Inside Engrim: How SQLite and RRF Escape the Token Tax in Cross-Model AI Workflows

Engrim has launched a local-first, project-scoped SQLite episodic memory standard that bridges Google Antigravity, Claude Code, and Cursor without cloud lock-in, compressing thousands of interaction tokens into a thin working context to escape token costs in cross-model AI workflows. The system blends FTS5 keyword search with model2vec static embeddings to address attention dilution and multiplicative token costs as context windows grow into the millions.

As AI coding assistants push context windows into the millions, developers face severe attention dilution and multiplicative token costs. Engrim introduces a local-first, project-scoped SQLite episodic memory standard that bridges Google Antigravity, Claude Code, and Cursor without cloud lock-in. By blending FTS5 keyword search with model2vec static embeddings, it compresses thousands of interaction tokens into a razor-thin working context.