How to Build Your Own AI Agent Using Gemini CLI: A Step-by-Step Guide

Learn to build an AI agent with Gemini CLI in this step-by-step guide. Create a to-do app, automate tasks, and extend with APIs.

July 26, 2025
11 min read

Introduction: Your Journey to Building an AI Sidekick Begins Here

Imagine having a tireless assistant who lives in your terminal, ready to debug code, automate tasks, or even whip up a web app from a sketch—all with a few keystrokes. Sounds like a developer’s dream, right? Welcome to the world of Gemini CLI, Google’s open-source AI agent that brings the power of Gemini 2.5 Pro directly to your command line. Whether you’re a seasoned coder or a curious beginner, this guide will walk you through creating your own AI agent using Gemini CLI, step by step, with a sprinkle of real-world magic to keep things exciting.

In this post, we’ll dive into what makes Gemini CLI a game-changer, explore its capabilities, and guide you through building a custom AI agent that can tackle tasks like summarizing documents, generating code, or even interacting with APIs. By the end, you’ll have a fully functional AI sidekick tailored to your needs. Ready to code smarter, not harder? Let’s get started!

Why Gemini CLI? The Power of AI in Your Terminal

Before we jump into the nitty-gritty, let’s talk about why Gemini CLI is worth your time. Launched in June 2025, Gemini CLI is Google’s answer to the growing demand for terminal-based AI tools. Unlike traditional IDE-based assistants like GitHub Copilot, Gemini CLI lives in your command line, offering a lightweight, versatile, and open-source solution for developers. Here’s why it’s making waves:

Open-Source Freedom: Licensed under Apache 2.0, Gemini CLI lets you inspect, modify, and contribute to its codebase, ensuring transparency and flexibility.
Massive Context Window: With up to 1 million tokens, it can handle entire codebases or lengthy documents without breaking a sweat.
Generous Free Tier: You get 60 requests per minute and 1,000 daily requests for free with a personal Google account, making it accessible to everyone.
Versatility: From coding and debugging to content generation and task automation, Gemini CLI is a jack-of-all-trades.
Extensibility: It supports the Model Context Protocol (MCP), allowing you to integrate custom tools and APIs for domain-specific tasks.

Think of Gemini CLI as a Swiss Army knife for developers—a tool that adapts to your workflow, whether you’re building a to-do app, analyzing a codebase, or automating GitHub issue labeling. Now, let’s roll up our sleeves and build your AI agent from scratch.

Prerequisites: Setting the Stage for Your AI Agent

Before we dive into the code, let’s ensure you have everything you need. Building an AI agent with Gemini CLI is straightforward, but it requires a few tools and setups. Here’s what you’ll need:

Node.js (Version 18 or Higher): Gemini CLI runs on Node.js, so make sure it’s installed. Check your version by running node -v in your terminal. If it’s not installed, download it from the official Node.js website.
A Google Account: You’ll need this to authenticate and access Gemini 2.5 Pro’s free tier. Alternatively, you can use an API key from Google AI Studio or Vertex AI for extended usage.
A Terminal: Whether you’re on Linux, macOS, or Windows, Gemini CLI works seamlessly. Linux and macOS users can use their default terminals, while Windows users might prefer WSL or PowerShell.
Basic Command-Line Knowledge: Familiarity with terminal commands like cd, npm, and git will make your life easier.
Optional: Google Cloud Project: If you want to use advanced features like custom MCP servers or APIs, set up a Google Cloud project. New users get $300 in free credits to experiment.

Got everything? Great! Let’s move on to installing Gemini CLI and setting up your environment.

Step 1: Installing Gemini CLI

Let’s kick things off by installing Gemini CLI. The process is quick and painless, taking just a couple of minutes. Here’s how to do it:

Open Your Terminal: Fire up your favorite terminal application.
Install Gemini CLI via npm: Run the following command to install Gemini CLI globally:
```
npm install -g @google/gemini-cli
```
This command fetches the latest version of Gemini CLI from npm and sets it up on your system.
Authenticate with Google: Once installed, run:
```
gemini
```
You’ll be prompted to log in with your Google account. Follow the browser-based authentication flow to grant access. If you’re on a headless server, check out the workaround for authentication using a custom script (more on this later).
Optional: Use an API Key: For extended usage or to avoid browser-based login, generate an API key from Google AI Studio and set it as an environment variable:
```
export GEMINI_API_KEY="YOUR_API_KEY"
```
Save this in a .env file in your project folder for convenience.

