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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
bce93f9c55
* 🎯 fix: Infer Agents Endpoint for Model Specs Naming an Agent A model spec whose preset names an `agent_id` but omits `endpoint` was unusable. `isModelSpecEndpointMatch` compares the request's endpoint to `preset.endpoint` by strict equality, so an undefined endpoint matched nothing and every request selecting the spec was rejected with a bare `Model spec mismatch` — an error naming neither the spec nor the missing field. The selector had the matching half of the same gap: `handleSelectSpec` read `preset.endpoint` directly, so it sent no endpoint and skipped assigning `agent_id` to `model`. Fixing only the server would leave the request malformed, so the resolution is shared between both. - Add `resolveModelSpecEndpoint` to `librechat-data-provider`, inferring the agents endpoint when a preset names an agent and none is set. An explicit `endpoint` always wins, so configured specs are unaffected. - Use it for endpoint matching and in the selector, so the menu and the request pipeline resolve a spec identically. * 🔁 refactor: Materialize Inferred Spec Endpoints at Config Load The review showed the lazy-resolver approach was unsound end to end: config validation rejected an endpoint-less spec before the resolver could ever run (`tPresetSchema` requires the `endpoint` key), and the resolver was applied at 2 of ~8 read sites, leaving selection handlers, startup presets, access filters, and provider-key reachability reading the raw preset. Materialize once at the boundary instead: - `tModelSpecPresetSchema` now makes `endpoint` optional (`nullish`). This is barely a widening — `endpoint: null` already validated — and only for model-spec presets; `tPresetSchema` is untouched. - `materializeModelSpecEndpoints` writes each spec's resolved endpoint back onto its preset. `createAppConfigService` applies it at both effective-config assembly points — YAML base load and DB-override merge — so admin-panel specs stored in override documents are covered. Identity-preserving, so cached configs see no new references when nothing needs filling in. - Every consumer now reads complete specs; the client's lazy resolve in `handleSelectSpec` is reverted to a raw read. `getModelSpecPreset` and the two hand-rolled preset constructions resolve the endpoint explicitly, which the narrowed preset type now enforces at compile time for any `TPreset`-shaped destination. - `isModelSpecEndpointMatch` keeps the resolver as request-time defense. * 🩹 fix: Materialize Spec Endpoints Before the YAML Missing-Endpoint Guard `processModelSpecs` warns and skips any spec whose preset lacks an endpoint, and it runs inside `loadBaseConfig` — so the previous commit's materialization received a YAML list from which the inferable spec had already been dropped. Only DB-override specs (merged after the guard) actually benefited. - Materialize at the entry of `processModelSpecs`, so inference happens before the guard and YAML agent specs survive it. The guard keeps skipping genuinely endpoint-less specs. The `createAppConfigService` calls stay: the base-path one guards alternate `loadBaseConfig` implementations, the merged-path one covers override documents, and both are identity-preserving no-ops when specs are already complete. - Constrain the widened schema: omitting `endpoint` is only legal when the preset names an `agent_id`. A preset with neither validated as a hard error before the key became optional, and silently accepting it would trade that startup-time error for a dead spec. An explicit `endpoint: null` (valid before this PR) keeps validating. * 🩹 fix: Infer Only From Non-Empty Agent IDs, Never Over Explicit Null Two edge cases in the inference contract: - `agent_id: ''` (what a form-backed writer persists for an untouched field) passed the nullish checks, validating and materializing a spec that names no agent. Both the refinement and the resolver now require a non-empty id, so such config fails validation loudly instead of producing a selectable spec that cannot work. - `endpoint: null` alongside an `agent_id` was treated as inferable, silently activating a spec that validated — and was skipped — before this PR. An explicit null is a statement, not an omission: the resolver now infers only when the key is absent, preserving prior behavior for previously valid configs. |
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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
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- 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
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🎨 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
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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
-
⚙️ 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.