mcp-agent-forge
Agent Forge is a tool for creating and managing AI agents with distinct personality traits. It facilitates multi-agent collaboration research projects and features natural language processing capabilities for deep multi-round dialogue interactions.
Agent Forge - 智能体锻造工具 (AI Agent Forge Tool)
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:
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 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 agentagent_answer
: Simulate agent responsesget_agent
: Get agent informationlist_agents
: List all agentsdelete_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:
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 file for details.