Hi there! We built Hal9 (GitHub) to make it radically simpler to create, deploy, and share applications powered by LLMs, diffusers, and other AI models. Whether you're working on chatbots, agents, APIs, or generative apps, Hal9 is designed to minimize the engineering overhead so you can focus on the AI itself.
Why Hal9?
Most generative AI projects end up dedicating the majority of their time to engineering challenges -- building interfaces, integrating tools, and managing infrastructure -- rather than focusing on the core AI work like refining prompts, implementing RAG strategies, or optimizing model performance.
Hal9 shifts that balance by drastically reducing engineering overhead. It offers a simple, lightweight interface built around Unix IO conventions like stdin and stdout, allowing you to focus entirely on AI innovation without the need to learn complex frameworks or deployment workflows.
With Hal9, you can prototype and run locally without extra dependencies, use our free online platform for quick deployments, or scale effortlessly to enterprise-grade solutions. We can also support organizations by enabling cloud deployments in their own environments or providing additional compute resources for enterprise customers.
Hal9 is designed to get out of your way so you can focus on building smarter, faster.
What is Hal9?
Hal9 is a deployment platform purpose-built for generative AI, enabling you to create and deploy generative (LLMs and diffusers) applications (chatbots, agents, APIs, apps) in seconds. Key features:
- Flexible: Use any library, and any model.
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Intuitive: No need to learn app frameworks, simply use
input()andprint(). - Scalable: Designed to integrate your app with scalable technologies like Docker and Kubernetes.
- Powerful: Using an OS process (stdin, stdout, files) as our app contract, enables long-running agents, multiple programming languages, complex system dependencies, and running arbitrary code in secure Kubernetes pods.
- Open: The code behind the Hal9 app, is also open source and open for contributions under our repo.
The Philosophy
We believe the Python ecosystem already provides great libraries for everything from LLM interactions to generative tasks. Instead of reinventing those wheels, Hal9 integrates them into a unified workflow, letting you focus on AI-specific challenges like retrieval-augmented generation (RAG), fine-tuning, alignment, and training.
Hal9 is perfect for developers who want to experiment, iterate, and deploy AI apps quickly without becoming mired in engineering tasks like frontend design or backend integration. It's also ideal for teams looking to collaborate, thanks to the open architecture and straightforward app structure.
Our Journey
We started Hal9 in 2021 with the goal of simplifying AI development. Initially, we focused on web developers, combining AI with technologies like D3.js and TensorFlow.js. While the low-code interface was popular, users wanted that, but with Python support.
In 2022, we took less-code a step further and embraced LLMs like GPT-3, moving towards automatic code generation and simplifying the UX. After several iterations, Hal9 has evolved into a platform that enables faster, easier AI app development.
Resources
We are actively publishing posts that demonstrate how to integrate your favorite frameworks with Hal9. Here are some of the technical blog posts already available:
Let us know your thoughts, feedback, and ideas -- Hal9 is as much about building apps as it is about creating a community of creators.
Top comments (1)
Feel free to post any questions or feedback, looking forward to this!