The memory framework ecosystem

Embedded vector + columnar database. Lance file format reads serverless from S3/local disk; no separate process to run. The pick for embedded apps and notebook workflows.

Vector database written in Rust. Strong filtering (payload-based pre-filter), HNSW index with quantization variants, gRPC + REST APIs. The performance pick when you cross 10M vectors.

Open-source embedding database for LLM applications. The default 'just install pip and start' vector store for prototypes, with first-party clients in Python and JS. SQLite-backed locally, distributed

Vector database with built-in modules for embedding, generative search, and reranking. Schema-first design appeals to teams used to traditional databases. Generative-search module pairs with local Oll

Distributed vector database designed for billion-scale workloads. Compute-storage separation, GPU-accelerated index builds, multi-tenant from the ground up. The pick when you've outgrown Qdrant single

Vector search inside the same Redis you already run. HNSW + flat indices, hybrid filtering with FT.SEARCH. The pragmatic pick when you don't want to add another service to ops.

Neo4j's official GraphRAG toolkit — Python library + reference patterns for building retrieval-augmented generation against a knowledge graph. The mature pick for enterprises already running Neo4j.

Temporal graph memory framework. Builds a bi-temporal knowledge graph from agent conversations, tracking when each fact was learned and when it was true. Powers Zep's hosted offering.

Long-term memory platform for AI agents. Sits above Graphiti as the application layer — sessions, facts, summaries, vector + graph hybrid retrieval. The 'memory backend you don't have to build' choice

Drop-in memory layer for LLM agents. Vector + graph memory variants (Mem0g) — the graph variant builds a directed labeled knowledge graph alongside the vector store, with conflict detection on contrad

Agent memory framework that models memory like an operating system. Main context = RAM, archival storage = disk; the agent itself decides when to page. Originally MemGPT, now Letta. Model-agnostic (An

Reference MCP server that gives an agent a persistent knowledge graph — entities, relations, observations stored to disk and surfaced back across sessions. The simplest path to making an agent remembe

Reference MCP server that exposes a Postgres database as a query surface. Read-only by default — but worth flagging that early versions had a SQL-injection class issue where the read-only wrapper coul

Document-oriented LLM frontend with workspaces. Connects to Ollama, LM Studio, OpenAI, Anthropic, etc. Strong document RAG.

Self-hosted ChatGPT-style web frontend. Pairs with Ollama or any OpenAI-compatible backend. Multi-user, RAG built in, fast.

LangChain's observability + evaluation platform. Trace agent runs, run evaluators against benchmark suites, version prompts. The dominant trace+eval tool for the LangChain/LangGraph ecosystem.

Open-source LLM tracing + evaluation. OpenInference standard for traces; runs locally with one pip install. The OSS-first pick for teams that want LangSmith-shaped functionality without vendor lock-in