📰 Story

I spent a year building agent memory on knowledge graphs. Here are my 5 mistakes

I spent the past year building a unified memory layer for my AI agents using knowledge graphs and ontologies on top of MongoDB. I followed every trend first. I reached for the shiny frameworks and tried to design the perfect upfront ontology. I made basically every mistake possible. Naive memory fails at scale. File search bloats the context window once memory gets big. Claude Code handles it this way out of the box. Even semantic search over history can't traverse the relationships between people, topics, objects, locations, and preferences. The fix was to stop treating memory as a retrieval problem and treat it as a *data-modeling problem*. Here are the 5 mistakes I made: 1. I reached for frameworks first. I tried LangGraph and CrewAI. The moment I needed custom ontology constraints, immutable observation logs, composite IDs, and multi-hop traversal, I was fighting the framework. Lesson: Own the memory and the harness yourself because frameworks encode assumptions your system rarely matches. 2. I overthought the ontology. Knowing it's a data-modeling problem, I tried to design the perfect ontology upfront. This froze projects for months. Lesson: Ontology design is a data-exploration loop. Start with POLE+O (Person / Object / Location / Event / Organization) and extend only on collisions. For example, I had a run tagging "Claude Code" as a Person when it's an Object. 3. I confused resolution with deduplication. Naming is not identity. Confusing them corrupts the graph. Resol