pydantic-ai-researcher

Description

pydantic-ai-researcher is a research system that orchestrates an asynchronous loop between two specialized agents: a research agent and an evaluator agent. The research agent answers complex queries using external MCP servers, while the evaluator agent assesses and refines these answers, iterating until a satisfactory response is attained.

Features

• Asynchronous Research Loop: Iterative evaluation and improvement of research answers.
• Dual Agent System: Utilizes a research agent for data gathering and an evaluator agent for feedback.
• Pluggable MCP Servers: Integrates with multiple MCP servers (Tavily, Brave Search, arXiv) for diverse data sources.
• Environment Configurable: Uses environment variables and .env files for API key configuration.
• Easy Setup: Straightforward installation via Pipfile or requirements.txt.

Installation / Setup

1. Prerequisites: Ensure Python 3.x is installed.
2. Installation:
   • Using Pipenv:

     pipenv install
     

   • Or using pip:

     pip install -r requirements.txt
     

3. Configuration:
   • Create a .env file at the project root.
   • Define the required environment variables:
     • OPENAI_API_KEY
     • TAVILY_API_KEY
     • BRAVE_API_KEY
   • (Other API endpoints may also need to be defined as needed.)

Usage

Run the project by executing the following command:

python main.py

Type your research queries in the interactive prompt. To exit the program, type exit.

Architecture

The system implements a two-agent evaluator-optimizer loop:

• Agent A (Research Agent): Generates initial answers to research queries using external data sources via MCP servers.
• Agent B (Evaluator Agent): Evaluates the answers provided by Agent A and offers feedback for improvement.
• Loop Mechanism: The system iterates through multiple cycles of evaluation until an answer is accepted or the maximum iteration limit is reached.

Below is the workflow diagram illustrating the evaluator-optimizer process:

Source: Building effective agents

Configuration

The project configuration relies on environment variables loaded via python-dotenv. Ensure that:

• OPENAI_API_KEY is set for the OpenAI model usage.
• TAVILY_API_KEY is set for accessing Tavily MCP Search.
• BRAVE_API_KEY is set for using Brave Search MCP.
• Additional configuration for arXiv MCP may be defined as needed.

Contributing / License

Contributions are welcome. Please submit issues and pull requests for bug fixes, improvements, or new features. Refer to the project's LICENSE file for licensing details.