AI Backend

Backend is where AI tools stopped being demos and started running production infrastructure. Supabase and Convex generate and manage databases with AI assistance built into setup and queries, useful if you don't want to hand write schema migrations. Claude Code, Cursor, and Codex CLI can scaffold API routes and server logic directly inside an existing repo, the same tools people use for frontend work but pointed at the backend half of the job. LangGraph, the OpenAI Agents SDK, and the Claude Agent SDK are lower level, meant for developers building their own agent behavior rather than picking a finished product. n8n and Dify sit at the no code end, letting you wire up backend logic and integrations without writing much code at all. Model Context Protocol isn't a tool exactly, it's the standard several of these now speak so agents can call the same tools and data sources consistently.

If you're production bound, weigh how much of the generated backend code you can actually audit and ship, not just how fast it scaffolds. Compare the full list below.