This guide reviews modern vector databases and long-term memory patterns for LLM-powered applications as of 2025‑12‑31, focusing on operational tradeoffs and integration patterns. Vector DBs (Pinecone, Milvus, Weaviate, Qdrant) store embeddings and metadata to enable retrieval-augmented generation (RAG), semantic search, and persistent agent memory. Pinecone is a managed service with low-friction SDKs and multi-tenant capabilities; Milvus (open-source) emphasizes large-scale performance and hybrid search; Weaviate combines a vector store with schema/knowledge-graph features and modular ML bindings; Qdrant is a Rust-based option focused on payload filtering, efficient upserts, and self-hosting. Long-term memory for agents and assistants is implemented by combining chunking, timestamped embeddings, hierarchical indexes (short-term vs episodic vs semantic memory), and policies for retention, versioning, and privacy. Practical selection criteria include latency, index update speed, ANN algorithm support (HNSW/IVF/PQ), hybrid dense+lexical search, compression and quantization, multi-tenancy, governance controls, and on‑prem vs managed deployment options. Integration ecosystems matter: LangChain provides retrieval and agent frameworks and connectors across these stores; low-code platforms like MindStudio simplify designing, testing, and operating stateful agents; enterprise tools such as Tabnine illustrate private, context-aware use cases (code search and secure knowledge retrieval) that benefit from private vector stores and strict data controls. Trends to consider for 2026: stronger enterprise governance and private deployments, wider adoption of hybrid retrieval strategies, and increased use of quantized indexes to reduce cost. This guide helps teams map requirements (scale, privacy, update patterns, and tooling integration) to the right vector DB and memory architecture.