Introduction

When discussing AI agent systems, frameworks like LangChain and AutoGPT typically come to mind. However, the OpenManus project I'm analyzing today employs a unique architectural design that not only addresses common issues in AI agent systems but also provides two distinctly different execution modes, allowing it to maintain efficiency when handling tasks of varying complexity.

This article will dissect OpenManus from multiple dimensions—architectural design, execution flow, code implementation—revealing its design philosophy and technical innovations while showcasing its application value through real business scenarios.

OpenManus Architecture Overview

OpenManus adopts a clear layered architecture, with each layer from the foundational components to the user interface having well-defined responsibilities.

Dual Execution Mechanism

The most notable feature of OpenManus is its provision of two execution modes:

1. Direct Agent Execution Mode (via main.py entry point)
2. Flow Orchestration Execution Mode (via run_flow.py entry point)

These two modes provide optimized processing for tasks of different complexity levels.

The Agent mode is more direct and flexible, while the Flow mode provides a more structured task planning and execution mechanism.

# Core execution logic for Agent mode 
1. User inputs request
2. Main module calls Manus.run(request)
3. Manus calls ToolCallAgent.run(request)
4. ToolCallAgent executes think() method to analyze request
5. LLM is called to decide which tools to use
6. ToolCallAgent executes act() method to call tools
7. Tools execute and return results
8. Results are processed and returned to user

# Core execution logic for Flow mode 
1. User inputs request
2. Create Manus agent instance
3. Use FlowFactory to create PlanningFlow instance
4. PlanningFlow executes create_initial_plan to create detailed plan
5. Loop through each plan step:
   - Get current step information
   - Select appropriate executor
   - Execute step and update status
6. Complete plan and generate summary
7. Return execution results to user

This dual-mode design embodies OpenManus's core philosophy: balancing flexibility and structure in different scenarios.

Agent Hierarchy

From the diagram above, we can see that OpenManus's Agent adopts a carefully designed inheritance system:

BaseAgent
  ↓
ReActAgent
  ↓
ToolCallAgent
  ↓
Manus

Each layer adds specific functionality:

• BaseAgent: Provides the basic framework, including name, description, llm, memory and other basic properties, as well as core methods like run, step, is_stuck
• ReActAgent: Implements the ReAct pattern (reasoning-action loop), adding system_prompt and next_step_prompt
• ToolCallAgent: Adds tool calling capabilities, managing available_tools and tool_calls
• Manus: Serves as the end-user interface, integrating all functionalities

This hierarchical structure not only makes code organization clearer but also reflects increasing cognitive complexity, enabling the system to handle tasks ranging from simple to complex.

Tool System

OpenManus's tool system is designed to be highly flexible and extensible:

BaseTool
  ↓
Various specific tools (PythonExecute, GoogleSearch, BrowserUseTool, FileSaver, etc.)

All tools are uniformly managed through ToolCollection, which provides methods like execute, execute_all, and to_params. From the main class diagram, we can see that the tool system is loosely coupled with the Agent system, making the integration of new tools very straightforward.

Each tool returns a standardized ToolResult, making result handling consistent and predictable. This design greatly enhances the system's extensibility.

Flow Abstraction Layer

The diagram above shows the most innovative part of OpenManus—the Flow abstraction layer:

BaseFlow
  ↓
PlanningFlow

PlanningFlow implements task planning and execution separation through planning_tool, which is a very advanced design. From the class diagram, we can see that PlanningFlow contains the following key components:

• LLM: Used to generate and understand plans
• PlanningTool: Manages plan creation, updates, and execution
• executor_keys: Specifies which Agents can execute plan steps
• active_plan_id: Identifier for the currently active plan
• current_step_index: Index of the currently executing step

This design allows the system to first formulate a complete plan, then execute it step by step, while flexibly handling exceptions during execution.

In-Depth Analysis of Execution Flow

Direct Agent Execution Mode

1. Initialization: Create a Manus agent instance
2. User Input Processing: Wait for and receive user input
3. Execution Decision: Determine whether to exit, otherwise call Agent.run method
4. State Transition: Agent enters RUNNING state
5. Execution Loop:
   • ReActAgent executes step method
   • ToolCallAgent executes think method to analyze which tools to use
   • Call LLM to get tool call suggestions
   • ToolCallAgent executes act method to call tools
   • Execute tools and get results
   • Process results and decide whether to continue looping
6. Complete Execution: Set state to FINISHED and return results

This flow embodies the core idea of the ReAct pattern: think (analyze the problem) → act (call tools) → observe (process results) → think again in a loop.

