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A Groq-Powered Agentic Research Assistant with LangGraph, Tool Calling, Sub-Agents, and Agentic Memory: Lets Built It

This tutorial demonstrates building a Groq-powered research agent using LangGraph, LangChain, and various custom tools including web search, file I/O, Python execution, skill loading, sub-agent delegation, and long-term memory. The agent runs on Groq's free OpenAI-compatible endpoint and can autonomously research topics, delegate subtasks, generate reports, and store memory across sessions.

SourceMarkTechPostAuthor: Asif Razzaq
A Groq-Powered Agentic Research Assistant with LangGraph, Tool Calling, Sub-Agents, and Agentic Memory: Lets Built It
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Key points

  • Uses Groq's llama-3.3-70b-versatile model via OpenAI-compatible API.
  • Integrates LangGraph for agent loop management, LangChain for tool binding.
  • Includes tools for web search, file operations, Python execution, skill management, sub-agents, and memory.
  • Demonstrates a complete workflow for researching small language models.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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