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11111111111 MCP Server

@mannaandpoem

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

mannaandpoem

提交者

烁 林

关于 11111111111 MCP Server

暂无概览

#openmanus#ai-agent#open-source

11111111111 MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

11111111111 MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

1

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.

2

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.

3

Does 11111111111 require a GPU?

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

4

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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