Why I Use Cline for AI Engineering
Author: Addy Osmani
Original: Read the full article
Summary
An evaluation report after assessing the entire landscape of AI coding assistants. Cline—a free VS Code extension—stands out for serious engineering work thanks to its system-level integration, model flexibility, and unique capabilities like computer use and MCP extensibility.
The AI coding assistant space is flooded with tools promising to revolutionize development workflows. As an engineer who has worked on complex systems for decades, I maintain a healthy skepticism toward such claims. After extensively testing the major contenders—Cursor, WindSurf, GitHub Copilot, and others—I've found Cline—a free VS Code extension—to be uniquely valuable for serious engineering work. Here's why, along with important caveats and tradeoffs to consider. This article was last updated July 21, 2025.
Core Philosophy: AI as a System-Level Tool
Cline's approach to AI assistance differs from most tools on the market. Rather than focusing solely on code generation or completion, it operates as a system-level tool that can interact with your entire development environment. This becomes particularly valuable when handling complex debugging scenarios, large-scale refactoring, or integration testing.
Key Capabilities That Matter
Flexible Context Management
One of Cline's most powerful features is its ability to efficiently integrate various types of context. The system provides several methods for adding context:
@file: Directly include file contents with intelligent parsing@folder: Batch-import directory contents with smart filtering@url: Automatically fetch documentation and convert to Markdown@problems: Integrate workspace diagnostic information
This flexibility becomes particularly valuable when working with large codebases or complex debugging scenarios. You can selectively include relevant files and documentation rather than overwhelming the context window with unnecessary information. The system's intelligent parsing ensures included content is properly formatted for the selected model.
These features align well with context engineering—providing the selected model with all the information and tools it needs to successfully complete a task.
Model Flexibility and Strategic Mixing
Unlike tools locked to a specific provider, Cline's model flexibility enables sophisticated workflows that leverage the strengths of different AI models. It supports the full range of models including Anthropic, OpenAI, Google Gemini, DeepSeek, and local models via LM Studio/Ollama.
For local/offline model users, Cline's integration with LM Studio has been significantly upgraded. Cline removed hardcoded temperature settings for LM Studio API calls, allowing users to customize the generation temperature for local models. It also added support for reasoning_content in LM Studio responses—meaning if a local model provides chain-of-thought or reasoning traces, Cline can capture and utilize it.
Cline also proactively tracks costs during sessions. This is most apparent when switching between model providers, helping you make informed decisions about model selection.
Cline has added real-time visual indicators for context size. This progress bar shows as you approach limits—invaluable for managing work within model constraints.
DeepSeek-R1 + Sonnet Hybrid Approach
Recent benchmarks and user experiences show that using DeepSeek-R1 for planning and Claude 3.5 Sonnet for implementation can reduce costs by up to 97% while improving overall output quality.
Here's why this combination works so well:
DeepSeek-R1 for Planning ($0.55/M tokens):
- Excels at logical reasoning and architectural analysis
- Open-source model allows customization
- Significantly lower costs (input tokens 5.45x cheaper than Sonnet, output 6.85x cheaper)
- Particularly effective for:
- Understanding complex codebases
- Creating sequence diagrams
- Mapping dependencies
- Identifying potential edge cases
- Generating architecture documentation
Claude 3.5 Sonnet for Implementation ("Execution"):
- Excels at runtime debugging and code generation
- Excellent browser automation capabilities
- Fast response times for iterative development
- Particularly effective for:
- Writing and optimizing code
- Testing implementations
- Debugging runtime issues
- Browser-based testing
- System interactions
The cost efficiency is striking. For planning-intensive tasks, using DeepSeek-R1 instead of premium models can reduce costs by an order of magnitude while maintaining or even improving output quality. Engineers report that roughly 70% of tasks that previously required more expensive models can rely on DeepSeek-R1.
You can also use Gemini (e.g., 2.0 Flash Thinking) with Cline for planning, which offers a 1M token context window. Cline's ability to seamlessly switch models makes this hybrid approach practical. With the v3.2.6 update, the system even remembers your preferred model for each mode, making it effortless to maintain optimal model selection for different types of tasks.
Checkpoints: Version Control Beyond Git
Cline's checkpoint system automatically captures workspace state after every AI operation.
Unlike traditional version control:
- Each checkpoint contains the complete environment state
- Changes can be compared and rolled back at fine granularity
- Browser sessions and terminal state are preserved
This is particularly valuable when:
- Exploring multiple solution approaches simultaneously
- Debugging complex runtime issues
- Refactoring across multiple files
- Testing different dependency configurations
The system operates independently from your regular git workflow, preventing experimental changes from polluting commit history.
Computer Use: Runtime Awareness
Perhaps Cline's most distinctive feature is its ability to interact with running systems. Using Claude's computer use capabilities, it can:
- Launch and interact with browsers (and even verify that interactions work!)
- Execute and monitor terminal commands
- Capture and analyze runtime behavior
- Respond to system output in real time
In practice, Cline can connect to a launched Chrome instance to verify that a set of changes renders correctly. It notices when there's a Next.js error and can proactively resolve the issue without you needing to copy-paste the problem back and forth. This is a game-changer.
