mirror of
https://github.com/danny-avila/LibreChat.git
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Enhanced ChatGPT Clone: Features Agents, MCP, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active.
https://librechat.ai/
aianthropicartifactsawsazurechatgptchatgpt-cloneclaudeclonedeepseekgeminigooglegpt-5librechatmcpo1openairesponses-apivisionwebui
0d94881c2d
* refactor: Remove deprecated and unused fields from endpoint schemas - Remove summarize, summaryModel from endpointSchema and azureEndpointSchema - Remove plugins from azureEndpointSchema - Remove customOrder from endpointSchema and azureEndpointSchema - Remove baseURL from all and agents endpoint schemas - Type paramDefinitions with full SettingDefinition-based schema - Clean up summarize/summaryModel references in initialize.ts and config.spec.ts * refactor: Improve MCP transport schema typing - Add defaults to transport type discriminators (stdio, websocket, sse) - Type stderr field as IOType union instead of z.any() * refactor: Add narrowed preset schema for model specs - Create tModelSpecPresetSchema omitting system/DB/deprecated fields - Update tModelSpecSchema to use the narrowed preset schema * test: Add explicit type field to MCP test fixtures Add transport type discriminator to test objects that construct MCPOptions/ParsedServerConfig directly, required after type field changed from optional to default in schema definitions. * chore: Bump librechat-data-provider to 0.8.404 * refactor: Tighten z.record(z.any()) fields to precise value types - Type headers fields as z.record(z.string()) in endpoint, assistant, and azure schemas - Type addParams as z.record(z.union([z.string(), z.number(), z.boolean(), z.null()])) - Type azure additionalHeaders as z.record(z.string()) - Type memory model_parameters as z.record(z.union([z.string(), z.number(), z.boolean()])) - Type firecrawl changeTrackingOptions.schema as z.record(z.string()) * refactor: Type supportedMimeTypes schema as z.array(z.string()) Replace z.array(z.any()).refine() with z.array(z.string()) since config input is always strings that get converted to RegExp via convertStringsToRegex() after parsing. Destructure supportedMimeTypes from spreads to avoid string[]/RegExp[] type mismatch. * refactor: Tighten enum, role, and numeric constraint schemas - Type engineSTT as enum ['openai', 'azureOpenAI'] - Type engineTTS as enum ['openai', 'azureOpenAI', 'elevenlabs', 'localai'] - Constrain playbackRate to 0.25–4 range - Type titleMessageRole as enum ['system', 'user', 'assistant'] - Add int().nonnegative() to MCP timeout and firecrawl timeout * chore: Bump librechat-data-provider to 0.8.405 * fix: Accept both string and RegExp in supportedMimeTypes schema The schema must accept both string[] (config input) and RegExp[] (post-merge runtime) since tests validate merged output against the schema. Use z.union([z.string(), z.instanceof(RegExp)]) to handle both. * refactor: Address review findings for schema tightening PR - Revert changeTrackingOptions.schema to z.record(z.unknown()) (JSON Schema is nested, not flat strings) - Remove dead contextStrategy code from BaseClient.js and cleanup.js - Extract paramDefinitionSchema to named exported constant - Add .int() constraint to columnSpan and columns - Apply consistent .int().nonnegative() to initTimeout, sseReadTimeout, scraperTimeout - Update stale stderr JSDoc to match actual accepted types - Add comprehensive tests for paramDefinitionSchema, tModelSpecPresetSchema, endpointSchema deprecated field stripping, and azureEndpointSchema * fix: Address second review pass findings - Revert supportedMimeTypesSchema to z.array(z.string()) and remove as string[] casts — fix tests to not validate merged RegExp[] output against the config input schema - Remove unused tModelSpecSchema import from test file - Consolidate duplicate '../src/schemas' imports - Add expiredAt coverage to tModelSpecPresetSchema test - Assert plugins is absent in azureEndpointSchema test - Add sync comments for engineSTT/engineTTS enum literals * refactor: Omit preset-management fields from tModelSpecPresetSchema Omit conversationId, presetId, title, defaultPreset, and order from the model spec preset schema — these are preset-management fields that don't belong in model spec configuration. |
