A Groq-Powered Agentic Research Assistant with LangGraph, Tool Calling, Sub-Agents, and Agentic Memory: Let’s Build It

A tutorial shows how to build a Groq-powered LangGraph research agent with tool calling, sub-agents, and long-term memory.

It first configures Groq as an OpenAI-compatible backend for LangChain using models like llama-3.3-70b-versatile.

The agent can search the web, fetch pages, run Python, load skills, delegate to sub-agents, and use memory.

The piece highlights a low-cost way to create a multi-step research workflow that produces structured outputs.