Pro Tip: If you hit authentication issues on a headless server, create a fake xdg-open script to capture the auth URL, as shared by a Reddit user. This involves saving the URL to a file and manually completing the login on another device.

Once authenticated, you’re ready to start using Gemini CLI. Run gemini again to confirm it’s working—you should see a prompt ready for your commands.

Step 2: Defining Your AI Agent’s Purpose

Before you start typing prompts, take a moment to define what your AI agent will do. A clear purpose shapes its capabilities and ensures it delivers value. Ask yourself: What problem do I want my agent to solve? Here are a few ideas to spark inspiration:

Code Generator: Build a web app, script, or API from a natural language description.
Debugger: Analyze and fix bugs in your codebase.
Workflow Automator: Manage GitHub issues, automate file operations, or run shell commands.
Research Assistant: Summarize documents or fetch real-time data from the web.
Creative Partner: Generate project documentation, flowcharts, or even a slide deck.

For this guide, let’s build a simple to-do app generator that creates a functional HTML, CSS, and JavaScript to-do app based on a natural language prompt. This is a great starting point to showcase Gemini CLI’s coding prowess.

Step 3: Configuring Your Project Environment

To make your AI agent context-aware, create a GEMINI.md file in your project’s root directory. This file acts like a blueprint, defining project rules, coding style, and tools your agent should use. Here’s how to set it up:

Create a Project Folder:
```
mkdir todo-app
cd todo-app
```
Create a GEMINI.md File:
```
touch GEMINI.md
```

Add Configuration Details: Open GEMINI.md in a text editor and add the following:

# Project: To-Do App Generator
- **Primary Language**: JavaScript
- **Framework**: Vanilla HTML/CSS/JS
- **Coding Style**: Use ES6, follow Airbnb style guide
- **Output Directory**: ./output
- **Tools**: Use built-in file write tool for generating files

This tells Gemini CLI to generate code in vanilla JavaScript, adhere to a specific style guide, and save output files in an output folder.

Optional: Add MCP Servers: If you want your agent to interact with external tools (e.g., GitHub APIs), configure MCP servers in a .gemini/settings.json file. For example, to add GitHub integration:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "YOUR_TOKEN"
      }
    }
  }
}

Run /mcp in Gemini CLI to verify the server is available.

With your environment set, Gemini CLI is ready to understand your project’s context and generate tailored responses.

Step 4: Crafting Your First Prompt

Now comes the fun part—telling Gemini CLI what to do. Since we’re building a to-do app generator, let’s craft a clear, concise prompt. Run gemini in your project folder, and type:

create a simple to-do app using HTML, CSS, and JavaScript. Include features to add, delete, and mark tasks as complete. Save the files in the ./output directory.

Gemini CLI will analyze the prompt, reference your GEMINI.md file, and generate three files: index.html, styles.css, and script.js. Here’s what you might expect:

index.html: A basic HTML structure with an input field, a button to add tasks, and a list to display tasks.
styles.css: Clean, responsive styling for the app, following the Airbnb style guide.
script.js: JavaScript logic to handle adding, deleting, and toggling task completion.

After running the prompt, check the output directory:

ls output

You should see the generated files. Open index.html in a browser to test the app. If something’s off, you can refine the prompt, like: “Add local storage to persist tasks in the to-do app.” Gemini CLI will update the code accordingly.

Real-World Example: A developer on Reddit shared how they used Gemini CLI to generate a to-do app in under a minute, then iterated with prompts to add features like task categories and due dates. The result? A fully functional app ready for deployment, all from the terminal.

