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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
8f6228095f
* 🖼️ fix: Stop Reporting a Failed Sandbox Image Read as an Unreadable Format `read_file` on a `/mnt/data` image collapsed every failure — rate limit, missing path, truncated transfer, transport error — into the binary-file hint, whose image branch reads as a permanent capability limit ("is an image file and cannot be read as text"). The model stopped retrying, and the operator had no way to tell which failure had actually occurred. The failure that surfaced it: the reader windows the bytes through `/exec` stdout, one call per window, against the Code API's per-user execution limiter (20 requests / 30s in the reference deployment). At a fixed 32KB window a 122KB chart cost 4 calls and a 1MB image cost 33 — more than a whole limiter window, so anything over ~640KB could never complete. A turn that generates several charts and reads them back exhausts the budget partway through and reports the 429s as unreadable images. - `handlers.ts`: failed reads name the real cause and the affordance that matches it — rate-limited waits and retries, a missing path lists the directory, a truncated transfer retries. The "cannot be read as text" phrasing is now reserved for bytes that are not a supported image. An image past the inline budget says to downscale and read the smaller copy rather than pointing at `bash_tool`, which cannot show an image. - `process.js`: the window is derived from the runner's stdout budget (48,960B vs 32,768B, so a 122KB chart is 3 calls and 1MB is 22), halves and re-reads the same offset when a runner truncates anyway (remembered per Code API, with the env remedy logged), waits out a rate limit it can afford instead of discarding the bytes already pulled, and stops at a 24-call ceiling so no read stalls a turn across limiter windows. - `utils/code.ts`: shared `getCodeApiRetryAfterMs` (header, then the `retry_after_seconds` body); `skillFiles.ts` drops its private copy. * ♻️ refactor: Move the Sandbox Image Reader Into TypeScript Addresses the review round on #15453. - P1: windowing, window sizing, the in-sandbox reader script, response parsing and byte assembly move to `packages/api/src/files/code/image.ts`, and the Code API rate-limit policy to `packages/api/src/utils/code.ts`. `process.js` keeps only the `/exec` transport it owns. Both new modules are tested against their real implementations (19 + 13 cases), including the round-trip ceiling, the changed-file guard and the shell-injection guard on the reader script. - P2: a configured stdout budget below the old 8KB window floor produced a window the runner could never emit, and narrowing was pinned to the same floor, so no retry could succeed. The window now aligns to the base64 group size instead, and the round-trip ceiling bounds what a small budget costs. - P2: `.env.example` documented a 32,768-byte default that no longer holds and never mentioned the stdout budget. It now describes `LIBRECHAT_CODE_SANDBOX_OUTPUT_MAX_SIZE` as the setting to tune, with the chunk override as the escape hatch. - P3: `process.spec.js` no longer reimplements the retry parser in its module mock. Behaviour is tested where it lives; the mock is a declared passthrough and the spec covers what the transport still owns — session forwarding, the profile header, parser hand-off and error logging. - P3: `getCodeApiRetryAfterMs` reads the response body through `in` narrowing rather than an `as` assertion. - P3: the window-sizing JSDoc no longer claims a multiple of 3 in the one case that is not — an explicit `LIBRECHAT_CODE_IMAGE_CHUNK_BYTES`. * 🎯 fix: Size Sandbox Image Reads to What the Limiter Can Deliver Addresses review round 2 on #15453. - Narrowing now halves the caller's own failing window instead of the shared learned value, and the learned value only moves down to the narrowest size any read needed. Reads run concurrently (one per tool call), so two reads failing at the same size were compounding into a window two steps smaller than the runner would have accepted, and persisting it for every later read. - Narrowing continues until a runner accepts a window or a floor below which no runner could serve an image at all. Two halvings stopped at 12,240 bytes — still ~16KB of base64 — so an 8-10KB stdout cap that was not configured up front stayed unreachable. - The round-trip ceiling is now one limiter window (20), and a read that cannot finish within it says so from the FIRST response rather than spending the whole budget rediscovering it: the first window reveals both the file size and what a call really delivers. A near-limit image no longer drains the turn's executions before failing, which is what made the wait budget look too small. - The round-trip message names the size this deployment can actually