durable-task-python

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Build durable, fault-tolerant workflows in Python using the Durable Task SDK with Azure Durable Task Scheduler. Use when creating orchestrations, activities, entities, or implementing patterns like function chaining, fan-out/fan-in, human interaction, or stateful agents. Applies to any Python application requiring durable execution, state persistence, or distributed transactions without Azure Functions dependency.

Azure-Samples By Azure-Samples schedule Updated 2/27/2026
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name: durable-task-python description: Build durable, fault-tolerant workflows in Python using the Durable Task SDK with Azure Durable Task Scheduler. Use when creating orchestrations, activities, entities, or implementing patterns like function chaining, fan-out/fan-in, human interaction, or stateful agents. Applies to any Python application requiring durable execution, state persistence, or distributed transactions without Azure Functions dependency.

Durable Task Python SDK with Durable Task Scheduler

Build fault-tolerant, stateful workflows in Python applications using the Durable Task SDK connected to Azure Durable Task Scheduler.

Quick Start

Required Packages

pip install durabletask durabletask-azuremanaged azure-identity

Or add to requirements.txt:

durabletask
durabletask-azuremanaged
azure-identity

Minimal Worker + Client Setup

import os
from azure.identity import DefaultAzureCredential
from durabletask import task
from durabletask.client import OrchestrationStatus
from durabletask.azuremanaged.client import DurableTaskSchedulerClient
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker


# Activity function
def hello(ctx: task.ActivityContext, name: str) -> str:
    return f"Hello {name}!"


# Orchestrator function
def my_orchestration(ctx: task.OrchestrationContext, input: str):
    result = yield ctx.call_activity(hello, input=input)
    return result


# Configuration - defaults to local emulator
taskhub = os.getenv("TASKHUB", "default")
endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
secure_channel = endpoint != "http://localhost:8080"
credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()

# Start worker and run orchestration
with DurableTaskSchedulerWorker(
    host_address=endpoint,
    secure_channel=secure_channel,
    taskhub=taskhub,
    token_credential=credential
) as worker:
    worker.add_orchestrator(my_orchestration)
    worker.add_activity(hello)
    worker.start()

    # Create client and schedule orchestration
    dts_client = DurableTaskSchedulerClient(
        host_address=endpoint,
        secure_channel=secure_channel,
        taskhub=taskhub,
        token_credential=credential
    )
    
    instance_id = dts_client.schedule_new_orchestration(my_orchestration, input="World")
    state = dts_client.wait_for_orchestration_completion(instance_id, timeout=60)
    
    if state and state.runtime_status == OrchestrationStatus.COMPLETED:
        print(f"Result: {state.serialized_output}")

Pattern Selection Guide

Pattern Use When
Function Chaining Sequential steps where each depends on the previous
Fan-Out/Fan-In Parallel processing with aggregated results
Human Interaction Workflow pauses for external input/approval
Durable Entities Stateful objects with operations (counters, accounts)
Sub-Orchestrations Reusable workflow components or version isolation
Eternal Orchestrations Long-running background processes with continue_as_new
Monitoring Periodic polling with configurable timeouts

See references/patterns.md for detailed implementations.

Orchestration Structure

Basic Orchestrator

def my_orchestration(ctx: task.OrchestrationContext, input: str):
    """Orchestrator function - MUST be deterministic"""
    # Call activities sequentially
    step1 = yield ctx.call_activity(step1_activity, input=input)
    step2 = yield ctx.call_activity(step2_activity, input=step1)
    return step2

Basic Activity

def my_activity(ctx: task.ActivityContext, input: str) -> str:
    """Activity function - can have side effects, I/O, non-determinism"""
    # Perform actual work here
    print(f"Processing: {input}")
    return f"Processed: {input}"

Registering with Worker

with DurableTaskSchedulerWorker(...) as worker:
    worker.add_orchestrator(my_orchestration)
    worker.add_activity(step1_activity)
    worker.add_activity(step2_activity)
    worker.start()

Critical Rules

Orchestration Determinism

Orchestrations replay from history - all code MUST be deterministic. When an orchestration resumes, it replays all previous code to rebuild state. Non-deterministic code produces different results on replay, causing failures.

NEVER do inside orchestrations:

  • datetime.now(), datetime.utcnow() → Use ctx.current_utc_datetime
  • uuid.uuid4() → Use ctx.new_uuid()
  • random.random() → Pass random values from activities
  • Direct I/O, HTTP calls, database access → Move to activities
  • time.sleep(), asyncio.sleep() → Use ctx.create_timer()
  • Environment variables that may change → Pass as input or use activities
  • Global mutable state → Pass state through activity results

ALWAYS use:

  • yield ctx.call_activity() - Call activities
  • yield ctx.call_sub_orchestrator() - Call sub-orchestrations
  • yield ctx.create_timer() - Durable delays
  • yield ctx.wait_for_external_event() - Wait for events
  • ctx.current_utc_datetime - Current time
  • ctx.new_uuid() - Generate GUIDs
  • ctx.set_custom_status() - Set status

