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.