Vector Stores & Memory
How AI systems remember. From vector databases for similarity search to higher-level memory layers and the embedding models that feed them, this is the retrieval backbone behind RAG and long-term context.
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Vector Databases
Stores for embeddings and similarity search
Chroma
Chroma
Open-source embedding database for RAG — store vectors and query by similarity in a few lines.
LanceDB
LanceDB
Embedded vector database built on the Lance columnar format — serverless and local-first
Milvus
Zilliz
Open-source vector database built for billion-scale similarity search
pgvector
pgvector
Postgres extension for vector similarity search — add agent memory and RAG without a separate database.
Qdrant
Qdrant
High-performance open-source vector database for similarity search and agent memory
Weaviate
Weaviate
Open-source vector database with hybrid search, filtering, and built-in vectorizers
Agent Memory
Long-term memory layers for agents
Mem0
Mem0
Memory layer for agents — extracts and recalls facts across sessions, not raw chat logs.
Zep
Zep
Long-term memory layer for agents, building a temporal knowledge graph from conversations
Embedding Models
Models that turn text into vectors
Sentence Transformers
Hugging Face
Run embedding models locally (e.g. bge-m3) to turn text into vectors for RAG and memory — no API.