🤖 MCP + CrewAI Agentic Integration 🚀

A powerful demonstration of Model Context Protocol (MCP) integrated with CrewAI orchestrations, featuring full observability through AgentOps and high-speed inference via Groq.

🌟 Overview

This project bridges the gap between context-aware tools and autonomous agents. It provides a custom MCP server for real-time external data (Weather, News, Notes) while leveraging CrewAI to orchestrate multi-agent workflows.

🏗️ Architecture

• MCP Layer: A FastMCP server exposing tools for real-time data retrieval.

• Agentic Layer: CrewAI agents specialized in Market Analysis and Research.

• Inference Layer: Ultra-fast LLMs (Llama 3.1) hosted on Groq.

• Observability Layer: AgentOps for tracing, cost management, and debugging.

Related MCP server: MCP Tool Server

✨ Key Features

🛠️ Custom MCP Server Tools

• ☀️ Weather Engine: Real-time meteorology data via WeatherAPI.

• 📰 News Intelligence: Global news retrieval via Serper (Google Search API).

• 📝 Contextual Notes: Locally persistent note management for long-term memory.

• � Auto-Summary: Intelligent summarization of collected context.

👥 Intelligence Crew

• 🔍 Market Researcher: Scours data to identify emerging trends.

• 📈 Data Analyst: Synthesizes research into actionable market insights.

• 🚀 Sequential Workflow: Fully orchestrated execution path for reliable results.

🛠️ Tech Stack

• Framework: CrewAI

• Server: FastMCP

• LLM Engine: Groq (Llama 3.1 8B/70B)

• Tracing: AgentOps

• Package Manager: uv

🚀 Getting Started

1. Prerequisites

Ensure you have the following installed:

• uv (Recommended) or Python 3.13+

• A valid Groq API Key

• A valid AgentOps API Key

• A Serper API Key (for News)

2. Installation

Clone the repository and sync dependencies:

git clone https://github.com/vad-007/MCP_Integration_crewai.git
cd MCP_Integration_crewai
uv sync

3. Configuration

Create a .env file in the root directory:

AGENTOPS_API_KEY=your_agentops_key
GROQ_API_KEY=your_groq_key
SERPER_API_KEY=your_serper_key
WEATHER_API_KEY=your_weather_key

4. Running the Project

🌐 Start the MCP Server

mcp dev main.py

🚢 Run the CrewAI Integration

python crewai_agentops_integration.py

🔍 Run Diagnostics

python test_agentops.py

📊 Observability with AgentOps

This project is fully instrumented. Every run generates a unique replay URL allowed you to:

• Watch Agent Self-Correction: See exactly how agents reason through tasks.

• Trace LLM Calls: Monitor every prompt and completion.

• Analyze Latency: Visualize the execution timeline of your crew.

Check your dashboard at: app.agentops.ai

📂 Project Structure

├── main.py                    # FastMCP Server implementation
├── crewai_agentops_integration.py # Main CrewAI orchestration
├── test_agentops.py           # Connectivity & Diagnostic tool
├── .env                       # Environment variables (private)
├── pyproject.toml             # Project configuration
├── uv.lock                    # Dependency lockfile
└── docs/                      # Troubleshooting & Optimization guides

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project

2. Create your Feature Branch (git checkout -b feature/AmazingFeature)

3. Commit your Changes (git commit -m 'Add some AmazingFeature')

4. Push to the Branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

🛡️ License

Distributed under the MIT License. See LICENSE for more information.

Developed with ❤️ for the AI Community.