agenr

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  AGENt memoRy

AY-JEN-ER - Human memory for AI agents.

This README is the overview and quick start. Canonical mechanism docs live in docs/ARCHITECTURE.md and docs/OPENCLAW.md.

AGENR is a local, structured, universal, living memory layer for AI agents. It tackles session amnesia, multi-surface fragmentation, and vendor lock-in together so you get one person, many agents, one brain.

It extracts structured knowledge from conversation transcripts - facts, decisions, preferences, todos, relationships, events, lessons - and stores them in a local database with semantic search. Entries strengthen when reinforced, decay when stale, and resolve contradictions. Your memory stays on your machine.

More: Vision | Roadmap | Architecture | OpenClaw guide

pnpm install -g agenr
agenr init

That's it. The interactive wizard handles everything: auth setup, platform detection, plugin installation, session ingestion, and watcher configuration. Run agenr init again anytime to reconfigure.

• Extract - An LLM reads your transcripts and pulls out structured entries. Smart filtering removes noise (tool calls, file contents, boilerplate - about 80% of a typical session) before the LLM sees it. Hedged or unverified agent claims are capped at importance 5 with an unverified tag.
• Store - Entries get embedded and compared against existing knowledge. Near-duplicates reinforce existing entries. New information gets inserted. Online dedup catches copies in real-time.
• Recall - Semantic search plus memory-aware ra