GCP – Agent Factory Recap: Deep Dive into Gemini CLI with Taylor Mullen
In the latest episode of the Agent Factory podcast, Amit Miraj and I took a deep dive into the Gemini CLI. We were joined by the creator of the Gemini CLI, Taylor Mullen, who shared the origin story, design philosophy, and future roadmap.
This post guides you through the key ideas from our conversation. Use it to quickly recap topics or dive deeper into specific segments with links and timestamps.
What is the Gemini CLI?
The Gemini CLI is a powerful, conversational AI agent that lives directly in your command line. It’s designed to be a versatile assistant that can help you with your everyday workflows. Unlike a simple chatbot, the Gemini CLI is agentic. This means it can reason, choose tools, and execute multi-step plans to accomplish a goal, all while keeping you informed. It’s open-source, extensible, and as we learned from its creator, Taylor Mullen, it’s built with a deep understanding of the developer workflow.
The Factory Floor
The Factory Floor is our segment for getting hands-on. This week, we put the Gemini CLI to the test with two real-world demos designed to tackle everyday challenges.
Onboarding to a New Codebase with Gemini CLI
Timestamp: [02:22]
I kicked off the demos by tackling a problem I think every developer has faced: getting up to speed with a new codebase. This included using the Gemini CLI to complete the following tasks:
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Clone the python ADK repository from GitHub with a simple, natural language command
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Generate a complete project overview
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Utilize the google-docs-mcp (Model Context Protocol) server to save the generated summary directly to Google Docs
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Analyze the project’s contribution history to understand contribution culture and workflow
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Find the best first task for a new contributor
Read more on MCP servers and how they work here.
Supercharging Your Research with Gemini CLI
Timestamp: [11:38]
For the next demo, Amit tackled a problem close to his heart: keeping up with the flood of new AI research papers. He showed how he built a personal research assistant using the Gemini CLI to complete the following tasks:
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Process a directory of research papers and generate an interactive webpage explainer for each one
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Iterate on a simple prompt, creating a detailed, multi-part prompt to generate a better output
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Save the complex prompt as a reusable custom slash command
Amit also shared gemini-cli-custom-slash-commands, a repository he put together that contains 10 practical workflow commands for Gemini CLI.
The Agent Industry Pulse
Timestamp: [17:26]
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Lang Chain 1.0 Alpha: The popular library is refocusing around a new unified agent abstraction built on Lang Graph, bringing production-grade features like state management and human-in-the-loop to the forefront.
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Embedding Gemma: Google’s new family of open, lightweight embedding models that allow developers to build on-device, privacy-centric applications.
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Agentic Design Patterns for Building AI Applications: A new book that aims to create a repository of educational resources around agent patterns.
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Gemma 3 270M: A tiny 270 million parameter model from Google, perfect for creating small, efficient sub-agents for simple tasks.
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Gemini CLI in Zed Code Editor: The Gemini CLI is now integrated directly into the Z Code editor, allowing developers to explain code and generate snippets without switching contexts.
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500 AI Agents Projects: A GitHub repository with a categorized list of open-source agent projects.
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Transformers & LLMs cheatsheet: A resource from a team at Stanford that provides a great starting point or refresher on the fundamentals of LLMs.
Taylor Mullen on the Gemini CLI
The highlight of the episode for me was our in-depth conversation with Taylor Mullen. He gave us a fascinating look behind the curtain at the philosophy and future of the Gemini CLI. Here are some of the key questions we covered:
Gemini CLI Origin Story
Timestamp: [21:00]
Taylor explained that the project started about a year and a half ago as an experiment with multi-agent systems. While the CLI version was the most compelling, the technology at the time made it too slow and expensive. He said it was “one of those things… that was almost a little bit too early.” Later, seeing the developer community embrace other AI-powered CLIs proved the demand was there. This inspired him to revisit the idea, leading to a week-long sprint where he built the first prototype.
On Building in the Open
Timestamp: [24:14]
For Taylor, the number one reason for making the Gemini CLI open source was trust and security. He emphasized, “We want people to see exactly how it operates… so they can have trust.” He also spoke passionately about the open-source community, calling it the “number one thing that’s on my mind.” He sees the community as an essential partner that helps keep the project grounded, secure, and building the right things for users.
Using Gemini CLI to Build Itself
Timestamp: [27:05]
When I asked Taylor how his team manages to ship an incredible 100 to 150 features, bug fixes, and enhancements every single week, his answer was simple: they use the Gemini CLI to build itself.
Taylor shared a story about the CLI’s first self-built feature: its own Markdown renderer. He explained that while using AI to 10x productivity is becoming easier, the real challenge is achieving 100x. For his team, this means using the agent to parallelize workflows and optimize human time. It’s not about the AI getting everything right on the first try, but about creating a tight feedback loop for human-AI collaboration at scale.
Gemini CLI under the hood: “Do what a person would do”
Timestamp: [30:58]
The guiding principle, Taylor said, is to “do what a person would do and don’t take shortcuts.” He revealed that, surprisingly, the Gemini CLI doesn’t use embeddings for code search. Instead, it performs an agentic search, using tools like grep, reading files, and finding references. This mimics the exact process a human developer would use to understand a codebase. The goal is to ground the AI in the most relevant, real-time context possible to produce the best results.
On Self-Healing and Creative Problem-Solving
Timestamp: [33:14]
We also discussed the agent’s ability to “self-heal.” When the CLI hits a wall, it doesn’t just fail; it proposes a new plan. Taylor gave an example where the agent, after being asked for a shareable link, created a GitHub repo and used GitHub Pages to deploy the content.
What’s Next: The Future is Extensible
Timestamp: [35:19]
The team is doubling down on extensibility. The vision is to create a rich ecosystem where anyone can build, share, and install extensions. These are not just new tools, but curated bundles of commands, instructions, and MCP servers tailored for specific workflows. He’s excited to see what the community will build and how users will customize the Gemini CLI for their unique needs.
Your turn to build
The best way to understand the power of the Gemini CLI is to try it yourself.
Check out the Gemini CLI on GitHub to see community projects, file an issue, or contribute. Additionally, don’t miss the full conversation: watch the episode and subscribe to The Agent Factory to join us for our next deep dive.
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