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Serverless Workers on Amazon Bedrock AgentCore Runtime - Python SDK

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On Amazon Bedrock AgentCore Runtime, you run a standard long-lived Python Worker inside an AgentCore Runtime handler. Temporal starts the handler when the Worker Controller Instance needs capacity. The handler starts a Worker that polls the Task Queue, then stops it when your idle policy decides to release capacity.

The Worker uses the normal Python SDK. The handler uses the bedrock-agentcore package to receive AgentCore Runtime invocations.

For the provider behavior, including autoscaling, Worker Versioning, and the Runtime session lifecycle, see Serverless Workers on Amazon Bedrock AgentCore Runtime.

Install the AgentCore Runtime SDK​

Install the AgentCore Runtime SDK alongside the Temporal Python SDK:

pip install bedrock-agentcore

Create a versioned Worker​

Serverless Workers require Worker Versioning. Create the Worker as you would any long-lived Python Worker, then set deployment_config to declare its Worker Deployment Version and enable versioning:

worker = Worker(
    # ...
    deployment_config=WorkerDeploymentConfig(
        version=WorkerDeploymentVersion(
            deployment_name=DEPLOYMENT_NAME,
            build_id=BUILD_ID,
        ),
        use_worker_versioning=True,
        default_versioning_behavior=VersioningBehavior.PINNED,
    ),
)

TEMPORAL_DEPLOYMENT_NAME and TEMPORAL_BUILD_ID must match the Worker Deployment Version that you create with temporal worker deployment create-version. Configure that Worker Deployment Version with the AgentCore Runtime endpoint that Temporal invokes. For the endpoint configuration, see Worker Versioning.

Every Workflow needs a versioning behavior, either PINNED or AUTO_UPGRADE. Setting default_versioning_behavior as shown applies PINNED behavior to every Workflow on the Worker. To set the behavior per Workflow instead, pass versioning_behavior to the @workflow.defn decorator.

Start the Worker from the Runtime handler​

AgentCore Runtime invokes an HTTP handler. Use BedrockAgentCoreApp to provide that handler, and use async_task so AgentCore keeps the Runtime active while the Worker polls:

bedrock_agentcore/strands-agent/agentcore_worker.py

@app.entrypoint
@app.async_task  # keeps /ping on "HealthyBusy" until this returns
async def invoke(payload: dict) -> dict:
    """Poll until idle, then drain. The payload is unused: every call is a new session and new worker."""
    api_key = os.environ.get("TEMPORAL_API_KEY") or None
    client = await Client.connect(
        os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"),
        namespace=os.environ.get("TEMPORAL_NAMESPACE", "default"),
        api_key=api_key,
        tls=bool(api_key),
        plugins=[StrandsPlugin()],
    )

    task_queue = os.environ.get("TEMPORAL_TASK_QUEUE", workflows.TASK_QUEUE)
    tracker = ActivityTracker()

    log.info("polling %s as %s/%s", task_queue, DEPLOYMENT_NAME, BUILD_ID)
    # execute_code is a sync Activity, so it needs an executor to block on.
    with ThreadPoolExecutor(max_workers=4) as activity_executor:
        worker = Worker(
            client,
            task_queue=task_queue,
            workflows=[workflows.StrandsAgentWorkflow],
            activities=[execute_code],
            activity_executor=activity_executor,
            interceptors=[tracker],
            deployment_config=WorkerDeploymentConfig(
                version=WorkerDeploymentVersion(
                    deployment_name=DEPLOYMENT_NAME, build_id=BUILD_ID
                ),
                use_worker_versioning=True,
                default_versioning_behavior=VersioningBehavior.PINNED,
            ),
            graceful_shutdown_timeout=DRAIN,
        )
        async with worker:
            await tracker.wait_until_idle(DEBOUNCE)
    log.info("worker idle for %ss; drained", DEBOUNCE)
    return {"message": "worker drained", "task_queue": task_queue}

The payload does not represent a Workflow input. The Worker Controller Instance invokes the endpoint to add Worker capacity. Applications start Workflows through the Temporal Client, as usual.

Configure the Temporal connection​

The temporalio.envconfig package loads Temporal Client configuration from environment variables and an optional TOML configuration file. Set the Temporal address, Namespace, Task Queue, and Worker Deployment Version values as Runtime environment variables. Store a Temporal Cloud API key or TLS material in a secret store rather than in the Runtime definition.