This bridges the critical gap between static code analysis and runtime behavior—particularly valuable when working with complex web applications or distributed systems.
Plan/Execute Modes: Control When It Matters
The plan/execute toggle fundamentally changes how you interact with AI assistance:
- Plan Mode: Design and review solutions before execution, with persistent model selection
- Execute Mode: Directly implement straightforward tasks, maintaining its own model preference
This separation provides necessary control for critical changes while maintaining efficiency for everyday tasks. The ability to switch modes mid-task is particularly valuable when requirements evolve during implementation.
Cline's plan/execute mode now globally stores plan/execute model preferences. If you prefer, say, DeepSeek or Gemini for planning and another model for execution, Cline will persistently remember those choices. This ensures consistent workflows across projects without manually switching settings each time.
Model Context Protocol (MCP): Extensibility in Practice
The Model Context Protocol fundamentally changes what's possible with AI assistance. Instead of being limited to predefined integrations, you can extend Cline's capabilities with custom tools. Some practical applications:
- Integration with internal monitoring systems
- Custom security scanning workflows
- Automated documentation generation
- Legacy system modernization pipelines
The protocol's simplicity (JSON-based API) makes it accessible while remaining powerful enough for complex integrations without specialized prompts or roles. This extensibility is particularly valuable in enterprise environments where custom tooling is the norm rather than the exception.
The latest version of Cline added an MCP Marketplace—like an app store for Cline, supporting one-click installation and auto-configuration, eliminating complex setup.
Cline can even use MCP to create and add tools for itself, like "add a tool that pulls the latest npm documentation." It handles everything from creating the MCP server to installing it, ready for future tasks.
The Fork Ecosystem: RooCode
While Cline has inspired several forks like RooCode (formerly RooCline) and Blackbox, each has taken a different direction in AI-assisted development. RooCode emphasizes role-based prompting and specialized workflows, while Cline maintains its vision of being a universal tool capable of handling any task without explicit role selection. This philosophy is reflected in features like plan/execute mode, which simplifies common interaction patterns without requiring users to manage complex prompt strategies.
The different approaches highlight an interesting tension in AI tool design—while specialized modes may seem appealing, they often add complexity to the user experience. Cline's approach focuses on reducing prompt fatigue and making interactions more natural.
Comparison with Current Tools
Cursor
(2K completions free, otherwise $20/month)
Strengths: Built on VS Code with high familiarity; stable and comprehensive feature set; effective multi-file operations via Composer; strong team environment support.
Limitations: Recent performance issues and bugs; fewer system-level integrations; limited runtime debugging.
WindSurf
(50 premium prompts free, otherwise $15/month)
Strengths: Excellent context awareness for medium-to-large codebases; clean, polished UI; cost-effective pricing; strong project context understanding.
Limitations: Fewer features compared to competitors; no browser automation; confusing "model flow action credits" system.
GitHub Copilot
(Copilot Pro is $10/user/month)
Strengths: Unlimited usage on paid tier; seamless VS Code integration; strong inline suggestions; native GitHub ecosystem integration.
Limitations: Basic multi-file editing capabilities; limited to editor environment; slower performance on complex tasks; no system-level operations; basic model support.
Aider
Strengths: Excellent terminal-based workflow; strong CLI integration; open-source flexibility; direct approach to task solving.
Limitations: Limited IDE integration; basic runtime capabilities; steeper learning curve for GUI-focused developers.
Why Cline Stands Out
Cline's distinction lies in its combination of:
Model Flexibility: Strategically leveraging different model capabilities (e.g., DeepSeek-R1 + Sonnet workflow) can reduce costs by up to 97% while improving output quality.
System Integration: Deep integration with browsers, terminals, and development tools enables true end-to-end assistance.
Control and Visibility: The human-in-the-loop approach with explicit approvals and checkpoints provides necessary oversight for critical systems.
Extensibility: The Model Context Protocol enables integration with custom tools and workflows, making it adaptable to specific needs.
The choice between tools ultimately depends on your specific needs:
- For cost-conscious teams, Cursor and WindSurf offer good value, though they're less comprehensive feature-wise than Cline
- For GitHub-centric workflows, GitHub Copilot integrates seamlessly
- For terminal enthusiasts, Aider provides a focused experience
- For maximum flexibility and control, Cline enables sophisticated workflows
Conclusion: Why Cline Makes Sense for Serious Engineering
For teams building complex systems, Cline's approach to AI assistance aligns well with professional engineering practice:
- It respects existing workflows rather than forcing new ones
- It provides control and visibility where it matters most
- It scales and customizes as needs evolve
- It treats AI as a tool rather than a magic solution
The tradeoff of increased complexity for greater control and capability makes sense for serious development work. While simpler tools may suffice for basic tasks, Cline's system-level approach offers unique value for complex engineering challenges.
If you're building complex systems and want AI assistance that respects engineering principles, Cline deserves serious consideration.
*The author has no affiliation with Cline beyond being a user. This evaluation is based on personal experience in production environments.