cyberwheel
A Reinforcement Learning (RL) simulation environment built for training and evaluating defensive and offensive autonomous models on simulated and emulated networks, with a focus on modularity and configurability.
With the latest major update to cyberwheel, we've implemented various new features, namely Emulation and Multi-Agent support! The first iteration of our emulator environment is now live, with instructions on setup located in cyberwheel/emulator/README.md. Cyberwheel environments now also support the ability to train and evaluate multiple RL agents in tandem, allowing you to train an RL Red Agent against an RL Blue Agent, both learning simultaneously. A full list of all of the new features are listed in more detail below:
⚡ 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/ornl-cyberwheel — read its card at https://meshkore.com/agent/ornl-cyberwheel/.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/ornl-cyberwheelFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ornl-cyberwheel/.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 '{ ... }' Capabilities
Do you own cyberwheel?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.