LangGraph agents

LangGraph agents are AI agent implementations built on LangGraph's graph-based execution model — where agent workflows are defined as directed graphs of nodes and edges, enabling complex multi-step, multi-agent, and stateful workflows with explicit control over execution flow, state management, and human-in-the-loop interactions.

How LangGraph structures agent execution

LangGraph represents agent workflows as directed graphs where each node is a function or model call and each edge defines the possible transitions between nodes. At each step, the graph determines which node to execute next — either following a fixed edge (deterministic transition) or evaluating a conditional that selects among multiple outgoing edges based on the current state. This explicit graph structure differs from linear chain execution and from the implicit decision-making of a loop-based agent: the developer defines the space of possible execution paths, and the model navigates within that space. State is a typed object passed between nodes, making execution state inspectable and recoverable.

Advantages for complex agent workflows

The graph model gives LangGraph advantages for workflows that are too complex for a simple loop. Branching execution lets an agent follow different paths based on intermediate results without embedding that logic in a prompt. Cycles allow the agent to revisit earlier nodes — useful for retry patterns, iterative refinement, and multi-hop reasoning — without risking an infinite loop because node execution is tracked in state. Parallel node execution allows multiple sub-tasks to run simultaneously and join before proceeding. Long-horizon workflows that span multiple interactions or require checkpointing — so that a partially completed workflow can resume after an interruption — are easier to implement in a graph model than in a loop.

Multi-agent patterns in LangGraph

LangGraph supports multi-agent architectures where a supervisor agent manages worker agents by defining them as subgraphs. The supervisor node dispatches tasks to worker nodes and collects their outputs before deciding the next step. Each worker agent can itself be a graph, with its own state and execution logic, running within the broader graph. This composability lets teams build modular agent systems where components can be developed and tested independently. The coordination overhead is explicit in the graph structure rather than implicit in prompt instructions, which makes multi-agent behavior easier to reason about and debug.

LangGraph agents — FAQ

When should I use LangGraph instead of a simpler agent loop?

LangGraph earns its added complexity for workflows that need: stateful execution across multiple model calls and tool invocations, explicit branching logic based on intermediate results, human-in-the-loop checkpoints where execution pauses for review, recovery from partial failures without restarting from scratch, or parallel execution of independent sub-tasks. For agents that run a simple linear sequence of steps or a basic tool-calling loop, a direct implementation without LangGraph is simpler and easier to debug.

Does LangGraph require using LangChain?

LangGraph was developed alongside LangChain and integrates tightly with it, but it can be used with other language model providers through its model interfaces. Teams that are already using LangChain will find LangGraph a natural extension; teams that are not may find the LangChain dependency overhead unwelcome for simple use cases.