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Cline

Open-source autonomous coding agent extension for planning, editing, running commands, and using tools from VS Code.

by Cline·added 2026-04-27·
CLI
HarnessCLI
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Canonical URL
https://heyclau.de/entry/tools/cline
Source URLs
https://docs.cline.bot, https://github.com/cline/cline, https://cline.bot
Brand
Cline
Brand domain
cline.bot
Brand asset source
brandfetch
Author
Cline
Claim status
unclaimed
Last verified
2026-04-27

Schema details

Install type
copy
Troubleshooting
No
Source repository stats
Scope
Source repo
Tool listing metadata
Pricing
open-source
Disclosure
heyclaude_pick
Application category
DeveloperApplication
Operating system
macOS, Windows, Linux
Full copyable content
## Editorial notes

Cline is useful for users who want a visible agent loop inside VS Code with command execution and file-editing control.

## Disclosure

Editorial listing. No paid placement or affiliate link is used.

About this resource

Editorial notes

Cline is useful for users who want a visible agent loop inside VS Code with command execution and file-editing control.

Disclosure

Editorial listing. No paid placement or affiliate link is used.

#vs-code#coding-agent

Source citations

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How it compares

Cline side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field
Cline logoCline

Open-source autonomous coding agent extension for planning, editing, running commands, and using tools from VS Code.

Open dossier
Roo Code logoRoo Code

Open-source AI coding agent for VS Code with modes for planning, editing, debugging, and workflow automation.

Open dossier
mini-SWE-agent logomini-SWE-agent

MIT-licensed command-line software-engineering agent for local coding tasks, GitHub issue fixing, trajectory inspection, and SWE-bench style evaluation.

Open dossier
Open SWE

Open-source framework for building internal coding agents that accept tasks via Slack, Linear, or GitHub, execute code changes in isolated cloud sandboxes, and open draft pull requests automatically.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety · Privacy · Safety · Privacy · Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandCline logoClineRoo Code logoRoo Codemini-SWE-agent logomini-SWE-agent—
Categorytoolstoolstoolstools
Sourcefirst-partysource-backedsource-backedsource-backed
AuthorClineRoo CodeSWE-agentLangChain
Added2026-04-272026-04-272026-06-032026-06-05
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes— missing— missing✓mini-SWE-agent is designed around language-model-generated terminal actions, so operators should review proposed actions before allowing changes in important repositories. The default CLI mode asks for confirmation before each proposed action, while automatic execution mode runs model-proposed actions without confirmation and should be limited to disposable or sandboxed environments. The project intentionally keeps the agent loop small and bash-oriented; that simplicity makes behavior easier to inspect, but it does not make actions safe, correct, authorized, or reversible. Generated patches, dependency changes, tests, issue updates, and benchmark runs still need human review before they affect protected branches, production systems, customer data, or shared infrastructure. Sandboxing choices such as local folders, containers, and isolated environments should be reviewed for mounted files, network access, dependency caches, and private access exposure. SWE-bench or other benchmark scores are useful evaluation signals, not proof that the agent is appropriate for a specific repository, policy boundary, or production workflow.✓Each task runs in an isolated cloud Linux sandbox (Modal, Daytona, Runloop, or LangSmith) to prevent production impact. The agent executes shell commands, file operations, web fetches, and HTTP requests inside the sandbox without confirmation prompts — review sandbox provider permissions before deployment. GitHub operations are performed through a GH_TOKEN proxy; scope token permissions to the minimum required repositories. Subagent orchestration can spawn parallel child agents — set appropriate step limits and monitor LangSmith traces to prevent runaway execution. AGENTS.md or CLAUDE.md at the repository root is injected into the system prompt; review this file to control agent behavior and conventions.
Privacy notes— missing— missing✓Prompts, issue text, repository snippets, diffs, command output, test logs, and error traces may be sent to the configured model provider, gateway, or local model runtime. The CLI documentation says mini saves the full history of the last run to the global configuration directory, which can include local paths, source excerpts, model messages, and command output. Trajectory browser files, batch output, benchmark artifacts, and saved histories should be treated as potentially sensitive development records with retention and redaction rules. Local sandbox or container configuration can expose mounted source trees, dependency caches, environment settings, and private project access if the operator grants broad access. Teams using mini-SWE-agent for issue fixing should document who can access trajectories, logs, model-provider records, and any generated patches before sharing outputs.✓Repository code, Linear issue history, and Slack thread history are sent to the configured model provider API. Sandbox providers (Modal, Daytona, Runloop, LangSmith) process task execution data according to their own privacy policies. LangSmith tracing, when enabled, logs full agent traces including tool inputs and outputs — configure retention and access controls in your LangSmith organization. GitHub OAuth tokens and model API keys should be stored as secrets and never committed to the repository.
Prerequisites— none listed— none listed
  • Python environment, package manager, and supported local shell environment for running the mini-SWE-agent CLI, batch workflows, or Python bindings.
  • Approved model route, provider account, local model setup, or gateway configuration prepared outside the repository where the agent will run.
  • Disposable checkout, sandbox, container, or tightly scoped working tree for tasks that may modify source files, dependencies, tests, or generated artifacts.
  • Clear task prompt, target branch, test command, rollback plan, and reviewer ownership before asking the agent to work on a real issue or codebase.
  • GitHub account with OAuth access for repository operations.
  • A model API key (Anthropic, OpenAI, or compatible provider).
  • A LangSmith API key when using LangSmith as the sandbox provider.
  • Slack workspace, Linear workspace, or GitHub repository access for the desired trigger integrations.
Install————
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