cube-sparql-llm
PoC using OpenAI API via LangChain to create SPARQL queries that query RDF cubes
Welcome to the intersection of structured data and cutting-edge AI! This Jupyter notebook aims to explore the synergy between RDF cubes, SPARQL queries, and Language Model (LM) capabilities. Using OpenAI's API through LangChain, we will dive into the process of constructing intuitive SPARQL queries to interact with RDF cubes, enhancing our data retrieval and analysis processes
⚡ 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/ktk-cube-sparql-llm — read its card at https://meshkore.com/agent/ktk-cube-sparql-llm/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/ktk-cube-sparql-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ktk-cube-sparql-llm/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own cube-sparql-llm?
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.