Mola-maker/dsh-kit--packages-memlab ↗★ 1
dsh-memlab
Benchmark cross-session memory plugins for DeepSeek Harness: teach a fictional project over several sessions, ask in fresh ones, score recall, fact updates, abstention and context cost
설치
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Usage
npm install -g dsh-memlab
cp $(npm root -g)/dsh-memlab/examples/lab.json lab.json # edit: home, modelPatch, systems
dsh-memlab setup --config lab.json # create one profile per system, install plugins
dsh-memlab run --config lab.json --mode passive --wait 120 --model "" none mine
dsh-memlab rescore --results ~/memlab/home/results/-passive.json --tasks zh # re-score saved answers
lab.json:
{
"home": "~/memlab/home",
"modelPatch": "~/memlab/model.patch.yml",
"systems": [
{ "id": "none" },
{ "id": "mine", "plugins": ["my-memory-plugin@1.0.0"], "patch": "- id: my-memory\n config:\n foo: 1\n",
"env": { "MY_USER": "memlab-{run}-{mode}" }, "reset": ["my-memory-store"] }
]
}
modelPatch: a Cordis patch file with your model provider and default model, put first in every profile.envvalues may use{run},{mode},{system},{workspace}so remote-backed plugins get a fresh namespace per run.reset: paths inside the DSH home removed before each run, for stores that would otherwise carry over (..and absolute paths are rejected).--wait: seconds to wait between the teach phase and probing, for plugins that commit memory asynchronously.