Every serious agent framework eventually reinvents the same primitive, badly, before adopting the real one. The agent calls a model, charges a card, waits nine hours for a human to click approve, then calls another model. Halfway through, the process dies — a deploy, an OOM kill, a spot instance reclaimed. What happens to the run?
If your answer is "it starts over," you don't have an agent, you have a slot machine. Durable execution is the fix: a workflow that resumes from its last completed step instead of the top, so the LLM call you already paid for isn't paid for twice and the card isn't charged again. In 2026 this stopped being exotic. The open question is no longer whether to make agent runs durable — it's where the durability lives.
Two answers dominate, and they are near-mirror images. (LangGraph's checkpointer is a third variant of the same idea — see LangGraph checkpointing vs Temporal durable execution — and the broader field spans Temporal vs Inngest vs Restate.)
Temporal: durability as a system beside your app#
Temporal is the incumbent, and its model is externalization. Your workflow code runs on dedicated worker processes. Those workers poll task queues from the Temporal Service — a cluster that persists every workflow's event history in its own datastore (Cassandra, PostgreSQL, or MySQL, depending on how you run it). Recovery works by replay: after a crash, a worker re-executes your workflow function against the saved history, fast-forwarding through steps whose results are already recorded until it reaches the first unfinished one.
That replay model is powerful and it has a tax. Workflow code must be deterministic: no direct I/O, no reading the wall clock, no unseeded randomness inside the workflow body — anything non-deterministic has to move into an activity. Get it wrong and you get non-determinism errors on replay, the single most common Temporal footgun. In exchange you get isolation, multi-region, mature retry/timeout tooling, polyglot workers, and throughput into the tens of thousands of state transitions per second. You also get a distributed system to deploy, monitor, upgrade, and page someone about.
DBOS: durability as a table you already have#
DBOS Transact makes the opposite bet: durability shouldn't be a system, it should be a library. You import it into your existing process. You decorate a function with @DBOS.workflow() and its constituent steps with @DBOS.step(). DBOS then checkpoints each step's result into Postgres — the same Postgres your app might already use for its business data. On restart, it replays from the last completed checkpoint. There is no separate orchestrator, no worker fleet, no second datastore. The Python SDK hit v2.26.0 on June 30, 2026 (MIT, ~1.5k stars), with a TypeScript SDK alongside and a Go SDK reported.
DBOS's own framing — "Postgres is all you need for durable execution" — is a marketing line, but it points at something real. Adoption is a decorator, not a rearchitecture.
The features are nearly the same. What differs is how much of a distributed system you agree to operate to get them.
The axis that actually decides it#
Here's the part the comparison tables miss. If you line up capabilities — exactly-once steps, resume-after-crash, timers, human-in-the-loop pauses — DBOS and Temporal look almost identical, because they are solving the identical problem. Reading feature lists will not tell you which to pick.
The real variable is operational surface. Temporal moves your durability into a system that lives next to your app; DBOS folds it into a database that lives inside your app's existing footprint. That single difference cascades:
- Isolation and scale are things you buy by running a separate cluster. If you need hard per-tenant isolation, multi-region, fan-out to hundreds of external APIs, or tens-of-thousands-per-second throughput, Temporal's separateness is exactly the point — pay for it.
- Simplicity is a thing you buy by not running one. A single agent service that already talks to Postgres, doing up to a few thousand transitions per second, with side effects that mostly land in that same database, gets durable for the cost of two decorators and zero new infrastructure.
So don't ask "which is more production-ready" — both are. Ask which failure you're provisioning against. If it's my agent redid a paid, irreversible step because the box rebooted, DBOS ends that story without adding an operational dependency. If it's I need isolation and scale a single database can't give me, that's the sentence that justifies operating Temporal.
Durability is table stakes now. The lasting decision is how many stateful systems you're willing to keep alive to have it — and for a lot of agents, the honest answer is: the one I already run.
