Cline AI Bot: the autonomous coding assistant for VS Code
Cline is an open-source AI coding assistant that lives inside Visual Studio Code. It splits planning from execution, works with whatever model you point it at, and runs entirely on your machine. This article covers what it does, what it costs and where it gets in your way.
Cline is an open-source AI coding assistant, shipped as an extension for Visual Studio Code under the Apache 2.0 licence. Since its launch in January 2025 it has passed three million downloads and more than 51,500 GitHub stars. It works in two modes: Plan Mode reads a task and writes an execution plan, Act Mode writes the code and runs the commands, and every file change and every terminal command needs your approval. Cline runs entirely client-side, so your code does not leave the development environment, and it supports models from OpenAI, Anthropic, Google, AWS and Azure as well as local models through LM Studio and Ollama. Developers report 40 to 50 percent less time spent on complex multi-file tasks, while heavy use can run up API costs between 5 and 50 euros a day.
What is Cline AI Bot?
Cline AI Bot is an open-source AI coding assistant that ships as a Visual Studio Code extension. Since it launched in January 2025 it has passed three million downloads and collected more than 51,500 GitHub stars. That is a lot of adoption for a tool with no marketing budget, and it says something about what developers want from this category.
- Core function:
- Cline handles code generation, file edits, terminal commands and browser automation inside your development environment.
- Main use:
- Software development, code review, debugging, writing tests and complex multi-file work.
- Who it is for:
- Developers, DevOps engineers, tech leads and the people who decide what a team's toolchain looks like.
What sets Cline apart
- Open source: Cline is published under Apache 2.0, so you can read the code and change it
- Client-side operation: your code never leaves your development environment, with no external upload or indexing
- Human in the loop: every file change and every terminal command waits for your explicit approval
- Multi-model support: models from OpenAI, Anthropic, Google, AWS and Azure, plus local ones
- Plan and Act modes: a structured approach that keeps planning and execution apart
Plan and Act architecture: interactive 3D view
Cline's dual-mode architecture keeps planning and execution cleanly apart. This 3D view shows the workflow: Plan Mode (blue, left) reads the task and writes an execution plan. The Cline Core (gold, centre) coordinates the process. The Act Mode (green, right) carries the plan out and writes the code. Every action waits for your approval.
Drag to rotate • scroll to zoom • right-click to pan
Source: Cline AI Bot architecture documentation (2025-12-14)
What Cline actually does
Cline pairs a language model with an architecture that separates thinking from doing. The dual-mode design saves wasted iterations: the approach gets worked out before a single line of code is written.
In Plan Mode, Cline reads your task and writes a detailed execution plan without touching your code. The mode exists so you can understand a complex task, and disagree with the plan, before anything changes on disk.
In Act Mode, Cline carries out the plan while keeping the context from the planning phase. It writes code, edits files and runs terminal commands. Each action waits for your approval.
Cline can create, read, edit and delete files. Every change lands in a diff view first, so you see exactly what would change before you approve or reject it.
Cline runs shell commands inside your development environment. That covers package installs, build scripts and test runs, each one with your explicit approval.
Using headless browsers, Cline handles web-facing work: interactive debugging, end-to-end tests, and visual debugging through screenshots and console log monitoring.
Cline reaches external services over MCP . That opens connections to databases, REST APIs , cloud services and design tools, which widens what the assistant can do considerably.
With .clinerules files you pin project-specific instructions, coding standards and patterns. Cline then works to your conventions rather than its own defaults.
Multi-model ecosystem: 3D network view
Cline is model-agnostic and works with providers across the board. This 3D view puts Cline (gold, centre) in the middle as the platform that connects them: OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), AWS Bedrock , Azure and local models (LM Studio, Ollama). Switching between them is how you trade quality against cost.
Drag to rotate • scroll to zoom • right-click to pan
Source: Cline AI Bot documentation, multi-model support (2025-12-14)
Cline in the German market: why it matters there
German software development is growing fast, and AI coding assistants have turned into a competitive question rather than a curiosity. Cline has a particular pull in a market where GDPR compliance and control over data decide what a team is allowed to install.
