---

name: runner-store-training description: "Operate Agent Lightning runners, LightningStore APIs, rollout status and retry behavior, custom algorithms, Trainer.fit, and Trainer.dev workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Runner, store, and training loop

Use this sub-skill when a user asks how to run training, manage rollouts/resources, debug store status, write algorithms, or use Trainer.fit/Trainer.dev.

Route by task

Request	Read/run
Understand LightningStore rollouts, attempts, resources, workers, spans	references/store-and-trainer-workflows.md and references/status-model.md
Write a custom algorithm or use @algo	references/store-and-trainer-workflows.md
Configure Trainer.fit or Trainer.dev	references/store-and-trainer-workflows.md
Debug retry/timeout/unresponsive statuses	references/status-model.md and references/troubleshooting.md
Run a CPU-safe store lifecycle smoke	python scripts/store_status_smoke.py
Start a store server via CLI	route to ../cli-and-services/SKILL.md

Key rules

• Use InMemoryLightningStore for local CPU tests and LightningStoreClient for an external store server.
• Algorithms enqueue rollouts and update resources; runners dequeue/start rollouts and write spans; Trainer wires both sides.
• Runner.step runs one rollout immediately. Runner.iter polls store queues until stopped or exhausted.
• Trainer.dev is the safest first dry-run because it exercises the runner/store/trainer infrastructure with a lightweight baseline algorithm.
• RolloutConfig controls timeout, unresponsive detection, max attempts, and retry conditions.

Minimal store loop

import agentlightning as agl

store = agl.InMemoryLightningStore()
resources_update = await store.add_resources({
    "prompt_template": agl.PromptTemplate(template="Task: {task}", engine="f-string")
})
rollout = await store.enqueue_rollout("hello", resources_id=resources_update.resources_id)
attempted = await store.dequeue_rollout(worker_id="worker-1")

Run scripts/store_status_smoke.py for a self-contained status/resource/span check.

Boundary

This sub-skill owns the control plane and training loop. Agent implementation details route to agent-authoring. Trace span semantics and adapters route to tracing-and-instrumentation. CLI/server commands route to cli-and-services. Example-specific optional dependencies route to examples-and-recipes.