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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
8a46ca070c
* 🧊 feat: Persist Context Fading Tier Across Agent Runs Carry the pruner's latched context-fading tier from `@librechat/agents` through `contextMeta` the way `calibrationRatio` already travels, so a historical tool result keeps the same truncated bytes from one run to the next and Anthropic's prefix-based prompt cache survives across turns (danny-avila/agents#497). - Add `IAgentFadingTier` to the message and conversation `contextMeta` types, schemas and the data-provider zod schema - Add `isAgentFadingTier`, `resolvePersistableFadingTier` and `resolveRunContextMeta` in `packages/api`; a tier is persisted once it carries information (masking active or a budget below the window), and a latched tier is kept even at a neutral calibration ratio - Seed `createRun({ fadingTier })` from the parent response's contextMeta regardless of encoding, since caps are character-based; the field rides the existing forward-compatible `runConfig` variable and older SDK versions ignore it, as does `Run.getFadingTier` being absent - Accept a valid `fading` field in event actor context and reject a malformed one, matching the calibration checks - Capture run context meta through one module-level helper for both completion paths instead of two inline copies Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Seed Fading Tier Across HITL Resume, Validate at the DB Layer Address review findings on fading tier persistence: - Leave the "is this tier worth persisting" decision to the SDK, whose `Run.getFadingTier()` now returns only informative tiers; comparing against the client's window misclassified untouched conversations whenever a reserve ratio was configured - Carry `contextMeta` through the HITL pause projection (staged approval, job metadata, resume state) and seed the rebuilt client from it, so a paused turn resumes with the same tier and calibration instead of re-deriving a shallower one and rewriting the prefix - Share run seeding between `chatCompletion` and `resumeCompletion`, and make capture tolerate partial runs and bare resume contexts - Validate `contextMeta.fading` in `commitAgentEventActorState` with a shared `isAgentFadingTier` guard in data-schemas, so a malformed tier is rejected at the DB layer instead of silently cold-starting the actor - Reuse `IAgentEventActorContextMeta` for `IMessage.contextMeta` instead of extending an inline duplicate Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 test: Cover Resume Seeding From Paused Context Meta Assert the resume controller seeds the rebuilt client from the context meta captured at the pause before rebuilding the run, and move the capture helper's JSDoc back above its function. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧹 style: Sort Imports in Fading Tier Plumbing Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Persist Fading Tier Through Redis Jobs and Branches Address Codex and Copilot review findings on fading tier persistence: - Deserialize `contextMeta` in `RedisJobStore`, whose explicit read mapper rebuilt every other pause field but this one, so Redis-backed deployments resumed with no tier to seed after every HITL pause - Copy server-private `contextMeta` onto the assistant message created by `POST /api/messages/branch`, so the next turn seeds its pruner the same way it would from the parallel source response - Constrain the `fading` subdocuments in both Mongoose schemas (version enum, positive budget, required fields) to match the zod schema - Throw a distinct "fading tier is invalid" error from both context meta validators instead of reporting a calibration failure Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Carry Context Meta Through Stop and Re-Pause Addresses the second Codex pass on the fading-tier persistence: - A re-pause whose resumed segment has nothing persistable now clears the job's `contextMeta` in the same transition instead of leaving the first pause's calibration and tier for the next resume to seed from. - The client publishes the run's live calibration and fading tier onto the job after each pre-invoke context snapshot (deduplicated on value), and the Stop path copies `jobData.contextMeta` onto the stopped response, so a follow-up from a stopped turn keeps the prompt prefix stable. - Mid-run captures (HITL pause, Stop) read the graph's live state; `Run` refreshes its own getters only after the stream settles, so the pause used to persist the seeds it started from. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Fence Context Meta Publication Ahead of Stop A Stop handled by another replica reads the job before the owner is signaled, so a fire-and-forget publish could lose the race, and a run stopped before its first context snapshot never published the tier it inherited. The client now publishes the inherited seed before the run streams or resumes, and the context-usage handler awaits each snapshot's publish so the write lands before the model call it describes begins. Failures still only log; the pre-run publish is optional-called so bare resume contexts keep working. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Read Back Context Meta After the Abort Claim The run owner publishes its context meta ahead of each model call and awaits the write, so a publish landing between the abort's initial job read and its terminal claim describes the call whose partial output the abort snapshot carries. abortJob now reads the same-epoch job back after the claim and content re-read and takes its context meta, so the stopped response persists the tier that produced its bytes. The refresh is best-effort and logs on failure like the content refresh beside it. