faktory
Python worker for the Faktory project
Decision gist · record as of 2026-08-14
Yes, if you are already committed to running Faktory and need a Python worker or client. The package is simple, dependency-free, and works with current Python versions. However, do not adopt it for new projects expecting active maintenance—the dormant status and lack of updates since 2023 mean you will be on your own for compatibility issues with future Faktory releases or Python versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7 or later; requires a running Faktory server accessible via TCP (default localhost:7419).
- Low install friction with no runtime dependencies.
- Maintenance is dormant—last release was 2022-04-30 and the repository has not been updated since 2023-09-11, so expect no active bug fixes or feature development.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing commercial and private use with minimal restrictions; you may use and modify this package freely provided you include the license notice.
last release 2022-04-30 (1567 days) · last repo commit 2023-09-11 · 71 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 405,536 downloads/mo, #6,904 on PyPI
Alternatives
Verify before relying
pip install faktory
from faktory import Worker
def your_function(x, y):
return x + y
w = Worker(queues=['default'], concurrency=1)
w.register('test', your_function)
w.run()- Whether the package works reliably with Faktory versions beyond 1.0 given the dormant maintenance status.
- Current state of the Django integration mentioned as 'in progress' in the description.
- Whether pickling issues with decorated functions in multiprocessing mode are documented or resolved.
What it is and what it does
Faktory is a Python client and worker library for the Faktory job server, a language-agnostic background job processor. It lets you push jobs from Python code to a Faktory server for processing, or run a Python worker that consumes and executes those jobs. The worker supports concurrency via multiple processes (default) or threads, graceful shutdown, TLS encryption, password authentication, and can report worker status and errors back to the server.
The package has no external runtime dependencies and installs with low friction. However, it requires a separate Faktory server to be running and accessible over the network. The library is in Alpha status and maintenance is dormant—the last release was in April 2022 and the repository has not been updated since September 2023, meaning you should expect no active support for new Faktory versions or bug fixes.
Use it for
- Run a Python worker that pulls jobs from a Faktory queue and executes them with configurable concurrency.
- Push background tasks to Faktory from a Python application for processing by any Faktory-compatible worker.
- Build a multi-language job processing pipeline where Python workers coexist with workers in other languages.
- Schedule delayed or retried jobs through Faktory with custom metadata from Python code.
- Monitor and control workers via Faktory's web UI while running Python job handlers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already committed to running Faktory and need a Python worker or client.
The package is simple, dependency-free, and works with current Python versions. However, do not adopt it for new projects expecting active maintenance—the dormant status and lack of updates since 2023 mean you will be on your own for compatibility issues with future Faktory releases or Python versions.
Install
faktory on PyPI
Before you install
Low install friction with no runtime dependencies. Maintenance is dormant—last release was 2022-04-30 and the repository has not been updated since 2023-09-11, so expect no active bug fixes or feature development.
Requires Python 3.7 or later; requires a running Faktory server accessible via TCP (default localhost:7419).
License in practice
BSD license is permissive, allowing commercial and private use with minimal restrictions; you may use and modify this package freely provided you include the license notice.
Quickstart
pip install faktory
from faktory import Worker
def your_function(x, y):
return x + y
w = Worker(queues=['default'], concurrency=1)
w.register('test', your_function)
w.run()Verify before relying
- Whether the package works reliably with Faktory versions beyond 1.0 given the dormant maintenance status.
- Current state of the Django integration mentioned as 'in progress' in the description.
- Whether pickling issues with decorated functions in multiprocessing mode are documented or resolved.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.7.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 1,567 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 405,536 / month, #6,904 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Topic :: System :: Distributed Computing |
Evidence: faktory-1.0.0-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 › “faktory job worker”
- faktoryA Python worker and client library for Faktory, a language-agnostic…
- saqSAQ is an async job queue framework that runs background tasks using…
- dask-jobqueueDeploys Dask distributed computing clusters on job queuing systems…
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 rq · pgqueuer · arq · pyfunceble-process-manager · taskiq · dvc-task · saq · loky · bounded-pool-executor · wmill