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I Built a CLI for AI Projects – Here’s My Experience
AI development is moving at breakneck speed. New models, APIs, and frameworks pop up every month, and keeping up can feel overwhelming. As someone who frequently prototypes AI applications, I found myself constantly rebuilding the same project scaffolding: setting up API keys, installing dependencies, creating Streamlit UIs, and integrating vector databases. So I thought: Why am I doing this manually every time? That’s when RunKit was born – a CLI tool that lets developers scaffold AI projects in seconds, not days. Here’s what I learned while building it. Why I Built a CLI for AI Projects Every AI project starts the same way: Decide on an LLM (Claude, Gemini, or a local model like Ollama) Set up environment variables and API keys Install dependencies Create a basic UI (usually Streamlit or FastAPI) Add optional features like caching, conversation memory, or a vector database I was repeating this setup so often that I started copy-pasting boilerplate code. But maintaining multiple AI projects like this was a nightmare. Dependencies would change, API requirements would shift, and suddenly my old setups were outdated. So I built RunKit: a CLI tool that lets you do this with a single command: pip install run-kit run-kit my-ai-project And boom! A ready-to-run AI project is generated with best practices, customizable features, and a clean architecture. The Challenges of Building a CLI Building a CLI isn’t just about automating tasks; it’s about making the experience smooth for users. Here are some of the challenges I faced: 1. Making the CLI Intuitive Nobody wants to read a 10-page manual to use a CLI. The goal was to make it as frictionless as possible. That’s why RunKit asks interactive questions:
AI development is moving at breakneck speed. New models, APIs, and frameworks pop up every month, and keeping up can feel overwhelming. As someone who frequently prototypes AI applications, I found myself constantly rebuilding the same project scaffolding: setting up API keys, installing dependencies, creating Streamlit UIs, and integrating vector databases.
So I thought: Why am I doing this manually every time?
That’s when RunKit was born – a CLI tool that lets developers scaffold AI projects in seconds, not days. Here’s what I learned while building it.
Why I Built a CLI for AI Projects
Every AI project starts the same way:
- Decide on an LLM (Claude, Gemini, or a local model like Ollama)
- Set up environment variables and API keys
- Install dependencies
- Create a basic UI (usually Streamlit or FastAPI)
- Add optional features like caching, conversation memory, or a vector database
I was repeating this setup so often that I started copy-pasting boilerplate code. But maintaining multiple AI projects like this was a nightmare. Dependencies would change, API requirements would shift, and suddenly my old setups were outdated.
So I built RunKit: a CLI tool that lets you do this with a single command:
pip install run-kit
run-kit my-ai-project
And boom! A ready-to-run AI project is generated with best practices, customizable features, and a clean architecture.
The Challenges of Building a CLI
Building a CLI isn’t just about automating tasks; it’s about making the experience smooth for users. Here are some of the challenges I faced:
1. Making the CLI Intuitive
Nobody wants to read a 10-page manual to use a CLI. The goal was to make it as frictionless as possible. That’s why RunKit asks interactive questions:
