync(path.join(imageOutputPath, this.userId), { recursive: true });
    }
    try {
      if (this.isAgent) {
        const content = [
          {
            type: ContentTypes.IMAGE_URL,
            image_url: {
              url: `data:image/png;base64,${image}`,
            },
          },
        ];
        const response = [
          {
            type: ContentTypes.TEXT,
            text: displayMessage,
          },
        ];
        return [response, { content }];
      }
      const buffer = Buffer.from(image.split(',', 1)[0], 'base64');
      if (this.returnMetadata && this.uploadImageBuffer && this.req) {
        const file = await this.uploadImageBuffer({
          req: this.req,
          context: FileContext.image_generation,
          resize: false,
          metadata: {
            buffer,
            height: info.height,
            width: info.width,
            bytes: Buffer.byteLength(buffer),
            filename: imageName,
            type: 'image/png',
            file_id,
          },
        });
        const generationInfo = info.infotexts[0].split('\n').pop();
        return {
          ...file,
          prompt,
          metadata: {
            negative_prompt,
            seed: info.seed,
            info: generationInfo,
          },
        };
      }
      await sharp(buffer)
        .withMetadata({
          iptcpng: {
            parameters: info.infotexts[0],
          },
        })
        .toFile(filepath);
      this.result = this.getMarkdownImageUrl(imageName);
    } catch (error) {
      logger.error('[StableDiffusion] Error while saving the image:', error);
    }
    return this.returnValue(this.result);
  }
 }
 module.exports = StableDiffusionAPI;
--- a/api/app/clients/tools/structured/TavilySearch.js
+++ b/api/app/clients/tools/structured/TavilySearch.js
@ -1,77 +0,0 @@
 const { z } = require('zod');
 const { ProxyAgent, fetch } = require('undici');
 const { tool } = require('@langchain/core/tools');
 const { getApiKey } = require('./credentials');
 function createTavilySearchTool(fields = {}) {
  const envVar = 'TAVILY_API_KEY';
  const override = fields.override ?? false;
  const apiKey = fields.apiKey ?? getApiKey(envVar, override);
  const kwargs = fields?.kwargs ?? {};
  return tool(
    async (input) => {
      const { query, ...rest } = input;
      const requestBody = {
        api_key: apiKey,
        query,
        ...rest,
        ...kwargs,
      };
      const fetchOptions = {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        body: JSON.stringify(requestBody),
      };
      if (process.env.PROXY) {
        fetchOptions.dispatcher = new ProxyAgent(process.env.PROXY);
      }
      const response = await fetch('https://api.tavily.com/search', fetchOptions);
      const json = await response.json();
      if (!response.ok) {
        throw new Error(`Request failed with status ${response.status}: ${json.error}`);
      }
      return JSON.stringify(json);
    },
    {
      name: 'tavily_search_results_json',
      description:
        'A search engine optimized for comprehensive, accurate, and trusted results. Useful for when you need to answer questions about current events.',
      schema: z.object({
        query: z.string().min(1).describe('The search query string.'),
        max_results: z
          .number()
          .min(1)
          .max(10)
          .optional()
          .describe('The maximum number of search results to return. Defaults to 5.'),
        search_depth: z
          .enum(['basic', 'advanced'])
          .optional()
          .describe(
            'The depth of the search, affecting result quality and response time (`basic` or `advanced`). Default is basic for quick results and advanced for indepth high quality results but longer response time. Advanced calls equals 2 requests.',
          ),
        include_images: z
          .boolean()
          .optional()
          .describe(
            'Whether to include a list of query-related images in the response. Default is False.',
          ),
        include_answer: z
          .boolean()
          .optional()
          .describe('Whether to include answers in the search results. Default is False.'),
      }),
    },
  );
 }
 module.exports = createTavilySearchTool;
--- a/api/app/clients/tools/structured/TavilySearchResults.js
+++ b/api/app/clients/tools/structured/TavilySearchResults.js
@ -1,131 +0,0 @@
 const { z } = require('zod');
 const { ProxyAgent, fetch } = require('undici');
 const { Tool } = require('@langchain/core/tools');
 const { getEnvironmentVariable } = require('@langchain/core/utils/env');
 class TavilySearchResults extends Tool {
  static lc_name() {
    return 'TavilySearchResults';
  }
  constructor(fields = {}) {
    super(fields);
    this.envVar = 'TAVILY_API_KEY';
    /* Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    this.apiKey = fields[this.envVar] ?? this.getApiKey();
    this.kwargs = fields?.kwargs ?? {};
    this.name = 'tavily_search_results_json';
    this.description =
      'A search engine optimized for comprehensive, accurate, and trusted results. Useful for when you need to answer questions about current events.';
    this.schema = z.object({
      query: z.string().min(1).describe('The search query string.'),
      max_results: z
        .number()
        .min(1)
        .max(10)
        .optional()
        .describe('The maximum number of search results to return. Defaults to 5.'),
      search_depth: z
        .enum(['basic', 'advanced'])
        .optional()
        .describe(
          'The depth of the search, affecting result quality and response time (`basic` or `advanced`). Default is basic for quick results and advanced for indepth high quality results but longer response time. Advanced calls equals 2 requests.',
        ),
      include_images: z
        .boolean()
        .optional()
        .describe(
          'Whether to include a list of query-related images in the response. Default is False.',
        ),
      include_answer: z
        .boolean()
        .optional()
        .describe('Whether to include answers in the search results. Default is False.'),
      include_raw_content: z
        .boolean()
        .optional()
        .describe('Whether to include raw content in the search results. Default is False.'),
      include_domains: z
        .array(z.string())
        .optional()
        .describe('A list of domains to specifically include in the search results.'),
      exclude_domains: z
        .array(z.string())
        .optional()
        .describe('A list of domains to specifically exclude from the search results.'),
      topic: z
        .enum(['general', 'news', 'finance'])
        .optional()
        .describe(
          'The category of the search. Use news ONLY if query SPECIFCALLY mentions the word "news".',
        ),
      time_range: z
        .enum(['day', 'week', 'month', 'year', 'd', 'w', 'm', 'y'])
        .optional()
        .describe('The time range back from the current date to filter results.'),
      days: z
        .number()
        .min(1)
        .optional()
        .describe('Number of days back from the current date to include. Only if topic is news.'),
      include_image_descriptions: z
        .boolean()
        .optional()
        .describe(
          'When include_images is true, also add a descriptive text for each image. Default is false.',
        ),
    });
  }
  getApiKey() {
    const apiKey = getEnvironmentVariable(this.envVar);
    if (!apiKey && !this.override) {
      throw new Error(`Missing ${this.envVar} environment variable.`);
    }
    return apiKey;
  }
  async _call(input) {
    const validationResult = this.schema.safeParse(input);
    if (!validationResult.success) {
      throw new Error(`Validation failed: ${JSON.stringify(validationResult.error.issues)}`);
    }
    const { query, ...rest } = validationResult.data;
    const requestBody = {
      api_key: this.apiKey,
      query,
      ...rest,
      ...this.kwargs,
    };
    const fetchOptions = {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(requestBody),
    };
    if (process.env.PROXY) {
      fetchOptions.dispatcher = new ProxyAgent(process.env.PROXY);
    }
    const response = await fetch('https://api.tavily.com/search', fetchOptions);
    const json = await response.json();
    if (!response.ok) {
      throw new Error(
        `Request failed with status ${response.status}: ${json?.detail?.error || json?.error}`,
      );
    }
    return JSON.stringify(json);
  }
 }
 module.exports = TavilySearchResults;
--- a/api/app/clients/tools/structured/TraversaalSearch.js
+++ b/api/app/clients/tools/structured/TraversaalSearch.js
@ -1,88 +0,0 @@
 const { z } = require('zod');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { getEnvironmentVariable } = require('@langchain/core/utils/env');
 /**
 * Tool for the Traversaal AI search API, Ares.
 */
 class TraversaalSearch extends Tool {
  static lc_name() {
    return 'TraversaalSearch';
  }
  constructor(fields) {
    super(fields);
    this.name = 'traversaal_search';
    this.description = `An AI search engine optimized for comprehensive, accurate, and trusted results.
    Useful for when you need to answer questions about current events. Input should be a search query.`;
    this.description_for_model =
      '\'Please create a specific sentence for the AI to understand and use as a query to search the web based on the user\'s request. For example, "Find information about the highest mountains in the world." or "Show me the latest news articles about climate change and its impact on polar ice caps."\'';
    this.schema = z.object({
      query: z
        .string()
        .describe(
          "A properly written sentence to be interpreted by an AI to search the web according to the user's request.",
        ),
    });
    this.apiKey = fields?.TRAVERSAAL_API_KEY ?? this.getApiKey();
  }
  getApiKey() {
    const apiKey = getEnvironmentVariable('TRAVERSAAL_API_KEY');
    if (!apiKey && this.override) {
      throw new Error(
        'No Traversaal API key found. Either set an environment variable named "TRAVERSAAL_API_KEY" or pass an API key as "apiKey".',
      );
    }
    return apiKey;
  }
  async _call({ query }, _runManager) {
    const body = {
      query: [query],
    };
    try {
      const response = await fetch('https://api-ares.traversaal.ai/live/predict', {
        method: 'POST',
        headers: {
          'content-type': 'application/json',
          'x-api-key': this.apiKey,
        },
        body: JSON.stringify({ ...body }),
      });
      const json = await response.json();
      if (!response.ok) {
        throw new Error(
          `Request failed with status code ${response.status}: ${json.error ?? json.message}`,
        );
      }
      if (!json.data) {
        throw new Error('Could not parse Traversaal API results. Please try again.');
      }
      const baseText = json.data?.response_text ?? '';
      const sources = json.data?.web_url;
      const noResponse = 'No response found in Traversaal API results';
      if (!baseText && !sources) {
        return noResponse;
      }
      const sourcesText = sources?.length ? '\n\nSources:\n - ' + sources.join('\n - ') : '';
      const result = baseText + sourcesText;
      if (!result) {
        return noResponse;
      }
      return result;
    } catch (error) {
      logger.error('Traversaal API request failed', error);
      return `Traversaal API request failed: ${error.message}`;
    }
  }
 }
 module.exports = TraversaalSearch;
--- a/api/app/clients/tools/structured/Wolfram.js
+++ b/api/app/clients/tools/structured/Wolfram.js
@ -1,95 +0,0 @@
 /* eslint-disable no-useless-escape */
 const { z } = require('zod');
 const axios = require('axios');
 const { Tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 class WolframAlphaAPI extends Tool {
  constructor(fields) {
    super();
    /* Used to initialize the Tool without necessary variables. */
    this.override = fields.override ?? false;
    this.name = 'wolfram';
    this.apiKey = fields.WOLFRAM_APP_ID || this.getAppId();
    this.description_for_model = `// Access dynamic computation and curated data from WolframAlpha and Wolfram Cloud.
