LangGraph: Building Stateful AI Agents That Actually Work

The biggest challenge in building AI agents isn't the LLM — it's managing state. LangGraph, built on top of LangChain, solves this with a graph-based approach to agent workflows.

Why State Management Matters

A simple agent that answers a question doesn't need state. But real-world agents do complex things:

All of these require remembering what happened in previous steps. This is the state management problem.

LangGraph's Core Concept

LangGraph models agent workflows as directed graphs:

```python

from langgraph.graph import StateGraph, END

from typing import TypedDict, List

class AgentState(TypedDict):

messages: List[dict]

next_step: str

research_results: List[str]

final_answer: str

graph = StateGraph(AgentState)

```

Building a Research Agent

```python

def researcher(state: AgentState) -> AgentState:

# Call LLM to decide what to research

query = llm.invoke(state["messages"])

results = search_tool(query)

return { **state, "research_results": results }

def writer(state: AgentState) -> AgentState:

# Use research results to write a response

prompt = f"Based on: {state['research_results']}\nWrite a response."

answer = llm.invoke(prompt)

return { **state, "final_answer": answer }

def reviewer(state: AgentState) -> str:

# Conditional edge — decide what to do next

if is_good_enough(state["final_answer"]):

return "end"

return "researcher" # Loop back

graph.add_node("researcher", researcher)

graph.add_node("writer", writer)

graph.add_conditional_edges("writer", reviewer, {"end": END, "researcher": "researcher"})

graph.set_entry_point("researcher")

app = graph.compile()

```

Key LangGraph Features

Persistence

LangGraph can checkpoint state to a database, allowing agents to resume after interruption:

```python

from langgraph.checkpoint.sqlite import SqliteSaver

memory = SqliteSaver.from_conn_string(":memory:")

app = graph.compile(checkpointer=memory)

```

Human-in-the-Loop

Pause execution and wait for human input at any node:

```python

app = graph.compile(interrupt_before=["action_node"])

```

Streaming

Stream agent thoughts and actions in real time:

```python

for event in app.stream(initial_state):

print(event)

```

When to Use LangGraph

Use LangGraph when your agent needs to:

For simple single-step agents, LangGraph is overkill. For production multi-step workflows, it's the right tool.

Talk to our team about building your LangGraph agent.