From IDEs to Vibe Coding to CLI AI Agents

For decades, coding mostly meant opening an editor or IDE, typing line after line of instructions, and managing everything through panels, toolbars, and menus. That workflow ruled the industry until AI copilots arrived and changed the way people wrote software. Instead of typing every detail, developers began practicing what’s now called vibe coding — explaining their intent in plain language and letting the AI generate the first draft of the code.

Now another shift is happening. Developers are bringing that same AI-driven workflow into the command line, the oldest tool in programming. With new assistants like OpenAI’s Codex CLI, Anthropic’s Claude Code, and Google’s Gemini CLI, you don’t just run commands in the terminal — you can ask the AI agents to edit files, test code, and even manage an entire repository. It feels like going back to basics, but with the power of modern AI built right in.

From Text Editors to IDEs

In the earliest days of programming, developers relied on simple text editors. These were tools like Notepad on Windows or Vim and Emacs on Unix systems. They allowed you to type out code, save the file, and run it through a compiler or interpreter. That was it. There were no hints, no suggestions, no built-in debugging. If you made a mistake, you had to figure it out by reading error logs or trial and error. This style of working demanded deep familiarity with the language and a lot of patience.

As software projects became larger and teams needed to collaborate, Integrated Development Environments (IDEs) emerged. IDEs brought all the essential parts of programming into one window. They combined an editor with tools for debugging, compiling, testing, and even version control. Popular examples include Eclipse for Java, Visual Studio for C# and C++, and later PyCharm and IntelliJ for Python and Java. Suddenly, instead of juggling many separate tools, a developer could do everything inside one environment.

This shift was massive. IDEs sped up workflows, reduced human error, and helped developers focus more on building features rather than managing files and compilers. They also opened the door for plug-ins and extensions that added even more power, from database management to UI designers. At the same time, IDEs could feel heavy. They consumed memory, took time to set up, and sometimes forced developers into specific workflows. Still, for nearly two decades, IDEs defined what “professional coding” looked like.

The Rise of Vibe Coding

After years of relying on IDEs, a new style of development started to take shape with the arrival of AI copilots. Instead of typing every line manually, developers began describing what they wanted in plain English and letting the AI draft the code. This practice became known as vibe coding. The term reflects the idea that you focus more on the “vibe” or intent of the solution rather than the low-level details of implementation.

Vibe coding took off because it lowered the barriers to programming. A junior developer, or even someone from a non-technical background, could say “create a function that sorts a list of names alphabetically” and the AI would generate a working version within seconds. Experienced developers also found it useful: instead of wasting time on boilerplate or repetitive patterns, they could let the AI handle the routine work while they concentrated on architecture, logic, and problem-solving.

This style of working became mainstream with tools like GitHub Copilot, ChatGPT, and Claude Code inside editors like VS Code. It made coding feel more conversational. Instead of looking up snippets on Stack Overflow or hunting through documentation, developers could simply ask, refine, and guide the AI until the output matched their needs. For the first time, the way we “talked” to computers started to feel more natural — not bound by syntax, but driven by intent.

Vibe coding didn’t replace IDEs, but it changed how people thought about them. The editor was no longer just a place to type code; it became a collaboration space between human and AI. This shift set the stage for the next leap: if AI could already understand intent inside an IDE, why not bring the same workflow to the command line, where projects are actually run, tested, and deployed?

What Is CLI-Based Coding?

The command line interface (CLI) has always been one of the oldest tools in programming. Long before IDEs became popular, developers used terminals to compile code, run programs, and manage files. Working in the CLI meant typing commands into a black-and-white screen and getting immediate text-based feedback. It was fast and efficient for those who knew the commands, but intimidating for beginners.

Now, with the rise of AI assistants, the command line has taken on a new role. CLI-based coding means you can talk to an AI directly from the terminal — just like you would with an IDE copilot — but instead of only suggesting code, the AI can also act on your project. From the terminal, you can ask the AI to edit a file, generate a new function, run tests, fix errors, or even commit changes to Git.