    // General guidelines:
    // - Use only getWolframAlphaResults or getWolframCloudResults endpoints.
    // - Prefer getWolframAlphaResults unless Wolfram Language code should be evaluated.
    // - Use getWolframAlphaResults for natural-language queries in English; translate non-English queries before sending, then respond in the original language.
    // - Use getWolframCloudResults for problems solvable with Wolfram Language code.
    // - Suggest only Wolfram Language for external computation.
    // - Inform users if information is not from Wolfram endpoints.
    // - Display image URLs with Image Markdown syntax: ![caption](https://imageURL/.../MSPStoreType=image/png&s=18). You must prefix the caption brackets with "!".
    // - ALWAYS use this exponent notation: \`6*10^14\`, NEVER \`6e14\`.
    // - ALWAYS use {{"input": query}} structure for queries to Wolfram endpoints; \`query\` must ONLY be a single-line string.
    // - ALWAYS use proper Markdown formatting for all math, scientific, and chemical formulas, symbols, etc.:  '$$\n[expression]\n$$' for standalone cases and '\( [expression] \)' when inline.
    // - Format inline Wolfram Language code with Markdown code formatting.
    // - Never mention your knowledge cutoff date; Wolfram may return more recent data. getWolframAlphaResults guidelines:
    // - Understands natural language queries about entities in chemistry, physics, geography, history, art, astronomy, and more.
    // - Performs mathematical calculations, date and unit conversions, formula solving, etc.
    // - Convert inputs to simplified keyword queries whenever possible (e.g. convert "how many people live in France" to "France population").
    // - Use ONLY single-letter variable names, with or without integer subscript (e.g., n, n1, n_1).
    // - Use named physical constants (e.g., 'speed of light') without numerical substitution.
    // - Include a space between compound units (e.g., "Ω m" for "ohm*meter").
    // - To solve for a variable in an equation with units, consider solving a corresponding equation without units; exclude counting units (e.g., books), include genuine units (e.g., kg).
    // - If data for multiple properties is needed, make separate calls for each property.
    // - If a Wolfram Alpha result is not relevant to the query:
    // -- If Wolfram provides multiple 'Assumptions' for a query, choose the more relevant one(s) without explaining the initial result. If you are unsure, ask the user to choose.
    // -- Re-send the exact same 'input' with NO modifications, and add the 'assumption' parameter, formatted as a list, with the relevant values.
    // -- ONLY simplify or rephrase the initial query if a more relevant 'Assumption' or other input suggestions are not provided.
    // -- Do not explain each step unless user input is needed. Proceed directly to making a better API call based on the available assumptions.`;
    this.description = `WolframAlpha offers computation, math, curated knowledge, and real-time data. It handles natural language queries and performs complex calculations.
    Follow the guidelines to get the best results.`;
    this.schema = z.object({
      input: z.string().describe('Natural language query to WolframAlpha following the guidelines'),
    });
  }
  async fetchRawText(url) {
    try {
      const response = await axios.get(url, { responseType: 'text' });
      return response.data;
    } catch (error) {
      logger.error('[WolframAlphaAPI] Error fetching raw text:', error);
      throw error;
    }
  }
  getAppId() {
    const appId = process.env.WOLFRAM_APP_ID || '';
    if (!appId && !this.override) {
      throw new Error('Missing WOLFRAM_APP_ID environment variable.');
    }
    return appId;
  }
  createWolframAlphaURL(query) {
    // Clean up query
    const formattedQuery = query.replaceAll(/`/g, '').replaceAll(/\n/g, ' ');
    const baseURL = 'https://www.wolframalpha.com/api/v1/llm-api';
    const encodedQuery = encodeURIComponent(formattedQuery);
    const appId = this.apiKey || this.getAppId();
    const url = `${baseURL}?input=${encodedQuery}&appid=${appId}`;
    return url;
  }
  async _call(data) {
    try {
      const { input } = data;
      const url = this.createWolframAlphaURL(input);
      const response = await this.fetchRawText(url);
      return response;
    } catch (error) {
      if (error.response && error.response.data) {
        logger.error('[WolframAlphaAPI] Error data:', error);
        return error.response.data;
      } else {
        logger.error('[WolframAlphaAPI] Error querying Wolfram Alpha', error);
        return 'There was an error querying Wolfram Alpha.';
      }
    }
  }
 }
 module.exports = WolframAlphaAPI;
--- a/api/app/clients/tools/structured/YouTube.js
+++ b/api/app/clients/tools/structured/YouTube.js
@ -1,137 +0,0 @@
 const { ytToolkit } = require('@librechat/api');
 const { tool } = require('@langchain/core/tools');
 const { youtube } = require('@googleapis/youtube');
 const { logger } = require('@librechat/data-schemas');
 const { YoutubeTranscript } = require('youtube-transcript');
 const { getApiKey } = require('./credentials');
 function extractVideoId(url) {
  const rawIdRegex = /^[a-zA-Z0-9_-]{11}$/;
  if (rawIdRegex.test(url)) {
    return url;
  }
  const regex = new RegExp(
    '(?:youtu\\.be/|youtube(?:\\.com)?/(?:' +
      '(?:watch\\?v=)|(?:embed/)|(?:shorts/)|(?:live/)|(?:v/)|(?:/))?)' +
      '([a-zA-Z0-9_-]{11})(?:\\S+)?$',
  );
  const match = url.match(regex);
  return match ? match[1] : null;
 }
 function parseTranscript(transcriptResponse) {
  if (!Array.isArray(transcriptResponse)) {
    return '';
  }
  return transcriptResponse
    .map((entry) => entry.text.trim())
    .filter((text) => text)
    .join(' ')
    .replaceAll('&amp;#39;', "'");
 }
 function createYouTubeTools(fields = {}) {
  const envVar = 'YOUTUBE_API_KEY';
  const override = fields.override ?? false;
  const apiKey = fields.apiKey ?? fields[envVar] ?? getApiKey(envVar, override);
  const youtubeClient = youtube({
    version: 'v3',
    auth: apiKey,
  });
  const searchTool = tool(async ({ query, maxResults = 5 }) => {
    const response = await youtubeClient.search.list({
      part: 'snippet',
      q: query,
      type: 'video',
      maxResults: maxResults || 5,
    });
    const result = response.data.items.map((item) => ({
      title: item.snippet.title,
      description: item.snippet.description,
      url: `https://www.youtube.com/watch?v=${item.id.videoId}`,
    }));
    return JSON.stringify(result, null, 2);
  }, ytToolkit.youtube_search);
  const infoTool = tool(async ({ url }) => {
    const videoId = extractVideoId(url);
    if (!videoId) {
      throw new Error('Invalid YouTube URL or video ID');
    }
    const response = await youtubeClient.videos.list({
      part: 'snippet,statistics',
      id: videoId,
    });
    if (!response.data.items?.length) {
      throw new Error('Video not found');
    }
    const video = response.data.items[0];
    const result = {
      title: video.snippet.title,
      description: video.snippet.description,
      views: video.statistics.viewCount,
      likes: video.statistics.likeCount,
      comments: video.statistics.commentCount,
    };
    return JSON.stringify(result, null, 2);
  }, ytToolkit.youtube_info);
  const commentsTool = tool(async ({ url, maxResults = 10 }) => {
    const videoId = extractVideoId(url);
    if (!videoId) {
      throw new Error('Invalid YouTube URL or video ID');
    }
    const response = await youtubeClient.commentThreads.list({
      part: 'snippet',
      videoId,
      maxResults: maxResults || 10,
    });
    const result = response.data.items.map((item) => ({
      author: item.snippet.topLevelComment.snippet.authorDisplayName,
      text: item.snippet.topLevelComment.snippet.textDisplay,
      likes: item.snippet.topLevelComment.snippet.likeCount,
    }));
    return JSON.stringify(result, null, 2);
  }, ytToolkit.youtube_comments);
  const transcriptTool = tool(async ({ url }) => {
    const videoId = extractVideoId(url);
    if (!videoId) {
      throw new Error('Invalid YouTube URL or video ID');
    }
    try {
      try {
        const transcript = await YoutubeTranscript.fetchTranscript(videoId, { lang: 'en' });
        return parseTranscript(transcript);
      } catch (e) {
        logger.error(e);
      }
      try {
        const transcript = await YoutubeTranscript.fetchTranscript(videoId, { lang: 'de' });
        return parseTranscript(transcript);
      } catch (e) {
        logger.error(e);
      }
      const transcript = await YoutubeTranscript.fetchTranscript(videoId);
      return parseTranscript(transcript);
    } catch (error) {
      throw new Error(`Failed to fetch transcript: ${error.message}`);
    }
  }, ytToolkit.youtube_transcript);
  return [searchTool, infoTool, commentsTool, transcriptTool];
 }
 module.exports = createYouTubeTools;
--- a/api/app/clients/tools/structured/credentials.js
+++ b/api/app/clients/tools/structured/credentials.js
@ -1,13 +0,0 @@
 const { getEnvironmentVariable } = require('@langchain/core/utils/env');
 function getApiKey(envVar, override) {
  const key = getEnvironmentVariable(envVar);
  if (!key && !override) {
    throw new Error(`Missing ${envVar} environment variable.`);
  }
  return key;
 }
 module.exports = {
  getApiKey,
 };
--- a/api/app/clients/tools/structured/specs/DALLE3-proxy.spec.js
+++ b/api/app/clients/tools/structured/specs/DALLE3-proxy.spec.js
@ -1,60 +0,0 @@
 const DALLE3 = require('../DALLE3');
 const { ProxyAgent } = require('undici');
 jest.mock('tiktoken');
 const processFileURL = jest.fn();
 describe('DALLE3 Proxy Configuration', () => {
  let originalEnv;
  beforeAll(() => {
    originalEnv = { ...process.env };
  });
  beforeEach(() => {
    jest.resetModules();
    process.env = { ...originalEnv };
  });
  afterEach(() => {
    process.env = originalEnv;
  });
  it('should configure ProxyAgent in fetchOptions.dispatcher when PROXY env is set', () => {
    // Set proxy environment variable
    process.env.PROXY = 'http://proxy.example.com:8080';
