ForcePilot/README.en.md

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<h1>Yuxi</h1>

<p><strong>A multi-tenant agent platform combining RAG and knowledge graphs</strong><br/>Make enterprise knowledge retrievable, reasoned over, and deliverable by agents</p>

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[![demo](https://img.shields.io/badge/demo-00A1D6.svg?style=flat&logo=bilibili&logoColor=white)](https://www.bilibili.com/video/BV1DF14BTETq/)

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[[Docs]](https://xerrors.github.io/Yuxi) · [[中文]](README.md)

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![arch](https://xerrors.oss-cn-shanghai.aliyuncs.com/github/gpt-iamge-2-arch.png)

**Image generated by GPT-Image-2.*

## Introduction

Yuxi is an LLM-powered platform for building knowledge-base and knowledge-graph agents. It unifies **RAG retrieval**, **LightRAG knowledge 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.

## Core Features

- 🤖 **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** — Graph construction and visualization based on LightRAG, with property graph support that feeds directly into 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.

## Tech Stack

| 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 |

## Quick Start

**Prerequisites**: [Docker](https://docs.docker.com/get-docker/) and Docker Compose installed, plus at least one OpenAI-compatible LLM API.

**1. Clone and initialize**

```bash
git clone --branch v0.7.0.dev0 --depth 1 https://github.com/xerrors/Yuxi.git
cd Yuxi

# Linux/macOS
./scripts/init.sh

# Windows PowerShell
.\scripts\init.ps1
```

**2. Start with Docker**

```bash
docker compose up --build
```

**3. 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-lite` for a lightweight LITE mode with faster cold starts. See the [docs](https://xerrors.github.io/Yuxi) for more deployment details.

## Examples and Demo

<table>
  <tr>
    <td align="center">
      <img src="https://xerrors.oss-cn-shanghai.aliyuncs.com/github/image-20260326125852369.png" width="100%" alt="Home"/>
      <br/>
      <strong>Home</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/d3e4fe09-fa48-4686-93ea-2c50300ade21" width="100%" alt="Dashboard statistics"/>
      <br/>
      <strong>Dashboard Statistics</strong>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://xerrors.oss-cn-shanghai.aliyuncs.com/github/image-20260326130528866.png" width="100%" alt="Agent configuration"/>
      <br/>
      <strong>Agent Configuration</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/06d56525-69bf-463a-8360-286b2cf8796f" width="100%" alt="Knowledge base invocation"/>
      <br/>
      <strong>Knowledge Base Invocation</strong>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/0548d89c-15a3-47cf-ba87-1b544f7dd749" width="100%" alt="Create knowledge base"/>
      <br/>
      <strong>Create Knowledge Base</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/21396d04-376b-4e9a-8139-eec8c3cc915a" width="100%" alt="Knowledge base management"/>
      <br/>
      <strong>Knowledge Base Management</strong>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/fc46a14b-16fb-47ea-84a0-148a451f3012" width="100%" alt="Knowledge graph"/>
      <br/>
      <strong>Knowledge Graph Visualization</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/d8b3de51-2854-455b-956f-2ae2d8d5f677" width="100%" alt="Project docs"/>
      <br/>
      <strong>Project Documentation</strong>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://xerrors.oss-cn-shanghai.aliyuncs.com/github/image-20260326130404306.png" width="100%" alt="Skills management"/>
      <br/>
      <strong>Extension Management (Skills)</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/9305d7a4-663b-4e5d-a252-211d6caa019b" width="100%" alt="MCPs management"/>
      <br/>
      <strong>Extension Management (MCPs)</strong>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/13bd22ea-ddde-4262-8c29-69fb948bce44" width="100%" alt="User and department permissions"/>
      <br/>
      <strong>User / Department Permission Management</strong>
    </td>
    <td align="center">
      <img src="https://github.com/user-attachments/assets/cc886b04-719e-4abd-807d-e9955080003d" width="100%" alt="Model provider configuration"/>
      <br/>
      <strong>Model Provider Configuration</strong>
    </td>
  </tr>
</table>

## Acknowledgements

Yuxi references and builds on the following excellent open-source projects:

- [LightRAG](https://github.com/HKUDS/LightRAG) - Used as the foundation for graph construction and retrieval.
- [DeepAgents](https://github.com/langchain-ai/deepagents) - Used as the deep agent framework.
- [DeerFlow](https://github.com/bytedance/deer-flow) - Referenced for Sandbox agent architecture ideas.
- [RAGflow](https://github.com/infiniflow/ragflow) - Referenced for document text chunking strategies.
- [LangGraph](https://github.com/langchain-ai/langgraph) - Multi-agent orchestration framework and the core architectural foundation of this project.
- [QwenPaw](https://github.com/agentscope-ai/QwenPaw) - Referenced for model configuration and personal file area design.

## Contributing

Thanks to all contributors for supporting this project!

<a href="https://github.com/xerrors/Yuxi/contributors">
  <img src="https://contrib.rocks/image?repo=xerrors/Yuxi&max=100&columns=10" />
</a>

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=xerrors/Yuxi)](https://star-history.com/#xerrors/Yuxi)

## 📄 License

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.

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**If this project helps you, please give us a ⭐️.**

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