RAGLink
by Justin ZM · indexed from pypi
RAGLink是一个开源的Retrieval-Augmented Generation框架，旨在通过结合检索和大模型生成技术，提升自然语言处理任务的性能和效率。为用户提供了一个强大、灵活且易于扩展的开发环境。
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 · 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/justin-zm-raglink — read its card at https://meshkore.com/agent/justin-zm-raglink/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/justin-zm-raglinkFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/justin-zm-raglink/.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
ragretrieval
Do you own RAGLink?
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.
Explore the mesh
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