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Cline is an open-source AI coding agent that can read and edit project files, run terminal commands, use a browser, and coordinate multi-file work from an editor or terminal. It is more than an autocomplete box: you describe an outcome, Cline can plan the work, ask for permission to act, show diffs, run checks, and revise the implementation. The software itself is free for individual developers, but the model inference behind each request may cost money unless you use a provider or local runtime with suitable free access.
What Cline is—and what it is not
Cline describes itself as “an AI coding agent that lives in your editor and your terminal.” Its repository uses the phrase “The open source coding agent in your IDE and terminal.” Those descriptions matter: Cline can take actions across a workspace instead of only proposing the next line of code.
- Project work: read files, create or modify several files, inspect diffs, create checkpoints, undo changes, and monitor build or linter output.
- Developer tools: execute terminal commands and use a browser when a task requires documentation lookup or checking a running application.
- Natural-language control: explain a goal such as “add pagination to the users endpoint and update the tests,” then review the proposed plan and changes.
- Extensibility: connect external services through MCP servers and plugins, including databases, APIs, and infrastructure tools.
It is not a guarantee that generated code is correct or safe. Treat every command, dependency change, migration, and external-tool call as code review material.
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Where Cline runs
Current Cline materials describe a product family rather than a single VS Code extension. The documented environments include VS Code, Cursor, Windsurf, JetBrains IDEs, Antigravity, and Zed, with Neovim support through ACP mode. The project also presents a CLI, a JetBrains plugin, a desktop application, Kanban, and an SDK for building agents and integrations. Availability and names can change, so check the current Cline documentation before standardizing an installation across a team.
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How the Plan and Act workflow works
Plan mode
Use Plan mode when you need discovery before modification. Cline can inspect the repository and propose files, implementation steps, tests, and risks. This is useful for unfamiliar codebases, broad refactors, and tasks with ambiguous requirements. Ask it to identify assumptions and list commands it expects to run before you approve execution.
Act mode
Act mode performs the approved work: editing files, running commands, checking output, and continuing through a task. Review each diff and command result. If a change is headed in the wrong direction, stop the task, revert or use a checkpoint, and restate the constraint instead of allowing more edits to accumulate.
Approval and auto-approve
Cline’s materials describe approval prompts for edits and commands, while an auto-approve setting can allow more autonomous operation. Approval prompts are a control point, not a complete security boundary. A command can have side effects you did not anticipate, and connected MCP servers may expose powerful capabilities. Keep auto-approve off for unfamiliar repositories, production credentials, destructive commands, and database operations.
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- Open a repository with a clean working tree, or create a branch for the experiment.
- Provide a narrowly scoped request, including the required behavior, files or modules to avoid, test commands, and compatibility constraints.
- Start in Plan mode and ask for a file-by-file plan plus potential risks.
- Switch to Act mode only after checking the plan. Approve reads and low-risk commands first.
- Inspect every diff. Confirm that generated files, lockfiles, configuration, and migrations are intentional.
- Run the project’s formatter, linter, unit tests, and relevant integration tests. Ask Cline to diagnose failures, but verify the diagnosis yourself.
- Commit only after the resulting patch meets your normal review standard.
A useful prompt is explicit about boundaries: “Implement the change in src/billing; do not alter authentication or database schema; add tests; run the existing test command; stop if a destructive command is required.”
Models, providers, and inference cost
Cline lets you choose how requests are served. Its provider documentation lists hosted options including Anthropic, OpenAI, Google, AWS Bedrock, OpenRouter, Azure, GCP Vertex, Groq, Cerebras, DeepSeek, and other OpenAI-compatible endpoints. It also names local runtimes such as Ollama and LM Studio. These are available integration choices, not a comparative ranking of quality or speed.
| Decision factor | Questions to ask |
|---|---|
| Quality | Can the model understand your language, framework, repository conventions, and tool outputs? |
| Latency | Will interactive edits remain responsive, or are long planning and test cycles acceptable? |
| Inference price | What are the provider’s current input, output, caching, and minimum-charge rules for the selected model? |
| Data routing | Does code leave your environment, and which provider, region, or gateway receives it? |
| Deployment | Do you need a hosted endpoint, a self-managed gateway, or a local runtime? |
| Permissions | Can the selected setup safely receive terminal, browser, filesystem, and MCP-tool access? |
The open-source client is free for individual developers according to Cline’s pricing page. That does not make every task free: hosted inference is billed according to the chosen provider or Cline’s model-access option, while bring-your-own-key usage is charged by that provider. Calculate expected spend from the model’s current rates and your token volume; there is no honest universal “cost per task.” Enterprise pricing is custom and includes features such as centralized billing, team management, access controls, and support.
