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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/
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* 🛡️ fix: Guard HITL checkpoint size against MongoDB 16MB limit A LangGraph HITL checkpoint embeds the whole serialized message history in a single BSON document, so a large conversation (inlined base64 media, big tool outputs, long history) can serialize past MongoDB's 16MB document ceiling. `MongoDBSaver.put` would then throw a raw `BSONObjectTooLarge` at pause time and the pause would be lost with no legible error. `LazyMongoSaver` now measures the serialized checkpoint on the persist path (rare HITL pauses only — the clean-exit common path is untouched): debug-logs the size, warns past a soft 8MB threshold, and throws a typed `CheckpointTooLargeError` before the doomed write past a 15MB hard limit (16MB minus headroom for the document's other fields). Thresholds are overridable via the constructor for testing. Adds integration coverage (real serde + mongodb-memory-server) for the under-threshold, soft-warn, and hard-reject cases. * 🧱 fix: Add explicit types for isolatedDeclarations build The production build (tsdown + rolldown-plugin-dts) compiles with --isolatedDeclarations, which requires explicit type annotations on exported bindings whose initializers it can't infer syntactically. `CHECKPOINT_HARD_LIMIT_BYTES` (arithmetic over two consts) tripped TS9010; annotate it and `CHECKPOINT_WARN_BYTES` as `number`. Verified with `tsc --isolatedDeclarations` over the package. * fix: Codex review — include metadata in the size guard + flush parked bookkeeping P1 (lost bookkeeping): `put` consumes the write anchor, then AWAITS assertCheckpointFitsDocument (checkpoint serialization). A bookkeeping-only putWrites dispatched in that window sees neither the anchor nor persistedIds, so it parks — and `put` never flushed it, dropping the marker (e.g. a completed Send-sibling's __no_writes__) so a resume re-executed the sibling. Extract flushBufferedBookkeeping (shared with the anchoring putWrites) and call it after super.put in the persist path. P2 (metadata ignored by guard): MongoDBSaver.put stores the serialized checkpoint AND metadata (plus metadata_search) in the SAME document, but the guard measured only the checkpoint — a just-under-limit checkpoint with large metadata fell through to a raw BSONObjectTooLarge. Measure checkpoint + metadata; the fixed headroom now only covers metadata_search/ids/framing. Two integration regressions added (both fail without the fix, pass with it): metadata-pushes-over-the-ceiling, and flush-during-the-serialization-window. * fix: count metadata_search (raw metadata copy) in the checkpoint size guard Codex follow-up: MongoDBSaver.put stores metadata a SECOND time as `metadata_search: metadata` — the whole raw metadata object as a queryable BSON subdocument in the same agent_checkpoints document. Measuring only checkpoint + serialized metadata undercounted by that raw copy, so a large metadata.writes payload could pass the 15 MB preflight while metadata_search pushed the actual BSON past 16 MB — the raw BSONObjectTooLarge the guard exists to prevent. Add mongoose.mongo.BSON.calculateObjectSize(metadata) for the metadata_search contribution (mongoose already imported; no new dep). Headroom now only covers ids + BSON framing. New integration test sizes a case where checkpoint + serialized metadata is under the limit but the raw metadata_search copy pushes it over — green (24/24). * ci: run packages/api agents integration specs (checkpointer) in CI The checkpointer.integration.spec.ts (durable HITL checkpointer vs a real in-process MongoDB) is a *.integration.spec.ts, which test:ci deliberately excludes — and cache-integration-tests.yml only covers cache/cluster/mcp/ stream, not src/agents/**. So it ran nowhere and its regressions guarded nothing. Add: - test:agents-integration script (jest over src/agents/*.integration.spec.ts, runInBand — mongodb-memory-server is in-process, no external service); - a dedicated agents-integration-tests.yml (mirrors the proven build setup, no Redis) triggered on packages/api/src/agents/** changes; - babel-plugin-replace-ts-export-assignment as a packages/api devDep: the spec imports @langchain/langgraph-checkpoint (whitelisted for babel transform, uses `export =`), whose transform needs this plugin — it was only present under client/node_modules, unresolvable from packages/api, so the suite couldn't load. 24/24 pass locally. |
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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
- Open-Source & Self-Hostable: powered by ClickHouse/code-interpreter
-
🔦 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
- Skills: Create reusable
SKILL.mdinstruction bundles for manual, automatic, or always-on agent workflows - Subagents: Delegate focused work to isolated child agent runs with their own context windows
- 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
-
🎨 Customizable Interface:
- Customizable Dropdown & Interface that adapts to both power users and newcomers
-
- 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
-
📥 Import & Export Conversations:
- 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
-
🎛️ Admin Panel:
- Browser-based UI to manage users, groups, roles, and configuration overrides
- Edit settings and per-role/group permissions live, without redeploying
- Bundled with the Docker Compose stacks for one-command setup
-
⚙️ Configuration & Deployment:
- Configure Proxy, Reverse Proxy, Docker, & many Deployment options
- Use S3 with CloudFront for stable media links, edge delivery, signed cookies, and secured downloads
- 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.