Temporal and Actae both make execution durable, and engineers reasonably ask which is better. The useful answer is not a winner; it is a boundary. Every durable-execution system has to answer two questions, and those answers explain almost every downstream difference:
- Who owns the application’s control flow — the durable engine, or your application and framework?
- What is the unit of replay — re-executing deterministic code, replaying a recorded result, or restoring a snapshot?
This is not an argument that Temporal should have been built differently. It is an argument that the choice is architectural, not a matter of quality.
Four ways to make an agent durable
In the agentic setting there are really four options, and they sit on a spectrum from “an engine owns the loop” to “you build everything” to “a layer sits beside the loop.”
1. A workflow engine (Temporal)
The control flow is expressed as a workflow definition, and the engine executes it. Workflow code is replayed deterministically against recorded history; activities are the side-effecting boundary, retried under policies you configure. The engine owns the loop — which is exactly what you want when the unit of value is a business process and the platform should drive it.
The constraint is the same property seen from the other side: durable logic is expressed through the workflow model. An agent framework’s own loop is typically treated as an activity, and the engine’s history format is the engine’s, not yours.
2. Framework-native persistence (a LangGraph checkpointer)
The framework persists its own state at checkpoints and resumes from a thread. It is the lowest-friction option if that framework is your entire world: no new infrastructure, and resume works for interrupted graphs.
The limits are structural. The durability model is the framework’s, so it does not span multiple frameworks or a plain async for loop. And, as a checkpoint records state, not effects, it cannot by itself tell you whether an external action landed.
3. Postgres plus a queue, built by hand
This is the honest default that many teams reach after outgrowing an in-memory queue. It has real advantages: total control, no new dependency, SQL you can inspect. Nothing is hidden.
It also means owning every subtlety yourself: ordering and atomicity, cursor semantics, lease expiry and fencing, idempotency-key lifetime, and the ambiguous window where an effect succeeded but its result was never recorded. Those are precisely the parts that bite in production, and they are more intricate than they first appear. The durability benchmark shows why: hand-rolled retry re-fired four already-applied effects in a six-step run.
4. A durability layer beside the loop (Actae)
The application or framework stays authoritative for prompts, models, tools, and planning. Actae records the facts of execution — ordered events, atomic state transitions, an idempotent tool-execution ledger, and fork lineage — and replays them for recovery and live delivery. It does not run the loop and it has no workflow definition that drives the application.
The constraint is equally clear: Actae is not a scheduler and does not own control flow. It is proprietary rather than open source, and it is newer than the alternatives above.
Side by side
When each one fits
- Temporal when a general workflow engine should own the application lifecycle, and durability should be expressed through the workflow model. Its maturity, scale, and ecosystem are real advantages.
- A framework checkpointer when one framework is the whole system and the only requirement is resuming a run.
- Postgres plus queues when control is the point and the team is prepared to own ordering, idempotency, and recovery semantics deliberately.
- Actae when the agent framework owns the loop, the stack may change over time, and the requirements include framework-neutral recovery, safe retries, and forking inherited state.
Coexistence is normal
These are not mutually exclusive. A platform can run business processes in a workflow engine and delegate agent-run durability to Actae, with the engine treating an Actae-backed agent as one of its activities. The failure modes they address overlap; the boundaries they own do not. There is no requirement to pick one globally.
The honest framing is the one on the comparison page: Actae is a narrower fit for teams that want framework-neutral durability and stateful execution lineage. It is not “better than Temporal.”
What Actae does not claim
- It is not a workflow engine or a scheduler. It does not own the execution loop and does not schedule work.
- It is not exactly-once by itself. A durable ledger closes the duplicate window only when the downstream honors the stable idempotency key. Without that, reconciliation remains the caller’s job.
- It does not sell a public SLA yet. The operating posture is stated plainly rather than overclaimed.
Why the boundary matters for agents specifically
The reason this decision is sharper for agents than for ordinary services is the shape of the work: a long, expensive, sampled reasoning step that may call tools with real side effects. That makes two properties first-class.
Exact recovery boundaries. Resume at the step, with the state that step actually saw, and replay the recorded result of any tool call already performed rather than re-executing it.
Forking. Keep steps 1..N, change step N+1, and get a second continuation that inherits real state with an auditable lineage. Actae makes this a primitive: fork at an exact boundary copies the state at or before the fork cursor into a child channel, records a lineage marker and an immutable receipt, and runs only the remaining steps. The deterministic demo measures 8,260 tokens saved (46.8%) by forking past the first five steps of a ten-step pipeline.
If the agent loop is going to stay where it is, and durability should move underneath it, Actae is built for that boundary. If a workflow engine should own the loop, Temporal is built for that one.