AI-IQ: Lightweight persistent memory with beliefs, predictions, and dream consolidation — in one SQLite file

Reddit r/LocalLLaMA / 3/31/2026

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Key Points

AI-IQ is a newly published lightweight persistent memory system for local AI agents that adds a “cognitive layer” beyond embedding-only vector stores.
It models knowledge as beliefs with confidence scores (updated via Bayesian inference) and predictions that can be resolved through a causal knowledge graph.
A “dream mode” performs autonomous consolidation by merging duplicates, resolving expired predictions, and detecting contradictions.
The system is designed to run locally in a single SQLite file using FTS5 plus sqlite-vec hybrid search, with no cloud and no vendor lock-in.
The author reports two months of production use with Claude Code, and claims self-learning via tracking which search results users actually use to tune retrieval weights.

Just published AI-IQ — a persistent memory system for local AI agents that goes beyond simple vector stores.

Most memory tools are "store embedding, retrieve embedding." AI-IQ adds a cognitive layer:

- **Beliefs** with confidence scores (0-1) that update via Bayesian inference

- **Predictions** you can resolve — the system propagates updates through a causal knowledge graph

- **Dream mode** — autonomous consolidation (merges duplicates, resolves expired predictions, detects contradictions)

- **Self-learning** — tracks what search results you actually use, auto-tunes retrieval weights

- **Identity layer** — discovers behavioral patterns from your decisions

All in a single SQLite file. FTS5 + sqlite-vec hybrid search. Zero cloud. Zero vendor lock-in.

```

pip install ai-iq

```

Been running it in production for 2 months with Claude Code (322 memories, 53 graph entities, 477 tests). Every decision, bug fix, and architecture choice — remembered and reasoned about.

The comparison to other tools is in the README: https://github.com/kobie3717/ai-iq

MIT licensed. Contributions welcome — CONTRIBUTING.md has good first issues.

submitted by /u/kobie0606
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