Cline review

A hands-on review of Cline, a bring-your-own-key AI coding assistant as a VSCode Plugin.

Notice & Disclaimer: AI Generated Content

This post is initially generated by a language model, usually by summarizing a human conversation or expanding a human-written summary. The goal is not to populate the internet with yet another piece of uncalled for, AI-generated slop (in which, unfortunately, people working in AI are complicit). Rather, it is to enable lower friction in sharing and distilling information. I have worked on, and often significantly rewritten, the post to ensure it accurately reflects the underlying human intentions and experiences, but there may be inaccuracies and biases that remain.

TL;DR

Cline is a VSCode extension offering a bring-your-own-key alternative to GitHub Copilot, with the ability to execute commands and plan multi-step edits. While promising in concept, its high token usage and several UX limitations make it difficult to recommend over Copilot Edit (as of Feb 2025). It’s also not as “smart” as you think it might be¹. Cost per coding session can range from $0.5-3 depending on your model choice.

Also see the verdict section.

Overview

Cline operates as a chatbot-style interface within VS Code, capable of code generation, modification, and terminal command execution (which sounds more promising than it actually is). The default plan is completely free, with the only cost being your LM API calls.

Core Functionality

Unlike Copilot’s real-time completions, Cline works in a turn-based manner similar to Copilot Edit, where you request specific changes or additions and the AI responds with complete code snippets or modifications. The two most important features are the internal feedback loop and more generous access: Cline can execute code changes in steps following its own plan, and it can modify files/execute commands on your computer.

Pros

1. Bring-your-own-key! Use any LM and provider you want
2. Cline has a “plan” mode, in which it gathers information and makes a plan
3. Can request access to files/execute terminal commands
4. Offers checkpoint features for reverting changes

Cons

1. Cline determines when a task is complete, not you. Once it declares the task is complete it’s done. I find this really weird.
2. Very token-consuming: first request is often 10k+ tokens, hitting context limit is realistic. Each session can be $0.5-3 depending on your model, so expect to spend more than copilot/cursor if you let it run by itself.
3. No effective code verification: Cline can, in principle, run commands and check outputs, but it doesn’t do it reliably and use command outputs productively.
   1. An example: I start a task telling Cline how to verify success (run the script with tests in it). Cline executes the command, and without checking the outputs, immediately declares the task is complete.
   2. In general it feels much like vanilla AI autocomplete: once Cline generates a plan, it executes it step-by-step, without verifying after steps or re-planning. Think about if your initial plan for a coding project every worked out completely!²
4. Cannot revert to checkpoints before AI modifications (as of Feb 2025). This could be a really simple fix, but they don’t yet have it. You’d better have another copy/commit before Cline starts working on your code.
5. Each session has its own context, so Cline always starts by gathering information. This can be frustrating if your codebase is complicated.
6. Doesn’t feel as polished compared to Copilot Edit

Verdict

While Cline offers flexibility through custom API keys, it doesn’t eliminate what I think is the biggest bottleneck in coding — your thinking speed. It’s not reliable enough for you to only care about the high-level functions/designs³, so you still have to be in the loop, understand every line of code, and tell it specifically what to do. If you treat it as a human capable of executing on your high-level goals, you will be thoroughly disappointed. But if you treat it like a multi-turn Copilot Edit, it’s not too bad and can definitely be a productivity tool.

Footnotes

1. Especially given the model we use is ranked 18th in the world in programming. Yes, I tried O1. Yes, I tried DeepSeek R1. As of Feb 2025, you can’t code hands-off yet.

2. Spoiler alert: these executions don’t often work.

3. My general impression of what works/what doesn’t work in AI coding: describing only the high level input-output behavior equals diaster. Giving pseudocode or a complete description of the implementation works (and saves you a lot of time), but usually you need to debug it yourself.