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jev-recall

Retrieve by relevance, not resemblance: filter an AI assistant's memories with TypeSafe's Jev

38 ★ on GitHub

What it is

Jev Recall gives you LLM re-ranker quality retreival at semantic search prices and speed

What the README says it does

  • Keyword search works only if you already know what you're looking for.
  • Semantic search works only if the memory that matters happens to look like the request.

From the project’s README.

Getting started

From the README:

uv sync                       # library + dev deps
uv sync --group bench         # baselines: fastembed, rank-bm25, anthropic, openai
cp .env.example .env          # TYPESAFE_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY

Where the decision sits

jev-recall calls the hosted decision API rather than running its own weights. That keeps the project small and means its behaviour moves whenever the vendor ships a new model version. It also means an API key and a network round trip sit in the path of every decision.

Models it references: Jev.

How active it is

Created 2026-09-19, last pushed 2026-09-20, 38 stars and 3 forks when we read it on 2026-11-02. Everything in this category is weeks old at most, so a stale push date means something different here than it would on a mature project. Check the repository before you depend on it.

Find out what your data actually supports

We turn your historical decisions into a frozen test set, then tell you which model, which threshold, and how much of it you can safely automate.