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OpenLore: Deterministic, local-first memory and guardrails for AI coding agents

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OpenLore: Deterministic, local-first memory and guardrails for AI coding agents
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OpenLore Deterministic, local-first memory and guardrails for AI coding agents — with no LLM in the hot path. One call tells your agent the code a task touches; one gate tells it what's unsafe to change. Re-record it yourself: docs/openlore-demo.tape.

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Original publisherGitHub
Canonical URLhttps://github.com/clay-good/OpenLore
Publication timeWed, 29 Jul 2026 23:06:53 +0000
Retrieval time2026-07-29T23:26:26.423Z
Last seen2026-07-29T23:26:26.423Z
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

OpenLore Deterministic, local-first memory and guardrails for AI coding agents — with no LLM in the hot path. One call tells your agent the code a task touches; one gate tells it what's unsafe to change. Grounded in static analysis. No API key. Same answer every time. A real, unedited recording — the published openlore on a fresh clone of ripgrep. install wires your agent and indexes the repo live — 235 files, 2,978 functions, 4,329 call edges in 14 seconds, no API key → orient returns the code a task touches → review catches a signature change that left 39 callers stale → prove projects the payoff. Re-record it yourself: docs/openlore-demo.tape. Install · What you get · Benchmarks · Governance · How it works · vs.

Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.

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