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Share In-Flight Context Meta Writes and Clear Neutral Live State Two snapshot callbacks in a parallel-agent run can capture the same tier before the first write settles; the publisher now caches the in-flight promise per serialized value so the second caller awaits the same durable write instead of treating an uncommitted value as published. A live snapshot with nothing left to persist after an earlier publish now writes a neutral record (ratio 1, no tier), since a running job's fields cannot be deleted through the metadata writer, so a Stop no longer persists an earlier non-neutral state onto the response. The pre-run seed publish is unchanged and is told apart from live snapshots by a flag from the context-usage sink. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 chore: Drop Unused Rest Parameter in Context Meta Test Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 feat: Persist Per-Agent Fading Tiers as Compact Context Meta Reconciles the host with @librechat/agents at 9aacad1a, where graph history stays canonical and each Run derives a provider-only projection from the latched tiers. LibreChat therefore persists only compact state: the calibration ratio, the default agent's tier, and now the per-agent tiers from Run.getFadingTiers(), stored as validated entries so agent IDs never become MongoDB field names and restored onto a null-prototype record for RunConfig.fadingTiers. Truncated messages, canonical tool content, projection provenance and per-message truncation state are never persisted; the SDK's latched flag is stripped on capture. The per-agent map travels everywhere the single tier already did: response contextMeta, event-actor checkpoint state, HITL pause and resume job metadata in both stores, the Stop path, and branch creation. A shared Mongoose definition keeps the message and conversation schemas identical, the zod schema validates entries, and commitAgentEventActorState rejects malformed maps. Comments no longer describe graph messages as truncated; only the provider projection is. Tests cover: tool results and inputs reach the persisted content parts at full size; contextMeta carries exactly the compact tier and calibration fields; a persisted snapshot round-trips into the seeded RunConfig unchanged and prototype-safe; per-agent tiers survive pause, Redis job round-trips, and event-actor context validation. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Restore Event Actor Types and Publish Seeds Before Abortable Setup The previous commit's doc rewording in the conversation types dropped the event-actor interfaces that precede the fading tier, which broke the declaration bundle in CI; they are restored unchanged. The inherited context meta is now published onto the job as soon as the parent's state is loaded and awaited at the top of chatCompletion and resumeCompletion, ahead of run creation and the other abortable setup stages, so a Stop during setup still persists the parent's tiers onto the stopped response. The partial response saved when every subscriber disconnects now carries the job's context meta like the Stop and pause paths do. The branch route keeps the source's context meta in the saved message but strips it from the HTTP response, matching client reads. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Serialize Distinct Context Meta Publications Overlapping snapshots from parallel agents could carry different tiers; each started its own write and both job stores keep whichever write finished last, so an older snapshot could overwrite a newer one. Each distinct publication is now issued after the previous one settles, in call order, so the newest snapshot is what a Stop reads back. Equal values still share one write. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Project Root Message Reads and Keep Inherited Meta on Setup Failure The root GET /api/messages paths returned raw documents: the single-message query read called getMessages without a projection and the cursor page performed an unprojected find. Both now apply CLIENT_MESSAGE_SELECT, with getMessagesByCursor accepting a select option, so the persisted fading tiers never reach the client through the paginated endpoint; the search-hydration read projects the same way. A turn that loads a parent's context meta but fails before its run exists used to replace the inherited meta with the capture of a missing run, so the persisted error response lost the last valid tiers. Both finalizers now keep the inherited meta when no run was created while a created run's neutral state may still clear it. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 refactor: Move Context Meta Publication Into packages/api The publication coordinator (ordering, deduplication, in-flight sharing, failure state) now lives in packages/api as createContextMetaPublisher, with selectRunContextMetaToPublish deciding what a publication carries; the legacy client keeps only the wiring to the job metadata writer. A transiently failing write is retried with a short backoff before the publication is reported and forgotten, so a snapshot's model call no longer proceeds on the first rejection while the job holds the previous record. The SDK swallows handler errors, so retrying is the only way to strengthen the fence without failing the turn. A terminal save now supplies contextMeta: null when the finished run has nothing to carry, and saveMessage unsets a previously stored value in that case, so a disconnect snapshot's record cannot outlive the run that completed neutrally. