limits
Rate limiting utilities
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive MIT license. It solves a common problem with well-documented, proven strategies. Install it if you need rate limiting with flexible storage and strategy choices; skip it only if your use case is trivial enough for a simple counter or if you are locked into a framework with built-in rate limiting.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low install friction with a pure-Python wheel and only three runtime dependencies (deprecated, packaging, typing-extensions).
- Actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows unrestricted use, modification, and distribution with minimal attribution requirements.
last release 2026-02-05 (190 days) · last repo commit 2026-08-05 · 642 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 45,697,217 downloads/mo, #619 on PyPI
Alternatives
Verify before relying
from limits import storage, strategies, parse
backend = storage.MemoryStorage()
strategy = strategies.MovingWindowRateLimiter(backend)
one_per_minute = parse("1/minute")
assert strategy.hit(one_per_minute, "namespace", "key")
assert not strategy.hit(one_per_minute, "namespace", "key")- Whether in-memory storage is suitable for production multi-process deployments without data loss.
- Performance characteristics and memory overhead of each strategy under high concurrency.
- Whether async support covers all three strategies equally or has limitations.
What it is and what it does
limits is a Python rate-limiting library that enforces request quotas using pluggable strategies and storage backends. It lets you define rate limits (e.g., '1/minute'), check whether incoming requests should be allowed, and query remaining quota and reset times. The library supports three algorithmic strategies—fixed window (memory-efficient but prone to burst at boundaries), moving window (precise but memory-intensive), and sliding window counter (a middle ground)—and can store state in Redis, Memcached, MongoDB, or in-memory. Both sync and async APIs are provided with identical method signatures.
You initialize a storage backend, wrap it in a strategy, define a rate limit, then call hit() to consume a request or test() to check without consuming. The library is designed for API gateways, web frameworks, and any service needing per-user or per-resource quotas. It has no compiled dependencies.
Use it for
- Enforce per-user API request limits in a web service using Redis as the shared backend.
- Implement sliding window rate limiting in an async application with identical sync/async APIs.
- Protect a backend service from burst traffic by testing limits before processing expensive operations.
- Track and reset quota windows for multiple namespaces (e.g., per-user, per-IP, per-endpoint).
- Use in-memory storage for rate limiting in development or single-process applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive MIT license. It solves a common problem with well-documented, proven strategies. Install it if you need rate limiting with flexible storage and strategy choices; skip it only if your use case is trivial enough for a simple counter or if you are locked into a framework with built-in rate limiting.
Install
limits on PyPI
Before you install
Low install friction with a pure-Python wheel and only three runtime dependencies (deprecated, packaging, typing-extensions). Actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.10 or later.
License in practice
MIT license (permissive) allows unrestricted use, modification, and distribution with minimal attribution requirements.
Quickstart
from limits import storage, strategies, parse
backend = storage.MemoryStorage()
strategy = strategies.MovingWindowRateLimiter(backend)
one_per_minute = parse("1/minute")
assert strategy.hit(one_per_minute, "namespace", "key")
assert not strategy.hit(one_per_minute, "namespace", "key")Verify before relying
- Whether in-memory storage is suitable for production multi-process deployments without data loss.
- Performance characteristics and memory overhead of each strategy under high concurrency.
- Whether async support covers all three strategies equally or has limitations.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdeprecatedpackagingtyping-extensions |
| Maintenance | Actively maintained 190 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 45,697,217 / month, #619 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/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: OS IndependentOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python Modules |
Evidence: limits-5.8.0-py3-none-any.whl
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See also throttled-py · upstash-ratelimit · rush · slowapi · Flask-Limiter · ratelim · asynciolimiter · requests-ratelimiter · pyrate-limiter · ratelimit