pydantic-ai-researcher

Name: pydantic-ai-researcher
Author: ibagur

@ibagur

リポジトリ

pydantic-ai-researcher について

#pydantic #ai-researcher #asynchronous-loop

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

ibagur

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "pydantic-ai-researcher": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is pydantic-ai-researcher?

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. It is designed for users who need automated, iterative research refinement.

How to use pydantic-ai-researcher?

After installing Python 3.x, use Pipenv (pipenv install) or pip (pip install -r requirements.txt) to install dependencies. Create a .env file with OPENAI_API_KEY, TAVILY_API_KEY, and BRAVE_API_KEY. Run python main.py and type research queries into the interactive prompt. To exit, type exit.

Key features of pydantic-ai-researcher

Asynchronous research loop for iterative evaluation and improvement
Dual agent system (research and evaluator agents)
Pluggable MCP servers: Tavily, Brave Search, arXiv
Environment configurable via .env files
Straightforward installation with Pipfile or requirements.txt

Use cases of pydantic-ai-researcher

Conduct iterative research on complex queries with automated feedback
Refine answers through multiple evaluation-and-improvement cycles
Gather data from diverse sources (web search, academic papers) via MCP servers
Automate back-and-forth improvement of research answers until satisfactory

FAQ from pydantic-ai-researcher

What dependencies are required to use pydantic-ai-researcher?

Python 3.x is required. API keys for OpenAI, Tavily, and Brave Search must be set in a .env file.

How do I configure pydantic-ai-researcher?

Create a .env file at the project root and define OPENAI_API_KEY, TAVILY_API_KEY, and BRAVE_API_KEY. Other API endpoints may be defined as needed.

How does the pydantic-ai-researcher architecture work?

It implements a two-agent evaluator-optimizer loop. The research agent generates initial answers using external MCP servers, and the evaluator agent provides feedback for improvement. The loop iterates until the answer is accepted or the maximum iteration limit is reached.