LangGraph vs Temporal for Stateful Agents

Building stateful AI agents that survive failures and run for a long time raises a familiar question: should you use a framework built for agents or a general-purpose durable execution engine? LangGraph and Temporal represent these two answers. LangGraph is purpose-built for LLM agent workflows, while Temporal is a battle-tested system for reliable, long-running workflows of any kind. They overlap on durability but differ deeply in focus.

Built for Agents vs Built for Workflows

LangGraph, part of the LangChain ecosystem, is designed specifically for stateful, multi-actor agent applications. It models an agent as a graph of nodes and edges, which fits how agents reason through cyclical, branching flows where the path is not known in advance. Because it is agent-native, it understands concepts like message history, tool use, and human-in-the-loop steps out of the box.

Temporal is a general-purpose durable execution engine. It was built to guarantee that workflow code runs to completion despite crashes, network outages, or restarts, and it is used for everything from payment processing to background jobs. It has no built-in notion of prompts, context windows, or LLM state, so it treats an agent as just another workflow whose logic you supply.

Durability and State

Both offer durability, but at different layers. LangGraph includes a persistence layer that checkpoints state between steps, so an interrupted agent can resume from its last recorded node. This is convenient for agents, though it generally captures state between nodes rather than partway through a single node's execution.

Temporal's durability is its core strength. It records full execution history and replays workflows to recover exactly where they left off, providing strong guarantees for mission-critical, long-running processes. If your concern is bulletproof execution across infrastructure failures over hours or days, Temporal's model is hard to beat. The trade-off is that you must build the AI-specific parts, such as managing message history and retrieval, yourself.

Agent-Native Features

This is where LangGraph pulls ahead for AI work. It offers agent-oriented observability with token usage, cost, latency, and model parameters captured per run, and it supports streaming, memory, and human oversight as first-class features. Its graph model matches the way agents actually loop and branch through reasoning.

Temporal offers none of this natively because it is domain-agnostic. To get the same agent visibility and conveniences, you would integrate additional tooling and write the orchestration of prompts and context manually. That is more work, but it buys you a uniform, durable foundation that is not tied to any AI framework.

Choosing or Combining Them

Pick LangGraph when your priority is agent reasoning, fast iteration, agent-native observability, and built-in support for memory and human-in-the-loop flows. Pick Temporal when your priority is rock-solid durability for long-running, mission-critical workflows that may span days and tolerate no lost progress.

Increasingly, sophisticated teams use both. Temporal orchestrates the macro-level, durable workflow, while a Temporal activity invokes a LangGraph agent to handle a reasoning-intensive subtask. This hybrid gives you Temporal's reliability for the overall process and LangGraph's agent-native flexibility where the model needs to think.