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.
What stood out to me here is the idea of orchestrating multiple coding agents across machines rather than treating AI coding as a single-agent workflow. Most discussions about AI-assisted development still assume one tool running locally, but the approach described here (task decomposition + parallel execution across machines) feels closer to how distributed build systems evolved.
The dependency-graph model is particularly interesting. If AI agents can operate on isolated git worktrees and resolve tasks in parallel, the bottleneck shifts from raw coding to how well the system can plan and coordinate tasks. In practice that probably means developers spend more time defining boundaries between tasks rather than writing every line themselves.
Another challenge I’ve noticed when experimenting with these tools is deciding which agent to use for which task. Different coding agents behave very differently depending on the type of work (refactoring, feature building, test generation, etc.). Having a runner that can dispatch tasks to different agents and machines could make that experimentation much easier.
Cool to see more people building in this space. I built something similar called Ashlr AO (https://ashlrao.com) — also uses tmux session isolation under the hood, but focused more on the real-time monitoring and fleet management side: live terminal output with ANSI rendering, automatic status detection (working/waiting/error), file conflict warnings when two agents touch the same file, and an optional intelligence layer for fleet-wide analysis.
One thing I've found building in this space: the DAG-based task decomposition (which Astro does well) and the runtime orchestration layer (spawning, monitoring, coordinating live agents) are actually two complementary problems. Most tools do one or the other — Astro focuses on the planning/decomposition side, while tools like mine focus on the "mission control" side of watching 5-10 agents run simultaneously.
The biggest unsolved challenge IMO is the feedback loop: when Agent A modifies a file that Agent B is also working on, how do you handle that gracefully? We detect it and warn, but actually resolving the conflict automatically is still an open problem.
I'm actually selling Ashlr AO ($9,500) if anyone here is interested in a production-ready version of this concept — 22 modules, 2,700+ tests, desktop app, PyPI package. Details: https://ashlrao.com or email mason@ashlr.ai
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[ 0.22 ms ] story [ 15.0 ms ] threadAstro 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.
The dependency-graph model is particularly interesting. If AI agents can operate on isolated git worktrees and resolve tasks in parallel, the bottleneck shifts from raw coding to how well the system can plan and coordinate tasks. In practice that probably means developers spend more time defining boundaries between tasks rather than writing every line themselves.
Another challenge I’ve noticed when experimenting with these tools is deciding which agent to use for which task. Different coding agents behave very differently depending on the type of work (refactoring, feature building, test generation, etc.). Having a runner that can dispatch tasks to different agents and machines could make that experimentation much easier.
For anyone exploring the broader ecosystem of agentic coding tools, this overview was useful as well: https://prommer.net/en/tech/guides/best-ai-agentic-coding-to...
It compares several of the current tools and workflows that are emerging around multi-agent development.
Curious how people think this model scales once teams start coordinating dozens of agents simultaneously.
One thing I've found building in this space: the DAG-based task decomposition (which Astro does well) and the runtime orchestration layer (spawning, monitoring, coordinating live agents) are actually two complementary problems. Most tools do one or the other — Astro focuses on the planning/decomposition side, while tools like mine focus on the "mission control" side of watching 5-10 agents run simultaneously.
The biggest unsolved challenge IMO is the feedback loop: when Agent A modifies a file that Agent B is also working on, how do you handle that gracefully? We detect it and warn, but actually resolving the conflict automatically is still an open problem.
I'm actually selling Ashlr AO ($9,500) if anyone here is interested in a production-ready version of this concept — 22 modules, 2,700+ tests, desktop app, PyPI package. Details: https://ashlrao.com or email mason@ashlr.ai