* 🚧 fix: Narrow Chat MCP Selections to What the Picker May Offer The chat MCP picker offers a server only when the operator has not hidden it (`chatMenu: false`) and the user reaches it directly rather than through an agent (`consumeOnly`). Nothing enforced either condition on the request, so a stale client, a replayed body, or a hand-written one could name any server and get its tools — `consumeOnly` in particular is a permission the registry derives per user, so ignoring it let a user reach in plain chat a server they had only been granted through an agent. Narrow the picker's selection in the two ephemeral loaders, against the same predicate the client's `selectableServers` uses. The resolver is injected, so `packages/api` stays free of the registry and the loaders keep their existing `deps` shape. Provenance is why this lives in the loaders rather than at the tool-resolution chokepoint: only here is a server name known to have come from the picker. Servers a model spec pins are the operator's own choice and are added after the narrowing, so pinning a chat-hidden server to a spec keeps working. `chatMenu` only ever comes from the config tier, so the overlay settles it with no lookup at all. Lookups for the rest run concurrently and are cached per server and user. A name the registry cannot resolve — request-tier, declared on the body and never registered — is kept, as is one whose lookup fails: this narrows an already-authenticated selection and is not the authorization boundary. * 📝 docs: Say What chatMenu Now Bounds The schema still described `chatMenu` as dropdown visibility alone, which is misleading now that it also bars a server from the chat selection a request carries. Spell out both halves, and what it deliberately does not restrict: agents, and servers a model spec pins. * 🛡️ fix: Close Four Gaps in the Chat MCP Narrowing Dedupe before resolving. The selection is request-supplied and the loaders used to build a `Set` immediately; mapping the raw array first let one body repeat a name enough times to launch a resolver — registry, cache, and ACL reads, with no single-flight — per occurrence. Resolve names through the tool-loading aliases. A configured `private server` is addressable as `private_server` downstream, so an exact-key lookup let the normalized spelling walk past a `chatMenu: false` check and still load tools. Names are returned as sent; only the decision is canonicalized. Let the request overlay settle `chatMenu`. The config tier is where the flag lives and is what the picker itself was built from, so an overlay that re-enables a server now outranks the durable config the registry returns, which was silently dropping a valid selection. `consumeOnly` still comes from the registry. Narrow the request body, not just the loader's copy. `applyContextToAgent` reads `req.body.ephemeralAgent.mcp` directly and prefers it over the agent's tools, so a hidden server's `serverInstructions` reached the model context even after its tools were dropped. * 🧭 fix: Ask the Registry Which MCP Servers the Picker May Offer Three rounds of review found the same mistake in three disguises: this narrowing re-derived the registry's tier precedence instead of asking for it. A config entry was treated as authoritative over a user-DB or process-backed server of the same name; the alias map was built from config names only, so a DB server named `private_server` was misrouted to a config `private server`; and the overlay short-circuit ran before any of that could be consulted. Resolve the accessible set through `resolveAllMcpConfigs` instead — the same call behind `GET /api/mcp/servers`, so what the request may use and what the picker was offered come from one place and cannot drift. Precedence, cross-tier name collisions, and the config overlay are the registry's job again, and the selectability test is a single flag check over what it returns. The result is smaller: no overlay branch, no per-server lookups, one catalog read regardless of how long the selection is, and aliases built over the whole accessible set so an exact name always outranks another server's normalized form. Publish the servers actually in play back onto the request body, spec pins included. `applyContextToAgent` prefers that list over the agent's tools, so narrowing to the picker selection alone had started dropping a spec-pinned server's `serverInstructions` while suppressing a hidden one's. |
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| .devcontainer | ||
| .github | ||
| .husky | ||
| .vscode | ||
| api | ||
| client | ||
| config | ||
| e2e | ||
| helm | ||
| otel/langfuse-fanout | ||
| packages | ||
| redis-config | ||
| scripts | ||
| search | ||
| skill | ||
| src/tests | ||
| utils | ||
| .codex | ||
| .dockerignore | ||
| .env.example | ||
| .gitattributes | ||
| .gitignore | ||
| .nvmrc | ||
| .prettierrc | ||
| AGENTS.md | ||
| bun.lock | ||
| CLAUDE.md | ||
| CONTEXT.md | ||
| deploy-compose.langfuse-fanout.yml | ||
| deploy-compose.yml | ||
| docker-compose.langfuse-fanout.yml | ||
| docker-compose.override.yml.example | ||
| docker-compose.yml | ||
| Dockerfile | ||
| Dockerfile.multi | ||
| eslint.config.mjs | ||
| librechat.example.yaml | ||
| LICENSE | ||
| package-lock.json | ||
| package.json | ||
| rag.yml | ||
| README.md | ||
| README.zh.md | ||
| tool-intent-spec.md | ||
| turbo.json | ||
LibreChat
English · 中文
🚀 What's New in v0.8.8-rc1
- Agent run control: Interrupt or steer an Agent mid-run, queue follow-up messages, and reclaim, edit, or escalate pending steers.
