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Deja Vu v0.1.0

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@focaxisdev focaxisdev released this 19 Apr 09:07
· 19 commits to main since this release
v0.1.0
7bdaf39

Deja Vu v0.1.0

Deja Vu is a familiarity-first memory engine for AI agents.

This first public release is aimed at developers who want to try a staged memory model instead of always-on retrieval:

  • familiarity detection before context loading
  • threshold-gated summary access
  • chunk retrieval only when the match is strong enough
  • plugin-first architecture for embeddings, storage, and scoring
  • in-memory adapters for quick local evaluation

Install

npm install deja-vu

Try It In 3 Minutes

import { createInMemorySemanticRecallEngine } from "deja-vu";

const engine = createInMemorySemanticRecallEngine();

await engine.addMemory({
  title: "Launch strategy",
  content: "Use familiarity-first recall before loading long project history.",
  tags: ["launch", "memory"],
});

const result = await engine.recall({
  text: "This sounds like the launch plan for the memory engine.",
  loadChunks: true,
});

console.log({
  matched: result.matched,
  familiarityLevel: result.familiarityLevel,
  score: result.score,
});

Included In v0.1.0

  • SemanticRecallEngine public API
  • hybrid scoring with semantic, recency, and importance signals
  • summary and chunk gating thresholds
  • in-memory storage and vector stores
  • mock embedding provider for deterministic local demos
  • examples and integration documentation

Current Scope

Deja Vu is a memory core, not a full hosted memory platform. This release is best suited for:

  • coding agents
  • project memory
  • long-running task assistants
  • host runtimes that want an embeddable memory module

Current Limitations

  • the default adapters are in-memory only
  • production use still needs persistent storage and a real embedding provider
  • the bundled mock embedding provider is for demos, not semantic accuracy at scale

Links

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