aiometer
A Python concurrency scheduling library, compatible with asyncio and trio
Decision gist · record as of 2026-08-14
Yes, if you need to manage concurrency in async code. The library is stable, has no known vulnerabilities, minimal dependencies, and solves a real problem—controlling task parallelism and rate. The aging maintenance (last release 497 days ago) is a minor concern but not a blocker given the stable status and active repository. Install it for production async workloads that need backpressure.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; asyncio or trio must be available in your environment.
- Low friction: pure Python wheel with only two lightweight runtime dependencies (anyio and exceptiongroup).
- Maintenance is aging—last release was 497 days ago—but the repository remains active with recent commits and the package is marked Production/Stable.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute aiometer freely in commercial and private projects with minimal restrictions.
last release 2025-04-04 (497 days) · last repo commit 2025-04-04 · 437 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 386,066 downloads/mo, #7,054 on PyPI
Alternatives
Verify before relying
import aiometer
import asyncio
async def task(item):
await asyncio.sleep(0.1)
return item
results = await aiometer.run_all(
[task(i) for i in range(100)],
max_at_once=10,
max_per_second=5
)- Whether the aging maintenance status (497 days since last release) affects stability or security for new Python versions.
- Performance characteristics under very high concurrency (e.g., thousands of concurrent tasks).
What it is and what it does
aiometer is a concurrency scheduler for async Python that lets you run many tasks at once while controlling how many run simultaneously and how fast they spawn. It works with both asyncio and trio, making it easy to apply backpressure—slowing down task execution to avoid overwhelming servers, databases, or other resources.
The library provides four main entry points: run_on_each() for fire-and-forget execution, run_all() to collect results in order, amap() to process results as they complete, and run_any() to return the first successful result. You control concurrency with max_at_once (concurrent task limit) and max_per_second (spawn rate). It's fully type-annotated, has 100% test coverage, and supports Python 3.8 through 3.13.
Use it for
- Make hundreds of HTTP requests concurrently without overwhelming the server by setting max_per_second and max_at_once limits.
- Run database queries in parallel while respecting connection pool limits via max_at_once.
- Process items from a queue with controlled concurrency to avoid resource exhaustion.
- Implement rate-limited API clients that respect per-second quotas.
- Coordinate async tasks across asyncio and trio codebases with a single scheduling interface.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to manage concurrency in async code.
The library is stable, has no known vulnerabilities, minimal dependencies, and solves a real problem—controlling task parallelism and rate. The aging maintenance (last release 497 days ago) is a minor concern but not a blocker given the stable status and active repository. Install it for production async workloads that need backpressure.
Install
aiometer on PyPI
Before you install
Low friction: pure Python wheel with only two lightweight runtime dependencies (anyio and exceptiongroup). Maintenance is aging—last release was 497 days ago—but the repository remains active with recent commits and the package is marked Production/Stable.
Requires Python 3.8 or later; asyncio or trio must be available in your environment.
License in practice
MIT license is permissive; you can use, modify, and distribute aiometer freely in commercial and private projects with minimal restrictions.
Quickstart
import aiometer
import asyncio
async def task(item):
await asyncio.sleep(0.1)
return item
results = await aiometer.run_all(
[task(i) for i in range(100)],
max_at_once=10,
max_per_second=5
)Verify before relying
- Whether the aging maintenance status (497 days since last release) affects stability or security for new Python versions.
- Performance characteristics under very high concurrency (e.g., thousands of concurrent tasks).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesanyioexceptiongroup |
| Maintenance | Aging 497 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 386,066 / month, #7,054 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/StableFramework :: AsyncIOFramework :: TrioIntended Audience :: DevelopersProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: aiometer-1.0.0-py3-none-any.whl
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See also aiotools · trio · asynciolimiter · httpx-limiter · aiojobs · pytest-asyncio-concurrent · pytest-trio · aiologic · asyncio-throttle · pytest-aio