Chat2Graph is a graph-native agentic system designed for exploring the synergy between graph technologies and AI. It targets developers and researchers interested in building advanced AI agents with enhanced reasoning and memory capabilities, offering a blueprint for "Graph + AI" innovation.

How It Works

The system employs a hybrid multi-agent architecture, combining "One-Active-Many-Passive" configurations. It features a dual-LLM reasoning machine that integrates fast and slow thinking processes, enabling more nuanced decision-making. Task decomposition and planning are managed through a Chain of Agents (CoA) approach, leveraging a graph planner for structured execution.

Quick Start & Requirements

Installation is primarily from source code. Specific prerequisites are not detailed in the provided text, but the project structure suggests a Python-based environment.

Highlighted Details

• One-Active-Many-Passive hybrid multi-agent architecture.
• Dual-LLM reasoning machine for fast/slow thinking.
• Chain of agents (CoA) with graph planner for task decomposition.
• Hierarchical memory system with vector and graph knowledge bases.

Maintenance & Community

The project welcomes community contributions via GitHub Issues/PRs. Contact is available through Discord and WeChat.

Licensing & Compatibility

The license is not specified in the provided text.

Limitations & Caveats

The project currently offers only basic agent system capabilities. Several features listed on the roadmap, such as workflow auto-generation, action recommendation, structured agent role management, agent task compilation, knowledge refinement, environment management, toolkit graph optimization, multimodal capabilities, and production enhancements, are marked as incomplete.