How to Create an AI Agent with LangChain and Google Gemini

Building your own AI agent no longer requires complex infrastructure. LangChain, a popular open-source framework, makes it straightforward to create LLM-powered applications and agents that can chat, reason, and respond to user queries. In this practical walkthrough, you’ll see how to build a simple yet functional command-line AI assistant using LangChain paired with Google’s Gemini models via the Gen AI Python SDK.

The process starts by setting up a Python project and defining the agent’s purpose: a lightweight desktop-style assistant that runs in the terminal, answers questions, and maintains an interactive chat. You’ll connect it to Gemini (such as a Flash or lightweight model) for fast responses, store your API key securely in a .env file obtained from Google AI Studio, and leverage LangChain’s integrations for chat models and related components.

A clear system prompt guides the agent’s behavior, while LangChain handles the underlying orchestration. The resulting tool supports both interactive chat mode and single-query execution. Once running, you can test it with real questions—such as factual queries that benefit from the model’s reasoning capabilities—and refine it as needed.

This approach keeps things accessible: no web frontend required, just a clean CLI experience. LangChain’s flexibility also opens the door to more advanced setups later with LangGraph for complex multi-step or multi-agent workflows. Whether you’re exploring AI development or prototyping useful tools, this method delivers a working agent quickly using modern, well-supported libraries.