How to Install Cline in VS Code Locally

Tool overview

Cline is an autonomous coding agent that runs inside your editor and terminal. It can read files, write code, run terminal commands, use a browser, and work through multi-step development tasks with human approval.

That last phrase matters. Cline is powerful because it can operate on a real project. It is risky for the same reason. A good local setup is not only about model configuration; it is about controlling file writes, terminal commands, context, and project history.

What "local Cline" means

Cline itself is the editor extension. The model can be local or cloud-hosted.

A local setup usually means:

• VS Code runs Cline.
• Ollama or LM Studio runs the model on your machine.
• Cline sends prompts to the local model endpoint.
• File reads, edits, and commands happen inside your local workspace.
• You approve each sensitive action.

This can keep model inference local. It does not mean every extension, dependency install, browser action, or command is automatically private. Review what the agent is doing.

Step-by-step setup

ollama --version
curl http://localhost:11434/api/tags

Step 1: Install the Cline extension

In VS Code:

1. Open the Extensions panel.
2. Search for Cline.
3. Install the official extension.
4. Open the Cline side panel.

You can also use the VS Code Marketplace link from Cline's official site or repository. Avoid unofficial forks unless you have a specific reason to inspect and trust them.

After installation, open a small project first. Do not start by giving a new coding agent access to a production repository with no Git history.

Step 2: Install and test Ollama

Download Ollama:

https://ollama.com/download

Pull a coding model:

ollama pull qwen2.5-coder:7b

Test it:

ollama run qwen2.5-coder:7b

Ask a small coding question, then exit the chat. Confirm the model is installed:

ollama list

Step 3: Connect Cline to Ollama

Open Cline settings and use:

Provider: Ollama
Base URL: http://localhost:11434
Model: qwen2.5-coder:7b

If the model does not appear, make sure it is installed:

ollama pull qwen2.5-coder:7b

Then restart VS Code or reopen the Cline panel.

Use http://localhost:11434, not the OpenAI-compatible /v1 path, when Cline's provider is set to Ollama.

Step 4: Choose a local coding model

For Cline, model choice matters more than in a normal chat UI. The model needs to follow instructions, edit code, reason across files, and produce structured actions.

Start with:

ollama pull qwen2.5-coder:7b

Then move up only if your machine can handle it:

ollama pull qwen2.5-coder:14b
ollama pull qwen2.5-coder:32b

You can also test other coding-capable local models, but do not assume every chat model is a good coding agent model. Weak local models often loop, miss files, or produce broken edits.

Verify it works

Step 5: Run the first task safely

Use a read-only first prompt:

Inspect this repository and summarize the project structure. Do not modify files or run commands yet.

Then try a narrow task:

Find one small bug or missing test in this project. Explain the proposed change before editing.

When Cline proposes file edits, inspect the diff before approving. When it proposes a terminal command, read the command and ask why it is needed if the purpose is not obvious.

Step 6: Use Git before autonomous edits

Before using Cline on real work:

git status

Commit or stash your own work first. Cline can make edits quickly, and Git is the cleanest way to separate your changes from the agent's changes.

Useful habits:

• Work on a branch.
• Keep tasks small.
• Review diffs often.
• Run tests yourself when needed.
• Do not let the model rewrite unrelated files.
• Ask Cline to explain before editing.

Step 7: Configure MCP servers in Cline

Cline supports Model Context Protocol servers. MCP can connect Cline to extra tools and data sources, such as local files, APIs, docs, databases, or internal services.

Use the MCP panel in Cline:

1. Open the Cline panel.
2. Click the MCP Servers icon.
3. Use Marketplace where available, or open the manual config.
4. Add entries under mcpServers.
5. Configure credentials or environment variables.
6. Verify the tools appear.
7. Test one safe tool call.

For Cline CLI, the MCP config can live at:

~/.cline/mcp.json

For the IDE extension, use the Cline MCP configuration button so you edit the correct settings JSON for that installation.

Step 8: Keep local autonomy under control

Cline's value comes from file and command autonomy. Do not make the first local setup fully automatic.

Safer defaults:

• Review file edits.
• Review terminal commands.
• Use a small workspace.
• Keep secrets outside the project.
• Do not approve deletion commands casually.
• Avoid broad prompts like "fix everything."
• Ask for a plan before implementation.
• Let Cline run tests only after you understand the command.

Local models can make confident mistakes. Human-in-the-loop approval is not a formality; it is the safety system.

