Three major AI agent platforms—Manus, OpenClaw, and Claude Code—store their memory in plain Markdown files instead of vector databases. The article breaks down how file-based context boosts token-cache economics, enables attention control, and layers optional semantic retrieval, plus when this approach starts to break down.

• Manus hit $100M ARR in eight months using append-only Markdown for memory instead of a vector database, since its 100:1 input-to-output token ratio makes cache-friendly file appends roughly 10x cheaper than invalidating a cache with database writes.
• OpenClaw layers SQLite-based vector search directly on top of its Markdown notes (vectorWeight = 0.7 plus temporal decay) rather than using an external vector database, blending keyword and embedding scores.
• Claude Code's CLAUDE.md files use directory/scope-based progressive disclosure to load only relevant context, replacing the need for a retrieval database entirely.
• All three systems treat the filesystem hierarchy itself as the retrieval mechanism, using plain text files for both long-term memory and active attention control (e.g. Manus's constantly rewritten todo.md).