Cline 與 goose：讓 AI 智能體存取程式碼庫前先檢查權限

KM101 Dispatch | Tech Radar | 6 November 2026

Two open-source AI agent projects recently caught my attention: Cline and goose.

Both can do more than generate code suggestions. They can work with files, execute commands and interact with tools.

That makes their permission models worth understanding before using either on a real project.

Cline: Coding Workflows with Configurable Approvals

Cline is built around AI-assisted software development.

Its IDE extensions and CLI support development tasks such as reading code, editing files and running commands.

The project's documentation describes approval controls for tool use.

For instance, a workflow can distinguish between reading source files and operations that modify files or execute potentially risky commands.

That distinction is useful when an agent needs to investigate a codebase before making changes.

goose: Local Automation with Multiple Permission Modes

goose is positioned as a more general-purpose local AI agent, with desktop, CLI and API interfaces.

It supports software development but also targets research and other automated workflows.

One particularly important detail is its documented permission configuration.

goose provides Autonomous, Manual Approval, Smart Approval and Chat Only modes.

According to the project's documentation, Autonomous Mode is the default.

That means anyone evaluating goose should check the configured mode before running tasks in an environment containing important files or credentials.

Manual Approval is available when more direct oversight is required.

What I'd Want to Verify in Practice

Reading about approval controls is useful, but actual behavior still needs testing.

For an evaluation, I'd start with a disposable repository and a few controlled tasks:

Read and explain a selected module without changing files.
Propose a small modification and inspect the resulting diff.
Check whether commands require approval under the selected configuration.
Verify what happens when an action is denied.
Compare the results against an expected outcome.

I'd also pay attention to the selected model provider.

Both projects support different model configurations, and open-source agent software does not necessarily mean that model inference is free.

Current Assessment

Cline and goose are both worth investigating, but I haven't run an independent hands-on comparison.

Their documentation indicates different areas of emphasis: Cline is centered on development workflows, while goose extends further into general local automation.

For now, the useful lesson is to treat an AI agent's capabilities and its permissions as two separate things worth evaluating.

Before assessing how much work an agent can automate, establish what it is allowed to do.

Sources:

KM101 Dispatch | Tech Radar