> **TL;DR.** Cline is an open-source AI coding agent that runs inside VS Code. You bring your own API key, it reads and writes your actual files, runs terminal commands, and controls a browser — all from a chat sidebar. If you want full transparency and control over which model you use and what you spend, Cline is the right tool.
What Cline Actually Is
Cline is a VS Code extension that wraps an AI model into an autonomous coding agent. Unlike GitHub Copilot or Cursor Tab, Cline doesn't just autocomplete — it takes multi-step tasks: reads files, edits them, runs shell commands, checks output, and loops until the task is done or you stop it.
The project is fully open-source (MIT license). The extension itself is free. You pay directly for API usage at your provider's rates. There is no Cline subscription, no token pool managed by a third party, no proprietary model. This is the core appeal for teams with existing API contracts or developers who want to audit exactly what's being sent.
It supports any provider with an OpenAI-compatible API: Anthropic (Claude), OpenAI (GPT-4o, o3), Google (Gemini), Mistral, and any local model via Ollama or LM Studio. Knowing how to use Cline in 2026 means understanding which model you're routing to and why.
Installation and First Run
1. Open VS Code.
2. Go to **Extensions** (Cmd+Shift+X / Ctrl+Shift+X).
3. Search **Cline** — the publisher is `saoudrizwan`. Install it.
4. After install, open the Cline panel from the sidebar icon or run `Cmd+Shift+P → Cline: Open in New Tab`.
5. On first open, you'll be prompted to configure an API provider.
That's the full install. No CLI setup, no Docker, no account creation.
API Provider Configuration (BYOK)
This is where Cline diverges from Cursor or GitHub Copilot. You configure your own key.
Open Cline settings (gear icon in the Cline panel):
**Anthropic:**
```
Provider: Anthropic
API Key: sk-ant-...
Model: claude-sonnet-4-6 (or your preferred model)
```
**OpenAI:**
```
Provider: OpenAI
API Key: sk-...
Model: gpt-4o
```
**Local model via Ollama:**
```
Provider: OpenAI Compatible
Base URL: http://localhost:11434/v1
API Key: ollama
Model: llama3.2 (or whatever you have pulled)
```
For local model setup, see [How to Use Ollama 2026](/en/rehberler/how-to-use-ollama-2026).
One significant practical point: Cline shows you the token count and estimated cost of every request in the UI. You know exactly what each task is spending before you approve it. For cost-sensitive teams, this is more useful than monthly bill surprises.
Core Workflow
The main loop is: describe a task in natural language → Cline proposes steps → you approve or reject each action → it executes and loops.
**Typical session:**
1. Open Cline panel in VS Code.
2. Type a task: `"Add input validation to the registration form — email format check, password minimum 8 chars, show inline errors."`
3. Cline reads relevant files, proposes edits.
4. Each edit or terminal command shows a diff or command preview before running.
5. You approve (or use auto-approve for trusted action types).
6. Cline runs, reads the result, and continues.
Cline has file-system access to your open workspace. It can:
- Read any file in the project
- Write and create files
- Run terminal commands (npm install, git, test runners, build scripts)
- Spawn a headless browser to check rendered output
- Search the web for documentation
The key mental model: Cline is an agent, not a chatbot. Give it goals, not instructions for each step. `"Make the tests pass"` works better than `"Edit line 47 of auth.ts to..."`.
Auto-Approve Settings
By default, Cline asks for approval before every file edit and every terminal command. In practice this gets tedious. Cline lets you configure which action categories to auto-approve.
Go to **Cline Settings → Auto-Approve**:
A sensible workflow: start a session with everything manual, watch what Cline does for a few steps, then flip to auto-approve once you trust the direction. For repetitive tasks like running tests after each edit, auto-approving read + write + test-run commands is fine.
There's also a checkpointing feature: Cline saves workspace state at decision points, so you can roll back to a previous state if an approved action produced bad output.
