agentrec
End-to-end training methodology for agent recommendation system
AgentRec is a library for building small end-to-end models for agent recommendation in multiagent systems. It contains functions for training, testing, and evaluating robust agent recommendation systems. Unlike traditional classification techniques, it is adaptive to new classes as the computation of prior embeddings need not change. In the original paper, this library was able to produce a model with a top-1 accuracy of 92.2% with a >=300 ms evaluation time.
⚡ 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/joshprk-agentrec — read its card at https://meshkore.com/agent/joshprk-agentrec/.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/joshprk-agentrecFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/joshprk-agentrec/.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 agentrec?
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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