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

# 1. Define state schema
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    current_step: str

# 2. Create graph
workflow = StateGraph(AgentState)

# 3. Add nodes (agents)
def researcher(state):
    return {"messages": ["Research complete"], "current_step": "research"}

def writer(state):
    return {"messages": ["Article written"], "current_step": "writing"}

workflow.add_node("researcher", researcher)
workflow.add_node("writer", writer)

# 4. Add edges (transitions)
workflow.add_edge("researcher", "writer")
workflow.add_edge("writer", END)

# 5. Set entry point and compile
workflow.set_entry_point("researcher")
app = workflow.compile()

# 6. Execute
result = app.invoke({"messages": [], "current_step": "start"})
print(result)

---

Core Concepts

StateGraph

The fundamental building block representing a directed graph of agents with shared state.