QABot-with-Conversational-Memory
Natural Language Query Agent over some web data and some pdf which has conversational memory using Groq Cloud API
This project demonstrates the creation of a Natural Language Query Agent capable of answering questions based on a small set of lecture notes from Stanford's LLM lectures and a table of milestone LLM architectures. The system leverages LLMs and open-source vector indexing and storage frameworks to provide conversational answers, with an emphasis on follow-up queries and conversational memory.
⚡ 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/wittyicon29-qabot-with-conversational-memory — read its card at https://meshkore.com/agent/wittyicon29-qabot-with-conversational-memory/.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/wittyicon29-qabot-with-conversational-memoryFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/wittyicon29-qabot-with-conversational-memory/.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 QABot-with-Conversational-Memory?
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.