Privacy and data handling
Cline’s FAQ says the open-source client runs locally. When you bring your own API key, requests go from your environment to the selected model provider. Requests made with Cline credits pass through Cline infrastructure, and Cline states that code and prompts are not used to train models. These are Cline’s statements, not an independent security audit.
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- Do not place production secrets, private keys, customer data, or regulated information in prompts unless your policy explicitly allows it.
- Use environment-specific credentials with the least privilege needed for the task.
- Review MCP server configuration, because an integration may read or change systems outside the repository.
- Record which provider, model, region, and account handled sensitive work.
Using MCP servers and plugins safely
MCP servers and plugins let Cline call external tools such as databases, APIs, and infrastructure systems. Start with read-only permissions and a test account. Document each server’s tools, inputs, network destinations, and credentials. Require approval for writes, shell execution, deployments, and data exports. Remove unused integrations rather than leaving broad access enabled indefinitely.
Performance, reliability, and cost controls
- Reduce context: point Cline at relevant directories and explain which generated or vendored trees to ignore.
- Use checkpoints: create a recoverable state before a large edit or dependency upgrade.
- Chunk work: separate planning, implementation, tests, and cleanup so failures are easy to localize.
- Set explicit stopping rules: tell Cline to stop on failing tests, schema changes, network access, or destructive commands.
- Watch usage: long logs, large repositories, repeated retries, and broad file searches increase inference consumption.
- Keep deterministic checks local: formatters, linters, type checks, and tests should decide whether a patch passes.
No controlled comparative benchmark establishes that one supported model is universally best with Cline. Choose based on the task and your privacy, latency, and budget requirements, then measure your own workload.
Troubleshooting common failures
Cline edits the wrong files
Return to Plan mode, specify allowed and forbidden paths, and ask for a file list before execution. Revert the patch or restore a checkpoint, then retry with a narrower prompt.
A command fails repeatedly
Read the complete stderr output, confirm the working directory and environment variables, and run the command manually. If the failure is caused by missing dependencies or credentials, fix that prerequisite rather than asking the agent to retry unchanged.
Context is too large or responses become inconsistent
Split the task, exclude generated artifacts, summarize settled decisions, and provide only the relevant logs. A smaller, well-defined task is usually cheaper and easier to verify.
Provider authentication or quota errors
Check the selected provider, API key scope, account quota, region, endpoint, and model name. Confirm that the key is available to the environment where Cline runs; do not paste a secret into chat.
Local models are too slow or inaccurate
Verify the runtime is serving the model, that the model fits available memory, and that Cline is pointed at the correct endpoint. For complex repository-wide work, compare the result with a hosted model while following your data policy.
An MCP tool behaves unexpectedly
Disable the server, inspect its configuration and logs, and reproduce the task without it. Re-enable only the minimum tools and permissions required.
Or skip the browser setup
If your coding workflow needs repeatable screenshots of a local or deployed page, ScreenshotNeo provides a website screenshot API and MCP server. It accepts consent banners before capture and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each cleanup step can be disabled. Only clean shots are billed: bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers identify the page verdict and billing status. Its MCP tools—take_screenshot, get_page_info, and capture_pdf—work with Claude, Cursor, and other MCP clients.
One-call example (see the ScreenshotNeo documentation):
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);It supports PNG, JPEG, WebP, and PDF; full-page and element captures; device presets, custom viewports and retina scale; dark mode; custom CSS and JavaScript; waits, clicks, hidden selectors, request blocking, headers, cookies, user agents, timezone, geolocation, transparent backgrounds, resizing, TTL caching, signed links, async webhooks, bulk capture of up to 100 URLs per call, usage reporting, and an OpenAPI specification. Plans include 1,000 free shots each month with no card; paid plans start at $5 for 3,000. Create a free ScreenshotNeo account.
Is Cline really free?
The open-source software is free for individual developers, but model inference may incur provider or Cline charges. Local runtimes can change the billing path without making hardware, setup, or model quality free.
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That depends on your Cline configuration and selected provider. Cline says its client runs locally and describes different routing for personal API keys and Cline credits; verify current provider retention, training, regional, and organizational-policy requirements before sending sensitive code.
How autonomous can Cline be?
Plan and Act modes, approval prompts, and auto-approve settings determine how much execution happens without a confirmation. Keep permissions narrow and review diffs, commands, and connected tools.
Frequently Asked Questions
Can Cline work without an internet connection?
Cline documents local runtimes such as Ollama and LM Studio, but a fully offline workflow also depends on the model, runtime, extensions, and tools you configure.
Does Cline replace code review?
No. Treat its diffs, dependency changes, commands, and test claims as submissions for normal human review.
Is there a guaranteed best model for Cline?
No controlled comparative benchmark establishes one. Select a provider and model using quality, latency, price, privacy, deployment, and permission requirements.
Quick Recap
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