寫在前面

Manus 是由中國初創公司 Monica.im 于 2025 年 3 月推出的全球首款通用型 AI 智能體（AI Agent），旨在實現“知行合一”，即不僅具備強大的語言理解和推理能力，還能自主執行復雜任務，直接交付完整成果。

什么是 Manus？

“Manus”一詞源自拉丁語“Mens et Manus”，意為“心智與雙手”，強調其不僅能思考，還能動手執行。與傳統 AI 助手（如 ChatGPT）主要提供建議或回答問題不同，Manus 能夠獨立完成從任務規劃到執行的全過程。例如，用戶只需輸入一句話，Manus 就能自動完成數據分析、代碼編寫、文檔生成等任務，真正實現從想法到成果的閉環。

Manus AI

https://manus.im/

這里放幾個用戶的案例：
● https://manus.im/share/jr5CKBMJJZDxvU2S5VdZLw?replay=1
● https://manus.im/share/c3onakN6Iajcm1Vt1xAVG7?replay=1
● https://manus.im/share/xFgpHb15vKqfRPWIs3JJPJ?replay=1

應用場景

• 數據分析與可視化：處理上傳的數據，生成圖表、趨勢分析或儀表盤。
• 市場調研：搜索網絡信息，整合數據并輸出結構化分析結果。
• 旅行規劃：根據用戶偏好生成詳細行程和旅行手冊。
• 代碼編寫與運行：生成代碼并測試，提供可執行程序或腳本。
• 文件操作與整理：解壓文件、對比文檔并生成比較表格。
• 網頁生成：根據需求設計并輸出展示型網頁。
• 多任務并行處理：支持同時處理多個任務，提升工作效率。

案例測試

貪吃蛇

寫的提示詞如下：

用HTML+CSS+JS 實現一個經典的 貪吃蛇游戲

最終的任務計劃總結：

The plan to develop a classic Snake game using HTML, CSS, and JavaScript has been successfully completed. All 10 steps of the plan have been accomplished, achieving a 100% completion rate. The project involved setting up the project structure and basic files, creating and styling the game canvas with HTML and CSS, and initializing game variables with JavaScript. The core functionalities implemented include the snake's movement logic, food generation, collision detection, and game-over conditions. Additionally, features for starting and restarting the game were added. The game underwent testing and debugging to ensure functionality, and the code was optimized with added comments for clarity. The project is now complete with no pending tasks.

實現效果如下：https://wzk.icu/manus/snake/index.html

PingPong

提示詞是這樣的：

用HTML+CSS+JS 實現一個經典的 PingPong 游戲

計劃匯總:

The plan to develop a Classic PingPong Game using HTML, CSS, and JavaScript (ID: plan_1741672648) has been successfully completed. All 9 steps of the plan have been accomplished, resulting in a fully functional game. The project began with setting up the necessary project structure, followed by designing the game layout. The core game logic was implemented using JavaScript, including the mechanics for paddle and ball movement. Collision detection was added to ensure interactions between paddles and walls were accurately represented. A scoring system and game over conditions were established to enhance gameplay. The game underwent thorough testing to ensure functionality and any bugs were addressed. Performance and responsiveness were optimized to provide a smooth user experience. Finally, the game was deployed to a web server or platform, making it accessible for users to play. The project was completed efficiently with no steps left in progress or blocked.

實現結果如下：https://wzk.icu/manus/pingpong/index.html

TODO List

提示詞如下：

用HTML+CSS+JS 實現一個經典的todolist 可以添加修改刪除

計劃匯總如下：

The plan to implement a TODO list using HTML, JavaScript, and CSS has been successfully completed. All nine steps outlined in the plan have been accomplished, achieving a 100% completion rate. The steps included setting up the basic HTML structure, styling the list with CSS, and implementing various JavaScript functionalities such as adding, deleting, editing, and clearing items. Additionally, the application was tested across different browsers to ensure compatibility, and the code was optimized for performance and readability. Finally, the TODO list application was deployed to a web server, marking the completion of the project. The plan was executed efficiently with no steps blocked or left in progress.

效果如下：https://wzk.icu/manus/todolist/index.html

原理分析

基本介紹

OpenManus 采用了一種 類似 Auto-GPT 的思考方式，即智能體不會直接輸出答案，而是會：
● 分析任務需求
● 制定解決方案
● 分解任務并執行
● 檢查結果并優化
● 返回最終答案

按照我之前的認知來看，是一種：ReACT 的模式。
除了 ReACT 以外，還有一個：CodeACT（Manus團隊采訪的時候說用的是 CodeACT）：

• https://github.com/xingyaoww/code-act

AutoGPT

● https://github.com/Significant-Gravitas/AutoGPT
● https://agpt.co/

（之前自動生成繪本的項目）在24年的時候，當時使用 AutoGPT做了一些實驗，當時GPT-4還非常的昂貴，做了一個給定任務：寫小紅帽的故事，借助生圖工具（DALLE），保持上下文統一風格，最終生成了一組故事。

在 LangChain 中有一個簡單案例，可以輕松的實現 ReACT：

# 下面是提示詞的模板，通過提示詞來激發大模型的 ReACT 行為：
# Answer the following questions as best you can. You have access to the following tools:
#
# {tools}
#
# Use the following format:
#
# Question: the input question you must answer
# Thought: you should always think about what to do
# Action: the action to take, should be one of [{tool_names}]
# Action Input: the input to the action
# Observation: the result of the action
# ... (this Thought/Action/Action Input/Observation can repeat N times)
# Thought: I now know the final answer
# Final Answer: the final answer to the original input question
#
# Begin!
#
# Question: {input}
# Thought:{agent_scratchpad}# Construct the ReAct agent
# tools = [搜索引擎、繪圖、SQL 等等]
agent = create_react_agent(llm, tools, prompt)
# Create an agent executor by passing in the agent and tools
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)message = agent_executor.invoke({"input": "what is LangChain?"})
print(f"message1: {message}")