- Human-in-the-loop Agents: Agents stream question progress, ask up to four related questions in one form, pause for input or tool approval, and resume.
- Unified Agent Builder: A redesigned Tools marketplace brings together Skills, MCP, Code Interpreter, orchestration, Programmatic Tool Calling, model-spec controls, and per-tool background and intent settings.
- Readable Agent activity: Generated activity-group headers, parent phase summaries, and live tool intent labels make long reasoning and tool runs easier to scan.
- Code Interpreter workflows: Code and shell tools can run in the background, sandbox images return as viewable artifacts, and highly experimental stateful sessions can reuse prewarmed conversation workspaces.
- Agent extensibility: Experimental Agent Plugins can bundle deployment Skills, MCP servers, and opt-in command hooks, while explicit subagents initialize only when selected.
- Memory, context, and identity: Agents can manage memory with optional per-agent isolation, expose support contacts safely, and show a more faithful Context Usage gauge.
- Sharing and files: Shared conversations show a badge and update at a stable URL, while signed-in viewers can continue them as personal copies.
- Artifact workflows: Open previews fullscreen, work with PowerPoint
.potxtemplates across upload, search, and code execution, upload shell scripts across common MIME variants, export Mermaid diagrams as SVG or PNG, and download original Office files from the artifact panel. - Models and reasoning: Added GPT-5.6 with Responses API reasoning controls, Claude Opus 5 and Sonnet 5, Gemini 3.7 and 3.6 Flash, and Gemini 3.5 Flash-Lite.
- Langfuse observability: Configure encrypted Langfuse connections in-app, let authorized admins open sampled sessions directly, optionally fan out traces by tenant, and suppress central export per run.
- Administration and security: Delegate config sections, encrypt registered secrets, enforce SSRF checks for speech, OCR, and web tools, and generate unique temporary credentials when secrets are blank.
- Messages and navigation: Right-aligned user turns, unified multi-part editing, full-message copy, a dock-style message rail, virtualized search, smooth streaming, and faster Agent startup.
- Streaming and tool reliability: Adaptive provider smoothing, Redis delta batching, dynamic MCP tool refresh, parsed MCP response media types, runtime OAuth recovery, and Agent stream circuit breakers improve long-running workflows.
- Deployment and reliability: Added configurable HTTP timeouts, Amazon DocumentDB 5.0+ support, low-noise Redis and browser observability, and a rolling-upgrade-safe generation protocol.
Read the full v0.8.8-rc1 changelog.
✨ 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 - Agent Plugins: Experimentally bundle deployment Skills and MCP servers into startup-loaded packages
- 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 create React, HTML, and Mermaid content directly in chat
- Open previews fullscreen and export Mermaid diagrams as SVG or PNG
-
🎨 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.