Getting Started with LangGraph

January 15, 2024 · 8 min read

Introduction

LangGraph is a powerful framework for building multi-agent systems with clear state management and control flow. This post covers the fundamentals and practical patterns for production use.

What is LangGraph?

LangGraph provides a way to define complex agent workflows as graphs. Each node represents a step, and edges define transitions based on conditions or outcomes.

Basic Pattern

from langgraph.graph import StateGraph

def node_1(state):
    return {"value": state["value"] + 1}

def node_2(state):
    return {"value": state["value"] * 2}

graph = StateGraph()
graph.add_node("step_1", node_1)
graph.add_node("step_2", node_2)
graph.add_edge("step_1", "step_2")

compiled = graph.compile()
result = compiled.invoke({"value": 1})

Best Practices

• Keep nodes focused and single-responsibility
• Use type hints for state management
• Implement proper error handling
• Log state transitions for debugging

Conclusion

LangGraph enables building complex, maintainable multi-agent systems. Start simple, iterate, and scale your workflows as needed.