Simon Willison’s Weblog

cysqlite—a new sqlite driver (via) Charles Leifer has been maintaining pysqlite3 - a fork of the Python standard library's sqlite3 module that makes it much easier to run upgraded SQLite versions - since 2018.

He's been working on a ground-up Cython rewrite called cysqlite for almost as long, but it's finally at a stage where it's ready for people to try out.

The biggest change from the sqlite3 module involves transactions. Charles explains his discomfort with the sqlite3 implementation at length - that library provides two different variants neither of which exactly match the autocommit mechanism in SQLite itself.

I'm particularly excited about the support for custom virtual tables, a feature I'd love to see in sqlite3 itself.

cysqlite provides a Python extension compiled from C, which means it normally wouldn't be available in Pyodide. I set Claude Code on it (here's the prompt) and it built me cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl, a 688KB wheel file with a WASM build of the library that can be loaded into Pyodide like this:

import micropip
await micropip.install(
    "https://simonw.github.io/research/cysqlite-wasm-wheel/cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl"
)
import cysqlite
print(cysqlite.connect(":memory:").execute(
    "select sqlite_version()"
).fetchone())

(I also learned that wheels like this have to be built for the emscripten version used by that edition of Pyodide - my experimental wheel loads in Pyodide 0.25.1 but fails in 0.27.5 with a Wheel was built with Emscripten v3.1.46 but Pyodide was built with Emscripten v3.1.58 error.)

You can try my wheel in this new Pyodide REPL i had Claude build as a mobile-friendly alternative to Pyodide's own hosted console.

I also had Claude build this demo page that executes the original test suite in the browser and displays the results:

# 11th February 2026, 5:34 pm / python, sqlite, charles-leifer, webassembly, pyodide, ai-assisted-programming, claude-code

MicroQuickJS. New project from programming legend Fabrice Bellard, of ffmpeg and QEMU and QuickJS and so much more fame:

MicroQuickJS (aka. MQuickJS) is a Javascript engine targetted at embedded systems. It compiles and runs Javascript programs with as low as 10 kB of RAM. The whole engine requires about 100 kB of ROM (ARM Thumb-2 code) including the C library. The speed is comparable to QuickJS.

It supports a subset of full JavaScript, though it looks like a rich and full-featured subset to me.

One of my ongoing interests is sandboxing: mechanisms for executing untrusted code - from end users or generated by LLMs - in an environment that restricts memory usage and applies a strict time limit and restricts file or network access. Could MicroQuickJS be useful in that context?

I fired up Claude Code for web (on my iPhone) and kicked off an asynchronous research project to see explore that question:

My full prompt is here. It started like this:

Clone https://github.com/bellard/mquickjs to /tmp

Investigate this code as the basis for a safe sandboxing environment for running untrusted code such that it cannot exhaust memory or CPU or access files or the network

First try building python bindings for this using FFI - write a script that builds these by checking out the code to /tmp and building against that, to avoid copying the C code in this repo permanently. Write and execute tests with pytest to exercise it as a sandbox

Then build a "real" Python extension not using FFI and experiment with that

Then try compiling the C to WebAssembly and exercising it via both node.js and Deno, with a similar suite of tests [...]

I later added to the interactive session:

Does it have a regex engine that might allow a resource exhaustion attack from an expensive regex?

(The answer was no - the regex engine calls the interrupt handler even during pathological expression backtracking, meaning that any configured time limit should still hold.)

Here's the full transcript and the final report.

Some key observations:

• MicroQuickJS is very well suited to the sandbox problem. It has robust near and time limits baked in, it doesn't expose any dangerous primitive like filesystem of network access and even has a regular expression engine that protects against exhaustion attacks (provided you configure a time limit).
• Claude span up and tested a Python library that calls a MicroQuickJS shared library (involving a little bit of extra C), a compiled a Python binding and a library that uses the original MicroQuickJS CLI tool. All of those approaches work well.
• Compiling to WebAssembly was a little harder. It got a version working in Node.js and Deno and Pyodide, but the Python libraries wasmer and wasmtime proved harder, apparently because "mquickjs uses setjmp/longjmp for error handling". It managed to get to a working wasmtime version with a gross hack.

