MCP 服务器

mcp-agent-forge

README

Agent Forge - 智能体锻造工具 (AI Agent Forge Tool)

English | 中文

中文版

Agent Forge 是一个智能体创建和管理平台，能够创建和管理具有特定性格特征的智能体，并模拟它们对问题的回答。通过Agent Forge MCP，你可以快速构建起一个类似于CO-STORM的多智能体协作研究项目。

功能特点

智能体锻造：创建具有特定性格特征的智能体
思维模拟：模拟智能体回答问题
完整管理：支持智能体的查询、列表、删除等操作
多轮对话：支持深度的多轮对话交互
自然语言处理：基于 DeepSeek API 的高级语言理解能力

系统要求

Go 1.24.1 或更高版本
DeepSeek API 密钥

安装

git clone https://github.com/HundunOnline/mcp-agent-forge.git
cd mcp-agent-forge && make build

MCP 配置

{
  "mcpServers": {
    "mcp-agent-forge": {
      "command": "/path/to/mcp-agent-forge",
      "env": {
        "DEEPSEEK_API_KEY": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxx",
      }
    }
  }
}

配置说明

Configuration

Environment Variables

变量名	描述	默认值	是否必需
`DEEPSEEK_API_KEY`	DeepSeek API 密钥	-	是
`LOG_LEVEL`	日志级别 (debug, info, warn, error)	info	否
`LOG_PATH`	日志文件路径	./logs	否
`CONFIG_PATH`	配置文件路径	./config/config.yaml	否
`PORT`	服务端口号	8080	否
`DEBUG`	调试模式开关	false	否

使用方法

expert_personality_generation: 创建新的智能体
agent_answer: 模拟智能体回答问题
get_agent: 获取智能体信息
list_agents: 列出所有智能体
delete_agent: 删除智能体

示例

基本用法

// 创建智能体
{
    "name": "expert_personality_generation",
    "arguments": {
        "agent_name": "马斯克思维模型",
        "core_traits": "系统思维,第一性原理,工程思维,风险管理,创新思维"
    }
}

// 智能体回答
{
    "name": "agent_answer",
    "arguments": {
        "agent_id": "your_agent_id",
        "context": "如何看待特斯拉的发展策略？",
        "planned_rounds": 3,
        "current_round": 1,
        "need_more_rounds": false
    }
}

实际应用案例

我们在 Claude AI 中创建了一个示例应用，展示了如何使用 Agent Forge 创建和管理专家智能体：

Claude AI Demo

这个示例展示了：

如何创建具有特定专业背景的智能体
如何进行多轮对话交互
如何利用智能体的专业知识解决问题
如何管理和调整智能体的行为

贡献指南

欢迎提交 Pull Request 或创建 Issue 来帮助改进这个项目。我们特别欢迎以下方面的贡献：

新的智能体模型和特征
性能优化
文档改进
Bug 修复
新功能建议

许可证

本项目采用 MIT 许可证。详见 LICENSE 文件。

English Version

Agent Forge is a platform for creating and managing AI agents with specific personality traits and simulating their responses to questions. Through agent forge mcp, you can quickly build a multi-agent collaboration research project similar to CO-STORM.

Features

Agent Forging: Create agents with specific personality traits
Thought Simulation: Simulate agent responses to questions
Complete Management: Support for agent querying, listing, deletion, and other operations
Multi-round Dialogue: Support for deep multi-round conversation interactions
Natural Language Processing: Advanced language understanding capabilities based on DeepSeek API

System Requirements

Go 1.24.1 or higher
DeepSeek API key

Installation

git clone https://github.com/HundunOnline/mcp-agent-forge.git
cd agent-forge && make build

MCP Configuration

{
  "mcpServers": {
    "mcp-agent-forge": {
      "command": "/path/to/mcp-agent-forge",
      "env": {
        "DEEPSEEK_API_KEY": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxx",
      }
    }
  }
}

Configuration Guide

Configuration

Environment Variables

Variable Name	Description	Default Value	Required
`DEEPSEEK_API_KEY`	DeepSeek API Key	-	Yes
`LOG_LEVEL`	Logging level (debug, info, warn, error)	info	No
`LOG_PATH`	Log file path	./logs	No
`CONFIG_PATH`	Configuration file path	./config/config.yaml	No
`PORT`	Service port	8080	No
`DEBUG`	Debug mode switch	false	No

Usage

expert_personality_generation: Create a new agent
agent_answer: Simulate agent responses
get_agent: Get agent information
list_agents: List all agents
delete_agent: Delete an agent

Examples

Basic Usage

// Create an agent
{
    "name": "expert_personality_generation",
    "arguments": {
        "agent_name": "Elon Musk Thinking Model",
        "core_traits": "Systems Thinking,First Principles,Engineering Mindset,Risk Management,Innovation"
    }
}

// Agent response
{
    "name": "agent_answer",
    "arguments": {
        "agent_id": "your_agent_id",
        "context": "What's your view on Tesla's development strategy?",
        "planned_rounds": 3,
        "current_round": 1,
        "need_more_rounds": false
    }
}