Building AI Agents with LangGraph - End-to-End Guide

Overview

This repository demonstrates how to build agentic AI applications using LangGraph framework - a library for building stateful, multi-agent applications with LangChain.

Prerequisites

• Python 3.8+
• pip
• Basic understanding of LangChain
• OpenAI API key

Installation

pip install langgraph langchain openai

Project Structure

.
├── README.md
├── agents/
│   ├── planner.py
│   └── executor.py
├── main.py
└── config.py

Key Components

1. Agent Types

   • Planner Agent: Breaks down tasks into subtasks
   • Executor Agent: Performs specific actions
   • Supervisor Agent: Monitors and coordinates

2. Graph Structure

   • Defines agent interactions
   • Manages state transitions
   • Handles message passing

Basic Implementation

from langgraph.graph import StateGraph
from langchain_core.messages import HumanMessage

# Define graph structure
graph = StateGraph()

# Add nodes and edges
graph.add_node("planner")
graph.add_node("executor")

# Configure state transitions
graph.set_entry_point("planner")

Running the Application

python main.py

Best Practices

• Use environment variables for API keys
• Implement error handling
• Add logging for debugging
• Test agent interactions thoroughly

Contributing

Pull requests are welcome. For major changes, please open an issue first.

License

MIT