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Overview

Last Updated: 3/11/2026

LangGraph Overview

LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. Trusted by companies like Klarna, Replit, and Elastic, LangGraph gives you precise control over agent workflows without abstracting away the underlying mechanics.

What is LangGraph?

LangGraph models agent workflows as graphs where:

  • Nodes are functions that do work (call LLMs, query databases, process data)
  • Edges define the flow between nodes
  • State is shared data that persists throughout execution

Unlike higher-level frameworks, LangGraph focuses entirely on orchestration. You define the logic, LangGraph handles the execution, persistence, and state management.

Core Benefits

Durable Execution

Build agents that persist through failures and run for extended periods. If your agent crashes, it automatically resumes from exactly where it left off using checkpoints.

Human-in-the-Loop

Incorporate human oversight by pausing execution at any point. Inspect agent state, make modifications, and resume—perfect for approval workflows and quality control.

Comprehensive Memory

Create stateful agents with:

  • Short-term memory: Conversation history within a session (thread-scoped)
  • Long-term memory: Facts and preferences across sessions (shared across threads)

Debugging with LangSmith

Gain deep visibility into agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.

Production-Ready Deployment

Deploy sophisticated agent systems with scalable infrastructure designed for stateful, long-running workflows.

When to Use LangGraph

Use LangGraph when you need:

  • Fine-grained control over agent flow and state
  • Long-running workflows that must survive failures
  • Human approval steps in your automation
  • Memory that persists across conversations
  • Complex branching logic or parallel execution

Consider higher-level tools when:

  • You want prebuilt agent patterns (use LangChain’s create_agent)
  • Your workflow is simple and stateless
  • You don’t need persistence or checkpointing

LangGraph Ecosystem

LangGraph integrates seamlessly with the LangChain ecosystem:

  • LangSmith: Observability, tracing, and evaluation for agent development
  • LangSmith Deployment: Purpose-built platform for deploying stateful agents
  • LangChain: Integrations and components for LLM applications (models, tools, prompts)

You can use LangGraph standalone or combine it with these tools for a complete agent development experience.

How LangGraph Works

At its core, LangGraph uses a message-passing algorithm inspired by Google’s Pregel:

  1. Define state: Create a schema for data that flows through your graph
  2. Add nodes: Write functions that read state, do work, and return updates
  3. Connect with edges: Define static or conditional transitions between nodes
  4. Compile: Build the executable graph with optional checkpointing
  5. Run: Invoke the graph with input and get output

Here’s a minimal example:

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
 
class State(TypedDict):
    text: str
 
def node_a(state: State) -> dict:
    return {"text": state["text"] + "a"}
 
def node_b(state: State) -> dict:
    return {"text": state["text"] + "b"}
 
graph = StateGraph(State)
graph.add_node("node_a", node_a)
graph.add_node("node_b", node_b)
graph.add_edge(START, "node_a")
graph.add_edge("node_a", "node_b")
graph.add_edge("node_b", END)
 
app = graph.compile()
result = app.invoke({"text": ""})
print(result)  # {'text': 'ab'}

Next Steps

  • Installation: Install LangGraph and dependencies
  • Quickstart: Build your first calculator agent
  • Core Concepts: Understand state, nodes, and edges in depth
  • Building Graphs: Learn the Graph API and state management patterns

Acknowledgements

LangGraph is inspired by Pregel  and Apache Beam . The public interface draws inspiration from NetworkX . LangGraph is built by LangChain Inc, but can be used without LangChain.

Installation