hypercache 0.2.1

Explicit, persistent caching for expensive Python functions and methods

hypercache

Explicit, persistent caching for expensive Python functions and methods.

What it does

• Caches expensive calls (API calls, embeddings, LLM generations)
• Works with sync and async methods
• Persists across restarts (disk, extensible to Redis)
• Normalizes non-hashable inputs (dicts, Pydantic models, dataclasses, bytes)
• Supports TTL, stale windows, and background refresh

Why not functools.lru_cache or cachetools

	lru_cache	cachetools	hypercache
Async support	No	No	Yes
Persistent storage	No	No	Yes
Non-hashable inputs	No	No	Yes (normalize)
Instance state in key	N/A	Manual	config=
TTL / stale / refresh	No	TTL only	Yes

Install

pip install hypercache

Basic usage

from datetime import timedelta
from hypercache import CachePolicy, CacheService, MemoryStore, cached


def _embedder_config(self) -> dict:
    return {"model": self.model, "dimensions": self.dimensions}


class Embedder:
    def __init__(self, model: str = "text-embedding-3-large", dimensions: int = 1536):
        self._cache = CacheService(MemoryStore(max_entries=512))
        self.model = model
        self.dimensions = dimensions

    @cached(
        version="embed:v1",
        policy=CachePolicy(
            ttl=timedelta(hours=6),
            stale=timedelta(minutes=30),
            refresh_in_background=True,
        ),
        config=_embedder_config,
    )
    async def embed(self, text: str) -> dict:
        return await call_embedding_api(text)

• Inputs are auto-captured from the function signature. No duplicate parameter lists.
• config= explicitly declares which instance state affects the cache key. No hidden method lookups.
• version= lets you invalidate all cached values when the implementation changes.

Sharing config across methods

Define the config function once, reference it from multiple decorators:

def _llm_config(self) -> dict:
    return {"model": self.model, "temperature": self.temperature}


class LLM:
    def __init__(self, model: str, temperature: float):
        self._cache = CacheService(MemoryStore())
        self.model = model
        self.temperature = temperature

    @cached(version="generate:v1", policy=CachePolicy(), config=_llm_config)
    async def generate(self, prompt: str) -> dict:
        ...

    @cached(version="structured:v1", policy=CachePolicy(), config=_llm_config)
    async def generate_structured(self, prompt: str, schema: dict) -> dict:
        ...

Excluding inputs from the key

Use exclude= to drop arguments that shouldn't affect caching:

@cached(
    version="embed:v1",
    policy=CachePolicy(),
    config=_embedder_config,
    exclude=frozenset({"request_id", "trace_id"}),
)
async def embed(self, text: str, request_id: str | None = None, trace_id: str | None = None):
    ...

Persistent cache

Swap the store — everything else stays the same:

from pathlib import Path
from hypercache import DiskCacheStore

cache = CacheService(DiskCacheStore(Path("./cache")))

Direct usage (no decorator)

result = cache.run(
    instance=embedder,
    operation="embed",
    version="embed:v1",
    inputs={"text": "hello"},
    config={"model": embedder.model},
    policy=CachePolicy(),
    compute=lambda: embedder.embed_uncached("hello"),
)

Invalidation

Embedder.embed.key_for(embedder, "hello")     # inspect key
Embedder.embed.invalidate(embedder, "hello")   # delete one entry
Embedder.embed.clear(embedder)                 # delete all entries for this method

Design principles

• No magic: no hidden method lookups, no Protocols that silently match by name
• Explicit: config= in the decorator, not a convention on the class
• DRY: inputs auto-captured from signature, no duplicate parameter lists
• IDE-friendly: named functions, not lambdas; errors surface at import time