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LibreChat
English · 中文
✨ Features
-
🖥️ UI & Experience inspired by ChatGPT with enhanced design and features
-
🤖 AI Model Selection:
- Anthropic (Claude), AWS Bedrock, OpenAI, Azure OpenAI, Google, Vertex AI, OpenAI Responses API (incl. Azure)
- Custom Endpoints: Use any OpenAI-compatible API with LibreChat, no proxy required
- Compatible with Local & Remote AI Providers:
- Ollama, groq, Cohere, Mistral AI, Apple MLX, koboldcpp, together.ai,
- OpenRouter, Helicone, Perplexity, ShuttleAI, Deepseek, Qwen, and more
-
- 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:
- LibreChat 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 for Tools
- LibreChat Agents:
-
🔍 Web Search:
- Search the internet and retrieve relevant information to enhance your AI context
- Combines search providers, content scrapers, and result rerankers for optimal results
- Customizable Jina Reranking: Configure custom Jina API URLs for reranking services
- Learn More →
-
🪄 Generative UI with Code Artifacts:
- Code Artifacts 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
- Text-to-image with DALL-E (3/2), Stable Diffusion, Flux, or any MCP server
- Produce stunning visuals from prompts or refine existing images with a single instruction
-
💾 Presets & Context Management:
- 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 for Advanced Context control
-
💬 Multimodal & File Interactions:
- 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:
- English, 中文 (简体), 中文 (繁體), العربية, Deutsch, Español, Français, Italiano
- 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
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🎨 Customizable Interface:
- Customizable Dropdown & Interface that adapts to both power users and newcomers
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- Never lose a response: AI responses automatically reconnect and resume if your connection drops
- Multi-Tab & Multi-Device Sync: Open the same chat in multiple tabs or pick up on another device
- Production-Ready: Works from single-server setups to horizontally scaled deployments with Redis
-
🗣️ Speech & Audio:
- Chat hands-free with Speech-to-Text and Text-to-Speech
- Automatically send and play Audio
- Supports OpenAI, Azure OpenAI, and Elevenlabs
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📥 Import & Export Conversations:
- Import Conversations from LibreChat, ChatGPT, Chatbot UI
- Export conversations as screenshots, markdown, text, json
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🔍 Search & Discovery:
- Search all messages/conversations
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👥 Multi-User & Secure Access:
- Multi-User, Secure Authentication with OAuth2, LDAP, & Email Login Support
- Built-in Moderation, and Token spend tools
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⚙️ Configuration & Deployment:
- 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 📚
🪶 All-In-One AI Conversations with LibreChat
LibreChat is a self-hosted AI chat platform that unifies all major AI providers in a single, privacy-focused interface.
Beyond chat, LibreChat provides AI Agents, Model Context Protocol (MCP) support, Artifacts, Code Interpreter, custom actions, conversation search, and enterprise-ready multi-user authentication.
Open source, actively developed, and built for anyone who values control over their AI infrastructure.
🌐 Resources
GitHub Repo:
- RAG API: github.com/danny-avila/rag_api
- Website: github.com/LibreChat-AI/librechat.ai
Other:
- Website: librechat.ai
- Documentation: librechat.ai/docs
- Blog: librechat.ai/blog
📝 Changelog
Keep up with the latest updates by visiting the releases page and notes:
⚠️ Please consult the changelog for breaking changes before updating.
⭐ Star History
✨ Contributions
Contributions, suggestions, bug reports and fixes are welcome!
For new features, components, or extensions, please open an issue and discuss before sending a PR.
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.
💖 This project exists in its current state thanks to all the people who contribute
🎉 Special Thanks
We thank Locize for their translation management tools that support multiple languages in LibreChat.