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.
Project description
gmlcache
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.
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, dependency-free 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
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