A guide to agentic coding CLI tools in 2025

It feels like just yesterday that "AI in coding" meant slightly better autocomplete. Now, it seems like we’ve blasted past that into a world of AI agents that can plan, write code, and work on complex tasks, all on their own. The latest development? AI agents that live right inside your command line.

This is a pretty big deal for how we write, debug, and even think about code. In this guide, I’m going to walk you through everything you need to know about this new wave of tools. We’ll cover what an "agentic coding CLI" actually is, compare the most popular options out there, get into the real-world trade-offs, and look at how these same ideas are starting to show up in other parts of a business, way beyond the terminal.

What is an agentic coding CLI?

The simplest way to think about an "agentic coding CLI" is to imagine a junior developer you can spin up in your terminal. Instead of just suggesting the next line of code, these tools can take on entire features or bugs. You delegate the work, and it gets to it.

But what makes a tool "agentic"? It really comes down to a few key things:

• It actually understands your project. It doesn’t just see the one file you have open. It can scan multiple files, figure out your project’s structure, and remember your conversation history to understand the bigger picture of what you’re trying to do.

• It can make a plan. You can give it a high-level goal, like "add a new API endpoint for user profiles," and it will map out the steps. It figures out which files need to be created or changed, what code to write, and which tests it should run to make sure it didn’t break anything.

• It does the work. This is the big leap. It doesn’t just suggest code; it writes new files, modifies existing ones, runs terminal commands to test its own work, and can even commit the final changes to Git for you.

This is a huge step up from the tools we’re used to. Early versions of GitHub Copilot were fantastic for churning out boilerplate, but you couldn’t really hand off a whole task to it. AI-first IDEs like Cursor brought some of these agentic abilities into a nice GUI, but for developers who live in the terminal, these CLI tools offer a raw, powerful, and scriptable way to get things done.

Comparing top agentic coding CLI platforms

The whole agentic coding scene is moving fast, but a few front-runners have already emerged. Each one has a slightly different approach and its own set of strengths. Let’s break down the most popular options by looking at how they work, which models they support, and what they’re going to cost you.

Aider: An open-source agent for Git

Aider is one of the originals in this space, and it was built from the ground up with a deep understanding of Git. It feels like a pair programmer that thinks in commits. Being open-source means it has a solid community behind it and supports a ton of different models.

• What it’s good at: Aider is great for conversational coding where you go back and forth, editing multiple files at once. It’s also really good at automatically committing its changes with clean, well-formatted messages. You can hook it up to models from OpenAI, Anthropic, or even models you’re running locally.

• Pricing: The tool itself is free. You just pay for the API usage of whatever large language model you connect it to. This sounds great, but it also means your costs can be a bit of a black box and can get high without you realizing it.

• Downsides: Compared to some of the newer tools, Aider’s terminal interface can feel a little clunky. It also often needs you to be very specific about which files it should be looking at, which can sometimes slow you down.

Claude Code: A powerful conversational coding agent

Claude Code is Anthropic’s big entry into the agentic CLI world. It’s known for its strong reasoning skills and its almost spooky ability to understand huge codebases with very little direction. It’s a closed-source, premium tool that feels polished and very capable from the get-go.

• What it’s good at: Its main selling point is how it just seems to get the context of your entire project without you having to feed it a list of relevant files. It also comes with some handy built-in tools like a web search, integrates with IDEs, and has some solid security features built in.

• Pricing: You pay as you go through an Anthropic API key, and this is where it gets a bit eye-watering. Developers consistently report that while Claude Code is powerful, it’s also expensive. Sessions can easily cost you $3 to $6 per hour, and that adds up incredibly fast on any non-trivial task.

• Downsides: The high, unpredictable cost is a major dealbreaker for a lot of people. Since it’s closed-source, you’re also completely reliant on Anthropic for updates and can’t tweak it yourself like you can with the open-source options.

A screenshot showing Claude Code's powerful integration within an IDE, a key feature of this agentic coding CLI.

Cline: The TDD-focused coding agent

Cline is an open-source tool built for a modern, test-driven development (TDD) workflow. It’s designed to be a true agent that doesn’t just write code, but also runs commands, reads the output, and keeps trying until the tests pass.

• What it’s good at: Cline’s ability to run shell commands is its standout feature. It can run your test suite, see what failed, and then take a shot at fixing the problem. It has good Git integration, supports pretty much any LLM through OpenRouter, and, helpfully, gives you a real-time running total of your API costs.

• Pricing: Just like Aider, Cline is a free tool where you pay for your own LLM usage. Users say the costs are generally more manageable, often landing in the $1, $3 per hour range.

• Downsides: The biggest annoyance is that Cline doesn’t have a persistent memory between sessions, so you have to re-feed it the context every time you start it up. Its "auto-approve" mode is powerful but can definitely get stuck in loops, so you have to keep an eye on it.