Why Cline lands well with German developers
What helps under GDPR
- Client-side operation: because Cline runs entirely on the client, your code never leaves the development environment. Under GDPR that removes the transfer question before it is asked.
- Local models: Cline supports local models through LM Studio and Ollama, so a team with a hard privacy requirement can work without any cloud API at all.
- Full control: every file change and every command waits for your approval, which keeps the audit trail intact and the compliance questions answerable.
- Open source transparency: the code can be read and changed, which is a different security posture from trusting a vendor statement.
Where the opportunity sits for companies
Developers report 40 to 50 percent less time on complex multi-file tasks. Plan Mode is where most of that saving comes from, because the approach gets settled before the editing starts.
Choosing the model per task lets you trade quality against spend. Local models remove the API bill entirely, while cloud models stay available for the hard problems.
Client-side operation and local model support make Cline workable for companies under strict data protection requirements, where a cloud-only assistant would not get approved.
Through .clinerules you tie Cline to your coding standards, your project requirements and your team's conventions, which is what makes the output consistent.
Business benefits: where Cline pays for itself
The case for Cline goes past raw speed. Here is what teams get out of it in practice.
Developers report 40 to 50 percent less time on complex multi-file tasks. Plan Mode prevents wasted iterations by settling the approach before code gets written. Fewer mistakes, faster delivery, more time for the work that needs a person.
Multi-model support means you pick the model per task. Cheap models handle the simple work, stronger models take the hard problems, and local models remove the API bill entirely for anything routine.
Plan Mode makes sure code is written deliberately, not just quickly. The structured approach shows up as better architecture and fewer bugs, and .clinerules lets you enforce project standards instead of hoping for them.
Client-side operation means your code never leaves the development environment. For companies under strict data protection requirements that is the difference between a pilot and a rejection. Local models add another layer.
How developers actually use Cline
The tool shows up in a handful of recurring shapes. These are the ones that come up most often.
A development team uses Plan Mode to structure big refactors. Cline reads the code, writes a detailed plan and then works through the changes step by step, each one approved before it lands. Reported result: half the time and noticeably fewer mistakes than doing it by hand.
A QA team has Cline generate tests for new features. It reads the code, works out what the feature does and writes the suite. Browser automation covers the end-to-end cases. Reported result: 60 percent faster test writing and better coverage.
A full-stack developer builds features that touch frontend, backend and the database. Cline plans the whole implementation, coordinates changes across files and runs the terminal commands for database migrations. Reported result: complex features shipped in half the time.
A team points Cline at an undocumented codebase. It reads the code, works out the logic and adds comments and documentation that say something. Reported result: code that a new joiner can read without a guided tour.
Challenges to weigh before you roll it out
Cline has real weak spots, and knowing them beforehand sets the right expectations.
Cline itself is free, but the models behind it are not. Heavy users report 5 to 50 euros a day. The fix is to route routine work to local models and keep cloud models for the hard problems. Cline tracks spend so the bill does not surprise you.
Plan and Act modes ask for a different way of working. Developers have to learn how to frame a task and how to read a plan critically. Start on small jobs and work up. The documentation and the community carry most of the onboarding.
Surveys put the share of developers who strongly trust AI output at 3 percent, against 46 percent who actively distrust it. The human-in-the-loop design helps, but code review does not become optional. Treat Cline as an assistant, not as a substitute for your judgement.
Getting Cline running, above all with local models, takes some technical knowledge, and the API configuration has to be right. The documentation is thorough and the community answers questions. In a company it is worth giving one tech lead the setup.
Practices that work
- Start small: begin with simple tasks and work up to the complex ones
- Use Plan Mode: it is where the mistakes get caught, so use it on anything non-trivial
- Always review: good output is still output you have not read yet
- Write .clinerules: project-specific rules are what make results consistent
- Watch the spend: Cline's cost tracking exists for a reason
Your rollout in four phases
Bringing Cline into a development workflow goes better with a plan. This is the sequence that works.
Phase 1: install and configure (days 1 to 2)
Install Cline from the Visual Studio Code marketplace and configure your first model, ideally a cloud model such as Claude or GPT-4. Try it on small tasks to see how the basics behave. Read the documentation on Plan and Act modes before you go further, because the mental model matters more than the settings.
