rag-with-reflection
It shows an advanced RAG with Reflection where LangGraph is used for workflow management.
여기에서는 LangGraph를 이용하여 기본 RAG를 구현하고, reflection과 query transformation을 이용하여 RAG의 성능을 향상시키는 방법을 비교하여 설명합니다. RAG를 이용함으로써 필요한 시점에 적절한 비용으로 기업의 데이터를 활용한 애플리케이션을 개발할 수 있습니다. 하지만, 자연으로 질의되는 사용자의 요청으로부터 사용자의 의도를 정확히 파악하고, 관련된 문서들로 부터 꼭 필요한 문서만을 선택하고, 한국어와 영어 문서를 모두 조회하려면 다양한 노력이 필요합니다.
⚡ 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/kyopark2014-rag-with-reflection — read its card at https://meshkore.com/agent/kyopark2014-rag-with-reflection/.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/kyopark2014-rag-with-reflectionFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/kyopark2014-rag-with-reflection/.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 rag-with-reflection?
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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