{"repo":"phenomenoner/openclaw-mem","free":true,"listed":false,"github":"https://github.com/phenomenoner/openclaw-mem","clone":"git clone https://github.com/phenomenoner/openclaw-mem.git","description":"The AI agent memory layer you can audit — local-first memory governance for AI agents: citations, trust policies, trace receipts, rollback. SQLite, sidecar-first, OpenClaw plugin.","language":"Python","stars":28,"topics":["openclaw","openclaw-plugin","agent-memory","llmops","observability","sqlite","context-engineering","local-first","memory-systems","provenance"],"license":"MIT","category":"databases-storage","readme_excerpt":"openclaw-mem The AI agent memory layer you can audit. Local-first memory governance for AI agents — every context item cited, every exclusion explained, every mutation reversible. Website · 30-second proof · Quickstart · Upgrade to v2 · v2.0.0 · Architecture · FAQ --- Most AI agent memory systems compete on recall — remember more, retrieve better. openclaw-mem competes on a different axis: governance . It captures agent activity as durable local records (SQLite + JSONL, no external database), then assembles bounded ContextPack bundles where every included memory carries a citation , every excluded memory carries a written reason , and every memory mutation ships with a rollback receipt . Built sidecar-first for OpenClaw, usable with Claude, Codex, Gemini, and generic agent harnesses. Not bigger memory — safer, explainable context. Why agent memory needs governance, not just recall Long-running agents don't just forget. Their memory degrades silently : - Stale notes still match queries long after they stop being true. - Untrusted or hostile content — tool output, scraped web text, injected instructions — retrieves well and slips into the prompt. This is the memory poisoning path of prompt injection, and similarity search alone cannot stop it. - Context bloat : prompts swell into unbounded memory dumps nobody can review. - No accountability : when the agent goes wrong, nothing explains why a memory was included. Recall-focused memory layers make these failures more likely as th","default_branch":null,"files":null,"tree":[],"storefront":"/r/phenomenoner","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/phenomenoner/openclaw-mem/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}