How Manus Works Revealed: Deconstructing the Multi-Intelligent Body Architecture for Next-Generation AI Agents

Introduction

Last night, Manus, an AI Agent product, was launched, instantly igniting the technology circle. At this moment, all AI enthusiasts are scrambling for the Manus invitation code, and even on a second-hand trading platform, the price of the invitation code has been speculated to 999 yuan to 50,000 yuan. Behind this boom is the strong expectation for the next generation of AI interaction.

Manus, as a general-purpose AI, builds a bridge between thinking and action: it not only thinks, but also delivers results. Whether it's work or life tasks, Manus can do it all efficiently while you rest. This "Leave it to Manus" philosophy is the perfect embodiment of the Multi-Agent system.

What is Manus?

Manus is a truly autonomous AI agent, capable of solving a variety of complex and constantly changing tasks. Its name comes from the Latin word for "hand", symbolizing its ability to transform thoughts into actions. Unlike traditional AI assistants, Manus not only provides suggestions or answers, but also directly delivers complete task results.

As a "general-purpose AI agent", Manus is able to autonomously perform tasks ranging from simple queries to complex projects without constant user intervention. Users only need to enter simple prompts, and no AI knowledge or experience is required to obtain high-quality output.

This "one-step solution to any problem" design concept differentiates Manus from traditional AI workflows and makes it easier for general users to use.

Core Architecture Analysis

The architecture of Manus reflects the typical characteristics of Multi-Agent systems, and its core consists of three major modules:

1. Planning Module

The planning module is the "brain" of Manus, which is responsible for understanding users' intentions, decomposing complex tasks into executable steps, and formulating execution plans. This module enables Manus to handle abstract task descriptions and translate them into concrete action steps.

As the decision-making hub of the system, the planning module implements:

• Task understanding and analysis

• Task decomposition and prioritization

• Execution plan development

• Resource allocation and tool selection

• Semantic Understanding and Intent Recognition (NLU)

• Complex Task Decomposition into DAG Structure

• Exception handling and process optimization

2. Memory Module (Memory)

The Memory module enables Manus to store and utilize historical information to improve the coherence and personalization of task execution. The module manages three key types of information:

1. User preferences: records user habits and preferences to personalize subsequent interactions.

2. Historical Interactions: Keeps a record of past conversations and task execution, providing contextual coherence.

3. Intermediate results: stores temporary data during task execution, supporting step-by-step execution of complex tasks

Building a long-term memory system:

class MemorySystem:
    def __init__(self):
        self.user_profile = UserVector()  # User preference vector
        self.history_db = ChromaDB()      # Interaction history database
        self.cache = LRUCache()           # Short-term memory cache

3. Tool Use Module

The Tool Use module is the "hand" of Manus, and is responsible for the actual execution of various operations. The module can call and use a variety of tools to complete the task, including:

• Web searching and information retrieval

• Data analysis and processing

• Code writing and execution

• Documentation generation

• Data Visualization

This multi-tool integration capability enables Manus to handle a variety of complex tasks, from information collection to content creation to data analysis.

Multi-Agent Systems: The Art of Intelligent Collaboration

Multi-Agent System (MAS) consists of multiple interacting intelligences, each of which is an autonomous entity capable of sensing, learning environment models, making decisions, and executing actions. These intelligences can be software programs, robots, drones, sensors, humans, or a combination of them.

In a typical Multi-Agent architecture, individual intelligences have specialized capabilities and goals. For example, a system may contain intelligences focusing on different tasks such as content summarization, translation, and content generation. They work together by sharing information and dividing tasks to achieve more complex and efficient problem-solving capabilities.

Operating Logic and Workflow

Manus adopts a Multiple Agent Architecture and runs in an independent virtual environment. The logic can be summarized as follows:

Complete execution process

1. Task Reception: The user submits a task request, which can be a simple query or a complex project requirement. Manus receives this input and starts processing.

2. Task Understanding: Manus analyzes the user input and understands the nature and goal of the task. In this phase, the memory module provides user preferences and historical interaction information to help understand user intent more accurately.

• Advanced natural language processing techniques are utilized for intent recognition and keyword extraction of user inputs

• Help users clarify their goals through conversational guidance when their needs are unclear

• Supports multimodal inputs such as text, pictures, documents, etc. to enhance the interactive experience.

3. Task decomposition: The planning module automatically decomposes a complex task into multiple executable subtasks and establishes task dependencies and execution order.

// todo.md
- [ ] Research popular tourist cities in Japan
- [ ] Collect transportation information
- [ ] Make itinerary
- [ ] Budget planning

4. Task Initialization and Environment Preparation: In order to ensure the isolation and security of task execution, the system creates an independent execution environment:

# Create task directory structure
mkdir -p {task_id}/
docker run -d --name task_{task_id} task_image

5. Execution Plan Creation: Create an execution plan for each subtask, including the required tools and resources. Historical interactions are recorded at this stage to help optimize the execution plan.

