LangGraph architecture

LangGraph's architecture organizes agent workflows as stateful directed graphs — where nodes represent computational steps, edges represent transitions, a typed state object is passed between nodes, and a checkpointer enables persistence and recovery — providing the structural primitives needed to build complex, long-running agent workflows.

Core architectural primitives

LangGraph builds on four primitives. State is a typed dictionary-like object that stores all data relevant to the current execution — conversation history, intermediate results, flags, and any other information nodes need to read or produce. Nodes are Python functions that take the current state and return an updated state; each node encapsulates one computational step, which may be a model call, a tool invocation, a state transformation, or a routing decision. Edges define transitions: a regular edge always moves from one node to the next; a conditional edge evaluates a function and selects among multiple possible next nodes based on the result. The graph object compiles these elements into an executable workflow that can be run, inspected, and interrupted.

Checkpointing and persistence

LangGraph's checkpointer saves the state of the graph at each node transition to an external store, enabling workflows to resume after interruption. This is significant for long-horizon agents: a workflow that requires hours of execution can be paused at any checkpoint and resumed without restarting, which reduces both cost and latency for tasks that cannot complete in a single invocation. Human-in-the-loop patterns work through checkpointing: the graph is defined to pause at designated points, the state is saved, a human reviews and approves or modifies it, and execution resumes. Without a checkpointer, state exists only in memory and is lost if the process terminates.

Deployment and observability

LangGraph Server provides an infrastructure layer for deploying LangGraph workflows as services — handling request routing, state persistence, and concurrent execution across multiple workflow instances. The execution model supports streaming intermediate outputs so that clients can observe agent progress as it happens rather than waiting for final results. Because state is explicit and typed, debugging is more tractable than in systems with implicit state: the state at any node transition can be inspected, replayed, or modified for testing. Observability tools that instrument LangChain also instrument LangGraph by default, since LangGraph uses LangChain's model interfaces.

LangGraph architecture — FAQ

How does LangGraph handle errors in node execution?

Node execution errors can be caught and handled within the graph by defining error-handling edges that route to a recovery node when a failure occurs. The specific error handling depends on the design: an agent might retry a failed tool call, fall back to an alternative approach, escalate to a human, or terminate with a logged failure. Without explicit error-handling edges, uncaught exceptions will terminate the graph execution. The checkpointing model allows the graph to resume from the last successful checkpoint rather than restarting entirely, which is useful for workflows with expensive earlier steps.

Can LangGraph be used for synchronous as well as asynchronous workflows?

LangGraph supports both synchronous and asynchronous execution. For applications requiring high throughput or long-latency tool calls, async execution allows the graph to handle multiple concurrent workflows without blocking. The graph code uses Python's async/await patterns, and async nodes and tools can be mixed with synchronous ones. Deployment with LangGraph Server handles the async execution infrastructure; for direct invocation, async graphs can be run with any Python async runtime.