Multi-Agent System with Pydantic AI

This is a simple example of how to build a multi-agent system with Pydantic AI including chat history memory.

The hierarchy of the multi-agent system is as follows:

graph TD
    Director[Director Agent]
    TaskMgr[Task Manager Agent]
    ResultHandler[Result Handler Agent]
    NumAgent[Secret Number Agent]
    WordAgent[Secret Word Agent]

    Director --> TaskMgr
    Director --> ResultHandler
    TaskMgr --> NumAgent
    TaskMgr --> WordAgent

    %% Styling
    classDef primary fill:#f9f,stroke:#333,stroke-width:2px;
    classDef secondary fill:#bbf,stroke:#333,stroke-width:2px;
    classDef tertiary fill:#dfd,stroke:#333,stroke-width:2px;
    
    class Director primary;
    class TaskMgr,ResultHandler secondary;
    class NumAgent,WordAgent tertiary;

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The Director Agent is the main agent that is responsible for the overall tasks. It can delegate tasks to the Task Manager Agent who can delegate tasks to the Secret Number Agent and the Secret Word Agent. The Secret Number Agent and the Secret Word Agent are responsible for providing a "secret" number and a "secret" word respectively. The Result Handler Agent is responsible for handling the result of the task and to postprocess the result. It reports back to the Director Agent.

Setup

1. Clone the repository

git clone https://github.com/achimstruve/pydanticAI-Multi-Agent-System.git
cd pydanticAI-Multi-Agent-System

2. Create a virtual environment

python -m venv venv

3. Activate the virtual environment

source venv/scripts/activate

4. Install the dependencies:

pip install -r requirements.txt

5. Create a .env file with the following variables:

OPENAI_API_KEY=your_openai_api_key

6. Run the agent system:

python main.py