Use GeminiCLI to quickly create AIAgent, from terminal deployment to practical cases

Gemini CLI (Command Line Interface) is an open source tool developed by Google, allowing you to interact directly with powerful Gemini AI models through terminals, and develop and manage various AI Agents based on this . It simplifies AI operations and allows you to complete complex AI tasks in the terminal.

1. Chat Q&A and content generation

Gemini CLI allows you to talk to AI in your terminal:

• Natural Language Interaction: Ask questions directly and get answers quickly, whether it is data query, problem solving or creative writing.
• Diverse content generation: Not only text, it can also generate programming, scripts, image descriptions, and even combine models such as Imagen to generate images and videos.

2. Programming assistance and development process optimization

It is a right-hand assistant to engineers, greatly improving efficiency:

• Code understanding and generation: Read, understand, modify, and generate program code to help you explain complex programs, refactor and fix bugs.
• Debugging and Automation: Give it a solution directly to give the error message. Embed instructions into scripts to automate repetitive encoding tasks such as unit testing and file generation.

3. Combining external tools and information sources

Gemini CLI is powerfully scalable and can be integrated with a variety of tools:

• Built-in Google Search: Integrate search functions, AI can refer to real-time web information, ensuring that information is instant and reliable, and is crucial to AI agents that require the latest information.
• Execute terminal instructions: Through specific syntax, AI Agent can not only generate content, but also perform actual system operations.
• Extended functions: Supports MCP and built-in expansion, can connect to external databases and APIs, allowing AI Agent to read archives, manage materials, or interact with third-party services.

4. Develop AI Agent

It is the core tool for developing AI Agents:

• Core Driver: Provides a stylized, highly controllable interface that allows you to design AI Agent behavior, connect multi-step tasks and deploy them.
• Multi-step reasoning: Helps AI Agent complete complex multi-step tasks such as analyzing requirements, generating solutions, executing instructions and verifying results through built-in tools and ReAct loops.

Gemini CLI is a multi-functional tool that brings the powerful capabilities of Google Gemini AI into developers’ terminal workflows, improves productivity, simplifies AI tasks, and provides a solid foundation for building an automated and intelligent AI Agent.

Here are the steps to install and start the Gemini CLI:

Preparation

• Node.js version requirements: Please confirm that your computer has Node.js version 18 or higher installed.

Execute CLI

You can choose any of the following ways to execute the Gemini CLI:

• Execute directly: Enter the following command in your terminal:

npx https://github.com/google-gemini/gemini-cli

or

• Global installation: Enter the following command to install, and then execute the gemini command:

" npm install -g @google/gemini-cli"

Quick Start

Execute "gemini" under the command prompt character

Setting and Verification

• Select Color Theme: After launching, you can select the color theme you like on the interface.
• Login Verification: Follow the prompts to log in with your Google account. This will allow you to send up to 60 model requests per minute and up to 1,000 model requests per day.

Complete the above steps and you can start using the Gemini CLI!

Then select the topic, and then select Auth Method. There are three ways to log in:

• Login with Google: Log in with your Google account (no API key required)
• Gemini API Key: If you have applied for Gemini API (via Google AI Studio)
• More...: Other advanced login methods

This will enable browser authorization here, and the authorization will be successful when you see the following screen.

Then you can see the following dialog box and you can start the conversation!

Enter /help first to view all available instructions and instructions.

Here are the basic features, instructions, and keyboard shortcuts of the Gemini CLI:

Basics

• Add context: Use the @ symbol to specify a file or folder as the context. For example: @src/myFile.ts can provide context information to Gemini for a specific file or folder.
• Shell mode: Execute terminal commands through the ! symbol. For example: !npm run start. You can also use natural language, such as: start the server.