For example, imagine you’re building a Python web app. Instead of switching between your IDE, test runner, and Git client, you could type into the CLI:

codex add login feature with email and password, include tests

The assistant would edit the relevant files, create test cases, and show you a diff of the changes. You can approve the patch, run the tests, and commit the update — all without leaving the terminal.

In simple terms, CLI-based coding makes the command line an AI-powered workspace. It feels like going back to basics — typing into the terminal — but the experience is entirely new. Instead of memorizing complex commands, you describe what you want, and the AI takes care of the rest. Tools like OpenAI Codex CLI, Anthropic Claude Code, and Google Gemini CLI are leading this movement and redefining what the command line can do.

Why CLI AI Fits the Vibe

Vibe coding is all about expressing intent in natural language and letting the AI handle the details. The command line turns out to be the perfect place for this approach. Instead of juggling between editor panes, menus, and plug-ins, developers can open a terminal, type a single instruction, and watch the AI handle the rest.

One big advantage is that the CLI lives close to the project itself. This is where you run tests, install dependencies, manage Docker containers, or push commits. By bringing AI into this space, developers can tie together coding, testing, and deployment in one continuous flow. For example, you might ask the AI:

fix the failing unit test for user authentication and rerun pytest

The assistant can patch the test, edit the code, and run the suite — giving you results in seconds without leaving the terminal.

Another strength is simplicity and speed. In the CLI, there’s no overhead of graphical interfaces. The AI can show diffs, logs, or errors directly as text. This makes the workflow lean, especially for developers who already prefer terminal-based tools like Git, Make, or Docker.

Finally, CLI coding complements the spirit of vibe coding: working at a higher level of abstraction. You’re not telling the computer how to do something line by line. You’re telling it what outcome you want, whether that’s fixing a bug, adding a feature, or cleaning up the repo. The terminal just happens to be the fastest and most natural place for that interaction.

The New Players in the CLI Race

With vibe coding pushing developers toward faster and more intent-driven workflows, it’s no surprise that the command line has become the new battleground for AI coding tools. Several major players are now competing to own this space.

OpenAI Codex CLI was one of the first widely known entrants. It lets you talk to the AI from your terminal, suggest code changes, run tests, and apply patches. The key strength of Codex CLI is flexibility — you can choose the model you want to use (such as GPT-5 Medium or High) and decide how much control the AI has. You can keep it in “suggest” mode, where it shows you diffs before applying changes, or switch to “auto” mode for hands-free coding.

Anthropic’s Claude Code takes a slightly different approach. While it’s available inside IDEs like VS Code, it also offers terminal-based repo awareness. Its strong reasoning skills make it good at handling multi-file changes and refactoring. Claude Code tends to generate cleaner, more cautious edits, which appeals to developers who want reliability in production work.

The biggest disruptor, though, came in June 2025 when Google released Gemini CLI. Unlike its competitors, Google launched it as an open-source, free tool for individual developers, powered by Gemini 2.5 Pro with a massive one-million-token context window. It can process entire repositories at once, ground its answers with Google Search, and integrate with workflows through the Model Context Protocol (MCP). The combination of free access and powerful features has quickly made it a favorite among developers looking to try CLI-based AI without worrying about costs.

Together, these tools are reshaping what the command line can do. What once was just a place for compiling and running code is now becoming the control center for full AI-driven development.

The way developers write code has always shifted with new tools — from plain text editors to powerful IDEs, then to vibe coding with AI copilots inside editors. Now the rise of CLI-based AI assistants shows another turn in this journey. The command line, once seen as old-fashioned, has become the stage for the newest wave of innovation. Tools like Codex CLI, Claude Code, and Gemini CLI prove that the fastest way to code today may not be clicking through menus, but simply telling an AI in plain language what you want and letting it make it happen. The terminal is back — and this time, it’s powered by intelligence far beyond what early programmers could have imagined.