# Learning Unfamiliar Codebases (`aspiring_hypotenuse_ior/codebase-learning-friction`) Actor

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

- **URL**: https://apify.com/aspiring\_hypotenuse\_ior/codebase-learning-friction.md
- **Developed by:** [Lore Nest](https://apify.com/aspiring_hypotenuse_ior) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.28 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## 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):

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

# Actor input Schema

## `files` (type: `string`):

Real parameter read as payload\['files'] in this tool's own generated code.

## Actor input object example

```json
{}
```

# Actor output Schema

## `output` (type: `string`):

Real dataset items pushed via Actor.push\_data() in src/main.py -- each item is exactly the dict this tool's own run() returns. This tool's own real, build-time-captured output had these top-level fields: knowledge\_graph, navigation\_hints, summary.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("aspiring_hypotenuse_ior/codebase-learning-friction").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("aspiring_hypotenuse_ior/codebase-learning-friction").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call aspiring_hypotenuse_ior/codebase-learning-friction --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,aspiring_hypotenuse_ior/codebase-learning-friction"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/cgFUn0X9grdWQldjV/builds/LozGHihBHUGFaLQfX/openapi.json