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Traditional tabular data processing technologies, such as distributed databases, data warehouses, and data lakes, have been continuously evolving and gradually maturing. In comparison, graph-based data processing technologies (graph databases, graph computing engines) provide new ideas and methods, but also face issues such as low ecological maturity and high barriers to product usage. With the rise of large language models (LLMs), how to effectively combine artificial intelligence technology with graph computing technology (Graph + AI) will be a very worthwhile direction to explore. On one hand, we can leverage cutting-edge technologies like LLMs and agents to lower the barriers to using graph computing products and enhance the user experience with graphs. On the other hand, graph computing technology can fully utilize the performance and interpretability advantages of graph data structures in relational analysis scenarios, assisting LLMs and intelligent agents in improving reasoning capabilities and generation quality.

Chat2Graph builds a multi-agent system (MAS) on top of a graph database to achieve intelligent capabilities in research and development, operations and maintenance, Q&A, generation, and more, helping users, developers, product managers, solution architects, operations engineers, and others to efficiently use graph databases, lower the barriers to using graphs, and accelerate content generation, implement dialogue with graphs. At the same time, leveraging the inherent advantages of graph data structures in relationship modeling, interpretability, etc., can enhance the key capabilities of intelligent agents such as reasoning, planning, memory, and tools, to achieve a deep integration of graph computing technology and artificial intelligence technology.

Chat2Graph currently provides basic capabilities of intelligent agent systems, but there are still many features that need to be improved together with the community.

• One-active-Many-Passive mixed multi-agent architecture.
• Dual-LLM reasoning machine combining fast and slow thinking.
• Task decomposition and graph planner oriented towards intelligent agent chains.
• Hierarchical memory system.
• Toolkit knowledge graph.
• Vector and graph knowledge base.
• Concise intelligent agent SDK.
• Service-oriented and web interaction.
• One-click configuration of agents.
• Structured agent role management.
• Workflow auto-generation.
• Operator layer action recommendation.
• Toolkit graph optimizer.
• Environmental information management.
• Agent task compiler.
• Unified resource manager.
• Tracking and control capabilities.
• Rich toolkit/MCP integration.
• Benchmark.
• Access to mainstream open-source ecosystems.
• Multimodal capabilities.
• Production enhancement.

Prepare the required versions of Python and NodeJS.

• Install Python: Python == 3.10 recommended.
• Install NodeJS: NodeJS >= v20 recommended.

You can also use tools like conda to install the python environment.

Build Chat2Graph as follows.

git clone https://github.com/TuGraph-family/chat2graph.git
cd chat2graph
./bin/build.sh

Then configure environment variables (e.g., LLM parameters) based on .env.template, startup Chat2Graph.

cp .env.template .env && vim .env
./bin/start.sh

When you see the following log, you can access Chat2Graph in the browser at http://localhost:5000/.

Starting server...
Web resources location: /Users/florian/code/chat2graph/app/server/web
Prepare database tables ...
Loading AgenticService from app/core/sdk/chat2graph.yml with encoding utf-8
Init application: Chat2Graph
Init the Leader agent
Init the Expert agents

  ____ _           _   ____   ____                 _     
 / ___| |__   __ _| |_|___ \ / ___|_ __ __ _ _ __ | |__  
| |   | '_ \ / _` | __| __) | |  _| '__/ _` | '_ \| '_ \ 
| |___| | | | (_| | |_ / __/| |_| | | | (_| | |_) | | | |
 \____|_| |_|\__,_|\__|_____|\____|_|  \__,_| .__/|_| |_|
                                            |_|          

 * Serving Flask app 'bootstrap'
 * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on http://127.0.0.1:5000
Press CTRL+C to quit

The SDK of Chat2Graph provides a very clear and concise API, allowing you to easily build access to your intelligent system.

You can quickly converse with the built-in Chat2Graph in the following ways.

SystemEnv.LLM_NAME="gpt-4o-mini"
SystemEnv.LLM_ENDPOINT="https://api.openai.com/v1"
SystemEnv.LLM_APIKEY="<YOUR-OPENAI-API-KEY>"

mas = AgenticService.load()
question = TextMessage(payload = "What is TuGraph ?")
answer = mas.execute(question).get_payload()

At the same time, the SDK also provides asynchronous dialogue capabilities.

job = mas.session().submit(question)
answer = job.wait().get_payload()

Of course, customizing your own intelligent agent is also allowed.

mas = AgenticService("Chat2Graph")
mas.expert(name="Graph Modeling Expert").workflow(
        (analysis_operator, concept_modeling_operator)
    ).build()

To facilitate the rapid configuration of agents, you can describe the details of the agent using a YAML file and load it directly.

mas = AgenticService.load("app/core/sdk/chat2graph.yml")

You can reference Contributing document and submit GitHub Issues/PRs to provide feedback and suggest improvements for Chat2Graph.

You can contact with us directly through TuGraph Discord and WeChat group provided below.

• WeChat：

• Discord：https://discord.gg/KBCFbNFj