AI in the Terminal: My Experience with Shell-GPT

November 18, 2025/Steve Brettschneider
shellgpt ai in terminal

Introduction

During a recent conversation with a fellow Pinnacleer, the topic of Warp came up — a modern terminal that integrates AI features right into the workflow. It’s an impressive tool, but it made me curious about open-source and more budget-friendly alternatives that offer similar AI-driven capabilities for the command line.

That curiosity led me to shell-gpt, a command-line utility that brings large language model power directly to your terminal. While it doesn’t have all the bells and whistles of Warp, shell-gpt still delivers a strong set of features and is a compelling choice for developers seeking AI assistance without vendor lock-in or extra costs.

This blog entry will walk you through my experience with shell-gpt.

What is Shell-GPT?

Shell-GPT is designed to bring the capabilities of large language models, like GPT-4, straight into your terminal. Unlike Warp—which aims to be a fully modern, AI-powered terminal experience—shell-gpt focuses on providing natural language command generation and execution for Bash, Zsh, PowerShell, or CMD, all from the command line you already use.

The tool advertises compatibility with local models through Ollama, but my attempts to use this feature were met with mixed results. The integration felt incomplete, and the documentation lagged behind the current implementation, so I ultimately had to reference the sgpt source code to piece things together. For most users, shell-gpt’s real value is in its ability to translate plain English into working shell commands, with the convenience of running them directly.

Installation & Setup

For the most current installation instructions, refer to the official shell-gpt GitHub README. In short: it’s a simple pip install shell-gpt, and configuration with your OpenAI API key is straightforward. The documentation covers all supported options.

While you can simply follow the instructions in shell-gpt’s README, as I developer, I take a more nuanced approach to installing it in order to keep my system clean and tidy.

On my setup, I use pyenv to manage multiple Python versions. To avoid cluttering my system Python installation, I created a dedicated virtual environment for shell-gpt using pyenv. This keeps dependencies isolated and makes it easy to upgrade or remove shell-gpt later. After installing, I added an alias to my .zshrc so I can invoke sgpt from any terminal session:

# Create a new virtual environment for shell-gpt
pyenv virtualenv 3.13.9 shell_gpt
pyenv activate shell_gpt
pip install shell-gpt
# Add this to your .zshrc alias
sgpt="$HOME/.pyenv/versions/shell_gpt/bin/sgpt"

With this setup, running sgpt in the terminal always uses the isolated environment.

Core Capabilities & Real-World Examples

Shell-GPT shines in several practical scenarios:

  • Command generation: Generate virtually any CLI command—simple or complex—across a wide range of tools and scenarios, from everyday tasks to intricate one-liners.
  • Code generation: Handy for generating small scripts, quick snippets, or adding comments to code—best for one-off tasks, not a substitute for full-featured IDEs like Cursor, Windsurf, or Claude Code.
  • Text analysis: Pipe in logs, scripts, or any text to get explanations, suggestions, summaries, or troubleshooting help—great for analyzing output, understanding code, or quickly making sense of complex results.
  • Git workflow: Automatically generate commit messages from diffs.
  • Shell integration: Of all the features, this one stands out for me: with a quick hotkey (such as Ctrl+L), you can instantly bring up command suggestions right in your terminal, making the workflow feel incredibly smooth and integrated.

Examples

Asking sgpt to come up with a find command for me

$ sgpt -s 'I forget how to recursively find .json files but exclude searching in the .venv directory'
find . -type d -name .venv -prune -o -type f -name "*.json" -print
[E]xecute, [D]escribe, [A]bort: d
Description:
Searches for all .json files under the current directory (.), excluding any directories named .venv and their contents.

Arguments and Options:

 • .: Start search in the current directory.
 • -type d -name .venv -prune: If a directory named .venv is found, do not descend into it.
 • -o: Logical OR; if the prune condition fails, evaluate the next.
 • -type f -name "*.json": Match files (-type f) with names ending in .json.
 • -print: Output the path of each matching file.
[E]xecute, [D]escribe, [A]bort: e
./settings.json
./test_results/html/status.json
$

Allowing sgpt to generate my git commit message for me


$ git diff | sgpt -s 'Commit my changes'
git add notifications.py && git commit -m "Add docstrings to notification helper functions"
[E]xecute, [D]escribe, [A]bort: e
[dev 87b4018] Add docstrings to notification helper functions
 1 file changed, 3 insertions(+)
$

Summarizing scripts/code to avoid grokking through a bunch of text

$ cat ~/.zshrc | sgpt summarize\ this
Here’s a summary of your .zshrc:

 • Oh-My-Zsh: Loads from ~/.oh-my-zsh, uses the agnoster theme, and enables plugins: git, extract, z.
 • Key Bindings: Enables vi mode (bindkey -v).
 • Custom Config: Sources ~/.zsh_functions and ~/.zsh_aliases.
 • Environment: Sets LESS, VISUAL, EDITOR, and Python-related variables (pyenv, pipenv).
 • PATH: Prepends custom, pyenv, and Codeium/Windsurf paths.
 • Tools: Initializes direnv, nvm, gh copilot, and 1Password SSH agent (commented).
 • Prompt: Customizes PS1 to show the current Git branch in JetBrains terminal.
 • Shell-GPT: Binds Ctrl+L to run sgpt for shell command suggestions.

Let me know if you want a more detailed breakdown of any section!
$

Shell integration with CTRL+L

Advanced Features Worth Exploring

  • Function calling: Define and use your own AI-powered functions for custom tasks.
  • Chat mode: Maintain context for multi-step or complex workflows.
  • Piping support: Seamlessly integrate shell-gpt into your existing CLI pipelines for even more automation.

Practical Considerations

  • Cost: Using the OpenAI API comes with usage fees. While local models (like Ollama) are technically an option, in practice the results aren’t great and setup is a bit of a hassle (as I mentioned earlier).
  • Security: If you work in DevOps or handle sensitive information (like API keys, secrets, or credentials), be very cautious about what you send to shell-gpt. Avoid pasting secrets or confidential data into prompts, and always consider the security implications before engaging the tool with sensitive material.
  • Workflow: Always review generated commands before execution; interactive mode helps prevent mistakes.
  • Best practice: Use shell-gpt as an assistant, not a replacement for your own understanding.

Conclusion

Shell-GPT is a significant productivity boost for anyone who spends serious time in the terminal. For consultants and experienced developers, it means faster problem-solving and less time lost to context switching. If you haven’t tried it yet, I encourage you to give it a spin! For more details, see the GitHub repo and official documentation.