futurist
Useful additions to futures, from the future.
Decision gist · record as of 2026-08-14
Yes, if you need executor alternatives with built-in statistics or eventlet support. The low install friction, permissive Apache-2.0 license, active maintenance, and zero known vulnerabilities make it a safe choice. Install only if your use case specifically requires the statistics gathering, eventlet executor, or synchronous executor—standard concurrent.futures covers most cases.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Low install friction with a single runtime dependency.
- Actively maintained as of June 2026, supporting current Python versions (3.11–3.14).
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-26 (49 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 501,086 downloads/mo, #6,317 on PyPI
Alternatives
Verify before relying
pip install futurist
from futurist import ThreadPoolExecutor
executor = ThreadPoolExecutor(max_workers=4)
future = executor.submit(lambda: x)
result = future.result()- Whether eventlet executor requires eventlet as an optional dependency or if it's bundled.
- Specific statistics gathering capabilities and output format.
- Whether synchronous executor is suitable for testing or production use.
- Concrete performance characteristics and typical use patterns in production.
What it is and what it does
Futurist is a futures library that extends Python's standard concurrent.futures with additional executors and observability features. It provides a synchronous executor for testing, an eventlet-based executor for greenlet-based concurrency, and built-in statistics gathering to monitor how asynchronous work executes. The library aims to make concurrent execution patterns more transparent and easier to reason about, particularly in OpenStack environments where it originated.
The package depends only on debtcollector for deprecation handling, keeping its footprint minimal. It supports Python 3.11 through 3.14 and is actively maintained. Developers use it when they need drop-in executor replacements with visibility into task execution patterns, or when working with eventlet-based concurrency models.
Use it for
- Replace standard executors with versions that gather execution statistics for monitoring and debugging.
- Use eventlet executor for greenlet-based concurrency in I/O-bound workloads.
- Test async code paths using the synchronous executor without managing threads.
- Monitor task execution patterns in distributed systems.
- Gather metrics on executor performance and task completion times.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need executor alternatives with built-in statistics or eventlet support.
The low install friction, permissive Apache-2.0 license, active maintenance, and zero known vulnerabilities make it a safe choice. Install only if your use case specifically requires the statistics gathering, eventlet executor, or synchronous executor—standard concurrent.futures covers most cases.
Install
futurist on PyPI
Before you install
Low install friction with a single runtime dependency. Actively maintained as of June 2026, supporting current Python versions (3.11–3.14).
Requires Python 3.11 or later.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install futurist
from futurist import ThreadPoolExecutor
executor = ThreadPoolExecutor(max_workers=4)
future = executor.submit(lambda: x)
result = future.result()Verify before relying
- Whether eventlet executor requires eventlet as an optional dependency or if it's bundled.
- Specific statistics gathering capabilities and output format.
- Whether synchronous executor is suitable for testing or production use.
- Concrete performance characteristics and typical use patterns in production.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagedebtcollector |
| Maintenance | Actively maintained 49 days since the last release |
| First released | |
| Downloads | 501,086 / month, #6,317 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: OpenStackIntended Audience :: Information TechnologyIntended Audience :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed |
Evidence: futurist-3.4.0-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 › “futures executor library”
- futuristFuturist provides futures utilities and executors that add…
- futuresBackport of Python 3's concurrent.futures module to Python 2.6 and…
- lokyLoky provides a reusable ProcessPoolExecutor that handles parallel…
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 debtcollector · Flask-Executor · unsync · requests-futures · futures · duet · synchronicity · eventlet · loky · taskflow