🍽️ KitchenAI

Instantly turn AI Jupyter Notebooks into production-ready APIs.

Falco Hatch Project Docs

kitchenai-list

What is KitchenAI?

KitchenAI bridges the gap between AI developers, Application developers, and Infrastructure developers making it easy to:

  • Author multiple AI techniques

  • Quickly test and iterate

  • Easily build and share

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  • For AI Developers: Focus on your techniques like RAG or embeddings—KitchenAI handles scalable, in the notebook you already feel comfortable in. KitchenAI will convert your notebook into a production-ready application.

  • For App Developers: Seamlessly integrate AI with a set of API’s you can build an application on top of. Quickly test to see which AI technique best fits your application.

  • For Infrastructure Developers: Integrate with AI tooling, customize Django backends, build plugins, and leverage built-in support for observability platforms like Sentry and OpenTelemetry. KitchenAI is extensible to modify for more advanced use cases.

Say goodbye to boilerplate!

🚀 Why KitchenAI?

Integrating AI into applications is getting more complicated, making it tough to test, tweak, and improve your code quickly. KitchenAI is here to fix that by meeting AI developers and data scientists where they already work. It makes the journey from Jupyter notebooks to a fully functional AI backend seamless—getting you up and running in just minutes.

With KitchenAI, you can bridge the gap between experimenting and going live, helping teams work faster and stay productive. The goal is simple: give you a set of tools that cuts the time it takes to turn AI ideas into production-ready solutions in half, so you can focus on what really matters—delivering results.

The ultimate tool in your AI development kit.

🔗 Learn more at docs.kitchenai.dev.

⚡ Quickstart

  1. Set Up Environment

    export OPENAI_API_KEY=<your key>
python -m venv venv && source venv/bin/activate && pip install kitchenai

  2. Start a Project

    kitchenai cook list && kitchenai cook select llama-index-starter

  3. Run the Server

    kitchenai init && kitchenai dev --module app:kitchen

    Alternatively, you can run the server with jupyter notebook:

    kitchenai dev --module app:kitchen --jupyter

  4. Test the API

    kitchenai client labels

    kitchenai client health

    kitchenai client labels

    kitchenai-client

  5. Build Docker Container

    kitchenai build . app:kitchenai

📖 Full quickstart guide at docs.kitchenai.dev.

✨ Features

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  • 📦 Quick Cookbook Creation: Build cookbooks in seconds.

  • 🚀 Production-Ready AI: Turn AI code into robust endpoints.

  • 🔌 Extensible Framework: Add custom recipes and plugins effortlessly.

  • 🐳 Docker-First Deployment: Deploy with ease.

🔧 Under the Hood

  • Django Ninja: Async-first API framework for high-performance endpoints.

  • Django Q2: Background workers for long-running tasks.

  • Plugin Framework: Django DJP integration

  • AI Ecosystem: Integrations to AI ecosystem and tools

  • S6 Overlay: Optimized container orchestration.

KitchenAI is built for developers, offering flexibility and scalability while letting you focus on AI.

🛠️ Roadmap

  • SDKs for Python, Go, JS, and Rust.

  • Enhanced plugin system.

  • Signal-based architecture for event-driven apps.

  • Built-in support for Postgres and pgvector.

🧑‍🍳 Contribute

KitchenAI is in alpha—we welcome your contributions and feedback!

🛠️ Setup

just bootstrap && just setup

  • Requirements: Python 3.11+, Hatch, and Just.

  • Creates a dev environment with pre-configured superuser (admin@localhost / admin).

Contributing details at docs.kitchenai.dev.

🙏 Acknowledgements

Inspired by the Falco Project. Thanks to the Python community for best practices and tools!

📊 Telemetry

KitchenAI collects anonymous usage data to improve the framework—no PII or sensitive data is collected.

Your feedback and support shape KitchenAI. Let’s build the future of AI development together!