GraphRAG_benchmarking
Benchmarking GraphRAG and AriGraph on MuSiQue, HotpotQA and BabiLong QA1, run locally with Ollama and Llama 3.
Retrieval-Augmented Generation (RAG) struggles to generate accurate answers to complex or “global” questions that require understanding the entire dataset, not just individual fragments. Traditional RAG approaches rely heavily on vector search, which often results in incomplete or misleading answers because it only considers the top-k most semantically similar text segments, missing the broader context and relationships within the data.
⚡ 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/baltsat-graphragbenchmarking — read its card at https://meshkore.com/agent/baltsat-graphragbenchmarking/.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/baltsat-graphragbenchmarkingFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/baltsat-graphragbenchmarking/.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 GraphRAG_benchmarking?
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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