LangGraph Basics

LangGraph Basics

Learn the fundamental concepts of LangGraph through hands-on examples. This folder contains essential building blocks for understanding how to create stateful, multi-step applications with Large Language Models.

📚 What You'll Learn

• State Management: How LangGraph maintains information across conversation turns
• Graph Construction: Building workflows as directed graphs with nodes and edges
• Basic Agents: Creating simple AI agents that can reason and act
• Human-in-the-Loop: Incorporating human feedback into agent workflows
• Streaming: Real-time response streaming from agents

📁 Files Overview

1. basic_langgraph.ipynb

Core LangGraph Concepts

• State definition and management
• Creating nodes (functions that agents execute)
• Building a simple graph structure
• Adding edges and conditional logic
• Compiling and running your first agent

Key Concepts Covered:

• StateGraph creation
• Node definition with @tool decorator
• State typing with TypedDict
• Graph compilation and execution

2. human_in_loop.ipynb

Interactive Agent Workflows

• Adding human approval steps
• Conditional execution based on user input
• Interrupt and resume functionality
• Building approval workflows

Use Cases:

• Content moderation before publishing
• Financial transaction approvals
• Critical decision checkpoints
• User preference collection

3. streaming.ipynb

Real-time Agent Responses

• Streaming responses from agents
• Real-time progress updates
• Handling partial results
• Stream processing and display

Benefits:

• Better user experience with immediate feedback
• Monitoring long-running processes
• Real-time debugging and observation

🚀 Quick Start Workflow

Basic Agent Creation

The basic pattern involves defining your state structure, creating nodes (agent functions), building the graph with proper edges, and compiling for execution.

Human-in-the-Loop Pattern

Add interrupt nodes for human input and implement conditional edges that route to human approval when needed, allowing for interactive workflows.

🎯 Learning Exercises

Exercise 1: Simple Chatbot

Create a basic conversational agent that:

1. Maintains conversation history
2. Responds to user queries
3. Tracks conversation state

Exercise 2: Approval Workflow

Build an agent that:

1. Generates content suggestions
2. Asks for human approval
3. Revises based on feedback
4. Finalizes approved content

Exercise 3: Multi-Step Reasoning

Create an agent that:

1. Breaks down complex problems
2. Solves each step incrementally
3. Streams progress updates
4. Provides final comprehensive answer

🔧 Common Patterns

State Definition

Define state using TypedDict with proper type annotations, including message handling and tracking current task or user preferences.

Conditional Logic

Implement routing logic functions that examine state and return appropriate next node names based on conditions like approval requirements or completion status.

🐛 Common Issues & Solutions

Issue: State Not Updating

Problem: Changes to state don't persist between nodes Solution: Always return state updates from node functions

Issue: Graph Execution Hangs

Problem: Missing edges or circular references Solution: Ensure all paths lead to END node

Issue: Type Errors

Problem: State type mismatches Solution: Use proper TypedDict definitions and type hints

📖 Next Steps

After mastering these basics, explore:

• LangGraph Tools - Add external capabilities
• LangGraph Multi Agents - Coordinate multiple agents
• LangGraph RAG Agents - Build knowledge-based systems

Remember: LangGraph is about creating stateful workflows. Think of each node as a step in a process, and the state as the information that flows between steps!