Summary

TGO is an open-source AI agent customer service platform designed for enterprises to build sophisticated AI agent teams. It integrates multi-channel support, advanced AI agent orchestration, Retrieval-Augmented Generation (RAG) knowledge bases, and seamless human-AI collaboration, aiming to enhance customer service operations.

How It Works

The platform employs a modular architecture centered around AI Agent Orchestration, enabling multi-agent configurations and integration with various LLM providers (e.g., OpenAI, Anthropic) via a streaming SSE interface for real-time responses. Its RAG knowledge base supports document uploads, Q&A pairs, and website crawling, powered by vector-based semantic search for precise information retrieval. Tool integration is facilitated through a "Tool Store" and custom tool management, with OpenAPI schema parsing for interactive forms. Communication leverages WuKongIM and WebSockets, while human-AI collaboration features smart handoff and a unified agent workspace.

Quick Start & Requirements

System requirements include a CPU with at least 2 Cores, 4 GiB of RAM, and macOS, Linux, or WSL2 operating systems. A one-click deployment is available via a bootstrap script:

REF=latest curl -fsSL https://raw.githubusercontent.com/tgoai/tgo/main/bootstrap.sh | bash

A separate script is provided for users in China. Further details can be found in the official Documentation.

Highlighted Details

• AI Agent Orchestration: Supports multi-agent setups, multi-model integration, and maintains context memory for coherent dialogues.
• RAG Knowledge Base: Enhances AI accuracy with document, Q&A, and website content, utilizing smart semantic retrieval.
• Multi-Channel Support: Offers an embeddable web widget and WeChat integration for broad customer reach.
• UI Widget System: Provides structured, rich UI components (e.g., order cards, logistics tracking) for interactive customer experiences.

Maintenance & Community

No specific details regarding notable contributors, sponsorships, community channels (like Discord/Slack), or roadmap were present in the provided README snippet.

Licensing & Compatibility

The license type and any compatibility notes for commercial use or closed-source linking were not specified in the provided README snippet.

Limitations & Caveats

No explicit limitations, known bugs, or alpha/beta status were mentioned in the provided README snippet.