11111111111 MCP Server

@mannaandpoem

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stdio

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mannaandpoem

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11111111111 MCP Server について

概要はまだありません

#openmanus#ai-agent #open-source

11111111111 MCP Server の設定

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": {
    "OpenManus": {
      "command": "uv",
      "args": [
        "venv",
        "--python",
        "3.12"
      ]
    }
  }
}

11111111111 MCP Server のツール

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。

概要

What is 11111111111?

11111111111 is an open-source framework for building general AI agents, developed by contributors from MetaGPT. It allows users to run agent tasks via terminal without needing an invite code, supporting multiple LLM backends.

How to use 11111111111?

Install via conda or the recommended uv tool, configure API keys in config/config.toml, and run the agent with python main.py. Additional commands include python run_mcp.py for the MCP tool version and python run_flow.py for the unstable multi-agent version.

Key features of 11111111111

Open-source, no invite code required.
Supports multiple LLMs (e.g., GPT-4o) with vision model.
Optional browser automation via Playwright.
MCP tool version and multi-agent flow version.
Simple terminal-based interaction.

Use cases of 11111111111

Quickly prototyping AI agent ideas without waiting for invite codes.
Automating browser tasks using Playwright and LLM vision.
Running multi-agent workflows with the unstable flow version.
Integrating with MCP tools for extended functionality.

よくある質問

What LLMs does 11111111111 support?

The configuration is flexible; the example shows GPT-4o with OpenAI API, but you can change `base_url` and `api_key` to use any compatible LLM provider.

How do I configure 11111111111?

Copy `config/config.example.toml` to `config/config.toml` and replace the placeholder API keys with your own keys. You can also customize model, temperature, and max tokens.

Does 11111111111 require a GPU?

No. It uses remote LLM APIs configured in `config.toml`, so no local GPU is required for inference.

Is 11111111111 stable for production?

The README describes it as a "simple implementation" and the multi-agent version (`run_flow.py`) is explicitly labeled "unstable". It is best suited for experimentation and prototyping.

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