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Zite is built for AI agents. External agents (Claude, ChatGPT, Cursor) have no training data on zitejs, so Zite delivers the context they need, when they need it.

The framework guide

Every create_sandbox response includes a framework guide — a concise spec of how Zite apps are structured (monorepo layout, data access, zite.sql() identifier rules, available imports and libraries, build workflow). Connecting the MCP and booting a sandbox is enough to build correctly, with no memorizing conventions in advance. The same conventions are documented for humans in the Framework reference.

llms.txt

This site publishes an llms.txt index and a full llms-full.txt so agents and IDEs can pull the docs into context. Point your tool at:

Give your agent Zite context

When an agent works outside a Zite sandbox — building against the Database API, or planning in your IDE before it connects — drop this into your project’s AGENTS.md (or a Cursor rule / CLAUDE.md):
For the full corpus, point your tool at this site’s llms-full.txt, and link recipes directly (e.g. /recipes/stripe-payments) for task-specific guidance.

Getting great results

  • Be specific about data — name the tables and fields; the agent designs the schema from your description.
  • Ask it to verify at runtime — a clean type-check isn’t proof. See the build loop.
  • Let it read .zite/db.ts — the source of truth for table and field names.
  • Connect integrations first — connect Slack or Stripe when the agent hands you a setup_integration link, so the token is available while it builds.