What are the system requirements?
Python 3.8 or higher is required. The library depends on packages such as `langchain-mcp-adapters`, `langgraph`, `schedule`, and provider-specific packages like `langchain-openai` and `langchain-anthropic`.
@Buidl-Land
基本情報
AgenticMaid is a Python library designed to interact with one or more Multi-Capability Protocol (MCP) servers.
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": {
"AutoMaid": {
"command": "python",
"args": [
"-m",
"uvicorn",
"AgenticMaid.api:app",
"--reload"
]
}
}
}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。
AgenticMaid Project is a Python library that interacts with one or more Multi-Capability Protocol (MCP) servers. It dynamically fetches tools from those servers, manages configurations for various AI/LLM services, schedules automated tasks, and handles chat service interactions. It is designed for developers building AI-powered applications that need to combine multiple capabilities and language models.
Install the required dependencies (python-dotenv, langchain-mcp-adapters, langgraph, etc.) and configure the library using either a Python dictionary, a JSON file, or a .env file for API keys. Instantiate ClientAgenticMaid with your configuration, call await client.async_initialize(), then use run_mcp_interaction() to invoke agents that leverage the fetched MCP tools.
.env fileslanggraph ReAct agentsPython 3.8 or higher is required. The library depends on packages such as `langchain-mcp-adapters`, `langgraph`, `schedule`, and provider-specific packages like `langchain-openai` and `langchain-anthropic`.
API keys and default model settings can be placed in a `.env` file inside the `AgenticMaid/` directory. They can be overridden by values passed directly in the main JSON or dictionary configuration.
Yes. The library is designed to connect to multiple MCP servers and dynamically fetch and combine their tools for use by agents.
OpenAI, Anthropic, Azure OpenAI, and local OpenAI-compatible servers (e.g., LM Studio) are supported. Providers are configured under the `ai_services` section of the configuration.
Define the task in the `scheduled_tasks` array of the configuration, providing a `cron_expression` (e.g., `"0 * * * *"`), a `prompt`, and a `model_config_name`. The schedule is handled by the `schedule` library.
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