Running Python code in a sandbox with MicroPython and WASM

Simon Willison released an alpha package called micropython-wasm that runs Python code in a sandbox using WebAssembly, along with a Datasette Agent plugin called datasette-agent-micropython that uses it for safe code execution. The sandbox is designed to enforce memory and CPU limits, control file and network access, and allow interaction with host functions, addressing a long-standing need for safely running plugin code within Python applications like Datasette and LLM.

Running Python code in a sandbox with MicroPython and WASM 6th June 2026 I’ve been experimenting with different approaches to running code in a sandbox for several years now, but my latest attempt feels like it might finally have all of the characteristics I’ve been looking for. I’ve released it as an alpha package called micropython-wasm https://github.com/simonw/micropython-wasm , and I’m using it for a code execution sandbox plugin for Datasette Agent https://github.com/datasette/datasette-agent called datasette-agent-micropython https://github.com/datasette/datasette-agent-micropython . Why do I want a sandbox? https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ why-do-i-want-a-sandbox- What I want from a sandbox https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ what-i-want-from-a-sandbox WebAssembly looks really promising here https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ webassembly-looks-really-promising-here MicroPython in WebAssembly https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ micropython-in-webassembly Building the first version https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ building-the-first-version Try it yourself https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ try-it-yourself Should you trust my vibe-coded sandbox? https://simonwillison.net/2026/Jun/6/micropython-in-a-sandbox/ should-you-trust-my-vibe-coded-sandbox- Why do I want a sandbox? My key open source projects— Datasette https://datasette.io/ , LLM https://llm.datasette.io/ , even sqlite-utils https://sqlite-utils.datasette.io/ —all support plugins. I absolutely love plugins as a mechanism for extending software. A carefully designed plugin system reduces the risk involved in trying new things to almost nothing—even the wildest ideas won’t leave a lasting influence on the core application itself. My software can grow a new feature overnight and I don’t even have to review a pull request There’s one major drawback: my plugin systems all use Python and Pluggy https://pluggy.readthedocs.io/en/latest/ , and plugin code executes with full privileges within my applications. A buggy or malicious plugin could break everything or leak private data. I’d love to be able to run plugin-style code in an environment where it is unable to read unapproved files, connect to a network, or generally operate in a way that’s risky or harmful to the rest of the application or the user’s computer. My interest covers more than just plugins. For Datasette in particular there are many features I’d like to support where arbitrary code execution would be useful. I’ve already experimented with this for Datasette Enrichments https://enrichments.datasette.io/ , where code can be used to transform values stored in a table. I’d love to build a mechanism where you can run code on a schedule that fetches JSON from an approved location, runs a tiny bit of code to reformat it into a list of dictionaries, then inserts those as rows in a SQLite database table. What I want from a sandbox My goal is to execute code safely within my own Python applications. Here’s what I need: - Dependencies that cleanly install from PyPI , including binary wheels across multiple platforms if necessary. I don’t want people using my software to have to take any extra steps beyond directly installing my Python package. - Executed code must be subject to both memory and CPU limits. I don’t want while True: s += "longer string" to crash my application or the user’s computer. - File access must be strictly controlled . Either no filesystem access at all or I get to define exactly which files can be read and which files can be written to. - Network access is controlled as well . Sandboxed code should not be able to communicate with anything without going through a layer I fully control. - Support for interaction with host functions . A sandbox isn’t much use if I can’t carefully expose selected platform features to the code that it’s running. - It has to be robust, supported, and clearly documented . I’ve lost count of the number of sandbox projects I’ve seen in repos with warnings that they aren’t actively maintained WebAssembly looks really promising here Web browsers operate in the most hostile environment imaginable when it comes to malicious code. Their job is to download and execute untrusted code from the web on almost every page load. Given this, JavaScript engines should be excellent candidates for sandboxes. Sadly those engines are also extremely complicated, and are not designed for easy embedding in other projects. Most of the v8-in-Python projects I’ve seen are infrequently maintained and come with warnings not to use them with completely untrusted code. WebAssembly is a much better candidate. It was designed from the start to support all of the characteristics I care about and has been tested in browsers for nearly a decade. The wasmtime https://pypi.org/project/wasmtime Python library is actively maintained and has binary wheels. MicroPython in WebAssembly WebAssembly engines like wasmtime run WebAssembly binaries. Some programming languages like Rust are easy to compile directly to WebAssembly. Dynamic languages like JavaScript and Python are harder—they support language primitives like eval , which means they need a full interpreter available at runtime. To run Python we need a full Python interpreter compiled to WebAssembly, wired up in a way that makes it easy to feed it code, hook up host functions and access the results. Pyodide offers an outstanding package for running Python using WebAssembly in the browser, but using Pyodide in server-side Python isn’t supported. The most recent advice I could find was from October 2024 https://github.com/pyodide/pyodide/discussions/5145 stating “Pyodide is built by the Emscripten toolchain