generic-ml-cache-cli 0.6.0

Terminal UI for generic-ml-cache: the gmlcache command. A thin inbound driver over generic-ml-cache-core -- reads config, provides the data source, maps commands onto the core library.

Detached ML Execution Cache — the terminal client

gmlcache runs, records, and replays detached ML workloads — record a real client (or API) call once, replay it forever by its content key, offline and byte-for-byte.

Single-user, local — not a gateway. gmlcache runs on your machine, as you, across the subscriptions and APIs you already hold. It is not a multi-user router and not a way to share one subscription — see Positioning.

Install

pip install generic-ml-cache-cli

This installs the gmlcache command and pulls in the engine, generic-ml-cache-core.

Use

gmlcache run    --client claude --model sonnet --prompt "…"   # record on a miss, replay on a hit
gmlcache check  --client claude --model sonnet --prompt "…"   # is this exact call already cached?
gmlcache list                                                 # stored executions, grouped by client/model
gmlcache stats                                                # totals, hit counts, token usage & cost
gmlcache inspect <key>                                        # pretty-print one stored execution
gmlcache doctor | models | status | init                     # environment & configuration helpers

What it does

• Records a real agentic CLI client (claude, codex, cursor-agent) or an API call — stdout, stderr, exit code, generated files, and token usage.
• Replays an identical request instantly and offline, byte-for-byte — gmlcache adds nothing to the client's output, so it is a transparent drop-in.
• Reports — list, group, inspect, and measure stored executions and their savings.

Built on a reusable engine

gmlcache is the terminal client — one inbound driver over the engine. The whole cache logic and every adapter live in generic-ml-cache-core, a stateless library. To embed the cache in your own application instead of driving it from a terminal, depend on the core and inject your own data source — you never reimplement the adapters.

Links

• Repository & docs: https://github.com/danielslobozian/generic-ml-cache
• Changelog (both packages, versioned in lockstep): CHANGELOG.md
• Security policy: SECURITY.md

License

Apache-2.0 — see LICENSE and NOTICE.