Learning Unfamiliar Codebases

Learning a codebase generates disproportionate frustration relative to the time spent on it, per the 2025 SO Developer Survey -- largely attributed to inadequate documentation.

Learning a new codebase is one of the most time-consuming and frustrating tasks developers face, often because documentation is sparse, outdated, or missing entirely. This tool programmatically inspects source files and builds a structural map of the code, extracting classes, functions, and call patterns so you can orient yourself in an unfamiliar project quickly.

Use Cases

• Mapping class hierarchies in a Python project by scanning every file for class definitions and recording which file each class lives in, giving you an instant index of the project's object-oriented structure.
• Cataloging function definitions across a repository so you can answer questions like "where is process_payment defined?" without manually opening dozens of files.
• Identifying files with the highest cyclomatic complexity using a heuristic that counts control-flow keywords (if, elif, for, while, except, with, return), helping you spot the files most likely to be bug-prone or hard to maintain.
• Tracing potential function call sites by extracting every identifier followed by an opening parenthesis, which surfaces candidate call relationships that can guide deeper manual investigation.
• Capturing per-file context snippets (the first 100 characters of each file) so the resulting map retains just enough textual context to disambiguate files with similar names.

How It Works

The CodebaseAnalyzer walks through each file's content and applies a set of compiled regular expressions tailored to Python syntax. One pattern extracts class names by matching the class keyword, another captures function definitions via the def keyword, and a broader pattern collects any identifier followed by a parenthesis to flag potential call sites. Each match is recorded in a symbol_map keyed by the symbol name, with the source filename appended so every entry remains traceable. In parallel, a _calculate_complexity routine tallies control-flow keyword occurrences per file into a complexity_map, and short content snippets are stored in file_map for quick reference.

Usage on Apify

Run it directly from the Apify Console ("Start"/"Try for free"), or call it via the API:

POST https://api.apify.com/v2/acts/codebase-learning-friction/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>
Content-Type: application/json
{}

Pricing

Pay-per-event, billed automatically by Apify -- no separate account or payment step:

• Actor start: $0.00005 per GB of memory (minimum one event per run)
• Result: $0.005 per item returned -- this is the primary, usage-based charge

Also independently available at $0.0100 USDC (Base) per call via the x402 payment protocol (POST /tools/codebase-learning-friction) for callers outside the Apify platform.

Example output

Real output captured from this tool's own build-time smoke test (input above):

{
  "summary": {
    "total_files_indexed": 0,
    "total_symbols_found": 0
  },
  "knowledge_graph": {
    "symbols": {},
    "file_complexity": {}
  },
  "navigation_hints": []
}