Feature comparison: Aider vs. Claude Code vs. Cline

This video demonstrates how to use the Claude Code agentic coding CLI to build a project in just a few minutes.

The trade-offs: When to use an agentic coding CLI (and when not to)

If you hang out in developer forums, you’ll see that an AI agent isn’t a silver bullet. The idea of a fully autonomous coder is cool, but it comes with some very real downsides you need to be aware of.

When should you use an agentic coding CLI?

These tools really shine when you’re dealing with tasks that are messy, confusing, or just plain big.

• Big, ugly tasks: When you need to refactor something across a dozen different files, add a feature to a part of the codebase you’ve never touched, or track down a really tricky bug, an agent can be a huge help. It can trace connections you might miss and do the tedious work for you.

• Getting started on something new: Scaffolding a new project or building a quick proof-of-concept is a perfect use case. You can describe the basics of what you want and let the agent handle all the boilerplate and setup.

• Learning on the fly: An agentic CLI can be an amazing learning tool. You can ask it to explain a confusing block of code, walk you through the history of a file with "git blame", or show you how to implement a design pattern you’re not familiar with.

Where an agentic coding CLI can fall short

For simple, everyday coding, these tools can honestly just get in the way.

• The API bill: Let’s talk about the elephant in the room: the cost. For a simple, well-defined change, it’s almost always cheaper and faster to just copy-paste some code into a regular chatbot. As

  

  agentic tools add a lot of overhead with their long system prompts and back-and-forth reasoning, all of which burns through expensive API tokens

  Reddit

• The time sink: If you’re an experienced dev and you know exactly which five lines in which three files you need to change, waiting for an agent to figure that all out from scratch is going to be slower than just doing it yourself.

• Trust and control: This is probably the biggest sticking point. These agents can and do make mistakes. They can misunderstand what you asked for, write buggy code, or get stuck in a weird loop. This means you constantly have to be supervising them, which creates a need for really good testing and a safe way to deploy their changes, guardrails that most of these tools just don’t have.

Beyond the agentic coding CLI: Applying agentic AI to business workflows

So, we have these incredibly powerful tools that are also expensive, unpredictable, and a bit wild. That feeling of "this is amazing, but I can’t quite trust it on its own" isn’t just a developer problem. It’s the core challenge of bringing agentic AI into any part of a business. The power is too big to keep it confined to a terminal.

This is where a platform like eesel AI comes into the picture. It takes those same core ideas of an autonomous agent and applies them to business-critical jobs like customer service and IT support, but wraps them in a package that’s easy to use, easy to control, and built for a team.

Here’s how eesel AI tackles the main headaches we saw with the coding tools:

• From complex setup to simple start: While developers are messing with API keys and command-line flags, eesel AI is a platform you can get running in just a few minutes. It has one-click integrations for help desks like Zendesk and Intercom, so there’s no complicated setup.

eesel AI's one-click integrations simplify the setup process compared to a manual agentic coding CLI.

• From unpredictable bills to clear pricing: That runaway API bill from the CLI tools makes it impossible to budget. eesel AI offers clear, predictable pricing plans. You know exactly what you’re paying, so you won’t get a surprise bill just because you had a busy month.

• From lack of control to total control: The biggest worry for developers is an unsupervised agent going off the rails. eesel AI was built to solve this exact problem. It has a powerful simulation mode that lets you test your AI on thousands of your company’s past support tickets in a totally safe environment. You can see exactly how it will perform before it ever interacts with a real customer. From there, you can roll it out gradually, telling it exactly which kinds of tickets to handle and which ones to pass to a human.

The eesel AI simulation mode provides total control, unlike the unpredictable nature of an agentic coding CLI.

The AI Agent from eesel AI uses that same agentic loop, understand the context, make a plan, take an action, to resolve support tickets on its own. It learns from your knowledge base and past conversations to provide accurate, on-brand support around the clock.

Agentic coding CLI: Next steps

So what’s the verdict on "agentic coding CLI" tools? They’re a fascinating peek into the future of software development. They give us a glimpse of a world where we delegate entire chunks of work to AI collaborators. For now, though, they’re a specialized tool for specific kinds of headaches, with real trade-offs in cost, speed, and control.

The ideas behind agentic AI, however, are here to stay. The real mission now is to make all that power safe, predictable, and accessible. For developers, that means figuring out the right balance of tools and human oversight. For businesses, it means finding a platform that delivers that power with the safety and control you need to actually use it.

Ready to bring agentic AI to your support team?

While developers are experimenting with the future of coding, your business can put a powerful, autonomous AI agent to work on customer support today. eesel AI gives you a fully controllable, easy-to-use platform that automates frontline support, helps draft replies, and sorts tickets, all inside the helpdesk you already use.

Start your free trial or book a demo to see how you can get up and running in minutes.