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LangGraph Tutorial |
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A deep technical walkthrough of LangGraph covering Building Stateful Multi-Actor Applications.
LangGraphView Repo is a library for building stateful, multi-actor applications with large language models (LLMs). It extends LangChain to provide fine-grained control over both the flow and state of your applications, making it ideal for building complex AI workflows and agent systems.
LangGraph enables developers to create sophisticated applications with multiple interacting components, persistent state management, and complex control flows that go beyond simple sequential chains.
flowchart TD
A[User Input] --> B[State Manager]
B --> C[Node Router]
C --> D[LLM Node]
C --> E[Tool Node]
C --> F[Conditional Node]
D --> G[State Update]
E --> G
F --> H{Condition Met?}
H -->|Yes| I[Next Node]
H -->|No| J[Alternative Path]
G --> K[Persistence Layer]
classDef input fill:#e1f5fe,stroke:#01579b
classDef core fill:#f3e5f5,stroke:#4a148c
classDef processing fill:#fff3e0,stroke:#ef6c00
classDef decision fill:#e8f5e8,stroke:#1b5e20
class A input
class B,C core
class D,E,F processing
class H,J decision
Welcome to your journey through stateful multi-actor applications! This tutorial explores how to build complex AI systems with LangGraph.
- Chapter 1: Getting Started with LangGraph - Installation, basic concepts, and your first graph
- Chapter 2: State Management - Managing application state and persistence
- Chapter 3: Nodes and Edges - Building graph components and connections
- Chapter 4: Conditional Logic - Implementing decision points and branching
- Chapter 5: Multi-Agent Systems - Coordinating multiple agents
- Chapter 6: Tool Integration - Connecting external tools and APIs
- Chapter 7: Persistence and Checkpoints - State persistence and recovery
- Chapter 8: Production Deployment - Scaling and deploying LangGraph applications
- repository:
langchain-ai/langgraph - stars: about 26.5k
- latest release:
cli==0.4.18(published 2026-03-15)
By the end of this tutorial, you'll be able to:
- Build Complex AI Workflows - Create multi-step processes with conditional logic
- Manage Application State - Handle persistent state across graph executions
- Coordinate Multiple Agents - Build systems with interacting AI components
- Integrate External Tools - Connect APIs, databases, and services
- Implement Error Handling - Build resilient applications with recovery mechanisms
- Scale Production Systems - Deploy and monitor LangGraph applications
- Debug Complex Graphs - Troubleshoot and optimize graph execution
- Customize Graph Behavior - Extend LangGraph with custom components
- Python 3.9+ (Python 3.8 no longer supported as of LangChain v0.3)
- Basic understanding of async/await patterns
- Familiarity with LangChain concepts (helpful but not required)
- Knowledge of graph theory basics (helpful but not required)
LangGraph Studio Transition: As of March 2025 (v0.0.37), LangGraph Studio Desktop is deprecated. Use the LangGraph CLI docs for local development and debugging.
Pydantic 2 Required: LangGraph now requires Pydantic 2 (Pydantic 1 reached end-of-life in June 2024). Update your code to use native Pydantic 2 syntax.
Perfect for developers new to graph-based AI systems:
- Chapters 1-2: Setup and basic state management
- Focus on understanding LangGraph fundamentals
For developers building AI applications:
- Chapters 3-5: Nodes, edges, and multi-agent coordination
- Learn to build sophisticated AI workflows
For production AI system development:
- Chapters 6-8: Tool integration, persistence, and deployment
- Master enterprise-grade AI graph applications
Ready to build stateful AI applications? Let's begin with Chapter 1: Getting Started!
- Start Here: Chapter 1: Getting Started with LangGraph
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