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What you can do with Jev Mem

Jev-Mem is a Python research codebase for agent memory, where a System-One controller manages memory writes and graph-based retrieval and a System-Two language model writes the final answer.

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libingzheren/Jev-Mem on GitHub

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What do you want to do?

4 use cases

  • Run the offline memory demo in under 15 minutes

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

    View prompt
    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.

  • Build and query Jev Mem over your own observations

    Load your JSON notes into a Jev Mem cache with live models and ask questions answered from retrieved evidence.

    View prompt
    In the Jev-Mem repository at <target-folder>, build a cache from <input-json-file> using `python -m jev_mem --mode build` with config/jev_mem.json, then query it with `--mode query` using the question <question>. Return the answer and confirm that the cache folder was created locally.

  • Run Jev Mem memory decisions on a local Laya model

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

    View prompt
    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.

  • Compare full Jev Mem against ablated controllers on LoCoMo

    Run LoCoMo questions with full, write-ablated, and read-ablated Jev Mem to see which control layer changes answers.

    View prompt
    In the Jev-Mem repository at <target-folder>, run the LoCoMo benchmark on data/locomo10.json for sample 0 with 10 questions, once with the full config and once each with --no-jev-write and --no-jev-read, using separate --cache-dir paths. Report the three overall scores and confirm that all three runs used the same dataset subset.

How to use Jev Mem

  1. Check you have what it needs: 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.
  2. Open libingzheren/Jev-Mem on GitHub and read the README and license.
  3. Clone the repository with git clone https://github.com/libingzheren/Jev-Mem and enter it with cd Jev-Mem. (inferred, check the README)
  4. Create a virtual environment with python3.11 -m venv .venv.
  5. Activate it with source .venv/bin/activate (on Windows, use .venv\Scripts\Activate.ps1).
  6. Install dependencies with python -m pip install -r requirements.txt.

These steps come from our top use case, Run the offline memory demo in under 15 minutes, which also has a copy-paste agent prompt.

Jev Mem alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • 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
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Questions about Jev Mem

What is Jev Mem?

Jev-Mem is a Python research codebase for agent memory, where a System-One controller manages memory writes and graph-based retrieval and a System-Two language model writes the final answer. It is an open-source project on GitHub: https://github.com/libingzheren/Jev-Mem.

Who is Jev Mem for?

Developers and researchers building or evaluating long-running AI agents who need persistent memory with traceable retrieval decisions.

What can I do with Jev Mem?

4 use cases so far, for example: Run the offline memory demo in under 15 minutes; Build and query Jev Mem over your own observations; Run Jev Mem memory decisions on a local Laya model. Each has steps and a copy-paste agent prompt.

What do I need to run Jev Mem?

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.

What are alternatives to Jev Mem?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Jev Mem safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.