durabletask.azuremanaged
Durable Task Python SDK provider implementation for the Azure Durable Task Scheduler
Decision gist · record as of 2026-08-14
Yes, if you are building on Azure and need durable orchestration. The package is actively maintained, has no known vulnerabilities, low install friction, and permissive licensing. It is production-stable (Development Status 5) and well-positioned in the ecosystem as the official Azure provider for the Durable Task framework. Install only if you are committed to Azure Durable Task Scheduler or Azure Durable Functions; it is not a general-purpose workflow library.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and Azure credentials/connection configuration to connect to Azure Durable Task Scheduler.
- Active maintenance with a recent release 15 days ago.
- Low install friction with only two runtime dependencies (durabletask and azure-identity).
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), allowing broad use including commercial applications with minimal restrictions.
last release 2026-07-30 (15 days) · last repo commit 2026-08-13 · 38 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 382,159 downloads/mo, #7,090 on PyPI
Alternatives
Verify before relying
pip install durabletask-azuremanaged
from durabletask.azuremanaged import AzureManagedTaskHubClient
client = AzureManagedTaskHubClient(connection_string)- Whether the package supports async/await patterns or only synchronous orchestration APIs.
- Specific performance characteristics or throughput limits for orchestration execution.
- Whether large payload externalization via azure-blob-payloads extra is required for typical workloads or optional.
What it is and what it does
durabletask-azuremanaged is a provider implementation within the Durable Task Python SDK ecosystem that connects orchestration logic to Azure Durable Task Scheduler. It sits between the core durabletask orchestration framework and Azure's managed infrastructure, handling the communication and state persistence needed to run reliable, long-running workflows.
The package is designed for developers building distributed workflows that need to survive failures, coordinate across services, or maintain state across long-running operations. It depends on durabletask for the orchestration model and azure-identity for authentication, and integrates with Azure's managed scheduler to handle task distribution and durability. Optional support for externalized payloads via the azure-blob-payloads extra allows workflows to handle large data without payload size constraints.
Use it for
- Building multi-step business processes that must survive service restarts or network failures.
- Orchestrating microservice workflows where tasks run asynchronously across multiple services.
- Implementing approval workflows or human-in-the-loop processes with durable state.
- Coordinating complex data pipelines with retry logic and error handling built in.
- Running scheduled or event-driven tasks that need to maintain execution history and state.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building on Azure and need durable orchestration.
The package is actively maintained, has no known vulnerabilities, low install friction, and permissive licensing. It is production-stable (Development Status 5) and well-positioned in the ecosystem as the official Azure provider for the Durable Task framework. Install only if you are committed to Azure Durable Task Scheduler or Azure Durable Functions; it is not a general-purpose workflow library.
Install
durabletask-azuremanaged on PyPI
Before you install
Active maintenance with a recent release 15 days ago. Low install friction with only two runtime dependencies (durabletask and azure-identity). Requires Python 3.10 or later.
Requires Python 3.10 or later and Azure credentials/connection configuration to connect to Azure Durable Task Scheduler.
License in practice
Licensed under MIT (permissive), allowing broad use including commercial applications with minimal restrictions.
Quickstart
pip install durabletask-azuremanaged
from durabletask.azuremanaged import AzureManagedTaskHubClient
client = AzureManagedTaskHubClient(connection_string)Verify before relying
- Whether the package supports async/await patterns or only synchronous orchestration APIs.
- Specific performance characteristics or throughput limits for orchestration execution.
- Whether large payload externalization via azure-blob-payloads extra is required for typical workloads or optional.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesdurabletaskazure-identity |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 382,159 / month, #7,090 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3 |
Evidence: durabletask_azuremanaged-1.9.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “durable task orchestration”
- durabletask.azuremanagedProvides a Python provider implementation for orchestrating durable…
- durabletaskDurabletask is a Python SDK for building durable orchestrations and…
- agent-framework-durabletaskIntegrates Microsoft Agent Framework agents with the Durable Task…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also azure-functions-durable · durabletask · restate-sdk · agent-framework-durabletask · dbos · absurd-sdk · FireWorks · agent-framework-azurefunctions · conductor-python · azure-mgmt-scheduler