For the supported connection variables, config-file format, and profiles, see Environment configuration.

Stop and drain the Worker​

AgentCore cannot tell when a Worker that is still polling has no Temporal work. The Runtime remains busy while the async_task handler runs, so it can remain active until its eight-hour maximum lifetime. To release capacity sooner, have the handler detect when the Worker has no useful work and return.

When the condition remains true for an idle period, leave the async with worker block. The Worker stops polling for new Tasks and gives in-flight Activities time to complete before the Runtime handler returns.

The following example from the AgentCore sample Worker defines an ActivityTracker. It uses an Activity inbound Interceptor to count running Activities.

bedrock_agentcore/strands-agent/agentcore_worker.py

# How long the Worker keeps polling after it goes idle.
DEBOUNCE = float(os.environ.get("AGENTCORE_DEBOUNCE_SECONDS", "60"))
# How long the drain waits for in-flight Activities (a model or tool call).
DRAIN = timedelta(seconds=120)


class ActivityTracker(Interceptor):
    """Tracks in-flight activities and blocks until AGENTCORE_DEBOUNCE_SECONDS elapses with no events."""

    def __init__(self) -> None:
        self.inflight = 0
        self.changed = asyncio.Event()

    def intercept_activity(
        self, next: ActivityInboundInterceptor
    ) -> ActivityInboundInterceptor:
        return _TrackedActivity(next, self)

    async def wait_until_idle(self, debounce: float) -> None:
        """Return once no Activity has run for ``debounce`` seconds."""
        while True:
            self.changed.clear()
            try:
                # Wake the moment an Activity starts or finishes; a timeout
                # instead means nothing has happened for the whole window.
                await asyncio.wait_for(self.changed.wait(), timeout=debounce)
            except asyncio.TimeoutError:
                if self.inflight == 0:
                    return


class _TrackedActivity(ActivityInboundInterceptor):
    def __init__(
        self, next: ActivityInboundInterceptor, tracker: ActivityTracker
    ) -> None:
        super().__init__(next)
        self._tracker = tracker

    async def execute_activity(self, input: ExecuteActivityInput):
        self._tracker.inflight += 1
        self._tracker.changed.set()
        log.info("activity in flight: %d", self._tracker.inflight)
        try:
            return await self.next.execute_activity(input)
        finally:
            self._tracker.inflight -= 1
            self._tracker.changed.set()

Register the tracker as a Worker Interceptor and wait for it inside the Worker context:

tracker = ActivityTracker()
worker = Worker(
    client,
    # ...
    interceptors=[tracker],
    graceful_shutdown_timeout=DRAIN,
)

async with worker:
    await tracker.wait_until_idle(DEBOUNCE)

ActivityTracker retires the Worker only after 60 seconds without an Activity starting or completing and with no Activity running. A long-running Activity keeps the count above zero, so the idle policy does not interrupt it. The two-minute graceful_shutdown_timeout is a safety limit for any Activity still in flight when shutdown starts.

Memory pressure can be another retirement condition. For example, the Runtime handler can monitor process memory and initiate the same graceful shutdown when usage crosses a threshold. Memory usage is not an idle signal. It tells you when to recycle a Worker, not whether it has work to do. Test any memory-based policy against the Runtime's memory limit and your Activity retry behavior.

AGENTCORE_DEBOUNCE_SECONDS controls the idle period. graceful_shutdown_timeout controls how long the Worker waits for in-flight Activities after it stops polling. Choose both values for your workload, and account for AgentCore's maximum Runtime lifetime. For the AgentCore lifecycle settings, see Lifecycle.

Keep Activities safe across Worker termination​

AgentCore can end the compute that runs a Worker. An Activity running at that time can be interrupted and retried. Use Activity Heartbeats so a retry resumes from its last recorded progress instead of starting over:

from temporalio import activity


@activity.defn
async def my_activity(items: list[str]) -> str:
    for i, item in enumerate(items):
        activity.heartbeat(i)
        # ... process item
    return "done"

Add observability​

An AgentCore Runtime Worker emits the same traces and metrics as a Worker on other compute. For metrics export and OpenTelemetry tracing interceptors, see Observability - Python SDK and the SDK metrics reference.