Why BrainDB?

Inspired by Karpathy's LLM wiki idea — give an LLM a persistent external memory it can read and write. BrainDB takes that further by adding structure, retrieval, and a graph on top of the "plain markdown files" baseline.

• vs. RAG. RAG is stateless: embed documents, retrieve similar chunks on every query, stuff them into context. There's no notion of an entity that persists, accrues connections, or ages. BrainDB stores typed entities (thoughts, facts, sources, documents, rules) with explicit supports / contradicts / elaborates / derived_from / similar_to relations, combined fuzzy + semantic search, graph traversal up to 3 hops, and temporal decay so stale items fade while accessed ones stay sharp. Retrieval returns a ranked graph neighbourhood, not a pile of chunks.
• vs. classic graph DBs (Neo4j, Memgraph). Those are general-purpose graph stores with their own query languages and ops cost. BrainDB is purpose-built for LLM agents: a plain HTTP API designed for tool-calling, semantically meaningful fields (certainty, importance, emotional_valence), built-in text + pgvector search with geometric-mean scoring, always-on rule injection, automatic provenance, and runs on plain PostgreSQL + pg_trgm + pgvector — no new infrastructure to operate.
• vs. markdown files as memory. Markdown wikis are flat and unstructured: the LLM has to grep, read whole files into context, and manage linking by hand. BrainDB's entities are atomic, queryable, ranked, and self-connecting. Facts extracted from a document automatically link back to the source via derived_from; recall returns relevant nodes plus their graph neighbourhood; nothing needs to be read in full unless the agent asks for it.