find https://ossinsight.io/blog/agent-memory-race-2026 Anyone tried some of them?

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.

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.

The old project page can be find in https://github.com/myui/hivemall