{"id":"evilander-audrey","name":"Audrey","af_score":59.0,"security_score":50.5,"reliability_score":33.8,"what_it_does":"Audrey is a local-first persistent memory layer for AI agents (including Claude Code). It encodes episodic events into a SQLite-backed store (with vector search via sqlite-vec), recalls relevant memories per prompt, and runs maintenance (“dream”/consolidation) to consolidate episodes into semantic/procedural knowledge with confidence decay and contradiction/validation handling. It can be used as a JS SDK, an MCP server with Claude Code memory tools, a CLI with lifecycle hooks, and a local REST API server.","best_when":"You want local, reviewable agent memory on a single machine (or per-user deployment) with MCP/CLI hooks for Claude Code, plus optional embeddings/LLM providers for consolidation and maintenance.","avoid_when":"You need centralized managed memory with enterprise-grade tenancy controls, or you cannot accept that embeddings/LLM consolidation may call external providers (when configured).","last_evaluated":"2026-03-30T15:37:57.474713+00:00","has_mcp":true,"has_api":true,"auth_methods":["API key via AUDREY_API_KEY (Bearer token) for REST server"],"has_free_tier":false,"known_gotchas":["Memory is stored locally in a SQLite file; concurrent agent/worker access may require careful process coordination (not documented here).","REST server appears local (default port 3487); exposing to networks without strong deployment security may be risky.","When using hosted embeddings/LLMs for consolidation, failures/timeouts and provider rate limits should be handled by the integrator (no explicit retry/rate-limit guidance described in README).","Because embeddings are re-generated on snapshot restore, cost/latency can spike depending on embedding provider configuration."],"error_quality":0.0}