and can only run in a browser or Node.js”. The other day I decided to take a look at MicroPython https://micropython.org as an option for this. The MicroPython site says: MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments. WebAssembly sure feels like a constrained environment to me Building the first version I had GPT-5.5 Pro do some research for me https://chatgpt.com/share/6a1e2a5c-58b8-8328-ba1c-0e6aadb0a051 , which turned up this PR against MicroPython https://github.com/micropython/micropython/pull/13676 by Yamamoto Takahashi https://github.com/yamt titled “Experimental WASI support for ports/unix”. It then produced this research.md document https://github.com/simonw/micropython-wasm/blob/c08fbd2276b15dc8c9bdff82845f750971f45647/research.md , so I let Codex Desktop and GPT-5.5 high loose on it https://gist.github.com/simonw/27461a16d76f28f8619c609444d544fe to see what would happen: read the research.md document and build this. You will probably need to write a script that compiles a custom WASM version of MicroPython as part of this project - fetch the MicroPython code to a /tmp directory for this as part of that script. It worked. I now had a prototype Python library that could execute Python code inside a WebAssembly sandbox The trickiest piece to solve was persistent interpreter state. The WASM build we are using here exposes a single entry point which starts the interpreter, runs the code and then stops the interpreter at the end. This works fine for one-off scripts, but for Datasette Agent I want variables and functions to stay resident in memory so I can reuse them across multiple code execution calls. A neat thing about working with coding agents is that you can get from an idea to a proof of concept quickly. I prompted: For keeping variables resident: what if we ran code inside micropython itself which called a host function get next python code and then passed that to eval - and that host function blocked until new code was available, maybe by running in a thread with a queue? Could that or a similar idea help here? After some iteration we got to a version of this that works In Python code you can now do this: python from micropython wasm import MicroPythonSession with MicroPythonSession as session: print session.run "x = 10\nprint x " .stdout print session.run "x += 5\nprint x " .stdout print session.run "print x 2 " .stdout Under the hood this starts a thread, sets up a request queue and then sends messages to that queue for the session.run command, each time waiting on a reply queue for the result of that execution. Inside WASM the MicroPython interpreter blocks waiting for a session next host function to return the next line of code, which it runs eval on before calling session result {"id": request id, "ok": True} when each block has been successfully executed. The other piece of complexity was supporting host functions, so my Python library could selectively expose functions that could then be called by code running in MicroPython. Codex ended up solving this with 78 lines of C https://github.com/simonw/micropython-wasm/blob/0.1a1/micropython wasm/usercmodule/host/hostmodule.c , which ends up compiled into the 362KB WebAssembly blob https://github.com/simonw/micropython-wasm/blob/0.1a1/micropython wasm/artifacts/micropython-wasi.wasm I’m distributing with the package. I am by no means a C programmer, but I’ve read the C and had two different models explain it to me here’s Claude’s explanation https://claude.ai/share/62f74371-cc3c-44f2-b406-33d03513de9e and I’ve subjected it to a barrage of tests. The great thing about working with WebAssembly is that if the C turns out to be fatally flawed the worst that can happen is the WebAssembly execution will fail with an exception. I can live with that risk. Memory limits are directly supported by wasmtime. CPU limits are a little harder: wasmtime offers a “fuel” concept to limit how many operations a WebAssembly call can execute, and that’s the correct fit for this problem, but the units are hard to reason about. I’m experimenting with a 20 million default “fuel” setting now but I’m not confident that it’s the most appropriate value. Try it yourself The micropython-wasm alpha is now live on PyPI https://pypi.org/project/micropython-wasm . You can try it from your own Python code as described in the README https://github.com/simonw/micropython-wasm . I’ve also added a simple CLI mode in version 0.1a2 https://github.com/simonw/micropython-wasm/releases/tag/0.1a2 which means you can try it using uvx without first installing it like so: uvx micropython-wasm -c 'print "Hello world" ' To see it run out of fuel: uvx micropython-wasm -c 's = ""; while True: s += "longer"' Outputs: micropython-wasm: guest exited with code 1 You can also try it in Datasette Agent https://agent.datasette.io/ like this: uvx llm keys set openai Paste in an OpenAI key, then: uvx --with datasette-agent \ --with datasette-agent-micropython \ --prerelease allow \ datasette --internal internal.db \ -s plugins.datasette-llm.default model gpt-5.5 \ --root -o Then navigate to http://127.0.0.1:8001/-/agent http://127.0.0.1:8001/-/agent and run the prompt: show me some micropython Should you trust my vibe-coded sandbox? Having complained about immature, loosely-maintained sandboxing libraries, it’s deeply ironic that I’ve now built my own I deliberately slapped an alpha release version on it, and I’m not ready to recommend it to anyone who isn’t willing to take a significant risk. I’ve put it through enough testing that I’m OK using it myself. I’ve shipped my first plugin that uses it, datasette-agent-micropython https://github.com/datasette/datasette-agent-micropython . I’ve also locked GPT-5.5 xhigh in that Datasette Agent plugin and challenged it to break out of the sandbox https://gist.github.com/simonw/5de497c44d25f9fd459c8aa2c959fe4a and so far it has not managed to. I’m hoping this implementation can convince some companies with professional security teams and high-stakes problems to commit to using Python in WebAssembly as a sandboxing approach and open source their own solutions. More recent articles Claude Opus 4.8: "a modest but tangible improvement" /2026/May/28/claude-opus-4-8/ - 28th May 2026 I think Anthropic and OpenAI have found product-market fit /2026/May/27/product-market-fit/ - 27th May 2026