Multi-Agent-RAG
Multi-agent RAG system using AutoGen for document-focused tasks in medical education, leveraging LangChain, ChromaDB, and OpenAI embeddings.
This project implements a Retrieval Augmented Generation (RAG) system using the AutoGen framework. The system leverages multiple agents capable of interacting with one another to execute tasks that require specific document knowledge, focusing on large unstructured textual data in medical education.
⚡ 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/sj9vrf-multi-agent-rag — read its card at https://meshkore.com/agent/sj9vrf-multi-agent-rag/.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/sj9vrf-multi-agent-ragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/sj9vrf-multi-agent-rag/.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 Multi-Agent-RAG?
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.