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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
3e064c2f2b
* feat: per-agent skill selection in builder and runtime scoping
Wire skills persistence on the Agent model and enable the skills
section in the agents builder panel. At runtime, scope the skill
catalog to only the skills configured on each agent (intersected
with user ACL). When no skills are configured, the full user catalog
is used as the default. The ephemeral chat toggle overrides per-agent
scoping to provide the full catalog.
* fix: add scopeSkillIds to @librechat/api mock in responses unit test
The test mocks @librechat/api but was missing the newly imported
scopeSkillIds, causing createResponse to throw before reaching the
assertions. Added a passthrough mock that returns the input array.
* fix: scope primeInvokedSkills by agent's configured skills
primeInvokedSkills was receiving the full unscoped accessibleSkillIds,
bypassing the per-agent skill scoping applied to initializeAgent. This
allowed previously invoked skills from message history to be resolved
and primed even when excluded from the agent's configured skill set.
Apply the same scopeSkillIds filtering to match the initializeAgent
calls, so skill resolution is consistent across catalog injection
and history priming.
* fix: preserve agent skills through form reset and union prime scope
Two related bugs in the per-agent skill selection flow:
1. resetAgentForm dropped the persisted skills array because the generic
fall-through at the end of the loop excludes object/array values.
Combined with composeAgentUpdatePayload always emitting skills, this
caused any save of a previously-configured agent to silently overwrite
skills with an empty array. Add an explicit case for skills mirroring
the agent_ids handling.
2. primeInvokedSkills processes the full conversation payload, including
prior handoff-agent invocations. Scoping it to only primaryAgent.skills
meant a skill invoked by a handoff agent in a prior turn could not be
resolved when the current primary agent had a different scope, leaving
message history reconstruction incomplete. Union the per-agent scoped
accessibleSkillIds across primary plus all loaded handoff agents so
any skill any active agent could invoke is resolvable from history.
* fix: mark inline skill removals as dirty
The inline X button on the skills list called setValue without
shouldDirty: true, so removing a skill via this control did not
mark the skills field as dirty in react-hook-form state. When a
user removed a skill with the X button and also staged an avatar
upload in the same save, isAvatarUploadOnlyDirty returned true and
onSubmit short-circuited to avatar-only upload, silently dropping
the PATCH that would persist the skill removal.
The dialog path (SkillSelectDialog) already passes shouldDirty: true
on add/remove; this aligns the inline control with that behavior.
* fix: restore full ACL scope for primeInvokedSkills history reconstruction
Reverting the earlier scoping of primeInvokedSkills to the active-agent
union. That change conflated runtime invocation scoping (which correctly
gates what the model can call now) with history reconstruction (which
restores bodies the model already saw in prior turns).
Per-agent scoping still applies at:
- Catalog injection (injectSkillCatalog via initializeAgent)
- Runtime invocation (handleSkillToolCall via enrichWithSkillConfigurable,
using each agent's scoped accessibleSkillIds in agentToolContexts)
History priming is a read of past context, not a grant of new capability.
Scoping it causes historical skill bodies to vanish from formatAgentMessages
when an agent's skills list is edited mid-conversation or when the ephemeral
toggle flips, which breaks message reconstruction and drops code-env file
continuity for /mnt/data/{skillName}/ references. The user's ACL-accessible
set is the correct and sufficient gate for history reconstruction.
* fix: close openai.js skill gap and pin undefined vs [] semantics
Three related gaps surfaced in review:
1. api/server/controllers/agents/openai.js was a third skill resolution
site alongside responses.js and initialize.js, but still used the old
activation gate (required ephemeralAgent.skills === true) and never
passed accessibleSkillIds through scopeSkillIds. Per-agent scoping
silently did not apply on this route. Mirror the same pattern used
in responses.js so all three routes behave identically.
2. scopeSkillIds previously collapsed undefined and [] into the same
"full catalog" fallback, making it impossible for a user to express
"this agent has no skills." Tighten the semantics before any data
is written under the old behavior:
- undefined / null = not configured, full catalog
- [] = explicitly none, returns []
- non-empty = intersection with ACL-accessible set
Update defaultAgentFormValues.skills from [] to undefined so a brand
new agent whose skills UI was never touched does not accidentally
persist "explicit none" on first save (removeNullishValues strips
undefined from the payload server side).
3. Add direct unit tests for scopeSkillIds covering all five cases
(undefined, null, empty, disjoint, overlap, exact match, empty
accessible set). 16 tests total in skills.test.ts pass.
* fix: add scopeSkillIds to @librechat/api mock in openai unit test
Same pattern as the earlier responses.unit.spec.js fix: the test mocks
@librechat/api with an explicit object, so each newly imported symbol
must be added to the mock. Without scopeSkillIds, OpenAIChatCompletion
controller throws on destructuring before reaching recordCollectedUsage,
causing the token usage assertions to fail.
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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
-
🔦 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.