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* 📊 feat: Real-Time Context Window & Token Usage Tracking * 🧪 fix: Align Pricing Spec Dep Signatures with TxDeps * 🩹 fix: Resolve Codex Findings for Context Usage Tracking * 📊 feat: Granular Tool Token Breakdown with Deferred Splits * 🧪 test: Cover Session Cost in Mock E2E and Scope Usage Selectors * 🧪 test: Live Host-Pipeline Usage Verification (Env-Gated) * 🧪 test: Local Real-Provider Multi-Turn E2E Harness * 🪙 fix: Keep Tagged Usage Buckets Out of the Live Context Estimate * 🩹 fix: Scoped Token-Config Fallback and Sequential Visibility for Usage Events * 🩹 fix: Address Usage Review Findings — Cost Timing, Scoped Caches, Finalized Output - carry the post-snapshot output estimate into the context snapshot at finalize so the gauge keeps the last response after live resets - accumulate per-rate billable units and price the session cost at render, so usage events arriving before the token-config load still count once it resolves - pass user-scoped token-config cache keys through loadConfigModels fetches and drop the controller's unscoped fallback to prevent serving another user's resolved config - tag emitted usage events with a per-run seq so resume dedupe never drops a distinct call with an identical payload - admit the static tokenConfig override in the custom endpoint schema so it survives zod parsing into req.config * 🩹 fix: Align Client Usage Accounting with Backend Cost Semantics - classify cache tokens by provider (shared inputTokensIncludesCache from data-provider, consumed by both the backend billing path and the client) instead of a magnitude heuristic, so Anthropic/Bedrock turns where cache is smaller than uncached input no longer under-bill input - mirror resolveCompletionTokens on the client so Vertex-style hidden thinking tokens are reflected in the Output row and session cost - prefer endpoint pricing over adapter-provider pricing so a custom endpoint can price a known model name without built-in rates shadowing it - carry static cacheRead/cacheWrite overrides through the tokenConfig schema and buildTokenConfigMap * 🩹 fix: Honor Static Token Config in Billing; Tighten Usage Freshness - initializeCustom now uses a static endpoint tokenConfig as the agent's endpointTokenConfig (billing + balance checks), not just the advertised UI config — previously the gauge showed admin rates while the agent billed against built-in tables - invalidate the token-config query alongside models on user-key add/ revoke so context windows and pricing refresh without a reload - include maxContextTokens in ChatForm's stabilized conversation memo so the gauge reflects a changed context-window setting immediately - feed the live output estimate from the legacy content path (direct and assistants streams), setting from cumulative part text rather than accumulating deltas * 🩹 fix: Resume Usage Dedup, Agent Pricing, and Partial Override Billing - fold usage events idempotently by (runId, seq) so resume backfill no longer resets the conversation totals — a mid-stream reconnect keeps the usage of prompts already completed earlier in the session - tap replayed pending message/reasoning/content events so output streamed past the resume snapshot reaches the live estimate, not just the message - resolve cost against the agent's backing endpoint (Agents conversations report endpoint `agents` / provider `openAI`, neither of which keys a custom endpoint's tokenConfig) - getMultiplier/getCacheMultiplier fall back to the standard tables for models absent from a partial endpointTokenConfig, so a partial static override no longer bills non-listed models at defaultRate while the UI shows the correct pattern rate * 🩹 fix: Repaired Output in Gauge, Cache-Rate Keys, Config Gate, Usage Cleanup - live/completed gauge counts the repaired completion (normalized output), so under-reporting providers don't drop the response from used context - translate static tokenConfig cacheWrite/cacheRead onto the write/read keys getCacheMultiplier reads, so cache tokens bill at the configured rate instead of the prompt-rate fallback - clear the token index and usage atoms when leaving a conversation, so visited histories don't accumulate in memory for the tab's lifetime - wait for startupConfig before mounting the gauge, so a deployment with contextUsage disabled never briefly mounts it or fires the token-config query on first load * 🩹 fix: Move Token-Config Resolution to TS; Key Live Usage by Created Convo - extract the token-config resolution (override gathering + cache lookup + buildTokenConfigMap) into resolveTokenConfigMap in packages/api, leaving the /api controller a thin request-scoped wrapper (CLAUDE.md TS rule) - getConvoKey prefers the user message's real conversationId once the `created` event stamps it, so a new chat's first-response live gauge and totals land under the id