gmlcache

generic-ml-cache-core

The hexagonal engine behind gmlcache — embeddable and stateless

The reusable engine behind gmlcache: record a real ML client (or API) call once, replay it by its content key. It contains the domain model, the use cases, the port contracts, and the default outbound adapters (SQLite execution repository, filesystem blob store, the claude/codex/cursor client runner, the API client, metrics, clock, fingerprinting) — plus the build_use_cases composition factory.

Pure Python and stateless: it bakes in structure (table names, blob naming, schema) but no location — you inject the data source.

Part of a single-user, local tool — not a gateway. gmlcache records and replays across the subscriptions and APIs you already hold; it is not a multi-user router. See Positioning.

Install

pip install generic-ml-cache-core

Embed it

Hand the library a data source and it wires the engine for you:

from generic_ml_cache_core import build_use_cases
from generic_ml_cache_core.application.port.inbound.run_managed_local_execution_command import (
    RunManagedLocalExecutionCommand,
)

wired = build_use_cases(store_root="/path/you/choose")   # you provide the data source
command = RunManagedLocalExecutionCommand(
    client="claude", model="sonnet", effort="", context="", prompt="…",
)
execution = wired.run_managed.execute(command)           # records on a miss, replays on a hit

You reuse the shipped adapters by injecting a data source — you never reimplement them (the Spring Batch model: the framework ships the writers, you provide the connection). Need a different store? Construct the use cases yourself against the ports and pass your own adapter.

What's inside

Domain model — executions, polymorphic call identities, artifacts, usage.
Use cases — managed-local / passthrough / API runs, and probe (check).
Ports (application/port/...) — client runner, blob store, execution repository, metrics, clock, fingerprint, API client.
Default adapters (adapter/out/...) + the build_use_cases composition factory.
generic_ml_cache_core.testing.InMemoryExecutionRepository — an in-memory reference adapter to test your code against the ports.

Inbound drivers — gmlcache today, a daemon later — map their surface (a terminal, a REST API) onto these public APIs; the core itself has no UI and reads no config file.

License

Apache-2.0 — see LICENSE and NOTICE.

Release files for generic-ml-cache-core 0.23.0

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Source distribution (sdist)

Source distribution for generic-ml-cache-core 0.23.0
File	Size	Uploaded
generic_ml_cache_core-0.23.0.tar.gz	155.4 kB	Jun 28, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for generic-ml-cache-core 0.23.0
File	Interpreter	ABI	Platform	Reset
generic_ml_cache_core-0.23.0-py3-none-any.whl 160.3 kB Jun 28, 2026	Python 3	none	any	Details

Total release size: 315.7 kB

Release files / generic_ml_cache_core-0.23.0.tar.gz

Download URL	generic_ml_cache_core-0.23.0.tar.gz
Size	155.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`701e9a4e69b4d969b51a5c41a5e1e531cdf82945fa0f53e4dc782e80e177cf48`
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Upload date	Jun 28, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.12`

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Publishing platform: GitHub Actions
Publishing repository: github.com/danielslobozian/generic-ml-cache
Publishing commit: github.com/danielslobozian/generic-ml-cache/tree/cf951f1232c4dec432f9a4a658209dba5fde266d

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Publishing configuration: github.com/danielslobozian/generic-ml-cache/blob/cf951f1232c4dec432f9a4a658209dba5fde266d/.github/workflows/release.yml
Publishing logs: github.com/danielslobozian/generic-ml-cache/actions/runs/28324581296/attempts/1

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Subject: generic_ml_cache_core-0.23.0.tar.gz
SHA-256 checksum: 701e9a4e69b4d969b51a5c41a5e1e531cdf82945fa0f53e4dc782e80e177cf48 Verifying attestations

Release files / generic_ml_cache_core-0.23.0-py3-none-any.whl

Download URL	generic_ml_cache_core-0.23.0-py3-none-any.whl
Size	160.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`96adc15f22c7cd444788aef7000931770cfe63a0909ab5c1382250498e54fe51`
BLAKE2b-256 checksum How to use checksums	`0d54888a1bbd7202b164fc829ebb4ef02d87ece5e4731005d414ca3757c95027`
Upload date	Jun 28, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.12`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/danielslobozian/generic-ml-cache
Publishing commit: github.com/danielslobozian/generic-ml-cache/tree/cf951f1232c4dec432f9a4a658209dba5fde266d

Workflow

Publishing configuration: github.com/danielslobozian/generic-ml-cache/blob/cf951f1232c4dec432f9a4a658209dba5fde266d/.github/workflows/release.yml
Publishing logs: github.com/danielslobozian/generic-ml-cache/actions/runs/28324581296/attempts/1

Artifact

Subject: generic_ml_cache_core-0.23.0-py3-none-any.whl
SHA-256 checksum: 96adc15f22c7cd444788aef7000931770cfe63a0909ab5c1382250498e54fe51 Verifying attestations