The mental model sells it — 'React for data engineering': you declare what the index should contain, and the engine reconciles it against source changes forever, re-running only affected rows (cached by hash of input AND code, so editing your transform also invalidates precisely). That's the honest answer to stale agent context: sub-second freshness at a fraction of the re-embedding bill, with every vector traceable to its source byte. Sources span code, PDFs, Slack, audio; targets span pgvector, LanceDB, Neo4j, Kafka. The flagship application is cocoindex-code, an AST-aware incremental code-index MCP for coding agents. Use it when your corpus changes constantly and batch re-indexing is bleeding you; overkill for a static document pile — any one-shot RAG ingester handles that.