Langgraph

LangGraph Workflows

Summary

LangGraph is a framework for building stateful, multi-agent applications with LLMs. It implements state machines and directed graphs for orchestration, enabling complex workflows with persistent state management, human-in-the-loop support, and time-travel debugging.

Key Innovation: Transforms agent coordination from sequential chains into cyclic graphs with persistent state, conditional branching, and production-grade debugging capabilities.

When to Use

✅ Use LangGraph When:

• Multi-agent coordination required
• Complex state management needs
• Human-in-the-loop workflows (approval gates, reviews)
• Need debugging/observability (time-travel, replay)
• Conditional branching based on outputs
• Building production agent systems
• State persistence across sessions

❌ Don't Use LangGraph When:

• Simple single-agent tasks
• No state persistence needed
• Prototyping/experimentation phase (use simple chains)
• Team lacks graph/state machine expertise
• Stateless request-response patterns

Quick Start

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator