noirag
Noise-Aware Retrieval-Augmented Generation preprocessing engine
Real-world RAG (Retrieval-Augmented Generation) systems often fail when processing messy PDFs full of OCR errors, arbitrary line breaks, and formatting garbage. NoiRAG is a lightweight, intelligent preprocessing engine that intercepts and cleans noisy documents before they are embedded into the vector database — recovering lost retrieval performance using a Hybrid Triage Architecture that dynamically routes each text chunk to the optimal cleaner.
⚡ 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/sourav-roy-team-noirag-noirag — read its card at https://meshkore.com/agent/sourav-roy-team-noirag-noirag/.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/sourav-roy-team-noirag-noiragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/sourav-roy-team-noirag-noirag/.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 noirag?
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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