ai-agent-tutorial
by super-rick · indexed from github
从 0 手写 AI Agent —— 完整 Python 教程 不依赖 LangChain / LlamaIndex 等任何 Agent 框架，纯手写实现一个完整的 AI Agent 应用， 覆盖 Agent 的全部核心概念：LLM 调用、工具、ReAct 循环、记忆、RAG、规划、多智能体、评估。
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/super-rick-ai-agent-tutorial — read its card at https://meshkore.com/agent/super-rick-ai-agent-tutorial/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/super-rick-ai-agent-tutorialFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/super-rick-ai-agent-tutorial/.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
llmrag
Do you own ai-agent-tutorial?
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
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.