    process.env.DALLE_API_KEY = 'test-api-key';
    // Create instance
    const dalleWithProxy = new DALLE3({ processFileURL });
    // Check that the openai client exists
    expect(dalleWithProxy.openai).toBeDefined();
    // Check that _options exists and has fetchOptions with a dispatcher
    expect(dalleWithProxy.openai._options).toBeDefined();
    expect(dalleWithProxy.openai._options.fetchOptions).toBeDefined();
    expect(dalleWithProxy.openai._options.fetchOptions.dispatcher).toBeDefined();
    expect(dalleWithProxy.openai._options.fetchOptions.dispatcher).toBeInstanceOf(ProxyAgent);
  });
  it('should not configure ProxyAgent when PROXY env is not set', () => {
    // Ensure PROXY is not set
    delete process.env.PROXY;
    process.env.DALLE_API_KEY = 'test-api-key';
    // Create instance
    const dalleWithoutProxy = new DALLE3({ processFileURL });
    // Check that the openai client exists
    expect(dalleWithoutProxy.openai).toBeDefined();
    // Check that _options exists but fetchOptions either doesn't exist or doesn't have a dispatcher
    expect(dalleWithoutProxy.openai._options).toBeDefined();
    // fetchOptions should either not exist or not have a dispatcher
    if (dalleWithoutProxy.openai._options.fetchOptions) {
      expect(dalleWithoutProxy.openai._options.fetchOptions.dispatcher).toBeUndefined();
    }
  });
 });
--- a/api/app/clients/tools/structured/specs/DALLE3.spec.js
+++ b/api/app/clients/tools/structured/specs/DALLE3.spec.js
@ -1,216 +0,0 @@
 const OpenAI = require('openai');
 const { logger } = require('@librechat/data-schemas');
 const DALLE3 = require('../DALLE3');
 jest.mock('openai');
 jest.mock('@librechat/data-schemas', () => {
  return {
    logger: {
      info: jest.fn(),
      warn: jest.fn(),
      debug: jest.fn(),
      error: jest.fn(),
    },
  };
 });
 jest.mock('tiktoken', () => {
  return {
    encoding_for_model: jest.fn().mockReturnValue({
      encode: jest.fn(),
      decode: jest.fn(),
    }),
  };
 });
 const processFileURL = jest.fn();
 const generate = jest.fn();
 OpenAI.mockImplementation(() => ({
  images: {
    generate,
  },
 }));
 jest.mock('fs', () => {
  return {
    existsSync: jest.fn(),
    mkdirSync: jest.fn(),
    promises: {
      writeFile: jest.fn(),
      readFile: jest.fn(),
      unlink: jest.fn(),
    },
  };
 });
 jest.mock('path', () => {
  return {
    resolve: jest.fn(),
    join: jest.fn(),
    relative: jest.fn(),
    extname: jest.fn().mockImplementation((filename) => {
      return filename.slice(filename.lastIndexOf('.'));
    }),
  };
 });
 describe('DALLE3', () => {
  let originalEnv;
  let dalle; // Keep this declaration if you need to use dalle in other tests
  const mockApiKey = 'mock_api_key';
  beforeAll(() => {
    // Save the original process.env
    originalEnv = { ...process.env };
  });
  beforeEach(() => {
    // Reset the process.env before each test
    jest.resetModules();
    process.env = { ...originalEnv, DALLE_API_KEY: mockApiKey };
    // Instantiate DALLE3 for tests that do not depend on DALLE3_SYSTEM_PROMPT
    dalle = new DALLE3({ processFileURL });
  });
  afterEach(() => {
    jest.clearAllMocks();
    // Restore the original process.env after each test
    process.env = originalEnv;
  });
  it('should throw an error if all potential API keys are missing', () => {
    delete process.env.DALLE3_API_KEY;
    delete process.env.DALLE_API_KEY;
    expect(() => new DALLE3()).toThrow('Missing DALLE_API_KEY environment variable.');
  });
  it('should replace unwanted characters in input string', () => {
    const input = 'This is a test\nstring with "quotes" and new lines.';
    const expectedOutput = 'This is a test string with quotes and new lines.';
    expect(dalle.replaceUnwantedChars(input)).toBe(expectedOutput);
  });
  it('should generate markdown image URL correctly', () => {
    const imageName = 'test.png';
    const markdownImage = dalle.wrapInMarkdown(imageName);
    expect(markdownImage).toBe('![generated image](test.png)');
  });
  it('should call OpenAI API with correct parameters', async () => {
    const mockData = {
      prompt: 'A test prompt',
      quality: 'standard',
      size: '1024x1024',
      style: 'vivid',
    };
    const mockResponse = {
      data: [
        {
          url: 'http://example.com/img-test.png',
        },
      ],
    };
    generate.mockResolvedValue(mockResponse);
    processFileURL.mockResolvedValue({
      filepath: 'http://example.com/img-test.png',
    });
    const result = await dalle._call(mockData);
    expect(generate).toHaveBeenCalledWith({
      model: 'dall-e-3',
      quality: mockData.quality,
      style: mockData.style,
      size: mockData.size,
      prompt: mockData.prompt,
      n: 1,
    });
    expect(result).toContain('![generated image]');
  });
  it('should use the system prompt if provided', () => {
    process.env.DALLE3_SYSTEM_PROMPT = 'System prompt for testing';
    jest.resetModules(); // This will ensure the module is fresh and will read the new env var
    const DALLE3 = require('../DALLE3'); // Re-require after setting the env var
    const dalleWithSystemPrompt = new DALLE3();
    expect(dalleWithSystemPrompt.description_for_model).toBe('System prompt for testing');
  });
  it('should not use the system prompt if not provided', async () => {
    delete process.env.DALLE3_SYSTEM_PROMPT;
    const dalleWithoutSystemPrompt = new DALLE3();
    expect(dalleWithoutSystemPrompt.description_for_model).not.toBe('System prompt for testing');
  });
  it('should throw an error if prompt is missing', async () => {
    const mockData = {
      quality: 'standard',
      size: '1024x1024',
      style: 'vivid',
    };
    await expect(dalle._call(mockData)).rejects.toThrow('Missing required field: prompt');
  });
  it('should log appropriate debug values', async () => {
    const mockData = {
      prompt: 'A test prompt',
    };
    const mockResponse = {
      data: [
        {
          url: 'http://example.com/invalid-url',
        },
      ],
    };
    generate.mockResolvedValue(mockResponse);
    await dalle._call(mockData);
    expect(logger.debug).toHaveBeenCalledWith('[DALL-E-3]', {
      data: { url: 'http://example.com/invalid-url' },
      theImageUrl: 'http://example.com/invalid-url',
      extension: expect.any(String),
      imageBasename: expect.any(String),
      imageExt: expect.any(String),
      imageName: expect.any(String),
    });
  });
  it('should log an error and return the image URL if there is an error saving the image', async () => {
    const mockData = {
      prompt: 'A test prompt',
    };
    const mockResponse = {
      data: [
        {
          url: 'http://example.com/img-test.png',
        },
      ],
    };
    const error = new Error('Error while saving the image');
    generate.mockResolvedValue(mockResponse);
    processFileURL.mockRejectedValue(error);
    const result = await dalle._call(mockData);
    expect(logger.error).toHaveBeenCalledWith('Error while saving the image:', error);
    expect(result).toBe('Failed to save the image locally. Error while saving the image');
  });
  it('should handle error when saving image to Firebase Storage fails', async () => {
    const mockData = {
      prompt: 'A test prompt',
    };
    const mockImageUrl = 'http://example.com/img-test.png';
    const mockResponse = { data: [{ url: mockImageUrl }] };
    const error = new Error('Error while saving to Firebase');
    generate.mockResolvedValue(mockResponse);
    processFileURL.mockRejectedValue(error);
    const result = await dalle._call(mockData);
    expect(logger.error).toHaveBeenCalledWith('Error while saving the image:', error);
    expect(result).toContain('Failed to save the image');
  });
 });
--- a/api/app/clients/tools/structured/specs/GoogleSearch.spec.js
+++ b/api/app/clients/tools/structured/specs/GoogleSearch.spec.js
@ -1,50 +0,0 @@
 const GoogleSearch = require('../GoogleSearch');
 jest.mock('node-fetch');
 jest.mock('@langchain/core/utils/env');
 describe('GoogleSearch', () => {
  let originalEnv;
  const mockApiKey = 'mock_api';
  const mockSearchEngineId = 'mock_search_engine_id';
  beforeAll(() => {
    originalEnv = { ...process.env };
  });
  beforeEach(() => {
    jest.resetModules();
    process.env = {
      ...originalEnv,
      GOOGLE_SEARCH_API_KEY: mockApiKey,
      GOOGLE_CSE_ID: mockSearchEngineId,
    };
  });
  afterEach(() => {
    jest.clearAllMocks();
    process.env = originalEnv;
  });
  it('should use mockApiKey and mockSearchEngineId when environment variables are not set', () => {
    const instance = new GoogleSearch({
      GOOGLE_SEARCH_API_KEY: mockApiKey,
      GOOGLE_CSE_ID: mockSearchEngineId,
    });
    expect(instance.apiKey).toBe(mockApiKey);
    expect(instance.searchEngineId).toBe(mockSearchEngineId);
  });
  it('should throw an error if GOOGLE_SEARCH_API_KEY or GOOGLE_CSE_ID is missing', () => {
    delete process.env.GOOGLE_SEARCH_API_KEY;
    expect(() => new GoogleSearch()).toThrow(
      'Missing GOOGLE_SEARCH_API_KEY or GOOGLE_CSE_ID environment variable.',
    );
    process.env.GOOGLE_SEARCH_API_KEY = mockApiKey;
    delete process.env.GOOGLE_CSE_ID;
    expect(() => new GoogleSearch()).toThrow(
      'Missing GOOGLE_SEARCH_API_KEY or GOOGLE_CSE_ID environment variable.',
    );
  });
 });
--- a/api/app/clients/tools/structured/specs/TavilySearchResults.spec.js
+++ b/api/app/clients/tools/structured/specs/TavilySearchResults.spec.js
@ -1,84 +0,0 @@
 const { fetch, ProxyAgent } = require('undici');
 const TavilySearchResults = require('../TavilySearchResults');
 jest.mock('undici');
 jest.mock('@langchain/core/utils/env');
 describe('TavilySearchResults', () => {
  let originalEnv;
  const mockApiKey = 'mock_api_key';
  beforeAll(() => {
    originalEnv = { ...process.env };
  });
  beforeEach(() => {
    jest.resetModules();
    jest.clearAllMocks();
    process.env = {
      ...originalEnv,
      TAVILY_API_KEY: mockApiKey,
    };
  });
  afterEach(() => {
    process.env = originalEnv;
  });
  it('should throw an error if TAVILY_API_KEY is missing', () => {