Step 5: Iterating and Refining Your Agent

Building an AI agent is an iterative process. Gemini CLI’s ReAct (Reason + Act) framework lets it break down complex tasks, try solutions, and adapt based on feedback. Here’s how to refine your agent:

Test Thoroughly: Run the to-do app and test edge cases, like empty inputs or long task names. If issues arise, prompt Gemini CLI to fix them: “Fix the bug where empty tasks are added to the list.”
Add Features: Enhance the app with prompts like: “Add a feature to filter tasks by completion status.”
Integrate APIs: Use MCP servers to connect to external APIs. For example, add a weather API to display local weather in the app: “Integrate a weather API to show today’s forecast in the to-do app header.”
Gather Feedback: Share your app with colleagues or the community (e.g., on GitHub) and incorporate their suggestions. Gemini CLI can handle feedback-driven updates like: “Refactor the code to improve readability based on user feedback.”

Pro Tip: Use the /chat save <tag> command to save your conversation history and /chat resume <tag> to pick up where you left off. This is perfect for long projects where context matters.

Step 6: Extending Your Agent with Advanced Features

Want to take your AI agent to the next level? Gemini CLI’s extensibility makes it a playground for creativity. Here are some advanced ideas:

Multi-Agent Systems: Use frameworks like LangGraph or CrewAI with Gemini CLI to build collaborative agents. For example, create a research agent to fetch data and a writer agent to draft reports.
Multimodal Capabilities: Leverage Gemini’s ability to process images or PDFs. Prompt: “Generate a web app from this PDF wireframe.”
Automation Workflows: Automate repetitive tasks, like running tests or updating dependencies. Example: “Scan my build.gradle.kts and list dependencies with available updates.”
GitHub Integration: Use MCP servers to manage GitHub issues, like labeling or closing them based on analysis.

Case Study: A Google Cloud blog post detailed how a developer used Gemini CLI with LangGraph to build a multi-agent system for trip planning, integrating event and hotel APIs to create a personalized itinerary. The agent handled complex queries like “Plan a weekend trip to Paris with concerts and budget hotels,” showcasing Gemini’s reasoning and API integration capabilities.

Once your AI agent is polished, it’s time to share it with the world. Here’s how to deploy and distribute your work:

Host on Google Cloud: Use Google Cloud’s Vertex AI or Cloud Run to deploy your app as a containerized service. Gemini CLI can generate the necessary Dockerfile and deployment scripts.
Share on GitHub: Push your project to a GitHub repository and invite community contributions. Since Gemini CLI is open-source, others can fork and enhance your agent.
Document Your Work: Ask Gemini CLI to generate documentation: “Create a README.md for my to-do app with setup instructions and usage examples.”
Monitor Usage: If you’re using a paid API key, track usage in Google AI Studio to avoid hitting limits.

Pro Tip: Join the Gemini CLI community on GitHub to share your agent, report issues, or explore example MCP servers. The official roadmap outlines upcoming features like enhanced MCP support and faster response times.

Challenges and Limitations to Watch For

No tool is perfect, and Gemini CLI has a few quirks to keep in mind:

Performance: Some users report slower responses compared to competitors like Claude Code, especially for complex integrations.
Error Handling: Occasional errors during API calls or complex tasks require manual troubleshooting.
Learning Curve: Beginners may find advanced features like MCP servers tricky to configure. Start simple and scale up.
Usage Limits: While the free tier is generous, heavy users may need a paid API key for unlimited requests.

Despite these, Gemini CLI’s open-source nature and active community mean issues are quickly addressed, and new features are constantly added.

Conclusion: Your AI Agent Awaits

Building your own AI agent with Gemini CLI is like crafting a trusty sidekick—one that’s always ready to tackle your coding challenges, automate your workflows, or spark creative ideas. From setting up your environment to generating a to-do app and extending it with APIs, you’ve now got the blueprint to create something truly powerful. The best part? Gemini CLI’s open-source ethos and massive context window mean your agent can evolve with your needs.

So, what will your AI agent do next? Maybe it’ll automate your GitHub workflow, generate a killer web app, or even help you plan your next vacation. The possibilities are endless, and the terminal is your playground. Fire up Gemini CLI, experiment, and share your creations with the world. Happy coding!

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