Memvid spotlights post-RAG agent memory stack

The post is lightweight, but the framing is right: AI app builders are moving from “use RAG everywhere” toward more specialized context layers for retrieval, memory, and tool state.

• –Memvid’s pitch is the most novel of the three, replacing database-heavy memory setups with a portable single-file layer built for long-running agents and fast local retrieval.
• –LlamaIndex remains the safest recommendation for document chat, repo search, and knowledge-base style apps where indexing and retrieval are still the core job.
• –Continue shows how production AI coding tools already blend retrieval, indexing, and persistent context instead of treating RAG as the whole architecture.
• –For developers, the real lesson is system design: use RAG for external knowledge, memory for ongoing state, and hybrids when agents need both.