    delete process.env.TAVILY_API_KEY;
    expect(() => new TavilySearchResults()).toThrow('Missing TAVILY_API_KEY environment variable.');
  });
  it('should use mockApiKey when TAVILY_API_KEY is not set in the environment', () => {
    const instance = new TavilySearchResults({
      TAVILY_API_KEY: mockApiKey,
    });
    expect(instance.apiKey).toBe(mockApiKey);
  });
  describe('proxy support', () => {
    const mockResponse = {
      ok: true,
      json: jest.fn().mockResolvedValue({ results: [] }),
    };
    beforeEach(() => {
      fetch.mockResolvedValue(mockResponse);
    });
    it('should use ProxyAgent when PROXY env var is set', async () => {
      const proxyUrl = 'http://proxy.example.com:8080';
      process.env.PROXY = proxyUrl;
      const mockProxyAgent = { type: 'proxy-agent' };
      ProxyAgent.mockImplementation(() => mockProxyAgent);
      const instance = new TavilySearchResults({ TAVILY_API_KEY: mockApiKey });
      await instance._call({ query: 'test query' });
      expect(ProxyAgent).toHaveBeenCalledWith(proxyUrl);
      expect(fetch).toHaveBeenCalledWith(
        'https://api.tavily.com/search',
        expect.objectContaining({
          dispatcher: mockProxyAgent,
        }),
      );
    });
    it('should not use ProxyAgent when PROXY env var is not set', async () => {
      delete process.env.PROXY;
      const instance = new TavilySearchResults({ TAVILY_API_KEY: mockApiKey });
      await instance._call({ query: 'test query' });
      expect(ProxyAgent).not.toHaveBeenCalled();
      expect(fetch).toHaveBeenCalledWith(
        'https://api.tavily.com/search',
        expect.not.objectContaining({
          dispatcher: expect.anything(),
        }),
      );
    });
  });
 });
--- a/api/app/clients/tools/structured/specs/openWeather.integration.test.js
+++ b/api/app/clients/tools/structured/specs/openWeather.integration.test.js
@ -1,224 +0,0 @@
 // __tests__/openWeather.integration.test.js
 const OpenWeather = require('../OpenWeather');
 describe('OpenWeather Tool (Integration Test)', () => {
  let tool;
  beforeAll(() => {
    tool = new OpenWeather({ override: true });
    console.log('API Key present:', !!process.env.OPENWEATHER_API_KEY);
  });
  test('current_forecast with a real API key returns current weather', async () => {
    // Check if API key is available
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      const result = await tool.call({
        action: 'current_forecast',
        city: 'London',
        units: 'Celsius',
      });
      console.log('Raw API response:', result);
      const parsed = JSON.parse(result);
      expect(parsed).toHaveProperty('current');
      expect(typeof parsed.current.temp).toBe('number');
    } catch (error) {
      console.error('Test failed with error:', error);
      throw error;
    }
  });
  test('timestamp action with real API key returns historical data', async () => {
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      // Use a date from yesterday to ensure data availability
      const yesterday = new Date();
      yesterday.setDate(yesterday.getDate() - 1);
      const dateStr = yesterday.toISOString().split('T')[0];
      const result = await tool.call({
        action: 'timestamp',
        city: 'London',
        date: dateStr,
        units: 'Celsius',
      });
      console.log('Timestamp API response:', result);
      const parsed = JSON.parse(result);
      expect(parsed).toHaveProperty('data');
      expect(Array.isArray(parsed.data)).toBe(true);
      expect(parsed.data[0]).toHaveProperty('temp');
    } catch (error) {
      console.error('Timestamp test failed with error:', error);
      throw error;
    }
  });
  test('daily_aggregation action with real API key returns aggregated data', async () => {
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      // Use yesterday's date for aggregation
      const yesterday = new Date();
      yesterday.setDate(yesterday.getDate() - 1);
      const dateStr = yesterday.toISOString().split('T')[0];
      const result = await tool.call({
        action: 'daily_aggregation',
        city: 'London',
        date: dateStr,
        units: 'Celsius',
      });
      console.log('Daily aggregation API response:', result);
      const parsed = JSON.parse(result);
      expect(parsed).toHaveProperty('temperature');
      expect(parsed.temperature).toHaveProperty('morning');
      expect(parsed.temperature).toHaveProperty('afternoon');
      expect(parsed.temperature).toHaveProperty('evening');
    } catch (error) {
      console.error('Daily aggregation test failed with error:', error);
      throw error;
    }
  });
  test('overview action with real API key returns weather summary', async () => {
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      const result = await tool.call({
        action: 'overview',
        city: 'London',
        units: 'Celsius',
      });
      console.log('Overview API response:', result);
      const parsed = JSON.parse(result);
      expect(parsed).toHaveProperty('weather_overview');
      expect(typeof parsed.weather_overview).toBe('string');
      expect(parsed.weather_overview.length).toBeGreaterThan(0);
      expect(parsed).toHaveProperty('date');
      expect(parsed).toHaveProperty('units');
      expect(parsed.units).toBe('metric');
    } catch (error) {
      console.error('Overview test failed with error:', error);
      throw error;
    }
  });
  test('different temperature units return correct values', async () => {
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      // Test Celsius
      let result = await tool.call({
        action: 'current_forecast',
        city: 'London',
        units: 'Celsius',
      });
      let parsed = JSON.parse(result);
      const celsiusTemp = parsed.current.temp;
      // Test Kelvin
      result = await tool.call({
        action: 'current_forecast',
        city: 'London',
        units: 'Kelvin',
      });
      parsed = JSON.parse(result);
      const kelvinTemp = parsed.current.temp;
      // Test Fahrenheit
      result = await tool.call({
        action: 'current_forecast',
        city: 'London',
        units: 'Fahrenheit',
      });
      parsed = JSON.parse(result);
      const fahrenheitTemp = parsed.current.temp;
      // Verify temperature conversions are roughly correct
      // K = C + 273.15
      // F = (C * 9/5) + 32
      const celsiusToKelvin = Math.round(celsiusTemp + 273.15);
      const celsiusToFahrenheit = Math.round((celsiusTemp * 9) / 5 + 32);
      console.log('Temperature comparisons:', {
        celsius: celsiusTemp,
        kelvin: kelvinTemp,
        fahrenheit: fahrenheitTemp,
        calculatedKelvin: celsiusToKelvin,
        calculatedFahrenheit: celsiusToFahrenheit,
      });
      // Allow for some rounding differences
      expect(Math.abs(kelvinTemp - celsiusToKelvin)).toBeLessThanOrEqual(1);
      expect(Math.abs(fahrenheitTemp - celsiusToFahrenheit)).toBeLessThanOrEqual(1);
    } catch (error) {
      console.error('Temperature units test failed with error:', error);
      throw error;
    }
  });
  test('language parameter returns localized data', async () => {
    if (!process.env.OPENWEATHER_API_KEY) {
      console.warn('Skipping real API test, no OPENWEATHER_API_KEY found.');
      return;
    }
    try {
      // Test with English
      let result = await tool.call({
        action: 'current_forecast',
        city: 'Paris',
        units: 'Celsius',
        lang: 'en',
      });
      let parsed = JSON.parse(result);
      const englishDescription = parsed.current.weather[0].description;
      // Test with French
      result = await tool.call({
        action: 'current_forecast',
        city: 'Paris',
        units: 'Celsius',
        lang: 'fr',
      });
      parsed = JSON.parse(result);
      const frenchDescription = parsed.current.weather[0].description;
      console.log('Language comparison:', {
        english: englishDescription,
        french: frenchDescription,
      });
      // Verify descriptions are different (indicating translation worked)
      expect(englishDescription).not.toBe(frenchDescription);
    } catch (error) {
      console.error('Language test failed with error:', error);
      throw error;
    }
  });
 });
--- a/api/app/clients/tools/structured/specs/openweather.test.js
+++ b/api/app/clients/tools/structured/specs/openweather.test.js
@ -1,358 +0,0 @@
 // __tests__/openweather.test.js
 const OpenWeather = require('../OpenWeather');
 const fetch = require('node-fetch');
 // Mock environment variable
 process.env.OPENWEATHER_API_KEY = 'test-api-key';
 // Mock the fetch function globally
 jest.mock('node-fetch', () => jest.fn());
 describe('OpenWeather Tool', () => {
  let tool;
  beforeAll(() => {
    tool = new OpenWeather();
  });
  beforeEach(() => {
    fetch.mockReset();
  });
  test('action=help returns help instructions', async () => {
    const result = await tool.call({
      action: 'help',
    });
    expect(typeof result).toBe('string');
    const parsed = JSON.parse(result);
    expect(parsed.title).toBe('OpenWeather One Call API 3.0 Help');
  });
  test('current_forecast with a city and successful geocoding + forecast', async () => {
    // Mock geocoding response
    fetch.mockImplementationOnce((url) => {
      if (url.includes('geo/1.0/direct')) {
        return Promise.resolve({
          ok: true,
          json: async () => [{ lat: 35.9606, lon: -83.9207 }],
        });
      }
      return Promise.reject('Unexpected fetch call for geocoding');
    });
    // Mock forecast response
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => ({
          current: { temp: 293.15, feels_like: 295.15 },
          daily: [{ temp: { day: 293.15, night: 283.15 } }],
        }),
      }),
    );
    const result = await tool.call({
      action: 'current_forecast',
      city: 'Knoxville, Tennessee',
      units: 'Kelvin',
    });
    const parsed = JSON.parse(result);
    expect(parsed.current.temp).toBe(293);
    expect(parsed.current.feels_like).toBe(295);
    expect(parsed.daily[0].temp.day).toBe(293);
