Thanks for the reply — your article really helped clarify what "distributed" means in agentfab.
From what I understand, it's closer to a Kubernetes-style scheduler/worker model — so more about parallelizing computation for fully controlled systems/applications, rather than "distributed" in the sense of independent agents across boundaries.
One thought I had (just a random thought): it could be interesting to push this toward a broader notion of distribution — not quite full P2P, but something like an "agent hub", where agents owned by different organizations coordinate via MCP/A2A or similar protocols. If there were a way to manage collaborative task execution across those boundaries, that would feel pretty cutting-edge and a genuinely different class of system.
Looks interesting and quite polished overall. I’m also working on an agentic workflow engine (graflow.ai) as an alternative to LangGraph, so this caught my attention. The sandboxing layer in particular looks well thought out and solid.
One question I had: in the demo, the agent builds a browser game. But that kind of task seems achievable even with vanilla Opus (plus Claude Skills / tool use), so I struggled a bit to see the core differentiation here.
Also, the project mentions "distributed" — is that mainly coordination between local processes, or does it already support cross-machine / networked execution? If it’s the former, tighter integration with remote execution (e.g. Tailscale-based peer agents or similar) could be a more meaningful differentiator. Not sure how practical that is in real deployments, but it feels like a clearer step beyond existing agent runtimes.
On the Graph-RAG part, I also couldn't fully understand how it's actually constructed internally (how the graph is built/updated and how retrieval is integrated into execution). A bit more detail on the internal mechanics there would help clarify the design.