magnet
MAGNet: Multi-agents control using Graph Neural Networks
The goal of this project is controlling multi-agents using reinforcement learning and graph neural networks. Multi-agent scenarios are usually sparsely rewarded. Graph neural networks have an advantage that each node can be trained robustly. With this property, we hypothesized that each agent in an environment can be controlled individually. Since there have been many research papers related to graph neural networks, we would like to apply it to reinforcement learning.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/tegg89-magnet — read its card at https://meshkore.com/agent/tegg89-magnet/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/tegg89-magnetFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tegg89-magnet/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own magnet?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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