Technical articles on persistent memory for AI agents, vector database architecture, and building production Node.js agent systems. No hype. No fluff.
We built Vektor for Node.js. We've also used most of these tools, read all the papers, and watched developers hit the same walls repeatedly. An honest breakdown of every major vector memory layer — Pinecone, Mem0, LangChain, Letta, Weaviate, Qdrant, Memori, Cognee and Voyage — including where Vektor falls short and what our roadmap addresses.
A deep dive into the Add/Update/Delete/None decision loop that keeps Vektor's memory graph clean — how it's prompted, how it resolves contradictions, and what happens when it gets it wrong.
Semantic, causal, temporal, entity — why four layers and not one? A walkthrough of the graph architecture behind Vektor and the peer-reviewed research it's built on.
Step-by-step: install Vektor Studio, wire up the MCP server, and have Claude remembering context across sessions. From zero to working in one sitting.
Most agents accumulate memory noise. REM Cycle compresses it. A technical breakdown of the 7-phase dream engine, the EverMemOS research it's based on, and the real-world results.
How to drop Vektor into an OpenAI Agents SDK workflow. Covers remember(), recall(), graph traversal, and handling the AUDN loop correctly in an async agent context.
RAG finds text by proximity. Associative memory finds context by connection. The architectural difference, why it matters for long-running agents, and when each approach is actually correct.
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