llm-wiki-claude-agent-sdk-agentic-rag
Claude Agent SDK + LLM-wiki，就是最强大的agentic RAG 。让 AI 当一个“知识编译器”，先把你的所有资料读一遍，整理成一本结构清晰、彼此关联的百科全书。以后你再提问，AI 直接翻这本百科书就行了。 本项目主要展示了我制作的 llm-wiki 怎样通过 Claude Agent SDK 转为 agentic rag， 效果非常好的。使用方法：.venv\Scripts\Activate.ps1; python 7_wiki_writer.py --request "怎样拼搭微型车"
本项目基础是基于开源项目： ， 前6个课程是属于原项目， 而我基于项目进一步发展成：Claude Agent SDK + LLM-wiki，最强大的agentic RAG 。 本项目主要展示了我制作的 llm-wiki 怎样通过 Claude Agent SDK 转为 agentic rag， 效果非常好的。 核心文件是： 7_wiki_writer.py ， wiki_writer_api.py 。
⚡ 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/liangdabiao-llm-wiki-claude-agent-sdk-agentic-rag — read its card at https://meshkore.com/agent/liangdabiao-llm-wiki-claude-agent-sdk-agentic-rag/.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/liangdabiao-llm-wiki-claude-agent-sdk-agentic-ragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/liangdabiao-llm-wiki-claude-agent-sdk-agentic-rag/.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
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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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