How to Build a Local AI Agent with LangGraph and Ollama

Building an AI agent that runs entirely on your local machine requires an orchestration framework and a local LLM. LangGraph provides the state management, while Ollama serves the model offline.

By keeping the data local, you eliminate API latency and bypass corporate data-sharing restrictions. This tutorial shows you how to build a basic research agent that can execute terminal commands without sending data to the cloud.

Prerequisites and Architecture

You need a machine with at least 16GB of RAM to run an 8B parameter model comfortably.

The architecture relies on two components:

1. Ollama: The runtime environment executing Llama 3 8B.
2. LangGraph: A library by LangChain that structures the agent's logic as a cyclical graph, allowing it to "think," use tools, and verify its own work.

Step 1: Install Dependencies

First, ensure Ollama is installed and the model is downloaded. Then, set up your Python environment.