An agent that answers in one request is a function call. An agent that books the travel, waits two days for the approval, then files the expense is a process — and processes die. The worker gets OOM-killed at hour six. The laptop closes. The model provider 529s on the eleventh of forty tool calls. When that happens, the only question that matters is whether the run resumes from where it stalled or restarts from the top, re-charging the card it already charged.
That is the problem durable execution solves, and it's why the orchestration layer has quietly become part of the agent stack rather than a backend detail. Three open-source engines own the conversation. They will all tell you they survive crashes — that part is table stakes; all three checkpoint and resume. What they actually disagree about is the recovery mechanism, and that disagreement reaches all the way up into how you're permitted to write the agent loop.
The journal that replays your code
Temporal is the heavyweight, and in 2026 it stopped being coy about agents: the OpenAI Agents SDK integration went generally available in March, and the company raised a $300M round on the back of "your agent is a workflow." The model is the cleanest expression of replay-based durability. Your workflow function runs, and every step's result is appended to an event history. If the process dies, Temporal spins up a fresh worker and re-executes the function from the top — except completed steps don't actually run again; their results are read back out of the history.
That replay is the whole trick, and it has a sharp consequence most teams discover the hard way: the workflow body must be strictly deterministic. No Date.now(), no random(), no direct network I/O — and, fatally for naive agent code, no raw LLM calls in the workflow itself. A model is the least deterministic thing you own; replay it and you get a different answer, and the history no longer matches the code. So every model call and every tool call has to be quarantined inside an Activity. Temporal forces a hard wall between control-flow code and side-effect code. The wall is the cost; exactly-once correctness across a multi-day run is what you buy with it.
The Rust engine that wraps the step, not the program
Restate makes the same replay bet as Temporal but packages it as something you can run as a single binary rather than a cluster. It markets "Durable AI Agents" as a headline use case, and its pitch is explicitly anti-lock-in: wrap the fallible step, the runtime journals the inputs and results, the agent resumes — "no SDK-specific magic." It demonstrates durable loops over the Vercel AI SDK, the OpenAI Agents SDK, and Pydantic AI rather than asking you to adopt a Restate-shaped agent framework.
Under the hood it's still a journal you replay, so the same determinism discipline applies: the LLM call lives inside a durable step, not the orchestration. What you get for choosing it over Temporal is operational weight — a Rust runtime that's closer to "a database you run" than "a platform you operate" — and durable promises that make human-in-the-loop pauses and inter-agent signals feel like ordinary language constructs instead of infrastructure.
The step function that never replays
Inngest refuses the replay model entirely, and that's the most important architectural fact about it. There's no re-execution from the top, so there's no determinism wall to design around. You write step functions; step.ai wraps each model call so only the failed step retries and its result is cached, not the whole workflow. Its AgentKit library builds single agents or multi-agent networks with a router, tools via MCP, and a useAgent React hook that streams a durable backend run straight to the frontend.
The trade is legible once you see the axis. Inngest lets you call an LLM inline in code that reads top-to-bottom, because nothing replays that code — you pay instead by structuring everything around explicit step boundaries and event triggers. You give up the strongest replay-based correctness guarantee and get back a programming model with far less ceremony, which for a lot of agent teams is the right trade.
The headline feature — "it survives crashes" — is the one thing all three already do. The real question is whether your agent loop has to be replay-deterministic.
The question to actually ask
These engines sit underneath whatever agent framework you've already chosen — the OpenAI Agents SDK, Pydantic AI, or Google's ADK — and wrap its loop in durability rather than replacing it. So the decision isn't "which framework," it's a layer below that. Stop comparing these on "does it recover," because they all do. Ask instead: does my orchestration code have to be deterministic?
If yes, you're in replay-land — Temporal for the mature platform with the OpenAI integration and the operational muscle for it, Restate for the same guarantees as a lightweight self-hostable binary that stays out of your framework's way. Both will make you isolate every LLM and tool call inside a durable step, and both will reward that discipline with exactly-once behavior across runs that outlive the process by days. If you'd rather call the model inline and think in events and steps than police determinism, Inngest is built for exactly that, and AgentKit means you don't bolt the agent layer on separately.
One honorable mention if you want durability without adopting a new runtime at all: DBOS (dbos-inc/dbos-transact-py, ~1.4k) checkpoints workflows directly into your own Postgres, no orchestration cluster required — durability as a library, for the team that already has a database and doesn't want a platform.
The mistake is picking by star count. Temporal's 21k versus Inngest's 5.5k measures maturity and reach, not fit. Fit is decided by one design choice you'll live inside every day: whether the engine replays your code, and therefore whether your agent loop is allowed to be as non-deterministic as the model at the center of it.
