Use case
AI agent memory that compounds
Most agents start every session blank. MADB gives them durable memory so they build on what they already learned.
Without memory, an agent re-derives the same context every run, repeats mistakes, and can’t reference what it did yesterday. Persistent memory for AI agents changes that: each decision and result is stored, then recalled when it’s relevant.
How MADB does it
Your agent calls remember to store events and recall to bring them back. Recall is composite-scored on similarity, recency, causal proximity, importance, and tag overlap — so the right memory returns, not just the most recent.
What you get
- Continuity across sessions, projects, and restarts
- Fewer repeated errors and redundant work
- A measured 4.65× recall ROI in our own production testing
Install in one command from the quickstart.