JavaScript execution as a tool for LLM
Install this plugin in the same environment as LLM.
llm install llm-tools-quickjsTo use this with the LLM command-line tool:
llm --tool QuickJS "Calculate 123 * 98742" --tools-debugWith 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' --tdI 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.
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' --tdOr 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.
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/activateNow install the dependencies and test dependencies:
llm install -e '.[test]'To run the tests:
python -m pytest