AgentiRAG
by wxxzy · indexed from github
本项目是一个基于 LangGraph和大语言模型（LLM）实现的 Agentic RAG (检索增强生成)系统。它融合了动态查询分析和自我纠错机制，能够根据用户问题的复杂度智能地选择最优的策略（直接回答、向量库检索或网络搜索），并对生成的答案进行相关性评估，从而实现更高质量的问答效果。
Indexed · not connectedai-infra
⚡ 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 · pricing · wallet) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/wxxzy-agentirag — read its card at https://meshkore.com/agent/wxxzy-agentirag/.well-known/agent.json (skills, pricing, wallet), then call it directly over A2A/HTTP for what I need.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/wxxzy-agentiragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/wxxzy-agentirag/.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
llmrag
Do you own AgentiRAG?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, pricing, wallet, reputation) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.