Machine-Learning-Chatbot
This repository demonstrates how to integrate your Dialogflow agent with 3rd-party services services using a Node.JS backend service. Integrating your service allows you to take actions based on end-user expressions and send dynamic responses back to the end-user.
This repository contains a backend service for an intelligent chatbot that onboards clients, answers questions, and sets up appointments on a retail website. The purpose of the backend service is to improve the customer experience that users have when interacting with a Dialogflow chatbot.
⚡ 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/ddayto21-machine-learning-chatbot — read its card at https://meshkore.com/agent/ddayto21-machine-learning-chatbot/.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/ddayto21-machine-learning-chatbotFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ddayto21-machine-learning-chatbot/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own Machine-Learning-Chatbot?
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.