6. Autonomous Execution: The tool uses modules to autonomously execute each subtask in the virtual environment, including searching for information, retrieving data, writing code, generating documentation, and analyzing and visualizing data. The intermediate results of the execution process are saved by the memory module for subsequent steps.

The system uses multiple specialized Agents to work together and perform their respective roles:

The execution results of each Agent are saved in the task catalog to ensure traceability:

class SearchAgent:
    def execute(self, task):
        # Call & Search API
        results = search_api.query(task.keywords)
        
        # Simulate browser behavior
        browser = HeadlessBrowser()
        for result in results:
            content = browser.visit(result.url)
            if self.validate_content(content):
                self.save_result(content)

• Search Agent: Responsible for searching the web for the latest and most relevant data, using a hybrid search strategy (keywords + semantics).

• Code Agent: Handle code generation and execution, realize automation operations, support Python/JS/SQL and other languages.

• Data Analysis Agent: Perform data analysis to extract valuable insights, Pandas/Matplotlib integration.

7. Dynamic quality inspection:

def quality_check(result):
    if result.confidence < 0.7:
        trigger_self_correction()
    return generate_validation_report()

8. Result Integration: Integrate the results of each sub-task into the final output to ensure the consistency and completeness of the content.

• Intelligently integrates the execution results of all Agents, eliminating redundancies and contradictions.

• Generate user-friendly multimodal output to ensure comprehensibility and usefulness.

9. Delivery of results: Provide users with the complete results of the task, which may be in the form of reports, analysis, code, charts, or other forms of output.

10. user feedback and learning: users provide feedback on the results, which is recorded by the memory module and used to improve future task execution. Reinforcement models are fine-tuned to continuously improve system performance.

Technical Features and Innovations

Manus has several technical features that make it stand out in the AI agent space:

Autonomous Planning Capability

Manus's ability to think and plan independently to ensure task execution is a key differentiator from previous tools. Manus achieved the latest SOTA (State-of-the-Art) score in the GAIA Benchmark (General AI Assistant Benchmark), a test designed to evaluate the ability of general AI assistants to solve problems in the real world. Achieving a 94% autocomplete rate on complex tasks.

Contextual Understanding

Manus is able to accurately recognize user needs from vague or abstract descriptions. For example, a user simply describes the content of a video, and Manus locates the appropriate video link on the platform. This efficient matching capability ensures a smoother user experience. Supports 10+ rounds of long dialog maintenance.

Multi-Agent Collaboration

Manus utilizes a multi-agent architecture, similar to Anthropic's Computer Use feature, running in separate virtual machines. This architecture enables different functional modules to work together to handle complex tasks.

Tool Integration

Manus is able to automate the invocation of various tools such as search, data analysis and code generation, significantly improving efficiency. This integration capability enables it to handle a variety of complex tasks, from information gathering to content creation to data analysis. Customized tool plug-in development is supported.

Secure Isolation

The gVisor-based sandbox environment ensures the security and stability of task execution.

Other Technical Advantages

1. Environmentally isolated task execution ensures security and stability.

2. Modular Agent design supports flexible expansion

3. Intelligent task scheduling mechanism to maximize resource utilization.

Future optimization direction

1. Task dependencies are upgraded to DAG (Directed Acyclic Graph) structure to support more complex task flows.

2. Introduce automated testing and quality control to improve the reliability of execution results.

3. Develop a hybrid human-computer interaction model that combines human insights with AI efficiency.

Technical Architecture Dependencies

The power of the system benefits from the collaboration of models at multiple levels:

1. Lightweight models: responsible for intent recognition and providing fast responses

2. Deepseek-r1: focuses on task planning, controlling the global strategy

3. Claude-3.7-sonnet: Handles complex multimodal tasks and provides deep understanding.

Application Scenario Extension

Comparison of Different Traditional AI Assistants

Pros:

• End-to-end task delivery: Not only provide recommendations, but also directly execute tasks and deliver results

• Task decomposition capability: The ability to decompose complex tasks into manageable steps

• Tool usage capability: Ability to invoke and use various tools to accomplish tasks

• Dynamic Environment Adaptability: Ability to adjust execution strategies based on task requirements.

• Long-term memory retention: Ability to remember user preferences and history of interactions to provide a personalized experience.

• Results Orientation: Focus on delivering complete task results, not just information

Disadvantages:

• Single interaction model: Traditional AI is mainly at the level of "conversation".

• Static response mechanism: Lack of autonomous execution capability

• Stateless design: Each conversation is independent and lacks continuity.

Conclusion

Multi-Agent systems represent the cutting edge of AI development, and the emergence of products such as Manus is a vivid manifestation of this trend. Although such systems still face the challenges of computational cost and task accuracy, their potential for collaborative intelligence is immeasurable.

In the future, with the optimization of model efficiency and the improvement of task execution reliability, we will see more "Leave it to Agent" application scenarios, truly realizing the seamless connection of AI from thinking to action.