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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
ed8547018c
* ⚡ feat: Persist HITL checkpoints only on pause (skip clean-exit writes) With `durability: 'exit'` (set by the SDK whenever a checkpointer is active) LangGraph persists ONE checkpoint at the exit boundary on EVERY run — paused or not. So a non-paused HITL turn writes a dead checkpoint whose only fate is to be pruned by deleteAgentCheckpoint: pure write+delete churn on the common path, given HITL only ever resumes an *interrupt* checkpoint. `InterruptOnlyMongoSaver` (a MongoDBSaver subclass) persists only interrupt checkpoints and discards clean-exit ones, so a non-paused turn writes nothing. How it tells them apart (verified empirically against @langchain/langgraph, not docs): when a run interrupts, the runner calls `putWrites` with the `INTERRUPT` ("__interrupt__") channel for the checkpoint it's about to create, and that write's `config.checkpoint_id` equals the `checkpoint.id` of the `put` that immediately follows. A clean exit calls `put` with no preceding interrupt `putWrites`. So we record the checkpoint id of any interrupt `putWrites` and persist a `put` only when its `checkpoint.id` was so marked. Keying on the globally-unique checkpoint id (not thread_id) keeps this correct even when two runs race on the same conversation (the job-replacement scenario). Correctness is preserved end-to-end: interrupt checkpoints + their pending writes persist exactly as before (resume unchanged); clean checkpoints were only ever written-then-pruned, so not writing them is observationally equivalent. The eager prune stays as the backstop. Tests (mongodb-memory-server): a bare put() is discarded; an interrupt-seeded checkpoint is persisted with its __interrupt__ pending write; and an end-to-end real-graph run writes 0 checkpoints on a clean completion and a resumable one on interrupt. NOTE: a non-paused turn's deleteAgentCheckpoint now finds nothing to delete (a 0-match no-op) — a follow-up can skip that call entirely once the lingering-abandoned-pause cleanup role is reassigned to the TTL + expiry sweeper. * ⚡ feat: Drop the redundant clean-path checkpoint prune With the lazy checkpointer (InterruptOnlyMongoSaver) a non-paused turn no longer writes a clean-exit checkpoint, so the post-completion prune in chatCompletion's finally had nothing left to delete. It was also already redundant: every fresh turn runs a pre-run prune (`deleteAgentCheckpoint` before `processStream`) that clears any checkpoint orphaned by a prior abandoned pause — verified empirically that a lingering interrupt checkpoint WOULD otherwise poison a fresh turn (LangGraph continues the abandoned state + re-interrupts), and that the pre-run prune is what prevents it. The Mongo TTL remains the backstop, and the resume path still prunes after a successful finalize. Removing the clean-path prune also deletes its job-replacement race surface (round-17 F21): an older run's late finally can no longer delete a newer paused run's checkpoint, because there is no longer a clean-path prune to race. Dropped the now-dead F21 predicate test. Net per non-paused HITL turn: from {pre-run prune + checkpoint write + post-run prune} down to {pre-run prune} — no write, no post-completion delete. * 🛡️ fix: Anchor any pending-write checkpoint; stale-only eviction (Codex) Broaden the lazy saver's keep-rule from "interrupt-only" to "persist any checkpoint that carries pending writes" (renamed InterruptOnlyMongoSaver → LazyMongoSaver). This makes it robust to delta-channel graphs without changing behavior for LibreChat's graph: - K1 (P1): a delta-channel graph can write a synthetic PARENT/anchor checkpoint (no __interrupt__ mark) that the interrupt checkpoint then points at, with the delta writes stored under the parent id. The old rule discarded that parent, breaking delta-state resume. Now any checkpoint that received putWrites is persisted, so the anchor parent and its writes survive and resume can walk the chain. - K3 (P2): for the same reason, clean delta-write rows are no longer orphaned — their checkpoint is persisted alongside them. (For LibreChat's standard Annotation/messages graph a clean run makes no putWrites at all — verified empirically — so the common path still writes nothing and the optimization is unchanged.) - K2 (P2): the 1024 FIFO cap could evict a valid in-flight id whose put() was just behind Mongo I/O, mis-classifying its interrupt checkpoint as a clean exit. Replaced with time-based eviction: only ids older than 5 min (a put always follows its putWrites within ms) are swept; a recent in-flight id is never dropped, and the map grows rather than evict a valid id if nothing is stale. New integration