Building AI Agents Workshop 🤖

A comprehensive 2-hour workshop on building AI agents from scratch, covering fundamental concepts and practical implementations using both custom code and LangGraph.

🎯 Workshop Overview

This workshop teaches you how to build intelligent agents that can reason, plan, and take actions to solve complex problems. You'll learn two approaches:

1. Building from Scratch: Understand the ReAct (Reasoning + Acting) pattern by implementing a simple agent
2. Using LangGraph: Leverage powerful frameworks to build more sophisticated agents

📚 Learning Objectives

By the end of this workshop, you will:

• ✅ Understand the core concepts of AI agents and the ReAct pattern
• ✅ Build a simple agent from scratch using OpenAI's API
• ✅ Implement action loops and tool integration
• ✅ Use LangGraph to create more advanced agent architectures
• ✅ Handle real-world scenarios like web search and multi-step reasoning

🛠️ Prerequisites

• Basic Python programming knowledge
• Familiarity with APIs and HTTP requests
• Basic understanding of Large Language Models (LLMs)

📋 Setup Instructions

1. Clone the Repository

git clone <your-repo-url>
cd building-agent-workshop

2. Create a Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Environment Variables

1. Copy the example environment file:

   cp env.example .env

2. Edit .env and add your API keys:

   OPENAI_API_KEY=your_openai_api_key_here
   TAVILY_API_KEY=your_tavily_api_key_here
   

5. Get Required API Keys

OpenAI API Key (Required)

1. Visit OpenAI API
2. Create an account and generate an API key
3. Add credits to your account (minimum $5 recommended)

Tavily API Key (Required for Lesson 2)

1. Visit Tavily
2. Sign up and get your free API key
3. Free tier includes 1000 searches/month

6. Start Jupyter Notebook

jupyter notebook

📖 Workshop Structure

Lesson 1: Simple ReAct Agent from Scratch (60 minutes)

File: notebooks/01_agent_from_scratch.ipynb

Learn the fundamentals by building a ReAct agent from scratch:

• Understanding the ReAct pattern (Reasoning + Acting)
• Building a basic Agent class
• Implementing tool functions (calculator, dog weight lookup)
• Creating action loops and observation handling

Lesson 2: Advanced Agents with LangGraph (60 minutes)

File: notebooks/02_agent_using_langgraph.ipynb

Build sophisticated agents using the LangGraph framework:

• Introduction to LangGraph components
• StateGraph and node management
• Tool binding and function calling
• Real-world web search integration

🎯 Workshop Timeline

Time	Activity	Duration
0:00-0:10	Setup & Introduction	10 min
0:10-1:10	Lesson 1: Agent from Scratch	60 min
1:10-1:20	Break	10 min
1:20-2:20	Lesson 2: LangGraph Agents	60 min

🔍 Troubleshooting

Common Issues

1. API Key Errors

   • Ensure your .env file is in the project root
   • Check that API keys are valid and have sufficient credits
   • Restart Jupyter after adding environment variables

2. Package Installation Issues

   • Use a fresh virtual environment
   • Upgrade pip: pip install --upgrade pip
   • Install packages one by one if bulk install fails

3. Notebook Not Starting

   • Ensure virtual environment is activated
   • Try: python -m jupyter notebook
   • Check if port 8888 is available

📚 Additional Resources

• OpenAI API Documentation
• LangGraph Documentation
• ReAct Paper

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Happy Building! 🚀