Non-Determinism Patterns (WRONG vs CORRECT)

Getting Current Time

# WRONG - datetime.now() returns different value on replay
def bad_orchestration(ctx: task.OrchestrationContext, _):
    current_time = datetime.now()  # Non-deterministic!
    if current_time.hour < 12:
        yield ctx.call_activity(morning_activity)

# CORRECT - ctx.current_utc_datetime is replayed consistently
def good_orchestration(ctx: task.OrchestrationContext, _):
    current_time = ctx.current_utc_datetime  # Deterministic
    if current_time.hour < 12:
        yield ctx.call_activity(morning_activity)

Generating UUIDs/Random Values

# WRONG - uuid4() generates different value on replay
def bad_orchestration(ctx: task.OrchestrationContext, _):
    order_id = str(uuid.uuid4())  # Non-deterministic!
    yield ctx.call_activity(create_order, input=order_id)

# CORRECT - ctx.new_uuid() replays the same value
def good_orchestration(ctx: task.OrchestrationContext, _):
    order_id = str(ctx.new_uuid())  # Deterministic
    yield ctx.call_activity(create_order, input=order_id)

Random Numbers

# WRONG - random produces different values on replay
def bad_orchestration(ctx: task.OrchestrationContext, _):
    delay = random.randint(1, 10)  # Non-deterministic!
    yield ctx.create_timer(timedelta(seconds=delay))

# CORRECT - generate random in activity, pass to orchestrator
def get_random_delay(ctx: task.ActivityContext, _) -> int:
    return random.randint(1, 10)  # OK in activity

def good_orchestration(ctx: task.OrchestrationContext, _):
    delay = yield ctx.call_activity(get_random_delay)  # Deterministic
    yield ctx.create_timer(timedelta(seconds=delay))

Sleeping/Delays

# WRONG - time.sleep blocks and doesn't persist
def bad_orchestration(ctx: task.OrchestrationContext, _):
    yield ctx.call_activity(step1)
    time.sleep(60)  # Non-durable! Lost on restart
    yield ctx.call_activity(step2)

# CORRECT - ctx.create_timer is durable
def good_orchestration(ctx: task.OrchestrationContext, _):
    yield ctx.call_activity(step1)
    yield ctx.create_timer(timedelta(seconds=60))  # Durable timer
    yield ctx.call_activity(step2)

HTTP Calls and I/O

# WRONG - HTTP call in orchestrator is non-deterministic
def bad_orchestration(ctx: task.OrchestrationContext, url: str):
    import requests
    response = requests.get(url)  # Non-deterministic!
    return response.json()

# CORRECT - move I/O to activity
def fetch_data(ctx: task.ActivityContext, url: str) -> dict:
    import requests
    response = requests.get(url)  # OK in activity
    return response.json()

def good_orchestration(ctx: task.OrchestrationContext, url: str):
    data = yield ctx.call_activity(fetch_data, input=url)  # Deterministic
    return data

Database Access

# WRONG - database query in orchestrator
def bad_orchestration(ctx: task.OrchestrationContext, user_id: str):
    import sqlite3
    conn = sqlite3.connect('db.sqlite')  # Non-deterministic!
    cursor = conn.execute("SELECT * FROM users WHERE id=?", (user_id,))
    user = cursor.fetchone()
    # ...

# CORRECT - database access in activity
def get_user(ctx: task.ActivityContext, user_id: str) -> dict:
    import sqlite3
    conn = sqlite3.connect('db.sqlite')  # OK in activity
    cursor = conn.execute("SELECT * FROM users WHERE id=?", (user_id,))
    return dict(cursor.fetchone())

def good_orchestration(ctx: task.OrchestrationContext, user_id: str):
    user = yield ctx.call_activity(get_user, input=user_id)
    # ...

Environment Variables

# WRONG - env var might change between replays
def bad_orchestration(ctx: task.OrchestrationContext, _):
    api_endpoint = os.getenv("API_ENDPOINT")  # Could change!
    yield ctx.call_activity(call_api, input=api_endpoint)

# CORRECT - pass config as input or read in activity
def good_orchestration(ctx: task.OrchestrationContext, config: dict):
    api_endpoint = config["api_endpoint"]  # From input, deterministic
    yield ctx.call_activity(call_api, input=api_endpoint)

# ALSO CORRECT - read env var in activity
def call_api(ctx: task.ActivityContext, _) -> str:
    api_endpoint = os.getenv("API_ENDPOINT")  # OK in activity
    # make the call...

