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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
376370d610
* ♻️ refactor: Compute Context Gauge Client-Side, Drop Projection Endpoint The /api/endpoints/context-projection endpoint re-fetched a conversation's messages from Mongo and re-tokenized them to project the context gauge for snapshot-less branches. The browser already holds those messages and their per-message tokenCounts, so this duplicated work on the request path (an unbounded read + server-side BPE tokenization until it was later capped). Move the snapshot-less estimate fully client-side, from the in-memory index: - sumBranch accumulates an uncalibrated char/4 estimate (estTokens) for count-less messages (imports / pre-feature) under the same summary cutoff - useTokenUsage folds estTokens (calibrated via the existing calibrationFamily ratio) into the existing fallback; known per-message counts render unchanged - delete the endpoint, controller, rate limiter, route, the getMessageTextStats data-schemas method, and the data-provider surface (endpoint/key/type/service/query) No DB read, no server tokenization, no rate-limit knobs; the gauge recomputes reactively from the index. Net -793 lines. * 🩹 fix: Count quotes and object-form content in client context estimate Address Codex review on the client-side context estimate: - messageChars now reads object-form content text (part.text.value), not only string text/think, so imported / pre-feature messages whose body lives in content parts are no longer estimated as zero. - Count-less user messages include their merged quote excerpts in the estimate, mirroring what the send path prepends into the prompt. * 🩹 fix: Cap over-window estimate and surface estimated tokens in breakdown Address remaining Codex review on the client-side context estimate: - Clamp the snapshot-less estimate's displayed usedTokens to maxTokens. The send path prunes an over-window branch before calling the model, so the gauge never actually exceeds the window; this avoids impossible values (e.g. 50k / 8k) without re-introducing client-side pruning. - Surface the calibrated count-less estimate as its own "Estimated" row in the breakdown popover, so a branch of only count-less imported / pre-feature messages is no longer shown as Input 0 / Output 0 under a non-zero header. * 🩹 fix: Refine client context estimate per Codex re-review - Drop calibration from the snapshot-less estimate. The removed projection never actually calibrated (the client never sent a ratio), and a ratio inflated by provider-injected context over-estimates visible imported text. - Exclude reasoning (think) / error parts from the estimate; the send path strips them, so they are not part of the next call's context. - Fold quote text into the estimate even when a tokenCount is present, since the edit route recounts tokenCount from text only and drops the merged quote. * 🩹 fix: Recount quoted user turns instead of topping up the stored count The previous round added quote chars on top of a quoted message's stored tokenCount, which double-counts the common (unedited) case where the count already includes the merged quote prompt. Match the removed projection instead: for quoted user turns, ignore the stored count and estimate the full merged text. This both avoids the double-count and still corrects the stale text-only count an edit leaves behind. * 🩹 fix: Trust stored counts for quoted turns; count tool-call parts - Quoted user turns: revert to trusting a present tokenCount. The send path's stored count already includes the merged quote (and any calibration), and the client's char/4 path is coarser, so recounting regressed normal turns. Only count-less messages estimate quotes from text. - Count tool-call name/args/output for count-less assistant messages; the formatter sends them back as context, so omitting them under-reported imported branches with tool history. * 🩹 fix: Exclude in-flight tail from estimate to avoid resume double-count On resume the live path seeds liveTokens from the partial response and also writes that content into the messages cache, where the count-less response is estimated into estTokens too — double-counting the in-flight output on the snapshot-less estimate path. sumBranch now exposes the tail message's own estimate (tailEstTokens); the estimate path drops it while a stream is live, so the in-flight response is counted once (via liveTokens). The breakdown's Estimated row uses the same in-flight-adjusted value. * 🩹 fix: Recount quoted user turns in context estimate (match send path) A quoted user turn's stored tokenCount is unreliable for the gauge: a text-only Save edit recomputes it from text alone, and the send path (needsCanonicalTokenCount in agents/client.js) recounts the quote-merged prompt every turn regardless of the stored value. Mirror that on the client — estimate quoted turns from the merged text+quotes and ignore the stored count — so snapshot-less branches don't under-report by the quote block. Reverts the earlier "trust the count" assumption, which the server disproves. * 🧹 chore: Route useResumableSSE diagnostics through the frontend logger Convert the [ResumableSSE]/[Debug] console.log and console.error diagnostics to the gated frontend `logger` (client/src/utils/logger), splitting the tag from the message so object arguments are passed through as real args (logged expandably, not stringified) and the logs stay tag-filterable and off the production console unless explicitly enabled. All log statements preserved; nothing removed. * 🩹 fix: Prefer content over text when estimating count-less messages A stopped agent response is saved with both a `text` field and a structured `content` array, and the send path formats from content. messageChars early-returned on `text`, dropping the content array (and the tool-call tokens it carries) from the snapshot-less estimate — also making the tool_call handling dead for such messages. Prefer content when present, fall back to text. |
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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
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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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🎛️ 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
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⚙️ 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
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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.