Field notes from a memory layer in production.
Engineering deep dives and tutorials on building, scaling, and shipping AI agents that remember. Written by the team behind Memanto.
What Memanto Actually Stores: All 13 Memory Types Explained
Not all context is the same. Memanto organizes every memory into one of 13 semantic types — each captured, retrieved, and surfaced differently. Here is what each type stores, why it exists, and when to use it.
One Memory Layer, Every Tool: Connect Memanto Across Your Entire Dev Stack
Your AI coding assistants forget everything between sessions. Here's how to wire Memanto into Claude Code, Cursor, Windsurf, and Cline simultaneously — so every tool knows your preferences, past decisions, and recurring errors.
Why We Don't Use HNSW For Agent Memory Search
Standard vector databases lean on knowledge graphs and parallel pipelines to compensate for HNSW's approximate search. Information-Theoretic Scoring removes the need for both — and matches hybrid systems on every public benchmark.
Stop Overengineering Agentic Memory: How Basic RAG Outperforms the Leading Memory Frameworks
Why complex graph-based memory architectures may be overkill, and how a highly-optimized RAG pipeline hits 89.8% on LongMemEval and 87.1% on LoCoMo with vector-only retrieval — no graph database, no multi-query orchestration.