* 💎 fix: Stop Double-Counting Cache Tokens for Gemini/OpenAI in Usage Spend (#12855) Different providers report `usage_metadata.input_tokens` with different semantics: - Anthropic / Bedrock: `input_tokens` EXCLUDES cache; cache reads/writes arrive separately and must be added to get the total prompt size. - Gemini / OpenAI: `input_tokens` ALREADY INCLUDES cached tokens (Google's `promptTokenCount`, OpenAI's `prompt_tokens`). Their `input_token_details.cache_*` are subsets of `input_tokens`. `recordCollectedUsage` treated both schemes as additive, so for cache-hit requests on Gemini/OpenAI it added cache tokens on top of an `input_tokens` value that already contained them — overcharging users by the cache_hit_rate (e.g., ~67% cache hit ≈ 1.67x overcharge). This matches the issue reporter's GCP billing comparison. Adds a small `splitUsage` helper that classifies the provider by model name and computes `inputOnly` (the non-cached portion) plus the all-inclusive `totalInput` for both the spend math and the returned `input_tokens` summary. The helper defaults to additive semantics (the historical behavior) so unknown providers are unaffected. Updates existing OpenAI-shaped tests that previously asserted the buggy additive math, and adds Gemini regression tests using the exact numbers from the issue report (input=11125, cache_read=7441 → input=3684). Anthropic / Bedrock paths remain bit-identical to before. * 🔧 refactor: Classify Cache-Token Semantics by Provider, Not Model Name Follows up the previous commit. Replaces a model-name regex (`gemini|gpt|o[1-9]|chatgpt`) with an explicit `Providers` enum lookup keyed off the `usage.provider` field — `UsageMetadata.provider` already exists in `IJobStore.ts` but was never being populated. - `callbacks.js#ModelEndHandler` now attaches `usage.provider` from `agentContext.provider` alongside `usage.model`. - `usage.ts` uses a `SUBSET_PROVIDERS` set (`openAI`, `azureOpenAI`, `google`, `vertexai`, `xai`, `deepseek`, `openrouter`, `moonshot`) backed by the canonical `Providers` enum from `librechat-data-provider`. - `xai`, `deepseek`, `openrouter`, `moonshot` extend `ChatOpenAI` so they inherit subset semantics (verified in node_modules). - Defaults to additive when `usage.provider` is missing, so the title flow (which doesn't propagate provider) and any pre-this-PR usage entries keep their existing behavior. Tests: switch fixtures from model-name signaling to explicit `provider` field, plus a Vertex AI case and a "missing provider" fallback case. |
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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
- 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
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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 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.