ruvector-ruvllm
Bounded native Rust key value cache and attention primitives for Python LLM pipelines
A bounded CPU key/value cache and attention primitive for Python LLM pipelines, backed by the actual ruvllm Rust crate. Store key/value token vectors and query them with scaled dot product attention. This focused Python package exposes cache operations, not text generation or full npm ruvllm feature parity. No model weights or hosted services are required.
⚡ 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/ruvector-team-ruvector-ruvllm — read its card at https://meshkore.com/agent/ruvector-team-ruvector-ruvllm/.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/ruvector-team-ruvector-ruvllmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ruvector-team-ruvector-ruvllm/.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 ruvector-ruvllm?
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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