LangChain_chatwithcsv_irisdataset
This project is using the LangChain library and OpenAI to create an agent that can answer questions about a dataset (in this case, the iris dataset). The agent is created using a CSV agent and an OpenAI language model, which allows the user to interact with the data using natural language queries.
This script reads data from a CSV file and creates an agent to answer questions about the data using OpenAI's language model. The data used in this example is the Iris dataset, which is a popular dataset in machine learning and consists of measurements of the sepal and petal sizes of three different species of the iris flower.
⚡ 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/tolgakurtuluss-langchainchatwithcsvirisdataset — read its card at https://meshkore.com/agent/tolgakurtuluss-langchainchatwithcsvirisdataset/.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/tolgakurtuluss-langchainchatwithcsvirisdatasetFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tolgakurtuluss-langchainchatwithcsvirisdataset/.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 LangChain_chatwithcsv_irisdataset?
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.