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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_KEYWhere 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.