Building Stateful Multi‑Agent LLM Workflows with LangGraph: A Step‑by‑Step Guide

LangGraph is a library in the LangChain ecosystem designed for building stateful, multi‑agent LLM applications. It expands LangChain by allowing developers to describe complex LLM workflows as graphs composed of nodes (computational steps) and edges (control/data flow), with a shared mutable state.

Core Concepts

Graph : The top‑level abstraction made of nodes and edges.

Node : A processing unit such as an LLM call, tool invocation, or custom Python function; can have inputs and outputs.

Edge : Connects nodes and can be conditional, enabling dynamic routing based on node output.

State : Shared mutable data that persists across node executions, enabling conversation history or other context.

Agent : In LangGraph, an agent can be represented by one or more nodes that cooperate to solve a task.

Typical Use Cases

Complex agent workflows that require multi‑step reasoning, tool usage, loops, and conditional logic.

Multi‑agent collaboration where several LLM agents need to exchange information.

Stateful dialogue systems that maintain and adapt to conversation history.

Autonomous agents capable of self‑correction, planning, and executing intricate tasks.

How It Works

Define Graph State : Specify which pieces of information the graph must keep, e.g., a list of messages and the latest user input.

Define Nodes : Create a node for each step, such as a call to an LLM or a custom processing function.

Define Edges : Connect nodes and optionally add conditional edges to control routing.

Compile the Graph : Use StateGraph to turn the node‑edge definition into an executable graph object.

Run the Graph : Invoke the compiled graph with an initial state and observe the evolving state and final output.

Example 1: Basic Graph ( example_1_basic_graph.py )

The example builds the simplest stateful graph containing one LLM‑call node and one custom processing node.

Key Elements

GraphState

: Defines the shared state with fields messages (dialogue history) and user_input. messages is declared as Annotated[List[BaseMessage], lambda x, y: x + y] so that each update appends a new message. call_llm node: Calls ChatOpenAI using the current messages and appends the LLM response as a new HumanMessage to the state. simple_processor node: Extracts the latest user input, transforms it to uppercase, and adds the result back to the state as a HumanMessage. StateGraph: The core class used to assemble the graph. add_node, set_entry_point, add_edge: Methods that register nodes, set the start node, and define the flow. In this example the edge from the LLM node leads to the processor node, which then points to END. compile(): Produces an executable app object. invoke(): Executes the compiled graph with an initial user message, returning a final state that contains all exchanged messages.

Execution Flow

Define GraphState to hold shared data.

Create call_llm and simple_processor nodes.

Connect them with add_edge to form a linear path: llm_node → processor_node → END.

Compile the graph and invoke it with a starting user message.

Highlights

Statefulness : GraphState lets nodes read and update a common context, preserving dialogue history.

Modularity : Separating LLM calls and custom logic into distinct nodes improves readability and maintainability.

Clear Flow Control : Using add_edge and the END marker makes the execution path explicit.

By following this example you can see how LangGraph decomposes a complex LLM workflow into manageable, state‑driven nodes and enables multi‑step reasoning.

References

How to: migrate from legacy LangChain agents to LangGraph – https://python.langchain.com/docs/how_to/migrate_to_langgraph