Hi HN, I built Astro because I was frustrated with running AI coding agents one at a time. You describe what you want, sit there waiting, then manually feed the next task. The ceiling isn't capability — it's coordination.

Astro sits above agents like Claude Code, Codex, and OpenClaw. You describe a goal once, it generates a dependency graph (DAG, not a flat list), and dispatches tasks in parallel across your machines. Each task runs in an isolated git worktree and opens a PR. Tasks that can run in parallel do — total time equals the longest path, not the sum of all tasks.

Key design decisions:

- Your machines run the agents with your API keys. The Astro server never calls AI models and never sees your keys. - Every task dispatch is cryptographically signed by your browser. The agent runner verifies the signature before executing. - The agent runner is fully open source (this repo). The server provides the planning UI and dashboard at astroanywhere.com. - Works with multiple agents: Claude Code, Codex, OpenClaw, OpenCode. It auto-detects what's installed. - Dispatches to local machines, HPC clusters (Slurm), and cloud VMs. One `npx @astroanywhere/agent` command sets everything up.

We also ship built-in templates (stock analysis reports, academic paper review, presentation generation) that run as parallel task graphs out of the box.

Quick start: install an agent (e.g. `npm i -g @anthropic-ai/claude-code`), register at astroanywhere.com, run `npx @astroanywhere/agent`, and you're connected.

Website with walkthrough: https://astroanywhere.com

Also see https://github.com/astro-anywhere/astro-examples for example outputs.

Happy to answer questions about the architecture, the planning approach, or anything else.