Durable Workflow Python SDK

Build durable Python workflows and activities against Durable Workflow Cloud or a self-hosted Server. The SDK uses the same language-neutral runtime protocol as the first-party PHP and Rust SDKs.

Install

pip install durable-workflow

Python 3.10 or newer is required.

Quickstart

import asyncio
from uuid import uuid4

from durable_workflow import Client, Worker, workflow, activity

@activity.defn(name="greet")
def greet(name: str) -> str:
    return f"hello, {name}"

@workflow.defn(name="greeter")
class GreeterWorkflow:
    def run(self, ctx, name):
        result = yield ctx.schedule_activity("greet", [name])
        return result

async def main():
    workflow_id = f"greet-{uuid4().hex}"
    async with Client(
        "http://server:8080",
        token="dev-token-123",
        namespace="default",
    ) as client:
        worker = Worker(
            client,
            task_queue="python-workers",
            workflows=[GreeterWorkflow],
            activities=[greet],
        )
        handle = await client.start_workflow(
            workflow_type="greeter",
            workflow_id=workflow_id,
            task_queue="python-workers",
            input=["world"],
        )
        await worker.run_until(workflow_id=workflow_id, timeout=30.0)
        result = await client.get_result(handle)
        print(result)  # "hello, world"

if __name__ == "__main__":
    asyncio.run(main())

Pass the Server origin to Client without a trailing /api. For Cloud, pass the complete namespace runtime URL exactly as provisioned. Cloud client and worker processes use separate runtime credentials:

client = Client(
    runtime_url,
    control_token=client_token,
    worker_token=worker_token,
    namespace=namespace,
)

Keep the client token in application processes and the worker token in worker processes when deploying them separately.

Capabilities

• Workflows, activities, child workflows, timers, and continue-as-new
• Signals, queries, validated updates, schedules, and message streams
• Activity retries, timeouts, cancellation, and heartbeats
• Deterministic parallel work, side effects, version markers, and sagas
• Replay verification and an in-process workflow test environment
• Avro payloads, external payload storage, metrics, and interceptors

See the capability matrix for the complete cross-SDK contract.

Documentation

• Python SDK portal and API reference
• Python SDK guide
• Complete SDK reference
• Runnable examples
• Separate workflow and activity worker processes
• Symmetric SDK playground

Runtime choices

Use Durable Workflow Cloud for a managed namespace, or run the published durableworkflow/server image yourself. Workflow and activity type names, task queues, and payloads are portable between both runtime choices.

Compatibility

Stable 2.x SDK releases follow semantic versioning and negotiate runtime capabilities with Server at startup. The installed package includes durable_workflow/server_compatibility.json, matching the release declarations in pyproject.toml. Its supported-server-release-baselines maps each supported Server major to its minimum stable release. Readers validate every entry before selection and refuse missing majors, malformed entries, or a contradiction with the unchanged legacy supported-server-versions 2.x baseline. An absent map retains legacy reader behavior. This declaration does not replace protocol or capability negotiation. The compatibility guide documents protocol and upgrade guarantees.

Worker protocol and cooperative cancellation

Workers use protocol 1.20 by default. For a Server that supports only 1.19, set DURABLE_WORKFLOW_WORKER_PROTOCOL_VERSION=1.19. This changes the request protocol without enabling optional worker features.

Use request_cancellation() for bounded, replayable workflow cleanup. Opt in against a Server that advertises protocol 1.20 and the required capabilities: include cooperative_cancellation in the Worker's capabilities. Durable local callback admission also needs prepared_local_activities, with prepared_local_activity_cancellation_policies for explicit local policies. Independently cancellable scopes remain disabled.

For synchronous Nexus completion waits, the cooperative worker also negotiates nexus_cooperative_cancellation when Server advertises the installed workflow_run binding. Owned target cleanup keeps the caller pending until the Server commits delivery at the original call. Async and acceptance-only calls keep their admission behavior. Existing terminal results and recorded legacy Nexus histories retain their replay semantics.

The cooperative worker supervises async and synchronous activity callbacks independently of application heartbeats. Existing terminal cancellation remains available. See the cancellation guide for immutable context, operation policies, shielded cleanup and recovery.

A suspended replay is not a workflow exit. Python may run finally blocks and context-manager exits when disposing of that replay. The SDK discards commands and ctx.logger messages produced only by disposal; queries observe the state at the pending command. Actual completion, failure and cooperative cancellation run cleanup through the normal history, and cold replay uses its recorded results. Put external cleanup in an Activity rather than direct I/O in a finalizer, and use ctx.cancellation_shield() when that Activity must finish after cancellation. The disposal guarantees require SDK 2.12.2 or later.

Both await activity.context().heartbeat() and synchronous activity.context().heartbeat_sync() stop at an explicit Server refusal. They raise ActivityLeaseAuthorityLost with its original .reason for an elapsed deadline, closed attempt or closed run. Cancellation continues to raise ActivityCancelled. The worker submits no result or failure for an attempt whose heartbeat refused continuation, even if application code catches that exception. A heartbeat check acts only when called. Code that never heartbeats can continue until it returns, subject to independent lease/cancellation supervision; a running native thread cannot be forcibly stopped or its effects undone by the SDK.

Development

The installed durable-workflow-replay-conformance command runs an offline Python replay shard. Its sdk-python artifact pin must match the installed distribution version reported by importlib.metadata.version("durable-workflow"). The report records that installed identity separately from the declared platform tuple and fails if metadata is missing or the SDK pin differs. Replay success does not prove installation or execution of the other declared artifacts; the aggregate runner supplies its own verified installation receipts.

pip install -e '.[dev]'
ruff check src/ tests/
mypy src/durable_workflow/
pytest tests/ -m "not integration"

Integration tests use Docker:

export COMPOSE_PROJECT_NAME=sdk-python-local
docker compose -f docker-compose.test.yml up -d --build --wait
SERVER_PORT=$(docker compose -f docker-compose.test.yml port server 8080 | sed 's/.*://')
DURABLE_WORKFLOW_SERVER_URL="http://127.0.0.1:$SERVER_PORT" DURABLE_WORKFLOW_AUTH_TOKEN=test-token pytest tests/integration/ -v
docker compose -f docker-compose.test.yml down -v

Candidate cooperative cancellation qualification is explicit. In a manual CI run, supply an exact public server_commit and set cooperative_qualification to true. CI verifies that checkout, builds the candidate Server, enables protocol 1.20, runs the connected cases and retains JUnit, raw observations, image authority and exact source provenance. An optional exact native_commit mounts that public Native checkout read-only into the test stack. The image's published Composer authority stays intact and the evidence identifies the source overlay. These are source qualification runs. For a local candidate, set DURABLE_WORKFLOW_COOPERATIVE_QUALIFICATION=1 for pytest. These cases fail if the runtime does not discover the required capability. Ordinary CI uses the SDK's default protocol and skips the optional connected cooperative cases.

License

MIT