---
id: cachetools-async
version: "0.0.5"
license: MIT
license_treatment: permissive
maintenance: active
---

# cachetools-async — Provides decorators that are inspired by and work closely with cachetools' for caching asyncio functions and methods.

License: permissive · Maintenance: active · Downloads: 512.7K/mo

## What it is and what it does

cachetools_async adds memoization decorators for asyncio coroutine functions, building on top of cachetools' cache implementations. It lets you decorate async functions with @cached to automatically store and reuse their results, using the same cache strategies (LRU, TTL, etc.) that cachetools provides for synchronous code. The cache itself is not asynchronous—it's a regular mutable mapping—but the decorator handles the async execution model correctly.

A key behavior: if multiple concurrent calls arrive for the same cached key before the first one completes, they all wait for that single result rather than each triggering a separate computation. This deduplication happens transparently, making it useful for scenarios like parallel requests for the same resource.

Use it for:

- Cache results from async HTTP requests (e.g., weather data, API responses) with TTL to avoid redundant network calls.
- Deduplicate concurrent async function calls in parallel workloads where multiple tasks request the same expensive computation.
- Implement LRU caching for async database queries or I/O operations to limit memory and improve response times.
- Add memoization to async data-fetching functions in web frameworks or async services without rewriting cache logic.
- Combine with cachetools' cache strategies to apply size limits and eviction policies to async coroutine results.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides async-aware memoization decorators for Python asyncio coroutines, extending cachetools with support for caching async function results using LRU, TTL, and other fixed-size cache strategies.

Yes. Low install friction, active maintenance, no known vulnerabilities, and MIT licensing make it a straightforward choice. Install it if you need memoization for asyncio functions and want to reuse cachetools' familiar cache strategies. The single cachetools dependency is lightweight and widely used.

## Install

pip install cachetools-async
uv add cachetools-async
poetry add cachetools-async

## Installing cachetools-async

Before you install: Low friction: pure Python wheel with a single runtime dependency on cachetools. Actively maintained with recent releases; repository is not archived and receives commits.

License in practice: MIT license is permissive and imposes no significant restrictions on use, modification, or distribution in most contexts.

Quickstart:

pip install cachetools_async

from cachetools import LRUCache
from cachetools_async import cached

@cached(cache=LRUCache(maxsize=32))
async def fetch_data(key):
    # async work here
    return result

await fetch_data(key)

Requires Python 3.9 or later.

Verify before relying:

- Whether concurrent calls to the same cached async function with different arguments are deduplicated or only identical calls are coalesced.
- Performance characteristics and memory overhead compared to direct cachetools usage in async contexts.
- Thread-safety guarantees when the same cached function is called from multiple asyncio tasks or event loops.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 512.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

async function caching, asyncio memoization decorator, cache async coroutines, cachetools async support, async result memoization, TTL cache for async functions, LRU cache asyncio, asyncio, caching, memoization

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