Gemini CLI is a new open-source AI agent from Google’s Gemini family, released in June 2025. It brings the power of the Gemini 2.5 Pro large language model directly into your terminal, letting you write code, create content, and solve problems using natural language commands. Because it runs in your CLI, Gemini CLI turns your shell into an intelligent assistant that can read and write files, run commands, and integrate with online tools. This matters because it makes advanced AI capabilities freely available to any developer: just log in with a Google account and you get access to Gemini’s models (with a 1 million-token context window) at no cost.
Installation and Setup
Getting started with Gemini CLI is straightforward on macOS, Linux, or Windows. First, make sure you have Node.js (v18 or higher) installed. Then you can install Gemini CLI via npm or run it on-the-fly with npx:
- Node.js installation: If you don’t have it, download Node.js 18+ from the official site.
- Global install (recommended): Open your terminal (Terminal.app on macOS, bash/zsh on Linux, PowerShell or CMD on Windows) and run
npm install -g @google/gemini-cli. You may needsudoon macOS/Linux. This makes thegeminicommand available system-wide. - Or use npx: Alternatively, you can launch it without installing by running
npx @google/gemini-cliin your shell.
After installation, simply type gemini and press Enter. On first run, Gemini CLI will guide you through a quick setup. You’ll choose a theme (color scheme) and then a sign-in method. The easiest choice is “Login with Google”, which opens a browser window to authenticate. Grant access with your Google account, and you’ll immediately get a free Gemini Code Assist license (which unlocks Gemini 2.5 Pro in the CLI). If you prefer, you can also enter an API key (set GEMINI_API_KEY) or use a Google AI Studio/Vertex AI key for higher limits. Once authenticated, Gemini CLI confirms your access and drops you into an interactive prompt ready to accept natural language commands.
Key Capabilities of Gemini CLI
- Massive 1M-token context window: Gemini 2.5 Pro can work with up to a million tokens in one conversation, vastly more than most models. In practice, developers often use around 100k tokens at a time for best performance. This huge context means you can feed entire large codebases or documents into Gemini CLI for analysis or summarization.
- MCP tool integration: Gemini CLI supports the open Model Context Protocol (MCP) standard. This lets the CLI plug into external tools and data sources seamlessly. For example, built-in MCP tools allow Gemini CLI to use Google Search for up-to-date web info, or to call media models like Imagen (for images) and Veo (for video) in a pipeline. You can also run custom MCP servers (see our blog on what MCP servers do) to connect enterprise systems, databases or files directly into your terminal AI sessions.
- VS Code agent mode integration: The Gemini CLI shares its AI engine with the Gemini Code Assist extension for VS Code. In Visual Studio Code’s chat interface, an “agent mode” lets you type high-level prompts (like “write tests for this function” or “refactor my code”) and have the AI automatically plan and execute multiple steps. In this mode, Gemini works as your coding partner inside the IDE, writing code, running tests, fixing errors, and more, all in response to your natural-language instructions.
- Natural-language terminal tasks: You can control your development workflow entirely with plain English. For example, you could ask Gemini CLI to “generate unit tests for the
calculateTaxfunction” or “refactor these files to modernize the syntax”. It can also automate command-line tasks: running Git commands (“create a new branch and commit all changes”), searching and processing logs, compiling code, and so on. Essentially, if you can describe a task, Gemini CLI can often do it. It can read and write your local files (using@filenamesyntax) and even execute shell commands when allowed, saving you manual effort.
Gemini CLI vs Copilot Agent Mode vs Claude Code
How does Gemini CLI stack up against other AI coding assistants? The table below compares Gemini CLI with GitHub’s Copilot Agent Mode and Anthropic’s Claude Code on context size, pricing, extensibility, and speed:
| Tool | Context Window | Price | Plugin Ecosystem | Speed |
|---|---|---|---|---|
| Gemini CLI | Up to 1 million tokens (~100k practical) |
Free (with Google account) | Built-in MCP tools (Search, Imagen, Veo, etc.) and extensions | Interactive, depends on network latency |
| GitHub Copilot (Agent Mode) | ~100k tokens (GPT-4 based) | Paid (Copilot subscription) | Deep VS Code and GitHub integration; plugins expanding | Slower (multiple API calls in loop) |
| Anthropic Claude Code | Up to 200k tokens (Claude Sonnet/4) | Free/paid (Anthropic plan) | Limited IDE support (emerging plugins) | Moderate (cloud-based inference) |
Strengths and Limitations
Strengths
- Free and open: No cost for individual developers. Apache-2.0 license means anyone can inspect, modify, and contribute to the code.
- Extremely large context: The million-token window lets it handle whole projects in one session, far beyond most assistants.
- Tool-savvy: Built-in support for MCP and Google services means it can reach real-time data and powerful media tools without extra coding.
- Flexibility: Works in any terminal or IDE. You can use it in VS Code or any text editor, and you can script it or run it manually.
Limitations
- Resource demands: Pushing very large contexts or many requests can strain memory and API limits. In practice, it works best with chunks up to ~100k tokens.
- Online required: Gemini CLI talks to Google’s cloud models, so it needs an internet connection. This also means you rely on Google’s servers and rate limits (though they are generous: 60 requests/minute and 1000/day on the free tier).
- New ecosystem: As a brand-new tool, the CLI may have early bugs and fewer community tutorials than older assistants. The plugin/tool ecosystem is growing but not as mature as some competitors.
- Speed variation: Complex, multi-step tasks (especially in agent mode) can take longer as the system loops through planning and execution. Simple edits are fast, but very open-ended requests may iterate a few times.
Open-Source License and Roadmap
- Apache 2.0 license: Gemini CLI’s code is on GitHub and open to all under the Apache-2.0 license. You’re free to use, modify, and share it.
- Community-driven roadmap: Google welcomes contributions, bug reports, and feature suggestions. Developers can track issues or propose enhancements on the official GitHub repo. Future releases will likely add more features and integrations based on user feedback.
Pro Tip: If you’re integrating external tools with Gemini CLI, learn how to secure them properly in MCP vs API Gateways. This guide covers authentication, data boundaries, and threat models for connected LLM workflows.
Takeaway: Gemini CLI brings Google’s top-tier AI into your command-line workflow, with massive context, built-in tools, and free usage for developers. It stands out as a highly capable, extensible, and cost-free coding assistant that lives right in your terminal (or VS Code). By supporting MCP and open contributions, it paves the way for powerful, plugin-driven AI workflows. Whether you’re generating code, refactoring, or automating tasks, Gemini CLI is poised to level up developer productivity with AI at the core of your toolchain.