AI coding assistants have become essential tools for developers, but many rely solely on IDE extensions like GitHub Copilot or Cursor. While these are valuable, AI CLI tools offer unique advantages that can significantly boost your productivity and code quality.
CLI-based AI tools provide a focused, distraction-free environment with full terminal context, making them ideal for complex tasks, debugging, and working with entire codebases. Anecdotally, developers at SSW have noticed that CLI tools often produce higher-quality, more accurate results than their IDE counterparts - possibly due to better context handling and fewer UI constraints.
Why use AI CLI tools?
✅ Benefits
Better context awareness - CLI tools can access your entire terminal history, file system, and git state, providing richer context for better suggestions
Higher quality output - SSW developers have observed that CLI tools consistently produce more accurate and contextually appropriate code than IDE extensions
Better for complex tasks - Debugging, refactoring, and multi-file operations often work better in the CLI
Universal workflow - Works across any editor or environment (Vim, VS Code, Notepad++, etc.)
Git integration - Natural integration with git commands and workflows
Batch operations - Easier to automate and script repetitive tasks
❌ Trade-offs
Learning curve - Requires comfort with command-line interfaces
No inline suggestions - Unlike IDE extensions, you won't get suggestions as you type
Context switching - Moving between your editor and terminal requires discipline
Setup overhead - May require additional configuration compared to IDE plugins
Less visual - No syntax highlighting or preview in suggestions (though output can be piped to editors)
Popular AI CLI tools
1. GitHub Copilot CLI
GitHub's official command-line tool for AI-assisted development. Unsurprisingly, Copilot CLI provides first-class integration with GitHub.
✅✅ Delegation - One standout feature not available with other CLI tools is the /delegate command which pushes your existing local session to a GitHub coding agent (with context!).
✅ Custom agents - You can define custom agents via markdown files with options to define agents at user, repository, and org level within GitHub.
❌Flexibility - Copilot CLI locks you into one provider: Copilot. If you're all in on Team GitHub, this is fine. But if you're looking for more options and
less vendor lock-in, you're better off with something like OpenCode.
Learn more: GitHub Copilot CLI Documentation
Bonus: Sandboxed Copilot CLI
One of SSW's Solution Architects has created a sandboxed version of Copilot CLI, Copilot Here. If you're working
in a high-sec environment or just want added protection around what AI can and cannot access during sessions, we highly recommend checking this out. It spins up within Docker,
is wired to its own reverse-proxy, and provides external controls around the agent itself.
2. OpenCode
OpenCode is an open-source agent that gives you the most options when it comes to models, providers, and extensibility. While it does provide some free models
OOTB, you are expected to BYO model subscription.
✅✅ Flexibility - OpenCode allows you to select from almost any model, as well as select your own provider. This kind of agnosticism is a huge win for users,
especially with the AI landscape being as volatile as it is. Being able to learn one CLI, define your own custom agents within it, and then and the models and providers
arbitrarily is a godsend.
✅ Custom agents - Custom agents are again defined by markdown files, and while they can be defined and used at a repository level, the general principle of OpenCode is for agents
to be user-level definitions.
✅ Subagent usage - OpenCode is better at reasoning about subagent usage automatically, rather than needing to be explicitly directed to do so. On a long enough agent session, context compacting is a real problem
and can degrade the quality of output, so having enthusiastic subagent usage is a big plus.
❌ Integration - Being an independent open-source agent, OOTB integration may not be as robust (or available as quickly) as proprietary tools like Copilot CLI.
Learn more: OpenCode GitHub Repository
3. Claude Code (via Anthropic CLI)
Claude Code is another fan favourite at SSW.
✅✅ Custom agents - Claude Code's custom agents are the most configurable we've used. While they provide all the functionality of other offerings (e.g. personas, allowed tools/paths, etc.),
Claude's custom agents also provide dedicated pre and post-use hooks. This is excellent to help with deterministic tool execution, allowing you to guarantee that certain scripts or commands will be run before and after
the agent.
✅ Integration - Claude has some interesting integrations that aren't available with other tools OOTB, such as Slack integration.
❌Flexibility - Similar to GitHub Copilot, Claude Code locks you in to one provider - in Claude's case; Anthropic.
Learn more: Anthropic CLI Documentation
4. Other notable tools
Warp AI - Built into Warp terminal for command suggestions
Fig AI - Autocomplete with AI explanations (acquired by AWS)
Shell GPT - Simple GPT integration for shell commands
Codex - OpenAI's open source CLI tool
When to use IDE vs CLI
CLI tools are great for…
Complex debugging sessions
Multi-file refactoring
Git operations and history analysis
Learning new commands or APIs
Automation and scripting
Working across different editors
Deep code analysis requiring full project context
✅ Figure: Good use cases for CLI tools
IDE tools are great for…
✅ Figure: Good use cases for IDE tools
Pro tip: You should be using both!
IDE extensions for real-time suggestions while coding, and CLI tools for complex problem-solving, debugging, and refactoring tasks.
Getting started
Choose a tool - Start with GitHub Copilot CLI if you already have a Copilot subscription, or try OpenCode for an open-source option
Install and configure - Follow the installation instructions for your chosen tool
Start small - Begin with simple queries like explaining commands or generating shell scripts
Integrate into workflow - Gradually incorporate CLI AI into your debugging and refactoring workflows
Experiment - Try different tools to find what works best for your workflow
Best practices
Provide context - The more information you give (file paths, error messages, what you've tried), the better the results
Iterate - CLI tools work best in a conversational workflow—refine your prompts based on responses
Review output - Always review and understand generated code before using it
Combine with version control - Use git to track AI-generated changes and easily revert if needed
Use for learning - Ask CLI tools to explain their suggestions to deepen your understanding
Subagent switching - Get comfortable with switching between specific subagents like Plan and Build
By incorporating AI CLI tools into your workflow alongside IDE extensions, you can leverage the strengths of both approaches for maximum productivity and code quality.