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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
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* 🪙 feat: Context-usage projection — data-provider + client wiring
Consumer side of the SDK-aligned context projection (agents
`projectAgentContextUsage`). Adds the `/api/endpoints/context-projection`
data-provider plumbing (endpoint, service, query key, `TContextProjectionRequest`)
and a `useContextProjectionQuery` gated to fire only when no fresh snapshot
covers the viewed branch.
Wires `useTokenUsage` precedence to: live snapshot → fresh persisted snapshot
(window matches the resolved one) → server projection → per-message estimate.
A model/window switch marks the baked snapshot stale (its `maxContextTokens`
no longer matches) and falls to the projection — closing the gauge's
window-switch (G1) and snapshot-less-branch (G2) gaps. Snapshot and projection
share the render-relevant fields, so they render uniformly.
Backend endpoint + agents version bump land in follow-up commits. Includes the
design spec (CONTEXT_PROJECTION_SPEC.md).
* 🪙 feat: Context-projection backend endpoint
POST /api/endpoints/context-projection → resolveContextProjection (packages/api):
reconstructs the viewed branch (parent-chain walk from messageId), resolves the
agent config (instructions/provider/model/maxContextTokens), reuses LibreChat's
stored per-message tokenCounts as the index map (no re-tokenizing), and calls
the agents SDK projectAgentContextUsage — no model call. Thin controller injects
db.getMessages/db.getAgent; route mirrors /token-config.
First cut targets message-windowing accuracy; tool-schema tokens are deferred to
a follow-up that reuses the full initializeAgent path.
* 🩹 fix: Codex review on context projection (G1 guard, IDOR, recount, summary)
- Guard `currentActive` against a stale window: a model/window switch on the
current branch left the live snapshot outranking the projection (G1 didn't
fire). Now defers to the projection unless streaming or the window matches.
- Scope branch lookups to the authenticated user (`getMessages` filter +
injected `userId`) — was loading any conversation by id (IDOR).
- Recount messages with no stored `tokenCount` via the tokenizer instead of
charging 0, so snapshot-less/imported histories don't under-report.
- Fall back (null) for already-summarized branches rather than projecting from
the full raw parent chain (the next call would send summary + tail); the
client's summary-baseline-aware estimate handles them until a follow-up
replays the summary boundary.
* 🩹 fix: Codex round 2 — drop agent load, summary marker, edit-invalidation
- Stop loading agent/model-spec config server-side (closes the agent-access
IDOR and the spec-prompt special-casing). Provider/model/window now come from
the client-resolved request (`limits.endpoint`/model — the agent's real
provider, not the `agents` endpoint, so the tokenizer is right). Agent/spec/
promptPrefix instructions are uniformly deferred to the full-fidelity follow-up.
- Detect summarized branches via the live path's `metadata.summaryUsedTokens`
marker (was the wrong `summaryTokenCount` field) and fall back to the
summary-aware estimate.
- Invalidate the projection query on in-place message edits via a branch
content `revision` in the cache key (the tail id is unchanged on edit).
Deferred (valid, not a regression): same-window endpoint/model switch keeps a
window-matched snapshot — needs endpoint/model persisted on the snapshot, which
lands with the fidelity follow-up. Smoke-tested: fits / prunes / summarized→null
/ no-window→null.
* 🛡️ fix: make context projection strictly additive (no-regression)
Revert the G1 window-match guard on the live/branch snapshot. When no explicit
maxContextTokens is set (the common default), the SDK's snapshot window is
reserve-derived (~0.9·(modelContext − maxOutputTokens)) while useTokenLimits
resolves the raw model context — so `snapshot.maxContextTokens === resolvedMax`
is false for the SAME model, and the guard would wrongly drop a valid
current-branch snapshot to projection/estimate post-stream (a regression in the
default case, per initialize.ts:1240-1243).
The projection now activates ONLY for snapshot-less branches (G2): the
precedence is live snapshot → persisted branch snapshot → projection → estimate,
where the first two are byte-for-byte the prior behavior and the projection just
slots ahead of the estimate. Window/model-switch (G1) detection needs the
snapshot to carry its model/window and defers to the fidelity follow-up.
* 🩹 fix: surface projections as estimates, not authoritative snapshots
A first-cut projection carries the SDK's windowing but omits instruction/tool
overhead, so rendering it as `isEstimate: false` showed a confident under-count
for snapshot-less branches. Mark projection-sourced views `isEstimate: true` +
`snapshotActive: false` (and drop the snapshot field) so they present as a
better estimate than sumBranch — improved used/window number, estimate framing,
no misleading granular breakdown with ~0 tools. Real snapshots stay
authoritative. (Codex round 3, projection.ts:139.)
* 🧹 chore: drop CONTEXT_PROJECTION_SPEC.md from the PR
* 🎨 style: fix import-sort order in projection.ts (CI sort-imports check)
* 🔧 chore: update @librechat/agents dependency to version 3.2.36 in package-lock.json and related package.json files
* chore: npm audit fix
* 🎨 style: fix import-sort order in data-service.ts (CI sort-imports check)
* 🩹 fix: drop dead calibrationRatio in projectionParams (tsc never error)
Inside the ternary, branchSnapshot is narrowed to null (the gate is
), so accessed a
property on (frontend typecheck failure). It was also dead — there is
never a snapshot to seed from in this branch — so just remove it.
* Revert "chore: npm audit fix"
This reverts commit
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| turbo.json | ||
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
- 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
-
⚙️ 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.