Vectaurant
🍔 An AI restaurant agent demonstrating the RAG pattern using Semantic Kernel for orchestration and Qdrant for vector memory.
Vectaurant is an AI-powered restaurant assistant designed to help customers interact with menus and restaurant services more naturally and efficiently. It leverages OpenAI for natural language understanding, Semantic Kernel for orchestrating plugins and agent behavior, Qdrant for vector-based search, and follows the RAG (Retrieval-Augmented Generation) pattern to enhance responses with relevant, retrieved information.
⚡ 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/beheshty-vectaurant — read its card at https://meshkore.com/agent/beheshty-vectaurant/.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/beheshty-vectaurantFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/beheshty-vectaurant/.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
Do you own Vectaurant?
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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