tgo

<p align="center"> <img src="resources/readme-banner-en.svg" width="100%" alt="Build AI Agent Teams for Customer Service"> </p> <p align="center"> <a href="./README.md">English</a> | <a href="./README_CN.md">简体中文</a> | <a href="./README_TC.md">繁體中文</a> | <a href="./README_JP.md">日本語</a> | <a href="./README_RU.md">Русский</a> </p> <p align="center"> <a href="https://tgo.ai">Website</a> | <a href="https://tgo.ai">Documentation</a> </p> ## TGO Introduction TGO is an open-source AI agent customer service platform dedicated to helping enterprises "Build AI Agent Teams for Customer Service". It integrates multi-channel access, agent orchestration, knowledge base management (RAG), and human agent collaboration. <img src="resources/home_en.png" width="100%"> ## 🚀 Quick Start ### One-Click Deployment Run the following command on your server to check requirements, clone the repository, and start the services: ```bash REF=latest curl -fsSL https://raw.githubusercontent.com/tgoai/tgo/main/bootstrap.sh | bash ``` > **For users in China** (using Gitee and Aliyun mirrors): > ```bash > REF=latest curl -fsSL https://gitee.com/tgoai/tgo/raw/main/bootstrap_cn.sh | bash > ``` --- For more details, please visit the [Documentation](https://tgo.ai). ## ✨ Features ### 🤖 AI Agent Orchestration - **Multi-Agent Support** - Configure multiple AI agents for different business scenarios - **Multi-Model Integration** - Connect with various LLM providers (OpenAI, Anthropic, etc.) - **Streaming Response** - Real-time AI responses via SSE for smooth conversation experience - **Context Memory** - Maintain conversation history for coherent dialogue ### 📚 Knowledge Base (RAG) - **Document Knowledge Base** - Upload documents to enhance AI response accuracy - **Q&A Knowledge Base** - Create question-answer pairs for quick knowledge expansion - **Website Knowledge Base** - Crawl website content to keep information up-to-date - **Smart Retrieval** - Vector-based semantic search for precise answers ### 🔧 MCP Tools Integration - **Tool Store** - Rich library of MCP tools, enable on demand - **Custom Tools** - Project-level tool configuration and management - **OpenAPI Schema** - Auto-parse schemas to generate interactive forms ### 🌐 Multi-Channel Access - **Web Widget** - Embeddable chat widget for websites - **WeChat Integration** - Official Account and Mini Program support - **Unified Management** - Manage all channels from a single dashboard ### 💬 Real-time Communication - **WuKongIM Integration** - Stable and reliable instant messaging - **WebSocket Connection** - Efficient bidirectional communication - **Message Sync** - Read/unread status, delivery confirmation - **Rich Media** - Support for text, images, files and more ### 👥 Human-AI Collaboration - **Smart Handoff** - Seamlessly transfer to human agents when needed - **Visitor Management** - Collect visitor info, assign sessions, track history - **Agent Workspace** - Unified interface for human agents ### 🎨 UI Widget System - **Structured Display** - Render orders, products, logistics as beautiful cards - **Rich Components** - Order cards, logistics tracking, product display, price comparison - **Action Protocol** - Standardized URI protocol for interactions ## 📦 Repository Structure | Repository | Description | Tech Stack | |:---|:---|:---| | [tgo-ai](repos/tgo-ai) | AI/ML operations service for managing agents, tool bindings, knowledge bases, and usage analytics | Python / FastAPI | | [tgo-api](repos/tgo-api) | Core business logic service handling user management, visitor tracking, assignment, and communication | Python / FastAPI | | [tgo-cli](repos/tgo-cli) | CLI tool & MCP Server enabling AI agents to execute customer service operations with 40+ built-in tools | TypeScript / Node.js | | [tgo-device-agent](repos/tgo-device-agent) | Embedded agent running on managed devices providing file and shell capabilities via TCP JSON-RPC | Go | | [tgo-device-control](repos/tgo-device-control) | Device control service managing TCP/JSON-RPC connections for remote device management with MCP Agent | Python / FastAPI | | [tgo-platform](repos/tgo-platform) | Multi-channel message intake service supporting WeChat, Feishu, DingTalk, Telegram, Slack, email, etc. | Python / FastAPI | | [tgo-plugin-runtime](repos/tgo-plugin-runtime) | Plugin lifecycle management and execution service with dynamic tool synchronization | Python / FastAPI | | [tgo-rag](repos/tgo-rag) | RAG service providing document processing, hybrid semantic/full-text search, and async processing | Python / FastAPI | | [tgo-web](repos/tgo-web) | Admin frontend with real-time chat, AI agent management, knowledge base, and MCP tool integration | TypeScript / React 19 | | [tgo-workflow](repos/tgo-workflow) | AI Agent workflow execution engine supporting DAG topology with LLM, API, condition, and tool nodes | Python / FastAPI | ### Widget SDKs | Repository | Description | Tech Stack | |:---|:---|:---| | [tgo-widget-js](repos/tgo-widget-js) | Embeddable customer service chat widget (Intercom-style) for websites | TypeScript / React 18 | | [tgo-widget-ios](repos/tgo-widget-ios) | Native iOS customer service chat SDK with SwiftUI views and UIKit bridging | Swift / SwiftUI | | [tgo-widget-flutter](repos/tgo-widget-flutter) | Cross-platform customer service chat widget for iOS and Android | Dart / Flutter | | [tgo-widget-cli](repos/tgo-widget-cli) | Visitor-facing CLI tool & MCP Server providing customer service interface | TypeScript / Node.js | | [tgo-widget-miniprogram](repos/tgo-widget-miniprogram) | WeChat Mini Program chat widget component with AI streaming responses and Markdown rendering | TypeScript | ## 🏗️ System Architecture <p align="center"> <img src="resources/architecture_en.svg" width="100%" alt="TGO System Architecture"> </p> ## Product Preview | | | |:---:|:---:| | **Dashboard** <br> <img src="resources/screenshot/en/home_dark.png" width="100%"> | **Agent Orchestration** <br> <img src="resources/screenshot/en/agent_dark.png" width="100%"> | | **Knowledge Base** <br> <img src="resources/screenshot/en/knowledge_dark.png" width="100%"> | **Q&A Debugging** <br> <img src="resources/screenshot/en/knowledge_qa_dark.png" width="100%"> | | **MCP Tools** <br> <img src="resources/screenshot/en/mcp_dark.png" width="100%"> | **Platform Admin** <br> <img src="resources/screenshot/en/platform_dark.png" width="100%"> | ## System Requirements - **CPU**: >= 4 Core - **RAM**: >= 8 GiB - **OS**: macOS / Linux / WSL2