Temporal vs LangGraph for AI agents (and when you need neither)

Temporal is a general-purpose durable execution engine; LangGraph is a framework for building agent graphs. They solve different problems — Temporal gives you durability but no agent primitives, LangGraph gives you agent structure but no durable runtime — which is why many production teams end up wanting both, or a platform that combines them.

"Temporal vs LangGraph" is one of the most common framing questions teams ask when they start moving agents toward production, and it's a slightly misleading one. The two tools sit at different layers of the stack. Comparing them head-to-head is a bit like comparing a database to a web framework: you can hold an opinion about each, but you rarely pick one insteadof the other. This post lays out what each actually is, why they aren't really competitors, what each still leaves you to build, and when a purpose-built agent runtime is the better answer than assembling either one yourself.

What Temporal is

Temporal is a general-purpose, code-first durable execution engine. You write ordinary code — called workflows and activities — and Temporal makes that code survive process crashes, deploys, and restarts by persisting its execution history and replaying it deterministically. A workflow can run for seconds or for months; if the worker running it dies, another worker picks it up and continues from exactly where it left off. It offers SDKs in several languages, including Python and TypeScript (as well as Go, Java, and .NET).

Temporal's core strength is that its durability is genuinely battle-tested. It is the proven foundation of the durable-execution category, used in production for mission-critical workloads well beyond AI — payments, provisioning, order fulfillment, long-running business processes. If you need work that absolutely must complete despite failures, Temporal is a serious, well-understood answer.

The important thing to be clear about: Temporal is not agent-specific. It knows nothing about LLMs, prompts, tools, model routing, or token cost. It gives you a durable place to run arbitrary code. Whether that code is an agent, a nightly batch job, or a checkout flow is entirely up to you — and so is everything that makes an agent an agent.

What LangGraph is

LangGraph is a code framework, from the LangChain ecosystem, for defining agents as graphs. You describe nodes (model calls, tools, decisions) and the edges between them, in Python or JavaScript, and LangGraph handles stepping through that graph — including cyclic, stateful, multi-actor flows that are awkward to express as a straight chain. It ships primitives that agent builders reach for constantly, including checkpointers for persisting graph state and an interrupt() mechanism for pausing to collect human input.

LangGraph's strength is agent structure plus ecosystem. It gives you a natural vocabulary for expressing agent control flow, and it sits inside one of the largest communities and integration libraries in the space. Observability and evaluations are handled by LangSmith, a separate product, rather than being part of the framework itself.

The thing to be clear about here: LangGraph is a framework you build with, not a durable runtime you operate. It helps you describe and run an agent graph in your own process. Standing up the production system around that graph — a hosted place to run it, crash recovery you can rely on, durable human-approval gates, per-step cost accounting, governance — is work that lands on you (or on additional products you adopt and wire together).

Why they aren't really competitors

Once you see the layers, the "versus" mostly dissolves. Temporal is a durability engine. LangGraph is an agent-authoring framework. One answers "how does this keep running when things fail?" The other answers "how do I express what this agent does?" Those are different questions, and a team can reasonably need both answers at once.

In fact, you can use them together: author the agent's control flow in LangGraph, and run it on top of Temporal so the execution is durable. Teams do exactly this. The cost is real, though — you are now operating two systems, owning the glue between them, and reasoning about two overlapping notions of state and retries. That integration is a project in its own right, and it's maintenance you carry indefinitely.

What each leaves you to build

The clearest way to compare them fairly is to look at the gap each one leaves for a production agent team.

With Temporal, you still build the agent product

Temporal hands you world-class durability primitives and then steps back. On top of them you build the agent layer:

• Agent primitives — LLM and tool nodes, prompts, graph structure.
• Model routing across providers and models.
• A human-approval interface (Temporal gives you signals; the queue and UI are yours to design).
• Cost attribution in dollars, per step.
• Evaluations to measure whether the agent is actually getting better.

With LangGraph, you still build (and operate) the runtime

LangGraph gives you the graph and useful primitives, and leaves the production runtime to you:

• A durable runtime you can operate, not just a checkpointer in your own process.
• Dependable crash recovery across restarts and deploys.
• Persistent approval gates that hold a paused run for hours or days.
• Per-step cost in USD (token traces are available through LangSmith).
• Governance — audit logs and role-based access control.

Notice that the two gap-lists are almost mirror images. Temporal has the durability and misses the agent product; LangGraph has the agent structure and misses the operated runtime and governance. That symmetry is exactly why teams end up wanting both.

When Temporal is the right call

Temporal is the better choice when durability is your dominant problem and agents are a small part of a much larger picture. Specifically:

• You need general workflow durability across many kinds of workloads, not just AI — and you want one proven engine underneath all of them.
• You have platform engineers who can own an SDK, run workers, and build the product layer that sits above the primitives.
• You value polyglot backends (Go, Java, .NET alongside Python and TypeScript) and extreme, well-understood scale.

If that describes you, Temporal is a proven foundation and a sound bet. You'll build the agent-specific layer yourself — but for a team where agents are one workload among many, that trade-off can be exactly right.

When LangGraph is enough

LangGraph is the better choice when you're focused on the agent logic itself and the operational concerns haven't arrived yet:

• You're prototyping or iterating on agent behavior, and getting the graph right is the whole job right now.
• Durability, governance, and cost attribution aren't yet the constraint — a checkpointer in your own process is good enough for where you are.
• Your team is happy living in code and in the LangChain ecosystem, and you want the largest library of integrations and examples.

For that stage, LangGraph is a productive, expressive home for agent logic. The question is what happens when the same agent has to run reliably in front of real users, which is where the missing runtime and governance start to matter.

When you need neither

There's a third path, and in fairness this is Ballast's pitch: skip the choice-and-glue entirely and use a purpose-built agent runtime that combines durable execution withagent primitives in one system. That's the category Ballast is in.

The bet is that most agent teams don't actually want a durability engine or an authoring framework in isolation — they want both layers, plus the product surface around them, without operating three systems. So Ballast puts them together: durable, checkpointed execution built specifically for agents, a visual builder for the graph, human approval gates that survive restarts indefinitely, per-step cost attribution in dollars, built-in evals, and governance with an audit log — in a single platform, with Python and TypeScript SDKs.

Concretely, that means the durability Temporal is known for and the agent structure LangGraph gives you arrive as one product rather than as an integration project. If you're weighing that trade-off directly, the Ballast vs Temporal and Ballast vs LangGraph pages go feature by feature.

The short version

If you only remember a handful of one-liners from the "Temporal vs LangGraph for AI agents" debate, make it these:

• Temporal is a general-purpose durable execution engine — world-class durability, no agent primitives.
• LangGraph is an agent-authoring framework — good agent structure and ecosystem, but not a durable runtime you operate.
• They're different layers, not rivals; you can even run LangGraph graphs on Temporal — at the cost of gluing two systems.
• Choose Temporal when durability spans many workloads and you have platform engineers to build the agent layer.
• Choose LangGraphwhen you're building agent logic and durability and ops aren't the concern yet.
• Choose a purpose-built agent runtime like Ballast when you want durable execution and agent primitives — plus a visual builder, approval gates, per-step cost, and evals — in one system.

Where to go next

• Ballast vs Temporal — durable execution built for general workloads versus built for agents.
• Ballast vs LangGraph — an agent framework plus separate observability versus one integrated platform.
• See the primitives and the run model on the product page.