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How to run the offline memory demo in under 15 minutes with Jev Mem

Install Jev Mem and watch it store observations, retrieve evidence, and print a decision trace with no API keys.

Who it is forA developer evaluating agent memory who wants to see how the pipeline works before spending money on live model calls.

The problemReading about write, connect, retrieve, and assess steps is hard to picture without seeing them run.

The outcomeA printed decision trace from synthetic observations, showing how the memory pipeline behaves on a laptop.

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. Clone the repository with git clone https://github.com/libingzheren/Jev-Mem and enter it with cd Jev-Mem. (inferred, check the README)
  2. Create a virtual environment with python3.11 -m venv .venv.
  3. Activate it with source .venv/bin/activate (on Windows, use .venv\Scripts\Activate.ps1).
  4. Install dependencies with python -m pip install -r requirements.txt.
  5. Run the demo with python -m jev_mem.demo and read the printed decision trace.

Prompt for your agent

Clone the Jev-Mem repository from https://github.com/libingzheren/Jev-Mem into <target-folder>, create a Python 3.11 virtual environment, install requirements.txt, and run `python -m jev_mem.demo`. Report the printed decision trace and confirm that no API keys were requested.

Caveat. The demo uses deterministic mock decisions and embeddings and does not measure answer quality, so it shows the pipeline rather than the paper's results.

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