LangGraph — Build Stateful AI Agent Workflows

What it is

LangGraph is a framework for building stateful AI agent workflows as directed graphs. Each node in the graph represents a step (LLM call, tool invocation, decision point), and edges define the flow between steps. Unlike linear chain abstractions, LangGraph supports cycles, conditional branching, and parallel execution.

LangGraph targets developers building production AI agents that need reliability features like state persistence, human approval gates, and automatic retries. It is part of the LangChain ecosystem.

How it saves time or tokens

Building a multi-step agent from scratch requires implementing state management, retry logic, and conversation persistence manually. LangGraph provides these as built-in primitives. State is automatically checkpointed between nodes, so agents can resume after failures without restarting.

The estimated token cost for this workflow is approximately 3,900 tokens. LangGraph's graph structure makes it easy to add or remove steps without rewriting the entire agent.

How to use

1. Install LangGraph:

pip install langgraph

1. Define a state schema and build a graph:

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

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

def research(state: State):
    # Call LLM or tool
    return {'messages': state['messages'] + ['research done']}

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

graph = StateGraph(State)
graph.add_node('research', research)
graph.add_node('draft', draft)
graph.add_edge(START, 'research')
graph.add_edge('research', 'draft')
graph.add_edge('draft', END)
app = graph.compile()

1. Run the graph:

result = app.invoke({'messages': [], 'next_step': ''})

Example

# Conditional branching example
from langgraph.graph import StateGraph, START, END

def router(state):
    if len(state['messages']) > 5:
        return 'summarize'
    return 'continue'

graph = StateGraph(State)
graph.add_node('process', process_fn)
graph.add_node('summarize', summarize_fn)
graph.add_conditional_edges('process', router, {
    'continue': 'process',
    'summarize': 'summarize'
})
graph.add_edge('summarize', END)
graph.add_edge(START, 'process')
app = graph.compile()

Related on TokRepo

• Multi-Agent Frameworks -- Deep-dive into LangGraph agent patterns
• AI Tools for Agents -- Compare agent frameworks and orchestration tools

Common pitfalls

• LangGraph graphs must have at least one path from START to END. Infinite loops without exit conditions will run until timeout or token limits.
• State must be serializable for persistence to work. Avoid putting non-serializable objects (database connections, file handles) in the state dict.
• Conditional edges require returning string keys that exactly match the routing map. Typos in routing keys cause silent failures.