A multi-tenant agent platform combining RAG and knowledge graphs
Make enterprise knowledge retrievable, reasoned over, and deliverable by agents
*Image generated by GPT-Image-2.
Yuxi is an LLM-powered platform for building knowledge-base and knowledge-graph agents. It unifies RAG retrieval, Milvus-backed in-knowledge-base graphs, and LangGraph multi-agent orchestration into a single multi-tenant workspace: administrators configure knowledge bases, models, and permissions, while users chat — in a ChatGPT-like interface — with agents that can mount Skills, MCPs, sub-agents, and sandbox tools, and receive answers with cited sources, graph-based reasoning, and deliverable artifacts.
Navigation: Introduction ｜ Quick Start ｜ Roadmap; for the latest updates, see the changelog.
- 🤖 Agent development — Built on LangGraph, with sub-agents (SubAgents), Skills, MCPs, Tools, and middleware; long-running tasks run asynchronously on a background worker, backed by a sandbox file system for persisting, previewing, and downloading tool artifacts.
- 📚 Knowledge base (RAG) — Multi-format document parsing (MinerU / PaddleX / OCR), configurable Embedding and Rerank models, knowledge base evaluation, in-app PDF / image preview, and retrieval sources backfilled as chat citations.
- 🕸️ Knowledge graph — Build, visualize, and retrieve entity-relation graphs inside Milvus knowledge bases, then fuse graph hits with chunk retrieval for agent reasoning.
- 🏢 Multi-tenancy & permissions — User / department-level access control, unified model provider configuration, and API Key authentication for external system integration.
- ⚙️ Platform & engineering — Vue + FastAPI architecture, ready-to-run Docker Compose deployment, dark mode, a lightweight LITE startup mode, and production-grade orchestration.
| Layer | Technologies |
|---|---|
| Frontend | Vue 3 · Vite · Pinia |
| Backend | FastAPI · LangGraph · ARQ (async worker) |
| Storage | PostgreSQL · Redis · MinIO · Milvus · Neo4j |
| Doc parsing | MinerU · PaddleX · RapidOCR |
| Deployment | Docker Compose |
Prerequisites: Docker and Docker Compose installed, plus at least one OpenAI-compatible LLM API.
1. Clone and initialize
git clone --branch v0.7.0 --depth 1 https://github.com/xerrors/Yuxi.git
cd Yuxi
# Linux/macOS
./scripts/init.sh
# Windows PowerShell
.\scripts\init.ps12. Start with Docker
docker compose up --build3. Open the platform
Once the services are ready, open http://localhost:5173 in your browser and sign in with the admin account generated during initialization.
💡 If you don't need heavy dependencies like knowledge bases / graphs, run
make up-litefor a lightweight LITE mode with faster cold starts. See the docs for more deployment details.
Home |
Dashboard Statistics |
Agent Configuration |
Knowledge Base Invocation |
Create Knowledge Base |
Knowledge Base Management |
Knowledge Graph Visualization |
Project Documentation |
Extension Management (Skills) |
Extension Management (MCPs) |
User / Department Permission Management |
Model Provider Configuration |
Yuxi references and builds on the following excellent open-source projects:
- LightRAG - Used as the foundation for graph construction and retrieval.
- DeepAgents - Used as the deep agent framework.
- DeerFlow - Referenced for Sandbox agent architecture ideas.
- RAGflow - Referenced for document text chunking strategies.
- LangGraph - Multi-agent orchestration framework and the core architectural foundation of this project.
- QwenPaw - Referenced for model configuration and personal file area design.
Thanks to all contributors for supporting this project!
This project is licensed under the MIT License. See LICENSE for details.
If this project helps you, please give us a ⭐️.