temporalio.contrib.aws.lambda

package documentation

(source)

A wrapper for running Temporal workers inside AWS Lambda.

A single run_worker call handles the full per-invocation lifecycle: connecting to the Temporal server, creating a worker with Lambda-tuned defaults, polling for tasks, and gracefully shutting down before the invocation deadline.

Quick start:

from temporalio.common import WorkerDeploymentVersion
from temporalio.contrib.aws.lambda_worker import LambdaWorkerConfig, run_worker

def configure(config: LambdaWorkerConfig) -> None:
    config.worker_config["task_queue"] = "my-task-queue"
    config.worker_config["workflows"] = [MyWorkflow]
    config.worker_config["activities"] = [my_activity]

lambda_handler = run_worker(
    WorkerDeploymentVersion(
        deployment_name="my-service",
        build_id="v1.0",
    ),
    configure,
)

Configuration

Client connection settings (address, namespace, TLS, API key) are loaded automatically from a TOML config file and/or environment variables via temporalio.envconfig. The config file is resolved in order:

TEMPORAL_CONFIG_FILE env var, if set.
temporal.toml in $LAMBDA_TASK_ROOT (typically /var/task).
temporal.toml in the current working directory.

The file is optional -- if absent, only environment variables are used.

The configure callback receives a LambdaWorkerConfig dataclass with fields pre-populated with Lambda-appropriate defaults. Override any field directly in the callback. The task_queue key in worker_config is pre-populated from the TEMPORAL_TASK_QUEUE environment variable if set.

Module	`otel`	OpenTelemetry helpers for Temporal workers running inside AWS Lambda.
Module	`_configure`	Configuration for the Lambda worker.
Module	`_defaults`	Lambda-tuned defaults for Temporal worker and client configuration.
Module	`_run_worker`	No module docstring; 0/1 variable, 3/4 functions, 1/1 class documented

From __init__.py:

Class	`LambdaWorkerConfig`	Passed to the configure callback of `run_worker`.
Function	`run_worker`	Create a Temporal worker Lambda handler.

def run_worker(version: WorkerDeploymentVersion, configure: Callable[[LambdaWorkerConfig], None]) -> Callable[[Any, Any], None]: (source) ¶

Create a Temporal worker Lambda handler.

Calls the configure callback to collect workflow/activity registrations and option overrides, then returns a Lambda handler function. On each invocation the handler connects to the Temporal server, starts a worker with Lambda-tuned defaults, polls for tasks until the invocation deadline approaches, and then gracefully shuts down.

The version parameter identifies this worker's deployment version. run_worker always enables Worker Deployment Versioning (use_worker_versioning=True). To provide a default versioning behavior for workflows that do not specify one at registration time, set deployment_config in worker_config in the configure callback.

The returned handler has the signature handler(event, context) and should be set as your Lambda function's handler entry point.

Example:

from temporalio.common import WorkerDeploymentVersion
from temporalio.contrib.aws.lambda_worker import (
    LambdaWorkerConfig,
    run_worker,
)

def configure(config: LambdaWorkerConfig) -> None:
    config.worker_config["task_queue"] = "my-task-queue"
    config.worker_config["workflows"] = [MyWorkflow]
    config.worker_config["activities"] = [my_activity]

lambda_handler = run_worker(
    WorkerDeploymentVersion(
        deployment_name="my-service",
        build_id="v1.0",
    ),
    configure,
)

Parameters
version:`WorkerDeploymentVersion`	The worker deployment version. Required.
configure:`Callable[[LambdaWorkerConfig], None]`	A callback that receives a `LambdaWorkerConfig` (pre-populated with Lambda defaults) and configures workflows, activities, and options on it.

Returns
`Callable[[Any, Any], None]`	A Lambda handler function.