llm-tools-quickjs
JavaScript execution as a tool for LLM
Installation
Install this plugin in the same environment as LLM.
llm install llm-tools-quickjs
Usage
To use this with the LLM command-line tool:
llm --tool QuickJS "Calculate 123 * 98742" --tools-debug
With the LLM Python API:
import llm
from llm_tools_quickjs import QuickJS
model = llm.get_model("gpt-4.1-mini")
result = model.chain(
"Calculate 123 * 98742",
tools=[QuickJS()]
).text()
print(result)
The QuickJS() instance maintains interpreter state between calls, so this kind of thing works:
quickjs = QuickJS()
conversation = model.conversation(tools=[quickjs])
print(conversation.chain("set a to 'rabbit'").text())
print(conversation.chain("calculate length of a times 50").text())
print(quickjs._get_context().eval("a"))
# Outputs 'rabbit'
Something a bit more fun:
llm -T QuickJS 'Draw a 40 character wide mandelbrot with JavaScript' --td
I tried this and got:
Tool call: QuickJS_execute_javascript({'javascript': "function mandelbrot(width, height, max_iter) {\n let result = '';\n for (let y = 0; y < height; y++) {\n for (let x = 0; x < width; x++) {\n let cx = (x / width) * 3.5 - 2.5;\n let cy = (y / height) * 2 - 1;\n let zx = 0, zy = 0, iter = 0;\n while (zx * zx + zy * zy < 4 && iter < max_iter) {\n let xtemp = zx * zx - zy * zy + cx;\n zy = 2 * zx * zy + cy;\n zx = xtemp;\n iter++;\n }\n if (iter === max_iter) {\n result += '#';\n } else {\n result += ' ';\n }\n }\n result += '\\n';\n }\n return result;\n}\n\nmandelbrot(40, 20, 100);"})
Here is a 40 character wide Mandelbrot set visualization in text form using JavaScript:
```
##
###
# ######
##########
###########
############
#### #############
##### ############
##########################
##### ############
#### #############
############
###########
##########
# ######
###
##
```
If you'd like, I can provide the JavaScript code used to generate this.
Function alternative
The QuickJS tool is a Toolbox - it persists state in between calls.
This plugin also provides a function variant with no persisted state. That can be used like this:
llm -T quickjs 'Calculate 123 * 98742' --td
Or in Python like this:
import llm
from llm_tools_quickjs import quickjs
model = llm.get_model("gpt-4.1-mini")
chain = model.chain(
"Draw a 40 character wide mandelbrot with JavaScript",
tools=[quickjs]
)
print(chain.text())
Some models that have trouble with class-based tools may work better with the function variant.
Development
To set up this plugin locally, first checkout the code. Then create a new virtual environment:
cd llm-tools-quickjs
python -m venv venv
source venv/bin/activate
Now install the dependencies and test dependencies:
llm install -e '.[test]'
To run the tests:
python -m pytest
Release files for llm-tools-quickjs 0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llm_tools_quickjs-0.1.tar.gz | 8.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llm_tools_quickjs-0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.1 kB
Release files / llm_tools_quickjs-0.1.tar.gz
| Download URL | llm_tools_quickjs-0.1.tar.gz |
|---|---|
| Size | 8.0 kB |
| Tags | Source |
|
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| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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