Infrastructure hub · 6 topics
The Infrastructure Behind AI Memory
AI memory infrastructure is the data layer agents rely on — embeddings, vector databases, knowledge graphs, RAG and hybrid search — explained through how agent memory is stored and retrieved.
Infra in the memory loop
Bridge
How infrastructure serves agent memory
Infrastructure is in service of the memory loop — not a generic database tutorial site. Every component exists to help agents remember and recall.
Topics
Memory infrastructure explained
Embeddings
Vectors encode meaning for semantic memory recall.
Read guide →Vector databases
Scale similarity search for long-term memory backends.
Read guide →Knowledge graphs
Relationships and time-aware facts for temporal memory.
Read guide →RAG
Static document retrieval vs dynamic agent memory.
Read guide →Hybrid search
Vector + keyword search for better memory retrieval.
Read guide →Storage backends
Redis, Postgres, pgvector and fast buffers.
Read guide →Decision
Vector vs knowledge graph for memory
Vector stores
Best for semantic similarity search, fast retrieval and per-user personalization at scale.
Knowledge graphs
Best for relationships, temporal facts and conflict resolution when information changes over time.
→ Vector databases for AI memory · Vector vs knowledge graph
FAQ
Frequently asked questions
What is the best vector database for AI agent memory?
Depends on your stack and scale. Pinecone, Qdrant, Weaviate and pgvector are common choices — match latency, ops burden and whether you need a separate memory framework on top. See vector databases for memory.
Do I need a knowledge graph for agent memory?
Not always. Vector stores suffice for semantic recall and personalization. Knowledge graphs add value when facts have relationships, change over time, or need conflict resolution — as with Zep's Graphiti. See vector vs knowledge graph.
Which embedding model should I use for memory?
Choose based on your language, dimension budget and latency needs. OpenAI, Cohere and open-source models (e.g. sentence-transformers) are common. The embedding model must stay consistent between write and retrieve. See embeddings for memory.
How does RAG infrastructure relate to agent memory?
RAG retrieves from a fixed document corpus; agent memory is dynamic and personal. They combine well: RAG for org knowledge, memory for user/session state. See RAG explained and memory vs RAG.
Is Redis enough for agent memory?
Redis excels as a fast buffer and semantic cache, but you typically implement memory logic yourself. For full CRUD memory APIs, use a framework like Engram, Mem0 or LangMem. See Redis for agent memory.
How do embeddings connect to long-term memory?
Text is embedded into vectors at write time; retrieval embeds the query and finds nearest neighbors in the store. The same model must be used for both. See embeddings for memory.
When should I use hybrid search for memory?
When pure vector search misses exact keyword matches (IDs, product names, codes). Hybrid combines vector similarity with keyword/BM25 search. See hybrid search.
Is pgvector production-ready for agent memory?
Yes for many workloads — especially when you already run Postgres and want SQL + vectors in one store. Scale and latency requirements may push you to dedicated vector DBs. See storage backends.