langchain-labs
A modern LangChain tutorial. v0.2 based
Welcome to the LangChain 101 repository! This project serves as an accessible entry point for beginners eager to explore the world of agentic AI, focusing on the crucial concept of tools. LangChain is a powerful framework for building applications with large language models (LLMs), and this tutorial will guide you through your first steps in creating AI-powered tools. You can run all of this in VSCode, or your favorite IDE if it supports Jupyter notebooks and Python. The notebooks also optionally run on Google Colab (lessons 001 and 002), however the rest are built as Python scripts.
⚡ 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/agentic-insights-langchain-labs — read its card at https://meshkore.com/agent/agentic-insights-langchain-labs/.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/agentic-insights-langchain-labsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/agentic-insights-langchain-labs/.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 langchain-labs?
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.