# OpenManus Structure

## Repository Overview

**Project**: OpenManus FoundationAgents\
**URL**: https://github.com/FoundationAgents/OpenManus\
**Purpose**: Open-source framework for building general AI agents

## Project Structure

```
OpenManus/
├── app/                          # Core application modules
│   ├── agents/                   # Agent implementations
│   ├── core/                     # Core framework components
│   ├── tools/                    # Agent tools and utilities
│   └── protocols/                # Communication protocols
├── assets/                       # Project assets
│   ├── logos/                    # Brand assets
│   └── images/                   # Documentation images
├── config/                       # Configuration management
│   ├── config.example.toml       # Configuration template
│   └── agent_configs/            # Agent-specific configurations
├── examples/                     # Usage examples and demos
│   ├── basic_agent.py           # Simple agent example
│   ├── multi_agent.py           # Multi-agent coordination
│   └── custom_tools.py          # Custom tool integration
├── protocol/                     # Communication protocols
│   └── a2a/                     # Agent-to-agent communication
│       ├── messages.py          # Message definitions
│       └── handlers.py          # Message handlers
├── tests/                        # Testing infrastructure
│   ├── sandbox/                 # Sandbox testing environment
│   ├── unit/                    # Unit tests
│   └── integration/             # Integration tests
├── workspace/                    # Agent workspace management
│   ├── shared/                  # Shared resources
│   └── agents/                  # Agent-specific workspaces
├── main.py                      # Primary entry point
├── run_flow.py                  # Multi-agent workflow runner
├── run_mcp.py                   # MCP (Main Control Process) runner
├── requirements.txt             # Python dependencies
├── setup.py                     # Package setup configuration
├── README.md                    # Project documentation
└── LICENSE                      # Open-source license
```

## Directory Analysis

### Core Application (`app/`)

**Purpose**: Contains the main framework components **Key Components**:

* **agents/**: Different agent type implementations
* **core/**: Framework base classes and utilities
* **tools/**: Reusable agent tools and capabilities
* **protocols/**: Communication and coordination protocols

**Design Patterns**:

* **Factory Pattern**: Agent creation and instantiation
* **Strategy Pattern**: Different agent behaviors and tools
* **Template Method**: Base agent class with customizable methods

### Configuration (`config/`)

**Purpose**: Configuration management and templates **Key Files**:

* **config.example.toml**: Configuration template with LLM settings
* **agent\_configs/**: Agent-specific configuration files

**Design Patterns**:

* **Strategy Pattern**: Runtime configuration of different LLM providers
* **Builder Pattern**: Complex configuration construction

### Protocol System (`protocol/a2a/`)

**Purpose**: Agent-to-agent communication infrastructure **Components**:

* **messages.py**: Message format definitions
* **handlers.py**: Message processing logic

**Design Patterns**:

* **Observer Pattern**: Event-driven agent communication
* **Command Pattern**: Message-based action execution
* **Mediator Pattern**: Centralized agent communication

### Entry Points

**Purpose**: Multiple ways to run and interact with agents

#### `main.py` - Single Agent Runner

* **Pattern**: Facade Pattern
* **Purpose**: Simple interface for single agent execution
* **Use Case**: Basic agent tasks and demonstrations

#### `run_flow.py` - Multi-Agent Workflow

* **Pattern**: Command + Composite Pattern
* **Purpose**: Orchestrate multiple agents in workflows
* **Use Case**: Complex multi-step agent coordination

#### `run_mcp.py` - MCP Tool Integration

* **Pattern**: Adapter Pattern
* **Purpose**: Integration with external MCP tools
* **Use Case**: Extended tool capabilities and integrations

### Testing Infrastructure (`tests/`)

**Purpose**: Comprehensive testing framework **Components**:

* **sandbox/**: Isolated testing environment for agents
* **unit/**: Component-level testing
* **integration/**: End-to-end workflow testing

**Design Patterns**:

* **Factory Pattern**: Test agent and scenario creation
* **Strategy Pattern**: Different testing approaches
* **Template Method**: Standardized test execution

### Workspace Management (`workspace/`)

**Purpose**: Agent runtime environment and resource management **Components**:

* **shared/**: Common resources and utilities
* **agents/**: Individual agent workspaces

**Design Patterns**:

* **Singleton Pattern**: Shared resource management
* **Factory Pattern**: Workspace creation and initialization
* **Observer Pattern**: Workspace state monitoring

## Architecture Insights

### Modular Design

* **Separation of Concerns**: Clear boundaries between configuration, execution, testing
* **Loosely Coupled**: Components can be modified independently
* **Extensible**: New agents and tools can be added without core changes

### Configuration-Driven Architecture

* **Runtime Flexibility**: Behavior modification through configuration
* **Environment Adaptation**: Easy deployment across different environments
* **Provider Independence**: Switch between different LLM providers seamlessly

### Multi-Modal Entry Points

* **Single Agent**: Simple tasks and demonstrations
* **Multi-Agent**: Complex workflows and coordination
* **MCP Integration**: Extended tool and system integration

### Production-Ready Features

* **Testing Infrastructure**: Comprehensive testing at multiple levels
* **Configuration Management**: Environment-specific settings
* **Workspace Isolation**: Safe agent execution environments
* **Protocol-Based Communication**: Standardized agent interactions

## Pattern Implementation Analysis

### Primary Patterns in Structure

1. **Facade Pattern**: Clean entry points (`main.py`, `run_*.py`)
2. **Factory Pattern**: Agent creation (`app/agents/`) and configuration
3. **Strategy Pattern**: Configurable behaviors and LLM providers
4. **Observer Pattern**: Agent communication (`protocol/a2a/`)
5. **Command Pattern**: Message-based agent coordination

### Supporting Patterns

1. **Template Method**: Base agent classes with customizable behavior
2. **Adapter Pattern**: MCP tool integration and external system connection
3. **Mediator Pattern**: Centralized agent communication management
4. **Singleton Pattern**: Shared resource and workspace management

This structure demonstrates a well-architected AI agent framework that effectively applies multiple design patterns to create a flexible, extensible, and production-ready system.
