# Aider Llm Coding Leaderboard Scraper (`kawsar/aider-llm-coding-leaderboard-scraper`) Actor

Collect LLM code-editing and multi-language benchmark scores from Aider's public leaderboards. Choose your mode, set a row cap, and instantly export clean datasets in JSON, CSV, or Excel forma

- **URL**: https://apify.com/kawsar/aider-llm-coding-leaderboard-scraper.md
- **Developed by:** [Kawsar](https://apify.com/kawsar) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.99 / 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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Aider LLM Coding Leaderboard Scraper

![Aider LLM Coding Leaderboard Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/aider-llm-coding-leaderboard-scraper-cover.png)

Gather LLM code-editing benchmark scores directly from Aider's edit and polyglot leaderboards. Select your preferred benchmark mode, adjust the output row limits, and export clean structured datasets in JSON, CSV, or Excel formats effortlessly.

### What it does

This Actor extracts leaderboard metrics from Aider's benchmark rankings and outputs each model's evaluation results into structured dataset rows. You can easily analyze pass rates, edit formats, total test counts, and benchmark dates. Results are automatically deduplicated by model name within each run to ensure clean, actionable datasets.

### Modes

- **Edit leaderboard**: Collects code-edit benchmark scores evaluating code modification performance.
- **Polyglot leaderboard**: Collects multi-language coding scores across diverse programming languages.

### Input example

```json
{
  "mode": "edit",
  "maxItems": 50,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30
}

```

| Parameter | Type | Default | Description |
| --- | --- | --- | --- |
| `mode` | string | `edit` | Choose between `edit` or `polyglot`. |
| `maxItems` | integer | `100` | Maximum number of successful rows to return (up to 1000). |
| `requestDelaySecs` | number | `1` | Delay between consecutive requests in seconds. |
| `userAgent` | string | `ActorResearch/1.0` | Custom User-Agent header string. |
| `timeoutSecs` | integer | `300` | Total overall run execution timeout in seconds. |
| `requestTimeoutSecs` | integer | `30` | Individual request timeout in seconds. |
| `proxyConfiguration` | object | No proxy | Optional proxy setup. Recommended only if experiencing access blocks. |

### Output example

```json
{
  "dirname": "2024-05-01-20-05-59--direct-opus-filenames-outside-fence",
  "test_cases": 133,
  "model": "claude-3-opus-20240229",
  "_released": "2024-02-29",
  "edit_format": "diff",
  "commit_hash": "f4b1797-dirty, f4b1797",
  "pass_rate_1": 53.4,
  "pass_rate_2": 68.4,
  "percent_cases_well_formed": 100.0,
  "error_outputs": 2,
  "num_malformed_responses": 0,
  "user_asks": 0,
  "lazy_comments": 0,
  "syntax_errors": 0,
  "indentation_errors": 0,
  "exhausted_context_windows": 0,
  "test_timeouts": 1,
  "command": "aider --opus",
  "date": "2024-05-01",
  "versions": "0.30.2-dev",
  "seconds_per_case": 32.4,
  "total_cost": 13.8395,
  "scraped_at": "2026-10-26T00:00:00+00:00"
}

```

The overview dataset table highlights key details such as model names, release dates, test cases, pass rates, and edit formats. If an individual request encounters an issue, the Actor records an error item detailing the status and error message without interrupting the rest of your run.

### Use cases

- **Model comparison**: Evaluate and rank AI coding models by their benchmark pass rates without manually parsing webpage layouts.
- **Benchmark tracking**: Run scheduled extractions to track performance improvements and pass rate changes over time.
- **Research & analytics**: Pull edit and polyglot evaluation data straight into CSV or Excel files for Jupyter Notebooks or internal reports.
- **Tooling optimization**: Monitor how edit formats and completion costs evolve across newer model releases.

### FAQ

**Who is this tool for?**

AI researchers, software engineers, and data analysts looking for structured Aider leaderboard benchmarks ready for spreadsheet analysis.

**What formats can I export?**

You can export datasets in JSON, CSV, Excel, and other standard formats supported by Apify.

**Can I run this on a regular schedule?**

Yes, you can schedule regular automated runs to keep your dataset updated automatically.

**How many rows can one run extract?**

A single run can collect up to 1000 items, governed by your `maxItems` setting.

### Trademark Notice

Aider and related names, logos, and trademarks belong to their respective owners. Their use in this documentation is solely to identify the source of the benchmark data and does not imply affiliation, sponsorship, endorsement, or partnership.

### Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or otherwise officially associated with Aider or its owners. All data is collected from publicly accessible benchmark resources. Users are responsible for complying with applicable terms of use, rate limits, and licensing requirements.

### Is web scraping legal?

This Actor extracts publicly accessible data without bypassing security controls or paywalls. While collecting public data is widely permitted, compliance depends on how data is gathered and used:

- **Personal data**: When collecting identifiers like names or contact details, ensure compliance with applicable data protection laws such as GDPR or CCPA.
- **Copyrighted content**: Public text, images, and visual assets are often protected by intellectual property rights. Avoid directly duplicating or redistributing full creative works.
- **Platform terms & rate limits**: Practice responsible data extraction by avoiding aggressive traffic spikes, adhering to rate limits, and respecting published platform policies.
- **Disclaimer**: This summary serves informational purposes only and does not constitute formal legal counsel. Seek professional legal advice for specific compliance queries.

For a detailed guide, see Apify's legal overview: [Is web scraping legal?](https://blog.apify.com/is-web-scraping-legal/).

# Actor input Schema

## `mode` (type: `string`):

Which Aider leaderboard YAML to collect.

## `maxItems` (type: `integer`):

Stop after this many successful rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait between requests.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

## `proxyConfiguration` (type: `object`):

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "mode": "edit",
  "maxItems": 100,
  "requestDelaySecs": 1,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Leaderboard rows in the overview table.

## `dataset` (type: `string`):

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# 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 = {
    "proxyConfiguration": {
        "useApifyProxy": false
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("kawsar/aider-llm-coding-leaderboard-scraper").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 = { "proxyConfiguration": { "useApifyProxy": False } }

# Run the Actor and wait for it to finish
run = client.actor("kawsar/aider-llm-coding-leaderboard-scraper").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 '{
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}' |
apify call kawsar/aider-llm-coding-leaderboard-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kawsar/aider-llm-coding-leaderboard-scraper"
        }
    }
}
```

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/b0b2GOQiHxYVfvR3R/builds/X3y1sf1CCVQHiX4Fb/openapi.json