Ingest
Feed raw text. EidolonDB extracts what is worth remembering.
Self-managing memory for AI agents
EidolonDB
Self-managing memory for AI agents.
Memory that extracts itself. Evolves itself. Stays useful.
The problem
Vector DBs store whatever you give them. You have to figure out what matters.
Agents forget everything between sessions. Context window isn't memory.
Memory bloat is real. Low-signal storage degrades retrieval quality.
How it works
Evolve
short_term becomes episodic, episodic distills to semantic. Noise decays.
Recall
Hybrid search surfaces the right memories at the right time.
Code
import { EidolonDB } from '@eidolondb/client';
const db = new EidolonDB({ url: 'http://localhost:3000', tenant: 'my-app' });
// Memory that extracts itself
await db.ingest("Today we decided on Fastify for the API. Port 4000. Jordan leads backend.");
// Recall across sessions
const context = await db.recall("project decisions");
// → ["We're using Fastify on port 4000", "Jordan leads backend development"]Eval results
| Metric | Without EidolonDB | With EidolonDB |
|---|---|---|
| Recall accuracy | 10% | 100% |
| Hallucinations | 1 | 0 |
| Overall score | 6% | 100% |
Features
Auto-Extraction
LLM extracts structured memories from raw text.
Tier System
short_term / episodic / semantic with automatic lifecycle.
LLM Distillation
Episodic memories condense into lasting semantic knowledge.
Hybrid Search
Vector + recency + importance scoring.
Deduplication
First-pass Jaccard + vector similarity dedup.
Full API + SDK
REST API, TypeScript SDK, zero-dependency.
Get started
npm install @eidolondb/client