Conditional Logic Based on External State

# WRONG - file existence can change between replays
def bad_orchestration(ctx: task.OrchestrationContext, path: str):
    if os.path.exists(path):  # Non-deterministic!
        yield ctx.call_activity(process_file, input=path)

# CORRECT - check in activity
def check_file_exists(ctx: task.ActivityContext, path: str) -> bool:
    return os.path.exists(path)  # OK in activity

def good_orchestration(ctx: task.OrchestrationContext, path: str):
    exists = yield ctx.call_activity(check_file_exists, input=path)
    if exists:  # Deterministic - based on activity result
        yield ctx.call_activity(process_file, input=path)

Dictionary/Set Iteration Order

# POTENTIALLY WRONG - dict iteration order may vary (Python < 3.7)
def risky_orchestration(ctx: task.OrchestrationContext, items: dict):
    for key in items:  # Order might not be guaranteed
        yield ctx.call_activity(process, input=key)

# CORRECT - use sorted keys for deterministic order
def good_orchestration(ctx: task.OrchestrationContext, items: dict):
    for key in sorted(items.keys()):  # Guaranteed order
        yield ctx.call_activity(process, input=key)

Thread-Local or Global State

# WRONG - global state can change
counter = 0

def bad_orchestration(ctx: task.OrchestrationContext, _):
    global counter
    counter += 1  # Non-deterministic across replays!
    yield ctx.call_activity(process, input=counter)

# CORRECT - pass state through orchestration input/output
def good_orchestration(ctx: task.OrchestrationContext, counter: int):
    counter += 1  # Local variable, deterministic
    yield ctx.call_activity(process, input=counter)
    # If continuing, pass counter forward
    ctx.continue_as_new(counter)

Using yield

In Python, orchestrator functions use yield to await durable operations:

# CORRECT - use yield
result = yield ctx.call_activity(my_activity, input="data")

# WRONG - will not work
result = ctx.call_activity(my_activity, input="data")  # Missing yield!

Error Handling

def orchestrator_with_error_handling(ctx: task.OrchestrationContext, input: str):
    try:
        result = yield ctx.call_activity(risky_activity, input=input)
        return result
    except task.TaskFailedError as e:
        # Activity failed - implement compensation
        ctx.set_custom_status({"error": str(e)})
        yield ctx.call_activity(compensation_activity, input=input)
        return "Compensated"

Retry Policies

from durabletask.task import RetryPolicy

retry_policy = RetryPolicy(
    first_retry_interval=5,  # seconds
    max_number_of_attempts=3,
    backoff_coefficient=2.0,
    max_retry_interval=60,  # seconds
    retry_timeout=300  # seconds
)

def orchestrator(ctx: task.OrchestrationContext, _):
    result = yield ctx.call_activity(
        unreliable_activity, 
        input="data",
        retry_policy=retry_policy
    )
    return result

Working with Custom Types

The SDK supports dataclasses, namedtuples, and custom classes:

from dataclasses import dataclass

@dataclass
class Order:
    product: str
    quantity: int
    cost: float

def process_order(ctx: task.ActivityContext, order: Order) -> str:
    return f"Processed {order.quantity}x {order.product}"

def order_workflow(ctx: task.OrchestrationContext, order: Order):
    result = yield ctx.call_activity(process_order, input=order)
    return result

Connection & Authentication

Local Emulator (Default)

# No authentication required
taskhub = "default"
endpoint = "http://localhost:8080"
credential = None
secure_channel = False

Azure with DefaultAzureCredential

from azure.identity import DefaultAzureCredential

taskhub = "my-taskhub"
endpoint = "https://my-scheduler.region.durabletask.io"
credential = DefaultAzureCredential()
secure_channel = True

Authentication Helper

def get_connection_config():
    endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
    taskhub = os.getenv("TASKHUB", "default")
    
    is_local = endpoint == "http://localhost:8080"
    
    return {
        "host_address": endpoint,
        "taskhub": taskhub,
        "secure_channel": not is_local,
        "token_credential": None if is_local else DefaultAzureCredential()
    }

config = get_connection_config()
worker = DurableTaskSchedulerWorker(**config)
client = DurableTaskSchedulerClient(**config)

Local Development with Emulator

# Pull and run the emulator
docker pull mcr.microsoft.com/dts/dts-emulator:latest
docker run -d -p 8080:8080 -p 8082:8082 --name dts-emulator mcr.microsoft.com/dts/dts-emulator:latest

# Dashboard available at http://localhost:8082

Client Operations

# Schedule new orchestration
instance_id = client.schedule_new_orchestration(my_orchestration, input="data")

# Schedule with custom instance ID
instance_id = client.schedule_new_orchestration(
    my_orchestration, 
    input="data",
    instance_id="my-custom-id"
)

# Wait for completion
state = client.wait_for_orchestration_completion(instance_id, timeout=60)

# Get current status
state = client.get_orchestration_state(instance_id)

# Raise external event
client.raise_orchestration_event(instance_id, "approval_received", data=approval_data)

# Terminate orchestration
client.terminate_orchestration(instance_id, output="User cancelled")

# Suspend/Resume
client.suspend_orchestration(instance_id)
client.resume_orchestration(instance_id)

References

  • patterns.md - Detailed pattern implementations (Fan-Out/Fan-In, Human Interaction, Entities, Sub-Orchestrations)
  • setup.md - Azure Durable Task Scheduler provisioning and deployment
Install via CLI
npx skills add https://github.com/Azure-Samples/Durable-Task-Scheduler --skill durable-task-python
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