RAG_LearnLittleCode
基于 RAG 的 AI 智能笔记助手「云尚」：FastAPI + LangGraph + ChromaDB + Redis 构建。ReAct / Plan-and-Execute 双 Agent 模式、深度思考、图片/视频多模态、MCP 联网搜索与网页抓取、DeepL 翻译 / Wolfram 计算 / Edge TTS 语音、PPT 生成、记忆压缩、知识库重排序、笔记回收站与邮件导出、Token 用量追踪。后续版本会持续优化并添加Skill功能
prompts/ 目录存放 12 个 Agent 提示词文件，可直接编辑调优，如 main_prompt.txt（主 Agent，已声明 MCP 与外部 API 工具的使用规则）、plan_generation.txt（计划生成）、classify_complexity.txt（L2 分类）、rag_summarize.txt（RAG 摘要）等。
⚡ 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/qiaojoin586-droid-raglearnlittlecode — read its card at https://meshkore.com/agent/qiaojoin586-droid-raglearnlittlecode/.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/qiaojoin586-droid-raglearnlittlecodeFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/qiaojoin586-droid-raglearnlittlecode/.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_LearnLittleCode?
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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