I'm really excited about this. MicroQuickJS is tiny, full featured, looks robust and comes from excellent pedigree. I think this makes for a very solid new entrant in the quest for a robust sandbox.

Update: I had Claude Code build tools.simonwillison.net/microquickjs, an interactive web playground for trying out the WebAssembly build of MicroQuickJS, adapted from my previous QuickJS plaground. My QuickJS page loads 2.28 MB (675 KB transferred). The MicroQuickJS one loads 303 KB (120 KB transferred).

Here are the prompts I used for that.

# 23rd December 2025, 8:53 pm / c, javascript, nodejs, python, regular-expressions, sandboxing, ai, webassembly, deno, pyodide, generative-ai, llms, claude-code, fabrice-bellard

MCP Run Python (via) Pydantic AI's MCP server for running LLM-generated Python code in a sandbox. They ended up using a trick I explored two years ago: using a Deno process to run Pyodide in a WebAssembly sandbox.

Here's a bit of a wild trick: since Deno loads code on-demand from JSR, and uv run can install Python dependencies on demand via the --with option... here's a one-liner you can paste into a macOS shell (provided you have Deno and uv installed already) which will run the example from their README - calculating the number of days between two dates in the most complex way imaginable:

ANTHROPIC_API_KEY="sk-ant-..." \
uv run --with pydantic-ai python -c '
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio(
    "deno",
    args=[
        "run",
        "-N",
        "-R=node_modules",
        "-W=node_modules",
        "--node-modules-dir=auto",
        "jsr:@pydantic/mcp-run-python",
        "stdio",
    ],
)
agent = Agent("claude-3-5-haiku-latest", mcp_servers=[server])

async def main():
    async with agent.run_mcp_servers():
        result = await agent.run("How many days between 2000-01-01 and 2025-03-18?")
    print(result.output)

asyncio.run(main())'

I ran that just now and got:

The number of days between January 1st, 2000 and March 18th, 2025 is 9,208 days.

I thoroughly enjoy how tools like uv and Deno enable throwing together shell one-liner demos like this one.

Here's an extended version of this example which adds pretty-printed logging of the messages exchanged with the LLM to illustrate exactly what happened. The most important piece is this tool call where Claude 3.5 Haiku asks for Python code to be executed my the MCP server:

ToolCallPart(
    tool_name='run_python_code',
    args={
        'python_code': (
            'from datetime import date\n'
            '\n'
            'date1 = date(2000, 1, 1)\n'
            'date2 = date(2025, 3, 18)\n'
            '\n'
            'days_between = (date2 - date1).days\n'
            'print(f"Number of days between {date1} and {date2}: {days_between}")'
        ),
    },
    tool_call_id='toolu_01TXXnQ5mC4ry42DrM1jPaza',
    part_kind='tool-call',
)

I also managed to run it against Mistral Small 3.1 (15GB) running locally using Ollama (I had to add "Use your python tool" to the prompt to get it to work):

ollama pull mistral-small3.1:24b

uv run --with devtools --with pydantic-ai python -c '
import asyncio
from devtools import pprint
from pydantic_ai import Agent, capture_run_messages
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio(
    "deno",
    args=[
        "run",
        "-N",
        "-R=node_modules",
        "-W=node_modules",
        "--node-modules-dir=auto",
        "jsr:@pydantic/mcp-run-python",
        "stdio",
    ],
)

agent = Agent( 
    OpenAIModel(                          
        model_name="mistral-small3.1:latest",
        provider=OpenAIProvider(base_url="http://localhost:11434/v1"),                
    ),            
    mcp_servers=[server],
)

async def main():
    with capture_run_messages() as messages:
        async with agent.run_mcp_servers():
            result = await agent.run("How many days between 2000-01-01 and 2025-03-18? Use your python tool.")
    pprint(messages)
    print(result.output)

asyncio.run(main())'

Here's the full output including the debug logs.

# 18th April 2025, 4:51 am / python, sandboxing, ai, deno, pyodide, generative-ai, local-llms, llms, claude, mistral, llm-tool-use, uv, ollama, pydantic, model-context-protocol