Show HN: Engrim – A universal, local-first SQLite memory engine for AI CLIs
Engrim is a new local-first SQLite memory engine designed to help AI CLI tools maintain project context across different models. It aims to solve the issue of 'attention dilution' by curating episodic memory for developers.
Why it matters
As AI context windows grow, managing long-term project state and decision history becomes critical for developer productivity.
The Universal Cross-Model & Cross-Agent Episodic Memory Store.
A local-first, project-scoped SQLite memory engine that allows developers to freely switch between models and environments ( Google Antigravity , Claude Code , Cursor MCP , Windsurf ) on the SAME project without losing architectural decisions, user constraints, or project state.
"Why pay for 200,000 tokens of forgotten noise on every turn? The models are disposable utilities; your project's decisions are not."
As context windows scale to 1M+ tokens, developers face attention dilution : reasoning degrades, cost multiplies with every conversational turn, and clearing context causes total amnesia.
engrim replaces attention dilution with 4,000 characters of curated episodic working memory :
Tested across 105 continuous sessions on a 50,000-line algorithmic trading system. Zero regressions across 186 unit tests, zero context amnesia across model switches.
In production testing on an active algorithmic trading codebase running real capital:
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