mneme-cache 1.0.1

A layered semantic cache for LLM applications: exact-match then semantic-match, embeddable, in-process, NumPy-only.

mneme

A layered semantic cache for LLMs and any expensive function with embeddable input.

mneme (Greek: μνήμη, "memory"; pronounced NEE-mee) is an embeddable, in-process Python library for semantic memoization: cache an expensive function once, return the cached result whenever a later input means the same thing. LLM completions are the canonical use case; the same machinery covers RAG retrievals, translations, classifications, deduplication, and agent memory. It pairs an exact-match layer (normalized query hash) with a semantic-match layer (cosine similarity over L2-normalized embeddings) and persists durably to a single SQLite file by default.

Full documentation: https://anthonynystrom.github.io/mneme/

from mneme import SemanticCache

with SemanticCache(path="cache.db", embedder=my_embedder) as cache:
    hit = cache.get("How do I reset my password?")
    if hit is None:
        response = call_my_llm("How do I reset my password?")
        cache.put("How do I reset my password?", response)
    else:
        response = hit.response

Why

• Cache before you call. Turn redundant expensive operations - LLM calls, RAG rerankers, paid translation APIs, slow classifiers - into a microsecond dict lookup or a millisecond NumPy matvec. For chatbots, agent loops, classification pipelines, and batch-style scoring, this is the difference between a viable product and one that pays for every paraphrase.
• One required dependency. NumPy. Optional extras for hnsw, redis, postgres, dynamodb, prometheus, otel. Bring your own embedder, your own LLM client, your own server.
• In-process, no daemon. A library you import, not a service you operate. Persists to a single SQLite file by default; swap in Redis / Postgres / DynamoDB for cross-host shared state.
• Strict typing, zero magic. Public surface is a small set of frozen @dataclasses and Protocols. py.typed shipped.

Features

• Layered cache - O(1) exact match, then cosine similarity over an in-memory matrix
• Sync + async APIs (SemanticCache, AsyncSemanticCache)
• 5 Store backends: Memory, SQLite (default), Redis, Postgres, DynamoDB
• 2 Index backends: NumPy (default; bandwidth-bound exact search, comfortable at typical d=768 to ~500k and at d=384 well past 1M) and hnswlib (opt-in; sub-millisecond approximate search at 1M+)
• 3 vector dtypes: float32, float16, int8 for memory-constrained deployments
• 3 multi-process modes: single, stale-tolerant, mmap-shared
• Multi-tenant via namespaces with per-namespace LRU quotas
• Calibration tooling (Python API + CLI) for tuning similarity thresholds
• Checkpoint export/import for backup and environment promotion
• Re-embed migration tool when the embedder changes
• Prometheus and OpenTelemetry metrics adapters

Install

pip install mneme-cache                       # core (NumPy only)
pip install "mneme-cache[hnsw]"               # approximate-NN at 1M+ entries
pip install "mneme-cache[redis]"              # RedisStore
pip install "mneme-cache[postgres]"           # PostgresStore
pip install "mneme-cache[dynamodb]"           # DynamoDBStore
pip install "mneme-cache[prometheus,otel]"    # metrics adapters
pip install "mneme-cache[all]"                # everything

Python 3.10+. The distribution is mneme-cache on PyPI; the import name is mneme. See the full install matrix.

Quickstart

from mneme import SemanticCache, MemoryStore

with SemanticCache(store=MemoryStore(), embedder=my_embedder) as cache:
    cache.put("How do I reset my password?", "Click 'Forgot password' on login.")
    hit = cache.get("Where do I reset my password?")  # paraphrase
    assert hit is not None
    print(hit.layer, hit.similarity, hit.response)

For the async API, see Async quickstart. For wrapping an actual LLM call, see Your first cached LLM.

Use cases

The same machinery covers more than LLM caching. Each pattern is the same three lines (cache.get, cache.put, your function); only what your function does changes.

Pattern	What it caches
LLM caching	Wrap any LLM call so paraphrases hit a microsecond cache instead of a multi-second model
RAG retrieval	Top-k chunks behind paraphrased questions; skips the cross-encoder reranker on cache hits
Translation	"Source text → translated text" per language pair; cuts billed translation API calls
Semantic deduplication	Read Hit.similarity directly to detect near-duplicate content in ingestion pipelines
Classification	Cache labels from any classifier (sklearn, fastText, BERT, rules engines)
Agent memory	Per-agent task → plan lookup; consistency on similar tasks across runs

Full walkthrough with runnable scripts →

Performance

Apple M4 Max baseline at 100k entries (full table on the docs site):

Operation	Latency
Exact-match get	~2.3 ms p99
Semantic get (fp32, d=768)	~2.7 ms p99
put (no eviction)	~0.9 ms p99
Single-thread throughput	~5,700 ops/sec

Documentation

Getting started	Sync + async quickstarts, bring your own embedder
Use cases	Five patterns: LLM, RAG retrieval, translation, dedup, classification, agent memory
How mneme is different	Where mneme makes different choices than other semantic-cache libraries
Concepts	Layered cache, embedders, quantization, multi-process, multi-tenant
Stores	Memory · SQLite · Redis · Postgres · DynamoDB
Guides	Calibration, checkpoints, re-embed migration, metrics, custom stores, perf tuning
API reference	Auto-generated from docstrings
Performance	Measured baseline against the original targets
Showcase	Flask demo covering all 5 use cases against Nemotron on a DGX Spark
Changelog	Release notes

Comparison

	mneme	GPTCache
Required runtime deps	NumPy	many (faiss, etc.)
Bundled embedder	no (BYOE)	yes
Bundled LLM client	no	yes
Sync + async parity	yes	partial
Strict typing (py.typed)	yes	no
Multi-process modes	3	n/a
Multi-tenant quotas	per-namespace LRU	n/a
Calibration tooling	yes (CLI + Python API)	no

Status

v1.0. Public surface locked; future minor versions are additive. See Changelog.

License

Apache 2.0. See LICENSE.