Overview: LibreChat is an open-source, self-hostable alternative to ChatGPT that enables users to integrate and switch between multiple AI models including OpenAI, Anthropic’s Claude, Google Gemini, Azure OpenAI, AWS Bedrock, and local models via Ollama or Deepseek. Designed for developers and organizations seeking control over their AI infrastructure, LibreChat eliminates vendor lock-in by supporting pay-per-call APIs and local deployments. It enhances the ChatGPT interface with advanced features like AI agents, code execution in multiple languages, web search integration, and multimodal inputs (image uploads, file analysis). Built with TypeScript and designed for Docker deployment, LibreChat is ideal for teams wanting a secure, customizable, and scalable conversational AI platform without relying on paid subscription services.
What You Get
- Multi-Model AI Support - Connect to OpenAI, Anthropic (Claude), Google Gemini, Azure OpenAI, AWS Bedrock, Deepseek, Mistral, Ollama, Groq, and more via direct API integration or custom endpoints without proxies.
- Code Interpreter API - Secure, sandboxed execution of Python, Node.js, Go, Java, Rust, C/C++, PHP, and Fortran code with file upload/download capabilities directly in chat.
- AI Agents & MCP Support - Build no-code custom assistants using Model Context Protocol (MCP), integrate tools like file search, web search, and code execution, and deploy community-built agents from a marketplace.
- Web Search with Jina Reranking - Perform internet searches with customizable rerankers using Jina API URLs to improve search result relevance.
- Code Artifacts & Generative UI - Generate and interact with React components, HTML pages, and Mermaid diagrams directly in chat responses.
- Image Generation & Editing - Create images with DALL-E 3/2, Stable Diffusion, Flux, or any MCP-compatible image model; support for text-to-image and image-to-image transformations.
- Presets & Context Management - Save, share, and switch between AI model configurations and prompt presets; fork conversations for branching context trees.
- Multimodal File & Image Analysis - Upload and analyze images with models like GPT-4o, Claude 3, Llama-Vision, and Gemini; chat with PDFs, documents, and other file types.
- Multilingual UI - Full interface localization in 20+ languages including Chinese, Arabic, Japanese, Russian, and Spanish.
- Speech-to-Text & Text-to-Speech - Enable hands-free interaction with OpenAI, Azure OpenAI, and ElevenLabs audio APIs.
- Multi-User Authentication - Secure access via OAuth2, LDAP, and email login with built-in moderation and token spend tracking.
- Import/Export Conversations - Import from ChatGPT or Chatbot UI; export as Markdown, JSON, text, or screenshots for record-keeping.
- Search All Messages - Full-text search across all conversations and messages for quick retrieval of past interactions.
- Docker & Cloud Deployment - Deploy via Docker, Railway, Zeabur, or Sealos with configurable reverse proxy and environment variables.
Common Use Cases
- Building a multi-tenant SaaS dashboard with AI agents - Enterprises use LibreChat to offer custom AI assistants powered by different models per customer, with secure authentication and usage tracking via OAuth2/LDAP.
- Creating a research assistant for academic teams - Researchers upload PDFs and images, run code analysis with the Code Interpreter, and use web search to gather latest papers—all within a single self-hosted interface.
- Problem: High ChatGPT Plus costs → Solution: LibreChat with local models - Teams replace $20/month subscriptions by deploying Llama-Vision or Deepseek locally via Ollama, reducing costs while maintaining multimodal capabilities.
- DevOps teams managing AI workflows across cloud providers - Engineers configure LibreChat to route queries to Azure OpenAI for enterprise compliance, AWS Bedrock for scalability, and Ollama for low-latency internal tools.
Under The Hood
LibreChat is a modular, extensible AI chat application built with TypeScript and JavaScript that supports integration with multiple large language models and tooling systems. It is structured as a monorepo with distinct backend and frontend components, enabling flexible deployment and development workflows.
Architecture
LibreChat adopts a layered architecture with clear separation between services, clients, and tools. It leverages design patterns such as strategy and factory to support diverse AI providers and tool integrations.
- Modular structure with distinct backend and frontend components
- Strategy and factory patterns for handling multiple AI model providers
- Clear separation of concerns between API, client, and database layers
- Extensible architecture supporting custom tools and agents
Tech Stack
LibreChat is built using a modern JavaScript/TypeScript ecosystem with React and Express.js as core technologies.
- TypeScript for type safety and enhanced developer experience
- React frontend with Vite for fast builds and development
- Express.js backend with MongoDB integration via Mongoose
- Extensive use of libraries like @langchain/core and @tanstack/react-query
Code Quality
The project maintains a strong testing culture with comprehensive test coverage across modules.
- Extensive test suite covering both backend API and frontend components
- Consistent error handling with informative messages and try/catch patterns
- Code linting and formatting configured with Prettier and ESLint
- Reasonable code consistency and naming conventions despite some technical debt
What Makes It Unique
LibreChat stands out by combining LLM client support, tooling, and agent-based architectures into a unified platform.
- Comprehensive integration of multiple AI providers with a consistent interface
- Modular and extensible framework for building custom AI-powered tools
- Agent-based architecture that enables complex multi-step reasoning workflows
- Unified system that bridges model providers with structured tooling capabilities