MCP Server Integration
Cline supports the Model Context Protocol (MCP), the same open standard used by Claude Code and other agents. This means you can connect external tools to Cline the same way you would with any MCP-compatible host.
Practical MCP servers worth adding:
- **filesystem** — explicit file access outside the workspace
- **github** — read/write GitHub issues, PRs, comments without leaving VS Code
- **postgres** / **sqlite** — let the agent query your database directly
- **puppeteer** — structured browser control
- **fetch** — give the agent HTTP access to APIs
To add an MCP server, edit `cline_mcp_settings.json` (accessible from Cline settings panel). Format matches the Claude Desktop config format, so existing MCP setups transfer directly.
This extensibility is what makes Cline viable for complex multi-system tasks: the agent can query a database, read the schema, edit your ORM models, and run migrations — all in one task loop. For comparison on another agentic workflow, see [How to Use Claude Code: 2026 Guide](/en/rehberler/how-to-use-claude-code).
Browser Tool and Computer Use
Cline includes a headless browser tool powered by Puppeteer. When enabled, the agent can:
- Navigate to a URL (including localhost)
- Click elements, fill forms, take screenshots
- Read page content and check for errors
- Automate QA steps after a code change
This is useful for UI development loops: you make a change, Cline opens the browser, checks the rendered result, reads the screenshot, and decides if the fix worked. It's slower than manual QA but runs unattended while you do other work.
To enable: Cline Settings → Browser → Enable Browser Tool. Requires Chrome or Chromium installed.
One honest caveat: the browser tool is best for simple navigation and visual checks. Complex interactive flows (drag-and-drop, canvas, OAuth popups) often fail or require explicit guidance. Don't expect it to replace a proper E2E test suite.
Cline vs. Alternatives: When to Pick Each
Knowing how to use Cline 2026 also means knowing when not to use it.
If your team has existing Anthropic or OpenAI API contracts and wants to avoid another SaaS subscription, Cline is the natural choice. If you want zero setup and best autocomplete, [Cursor](/en/rehberler/how-to-use-cursor-2026) wins. If you prefer the terminal and work across large repos, [Claude Code](/en/rehberler/how-to-use-claude-code) is more suited.
Cline also runs well alongside Cursor — use Cursor Tab for autocomplete, Cline for autonomous multi-step tasks. They don't conflict.
Practical Tips for Using Cline Effectively
**Be specific about scope.** Cline's biggest failure mode is scope creep. `"Clean up the codebase"` will produce unexpected changes. `"Refactor the auth middleware to use async/await, only touch src/middleware/auth.ts"` is tractable.
**Set a context window budget.** Long tasks accumulate context. Cline shows context usage in the UI. If a task runs long, start a new task rather than letting the context fill — long context degrades instruction-following.
**Use `.clinerules`** (formerly `.clineignore`) to tell Cline which files and directories to never touch. Put generated files, build output, and secrets configuration here.
**Checkpoint before risky changes.** Before asking Cline to do anything database-related or deployment-related, use the snapshot feature so you have a clean rollback point.
**Model selection matters.** For complex reasoning tasks (architecture decisions, debugging subtle bugs), use a frontier model like Claude Sonnet or GPT-4o. For repetitive grunt work (formatting, test stub generation), a cheaper or local model is sufficient. Switching mid-session is two clicks in Cline settings.
For projects where you're building on top of AI APIs, see [How to Use Claude API 2026](/en/rehberler/how-to-use-claude-api) to understand what Cline is calling under the hood.
Next Steps
- Set up your first Cline session with a real task in an existing project, not a toy repo
- Configure at least one MCP server relevant to your stack (GitHub or database)
- Compare your workflow against [How to Use Windsurf 2026](/en/rehberler/how-to-use-windsurf) if you want a less-config alternative
- If you're building an AI-powered product, read [How to Start AI SaaS 2026](/en/rehberler/how-to-start-ai-saas-2026) for the broader architecture decisions Cline will help you execute