10 Consensuses on Agent Memory Architecture
pauliusztin · reddit · 2026-07-15
The author systematically reviewed the implementations of Cognee, Graphiti, and Neo4j `agent-memory`, concluding that despite different product forms, their core architectures are highly similar. ## Shared Architectural Principles 1. **Coexistence of Knowledge Graphs and Vector/Text Retrieval**: Rather than choosing one, they are fused within the same storage layer. 2. **Ontology/Data Model is Core**: All extraction and querying revolve around a unified schema. The author mentions Neo4j's **POLE+O** (Person, Object, Location, Event, Organization) along with `preference` and `fact` nodes. 3. **Single Database Preference**: Placing text search, graphs, and indexes in one database reduces operational complexity; MongoDB is suggested as a viable single-DB solution. 4. **LLM-driven Extraction**: Nodes and edges are extracted from chunked inputs, using batching and cheaper models to control costs. 5. **Separating Resolution and Deduplication**: Resolve names first, then merge identities, because "false merges" are irreversible. 6. **Nightly Cleaning/Re-deduplication**: Scheduled reruns of dedup act like memory "sleep consolidation." 7. **Logs vs. Single Collections**: Append-only offers versioning and temporality but costs more memory; single collections are more practical when versioning isn't needed. 8. **Three Query Methods**: Graph search, agentic search, and on-the-fly generated LLM wikis. 9. **Closed-source APIs First, Open-source SLMs Later**: Switch when cost, latency, or privacy demands it. 10. **Exposure via MCP and Skills**: Package it as a memory app rather than a database wrapper, providing only essential ingest/write primitives. The author concludes by noting that their biggest challenge in building agent memory is balancing depth and breadth in ontology design.
Related event: Deep Dive into Mainstream Agent Memory Architectures(2 posts)→
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