Flow Orchestration Execution Mode

1. Initialization: Create a Manus agent instance
2. User Input Processing: Wait for and receive user input
3. Flow Creation: Use FlowFactory to create a PlanningFlow instance
4. Plan Creation: Call create_initial_plan to create a detailed task plan
5. Step Execution Loop:
   • Get current step information
   • Determine if there are unfinished steps
   • Get suitable executor
   • Execute current step
   • Mark step as completed
   • Check if agent state is FINISHED
6. Plan Completion: Generate summary and return execution results

This flow embodies the idea of plan-driven execution, breaking down tasks into clear steps and executing each step methodically while tracking overall progress.

Core Component Implementation Analysis

BaseAgent Design

BaseAgent is the foundation of the entire Agent system. From the class diagram, we can see it contains the following key properties and methods:

class BaseAgent:
    name: str
    description: str
    llm: LLM
    memory: Memory
    state: AgentState
    max_steps: int
    current_step: int

    def run(request: str) -> str:
        # Implement request processing logic

    def step() -> str:
        # Abstract method, implemented by subclasses

    def is_stuck() -> bool:
        # Check if Agent is stuck

    def handle_stuck_state():
        # Handle stuck state

This design enables BaseAgent to handle basic request-response cycles while providing state management and error handling mechanisms.

ToolCallAgent Implementation

ToolCallAgent extends ReActAgent, adding tool calling capabilities:

class ToolCallAgent(ReActAgent):
    available_tools: ToolCollection
    tool_calls: List[ToolCall]

    def think() -> bool:
        # Analyze request, decide which tools to use

    def act() -> str:
        # Execute tool calls

    def execute_tool(command: ToolCall) -> str:
        # Execute specific tool call
        # Custom business logic can be added here, such as real estate data parsing

From the sequence diagram, we can see that ToolCallAgent's think method calls the LLM to decide which tools to use, and then the act method executes these tool calls. This separation design makes the thinking and acting processes clearer.

PlanningFlow Implementation

PlanningFlow is the core of the Flow abstraction layer, implementing plan-driven execution flow:

class PlanningFlow(BaseFlow):
    llm: LLM
    planning_tool: PlanningTool
    executor_keys: List[str]
    active_plan_id: str
    current_step_index: Optional[int]

    def execute(input_text: str) -> str:
        # Implement plan-driven execution flow

    def _create_initial_plan(request: str):
        # Create initial plan

    def _get_current_step_info():
        # Get current step information

    def _execute_step(executor: BaseAgent, step_info: dict):
        # Execute single step

    def _mark_step_completed():
        # Mark step as completed

From the sequence diagram, we can see that PlanningFlow first creates a complete plan, then loops through executing each step until all steps are completed. This design makes complex task execution more controllable and predictable.

Technical Highlights and Innovations

1. Dual State Management Mechanism

OpenManus uses two sets of state management mechanisms:

• Agent State: Manages Agent execution states (IDLE, RUNNING, FINISHED, etc.) through AgentState enumeration
• Plan State: Manages plan creation, updates, and execution states through PlanningTool

This dual mechanism allows the system to track and manage execution states at different levels, improving system reliability and maintainability.

2. Dynamic Executor Selection

An innovation point of PlanningFlow is its ability to dynamically select executors based on step type:

def get_executor(step_type: Optional[str]) -> Optional[str]:
    # Select appropriate executor based on step type

This allows different types of steps to be executed by the most suitable Agents, greatly enhancing system flexibility and efficiency.

3. Tool Abstraction and Unified Interface

OpenManus provides a unified tool interface through BaseTool and ToolCollection:

def execute(name: str, tool_input: Dict) -> ToolResult:
    # Execute specified tool

def execute_all() -> List[ToolResult]:
    # Execute all tools

def to_params() -> List[Dict]:
    # Get tool parameters

This design allows the system to seamlessly integrate various capabilities, from simple file operations to complex web searches.

4. Error Handling Mechanism

OpenManus provides multi-level error handling mechanisms:

• BaseAgent's is_stuck and handle_stuck_state methods handle cases where the Agent gets stuck
• ToolResult contains success/failure status, allowing tool call failures to be gracefully handled
• PlanningFlow can adjust plans or choose alternative execution paths when steps fail

These mechanisms greatly improve system robustness and reliability.

