AI Agent Tutorial - Building Intelligent Agents from Scratch

Complete implementation of AI agents using LangChain, featuring multiple patterns, production-ready code, and comprehensive examples.

📖 Read the full tutorial: Building AI Agents from Scratch: A Complete Guide (2026)

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• OpenAI API key (or Anthropic/Google)
• Basic understanding of Python and APIs

Installation

# Clone the repository
git clone https://github.com/DailyDevDotIn/AI-Agent-Tutorial.git
cd ai-agent-tutorial

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env and add your API keys

Run Your First Agent

python examples/simple_agent.py

🎯 What's Included

Core Features

• ✅ ReAct Pattern Agent - Reasoning + Acting
• ✅ Plan-and-Execute Agent - Strategic task decomposition
• ✅ Memory Systems - Short-term and long-term memory
• ✅ Custom Tools - Web search, code execution, calculations
• ✅ Error Handling - Robust retry logic and fallbacks
• ✅ Token Optimization - Cost tracking and optimization
• ✅ Caching - Response caching for common queries
• ✅ Security - Input validation and output sanitization
• ✅ API Server - FastAPI-based REST API
• ✅ Monitoring - LangSmith integration

Example Agents

1. Research Assistant - Web search + summarization
2. Code Reviewer - Automated code analysis
3. Data Analyst - SQL queries + visualizations
4. Customer Support - FAQ + ticket management

📚 Documentation

• Architecture Overview
• Agent Patterns Explained
• Deployment Guide
• Best Practices
• Troubleshooting

🧪 Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src tests/

# Run specific test file
pytest tests/test_agent.py -v

🐳 Docker Deployment

# Build image
docker build -t ai-agent .

# Run container
docker run -p 8000:8000 --env-file .env ai-agent

# Or use docker-compose
docker-compose up

📊 Performance

Benchmarked on 1000 queries:

Metric	Value
Success Rate	98.5%
Avg Response Time	3.2s
Avg Tokens/Query	1,250
Avg Cost/Query	$0.025

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide first.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LangChain for the amazing framework
• OpenAI for GPT models
• DailyDev.in community for feedback and testing

📞 Support

• 📧 Email: hello@dailydev.in
• 🐦 Twitter: @dailydevdotin
• 💼 LinkedIn: DailyDevDotIn

⭐ Star History

If you find this useful, please star the repo!

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