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How to run Jev Mem memory decisions on a local Laya model with Jev Mem

Swap the System-One backend to Laya so memory decisions run locally without a TypeSafe API key.

Who it is forA developer who wants to keep memory decisions on their own machine, or on Apple Silicon, instead of calling a hosted decision API.

The problemThe default setup depends on a TypeSafe API key for memory decisions, which may not fit a local or offline workflow.

The outcomeA local Laya-backed run on the synthetic LoCoMo example that prints results without a TypeSafe key.

What you need first

  • Python 3.11 or newer and a terminal
  • A local copy of the repository (the README assumes you run commands from the repository root)
  • For live runs: a TypeSafe API key and an OpenAI API key, plus network access, since live runs may incur charges
  • For benchmarks: the LoCoMo dataset placed at data/locomo10.json, obtained from its original distributor per the data setup guide

Steps

  1. Complete the offline demo setup first so the Python environment exists (see the offline demo use case).
  2. Install the Laya extra with python -m pip install '.[laya]'.
  3. Run the benchmark on synthetic data with USE_TF=0 python -m jev_mem.benchmarks.locomo --dataset examples/locomo_synthetic.json --jev-config config/laya_mem.json --sample 0 --max-questions 2 --best-of-n 1 --no-parallel. (inferred, check the README)
  4. For Apple Silicon, use python -m pip install '.[mlx]' and config/laya_mlx_mem.json instead. (inferred, check the README)

Prompt for your agent

In the Jev-Mem repository at <target-folder>, install the '.[laya]' extra and run the LoCoMo benchmark on examples/locomo_synthetic.json with config/laya_mem.json, using --max-questions 2. Report the output and confirm that no TypeSafe API key was set.

Caveat. The README says the reported results use Jev and Laya has not been benchmarked for the paper, and answer generation and evaluation still need your OpenAI-compatible provider.

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