Commands

• /help: Shows all help information for the Gemini CLI.
• /docs: Enable the full Gemini CLI file in the browser.
• /clear: Clear the screen and clear the conversation history.
• /theme: Change the interface theme color.
• /auth: Change the authentication method.
• /editor: Set external editor preferences.
• /privacy: Show privacy statement.
• /stats: View session statistics.
• /mcp: Lists the configured MCP (Model Context Protocol) servers and tools.
• /memory: manages memory. Usage: /memory [New text to be added].
• /tools: Lists all available Gemini CLI tools.
• /about: Display version information.
• /bug: Submit a bug report.
• /chat: Manage conversation history. Usage: /chat [Label].
• /compress: Compress the context by replacing the context with a summary.
• !: Execute the Shell command.

Let's take the application of Gemini CLI to a more advanced level: creating an AI Agent that can generate and assist in the development of "Snake Eater Games" code. This will demonstrate the powerful capabilities of Gemini models in code generation and how you can use the Gemini CLI to drive complex tasks like this.

Goal: Let the AI ​​Agent understand our needs, generate Python code for the Snake Eater game, and provide modification suggestions if necessary.

Core practice concept:

• Multiple rounds of conversations and context management: AI Agent needs to remember the previous conversations in order to understand subsequent requests.
• Precise prompt word engineering: In order for Gemini to generate correct and runnable code, prompt words must be very specific.
• Code execution and verification (optional but recommended): More advanced AI Agents can even try to execute and debug errors after the code is generated.

Ask Gemini CLI to create a simple snake-eating game. Gemini CLI will explain to you its technical choices and new features, and how it will be visually presented, and finally explain the archive structure.

The first execution problem

When the web page is opened at the beginning, the game starts immediately and ends quickly, without any prompts or restart buttons, causing the player to Game Over before he responds.

The first game produced, the webpage ends as soon as it is opened, and there is no button to restart, so it will end if you don’t have time to react.

Make a request for correction

We ask Gemini CLI to join the reset mechanism and explain the reasons for the error as follows:

• The reason for the problem: The initial state directly enters the game circle, and the player is not prepared or the mechanism for restarting is provided.
• Solution: Add the "Restart" button, pause and restart the control process, and add score records.

The Gemini CLI immediately responded and automatically modified the code, adding the following functions:

• The Restart button is displayed in the center of the screen, and you can start again after the game is over.
• A new score statistics function has been added, and the current score is displayed in the upper right corner of the screen.
• Correct the initialization and ending logic to avoid the game being terminated if it flashes.

Final result

After correction, a normal game of gluttony snake was successfully produced, with:

• Smooth operation direction control
• Scoring system and instant updates
• The Restart button allows players to challenge repeatedly
• The interface is simple and the gaming experience is close to Nokia classic version

This task demonstrates the strength of Gemini CLI to assist in the development of interactive applications, from error recognition to complete functions, and fully automatic completion of the process.

In the AI ​​field, Gemini CLI and ChatGPT are powerful tools, but they differ in design concepts and application scenarios, which also determines their applicability when developing AI Agents.

ChatGPT (through its API or web interface)

ChatGPT mainly focuses on dialogue and user experience. Its original design is to provide smooth and natural multi-round dialogue capabilities, and attract users through extremely intuitive and easy-to-use web interfaces. The API is also suitable for developers to quickly integrate dialogue functions. For most general users, ChatGPT is more like a "call-on-use" black box service. Although developers can transmit prompt words through the API, their meticulous control over model behavior is relatively limited. therefore, ChatGPT is more suitable for smart conversation applications that are directly targeted to end users, or for prototyping and concept development for fast verification of conversational applications. 。

Gemini CLI (with its underlying Gemini API)

Gemini CLI is a tool designed for developers, providing command-line interfaces that directly expose the functions of Gemini API. It has focused on the capabilities of programming, automation and flexible integration since the beginning of design. It is highly controllable. Developers can accurately control various parameters of the model, such as temperature, top_p, top_k through CLI or Python function libraries, and can process multimodal inputs (such as pictures), and even use function calls to further expand the model's action capabilities. In addition, Gemini CLI adopts modular design, like Lego bricks in the hands of developers, which can be easily embedded in existing workflows, scripts, automation systems or large-scale applications. It not only supports text generation, but also produces program codes and text files, and processes complex input and output structures. Through CLI, developers can also have more direct contact with the underlying Gemini model, which is particularly important for application scenarios that require highly customized and optimized AI Agent behavior.

