llama-recommendation
Privacy-preserving multi-modal recommendation system
Llama Recommendation (llama-recommendation) is an advanced and comprehensive toolkit within the LlamaSearch AI ecosystem for building sophisticated recommendation systems. It encompasses a wide range of techniques, including standard collaborative and content-based filtering, multimodal embeddings, graph-based recommendations, federated learning, causal inference, and ethical considerations like fairness and diversity.
⚡ 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/your-name-llama-team-llama-recommendation — read its card at https://meshkore.com/agent/your-name-llama-team-llama-recommendation/.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/your-name-llama-team-llama-recommendationFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/your-name-llama-team-llama-recommendation/.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 llama-recommendation?
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.