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).

Karpathy proposes replacing on-the-fly retrieval with an LLM-maintained markdown wiki that ingests sources, compiles structured pages, and self-updates through ingest, query, and lint cycles. This approach builds a persistent, compounding knowledge base without vectors or re-retrieval, though it currently lacks enterprise controls.

• Karpathy's "LLM Wiki" replaces vector-based RAG with an LLM that reads sources once and maintains a persistent, self-updating markdown wiki instead of re-retrieving chunked fragments per query.
• The system runs three operations—ingest (add new material), query (answer from the wiki, then save new answers back into it), and lint (scheduled scans for outdated info, broken links, and gaps)—so every interaction compounds the knowledge base rather than starting fresh.
• It directly targets two RAG failure modes: stateless retrieval that repeats embedding work every query, and chunking that destroys document context.
• The idea echoes Vannevar Bush's 1945 Memex concept of an associative, hyperlinked personal knowledge library, though it currently lacks enterprise-grade controls.

This article compares two main options for setting up an AI agent: the user-friendly Claude and the customizable Hermes. It explains the differences between AI agents and chatbots, outlines the setup processes for each option, and emphasizes the importance of .md files for effective AI interaction.

• Claude's Cowork mode requires a paid subscription starting at $20/month to let it act autonomously on your computer.
• Hermes has no known security vulnerabilities as of April 2026, whereas OpenClaw has had multiple.
• Hermes offers better memory and learning from past interactions than OpenClaw, at the cost of a more involved setup.