memory

Engram left stealth with $98M at a reported $600M valuation and 13 employees, building a learned memory layer that studies an organization in advance instead of re-reading its documents on every query. The technical bet is real and specific. The moat question is harder than the round makes it look.

I have shipped Mastra across six TypeScript codebases over the full 1.x line — a market-intelligence API, an autonomous-CMO product, a crypto trading agent, a personal assistant. This is the comprehensive operator playbook: memory's four layers, tools and workflows, supervisors, MCP on both ends, RAG and evals, the free server and auth layers, observability, the whole 1.x migration history, and the production bugs that taught me the most.

Every agent-memory tutorial opens with a vector DB. That is a product opinion, not a default. Here is what mature agent runtimes actually use, and the narrow window where Qdrant or sqlite-vec earns its keep.