Unveiling Manus: Understanding the principles and architecture behind it

1. The overall architecture of Manus

The architecture of Manus can be likened to  " a thinking cloud robot " . It   consists of three parts: the brain (model layer) , hands (tool layer)  and  the workbench (execution environment) :

1.  Brain ( Model Layer )

• Function : Responsible for understanding user instructions, planning task steps, and monitoring the execution process.
• Technical implementation :  

• (1) Based on the collaborative work of   multiple large models (such as  Claude 3.5  and  Qwen ), the division of labor between models is clear:

• Planning model : break down tasks (e.g. breaking down “writing a travel guide” into checking air tickets, choosing a hotel, and arranging an itinerary);  
• Execution model : calling tools (such as browser search, writing code, generating documents);  
• Audit model : Verify the results (e.g. check whether the hotel price is reasonable).

• (2) Dynamic learning : Adjust the execution strategy based on user feedback (for example, if users often choose economy hotels, subsequent recommendations will prioritize cost-effectiveness).

2.  Hands ( Tool Layer )

• Functionality : Provides the tools needed to perform tasks, such as browsers, code editors, and file managers.
• Technical implementation :  

• (1) Built-in tool chain : integrated  Python interpreter , web crawler , and Office interface , which can directly operate files and data;  
• (2) Private API access : for example, calling the flight query interface to obtain real-time ticket prices, or connecting to the company's internal database to extract customer information.

3.  Workbench ( execution environment )

• Function : Provides a secure cloud environment to isolate different tasks to avoid interference.
• Technical implementation :  

•  (1) Virtual machine isolation : Each task runs in an independent cloud virtual machine to prevent data leakage;   (2) Permission control : Dynamically assign permissions based on task requirements (such as only allowing reading of specified folders).
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2. Working Principle of Manus

Manus's workflow is similar to  that of a " human intern " , and is divided   into four stages: understanding the task → breaking down the steps → performing the operation → providing feedback on the results :

1.  Understand the task

• Example : User input: "Help me filter out 10 resumes suitable for algorithm engineers."  
• principle :  

• Model analysis keywords (“algorithm engineer”) to identify implicit requirements (such as programming skills and project experience);  
• Confirm details through  contextual understanding  (such as whether fresh graduates need to be excluded).

2.  Disassembly steps

• Case : The task is broken down into: unzip the file → read each file one by one → extract skill keywords → score and sort.  
• principle :  

• Agent Base system : decomposes the task into a subtask tree, each of which is handled by a different model or tool;  
• MCP protocol : coordinates dependencies between subtasks (e.g., a file must be decompressed before a resume can be read).

3.  Perform an action

• Case : Automatically call a Python script to decompress files and use a browser plug-in to crawl LinkedIn information.  
• principle :  

• Tool call : Model generation code unzip resumes.zip And execute, if an error occurs, a retry is triggered;  
• Asynchronous execution : The task runs independently in the cloud. The user can close the page and receive an email notification upon completion.

4.  Feedback results

• Example : Generate an Excel table containing candidate rankings, skill matching, and reasons for recommendation.  
• principle :  

• Multimodal output : combining text, graphics, links (such as GitHub projects);  
• Audit mechanism : The audit model checks for logical errors (such as misjudging “3 years of experience” as “5 years”).

3. Core technical highlights of Manus

1.  Design with both hands and brain

• Traditional AI : Can only generate recommendations (such as “You should screen resumes for people with Python experience”).
• Manus : Directly output results (such as a resume form with a score), which is equivalent to  the combination of "thinking + doing"  .

2.  Dynamic learning ability

• Case : After the user modifies the color scheme of the generated PPT several times, Manus automatically remembers the preference and uses the dark blue theme by default.  
• Principle : Optimize the model through  the AHPU indicator (the number of hours a user uses the Agent) instead of simply increasing the number of users.

3.  Balance between safety and efficiency

• Virtual machine isolation : Even if a task fails (such as the crawler's IP is blocked), it will not affect other tasks;  
• Cost control : A single task costs about US$2, which is only 1/5 of the same task of GPT-4.

4. The essential difference from ordinary large models

5. Typical application scenarios

1.  Resume screening

• Process : Upload compressed package → automatically decompress → extract skill keywords → generate ranking table → recommend interview questions .  
• Advantages : HR saves 80% of time and avoids manual screening and missed talents.

2.  Travel Planning

• Process : Input "Cherry Blossom Viewing in Japan in April + Budget 10,000" → Automatically check air tickets and hotels → Generate itinerary PDF → Booking link summary .  
• Advantages : Users do not need to switch between multiple apps to compare prices.

VI. Controversies and Limitations

1. Low technical transparency : No technical documentation is made public, and the project is suspected of relying on existing models (such as Claude) rather than originality.  
2. Task complexity limitation : It cannot handle tasks that require deep cross-platform interaction (such as automatically installing Steam games).  
3. Risk of over-marketing : Some demonstration videos may be edited and optimized, resulting in a gap in actual effect.

Manus's architecture design makes it more like a  "digital employee who can work autonomously" rather than a traditional conversational AI. Its value lies in  lowering the professional threshold (ordinary people can also complete complex tasks) and  improving efficiency (from "talking" to "doing"), but the maturity of the technology still needs to be verified. For ordinary users, they can give priority to tasks with clear requirements (such as data analysis), and it is recommended to keep manual review for complex scenarios.