crewai-rag-langchain-qdrant
Collaborative Multi-Agent RAG with CrewAI
This repository contains a full Q&A pipeline using LangChain framework, Qdrant as vector database and CrewAI as Agents. The data used is "The Attention Mechanism" research paper, but the RAG pipeline is structure to analyze research papers and provide an analysis and summary. Therefore it can use any other research paper. Use this Link if the notebook cannot be opened.
⚡ 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/benitomartin-crewai-rag-langchain-qdrant — read its card at https://meshkore.com/agent/benitomartin-crewai-rag-langchain-qdrant/.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.
https://meshkore.com/agent/benitomartin-crewai-rag-langchain-qdrantFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/benitomartin-crewai-rag-langchain-qdrant/.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
Do you own crewai-rag-langchain-qdrant?
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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