aws-stepfunctions-toolkit 0.1.1

Run AWS Step Functions state machines end-to-end locally, with per-step control over how each step runs.

AWS Step Functions Toolkit

Run a Step Functions state machine end-to-end on your laptop — and choose, per step, how each one runs.

The motivation to create this package was the need to test a Step Functions pipeline built almost entirely from batch:submitJob.sync steps. AWS's test_state API does not currently support .sync integrations or .waitForTaskToken, and deploying and running the state machine on AWS to validate each change made iterating impractical.

AWS's docs suggest chaining test_state calls — feeding each state's output and next state into the next call — to exercise a complete execution path, but leave you to build that driver loop yourself. This toolkit packages that logic as a framework so you don't have to: it walks your real ASL definition state by state, delegates the engine logic (Path/Parameters/ResultSelector/Output and next-state resolution) to test_state, and handles the parts that aren't a linear chain — Map and Parallel fan-out, any-depth recursion, and nested subflows. For the steps test_state cannot run (.sync, .waitForTaskToken), you plug in a strategy — typically by building and running that step's container locally with Docker. The result: run an entire pipeline locally and swap any step between a mock, your own function, a local container, or a real AWS call.

_{The real console graph of the docker-batch example, annotated to show how the toolkit handles each state.}

Install

pip install aws-stepfunctions-toolkit

Quick start

To run the bundled quickstart example (no Docker — its task steps are mocked), head to examples/quickstart/ — it has step-by-step instructions. You'll need AWS credentials + a region and an IAM role allowed to call test_state (see the Setup guide).

What your code looks like

The snippet below shows the shape of a run — it is not runnable on its own (you supply your own state machine); the quickstart folder above is the runnable version.

import json
from aws_stepfunctions_toolkit import WorkflowRunner, StaticMockResponseStrategy, CallableStrategy

# Bring your own: your Step Functions state machine, exported as Amazon States Language (ASL)
# JSON. (In the AWS console: the state machine's "Definition" tab. This is the workflow you
# want to test — the toolkit does not generate it for you.)
definition = json.loads(open("state_machine.asl.json").read())

# For each step you don't want to run for real, say how to produce its result. Keys are the
# state names from your definition.
mock_mapping = {
    # A mocked lambda:invoke result puts the function's return under "Payload" as a JSON string.
    "Enrich": CallableStrategy(lambda data: {"Payload": json.dumps({"enriched": True})}),          # your own function
    "Notify": StaticMockResponseStrategy(json.dumps({"Payload": json.dumps({"status": "sent"})})),  # a fixed payload
}

runner = WorkflowRunner(
    role_arn="arn:aws:iam::<account>:role/<role-with-test-state-perms>",  # the only value you must set; needs AWS creds + a region in your environment
    asl_registry={"main": definition},
    mock_mapping=mock_mapping,
)

final_output = runner.start(initial_input={"order_id": 123})
print(final_output)

asl_registry maps state-machine names to their ASL definitions: put the workflow you're testing under the key "main" (the entry point). If your machine starts nested state machines (via startExecution.sync:2), register each one too, keyed by the name of the state that starts it — see Control flow.

JSONata throughout. The examples (and the toolkit's mock-result handling) use the JSONata query language. Set "QueryLanguage": "JSONata" on each state (not just at the top level): the runner tests one state at a time, so a state must declare its own query language.

States without an entry in mock_mapping are handled automatically (test_state for ordinary states; built-in recursion for Map / Parallel / nested state machines). To run a step in a real local container instead of mocking it, use DockerBatchStrategy.

Features

Each links to docs with a code snippet, or to a runnable example.

• Local end-to-end runs against your real ASL definition — no deploy. → runnable example
• Per-step execution choice — test_state, a static mock, your own function, a local subprocess, a local Docker container, or a real AWS Batch submission. → docs/strategies.md
• Pluggable image sources — plain Dockerfile (default), prebuilt/ECR image, or docker buildx bake; bring your own. A bake file is not required. → docs/strategies.md#image-sources
• Subflows (nested state machines) via startExecution.sync:2. → docs/control-flow.md#subflows-nested-state-machines
• Map and Parallel — fan-out and any-depth recursion. → docs/control-flow.md#map-states
• Run a sub-range — start and/or stop at specific states. → docs/control-flow.md#running-a-sub-range
• Container-side handler (BatchJobInterface) for code inside your job containers. → docs/container-handler.md
• Mock generation from a real execution, plus history inspection. → docs/cli-and-history.md

Documentation

Full documentation is hosted at aws-stepfunctions-toolkit.readthedocs.io.

Page	What's in it
Setup	AWS credentials, the test_state IAM role, and Docker.
How it works	Motivation, the test_state gap, and the run loop.
Usage walkthrough	A complete, end-to-end worked example.
Selecting how each step runs	The strategy catalog + image sources.
Control flow	Subflows, Map/Parallel, recursion, start/end control.
Container-side handler	BatchJobInterface contract.
CLI & history	sfn-toolkit generate-mock, ExecutionHistory.

Requirements

• Python 3.13+
• An AWS role/credentials allowed to call test_state (the toolkit calls the real API for the engine logic).
• Docker, only if you use DockerBatchStrategy to run steps in containers.

New to this? The Setup guide covers AWS credentials, creating the test_state IAM role, and Docker, step by step.

Examples

Each folder under examples/ is self-contained (its own ASL, runnable script, and README) — see the examples index. Start with:

• examples/quickstart/ — copy-and-run starter; set ROLE_ARN and python run.py. No Docker (task steps are mocked).
• examples/local-subprocess/ — run a step's code as a local subprocess (LocalExecutionStrategy). No Docker.
• examples/docker-batch/ — run steps in real local containers (DockerfileImage + BakeImage) plus a nested machine. make run-example runs it (needs Docker, AWS creds, and a ROLE_ARN env var).

License

See LICENSE.