Common problems

Cline cannot connect to Ollama

Check Ollama:

curl http://localhost:11434/api/tags

In Cline, confirm:

Provider: Ollama
Base URL: http://localhost:11434

Then restart VS Code.

The model is missing from Cline

Pull it:

ollama pull qwen2.5-coder:7b

Then run:

ollama list

Cline edits the wrong files

Stop the task, inspect the diff, and narrow the prompt. Use a smaller workspace or ask Cline to list target files before editing.

Terminal commands fail

Check your project dependencies and shell environment. Cline can run commands, but it cannot fix a missing Node, Python, compiler, or environment variable without clear instruction.

The local model loops or produces poor edits

Use a stronger coding model, reduce the task size, or switch to a cloud model for complex refactors. Small local models are useful, but they are not equally reliable for autonomous coding.

Security notes

Treat Cline like a developer with shell access.

Use these rules:

• Keep Git clean.
• Review every command.
• Keep secrets out of the workspace.
• Use test repositories for experiments.
• Avoid automatic approval until you trust the exact workflow.
• Do not mount unnecessary directories into MCP servers.
• Watch for commands that install packages, delete files, change Git history, or modify environment files.

Questions to ask before installing

• Do you need Cline to run with local models only, or do you want a cloud fallback for difficult tasks?
• Which directories and project files are allowed for edits and command execution?
• Who approves terminal commands in your team workflow?
• Are there regulated or private repos that need strict approval controls?
• Do you have a restore point or clean branch for quick rollback?

Approximate U.S. planning cost

• Software and install cost: mostly free to start. Cline's extension source is public and Apache 2.0 licensed; local Ollama/LM Studio runtimes are free downloads.
• Local hardware cost: depends on model size and project complexity. Larger local coding models benefit from more RAM and VRAM.
• API-token spend: if you use hosted models for harder tasks, budget for pay-as-you-go token charges.
• Paid plans: cloud endpoints (OpenAI, Anthropic, etc.) have paid usage; Cline may also have enterprise feature paths for teams and managed controls.
• VPS/managed service option: run Cline-compatible backends off-machine for team setups or strict control points, which adds host and maintenance cost.

For live model/API pricing use:

• OpenAI API: https://platform.openai.com/docs/pricing
• Anthropic pricing: https://www.anthropic.com/pricing
• Cline repository license and enterprise controls: https://github.com/cline/cline

Cost breakdown

• One-time cost: install time, local model downloads, and any hardware upgrades.
• Recurring cost: cloud API usage, optional managed service fees, and backup/storage overhead.
• Operational cost: review time on command approvals, review diffs, and periodic security hardening.

Trust boundaries

• Human-in-the-loop: keep command and file-edit approvals active until prompts and workspace are stable.
• Project boundary: isolate each task in branches or temporary repos.
• Provider boundary: know which model endpoint and provider handles each task.
• Execution boundary: do not let Cline run destructive commands without explicit approval.

Workspace and file-system permissions

• Start with a small test repo, not production code.
• Do not mount broad home directories into MCP servers.
• Keep credentials outside project directories.
• Require a clean branch before enabling autonomous edits.

Key and secret management

• Keep provider keys in environment variables or secure store, not in prompts.
• Use separate keys per environment and rotate on role or team changes.
• Set least-privilege API keys where supported.
• If one project is compromised, revoke and replace keys before rerunning.

Red flags

• New task asks Cline to "fix everything" without plan-based checkpoints.
• Auto-run terminal commands without review toggles.
• Model endpoint set to unknown or untrusted URL.
• Broad "approve all" shortcuts in repetitive workflows.
• Key reuse across unrelated environments.

Rollback and update guidance

Rollback:

1. Stop current Cline tasks.
2. Reopen extension and disable risky MCP servers.
3. Reset workspace with git checkout -- for accidental file edits.
4. Restart VS Code and rerun a read-only test prompt.

Update best practice:

• Update Cline, Ollama, and model packages separately.
• Keep a pinned fallback model installed.
• Re-run small smoke tests after each update.

Bottom line

Install Cline from VS Code, run Ollama locally, pull a coding model such as qwen2.5-coder:7b, and configure Cline with Provider: Ollama and Base URL: http://localhost:11434.

The model setup is the easy part. The high-quality setup is the workflow: Git first, small tasks, reviewed diffs, reviewed commands, and limited MCP permissions.