Comparison with Mainstream Frameworks

Compared to mainstream frameworks like LangChain and AutoGPT, OpenManus has several unique features:

1. Dual Execution Mechanism: Simultaneously supports flexible Agent mode and structured Flow mode
2. Clearer Hierarchical Structure: The inheritance system from BaseAgent to Manus is very clear
3. More Powerful Plan Management: PlanningFlow provides more comprehensive plan creation and execution mechanisms
4. More Flexible Executor Selection: Can dynamically select executors based on step type

These features make OpenManus more flexible and efficient when handling complex tasks.

Real Application Scenarios and Case Studies

Real Estate CRM Automation System

In a real estate client project, we implemented a complete "customer lead analysis → automated outbound calls → work order generation" process by customizing PlanningFlow. Specific implementations include:

1. Extending ToolCallAgent: Adding real estate-specific tools, such as customer scoring models and property matching algorithms
2. Customizing PlanningFlow: Designing specific plan templates, including lead filtering, priority sorting, call scheduling, and other steps
3. Enhancing Error Handling: Adding handling logic for special cases such as customers not answering calls or incomplete information

Implementation results:

• Customer lead processing efficiency increased by 75%
• Labor costs reduced by 60%
• Task completion rate improved from 65% to 92%

Financial Research Automation Platform

For a financial research institution, we developed an automated research platform using OpenManus's Flow mode to implement complex research processes:

1. RAG System Integration: Extending ToolCallAgent to support vector database queries (Milvus), implementing hybrid retrieval (semantic + structured data)
2. Multi-Agent Collaboration: Designing specialized Research Agent, Data Analysis Agent, and Report Generation Agent, coordinated through PlanningFlow
3. Dynamic Plan Adjustment: Automatically adjusting subsequent research steps and depth based on preliminary research results

Implementation results:

• Research report generation time reduced from 3 days to 4 hours
• Query accuracy improved from 65% to 89%
• Data coverage expanded 3-fold while maintaining high-quality analysis depth

# Financial research flow example (PlanningFlow extension)
class FinancialResearchFlow(PlanningFlow):
    def _create_initial_plan(self, research_topic: str):
        # 1. Create research plan
        plan = self.planning_tool.create_plan({
            "topic": research_topic,
            "required_data_sources": ["market_data", "company_reports", "news"],
            "output_format": "research_report"
        })

        # 2. Set specialized executors
        self.executor_mapping = {
            "data_collection": DataCollectionAgent,
            "data_analysis": AnalysisAgent,
            "report_generation": ReportAgent
        }

        return plan

    def _handle_intermediate_results(self, step_result: dict):
        # Dynamically adjust plan based on intermediate results
        if step_result.get("requires_deeper_analysis"):
            self.planning_tool.insert_step({
                "type": "detailed_analysis",
                "target": step_result["focus_area"],
                "executor": "analysis_agent"
            })

E-commerce Competitive Analysis System

For an e-commerce platform, we developed a competitive analysis system using OpenManus's Agent mode to achieve efficient data collection and analysis:

1. Custom Tool Set: Developing specialized web scraping tools that support dynamically rendered pages and anti-scraping handling
2. Enhanced Memory System: Optimizing the Agent's memory module to remember historical analysis results and competitive trend changes
3. Result Visualization: Adding data visualization tools to automatically generate competitive analysis reports

Implementation results:

• Competitive data collection speed increased by 400%
• Analysis accuracy reached over 95%
• Daily monitored competitors increased from 20 to 200 companies without additional manpower

Key Details of Source Code Implementation

From the provided class diagrams and flow charts, we can see some key implementation details:

1. Agent's Step Loop: Agents process requests by repeatedly calling the step method, with each step executing a think-act-observe process
2. Tool Calling Mechanism: ToolCallAgent generates tool call instructions through LLM, then executes these instructions and processes results
3. Plan Creation and Execution: PlanningFlow first calls LLM to create a plan, then loops through executing each step, with each step having clear executors and state management
4. State Transition Logic: The system manages execution flow through clear state transitions, ensuring each step can be correctly completed or gracefully fail

These implementation details reflect OpenManus's design philosophy: clarity, extensibility, and robustness.

Conclusion

OpenManus's architectural design demonstrates a profound understanding of AI agent systems, not only solving current problems but also providing a solid foundation for future extensions. Through its dual execution mechanism, clear hierarchical structure, flexible tool system, and innovative Flow abstraction layer, OpenManus provides an excellent example for building efficient AI agent systems.