LangGraph: Stateful multi-agent systems

LangGraph is an open-source framework developed by the creators of LangChain that enables the construction of stateful, multi-agent applications using a graph-based architecture. Unlike traditional linear chains, LangGraph allows developers to define complex workflows with loops, conditional branching, and persistent memory.

As we are moving towards AI Agents, the transition from simple chatbots to autonomous agents requires a sophisticated orchestration layer. LangGraph addresses the limitations of Directed Acyclic Graphs (DAGs) by providing a system where agents can revisit previous steps, self-correct, and maintain a shared state over long-running interactions.

What is LangGraph?

LangGraph is a library for building stateful, multi-actor applications with Large Language Models (LLMs). It is built on top of LangChain but operates as a distinct architectural paradigm. While LangChain excels at creating linear sequences of tasks, LangGraph is designed for “agentic” workflows that are inherently non-linear.

The framework centers on three core concepts:

• Nodes: These are the building blocks of the graph. Each node is a Python function or a Runnable that performs a specific action, such as calling an LLM, searching a database, or executing code.
• Edges: These define the transitions between nodes. Edges determine the flow of execution, including conditional edges that route the workflow based on the output of a previous node.
• State: This is a shared data structure (typically a TypedDict or Pydantic model) that represents the current snapshot of the application. Every node reads from and writes to this state, ensuring context persistence.

What to use LangGraph for

LangGraph is specifically engineered for scenarios where a single-pass, linear execution is insufficient. Organizations leverage LangGraph to build advanced AI agents that require high levels of autonomy and reliability.

1. Multi-agent collaboration

Complex tasks often require specialized agents working together. For example, a software development workflow might involve one agent writing code, a second agent reviewing it, and a third agent executing tests. LangGraph manages the handoffs and communication between these agents through the shared state.

2. Human-in-the-loop (HITL) workflows

For high-stakes enterprise applications, human oversight is mandatory. LangGraph’s checkpointing system allows a workflow to pause execution, persist its state to a database, and wait for human approval or intervention before resuming.

3. Iterative RAG (Retrieval-Augmented Generation)

Standard RAG often fails if the initial retrieval is irrelevant. LangGraph enables Corrective RAG (CRAG), where an agent evaluates the retrieved documents and, if they are found wanting, loops back to refine the search query or try a different data source.

4. Long-running business processes

Processes such as automated customer onboarding or legal contract analysis involve multiple stages that may take hours or days to complete. LangGraph’s persistence layer ensures that the system can recover from failures and maintain progress across sessions.

How to use LangGraph

Implementing a LangGraph application involves a structured four-step process: defining the state, creating nodes, establishing edges, and compiling the graph.

Step 1: Define the state

The state is the “source of truth” for the graph. You must define what information needs to be tracked.
Python:

from typing import Annotated, TypedDict
from langgraph.graph.message import add_messages

class State(TypedDict):
    # 'add_messages' is a reducer that appends new messages to history
    messages: Annotated[list, add_messages]

Step 2: Create the nodes

Nodes are functions that take the current state as input and return an update to that state.
Python:

def assistant(state: State):
    return {"messages": [llm.invoke(state["messages"])]}

Step 3: Define edges and logic

You connect the nodes using add_edge for direct transitions or add_conditional_edges for logic-based routing.
Python:

from langgraph.graph import StateGraph, START, END

workflow = StateGraph(State)
workflow.add_node("assistant", assistant)
workflow.add_edge(START, "assistant")
workflow.add_edge("assistant", END)

Step 4: Compile and execute

Compiling the graph validates the structure and creates a CompiledGraph object that can be invoked or streamed. To see a practical application in your organization, you can book an AI demo to explore custom implementations.

Benefits and downsides of LangGraph

While LangGraph provides significant power, it introduces a level of architectural complexity that must be weighed against the project requirements.

Benefits

• Cycles and loops: Unlike LangChain’s Chain objects, LangGraph supports cyclical graphs, allowing for iterative reasoning and self-correction.
• Granular control: Developers have precise control over the execution flow, making it easier to debug specific agent behaviors.
• Persistence: Built-in checkpointing allows for “time-travel debugging,” where you can inspect and re-run previous states of the graph.
• Ecosystem integration: It integrates seamlessly with LangSmith for observability and LangServe for deployment.

Downsides

• Steeper learning curve: Understanding graph theory and state management is more difficult than building linear chains.
• Verbosity: Simple tasks often require more boilerplate code in LangGraph than in standard LangChain implementations.
• Architectural overhead: For simple RAG applications or Q&A bots, the complexity of a graph might be unnecessary.

Competitor analysis: How LangGraph compares

Several frameworks compete in the AI agent orchestration space, each with different philosophies regarding structure and ease of use.

Comparison table: AI agent frameworks

Feature	LangGraph	CrewAI	Microsoft Autogen	PydanticAI
Primary Focus	Stateful orchestration	Role-based teams	Conversational agents	Type-safe agents
Architecture	Graph-based	Process-based	Dialogue-based	Schema-based
State Management	Explicit & Persistent	Event-driven	Implicit	Strict Pydantic
Ease of Use	Moderate (Low-level)	High (High-level)	Moderate	High
Cyclical Support	Native	Limited	Native	Native

CrewAI

CrewAI focuses on “role-based” agents. It is highly intuitive for creating teams where agents have specific “backstories” and “goals.” However, it is less flexible than LangGraph for custom branching logic and lacks the same level of granular state control.

Microsoft AutoGen

AutoGen emphasizes conversation as the primary mode of interaction. While powerful for multi-agent dialogue, it can be harder to control in production environments where strict, non-conversational state transitions are required.

PydanticAI

PydanticAI is a newer entrant that prioritizes type safety and developer experience using Pydantic models. It is excellent for data-heavy enterprise applications but does not yet have the extensive ecosystem of LangGraph.

Conclusion

LangGraph represents a shift from “AI as a sequence” to “AI as a system.” By introducing graph-based state management, it enables the development of resilient, autonomous agents capable of handling the complexities of real-world business logic. While the framework requires a more disciplined architectural approach, the benefits of persistence, cyclical execution, and human-in-the-loop capabilities make it a leading choice for production-grade AI systems.

For organizations looking to move beyond basic prototypes, a structured AI workshop can help identify where LangGraph’s capabilities align with specific business objectives.

Frequently asked questions