Discovering Gemini CLI: My Journey with Google's Latest AI Tool
I never expected a simple terminal agent to revolutionize my coding workflow, but when Google released Gemini CLI about a month ago, it opened up new possibilities with its free access to advanced AI features and community-driven enhancements.
TL;DR
I dove into Gemini CLI's launch, gaining free access to a powerful AI model with a massive context window and generous request limits.
It introduced plan and implementation modes that break down tasks step by step, making coding more structured and efficient for everyday use.
Community contributions added custom commands and seamless integrations with tools like VS Code, enhancing my workflow with context-aware suggestions.
GitHub actions in beta let me automate code reviews and tasks, turning Gemini CLI into a reliable AI teammate for faster development.
Ongoing updates from contributors promise even more reliability, sparking curiosity about its future as a top coding agent.
As I first encountered Gemini CLI, I was impressed by its core offerings. It runs directly in the terminal, providing free access to the Gemini 2.5 Pro model with a 1 million token context window, along with 60 requests per minute and 1,000 per day—plenty for my daily needs without any cost.
This update brought powerful new features that positioned it as a strong alternative to tools like Claude Code. I explored modes like plan and implementation, where the AI helps break down tasks into a step-by-step roadmap before executing the code, guiding me through the process seamlessly.
In the planning mode, for instance, I used it to devise a strategy for adding a new model selector, and then switched to implementation mode to apply those steps automatically. This setup lets me create reusable prompts in a TOML file, streamlining my interactions and making workflows faster by setting prompts, arguments, and shell commands for easy reuse.
Another highlight was the introduction of namespacing, which organizes commands based on their file paths, allowing me to structure subdirectories cleanly and scale my setups without confusion. If I wanted to build custom slash commands, I found it straightforward, though I referred to community resources for detailed guidance.
What really stood out was the integration with VS Code and other editors like Cursor. I simply opened the terminal in VS Code and ran the install command, enabling context-aware suggestions that pull from my open files and selected text, providing targeted recommendations without leaving my editor.
This feature includes in-editor diffing, so I could review and apply changes visually, which made my workflow more intuitive and efficient. In my experience, this level of awareness gives Gemini CLI an edge over similar tools, as it aligns perfectly with what I'm working on in real time.
Then there's the GitHub actions integration, currently in beta, which acts as an AI coding teammate. I used it to automate issue triage and pull request reviews, delegating tasks like bug fixes or feature implementations directly from my repository.
For example, I created a feature request, and the agent handled it autonomously, suggesting code changes and improvements. Setting it up was easy—just ensure the latest Gemini CLI version and run the setup command—and it connected smoothly with my GitHub account.
Beyond these, the open-source community has been instrumental. Over the past month, contributors have added features like automatic loop detection to prevent code from getting stuck, Docker support, and even Vim and Emacs modes, all merged into the official repository through weekly updates.
These enhancements have made Gemini CLI more reliable and user-friendly, with ongoing PRs ensuring it's continually improving as a reputable coding agent.
Reflecting on this, Gemini CLI has not only streamlined my daily coding but also highlighted the power of community collaboration in AI development, setting the stage for even more innovative tools in the future.
Key Takeaways
Gemini CLI offers free access to advanced AI with a large context window, making it accessible for efficient coding.
Its plan and implementation modes provide structured task management, enhancing productivity through step-by-step guidance.
Integrations with VS Code and GitHub actions deliver context-aware suggestions and automation, rivaling other AI tools.
Community contributions drive ongoing improvements, adding features like loop detection and editor support for better reliability.
