Today we're launching LunaOS on Product Hunt. 140+ commands that compose into pipelines —
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LunaOS runs entirely on Cloudflare Workers, D1, KV, and Vectorize. No traditional
servers. In this post we explain our architecture: how we handle SSE streaming from
Workers, manage RAG with Vectorize embeddings, and keep cold starts under 5ms. We also
cover our 200-line-per-file rule and how it keeps the codebase maintainable.
Most AI tools lack context about your specific codebase. LunaOS solves this with
built-in RAG: connect your GitHub repo, and every agent automatically receives relevant
code context before generating results. We use Cloudflare Vectorize with BGE embeddings
for fast, accurate semantic search across your entire repository.
A single agent is useful. A chain of agents is powerful. In this tutorial, we walk
through creating a "Full Review" chain that runs code-review, security-audit, and
test-generator in sequence — passing context between each step. You can build custom
chains in the Studio IDE or define them in YAML.