Phase 2: fold it into your workflow (weeks 1 to 2)
Start using Cline in daily work. Run Plan Mode on complex tasks so you see what it buys you. Write .clinerules files for your projects to pin the coding standards. Review every change carefully, and pay attention to where Cline is genuinely useful and where it is not.
Phase 3: tune and extend (weeks 3 to 4)
Try different models to find your own quality-to-cost ratio. For privacy-critical projects, set up local models through LM Studio or Ollama. Use MCP integrations to connect Cline to the services you already run. Keep an eye on the API spend and adjust the model choice accordingly.
Phase 4: take it to the team (months 2 to 3)
Share what you learned and write it down as team practice. Build shared .clinerules for team projects. Run a session so everyone can use the tool properly. Above all, agree the rules on code review and quality before the volume of machine written code goes up.
Strategic value: Cline as an advantage
AI coding assistants are becoming standard equipment, which makes the differences between them the interesting part. Where many tools send your code to the cloud, Cline keeps it where it is and shows you what it is doing.
Being open source means you can read Cline's code, extend it and fix it. That is a different kind of trust from a vendor promise, and the active community keeps the improvements coming.
The model-agnostic design means you are not tied to one provider. When a better model appears, you switch, and your workflow does not change. Over a few years that is worth more than any single model's quality today.
For companies where GDPR compliance decides what gets installed, client-side operation and local model support are the combination that gets a tool through review.
Where this leaves you
Three million downloads in nine months is not a story about model quality, because Cline does not ship a model. It is a story about what developers want around the model: control, visibility and the freedom to swap the engine.
The points worth keeping
- Plan and Act modes: separating the two prevents wasted iterations and produces better results
- Client-side operation: your code never leaves the development environment, which is what makes GDPR conversations short
- Multi-model flexibility: pick a model per task, or run local ones and pay nothing per token
- Open source: the code is readable and changeable, and the community keeps working on it
- Productivity: 40 to 50 percent time saved on complex tasks, with you still approving every change
If privacy and adaptability are the constraints you work under, Cline sits in a good place. Install it, point it at a small task and see how the plan reads. The learning curve is moderate and the answer comes quickly.
Further reading
Frequently asked questions about Cline AI Bot
What is Cline AI Bot and how does it work?
Cline is an open-source AI coding assistant that ships as a Visual Studio Code extension. It runs language models against your codebase to generate code, edit files and debug. Two modes carry the work: Plan Mode reads the task and writes an execution plan, Act Mode carries that plan out by writing code and running commands. Every action needs your approval first. Because Cline runs client-side, your code stays inside your development environment.
Which AI models does Cline support?
Cline is model-agnostic. It works with Anthropic Claude, the OpenAI GPT-4 series, Google Gemini, AWS Bedrock, Azure and GCP Vertex. Local models run through LM Studio or Ollama. You can switch between models per task, which is how most users balance quality against cost.
How secure is Cline for companies under GDPR?
Three properties matter here. Cline runs client-side, so your code is never uploaded or indexed externally. Every file change and every terminal command needs explicit approval through the interface. And .clinerules files let you pin project-specific instructions and coding standards. For the strictest privacy requirements, run local models and no cloud API is involved at all.
What does Cline cost to run?
Cline itself is free and open source under Apache 2.0. The cost comes from the model APIs behind it. Light users get by on a few euros a month. Heavy users report 5 to 50 euros a day on API tokens, depending on the model, how often they run it and how large the tasks are. Cline tracks spend per task, and local models through LM Studio or Ollama remove the API bill entirely.
How is Cline different from GitHub Copilot?
Cline is open source under Apache 2.0, so the code can be read and modified. It separates planning from execution through Plan and Act modes rather than completing code inline. It runs client-side where many tools send code to the cloud. It drives the terminal and a headless browser, not just the editor. And it extends to external services over the Model Context Protocol.
Can I run Cline with local AI models?
Yes. Cline supports local models through LM Studio and Ollama, which means you can work without any cloud API. That removes the API bill and keeps every token on your own hardware. Setup needs some technical knowledge, and local models tend to run slower than cloud models, but the control is complete.