    expect(parsed.daily[0].temp.night).toBe(283);
  });
  test('timestamp action with valid date returns mocked historical data', async () => {
    // Mock geocoding response
    fetch.mockImplementationOnce((url) => {
      if (url.includes('geo/1.0/direct')) {
        return Promise.resolve({
          ok: true,
          json: async () => [{ lat: 35.9606, lon: -83.9207 }],
        });
      }
      return Promise.reject('Unexpected fetch call for geocoding');
    });
    // Mock historical weather response
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => ({
          data: [
            {
              dt: 1583280000,
              temp: 283.15,
              feels_like: 280.15,
              humidity: 75,
              weather: [{ description: 'clear sky' }],
            },
          ],
        }),
      }),
    );
    const result = await tool.call({
      action: 'timestamp',
      city: 'Knoxville, Tennessee',
      date: '2020-03-04',
      units: 'Kelvin',
    });
    const parsed = JSON.parse(result);
    expect(parsed.data[0].temp).toBe(283);
    expect(parsed.data[0].feels_like).toBe(280);
  });
  test('daily_aggregation action returns aggregated weather data', async () => {
    // Mock geocoding response
    fetch.mockImplementationOnce((url) => {
      if (url.includes('geo/1.0/direct')) {
        return Promise.resolve({
          ok: true,
          json: async () => [{ lat: 35.9606, lon: -83.9207 }],
        });
      }
      return Promise.reject('Unexpected fetch call for geocoding');
    });
    // Mock daily aggregation response
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => ({
          date: '2020-03-04',
          temperature: {
            morning: 283.15,
            afternoon: 293.15,
            evening: 288.15,
          },
          humidity: {
            morning: 75,
            afternoon: 60,
            evening: 70,
          },
        }),
      }),
    );
    const result = await tool.call({
      action: 'daily_aggregation',
      city: 'Knoxville, Tennessee',
      date: '2020-03-04',
      units: 'Kelvin',
    });
    const parsed = JSON.parse(result);
    expect(parsed.temperature.morning).toBe(283);
    expect(parsed.temperature.afternoon).toBe(293);
    expect(parsed.temperature.evening).toBe(288);
  });
  test('overview action returns weather summary', async () => {
    // Mock geocoding response
    fetch.mockImplementationOnce((url) => {
      if (url.includes('geo/1.0/direct')) {
        return Promise.resolve({
          ok: true,
          json: async () => [{ lat: 35.9606, lon: -83.9207 }],
        });
      }
      return Promise.reject('Unexpected fetch call for geocoding');
    });
    // Mock overview response
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => ({
          date: '2024-01-07',
          lat: 35.9606,
          lon: -83.9207,
          tz: '+00:00',
          units: 'metric',
          weather_overview:
            'Currently, the temperature is 2°C with a real feel of -2°C. The sky is clear with moderate wind.',
        }),
      }),
    );
    const result = await tool.call({
      action: 'overview',
      city: 'Knoxville, Tennessee',
      units: 'Celsius',
    });
    const parsed = JSON.parse(result);
    expect(parsed).toHaveProperty('weather_overview');
    expect(typeof parsed.weather_overview).toBe('string');
    expect(parsed.weather_overview.length).toBeGreaterThan(0);
    expect(parsed).toHaveProperty('date');
    expect(parsed).toHaveProperty('units');
    expect(parsed.units).toBe('metric');
  });
  test('temperature units are correctly converted', async () => {
    // Mock geocoding response for all three calls
    const geocodingMock = Promise.resolve({
      ok: true,
      json: async () => [{ lat: 35.9606, lon: -83.9207 }],
    });
    // Mock weather response for Kelvin
    const kelvinMock = Promise.resolve({
      ok: true,
      json: async () => ({
        current: { temp: 293.15 },
      }),
    });
    // Mock weather response for Celsius
    const celsiusMock = Promise.resolve({
      ok: true,
      json: async () => ({
        current: { temp: 20 },
      }),
    });
    // Mock weather response for Fahrenheit
    const fahrenheitMock = Promise.resolve({
      ok: true,
      json: async () => ({
        current: { temp: 68 },
      }),
    });
    // Test Kelvin
    fetch.mockImplementationOnce(() => geocodingMock).mockImplementationOnce(() => kelvinMock);
    let result = await tool.call({
      action: 'current_forecast',
      city: 'Knoxville, Tennessee',
      units: 'Kelvin',
    });
    let parsed = JSON.parse(result);
    expect(parsed.current.temp).toBe(293);
    // Test Celsius
    fetch.mockImplementationOnce(() => geocodingMock).mockImplementationOnce(() => celsiusMock);
    result = await tool.call({
      action: 'current_forecast',
      city: 'Knoxville, Tennessee',
      units: 'Celsius',
    });
    parsed = JSON.parse(result);
    expect(parsed.current.temp).toBe(20);
    // Test Fahrenheit
    fetch.mockImplementationOnce(() => geocodingMock).mockImplementationOnce(() => fahrenheitMock);
    result = await tool.call({
      action: 'current_forecast',
      city: 'Knoxville, Tennessee',
      units: 'Fahrenheit',
    });
    parsed = JSON.parse(result);
    expect(parsed.current.temp).toBe(68);
  });
  test('timestamp action without a date returns an error message', async () => {
    const result = await tool.call({
      action: 'timestamp',
      lat: 35.9606,
      lon: -83.9207,
    });
    expect(result).toMatch(
      /Error: For timestamp action, a 'date' in YYYY-MM-DD format is required./,
    );
  });
  test('daily_aggregation action without a date returns an error message', async () => {
    const result = await tool.call({
      action: 'daily_aggregation',
      lat: 35.9606,
      lon: -83.9207,
    });
    expect(result).toMatch(/Error: date \(YYYY-MM-DD\) is required for daily_aggregation action./);
  });
  test('unknown action returns an error due to schema validation', async () => {
    await expect(
      tool.call({
        action: 'unknown_action',
      }),
    ).rejects.toThrow(/Received tool input did not match expected schema/);
  });
  test('geocoding failure returns a descriptive error', async () => {
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => [],
      }),
    );
    const result = await tool.call({
      action: 'current_forecast',
      city: 'NowhereCity',
    });
    expect(result).toMatch(/Error: Could not find coordinates for city: NowhereCity/);
  });
  test('API request failure returns an error', async () => {
    // Mock geocoding success
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: true,
        json: async () => [{ lat: 35.9606, lon: -83.9207 }],
      }),
    );
    // Mock weather request failure
    fetch.mockImplementationOnce(() =>
      Promise.resolve({
        ok: false,
        status: 404,
        json: async () => ({ message: 'Not found' }),
      }),
    );
    const result = await tool.call({
      action: 'current_forecast',
      city: 'Knoxville, Tennessee',
    });
    expect(result).toMatch(/Error: OpenWeather API request failed with status 404: Not found/);
  });
  test('invalid date format returns an error', async () => {
    // Mock geocoding response first
    fetch.mockImplementationOnce((url) => {
      if (url.includes('geo/1.0/direct')) {
        return Promise.resolve({
          ok: true,
          json: async () => [{ lat: 35.9606, lon: -83.9207 }],
        });
      }
      return Promise.reject('Unexpected fetch call for geocoding');
    });
    // Mock timestamp API response
    fetch.mockImplementationOnce((url) => {
      if (url.includes('onecall/timemachine')) {
        throw new Error('Invalid date format. Expected YYYY-MM-DD.');
      }
      return Promise.reject('Unexpected fetch call');
    });
    const result = await tool.call({
      action: 'timestamp',
      city: 'Knoxville, Tennessee',
      date: '03-04-2020', // Wrong format
    });
    expect(result).toMatch(/Error: Invalid date format. Expected YYYY-MM-DD./);
  });
 });
--- a/api/app/clients/tools/util/fileSearch.js
+++ b/api/app/clients/tools/util/fileSearch.js
@ -1,196 +0,0 @@
 const { z } = require('zod');
 const axios = require('axios');
 const { tool } = require('@langchain/core/tools');
 const { logger } = require('@librechat/data-schemas');
 const { generateShortLivedToken } = require('@librechat/api');
 const { Tools, EToolResources } = require('librechat-data-provider');
 const { filterFilesByAgentAccess } = require('~/server/services/Files/permissions');
 const { getFiles } = require('~/models');
 /**
 *
 * @param {Object} options
 * @param {ServerRequest} options.req
 * @param {Agent['tool_resources']} options.tool_resources
 * @param {string} [options.agentId] - The agent ID for file access control
 * @returns {Promise<{
 *   files: Array<{ file_id: string; filename: string }>,
 *   toolContext: string
 * }>}
 */
 const primeFiles = async (options) => {
  const { tool_resources, req, agentId } = options;
  const file_ids = tool_resources?.[EToolResources.file_search]?.file_ids ?? [];
  const agentResourceIds = new Set(file_ids);
  const resourceFiles = tool_resources?.[EToolResources.file_search]?.files ?? [];
  // Get all files first
  const allFiles = (await getFiles({ file_id: { $in: file_ids } }, null, { text: 0 })) ?? [];
  // Filter by access if user and agent are provided
  let dbFiles;
  if (req?.user?.id && agentId) {
    dbFiles = await filterFilesByAgentAccess({
      files: allFiles,
      userId: req.user.id,
      role: req.user.role,
      agentId,
    });
  } else {
    dbFiles = allFiles;
  }
  dbFiles = dbFiles.concat(resourceFiles);
  let toolContext = `- Note: Semantic search is available through the ${Tools.file_search} tool but no files are currently loaded. Request the user to upload documents to search through.`;
  const files = [];
  for (let i = 0; i < dbFiles.length; i++) {
    const file = dbFiles[i];
    if (!file) {
      continue;
    }
    if (i === 0) {
