OpenManus 项目分析报告

本报告由 OpenClaw 自动生成

研究日期: 2026-03-27

项目路径: /Users/daoyu/Documents/ai-repo/OpenManus

---

1. 项目概述

Manus is incredible, but OpenManus can achieve any idea without an Invite Code 🛫!
Our team members @Xinbin Liang and @Jinyu Xiang (core authors), along with @Zhaoyang Yu, @Jiayi Zhang, and @Sirui Hong, we are from @MetaGPT. The prototype is launched within 3 hours and we are keeping building!
It’s a simple implementation, so we welcome any suggestions, contributions, and feedback!
Enjoy your own agent with OpenManus!
We’re also excited to introduce OpenManus-RL, an open-source project dedicated to reinforcement learning (RL)- based (such as GRPO) tuning methods for LLM agents, developed collaboratively by researchers from UIUC and OpenManus.

Project Demo

Installation

We provide two installation methods. Method 2 (using uv) is recommended for faster installation and better dependency management.

Method 1: Using conda

1. Create a new conda environment:

2. 技术栈

3. 项目结构

./run_mcp.py
./app/config.py
./app/__init__.py
./app/llm.py
./app/logger.py
./app/exceptions.py
./app/bedrock.py
./app/schema.py
./setup.py
./run_mcp_server.py
./run_flow.py
./sandbox_main.py
./main.py

4. 技术实现分析

核心架构

待深入分析

关键模块

待深入分析

设计模式

待深入分析

5. 产品意义

解决的问题

待分析

目标用户

待分析

应用场景

待分析

6. 借鉴点

技术层面

1. 待分析
2. 待分析

产品层面

1. 待分析
2. 待分析

工程实践

1. 待分析
2. 待分析

7. 待深入研究

• 阅读核心源码
• 运行示例
• 分析测试用例
• 研究 API 设计

---

本报告由 OpenClaw 自动生成，需要进一步人工补充