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Widen the saveMessage Method Type for the Null Unset The exported method interface still typed contextMeta as the message field, so the spec exercising the null unset failed typechecking; it now mirrors the implementation signature. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Publish Every Agent's Snapshot, Sanitize Search Hits, Unbound Agent IDs The context-usage handler now awaits the run's context meta publish for every ON_CONTEXT_USAGE event, hidden sequential agents included, while the visible-snapshot bookkeeping and the client emission keep their gate; a Stop before the next visible snapshot finds the tier a hidden agent's model call latched. Search results are built from the hydrated hit minus its server-private contextMeta, since Meilisearch hydration projects every schema field. The per-agent tier entry guard no longer caps the agent ID length: ephemeral agent IDs encode endpoint, model and sender without a bound, and dropping such an entry would let that agent alone re-derive its tier on the next turn. Also formats the publication spec so the static checks pass. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Unset Context Meta Atomically, Keep Publication State After a Failed Write - `saveMessage` folds the `contextMeta` unset into the same update that persists the terminal response, on both the plain and the provenance-merge paths, so a failure between two writes can no longer leave a completed row carrying a disconnect snapshot's state. - `createContextMetaPublisher` tracks whether any record has committed separately from the retryable latest publication, so `hasPublished` stays true after a later publication exhausts its retries and a following neutral snapshot still overwrites the earlier record on the job. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Carry Context Meta Through the Resumable Stop Route and Neutral Resumed Completions - The resumable Stop route copies the refreshed `jobData.contextMeta` onto the terminal assistant message it persists, as the abort middleware already does, so a stopped resumable response keeps the tiers that produced its partial output. - A resumed run that completes with neutral context state now saves `contextMeta: null`, which unsets the paused segment's record instead of letting an omitted field keep it on the completed row. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Trust Only Server-Authored Context Meta, Unset It on a Neutral Stop, Read It From the Job on Disconnect - `POST /api/messages/:conversationId` and conversation imports drop any client-supplied `contextMeta`, and the run seeds its calibration and fading tiers only from a server-authored parent response, so a forged tier can never shape a provider projection. - Both Stop paths save `contextMeta: jobData.contextMeta ?? null`, so a job that re-paused with neutral state unsets what an earlier pause stored on the row instead of leaving it behind. - The disconnect partial save reads the same-epoch job record for the run's published context meta, since the client-facing resume snapshot never carries server-private state. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧪 test: Mock the Job Store in the Resume Metadata Spec The disconnect partial save now reads the same-epoch job record through `GenerationJobManager.getJobStore()`, so the resume metadata spec's manager mock provides it as the other disconnect spec already does. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 🧊 fix: Keep Context Meta Off the POST Message Response `POST /api/messages/:conversationId` returned the saved row verbatim, so a client write against an existing server-authored message echoed the row's stored context meta. The route now projects the row through the same client projection the branch route uses, which drops the server-private field. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF * 📦 chore: Bump @librechat/agents to 3.7.16 for Persisted Fading Tiers 3.7.16 is the first release that carries danny-avila/agents#497, so `Run.getFadingTier()` / `getFadingTiers()` now report the latched tier this branch persists and `RunConfig.fadingTier` / `fadingTiers` seed the next run from it. The dependency set is unchanged from 3.7.15; only the package entry moves. Adds an SDK-backed round trip to the fading spec: a first pruner latches an informative tier, `resolveRunContextMeta` reduces it to the persisted shape, and a second pruner seeded from that shape reproduces the first run's projected tool-exchange bytes under instruction and calibration drift, while an unseeded pruner under the same drift relaxes its tier and rewrites them. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WNbgMeR2aZDyhsqAy4L8gF --------- Co-authored-by: Claude <noreply@anthropic.com> |
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| LICENSE | ||
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| turbo.json | ||
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
-
💬 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.