A multi-agent system built using CrewAI and local LLMs via Ollama. This intelligent team of AI agents collaborates to research, write, and review beginner-friendly Python tutorials automatically.
- 🤖 Multi-agent architecture - Specialized Researcher, Writer, and Reviewer agents
- 🧠 Local LLM powered - Runs on Mistral, Qwen, LLaMA, or other Ollama models
- ⚙️ YAML-based configuration - Easy to customize and extend
- 📝 Markdown output generation - Clean, structured tutorial files
- 🔐 Environment variable support - Secure configuration management
- 🌐 Extensible design - Ready for web search tools and API integrations
crewai_python_tutorial_agent/
├── agents/
│ ├── researcher.py # Research agent implementation
│ ├── writer.py # Writing agent implementation
│ └── reviewer.py # Review agent implementation
├── tools/
│ └── web_search.py # Web search utilities
├── tasks/
│ └── generate_tutorial.py # Task definitions
├── models/
│ └── local_llm.py # Local LLM configuration
├── configs/
│ └── crew_config.yaml # Crew configuration
├── output/ # Generated tutorials
├── main.py # Main entry point
├── requirements.txt # Python dependencies
├── .env # Environment variables
└── README.md # This file
- Python 3.8+
- Ollama installed and running
-
Clone the repository
git clone https://github.com/yourusername/ai-tutorial-crew.git cd ai-tutorial-crew -
Install Ollama and pull a model
# Install Ollama (if not already installed) curl -fsSL https://ollama.com/install.sh | sh # Pull your preferred model ollama pull mistral # Or try: ollama pull qwen2 or ollama pull llama3
-
Install Python dependencies
pip install -r requirements.txt
-
Set up environment variables
cp .env.example .env # Edit .env with your configurations
-
Run the tutorial generation crew
python main.py
-
Check the output
# Generated tutorial will be saved at: cat output/python_tutorial.md
The system employs three specialized AI agents working in sequence:
- 🔍 Researcher Agent - Gathers information about Python concepts and best practices
- ✍️ Writer Agent - Creates structured, beginner-friendly tutorial content
- 📝 Reviewer Agent - Reviews and improves the tutorial for clarity and accuracy
Each agent uses the local LLM through Ollama to perform their specialized tasks, ensuring privacy and eliminating API costs.
Customize the crew behavior by editing configs/crew_config.yaml:
# Example configuration
agents:
researcher:
role: "Python Tutorial Researcher"
goal: "Research comprehensive Python concepts"
backstory: "Expert in Python programming..."
writer:
role: "Tutorial Writer"
goal: "Create engaging beginner tutorials"
backstory: "Skilled technical writer..."# Understanding Python Lists: A Beginner's Guide
## What is a List in Python?
A list is a versatile data structure in Python that allows you to store multiple items in a single variable. Lists are ordered, changeable, and allow duplicate values...
## Creating Lists
```python
# Creating a simple list
fruits = ["apple", "banana", "orange"]
print(fruits) # Output: ['apple', 'banana', 'orange']append()- Add item to endinsert()- Add item at specific position ...
## 🛠️ Development
### Adding New Agents
1. Create a new agent file in `agents/`
2. Define the agent's role, goal, and backstory
3. Add the agent to your crew configuration
4. Update `main.py` to include the new agent
### Adding Custom Tools
1. Create tool files in `tools/`
2. Implement your custom functionality
3. Register tools with appropriate agents
## 🔮 Roadmap
- [ ] **Web Search Integration** - Add real-time research capabilities
- [ ] **Multiple Topic Support** - Generate tutorials for various Python topics
- [ ] **Interactive Mode** - Allow users to specify tutorial topics
- [ ] **Output Formats** - Support for HTML, PDF, and other formats
- [ ] **Quality Metrics** - Automated tutorial quality assessment
- [ ] **Unit Tests** - Comprehensive test coverage
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
1. Fork the repository
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
## 📋 Requirements
- Python 3.8+
- CrewAI
- Ollama
- PyYAML
- python-dotenv
See `requirements.txt` for complete dependencies.
## 🐛 Troubleshooting
### Common Issues
**Ollama not responding:**
```bash
# Check if Ollama is running
ollama list
# Restart Ollama service
ollama serve
Model not found:
# Pull the required model
ollama pull mistralPermission errors:
# Ensure output directory exists and is writable
mkdir -p output
chmod 755 outputThis project is licensed under the MIT License - see the LICENSE file for details.
- CrewAI - For the amazing multi-agent framework
- Ollama - For local LLM infrastructure
- The open-source community for inspiration and support
- GitHub: @yourusername
- Email: your.email@example.com
⭐ If you find this project helpful, please consider giving it a star on GitHub!