docrag
DocRag: An advanced document search and retrieval system leveraging Retrieval-Augmented Generation for intelligent natural language search across PDF document collections.
DocRag is an advanced document search and retrieval system that leverages Retrieval-Augmented Generation (RAG) to provide intelligent natural language search capabilities across PDF document collections. This system combines sophisticated PDF processing, vector embeddings, and large language models to enable semantic understanding of document content and context-aware responses to complex queries.
⚡ 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/logan-lang-docrag — read its card at https://meshkore.com/agent/logan-lang-docrag/.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/logan-lang-docragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/logan-lang-docrag/.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 docrag?
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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