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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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* chore: Update dependencies by adding ai-tokenizer and removing tiktoken - Added ai-tokenizer version 1.0.6 to package.json and package-lock.json across multiple packages. - Removed tiktoken version 1.0.15 from package.json and package-lock.json in the same locations, streamlining dependency management. * refactor: replace js-tiktoken with ai-tokenizer - Added support for 'claude' encoding in the AgentClient class to improve model compatibility. - Updated Tokenizer class to utilize 'ai-tokenizer' for both 'o200k_base' and 'claude' encodings, replacing the previous 'tiktoken' dependency. - Refactored tests to reflect changes in tokenizer behavior and ensure accurate token counting for both encoding types. - Removed deprecated references to 'tiktoken' and adjusted related tests for improved clarity and functionality. * chore: remove tiktoken mocks from DALLE3 tests - Eliminated mock implementations of 'tiktoken' from DALLE3-related test files to streamline test setup and align with recent dependency updates. - Adjusted related test structures to ensure compatibility with the new tokenizer implementation. * chore: Add distinct encoding support for Anthropic Claude models - Introduced a new method `getEncoding` in the AgentClient class to handle the specific BPE tokenizer for Claude models, ensuring compatibility with the distinct encoding requirements. - Updated documentation to clarify the encoding logic for Claude and other models. * docs: Update return type documentation for getEncoding method in AgentClient - Clarified the return type of the getEncoding method to specify that it can return an EncodingName or undefined, enhancing code readability and type safety. * refactor: Tokenizer class and error handling - Exported the EncodingName type for broader usage. - Renamed encodingMap to encodingData for clarity. - Improved error handling in getTokenCount method to ensure recovery attempts are logged and return 0 on failure. - Updated countTokens function documentation to specify the use of 'o200k_base' encoding. * refactor: Simplify encoding documentation and export type - Updated the getEncoding method documentation to clarify the default behavior for non-Anthropic Claude models. - Exported the EncodingName type separately from the Tokenizer module for improved clarity and usage. * test: Update text processing tests for token limits - Adjusted test cases to handle smaller text sizes, changing scenarios from ~120k tokens to ~20k tokens for both the real tokenizer and countTokens functions. - Updated token limits in tests to reflect new constraints, ensuring tests accurately assess performance and call reduction. - Enhanced console log messages for clarity regarding token counts and reductions in the updated scenarios. * refactor: Update Tokenizer imports and exports - Moved Tokenizer and countTokens exports to the tokenizer module for better organization. - Adjusted imports in memory.ts to reflect the new structure, ensuring consistent usage across the codebase. - Updated memory.test.ts to mock the Tokenizer from the correct module path, enhancing test accuracy. * refactor: Tokenizer initialization and error handling - Introduced an async `initEncoding` method to preload tokenizers, improving performance and accuracy in token counting. - Updated `getTokenCount` to handle uninitialized tokenizers more gracefully, ensuring proper recovery and logging on errors. - Removed deprecated synchronous tokenizer retrieval, streamlining the overall tokenizer management process. * test: Enhance tokenizer tests with initialization and encoding checks - Added `beforeAll` hooks to initialize tokenizers for 'o200k_base' and 'claude' encodings before running tests, ensuring proper setup. - Updated tests to validate the loading of encodings and the correctness of token counts for both 'o200k_base' and 'claude'. - Improved test structure to deduplicate concurrent initialization calls, enhancing performance and reliability. |
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LibreChat
✨ 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
- 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 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.