Analysis of the Manus Multi‑Agent AI System Architecture and Workflow

Manus is a newly released general AI agent that has sparked great interest in the tech community. It aims to connect thinking and action by autonomously handling tasks from user prompts to final deliverables.

What is Manus

Manus is a truly autonomous AI agent whose name derives from the Latin word for “hand”, symbolizing its ability to turn ideas into actions. Unlike traditional assistants, it not only provides suggestions but also delivers complete task results.

Core Architecture

The system follows a typical multi‑agent design and consists of three main modules:

Planning module – the “brain” that understands user intent, decomposes complex tasks into executable steps, and creates execution plans.

Memory module – stores user preferences, interaction history, and intermediate results to maintain context and continuity.

Tool‑use module – the “hand” that invokes various tools such as web search, data analysis, code generation, document creation, and visualization.

Planning Module

The planning module acts as the decision‑making core, performing task understanding, decomposition, priority sorting, plan generation, resource allocation, NLU, DAG construction, and exception handling.

Memory Module

The memory module enables long‑term context by managing three key information types: user preferences, historical interactions, and intermediate results.

class MemorySystem:
    def __init__(self):
        self.user_profile = UserVector()  # 用户偏好向量
        self.history_db = ChromaDB()      # 交互历史数据库
        self.cache = LRUCache()           # 短期记忆缓存

Tool‑Use Module

This module executes actions by calling tools for web search, data processing, code execution, document generation, and data visualization.

Multi‑Agent System: The Art of Intelligent Collaboration

A Multi‑Agent System (MAS) consists of multiple interacting intelligent agents, each capable of perception, learning, decision‑making, and action. Agents may be specialized for tasks such as summarization, translation, or content generation and cooperate through information sharing and task division.

Operation Logic and Workflow

Manus runs a Multiple Agent Architecture in isolated virtual environments. The overall workflow can be summarized as:

Task Reception : User submits a request, ranging from simple queries to complex project requirements.

Task Understanding : The system parses the input, leveraging the memory module for user preferences and history to refine intent.

Task Decomposition : The planning module breaks the task into sub‑tasks and builds a dependency DAG. // todo.md - [ ] 调研日本热门旅游城市 - [ ] 收集交通信息 - [ ] 制定行程安排 - [ ] 预算规划

Environment Preparation : An isolated execution environment is created. # 创建任务目录结构 mkdir -p {task_id}/ docker run -d --name task_{task_id} task_image

Plan Execution : Each sub‑task receives a specific plan, selecting appropriate tools and resources.

Autonomous Execution : Specialized agents run their tasks. class SearchAgent: def execute(self, task): # 调用搜索 API results = search_api.query(task.keywords) # 模拟浏览器行为 browser = HeadlessBrowser() for result in results: content = browser.visit(result.url) if self.validate_content(content): self.save_result(content) Search Agent : Performs web searches using keyword and semantic strategies. Code Agent : Generates and runs code in Python/JS/SQL. Data Analysis Agent : Analyzes data with Pandas/Matplotlib.

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

Result Integration : Consolidates sub‑task outputs into a coherent final deliverable.

Result Delivery : Provides the user with reports, code, visualizations, or other formats.

User Feedback & Learning : Feedback is stored in memory to improve future executions.

Technical Features and Innovations

Autonomous Planning : Achieves a 94% automatic completion rate on complex tasks (GAIA benchmark).

Context Understanding : Handles ambiguous or abstract inputs and maintains long‑term conversations (10+ turns).

Multi‑Agent Collaboration : Runs agents in sandboxed VMs (gVisor) for safe execution.

Tool Integration : Seamlessly calls search, data analysis, and code generation tools.

Security Isolation : Uses gVisor sandboxing to ensure safe task execution.

Modular design supports extensible plugins and custom tools.

Future Optimization Directions

Upgrade task dependency graphs to full DAG structures for more complex workflows.

Introduce automated testing and quality control to improve reliability.

Develop human‑AI hybrid interaction modes combining human insight with AI efficiency.

Technical Architecture Dependencies

The system relies on a hierarchy of models:

Lightweight model for fast intent recognition.

Deepseek‑r1 for global task planning.

Claude‑3.7‑sonnet for handling complex multimodal tasks.

Application Scenarios

Scenario

Typical Case

Output Form

Travel Planning

Custom Japan itinerary

Interactive map + budget sheet

Financial Analysis

Tesla stock multi‑dimensional analysis

Dynamic dashboard + risk assessment

Education Support

Momentum theorem teaching plan

Interactive lesson + simulation

Business Decision

Insurance product comparison

Visual matrix + recommendation report

Market Research

Amazon market sentiment analysis

Quarterly trend report + forecast model

Comparison with Traditional AI Assistants

+ End‑to‑end task delivery: not just advice, but execution and results
+ Task decomposition ability: breaks complex tasks into manageable steps
+ Tool‑use capability: invokes diverse tools to accomplish tasks
+ Dynamic environment adaptation: adjusts strategies per task needs
+ Long‑term memory: retains user preferences and interaction history
+ Result‑oriented: focuses on delivering complete outputs
- Single‑turn interaction: traditional AI stays at conversation level
- Static response: lacks autonomous execution
- Stateless design: each dialogue is independent, no continuity

Conclusion

Multi‑Agent systems represent a frontier in AI development, and products like Manus exemplify this trend. Although challenges such as computational cost and task accuracy remain, the collaborative intelligence potential is immense. As model efficiency improves, we will see more “Leave it to Agent” scenarios, achieving seamless transition from AI thought to action.