      toolContext = `- Note: Use the ${Tools.file_search} tool to find relevant information within:`;
    }
    toolContext += `\n\t- ${file.filename}${
      agentResourceIds.has(file.file_id) ? '' : ' (just attached by user)'
    }`;
    files.push({
      file_id: file.file_id,
      filename: file.filename,
    });
  }
  return { files, toolContext };
 };
 /**
 *
 * @param {Object} options
 * @param {string} options.userId
 * @param {Array<{ file_id: string; filename: string }>} options.files
 * @param {string} [options.entity_id]
 * @param {boolean} [options.fileCitations=false] - Whether to include citation instructions
 * @returns
 */
 const createFileSearchTool = async ({ userId, files, entity_id, fileCitations = false }) => {
  return tool(
    async ({ query }) => {
      if (files.length === 0) {
        return ['No files to search. Instruct the user to add files for the search.', undefined];
      }
      const jwtToken = generateShortLivedToken(userId);
      if (!jwtToken) {
        return ['There was an error authenticating the file search request.', undefined];
      }
      /**
       * @param {import('librechat-data-provider').TFile} file
       * @returns {{ file_id: string, query: string, k: number, entity_id?: string }}
       */
      const createQueryBody = (file) => {
        const body = {
          file_id: file.file_id,
          query,
          k: 5,
        };
        if (!entity_id) {
          return body;
        }
        body.entity_id = entity_id;
        logger.debug(`[${Tools.file_search}] RAG API /query body`, body);
        return body;
      };
      const queryPromises = files.map((file) =>
        axios
          .post(`${process.env.RAG_API_URL}/query`, createQueryBody(file), {
            headers: {
              Authorization: `Bearer ${jwtToken}`,
              'Content-Type': 'application/json',
            },
          })
          .catch((error) => {
            logger.error('Error encountered in `file_search` while querying file:', error);
            return null;
          }),
      );
      const results = await Promise.all(queryPromises);
      const validResults = results.filter((result) => result !== null);
      if (validResults.length === 0) {
        return ['No results found or errors occurred while searching the files.', undefined];
      }
      const formattedResults = validResults
        .flatMap((result, fileIndex) =>
          result.data.map(([docInfo, distance]) => ({
            filename: docInfo.metadata.source.split('/').pop(),
            content: docInfo.page_content,
            distance,
            file_id: files[fileIndex]?.file_id,
            page: docInfo.metadata.page || null,
          })),
        )
        .sort((a, b) => a.distance - b.distance)
        .slice(0, 10);
      if (formattedResults.length === 0) {
        return [
          'No content found in the files. The files may not have been processed correctly or you may need to refine your query.',
          undefined,
        ];
      }
      const formattedString = formattedResults
        .map(
          (result, index) =>
            `File: ${result.filename}${
              fileCitations ? `\nAnchor: \\ue202turn0file${index} (${result.filename})` : ''
            }\nRelevance: ${(1.0 - result.distance).toFixed(4)}\nContent: ${result.content}\n`,
        )
        .join('\n---\n');
      const sources = formattedResults.map((result) => ({
        type: 'file',
        fileId: result.file_id,
        content: result.content,
        fileName: result.filename,
        relevance: 1.0 - result.distance,
        pages: result.page ? [result.page] : [],
        pageRelevance: result.page ? { [result.page]: 1.0 - result.distance } : {},
      }));
      return [formattedString, { [Tools.file_search]: { sources, fileCitations } }];
    },
    {
      name: Tools.file_search,
      responseFormat: 'content_and_artifact',
      description: `Performs semantic search across attached "${Tools.file_search}" documents using natural language queries. This tool analyzes the content of uploaded files to find relevant information, quotes, and passages that best match your query. Use this to extract specific information or find relevant sections within the available documents.${
        fileCitations
          ? `
 **CITE FILE SEARCH RESULTS:**
 Use the EXACT anchor markers shown below (copy them verbatim) immediately after statements derived from file content. Reference the filename in your text:
 - File citation: "The document.pdf states that... \\ue202turn0file0"  
 - Page reference: "According to report.docx... \\ue202turn0file1"
 - Multi-file: "Multiple sources confirm... \\ue200\\ue202turn0file0\\ue202turn0file1\\ue201"
 **CRITICAL:** Output these escape sequences EXACTLY as shown (e.g., \\ue202turn0file0). Do NOT substitute with other characters like † or similar symbols.
 **ALWAYS mention the filename in your text before the citation marker. NEVER use markdown links or footnotes.**`
          : ''
      }`,
      schema: z.object({
        query: z
          .string()
          .describe(
            "A natural language query to search for relevant information in the files. Be specific and use keywords related to the information you're looking for. The query will be used for semantic similarity matching against the file contents.",
          ),
      }),
    },
  );
 };
 module.exports = { createFileSearchTool, primeFiles };
--- a/api/app/clients/tools/util/handleTools.js
+++ b/api/app/clients/tools/util/handleTools.js
@ -1,491 +0,0 @@
 const { logger } = require('@librechat/data-schemas');
 const {
  EnvVar,
  Calculator,
  createSearchTool,
  createCodeExecutionTool,
 } = require('@librechat/agents');
 const {
  checkAccess,
  createSafeUser,
  mcpToolPattern,
  loadWebSearchAuth,
 } = require('@librechat/api');
 const { getMCPServersRegistry } = require('~/config');
 const {
  Tools,
  Constants,
  Permissions,
  EToolResources,
  PermissionTypes,
  replaceSpecialVars,
 } = require('librechat-data-provider');
 const {
  availableTools,
  manifestToolMap,
  // Basic Tools
  GoogleSearchAPI,
  // Structured Tools
  DALLE3,
  FluxAPI,
  OpenWeather,
  StructuredSD,
  StructuredACS,
  TraversaalSearch,
  StructuredWolfram,
  createYouTubeTools,
  TavilySearchResults,
  createOpenAIImageTools,
 } = require('../');
 const { primeFiles: primeCodeFiles } = require('~/server/services/Files/Code/process');
 const { createFileSearchTool, primeFiles: primeSearchFiles } = require('./fileSearch');
 const { getUserPluginAuthValue } = require('~/server/services/PluginService');
 const { createMCPTool, createMCPTools } = require('~/server/services/MCP');
 const { loadAuthValues } = require('~/server/services/Tools/credentials');
 const { getMCPServerTools } = require('~/server/services/Config');
 const { getRoleByName } = require('~/models/Role');
 /**
 * Validates the availability and authentication of tools for a user based on environment variables or user-specific plugin authentication values.
 * Tools without required authentication or with valid authentication are considered valid.
 *
 * @param {Object} user The user object for whom to validate tool access.
 * @param {Array<string>} tools An array of tool identifiers to validate. Defaults to an empty array.
 * @returns {Promise<Array<string>>} A promise that resolves to an array of valid tool identifiers.
 */
 const validateTools = async (user, tools = []) => {
  try {
    const validToolsSet = new Set(tools);
    const availableToolsToValidate = availableTools.filter((tool) =>
      validToolsSet.has(tool.pluginKey),
    );
    /**
     * Validates the credentials for a given auth field or set of alternate auth fields for a tool.
     * If valid admin or user authentication is found, the function returns early. Otherwise, it removes the tool from the set of valid tools.
     *
     * @param {string} authField The authentication field or fields (separated by "||" for alternates) to validate.
     * @param {string} toolName The identifier of the tool being validated.
     */
    const validateCredentials = async (authField, toolName) => {
      const fields = authField.split('||');
      for (const field of fields) {
        const adminAuth = process.env[field];
        if (adminAuth && adminAuth.length > 0) {
          return;
        }
        let userAuth = null;
        try {
          userAuth = await getUserPluginAuthValue(user, field);
        } catch (err) {
          if (field === fields[fields.length - 1] && !userAuth) {
            throw err;
          }
        }
        if (userAuth && userAuth.length > 0) {
          return;
        }
      }
      validToolsSet.delete(toolName);
    };
    for (const tool of availableToolsToValidate) {
      if (!tool.authConfig || tool.authConfig.length === 0) {
        continue;
      }
      for (const auth of tool.authConfig) {
        await validateCredentials(auth.authField, tool.pluginKey);
      }
    }
    return Array.from(validToolsSet.values());
  } catch (err) {
    logger.error('[validateTools] There was a problem validating tools', err);
    throw new Error(err);
  }
 };
 /** @typedef {typeof import('@langchain/core/tools').Tool} ToolConstructor */
 /** @typedef {import('@langchain/core/tools').Tool} Tool */
 /**
 * Initializes a tool with authentication values for the given user, supporting alternate authentication fields.
 * Authentication fields can have alternates separated by "||", and the first defined variable will be used.
 *
 * @param {string} userId The user ID for which the tool is being loaded.
 * @param {Array<string>} authFields Array of strings representing the authentication fields. Supports alternate fields delimited by "||".
 * @param {ToolConstructor} ToolConstructor The constructor function for the tool to be initialized.
 * @param {Object} options Optional parameters to be passed to the tool constructor alongside authentication values.
 * @returns {() => Promise<Tool>} An Async function that, when called, asynchronously initializes and returns an instance of the tool with authentication.
