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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: Return Sandbox Images From `read_file` as Viewable Artifacts The code-execution sandbox `read_file` path refused every image extension because it reads files via `cat` over codeapi's JSON `/exec` transport, which lossily corrupts non-UTF-8 bytes. The skill-file read path already surfaced images as artifacts; this brings the sandbox path to parity so an agent can actually see a chart/screenshot it reads. - `readSandboxImage` (process.js): a Python base64 reader over `/exec` with an in-sandbox size guard so oversize images never cross the wire; base64 is ASCII-safe where `cat` corrupts. - `handleSandboxImageRead` (handlers.ts): byte-integrity check (guards against a truncated `/exec` stdout), MIME resolved purely from the magic-byte sniff (extension only routes; a mislabeled non-image falls back to the bash hint), and graceful degradation on every failure mode. - Shared `buildImageArtifactResult` used by both read paths; the result's `artifact.content` image_url reaches the UI (tool-end callbacks save it as an attachment) and the LLM (SDK folds it into the model-visible message for Anthropic/OpenAI/Google). * ✅ test: Sync read_file code-only description assertions with image wording * 🛡️ fix: Harden sandbox image reads (regular-file guard, completeness check) Addresses Codex review on PR #14277: - readSandboxImage now os.stat's the target and rejects non-regular files (FIFOs, sockets, /dev/* symlinks) via stat.S_ISREG, and bounds the read at limit+1 bytes — a device/FIFO can no longer stream unbounded into memory until the request times out. - handleSandboxImageRead validates completeness (not just the magic header): PNG must end with the IEND trailer and WebP's RIFF size must match the byte length, so a truncated/interrupted image degrades to the bash hint instead of being sent as a corrupt image_url. JPEG/GIF stay header-level (they can carry trailing metadata; a strict end-marker would risk false rejections). * 🩹 fix: Chunk sandbox image reads to fit the runner stdout cap Inlining any real image failed with "is an image file (.png) and cannot be read as text". Root cause: readSandboxImage base64-encodes the file to STDOUT, but the runner caps stdout at SANDBOX_OUTPUT_MAX_SIZE (1024 bytes by default) and SIGKILLs the job on overflow (status OL), truncating the JSON mid-base64. The parse then threw and the handler degraded to the binary hint. The in-sandbox MAX_BINARY_BYTES=5MB guard never fired because the *transport*, not the file size, is the real ceiling: a 5MB image needs ~6.8MB of stdout. Reproduced against a live MicroVM — a 186KB matplotlib PNG died with 'stdout length exceeded' at exactly the 65536-byte cap. Read the file in windows instead: each /exec pulls raw bytes at an offset and base64s only that slice, so every response stays under the cap regardless of how the runner is configured; the chunks are reassembled and verified against the sandbox-reported total. Verified end-to-end on a real MicroVM: 25KB and 186KB PNGs both round-trip byte-exact (sha256 match). Also: - Detect the truncation explicitly (status OL) and name the fixable cause (chunk size / SANDBOX_OUTPUT_MAX_SIZE) instead of "unexpected output". - Parse the LAST stdout line so a shell banner can't break the read, and include a stdout snippet when it genuinely is unparseable. - LIBRECHAT_CODE_IMAGE_CHUNK_BYTES (default 32KB) tunes the window. - Tests drive the real reader against a mocked /exec transport rather than mocking readSandboxImage, which is why the existing suite stayed green through this bug. * 🎯 fix: Cap sandbox inline images at 1MB, separate from skill-file reads The sandbox and skill-file image paths shared MAX_BINARY_BYTES (5MB), but their transports differ: skill files stream from storage, while sandbox bytes come back base64 over /exec stdout under the runner's output cap, so the reader windows the file and cost scales in round-trips (~160 at 5MB vs ~32 at 1MB). Nothing is gained by allowing more — vision providers downsample to ~1.5-2k px regardless, so multi-MB originals buy no fidelity while grinding through round-trips. Give the sandbox path its own MAX_SANDBOX_INLINE_IMAGE_BYTES (1MB), used for both the read cap and the over-limit message (which previously quoted 5MB while the reader enforced something else). Skill-file reads keep 5MB. Verified against a live MicroVM: a 186KB PNG round-trips byte-exact, and a 1.4MB file returns tooLarge in a single round-trip with zero bytes transferred, degrading to the existing bash_tool hint. |
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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
-
💾 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.