Compare Temporal, a durable execution platform for deterministic workflow code, with Agent Swarm, an open-source operating system for AI work.
The real comparison is not which abstraction is nicer. It is whether you want to build an agent system or run a persistent agent team.
Temporal runs deterministic workflow code with replay guarantees: every state change lands in an event history, and the engine rebuilds exact state by replaying it. Agent Swarm orchestrates non-deterministic model and agent work: a lead agent delegates tasks to workers such as Claude Code or Codex in isolated containers, with persistent memory, step checkpoints, and human-in-the-loop approvals. Agent Swarm does not replace Temporal for deterministic transaction workflows such as payments or order sagas. Choose Temporal when correctness depends on deterministic replay. Choose Agent Swarm when the work is judgment-heavy AI work that cannot be replayed, only supervised.
Payments, order sagas, inventory reservations: if a wrong recovery is a double charge or a lost order, you want Temporal's model. Workflow code must be deterministic, every event is recorded, and the platform rebuilds exact state by replaying the event history after any crash. Note the boundary: Temporal replays Workflow code, but it retries Activities. An Activity that charges a card or calls an external API can run again after a failure, so Temporal expects Activity code to be idempotent.
Temporal is an SDK and a service you embed in your own application, with official SDKs for Go, Java, .NET, PHP, Python, Ruby, Rust, and TypeScript (as of 2026-10-06). Your engineers write workflow code in the language they already ship, run workers as their own processes, and keep the orchestration inside the product boundary.
Temporal is the older, larger project by a wide margin: more than 23,000 GitHub stars against fewer than 1,000 for Agent Swarm (as of 2026-10-06), signals, queries and updates for interacting with live workflows, child workflows for fault isolation, worker versioning for safe deploys, and a managed Temporal Cloud with a 99.9% SLA and a 99.99% high-availability option.
An LLM call can return a different answer on retry, so durability for agent work means checkpointing outcomes, not recomputing them. Agent Swarm writes an atomic checkpoint after every workflow step: completed steps are not re-run. A step interrupted mid-flight has no checkpoint and runs again after resume, including its model call. Temporal's docs agree: LLM invocations belong outside the replay path.
Agent Swarm is a running system you hand work to. A lead agent receives tasks from Slack, GitHub, Linear, email, or the API, breaks goals into tasks, and delegates them to workers with dependencies, priority queues, and review gates. Persistent memory and identity per agent mean the swarm recalls prior decisions instead of starting cold on each run.
Schedules, DAG workflows with webhook and manual triggers, and human-in-the-loop approval nodes that pause a run and post an approval card in Slack are built in. A heartbeat sweeps the swarm every 90 seconds, detects stalled tasks, and recovers them. The whole stack self-hosts free under MIT with Docker Compose or Helm.
Pick one canonical workflow that matters to your business and write down its side effects first: the pull request it opens, the message it sends, the charge it makes.
Implement it on Temporal with workflow code and activities, and on Agent Swarm as a delegated task or a DAG workflow.
Force-kill a worker mid-step on both systems.
Pass/fail, duplicate side effects: after recovery, check for a double pull request, a double message, a double charge. Zero duplicate side effects is the pass bar.
Pass/fail, recovery budget: before the test, the team sets a budget for time from the kill to a correct completed run. Measure both systems against that budget.
Pass/fail, manual intervention: record every manual action a human took to finish the run or clean up after it. Set the allowance before the run, for example zero or one documented action per run.
Migration acceptance: move a step only when it passes all three criteria on repeated runs, not on a single lucky run.
Decide per step, not per platform: keep deterministic money and state steps on Temporal and give judgment-heavy steps to agents.
A useful comparison says where each tool actually wins. agent-swarm.dev is for a persistent, owned operating team; the alternative wins when its shape fits your job better.
Event history replay reconstructs exact workflow state, which is why Temporal is the right answer for payments, order sagas, and any step where a wrong retry is a double charge. The guarantee covers Workflow code: Activities are retried, not replayed, so an Activity that charges a card or calls an external API must be idempotent. Agent Swarm checkpoints after every step: completed steps are not re-run, but a step interrupted mid-flight runs again after resume, including its model call, and can repeat a side effect unless the task is idempotent. For deterministic transaction workflows, keep Temporal.
As of 2026-10-06, Temporal has more than 23,000 GitHub stars against Agent Swarm's fewer than 1,000, official SDKs in eight languages, and a managed cloud with published SLAs and Business and Enterprise support tiers. Agent Swarm is the younger project, and its Cloud is a waitlist, not a product you can buy today.
Durable timers that sleep for months, Temporal Schedules with backfill, and continue-as-new for unbounded histories are built into the platform. Agent Swarm schedules recurring agent work well, but a deterministic three-month wait inside a transaction flow belongs in Temporal.
The strongest architecture keeps both. Temporal stays the deterministic backplane for money, state machines, and sagas. An Activity then hands selected judgment-heavy steps to Agent Swarm through its HTTP API or a webhook trigger, and records the result when the swarm finishes. Because Activities are retried, that hand-off Activity needs an idempotency key or dedup, so a retry does not start a second swarm task for the same step. This is a pattern, not a product: there is no built-in connector today, so you wire the HTTP call yourself. The honest detail is that Temporal's own documentation already tells you to put LLM invocations in Activities, which is exactly where the hand-off lives.
Agent Swarm is open source under MIT and the fastest way to judge it is a real judgment-heavy task. The bunx @desplega.ai/agent-swarm onboard wizard collects credentials, generates the compose files, starts the stack, and verifies health. Hand the swarm a job from Slack or GitHub, then run the proof-of-concept checklist above beside your Temporal evaluation.
Not for deterministic transaction workflows. Agent Swarm does not replace Temporal for payments, order sagas, or any flow where correctness depends on replaying an event history to exact state. It is an alternative to building agent orchestration yourself when the work is non-deterministic model and agent work: a lead agent, isolated workers, persistent memory, schedules, and review gates on infrastructure you control.
No, and it does not try to. Model calls are non-deterministic, so Agent Swarm writes an atomic checkpoint after every step and resumes from the last checkpoint after a crash. Completed steps are not re-run; a step interrupted mid-flight has no checkpoint and runs again, including its model call, so that step can repeat a side effect unless the task is idempotent. A failed agent-task node is re-dispatched as a fresh task only if the node has a retry policy. A crashed task is pinned back to the same agent, and if the agent never returns, the heartbeat escalates it to the Lead after about ten minutes (HEARTBEAT_RESUME_PIN_GRACE_MIN, default 10); otherwise the heartbeat can reassign or fail the task. Task results themselves are first-call-wins and idempotent on an identical repeat.
Yes, and for many teams that is the right end state. Temporal keeps the deterministic backplane: transactions, state machines, sagas, long timers. An Activity hands judgment-heavy steps to Agent Swarm over its HTTP API or a webhook trigger and records the outcome. Because Activities are retried, give that hand-off an idempotency key or dedup so a retry cannot start a second swarm task. It is an integration pattern you wire yourself, not a built-in connector.
Keep Temporal whenever a wrong retry has a real cost: charging a card twice, double-shipping an order, losing a reservation. Deterministic replay protects Workflow code, and idempotent Activities protect the side effects, which is the strongest correctness model in the category. Also keep it when your team already operates it fluently and the workflow is deterministic business logic.
Yes. Agent Swarm is open source under MIT and self-hosts with Docker Compose or Helm on Kubernetes, with no license fee and no enforced worker cap. A hosted Cloud is on a waitlist; the contact address is the way in.
This page compares product categories and operating models from public product documentation and repositories. We do not claim the tools are interchangeable.
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