EidolonDB – Self-managing memory for AI agents

I got tired of my agents making things up in long-horizon or multi-session workflows. So I built a memory layer that refuses to. EidolonDB gives agents three memory tiers — short_term, episodic, and semantic — with automatic promotion and decay. You ingest raw conversation text, and an LLM pipeline extracts structured memories, classifies them by tier, scores importance, and deduplicates. Over time, short-term facts either promote to long-term knowledge or expire. Key idea: if something isn’t in memory, the system rejects the premise instead of guessing. How I validated it I built an eval harness with 8 multi-session scenarios: project assistant personal assistant technical support preference drift ambiguous recall contradictory memory incomplete recall temporal retrieval Each scenario spans 3 sessions, with a held-out judge scoring: recall accuracy hallucination / false-premise acceptance Results No-memory baseline: 0.158 RAG baseline: 0.933 (same rejection prompting; naive RAG ~0.65)