rag-agent
A powerful Retrieval Augmented Generation system that turns your Markdown documentation into an interactive knowledge base using vector search and LLMs.
Markdown Knowledge RAG transforms your Markdown files into a searchable knowledge base using vector embeddings. Ask questions in natural language and get accurate answers based on your documentation content. The system combines Milvus for vector storage with your choice of Ollama's local LLMs or OpenAI's models for embeddings and text generation.
⚡ 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/kevwan-rag-agent — read its card at https://meshkore.com/agent/kevwan-rag-agent/.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/kevwan-rag-agentFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/kevwan-rag-agent/.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 rag-agent?
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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