Why is Gemini CLI more suitable for developing AI Agents?

Gemini CLI has several features that make it an ideal tool for developing AI Agents. First, in terms of automation and scripting, it allows developers to easily embed AI model calls into any script to achieve a task execution process without manual intervention. Its meticulous parameter control function provides extremely high degree of freedom for the behavioral adjustment of AI Agent, and can flexibly adjust the model's response method according to task requirements. For applications that require processing multiple data types, The model behind Gemini CLI also supports powerful multimodal capabilities, which can understand and generate text, pictures, PDFs, and codes (more than videos and audio are supported yet). Furthermore, its function call function is more critical, allowing the model to decide whether to call external tools or functions based on the situation, such as querying databases, sending emails or executing commands, allowing the AI ​​Agent to truly have the ability to actually perform tasks. . Finally, as a part of the Google AI ecosystem, Gemini CLI is built on a stable cloud infrastructure and is highly integrated with a diverse development toolchain, making deployment and expansion of AI Agents smoother and more scalable.

In short, if your goal is to build an AI Agent that automates tasks, interacts with external systems, and requires highly customized behavior, Gemini CLI is undoubtedly a more appropriate and powerful choice than ChatGPT, which focuses primarily on dialogue.

Gemini CLI is not only a powerful tool at present, it also carries the huge potential to reshape the future AI tool development ecosystem. With the rapid development of AI technology, we are moving from simply “using AI” to “using AI to developing AI Agents and smart tools”. , and Gemini CLI is the key catalyst in this transition.

Here are a few key aspects of how Gemini CLI may affect the future AI development ecosystem:

Accelerate the popularization and customization of AI Agent:

• Lower threshold: Through standardized command line interfaces, developers in non-AI fields can also be more easily integrated into their applications, thereby accelerating the development and deployment of AI Agents in various industries.
• Specialized Agent: We will see more highly customized AI Agents for specific areas (such as law, healthcare, finance) that will be able to perform more precise and professional tasks.

Promote AI-First Development paradigm:

• Traditional software development usually designs application logic first and then considers how to integrate AI. Gemini CLI will encourage developers to think about how to use AI as the core component at the beginning of design, making AI Agent the core driver of applications.
• This will give rise to more application modes that “functions are defined by AI, fine-tuned and supervised by people”.

Strengthen the application breadth of multimodal AI:

• As the Gemini model itself advances in handling multimodal information, Gemini CLI will become a tool for developers to build AI Agents that can understand and generate multivariate data such as text, images, audio and even videos.
• This will open up new application scenarios such as Visual Content Analysis Agent, Advanced Version of Smart Voice Assistant, etc.

Promote open source collaboration and toolchain integration:

• As a command column tool, Gemini CLI is naturally suitable for integration with a wide range of existing open source tools, CI/CD pipelines, and automated scripts.
• This will encourage developers to build a richer ecosystem of open source tools and library around the Gemini CLI, further reducing development complexity.

Redefine how developers interact with AI:

• Developers will no longer be just consumers of AI models, but rather "arrangers" and "directors" of AI models.
• They will carefully design prompt words, set model parameters through the Gemini CLI, and weave AI Agent into more complex systems.
• This will prompt the developer community to share more best practices about "prompt word engineering" and "agent design patterns".

The development of edge AI and embedded Agent:

• With the improvement of model compression and inference efficiency, a lightweight Gemini model version may appear in the future, with Gemini CLI running on edge devices, enabling more immediate and lower latency local AI Agents.

all in all, Gemini CLI is not just a tool, it is also a critical infrastructure that leads the arrival of the AI ​​Agent era. It will design, construct and deploy next-generation intelligent applications in a more flexible, efficient and creative way to truly reshape the AI ​​tool development ecosystem we know 。

Start your AI Agent development journey!

Try downloading Gemini CLI and create your own AI Agent. If you want to experience a more convenient UI interface, you might as well make an appointment to try out our development GenApe AI Agent to explore more possibilities for automation and intelligence integration!

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