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Agent2 — 模块化 Agent 系统框架

一个从零构建的 Python Agent 系统框架,用于深入理解 AI Agent 的核心架构和设计模式。

核心特性

特性 说明
🧠 LLM 抽象层 统一 OpenAI 兼容接口,支持 OpenAI / DeepSeek / Ollama / vLLM / Qwen 等
🔧 工具系统 @tool 装饰器自动生成 JSON Schema,支持同步/异步
🔄 ReAct 模式 Thought → Action → Observation 推理循环
📋 Plan-and-Execute 先规划后执行,支持动态重规划
🪞 自我反思 ReflectionMixin 添加输出自评和迭代改进
💾 记忆系统 短期 (WorkingMemory) + 长期 (LongTermMemory/TF-IDF)
👥 多 Agent 编排 顺序/监督者/辩论 三种协作模式

快速开始

# 安装
uv pip install -e "."

# 设置 API Key
export AGENT2_API_KEY=sk-...

最简示例

import asyncio
from agent2.llm import create_llm
from agent2.agent import ReActAgent
from agent2.tools.builtin import python_exec

async def main():
    llm = create_llm("openai", model="gpt-4o-mini")
    agent = ReActAgent("assistant", llm=llm, tools=[python_exec])
    result = await agent.run("What is 2^100?")
    print(result)

asyncio.run(main())

示例

uv run examples/01_single_agent.py   # 单 Agent ReAct
uv run examples/02_tool_use.py       # 自定义工具
uv run examples/03_planning.py       # Plan-and-Execute
uv run examples/04_memory.py         # 记忆系统(无需 API Key)
uv run examples/05_multi_agent.py    # 多 Agent 协作

架构

agent2/
├── llm/        # LLM 抽象层 — 统一多提供商接口
├── tools/      # 工具系统 — @tool 装饰器 + Registry
├── agent/      # Agent 核心 — ReAct / Planner / Reflection
├── memory/     # 记忆系统 — Working / LongTerm
├── crew/       # 多 Agent — Sequential / Supervisor / Debate
└── utils/      # 配置 + 日志

License

MIT

Metadata

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