CrewAI with FastMCP Server Integration Course

This course teaches beginners how to use CrewAI with FastMCP server access through step-by-step programming examples.

Course Overview

This course is designed for beginner developers with basic Python knowledge who want to learn how to integrate CrewAI agents with FastMCP servers. The course covers fundamental concepts, practical implementation, and advanced patterns for building intelligent agent workflows.

Lessons

Lesson 1: Setting up CrewAI with MCP Server Access

• Install required packages
• Set up environment variables
• Create a basic CrewAI agent
• Execute simple tasks

graph TD
    A[Install Packages] --> B[Set Environment Variables]
    B --> C[Create CrewAI Agent]
    C --> D[Execute Task]

Lesson 2: Integrating MCP Server with CrewAI

• Create custom tools for MCP server access
• Configure authentication and connection settings
• Use MCP server data in agent tasks
• Handle errors and exceptions

graph TD
    A[Create FastMCP Tool] --> B[Configure Authentication]
    B --> C[Create Agent with Tool]
    C --> D[Execute Task with MCP Data]
    D --> E[Handle Response]

Lesson 3: Advanced CrewAI Patterns with MCP Server

• Implement multi-agent workflows
• Use hierarchical processes
• Share data between agents through the MCP server
• Store and retrieve research findings
• Implement quality assurance processes

graph TD
    A[Researcher Agent] -->|Query| B(FastMCP Server)
    B -->|Return Data| A
    A -->|Share Findings| C[Writer Agent]
    C -->|Create Report| D[Reviewer Agent]
    D -->|Provide Feedback| C
    C -->|Final Report| E[Output]

Getting Started

1. Install the required packages:

pip install -r requirements.txt

2. Set up your environment variables:

export FASTMCP_URL=http://your-fastmcp-server-url:port
export FASTMCP_API_KEY=your-api-key

3. Run the examples:

python lesson1_setup.py
python lesson2_mcp_integration.py
python lesson3_advanced_patterns.py

Requirements

• Python 3.8+
• CrewAI library
• FastMCP library
• Access to an MCP server

Course Structure

Each lesson includes:

• A Python script with comprehensive comments
• Clear objectives and expected outcomes
• Step-by-step implementation
• Best practices for error handling and security

Next Steps

After completing this course, you should be able to:

• Create and configure CrewAI agents
• Integrate MCP servers with agent workflows
• Build complex multi-agent systems
• Implement data sharing between agents
• Design robust error handling for production systems