* fix: skipBufferReplay for job resume connections - Introduced a new option `skipBufferReplay` in the `subscribe` method of `GenerationJobManagerClass` to prevent duplication of events when resuming a connection. - Updated the logic to conditionally skip replaying buffered events if a sync event has already been sent, enhancing the efficiency of event handling during reconnections. - Added integration tests to verify the correct behavior of the new option, ensuring that no buffered events are replayed when `skipBufferReplay` is true, while still allowing for normal replay behavior when false. * refactor: Update GenerationJobManager to handle sync events more efficiently - Modified the `subscribe` method to utilize a new `skipBufferReplay` option, allowing for the prevention of duplicate events during resume connections. - Enhanced the logic in the `chat/stream` route to conditionally skip replaying buffered events if a sync event has already been sent, improving event handling efficiency. - Updated integration tests to verify the correct behavior of the new option, ensuring that no buffered events are replayed when `skipBufferReplay` is true, while maintaining normal replay behavior when false. * test: Enhance GenerationJobManager integration tests for Redis mode - Updated integration tests to conditionally run based on the USE_REDIS environment variable, allowing for better control over Redis-related tests. - Refactored test descriptions to utilize a dynamic `describeRedis` function, improving clarity and organization of tests related to Redis functionality. - Removed redundant checks for Redis availability within individual tests, streamlining the test logic and enhancing readability. * fix: sync handler state for new messages on resume The sync event's else branch (new response message) was missing resetContentHandler() and syncStepMessage() calls, leaving stale handler state that caused subsequent deltas to build on partial content instead of the synced aggregatedContent. * feat: atomic subscribeWithResume to close resume event gap Replaces separate getResumeState() + subscribe() calls with a single subscribeWithResume() that atomically drains earlyEventBuffer between the resume snapshot and the subscribe. In in-memory mode, drained events are returned as pendingEvents for the client to replay after sync. In Redis mode, pendingEvents is empty since chunks are already persisted. The route handler now uses the atomic method for resume connections and extracted shared SSE write helpers to reduce duplication. The client replays any pendingEvents through the existing step/content handlers after applying aggregatedContent from the sync payload. * fix: only capture gap events in subscribeWithResume, not pre-snapshot buffer The previous implementation drained the entire earlyEventBuffer into pendingEvents, but pre-snapshot events are already reflected in aggregatedContent. Replaying them re-introduced the duplication bug through a different vector. Now records buffer length before getResumeState() and slices from that index, so only events arriving during the async gap are returned as pendingEvents. Also: - Handle pendingEvents when resumeState is null (replay directly) - Hoist duplicate test helpers to shared scope - Remove redundant writableEnded guard in onDone |
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| api | ||
| client | ||
| config | ||
| e2e | ||
| helm | ||
| packages | ||
| redis-config | ||
| src/tests | ||
| utils | ||
| .dockerignore | ||
| .env.example | ||
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| AGENTS.md | ||
| bun.lock | ||
| CLAUDE.md | ||
| deploy-compose.yml | ||
| docker-compose.override.yml.example | ||
| docker-compose.yml | ||
| Dockerfile | ||
| Dockerfile.multi | ||
| eslint.config.mjs | ||
| librechat.example.yaml | ||
| LICENSE | ||
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| package.json | ||
| rag.yml | ||
| README.md | ||
| turbo.json | ||
LibreChat
✨ Features
-
🖥️ UI & Experience inspired by ChatGPT with enhanced design and features
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🤖 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
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- 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
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💾 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
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💬 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 🗃️
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🌎 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
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🧠 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
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🗣️ 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
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📖 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.