lectrace 1.1.26

Write Python lecture code. Get an interactive viewer on GitHub Pages.

lectrace

Executable Python lecture notes.

Write a Python script. Run lectrace serve to get an interactive step-through viewer in your browser. Push to GitHub to deploy it to GitHub Pages, automatically.

No Node.js. No configuration. No build step for your students. Just Python.

Inspired by edtrace by Percy Liang.

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See it live: AI Lectures, a real course built with lectrace (source)

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Install

uv add lectrace
# or
pip install lectrace

Requires Python 3.11+. Zero mandatory dependencies. lectrace uses the standard library only. numpy, torch, and sympy are detected and rendered automatically if already installed in your environment.

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Write a lecture

A lecture file is a plain Python script. Define main() first, put helper functions below it, call main() at the end:

# 01_binary_search.py
from lectrace import text, note, plot

def main():
    text("# Binary Search")
    text("Finds a target in a sorted array in $O(\\log n)$ time.")

    arr = [2, 5, 8, 12, 16, 23, 38, 42]  # @inspect arr
    result = binary_search(arr, 23)        # @inspect result

    text(f"Found 23 at index `{result}`.")
    note("Binary search only works on sorted arrays.")

def binary_search(arr, target):
    lo, hi = 0, len(arr) - 1
    while lo <= hi:
        mid = (lo + hi) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid - 1
    return -1

if __name__ == "__main__":
    main()

This gives you three things at once:

• python 01_binary_search.py: runs as a plain script, no lectrace involvement
• lectrace serve: opens the interactive viewer in your browser
• git push: deploys to GitHub Pages automatically

The tracer only traces code that runs inside main(). Imports and function definitions are invisible (they're just setup). Helper functions appear in the viewer only when main() actually calls them.

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How tracing works

lectrace loads your file silently (running imports and defining functions), then calls main() with sys.settrace active:

• Module-level code (imports, def statements): never generates a step
• main() and every function it calls: stepped through line by line
• Functions defined but never called: invisible

When execution enters a helper function, the viewer shows the def line first with arguments already in the variable panel, then steps through the body. When the function returns, the viewer jumps back to the call site.

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Variable panel

The variable panel on the right tracks state at every step:

• Inside main(): only variables marked with # @inspect are shown
• Inside any helper function: all local variables are shown automatically, no directives needed
• Call stack: shown above variables when inside a helper, displaying the full chain of calls
• New variables: highlighted in green when they first appear
• Changed variables: highlighted in amber when their value changes

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Directives

Inline comments that control tracing and display:

Directive	Effect
# @inspect x y	Show x and y in the variable panel after this line
# @inspect	Show all local variables at this line
# @clear x	Remove x from the variable panel
# @stepover	Execute this line without stepping into any calls it makes
# @hide	Run this line silently, never shown in the viewer

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Rendering functions

Call these anywhere inside main() or any function it calls:

Function	What it renders
text("# Heading")	Markdown with LaTeX math ($...$ inline, $$...$$ display)
text("...", verbatim=True)	Monospace, whitespace preserved
image("fig.png", width=400)	Local file or remote URL (cached)
video("demo.mp4")	Embedded video with controls
link(my_function)	Clickable jump to that function in the viewer
link(title="Paper", url="...", authors=["Smith"], date="2024")	Reference card with hover metadata
plot({...})	Interactive Vega-Lite chart
note("speaker annotation")	Presenter note shown as a styled callout
system_text(["python3", "--version"])	Shell command output as verbatim text

Citing arXiv papers

Pass an arXiv URL to link() and lectrace fetches the title, authors, date, and abstract for you automatically. No manual metadata needed:

# lectrace fetches everything from arXiv
link(url="https://arxiv.org/abs/1706.03762")

# without arXiv auto-fetch, you'd write this by hand
link(title="Attention Is All You Need", authors=["Vaswani", "Shazeer", "Parmar", "..."], date="2017", url="https://arxiv.org/abs/1706.03762")

Both /abs/ and /pdf/ arXiv URLs are supported. Metadata is cached locally so the network request only happens once per paper.

For more robust HTML parsing, install the arxiv extra:

pip install lectrace[arxiv]
# or
uv add lectrace[arxiv]

Without it, lectrace falls back to the standard library's HTML parser, which works for most papers.

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Custom type rendering

Implement __lectrace__ on any class to control how it appears in the variable panel:

class Node:
    def __init__(self, val, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

    def __lectrace__(self):
        return {
            "val": self.val,
            "left": self.left.val if self.left else None,
            "right": self.right.val if self.right else None,
        }

Without __lectrace__, nested objects show their full repr. With it, you control exactly what students see.

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Viewer

A mobile-responsive React app that works in any browser. Students need nothing installed.

Keyboard shortcuts:

Key	Action
→ or l	Step forward
← or h	Step backward
Shift+→ or j	Step over forward (skip sub-calls)
Shift+← or k	Step over backward
u	Step out of current function
R	Toggle raw code view
A	Toggle reveal animation
E	Toggle variable panel
F	Toggle fullscreen

Mobile: swipe left/right to step, tap the Variables bar to expand the variable panel.

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File naming

Pattern	Behaviour
01_intro.py	Lecture, appears in sidebar, traced and deployed
02_sorting.py	Lecture, sidebar order follows alphabetical sort
_utils.py	Helper, imported normally, never traced or shown

my-course/
  _data.py            ← shared data, ignored by lectrace
  01_intro.py         ← first in sidebar
  02_complexity.py    ← second
  03_sorting.py       ← third

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CLI

lectrace serve                  # build + serve all lectures at http://localhost:7000
lectrace serve 01_intro.py      # serve a single file
lectrace build --output _site   # build static site for deployment
lectrace init                   # generate GitHub Actions workflow + lectrace.toml
lectrace run 01_intro.py        # execute and print trace stats (no server)

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Deploy to GitHub Pages

lectrace init   # generates .github/workflows/lectrace.yml
git add .
git commit -m "add lectures"
git push

Enable GitHub Pages in your repo settings (Source: GitHub Actions). Every push to main rebuilds and redeploys automatically.

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How it works

• Tracer: loads the module without tracing, then activates sys.settrace and calls main(). Every step shown is inside a function that was actually called.
• Serializer: converts Python values to JSON. Primitives are direct. Collections recurse. numpy/torch/sympy are detected lazily.
• Builder: discovers lecture files, runs each through the tracer, writes traces/*.json plus a manifest. Incremental: files are skipped if their SHA-256 hash hasn't changed.
• Viewer: a pre-built React + TypeScript SPA bundled into the pip package. Uses HashRouter so it works at any URL depth with zero configuration. Math via KaTeX, charts via Vega-Lite, syntax highlighting via highlight.js.

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Documentation

Full documentation: https://praisegee.github.io/lectrace/

• Getting Started
• Directives
• Rendering API
• Custom Types
• Deploying
• API Reference