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.
pip install durable-workflowPython 3.10 or newer is required.
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.
- 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.
- Python SDK portal and API reference
- Python SDK guide
- Complete SDK reference
- Runnable examples
- Symmetric SDK playground
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.
Stable 2.x SDK releases follow semantic versioning and negotiate runtime
capabilities with Server at startup. Use stable 2.x SDK and Server channels
for new applications. The compatibility guide
documents protocol and upgrade guarantees.
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.
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 -vCandidate 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.