Python
Build durable workflows and AI agents in Python with the Vercel SDK.
The Python SDK is currently in beta. APIs and behavior may change.
You can build durable workflows in Python using the vercel-workflow SDK. Your workflow code can pause, resume, and maintain state, just like the JavaScript and TypeScript Workflow SDK.
Getting started
Add the vercel-workflow package and workflow entrypoint to pyproject.toml:
[project]
requires-python = ">=3.12"
dependencies = ["vercel-workflow"]
[[tool.vercel.workflows]]
entrypoint = "app.workflows:wf"The workflow entrypoint uses the module:object format and points to the exported Workflows registry.
Workflows
A workflow is a stateful function that coordinates multi-step logic over time. Create a Workflows instance and use the @wf.workflow decorator to mark a function as durable:
from vercel import workflow
wf = workflow.Workflows()from app.workflow import wf
from app.steps.generate_draft import generate_draft, summarize_draft
@wf.workflow
async def ai_content_workflow(*, topic: str):
draft = await generate_draft(topic=topic)
summary = await summarize_draft(draft=draft)
return {
"draft": draft,
"summary": summary,
}Export the registry from the workflow package and import the module containing your workflow so its definitions are registered:
from app.workflow import wf
from app.workflows import ai_content_workflow
__all__ = ["ai_content_workflow", "wf"]Under the hood, the workflow compiles into a route that orchestrates execution. All inputs and outputs are recorded in an event log. If a deploy or crash happens, the system replays execution deterministically from where it stopped.
Steps
A step is a stateless function that runs a unit of durable work inside a workflow. Use @wf.step to mark a function as a step:
import random
from app.workflow import wf
@wf.step
async def generate_draft(*, topic: str):
return await ai_generate(prompt=f"Write a blog post about {topic}")
@wf.step
async def summarize_draft(*, draft: str):
summary = await ai_summarize(text=draft)
# Simulate a transient error. The step automatically retries.
if random.random() < 0.3:
raise Exception("Transient AI provider error")
return summaryEach step executes separately from the workflow orchestrator. While the step executes, the workflow suspends without consuming resources. When the step completes, the workflow resumes automatically where it left off.
Starting a workflow
Call workflow.start() from server-side code to start a workflow. It returns a Run that you can use to identify the run, check its status, and wait for its result:
from app.workflows.ai_content_workflow import ai_content_workflow
from vercel import workflow
@app.post("/api/generate")
async def generate_content(*, topic: str):
run = await workflow.start(ai_content_workflow, topic=topic)
print(run.run_id)
print(await run.status())
# Wait until the workflow completes and return its result.
return await run.return_value()Starting a workflow only waits until the run has been created and queued. Await return_value() to wait for the workflow to finish, or save its run_id and recreate the handle later with workflow.Run(run_id).
Sleep
Sleep pauses a workflow for a specified duration without consuming compute resources:
from datetime import timedelta
from app.workflow import wf
from vercel import workflow
@wf.workflow
async def ai_refine_workflow(*, draft_id: str):
draft = await fetch_draft(draft_id)
await workflow.sleep(timedelta(days=7)) # Wait 7 days to gather more signals.
refined = await refine_draft(draft)
return {
"draft_id": draft_id,
"refined": refined,
}The parameter accepts four forms:
The string form accepts one or more <value><unit> pairs. Supported units:
sleep() must be called from the workflow body, not from inside a step. Calling it from a step raises a RuntimeError.
The sleep consumes no resources. The workflow resumes automatically when the time expires.
Hooks
A hook lets a workflow wait for external events such as user actions, webhooks, or third-party API responses.
Define a hook model with Pydantic and workflow.BaseHook:
import typing
import pydantic
from app.workflow import wf
from vercel import workflow
class Approval(pydantic.BaseModel, workflow.BaseHook):
"""Human approval for AI-generated drafts"""
decision: typing.Literal["approved", "changes"]
notes: str | None = None
@wf.workflow
async def ai_approval_workflow(*, topic: str):
draft = await generate_draft(topic=topic)
# Wait for human approval events
async for event in Approval.wait(token="draft-123"):
if event.decision == "approved":
await publish_draft(draft)
break
revised = await refine_draft(draft, event.notes)
await publish_draft(revised)Resume the workflow when data arrives:
from app.workflows.approval import Approval
@app.post("/api/resume")
async def resume(approval: Approval):
"""Resume the workflow when an approval is received"""
await approval.resume("draft-123")
return {"ok": True}When a hook receives data, the workflow resumes automatically. You don't need polling, message queues, or manual state management.
Streaming
Steps can stream progress while a workflow is running. Get the run's writable stream inside a step, write values to it, and close it when no more values will be sent:
from app.workflow import wf
from vercel import workflow
@wf.step
async def write_progress():
writable = workflow.get_writable()
for message in ["Drafting", "Reviewing", "Complete"]:
await writable.write(message)
await writable.close()
@wf.workflow
async def streaming_workflow():
await write_progress()Read the values from the returned Run as they arrive:
from app.workflows.streaming import streaming_workflow
from vercel import workflow
@app.post("/api/stream")
async def stream_progress():
run = await workflow.start(streaming_workflow)
async for message in run.readable():
print(message)Streams are not closed automatically. Close the writable in the last step that writes to it so readers know when the stream is complete.
Next steps
- Learn more about the Foundations.
- Check Errors if you encounter issues.
- Explore the API Reference.