What are the required dependencies?
Python 3.8+, Node.js and npm, a local Ollama installation, and access to an MCP server.
@RyanNg1403
基本情報
This repo makes use of MCP servers to seamlessly integrate multiple tools for the agent.
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-mcp-agent-with-chainlit": {
"command": "python",
"args": [
"pydantic_mcp_agent.py"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
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。
An AI agent implementation using Pydantic and Chainlit that provides web browsing and automated interactions through the Multi-Command Protocol (MCP). It integrates with Ollama for local LLM support and uses a Chainlit-based interactive chat interface. This server is designed for developers building type-safe, configurable AI agents with local language models.
After installing Python and Node.js dependencies and configuring mcp_config.json, run the Chainlit interface with chainlit run pydantic_mcp_chainlit.py or execute the agent directly with python pydantic_mcp_agent.py. Environment variables EXA_API_KEY and OLLAMA_HOST can be set in a .env file.
Python 3.8+, Node.js and npm, a local Ollama installation, and access to an MCP server.
Copy `mcp_config.template.json` to `mcp_config.json` and edit the file with your configuration settings. The file is ignored by git for security.
`EXA_API_KEY` for your MCP API key and `OLLAMA_HOST` for the Ollama host address (defaults to `http://localhost:11434`).
Yes, Ollama integration enables local LLM support, though MCP server access may require network connectivity depending on configuration.
This project is licensed under the MIT License.

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