# Serverless Workers on Amazon Bedrock AgentCore Runtime - Python SDK

> For the complete documentation index, see [llms.txt](https://docs.temporal.io/llms.txt).
> Any documentation page is available as raw Markdown by appending `.md` to its URL.

> Run a Temporal Worker on Amazon Bedrock AgentCore Runtime using the Python SDK.

> **Pre-release**
> Amazon Bedrock AgentCore Runtime support is in Pre-release, and its APIs may change in backwards-incompatible ways.

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](/serverless-workers/agentcore).

## Install the AgentCore Runtime SDK 

Install the AgentCore Runtime SDK alongside the Temporal Python SDK:

```bash
pip install bedrock-agentcore
```

## Create a versioned Worker 

Serverless Workers require [Worker Versioning](/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:

```python
import os

from temporalio.client import Client
from temporalio.common import VersioningBehavior, WorkerDeploymentVersion
from temporalio.worker import Worker, WorkerDeploymentConfig

from my_activities import my_activity
from my_workflows import MyWorkflow

def create_worker(client: Client) -> Worker:
    return Worker(
        client,
        task_queue=os.environ["TEMPORAL_TASK_QUEUE"],
        workflows=[MyWorkflow],
        activities=[my_activity],
        deployment_config=WorkerDeploymentConfig(
            version=WorkerDeploymentVersion(
                deployment_name=os.environ["TEMPORAL_DEPLOYMENT_NAME"],
                build_id=os.environ["TEMPORAL_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](/serverless-workers/agentcore#worker-versioning).

Every Workflow needs a [versioning behavior](/worker-versioning#versioning-behaviors), 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. The complete handler in
[Stop and drain the Worker](#stop-and-drain-the-worker) shows how to add a retirement policy.

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](/develop/python/client/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](/develop/environment-configuration).

## Stop and drain the Worker 

Decide what condition means that a Worker can retire. Observe that condition in the Runtime handler. When it remains
true for an idle period, stop polling and drain the Worker. The Temporal Python SDK handles the draining after you leave
the `async with worker` block.

The following example defines an `ActivityTracker`. It uses an
[Activity inbound Interceptor](/develop/python/workers/interceptors) to count running Activities.

```python
import asyncio
import os
from datetime import timedelta

from bedrock_agentcore.runtime import BedrockAgentCoreApp
from temporalio.client import Client
from temporalio.common import VersioningBehavior, WorkerDeploymentVersion
from temporalio.envconfig import ClientConfig
from temporalio.worker import (
    ActivityInboundInterceptor,
    ExecuteActivityInput,
    Interceptor,
    Worker,
    WorkerDeploymentConfig,
)

from my_activities import my_activity
from my_workflows import MyWorkflow

DEBOUNCE = float(os.environ.get("AGENTCORE_DEBOUNCE_SECONDS", "60"))
DRAIN = timedelta(seconds=120)

class ActivityTracker(Interceptor):
    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:
        while True:
            self.changed.clear()
            try:
                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):
        super().__init__(next)
        self.tracker = tracker

    async def execute_activity(self, input: ExecuteActivityInput):
        self.tracker.inflight += 1
        self.tracker.changed.set()
        try:
            return await self.next.execute_activity(input)
        finally:
            self.tracker.inflight -= 1
            self.tracker.changed.set()

app = BedrockAgentCoreApp()

@app.entrypoint
@app.async_task
async def invoke(_: dict) -> dict:
    client = await Client.connect(**ClientConfig.load_client_connect_config())
    tracker = ActivityTracker()
    worker = Worker(
        client,
        task_queue=os.environ["TEMPORAL_TASK_QUEUE"],
        workflows=[MyWorkflow],
        activities=[my_activity],
        interceptors=[tracker],
        graceful_shutdown_timeout=DRAIN,
        deployment_config=WorkerDeploymentConfig(
            version=WorkerDeploymentVersion(
                deployment_name=os.environ["TEMPORAL_DEPLOYMENT_NAME"],
                build_id=os.environ["TEMPORAL_BUILD_ID"],
            ),
            use_worker_versioning=True,
            default_versioning_behavior=VersioningBehavior.PINNED,
        ),
    )

    async with worker:
        await tracker.wait_until_idle(DEBOUNCE)

    return {"message": "Worker drained"}
```

`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](/serverless-workers/agentcore#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](/develop/python/activities/timeouts#activity-heartbeats) so a retry resumes from its last
recorded progress instead of starting over:

```python
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](/develop/python/platform/observability) and the
[SDK metrics reference](/references/sdk-metrics).