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

Prerequisites: Ensure Python 3.x is installed.

Installation:

Using Pipenv:
```
pipenv install
```
Or using pip:
```
pip install -r requirements.txt
```

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:

evaluator-optimizer-workflow

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.