TokenUsage subscribes to instead of NEW_CONVO * 🩹 fix: Clear Stale Redis Job Usage; Live-Tap Legacy Streams; Share Fetched Config - DEL the Redis job hash before re-creating it so a reused streamId can't inherit a prior run's contextUsage/tokenUsage and backfill stale usage - tap the legacy {message,text} stream branch (non-agent OpenAI/Anthropic streams) into the live estimate, not just the content path - copy a deduped fetch's token config to every sibling endpoint sharing the baseURL/key/headers, so /token-config resolves each by its own name * ⏪ revert: Don't DEL Redis job hash in createJob (breaks cross-replica resume) createJob is an idempotent join — a second replica calls it for the same streamId to share an in-flight stream's state. DELeting the hash wiped the prior replica's persisted created/usage state, so a joining replica missed the created event (GenerationJobManager cross-replica integration test). Reverts the F1 change from 2bfce0c34b; the stale-usage concern doesn't arise in practice (streamId is unique per generation). * 🩹 fix: Best-Effort Usage Emit; Tag Hidden Sequential-Agent Usage - wrap the ModelEndHandler usage emit in try/catch so a failed telemetry delivery (closed SSE / Redis publish error) can't abort the handler before thought-signature capture, which would break resumed tool calls - tag hidden sequential-agent usage as 'sequential' (non-primary) so the client folds it into session cost/totals but not the live context gauge, instead of letting an undefined usage_type inflate the visible gauge * 🩹 fix: Refetch Stale Token Config on Mount; Normalize Vertex for Lookup - useTokenConfigQuery refetches on mount when stale, so a user-key change that invalidates tokenConfig while the gauge is unmounted takes effect on return instead of serving the prior key's resolved config - normalize a Vertex-backed agent's provider (vertexai) to the google token-config key, so Gemini context windows and rates resolve instead of showing unknown context / $0 cost * ✨ feat: Server-Side Per-Event Cost (Authoritative Pricing for the Gauge) Move usage-cost pricing to the single source of truth. The backend prices each model call with the same billing functions (premium tiers via getMultiplier(inputTokenCount), cache rates) and emits the USD cost on on_token_usage when interface.contextCost is enabled; the client sums emitted costs instead of re-deriving from base token-config rates. - computeUsageCostUSD reuses prepareTokenSpend/prepareStructuredTokenSpend so the emitted cost matches what is billed (incl. premium thresholds) - getDefaultHandlers gains a usageCost pricing context; initialize.js wires db.getMultiplier/getCacheMultiplier gated on contextCost (agents path) - client UsageTotals carries a summed costUSD; retire the client-side rate lookups (costFromUnits/calcUsageCost) that drifted from backend pricing and produced the provider-keying / cache-key / Vertex / premium findings - keep normalizeUsageUnits for the displayed token counts; token-config is still used for the context-window meter Fixes the premium-tier session-cost under-report (gpt-5.x / gemini-3.1 above their input thresholds). * 🩹 fix: Branch-Accurate Usage Snapshot + Clearer Gauge Track Contrast - re-anchor the context snapshot from the user message to the response message at finalize. Regenerating a response branches off a shared user message, so anchoring on it made the snapshot read as "active" on both branches — switching to the sibling branch showed the wrong (other branch's) context. The response message is branch-unique, so sibling branches now correctly fall back to their own per-branch totals. - raise the gauge ring's track/fill contrast (muted track, prominent fill) so the used portion reads clearly as a fill-level indicator * 🩹 fix: Tag Sequential Usage in Billing; Emit Subagent Cost; Reset Live on Resume Errors - tag hidden sequential-agent usage `usage_type: 'sequential'` on the COLLECTED usage (not just the emit), and treat it as non-primary in recordCollectedUsage (billed, excluded from the reported output total) so hidden intermediate output stops inflating the parent's tokenCount/pruning - emit on_token_usage from the subagent usage sink (tagged `subagent`, with authoritative cost when contextCost is on) so the gauge's session cost/totals include billed subagent usage; it stays out of the live meter - call resetLive on the resumable 404 and max-retry terminal branches so the gauge doesn't keep counting stale in-flight tokens after the stream ends * 🎨 fix: Contrast the Popup Context Bar; Revert Ring Restyle - raise the popup breakdown's context progressbar contrast (muted surface-tertiary track, prominent text-primary fill) — that's the bar the contrast feedback was about - revert the gauge ring restyle (kept its original border-heavy track / text-secondary fill); the ring wasn't the element in question * 🩹 fix: Stop Snapshot Granularity Leaking Across Branches; Revert Tree Memo - a null-anchor context snapshot was treated as active on every branch, leaking one generation's granular breakdown onto sibling branches. Require a non-null (response-message) anchor on the viewed branch instead, so siblings without a matching snapshot fall back to their own totals. - revert the buildTree WeakMap memo in messages.ts. buildTree is pure (builds from shallow copies) so the memo was behaviorally identical, but it was the feature's only change to core branch-navigation selectors — removing it matches upstream and rules it out of branch-navigation debugging. * 🪙 fix: Thread Endpoint Token Config to Agent Billing, Cost, and Context Limits Custom-endpoint agents resolve an endpointTokenConfig during agent init but it never reached the AgentClient, so spending, emitted cost, and runtime max-token resolution all fell back to default rates for those agents. - Surface options.endpointTokenConfig on the returned InitializedAgent. - Pass it to the AgentClient (this.options.endpointTokenConfig) so the spending path bills at configured rates. - Thread it through usageCost to computeUsageCostUSD so emitted per-event cost matches billing. - getModelMaxTokens/getModelMaxOutputTokens fall back to the built-in map for models absent from a partial override (matches buildTokenConfigMap); consolidates the duplicated fallback in pricing.ts. * 🪙 fix: Preserve Granular Breakdown Across Branch Switches The granular context breakdown lives only in the live on_context_usage snapshot — a single per-conversation slot, anchored to the latest response and overwritten by each generation. Switching to a branch generated earlier this session lost its tool/skill/system rows and fell back to coarse totals. Retain each generation's finalized snapshot in a per-conversation map keyed by its branch-unique response id (snapshotsByAnchorFamily). When the live snapshot is off the viewed branch, walk the branch tail for its deepest stored anchor and render that breakdown. Bounded by generation count and cleared on conversation switch; the live/just-generated path is unchanged. * 🪙 fix: Harden Resume Seeding and Subagent Usage Emission - useResumableSSE: skip the trailing-output live seed when the resume carries a context snapshot; the snapshot's messageTokens already counts produced output, so seeding it again inflated usage until the next reset. - AgentClient subagent emitter: await GenerationJobManager.emitChunk like every other caller (it persists before publishing), so a floating promise can't race job cleanup and a Redis/publish failure is caught by the emitter's try/catch instead of surfacing as an unhandled rejection. * 🧪 test: Playwright Coverage for Context Breakdown Granularity Add a test-only data-testid distinguishing the granular snapshot breakdown (context-breakdown) from the coarse message-history estimate (context-estimate), then assert granularity in the mock e2e harness: - renders the granular breakdown from the live on_context_usage snapshot (guards that the snapshot event actually reaches the popover, not just the usage totals). - preserves the granular breakdown after switching branches — regenerate to overwrite the single live snapshot, switch back, and confirm the rows survive via the per-anchor snapshot history map. Branch regenerate/sibling selectors mirror the existing chat.spec branch test. All three usage specs pass against the mock pipeline. * 🪙 fix: Correct Resume Live-Seed, Fallback Re-index, and Subagent Emit Flush Codex round on the prior commit: - countTrailingOutputChars now counts only output at the very END of the aggregated content (0 when the model paused at a tool call), and the resume path always seeds it. The earlier skip-trailing-tool-parts behavior plus the skip-seed-when-snapshot gate together over- or under-counted in-flight output on resume; one rule fixes both — pre-invoke snapshot budget is never double-counted, and genuine in-flight output is no longer dropped. - useTokenUsage re-indexes from the messages cache on tail change while submitting. The cache subscriber is muted during streaming, so without a context snapshot (non-agent streams) sumBranch missed the created tail and dropped history + prompt until finalize. Bounded — tailId only shifts on created/finalize/branch-switch. - AgentClient tracks subagent usage emit promises and flushes them in chatCompletion's finally. The sink fires the emitter without awaiting, and resume reads the usage emitChunk persists (HSET), so cleanup must not race it or resumed clients miss billed subagent usage.
LibreChat
English · 中文
✨ Features
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🖥️ 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
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🔦 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.