One of the main barriers to a wider adoption of open-source agents is the dependency on extra tools and frameworks that need to be installed before the agents can be run. In this post, we show how to write agents as HTML files, which can just be opened and run in a browser.
One of the main barriers to a wider adoption and experimentation with open-source agents is the dependency on extra tools and frameworks that need to be installed before the agents can be run. In this post, we introduce the Wasm agents blueprint , aimed at showing how to write agents as HTML files, which can just be opened and run in a browser, without the need for any extra dependencies. This is still an experimental project, but we chose to share it early so that people can both test its capabilities and build new applications based on it.
Why?
While developing our agent-factory tool, we have been wondering about ways to package an agent so it could be easily shared and executed on different systems. In the past, we addressed a similar problem with marimo , both when trying to convey our thoughts with code examples and when introducing new tools for algorithmic timelines . In both cases, the main advantage was that people could run code directly on their devices, more precisely inside their own browsers, thanks to the possibility of exporting marimo notebooks as standalone HTML files powered by WebAssembly and Pyodide .
WebAssembly (Wasm) is a binary instruction format that allows code written in languages like C, C++, Rust, and Python to run at near-native speed in web browsers. Pyodide is a Python distribution for the browser that runs entirely in WebAssembly, enabling you to execute Python code and many of its libraries directly in web applications. As most of our agentic code is in Python, it was quite natural for us to think about adopting a similar solution: the result is a collection of simple, single-file agents that run in a browser tab, in an environment that is sandboxed by construction, and without the need to manually install extra libraries or development frameworks.
How it works
All our demos are standalone HTML files, which contain both the UI and the running code of an agent. If you look at their source code, you will see three calls to the pyodide.runPythonAsync command. They deal, respectively, with installing the required Python dependencies (with the micropip customisation Pyodide library); disabling traces (this is currently required only when running the openai-agents framework – you can find more information in the customization section below); and running the actual agent code.
The agent code is nothing more than a Python script that relies on the openai-agents-python library to run an AI agent backed by an LLM served via an OpenAI-compatible API. What this means is that while it runs with OpenAI out-of-the-box (using gpt-4o as a default model), you can also specify a different, self-hosted model, as long as it is compatible with the API. This includes, for instance, models served via HuggingFace TGI , vLLM , or even Ollama running locally on your computer.
Give it a try
If you want to try our agents, follow the setup instructions in our GitHub repo. The TL/DR is: if you have a ready-to-use OpenAI API key, you can just paste it into the provided config.js file. If instead you want to try a local model, you should make sure that it is running and that you know the URL to access it. For example, if you want to run qwen3:8b on Ollama, you should first download the application, then the model (with Ollama pull qwen3:8b). After that, open one of the HTML files in your browser and follow the instructions provided: your agent should be up and running in a few seconds.
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