test: a checkpoint anchored by a NON-interrupt write is persisted. Full agents/HITL suites green (108). * style(checkpointer): fix import order to satisfy sort-imports CI * fix(checkpointer): don't persist failed-turn (error-only) checkpoints LazyMongoSaver anchored on ANY pending write, so a non-paused turn that errors (LangGraph records an __error__ write then a put) was persisted and, with the clean-path prune removed, lingered until the next fresh-turn prune or the Mongo TTL. Anchor only on resumable writes — INTERRUPT or a real (non-__-prefixed) state/delta channel — so error/bookkeeping-only checkpoints are discarded at the source. Addresses Codex P3. Codex P2 (delta-stub parent orphan) is not reachable: the SDK graph uses standard Annotation/MessagesAnnotation channels (no DeltaChannel), and under durability:'exit' putWrites precedes put with a parentless boundary checkpoint — probe-confirmed against @langchain/langgraph@1.4. Documented the durability:'exit' invariant the saver depends on. Tests: error-only put discarded; e2e throwing graph persists 0 checkpoints. * fix(checkpointer): drop bookkeeping-only write batches, not just the checkpoint The prior fix stopped the failed-turn CHECKPOINT from persisting, but putWrites still forwarded the __error__ batch to MongoDBSaver.putWrites — writing a row to agent_checkpoint_writes whose parent checkpoint is then discarded. With the post-run deleteThread removed, that orphan row lingered until the Mongo TTL or the conversation's next pre-run prune. putWrites now drops a non-resumable (bookkeeping-only) batch entirely instead of forwarding it. Probed against a real MongoDBSaver (mongodb-memory-server): a throwing graph now leaves 0 checkpoints AND 0 write rows (was 0 + 1 orphan), while interrupt->resume is unaffected — the __interrupt__ write is resumable so it is still forwarded. Addresses Codex P2 (round 3). Tests: error-only put leaves no checkpoint and no write row; e2e throwing graph leaves both collections empty; new e2e interrupt->resume completes with the approval value. * fix(checkpointer): un-anchor a checkpoint whose putWrites failed; freshen comments Self-review findings on the converged PR: 1. LangGraph dispatches put() concurrently with putWrites (probe-confirmed on 1.4.5), and put() still completes when putWrites rejects — so a transient Mongo failure during the interrupt write could persist a checkpoint whose __interrupt__ row is missing (an unresumable phantom pause). putWrites now deletes the write anchor on rejection (best-effort) and rethrows, so that put() discards the checkpoint instead. The pre-recorded anchor stays where it is — recording after the await would drop slow-I/O interrupts on the success path, which the same probe showed is reachable. 2. Renamed leftovers: two comments still said InterruptOnlyMongoSaver; the class is LazyMongoSaver. 3. Documented why the pre-run prune is deliberately unconditional per HITL turn (any cheaper gate can go stale across replicas and skip the prune exactly when an orphaned interrupt exists). Test: failed putWrites → subsequent put persists nothing (14/14 green). * fix(checkpointer): bookkeeping write batches follow their checkpoint's fate The round-3 rule dropped bookkeeping-only putWrites batches (__error__/ __resume__/__no_writes__) unconditionally — batch-scoped, when the decision must be checkpoint-scoped. Probe-confirmed (langgraph 1.4.5, durability:'exit'): a Send fan-out that pauses on one sibling records the completed siblings as pure __no_writes__ batches on the RETAINED interrupt checkpoint; dropping those markers makes resume re-execute the completed siblings (side effects measured twice). Addresses Codex M2 (P2). putWrites now PARKS a bookkeeping-only batch in memory until the checkpoint's fate is known: forwarded when the checkpoint is anchored (or was just persisted — put is dispatched concurrently), dropped when put discards it. Net: an errored turn still leaves nothing durable (0 checkpoints, 0 write rows), and a retained checkpoint stores byte-for-byte what a plain MongoDBSaver would. Codex M1 (__resume__ lost on re-pause) did not reproduce: the re-pause emits [__interrupt__,__resume__] as ONE batch (anchored, forwarded whole) and a second resume on a rebuilt graph replays both answers correctly — but the fate-scoped buffering now covers a lone __resume__ batch in any ordering too. Tests: bookkeeping preserved on a retained checkpoint in either arrival order; e2e Send-sibling pause/resume with side-effect counters (was {a:2,c:2} under the drop rule, now {a:1,c:1}); error-only turn still leaves both collections empty. 16/16 green. |
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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.