inline, so the model has a downscale target instead of a guess. - `python3: not found` from the sandbox reader was matching the generic "not found" in `isSandboxMissingFileError` and being reported as a missing image path, sending the model to `ls /mnt/data` while hiding a runner dependency. Image reads now classify only path-specific ENOENT forms as a missing file. * 📐 fix: Size a Truncated Read's Retry From the Cap It Revealed Addresses review round 3 on #15453. A ~1KB sandbox stdout cap is a real runner default, and the 1,536-byte narrowing floor sat above it: discovery stopped one step short and declared every image unreadable, even though small ones fit comfortably in the 576-byte window the configured path already derives. Narrowing now runs until no smaller window exists. It also no longer has to grope its way down: a truncated response emitted exactly as much as the runner allows, so its length measures the cap this deployment never declared, and the retry is derived from it directly — one round-trip instead of the eight blind halvings it takes to fall from ~49KB to ~1KB. Blind halving remains the fallback and bounds the result either way, so every retry is strictly smaller than the window that just failed. |
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| deploy-compose.langfuse-fanout.yml | ||
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LibreChat
English · 中文
🚀 What's New in v0.8.8-rc1
- Agent run control: Interrupt or steer an Agent mid-run, queue follow-up messages, and reclaim, edit, or escalate pending steers.
- Human-in-the-loop Agents: Agents stream question progress, ask up to four related questions in one form, pause for input or tool approval, and resume.
- Unified Agent Builder: A redesigned Tools marketplace brings together Skills, MCP, Code Interpreter, orchestration, Programmatic Tool Calling, model-spec controls, and per-tool background and intent settings.
- Readable Agent activity: Generated activity-group headers, parent phase summaries, and live tool intent labels make long reasoning and tool runs easier to scan.
- Code Interpreter workflows: Code and shell tools can run in the background, sandbox images return as viewable artifacts, and highly experimental stateful sessions can reuse prewarmed conversation workspaces.
- Agent extensibility: Experimental Agent Plugins can bundle deployment Skills, MCP servers, and opt-in command hooks, while explicit subagents initialize only when selected.
- Memory, context, and identity: Agents can manage memory with optional per-agent isolation, expose support contacts safely, and show a more faithful Context Usage gauge.
- Sharing and files: Shared conversations show a badge and update at a stable URL, while signed-in viewers can continue them as personal copies.
- Artifact workflows: Open previews fullscreen, work with PowerPoint
.potxtemplates across upload, search, and code execution, upload shell scripts across common MIME variants, export Mermaid diagrams as SVG or PNG, and download original Office files from the artifact panel. - Models and reasoning: Added GPT-5.6 with Responses API reasoning controls, Claude Opus 5 and Sonnet 5, Gemini 3.7 and 3.6 Flash, and Gemini 3.5 Flash-Lite.
- Langfuse observability: Configure encrypted Langfuse connections in-app, let authorized admins open sampled sessions directly, optionally fan out traces by tenant, and suppress central export per run.
- Administration and security: Delegate config sections, encrypt registered secrets, enforce SSRF checks for speech, OCR, and web tools, and generate unique temporary credentials when secrets are blank.
- Messages and navigation: Right-aligned user turns, unified multi-part editing, full-message copy, a dock-style message rail, virtualized search, smooth streaming, and faster Agent startup.
- Streaming and tool reliability: Adaptive provider smoothing, Redis delta batching, dynamic MCP tool refresh, parsed MCP response media types, runtime OAuth recovery, and Agent stream circuit breakers improve long-running workflows.
- Deployment and reliability: Added configurable HTTP timeouts, Amazon DocumentDB 5.0+ support, low-noise Redis and browser observability, and a rolling-upgrade-safe generation protocol.
Read the full v0.8.8-rc1 changelog.
✨ 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 - Agent Plugins: Experimentally bundle deployment Skills and MCP servers into startup-loaded packages
- 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 create React, HTML, and Mermaid content directly in chat
- Open previews fullscreen and export Mermaid diagrams as SVG or PNG
-
🎨 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
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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 🗃️
-
🌎 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.