This is my own study notes, including organized content and simple experiments.

1. Analysis of the core functions of the project

1.1 Core Positioning

OpenManus is a multifunctional AI agent framework that automates complex tasks through a combined toolchain. Its core features include:

• Multi-tool linkage
  : Supports Python execution, web search, file operations, browser control and other tools
• Intelligent process planning
  : Built-in Planning Agent automatically breaks down tasks
• LLM driver interaction
  ：Realize natural language interaction based on GPT-4 and other models
• Cross-platform compatibility
  : Support Docker deployment and multiple operating systems

2. In-depth analysis of code architecture

2.1 Core module structure

2.2 Detailed explanation of key components

Agent

• Manus
  kind(app/agent/manus.py):

  class Manus ( ToolCallAgent ) : 
      available_tools  =  ToolCollection (
          PythonExecute ( ) ,  WebSearch ( ) ,  BrowserUseTool ( ) ,  FileSaver ( ) ,  Terminate ( )
      )
  

• Planning Agent (app/agent/planning.py):

  class PlanningAgent ( ToolCallAgent ) : 
      async def act ( ) :  
          # Execute the tool and update the plan status
  

Tool layer

• Basic tool abstraction:

  class BaseTool : 
      name : str 
      description : str 
      async def execute ( self , ** kwargs ) : . . .    
  

• Specific tool implementation:

  class WebSearch ( BaseTool ) : 
      async def execute ( self ,  query ) :  
          # Call the search engine API
  

Process Control

• Process Plant (app/flow/flow_factory.py):

  class FlowFactory : 
      @staticmethod
      def create_flow ( flow_type ) : 
          if  flow_type  ==  FlowType . PLANNING :
              return  PlanningFlow ( agents = agents )
  

3. Practical Guide to Development Process

3.1 Quick Start Steps

# Recommended installation using uv (fragment 4)
uv pip  install -r  requirements.txt 
python run_flow.py

3.2 Core Execution Process

3.3 Configuration Key Points

• API key configuration (config/config.toml):

  [ llm ]
  model = "gpt-4o"  
  api_key = "sk-..."  
  

• Browser configuration (app/config.py):

  class Config : 
      def browser_config ( self ) - >  BrowserSettings :  
          # Return browser setting parameters
  

4. Typical Application Scenarios

4.1 Japan Travel Planning Case

User input :

Need to create a 7-day Japan itinerary departing from Seattle from April 15-23, with a budget of $2500-5000, including elements such as historical sites, cultural experiences (kendo, tea ceremony), and Nara deer. Need to generate an HTML manual with maps and Japanese phrases.

Execution process :

1. Planning Phase
   :

• use WebSearch Query attraction information
• PythonExecute
   Processing budget calculations
• Execution Phase
  :
• BrowserUseTool
   Visit travel websites
• FileSaver
   Save the generated HTML manual
• Output
  :

  await  tool . execute ( save_path = "/output/travel_plan.html" )
  

Sample output :






5. In-depth analysis of code structure

5.1 Directory structure diagram




5.2 Core Class Relationships

• Agent-Tool Collaboration :

  class Manus ( ToolCallAgent ) : 
      async def act ( ) :  
          tool  =  self . available_tools . get ( "WebSearch" )
          result  = await  tool . execute ( query ) 
  

• Process Executor :

  async def run_flow ( ) :  
      flow  =  FlowFactory . create_flow ( FlowType . PLANNING )
      await  flow . execute ( user_prompt )
  

---

6. Advanced Development Skills

6.1 New tool development

# Create a new tool/my_tool.py
class MyTool ( BaseTool ) :
    name  = "my_tool"
    async def execute ( self ,  param ) :
        # Implement tool logic
        return "execution result"

# Register with the agent
class CustomAgent ( Manus ) :
    available_tools . add_tool ( MyTool ( ) )

6.2 Customized prompt words

# Modify prompt/manus.py
SYSTEM_PROMPT  = "You are now a travel planning expert..." 
NEXT_STEP_PROMPT  = "Use WebSearch to obtain the latest tourist attraction information first..." 

7. Ecology and Community

7.1 Dependency Management

8. Conclusion

Through modular design and a powerful tool chain, OpenManus achieves full process automation from simple queries to complex tasks. Its core advantages are:

• Flexible proxy architecture
  : Supports multiple agent types and tool combinations
• Clear execution process
  : Complete life cycle management from planning to execution
• Rich scalability
  : Functional extension through plug-in mechanism

Through the in-depth analysis of this article, developers can quickly grasp the core principles and development methods of the project and achieve a complete leap from understanding to practice.