LangGraph — Stateful AI Agent Graphs by LangChain

What it is

LangGraph is a framework by LangChain for building stateful, multi-actor AI agent applications as directed graphs. Instead of linear chains, LangGraph lets agents loop, branch, wait for human input, and checkpoint state automatically.

It targets AI engineers and teams building production agent systems that require retry logic, parallel tool calls, approval gates, or long-running workflows.

How it saves time or tokens

LangGraph eliminates the boilerplate of building state machines for agent orchestration. Its built-in persistence means you do not need to implement checkpointing or crash recovery. The human-in-the-loop primitives (interrupt_before, interrupt_after) replace custom webhook and polling logic. By modeling agents as graphs, you can visualize and debug complex multi-step flows instead of tracing through nested function calls.

How to use

1. Install LangGraph:

pip install langgraph

1. Define state and nodes:

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

class State(TypedDict):
    messages: list
    next_step: str

def researcher(state: State):
    return {'messages': state['messages'] + ['research done']}

def writer(state: State):
    return {'messages': state['messages'] + ['draft complete']}

1. Build and compile the graph:

graph = StateGraph(State)
graph.add_node('research', researcher)
graph.add_node('write', writer)
graph.add_edge('research', 'write')
graph.add_edge('write', END)
graph.set_entry_point('research')
app = graph.compile()
result = app.invoke({'messages': [], 'next_step': ''})

Example

A ReAct agent with validation loop:

def should_continue(state: State):
    if 'valid' in state['messages'][-1]:
        return 'end'
    return 'retry'

graph = StateGraph(State)
graph.add_node('act', researcher)
graph.add_node('validate', writer)
graph.add_conditional_edges('validate', should_continue, {
    'retry': 'act',
    'end': END
})
graph.add_edge('act', 'validate')
graph.set_entry_point('act')
app = graph.compile()

Related on TokRepo

• Multi-agent frameworks — compare LangGraph with CrewAI, AutoGen, and others
• LangGraph deep-dive — dedicated LangGraph resources on TokRepo

Common pitfalls

• Creating infinite loops without a max-iteration guard causes runaway token spend. Always add a counter or timeout to conditional edges.
• Storing large objects in graph state slows checkpoint serialization. Keep state lean and reference external storage for big payloads.
• Mixing synchronous blocking calls inside async graph nodes degrades throughput. Use async-native tool implementations.