 */
 const loadToolWithAuth = (userId, authFields, ToolConstructor, options = {}) => {
  return async function () {
    const authValues = await loadAuthValues({ userId, authFields });
    return new ToolConstructor({ ...options, ...authValues, userId });
  };
 };
 /**
 * @param {string} toolKey
 * @returns {Array<string>}
 */
 const getAuthFields = (toolKey) => {
  return manifestToolMap[toolKey]?.authConfig.map((auth) => auth.authField) ?? [];
 };
 /**
 *
 * @param {object} params
 * @param {string} params.user
 * @param {Record<string, Record<string, string>>} [object.userMCPAuthMap]
 * @param {AbortSignal} [object.signal]
 * @param {Pick<Agent, 'id' | 'provider' | 'model'>} [params.agent]
 * @param {string} [params.model]
 * @param {EModelEndpoint} [params.endpoint]
 * @param {LoadToolOptions} [params.options]
 * @param {boolean} [params.useSpecs]
 * @param {Array<string>} params.tools
 * @param {boolean} [params.functions]
 * @param {boolean} [params.returnMap]
 * @param {AppConfig['webSearch']} [params.webSearch]
 * @param {AppConfig['fileStrategy']} [params.fileStrategy]
 * @param {AppConfig['imageOutputType']} [params.imageOutputType]
 * @returns {Promise<{ loadedTools: Tool[], toolContextMap: Object<string, any> } | Record<string,Tool>>}
 */
 const loadTools = async ({
  user,
  agent,
  model,
  signal,
  endpoint,
  userMCPAuthMap,
  tools = [],
  options = {},
  functions = true,
  returnMap = false,
  webSearch,
  fileStrategy,
  imageOutputType,
 }) => {
  const toolConstructors = {
    flux: FluxAPI,
    calculator: Calculator,
    google: GoogleSearchAPI,
    open_weather: OpenWeather,
    wolfram: StructuredWolfram,
    'stable-diffusion': StructuredSD,
    'azure-ai-search': StructuredACS,
    traversaal_search: TraversaalSearch,
    tavily_search_results_json: TavilySearchResults,
  };
  const customConstructors = {
    youtube: async (_toolContextMap) => {
      const authFields = getAuthFields('youtube');
      const authValues = await loadAuthValues({ userId: user, authFields });
      return createYouTubeTools(authValues);
    },
    image_gen_oai: async (toolContextMap) => {
      const authFields = getAuthFields('image_gen_oai');
      const authValues = await loadAuthValues({ userId: user, authFields });
      const imageFiles = options.tool_resources?.[EToolResources.image_edit]?.files ?? [];
      let toolContext = '';
      for (let i = 0; i < imageFiles.length; i++) {
        const file = imageFiles[i];
        if (!file) {
          continue;
        }
        if (i === 0) {
          toolContext =
            'Image files provided in this request (their image IDs listed in order of appearance) available for image editing:';
        }
        toolContext += `\n\t- ${file.file_id}`;
        if (i === imageFiles.length - 1) {
          toolContext += `\n\nInclude any you need in the \`image_ids\` array when calling \`${EToolResources.image_edit}_oai\`. You may also include previously referenced or generated image IDs.`;
        }
      }
      if (toolContext) {
        toolContextMap.image_edit_oai = toolContext;
      }
      return createOpenAIImageTools({
        ...authValues,
        isAgent: !!agent,
        req: options.req,
        imageOutputType,
        fileStrategy,
        imageFiles,
      });
    },
  };
  const requestedTools = {};
  if (functions === true) {
    toolConstructors.dalle = DALLE3;
  }
  /** @type {ImageGenOptions} */
  const imageGenOptions = {
    isAgent: !!agent,
    req: options.req,
    fileStrategy,
    processFileURL: options.processFileURL,
    returnMetadata: options.returnMetadata,
    uploadImageBuffer: options.uploadImageBuffer,
  };
  const toolOptions = {
    flux: imageGenOptions,
    dalle: imageGenOptions,
    'stable-diffusion': imageGenOptions,
  };
  /** @type {Record<string, string>} */
  const toolContextMap = {};
  const requestedMCPTools = {};
  for (const tool of tools) {
    if (tool === Tools.execute_code) {
      requestedTools[tool] = async () => {
        const authValues = await loadAuthValues({
          userId: user,
          authFields: [EnvVar.CODE_API_KEY],
        });
        const codeApiKey = authValues[EnvVar.CODE_API_KEY];
        const { files, toolContext } = await primeCodeFiles(
          {
            ...options,
            agentId: agent?.id,
          },
          codeApiKey,
        );
        if (toolContext) {
          toolContextMap[tool] = toolContext;
        }
        const CodeExecutionTool = createCodeExecutionTool({
          user_id: user,
          files,
          ...authValues,
        });
        CodeExecutionTool.apiKey = codeApiKey;
        return CodeExecutionTool;
      };
      continue;
    } else if (tool === Tools.file_search) {
      requestedTools[tool] = async () => {
        const { files, toolContext } = await primeSearchFiles({
          ...options,
          agentId: agent?.id,
        });
        if (toolContext) {
          toolContextMap[tool] = toolContext;
        }
        /** @type {boolean | undefined} Check if user has FILE_CITATIONS permission */
        let fileCitations;
        if (fileCitations == null && options.req?.user != null) {
          try {
            fileCitations = await checkAccess({
              user: options.req.user,
              permissionType: PermissionTypes.FILE_CITATIONS,
              permissions: [Permissions.USE],
              getRoleByName,
            });
          } catch (error) {
            logger.error('[handleTools] FILE_CITATIONS permission check failed:', error);
            fileCitations = false;
          }
        }
        return createFileSearchTool({
          userId: user,
          files,
          entity_id: agent?.id,
          fileCitations,
        });
      };
      continue;
    } else if (tool === Tools.web_search) {
      const result = await loadWebSearchAuth({
        userId: user,
        loadAuthValues,
        webSearchConfig: webSearch,
      });
      const { onSearchResults, onGetHighlights } = options?.[Tools.web_search] ?? {};
      requestedTools[tool] = async () => {
        toolContextMap[tool] = `# \`${tool}\`:
 Current Date & Time: ${replaceSpecialVars({ text: '{{iso_datetime}}' })}
 **Execute immediately without preface.** After search, provide a brief summary addressing the query directly, then structure your response with clear Markdown formatting (## headers, lists, tables). Cite sources properly, tailor tone to query type, and provide comprehensive details.
 **CITATION FORMAT - UNICODE ESCAPE SEQUENCES ONLY:**
 Use these EXACT escape sequences (copy verbatim): \\ue202 (before each anchor), \\ue200 (group start), \\ue201 (group end), \\ue203 (highlight start), \\ue204 (highlight end)
 Anchor pattern: \\ue202turn{N}{type}{index} where N=turn number, type=search|news|image|ref, index=0,1,2...
 **Examples (copy these exactly):**
 - Single: "Statement.\\ue202turn0search0"
 - Multiple: "Statement.\\ue202turn0search0\\ue202turn0news1"
 - Group: "Statement. \\ue200\\ue202turn0search0\\ue202turn0news1\\ue201"
 - Highlight: "\\ue203Cited text.\\ue204\\ue202turn0search0"
 - Image: "See photo\\ue202turn0image0."
 **CRITICAL:** Output escape sequences EXACTLY as shown. Do NOT substitute with † or other symbols. Place anchors AFTER punctuation. Cite every non-obvious fact/quote. NEVER use markdown links, [1], footnotes, or HTML tags.`.trim();
        return createSearchTool({
          ...result.authResult,
          onSearchResults,
          onGetHighlights,
          logger,
        });
      };
      continue;
    } else if (tool && mcpToolPattern.test(tool)) {
      const [toolName, serverName] = tool.split(Constants.mcp_delimiter);
      if (toolName === Constants.mcp_server) {
        /** Placeholder used for UI purposes */
        continue;
      }
      if (
        serverName &&
        (await getMCPServersRegistry().getServerConfig(serverName, user)) == undefined
      ) {
        logger.warn(
          `MCP server "${serverName}" for "${toolName}" tool is not configured${agent?.id != null && agent.id ? ` but attached to "${agent.id}"` : ''}`,
        );
        continue;
      }
      if (toolName === Constants.mcp_all) {
        requestedMCPTools[serverName] = [
          {
            type: 'all',
            serverName,
          },
        ];
        continue;
      }
      requestedMCPTools[serverName] = requestedMCPTools[serverName] || [];
      requestedMCPTools[serverName].push({
        type: 'single',
        toolKey: tool,
        serverName,
      });
      continue;
    }
    if (customConstructors[tool]) {
      requestedTools[tool] = async () => customConstructors[tool](toolContextMap);
      continue;
    }
    if (toolConstructors[tool]) {
      const options = toolOptions[tool] || {};
      const toolInstance = loadToolWithAuth(
        user,
        getAuthFields(tool),
        toolConstructors[tool],
        options,
      );
      requestedTools[tool] = toolInstance;
      continue;
    }
  }
  if (returnMap) {
    return requestedTools;
  }
  const toolPromises = [];
  for (const tool of tools) {
    const validTool = requestedTools[tool];
    if (validTool) {
      toolPromises.push(
        validTool().catch((error) => {
          logger.error(`Error loading tool ${tool}:`, error);
          return null;
        }),
      );
    }
  }
  const loadedTools = (await Promise.all(toolPromises)).flatMap((plugin) => plugin || []);
  const mcpToolPromises = [];
  /** MCP server tools are initialized sequentially by server */
  let index = -1;
  const failedMCPServers = new Set();
  const safeUser = createSafeUser(options.req?.user);
  for (const [serverName, toolConfigs] of Object.entries(requestedMCPTools)) {
    index++;
    /** @type {LCAvailableTools} */
    let availableTools;
    for (const config of toolConfigs) {
      try {
        if (failedMCPServers.has(serverName)) {
          continue;
        }
        const mcpParams = {
          index,
          signal,
          user: safeUser,
          userMCPAuthMap,
          res: options.res,
          model: agent?.model ?? model,
          serverName: config.serverName,
          provider: agent?.provider ?? endpoint,
        };
        if (config.type === 'all' && toolConfigs.length === 1) {
          /** Handle async loading for single 'all' tool config */
          mcpToolPromises.push(
            createMCPTools(mcpParams).catch((error) => {
              logger.error(`Error loading ${serverName} tools:`, error);
              return null;
            }),
          );
          continue;
        }
        if (!availableTools) {
          try {
            availableTools = await getMCPServerTools(safeUser.id, serverName);
          } catch (error) {
            logger.error(`Error fetching available tools for MCP server ${serverName}:`, error);
          }
        }
        /** Handle synchronous loading */
        const mcpTool =
          config.type === 'all'
            ? await createMCPTools(mcpParams)
            : await createMCPTool({
                ...mcpParams,
                availableTools,
                toolKey: config.toolKey,
              });
        if (Array.isArray(mcpTool)) {
          loadedTools.push(...mcpTool);
        } else if (mcpTool) {
          loadedTools.push(mcpTool);
        } else {
          failedMCPServers.add(serverName);
          logger.warn(
            `MCP tool creation failed for "${config.toolKey}", server may be unavailable or unauthenticated.`,
          );
        }
      } catch (error) {
        logger.error(`Error loading MCP tool for server ${serverName}:`, error);
      }
    }
  }
  loadedTools.push(...(await Promise.all(mcpToolPromises)).flatMap((plugin) => plugin || []));
  return { loadedTools, toolContextMap };
 };
 module.exports = {
  loadToolWithAuth,
  validateTools,
  loadTools,
 };
--- a/api/app/clients/tools/util/handleTools.test.js
+++ b/api/app/clients/tools/util/handleTools.test.js
@ -1,286 +0,0 @@
 const mongoose = require('mongoose');
 const { MongoMemoryServer } = require('mongodb-memory-server');
 const mockPluginService = {
  updateUserPluginAuth: jest.fn(),
  deleteUserPluginAuth: jest.fn(),
  getUserPluginAuthValue: jest.fn(),
 };
 jest.mock('~/server/services/PluginService', () => mockPluginService);
 jest.mock('~/server/services/Config', () => ({
  getAppConfig: jest.fn().mockResolvedValue({
    // Default app config for tool tests
    paths: { uploads: '/tmp' },
    fileStrategy: 'local',
    filteredTools: [],
    includedTools: [],
  }),
  getCachedTools: jest.fn().mockResolvedValue({
    // Default cached tools for tests
    dalle: {
      type: 'function',
      function: {
        name: 'dalle',
        description: 'DALL-E image generation',
        parameters: {},
      },
    },
  }),
 }));
 const { Calculator } = require('@librechat/agents');
 const { User } = require('~/db/models');
 const PluginService = require('~/server/services/PluginService');
 const { validateTools, loadTools, loadToolWithAuth } = require('./handleTools');
 const { StructuredSD, availableTools, DALLE3 } = require('../');
 describe('Tool Handlers', () => {
  let mongoServer;
  let fakeUser;
  const pluginKey = 'dalle';
  const pluginKey2 = 'wolfram';
  const ToolClass = DALLE3;
  const initialTools = [pluginKey, pluginKey2];
  const mockCredential = 'mock-credential';
  const mainPlugin = availableTools.find((tool) => tool.pluginKey === pluginKey);
  const authConfigs = mainPlugin.authConfig;
  beforeAll(async () => {
    mongoServer = await MongoMemoryServer.create();
    const mongoUri = mongoServer.getUri();
    await mongoose.connect(mongoUri);
    const userAuthValues = {};
    mockPluginService.getUserPluginAuthValue.mockImplementation((userId, authField) => {
      return userAuthValues[`${userId}-${authField}`];
    });
    mockPluginService.updateUserPluginAuth.mockImplementation(
      (userId, authField, _pluginKey, credential) => {
        const fields = authField.split('||');
        fields.forEach((field) => {
          userAuthValues[`${userId}-${field}`] = credential;
        });
      },
    );
    fakeUser = new User({
      name: 'Fake User',
      username: 'fakeuser',
      email: 'fakeuser@example.com',
      emailVerified: false,
      // file deepcode ignore NoHardcodedPasswords/test: fake value
      password: 'fakepassword123',
      avatar: '',
      provider: 'local',
      role: 'USER',
      googleId: null,
      plugins: [],
      refreshToken: [],
    });
    await fakeUser.save();
    for (const authConfig of authConfigs) {
      await PluginService.updateUserPluginAuth(
        fakeUser._id,
        authConfig.authField,
        pluginKey,
        mockCredential,
      );
    }
  });
  afterAll(async () => {
    await mongoose.disconnect();
    await mongoServer.stop();
  });
  beforeEach(async () => {
    // Clear mocks but not the database since we need the user to persist
    jest.clearAllMocks();
    // Reset the mock implementations
    const userAuthValues = {};
    mockPluginService.getUserPluginAuthValue.mockImplementation((userId, authField) => {
      return userAuthValues[`${userId}-${authField}`];
    });
    mockPluginService.updateUserPluginAuth.mockImplementation(
      (userId, authField, _pluginKey, credential) => {
        const fields = authField.split('||');
        fields.forEach((field) => {
          userAuthValues[`${userId}-${field}`] = credential;
        });
      },
    );
    // Re-add the auth configs for the user
    for (const authConfig of authConfigs) {
      await PluginService.updateUserPluginAuth(
        fakeUser._id,
        authConfig.authField,
        pluginKey,
        mockCredential,
      );
    }
  });
  describe('validateTools', () => {
    it('returns valid tools given input tools and user authentication', async () => {
      const validTools = await validateTools(fakeUser._id, initialTools);
      expect(validTools).toBeDefined();
      expect(validTools.some((tool) => tool === pluginKey)).toBeTruthy();
      expect(validTools.length).toBeGreaterThan(0);
    });
    it('removes tools without valid credentials from the validTools array', async () => {
      const validTools = await validateTools(fakeUser._id, initialTools);
      expect(validTools.some((tool) => tool.pluginKey === pluginKey2)).toBeFalsy();
    });
    it('returns an empty array when no authenticated tools are provided', async () => {
      const validTools = await validateTools(fakeUser._id, []);
      expect(validTools).toEqual([]);
    });
    it('should validate a tool from an Environment Variable', async () => {
      const plugin = availableTools.find((tool) => tool.pluginKey === pluginKey2);
      const authConfigs = plugin.authConfig;
      for (const authConfig of authConfigs) {
        process.env[authConfig.authField] = mockCredential;
      }
      const validTools = await validateTools(fakeUser._id, [pluginKey2]);
      expect(validTools.length).toEqual(1);
      for (const authConfig of authConfigs) {
        delete process.env[authConfig.authField];
      }
    });
  });
  describe('loadTools', () => {
    let toolFunctions;
    let loadTool1;
    let loadTool2;
    let loadTool3;
    const sampleTools = [...initialTools, 'calculator'];
    let ToolClass2 = Calculator;
    let remainingTools = availableTools.filter(
      (tool) => sampleTools.indexOf(tool.pluginKey) === -1,
    );
    beforeAll(async () => {
      const toolMap = await loadTools({
        user: fakeUser._id,
        tools: sampleTools,
        returnMap: true,
        useSpecs: true,
      });
      toolFunctions = toolMap;
      loadTool1 = toolFunctions[sampleTools[0]];
      loadTool2 = toolFunctions[sampleTools[1]];
      loadTool3 = toolFunctions[sampleTools[2]];
    });
    let originalEnv;
    beforeEach(() => {
      originalEnv = process.env;
      process.env = { ...originalEnv };
    });
    afterEach(() => {
      process.env = originalEnv;
    });
    it('returns the expected load functions for requested tools', async () => {
      expect(loadTool1).toBeDefined();
      expect(loadTool2).toBeDefined();
      expect(loadTool3).toBeDefined();
      for (const tool of remainingTools) {
        expect(toolFunctions[tool.pluginKey]).toBeUndefined();
      }
    });
    it('should initialize an authenticated tool or one without authentication', async () => {
      const authTool = await loadTool1();
      const tool = await loadTool3();
      expect(authTool).toBeInstanceOf(ToolClass);
      expect(tool).toBeInstanceOf(ToolClass2);
    });
    it('should initialize an authenticated tool with primary auth field', async () => {
      process.env.DALLE3_API_KEY = 'mocked_api_key';
      const initToolFunction = loadToolWithAuth(
        'userId',
        ['DALLE3_API_KEY||DALLE_API_KEY'],
        ToolClass,
      );
      const authTool = await initToolFunction();
      expect(authTool).toBeInstanceOf(ToolClass);
      expect(mockPluginService.getUserPluginAuthValue).not.toHaveBeenCalled();
    });
    it('should initialize an authenticated tool with alternate auth field when primary is missing', async () => {
      delete process.env.DALLE3_API_KEY; // Ensure the primary key is not set
      process.env.DALLE_API_KEY = 'mocked_alternate_api_key';
      const initToolFunction = loadToolWithAuth(
        'userId',
        ['DALLE3_API_KEY||DALLE_API_KEY'],
        ToolClass,
      );
      const authTool = await initToolFunction();
      expect(authTool).toBeInstanceOf(ToolClass);
      expect(mockPluginService.getUserPluginAuthValue).toHaveBeenCalledTimes(1);
      expect(mockPluginService.getUserPluginAuthValue).toHaveBeenCalledWith(
        'userId',
        'DALLE3_API_KEY',
        true,
      );
    });
    it('should fallback to getUserPluginAuthValue when env vars are missing', async () => {
      mockPluginService.updateUserPluginAuth('userId', 'DALLE_API_KEY', 'dalle', 'mocked_api_key');
      const initToolFunction = loadToolWithAuth(
        'userId',
        ['DALLE3_API_KEY||DALLE_API_KEY'],
        ToolClass,
      );
      const authTool = await initToolFunction();
      expect(authTool).toBeInstanceOf(ToolClass);
      expect(mockPluginService.getUserPluginAuthValue).toHaveBeenCalledTimes(2);
    });
    it('should throw an error for an unauthenticated tool', async () => {
      try {
        await loadTool2();
      } catch (error) {
        expect(error).toBeDefined();
      }
    });
    it('returns an empty object when no tools are requested', async () => {
      toolFunctions = await loadTools({
        user: fakeUser._id,
        returnMap: true,
        useSpecs: true,
      });
      expect(toolFunctions).toEqual({});
    });
    it('should return the StructuredTool version when using functions', async () => {
      process.env.SD_WEBUI_URL = mockCredential;
      toolFunctions = await loadTools({
        user: fakeUser._id,
        tools: ['stable-diffusion'],
        functions: true,
        returnMap: true,
        useSpecs: true,
      });
      const structuredTool = await toolFunctions['stable-diffusion']();
      expect(structuredTool).toBeInstanceOf(StructuredSD);
      delete process.env.SD_WEBUI_URL;
    });
  });
 });
--- a/Show more
+++ b/Show more
		`@ -1,2 +0,0 @@`
			`[ -n "$CI" ] && exit 0`
			`npx lint-staged --config ./.husky/lint-staged.config.js`