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* 🪙 fix: Persist Context Snapshot + Summary Marker After Summarization The post-summarization context is correctly compacted by the SDK, but the breakdown wasn't reliably reaching the client, leaving the gauge on the whole-history estimate (stuck at 100% forever once a conversation compacts). Two server changes in buildResponseMetadata: - Snapshot guard: persist the breakdown when a PRIMARY usage event follows the latest snapshot (tracked via contextUsageSink.latestUsageIndex, recorded in the on_context_usage handler) instead of a brittle snapshot-vs-primary count. A summarization detour adds an extra snapshot whose only following usage is tagged 'summarization', which the count guard could miscount and drop. - Summary marker: whenever a turn compacts (summaryTokens > 0), persist a lightweight metadata.summaryUsedTokens (the pre-invoke compacted context size) UNCONDITIONALLY — so even when the full snapshot can't be saved (interrupted final call) or never reaches the client, the per-message estimate has a signal to cap the discarded history. Tests: client.contextMetadata.spec (guard + marker, incl. marker-survives-drop) and a real-pipeline summarization integration test. * 🪙 fix: Cap the Context Estimate at the Summary Marker When the gauge falls back to the per-message estimate (no usable snapshot on the branch), sumBranch summed the ENTIRE branch history — after a summarization that discarded most of it, this over-counts and pins the gauge at 100% in perpetuity. sumBranch now stops at the deepest summarized response (metadata.summaryUsedTokens) and records it as summaryBaseline; the walk counts only post-summary messages, and useTokenUsage adds the baseline. So the estimate reflects the compacted context (summary + recent turns), not the discarded history. USD/default behavior unchanged when no marker is present. Test: sumBranch caps a huge pre-summary history at the compacted baseline. * 🪙 fix: Address Codex Review on the Summarization Marker - Branch cost/usage is no longer truncated at the summary marker — sumBranch caps only the CONTEXT-window count there and keeps accumulating provider usage/cost to the root (cumulative spend isn't discarded by compaction). - findBranchSnapshotAnchor stops at a summarized response with no snapshot of its own, so it can't recover a stale PRE-summary snapshot and show discarded history; the summary-baseline estimate is used instead. - Abort path: buildAbortedResponseMetadata now persists the summaryUsedTokens marker (pre-invoke, no completedOutputTokens ambiguity, so safe on abort) so a STOPPED summarized turn isn't re-summed on reload. - Marker baseline fallback now includes summaryTokens (a separate breakdown field) so it doesn't under-report the compacted size. DRY'd into a shared computeSummaryUsedTokens used by the completion and abort paths. - Estimate popover surfaces the summary baseline as a row so the displayed rows reconcile with the header total. Tests: sumBranch cost-not-truncated + anchor-stops-at-marker (client); computeSummaryUsedTokens fallback + abort marker (packages/api). * 🪙 fix: Attribute Persisted Context Usage to the Snapshot Run Match the post-snapshot primary usage to the latest snapshot's runId before persisting metadata.contextUsage. Parallel/direct runs interleave snapshots and usage (A snapshot → B snapshot → A usage → B no-usage); the prior index-only guard persisted B's snapshot with A's output. finalCallOutputTokens now filters completedOutputTokens to the snapshot's run. Untagged events (older lib/resume) match any run for back-compat. * 🪙 fix: Harden Summary Marker Against Tool-Loops, Stale Anchors, and Emit Races Codex round on the summarization marker: - Avoid double-counting earlier tool-loop outputs in the summary marker: those outputs sit in BOTH the latest snapshot's pre-invoke baseline AND the response message's tokenCount the client estimate adds on top. computeSummaryUsedTokens now subtracts the run's prior primary outputs (priorRunOutputTokens) — the live path bounds them by the snapshot's usage index, the abort path by all primaries (an interrupted final call emits none). Single-call turns subtract 0. - Stop treating pre-summary anchors as active: sumBranch no longer sets containsAnchor once the context is capped at a summary marker, so a stale pre-summary snapshot can't override the summary-baseline estimate. - Capture latestUsageIndex BEFORE awaiting emitEvent: a yield (resumable SSE / Redis) during parallel runs could let this call's own usage advance the index past the event that proves the snapshot completed, dropping a valid breakdown. * 🪙 fix: Subtract Summarization Output from the Summary Marker recordCollectedUsage folds the summarization call's completion into the response message's tokenCount, while the generated summary is also in the snapshot baseline as summaryTokens. The client estimate (summaryBaseline + responseTokenCount) thus counted the summary twice — inflating the gauge after compaction even on a single-call turn whenever the full snapshot is unavailable. priorRunOutputTokens now also counts summarization-tagged output (still excluding subagent/sequential, which recordCollectedUsage keeps out of the reported total), so the marker subtracts it. Updated unit + guard tests. * 🪙 fix: Refine Marker Subtraction for Summarization RunId and Abort Boundary Two Codex follow-ups on the marker-subtraction logic: - Subtract summarization output regardless of runId: the summarize detour is its own model-end call that may carry a distinct runId, but its output still lands in this response's tokenCount AND the snapshot baseline (summaryTokens). It is now counted unconditionally (still within the response's own usageEmitSink), while primaries keep the parallel-run runId filter. - Don't subtract primaries on the abort path: the job stores no snapshot/usage boundary, so a primary that completed AFTER the latest snapshot is NOT in the baseline; subtracting it would cancel real output and under-report. priorRun- OutputTokens gains an includePrimary flag (false for abort) — abort subtracts only the always-pre-snapshot summarization output. * 🪙 fix: Run-Scope Summary Subtraction and Stop Subtracting on Abort Two Codex follow-ups, resolved by reverting the round-4 detour: - Run-scope the summarization subtraction: the summarize detour inherits the graph run id (traceConfig spreads config.metadata.run_id), so its usage shares the answer snapshot's runId — it is NOT a distinct run. priorRunOutputTokens now filters summarization by runId like primaries, so a parallel sibling run's summary (different runId, in the sibling's baseline) is no longer subtracted from this branch's marker. Drops the includePrimary flag added last round. - Stop subtracting on the abort path: abort tokenCount is countTokens(text) (abortMiddleware) or absent (agents route) — it does not fold in summarization or earlier-call output the way recordCollectedUsage does, so the marker must keep the full baseline. buildAbortedResponseMetadata now subtracts nothing.
LibreChat
English · 中文
✨ Features
-
🖥️ UI & Experience inspired by ChatGPT with enhanced design and features
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🤖 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
-
🔦 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 →
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🪄 Generative UI with Code Artifacts:
- Code Artifacts allow creation of React, HTML, and Mermaid diagrams directly in chat
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🎨 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
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💾 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 🗃️
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🌎 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
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🧠 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
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- 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
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🗣️ Speech & Audio:
- Chat hands-free with Speech-to-Text and Text-to-Speech
- Automatically send and play Audio
- Supports OpenAI, Azure OpenAI, and Elevenlabs
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📥 Import & Export Conversations:
- Import Conversations from LibreChat, ChatGPT, Chatbot UI
- Export conversations as screenshots, markdown, text, json
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🔍 Search & Discovery:
- Search all messages/conversations
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👥 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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⚙️ 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
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📖 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.