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
    }
}

Real Application Case

We created a sample application in Claude AI that demonstrates how to use Agent Forge to create and manage expert agents:

Claude AI Demo

This example shows:

• How to create agents with specific professional backgrounds
• How to conduct multi-round dialogue interactions
• How to utilize agents' expertise to solve problems
• How to manage and adjust agent behavior

Contributing

We welcome Pull Requests or Issues to help improve this project. We especially welcome contributions in the following areas:

• New agent models and traits
• Performance optimizations
• Documentation improvements
• Bug fixes
• New feature suggestions

License

This project is licensed under the MIT License. See the LICENSE file for details.