# Ai Citation Finder (`muhammad-bilal/ai-citation-finder`) Actor

Find which websites influence AI answers by extracting and ranking the citations used by
ChatGPT, Gemini, Claude, and Perplexity.
For every question you provide, this Actor asks each selected AI answer engine, extracts every
citation and URL from the answer, normalizes domains, and counts frequency.

- **URL**: https://apify.com/muhammad-bilal/ai-citation-finder.md
- **Developed by:** [Muhammad Bilal](https://apify.com/muhammad-bilal) (community)
- **Categories:** AI, Agents, SEO tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.25 / actor start

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#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

## AI Citation Finder

**Find which websites influence AI answers** by extracting and ranking the citations used by
ChatGPT, Gemini, Claude, and Perplexity.

[![Apify SDK](https://img.shields.io/badge/Apify-SDK%20v3-green)](https://sdk.apify.com)
[![Playwright](https://img.shields.io/badge/Playwright-v1-blue)](https://playwright.dev)
[![TypeScript](https://img.shields.io/badge/TypeScript-strict-blue)](https://www.typescriptlang.org)

### Overview

For every question you provide, this Actor asks each selected AI answer engine, extracts **every
citation and URL** from the answer, **normalizes domains**, and **counts frequency**. It then ranks
the most influential sources overall and per category (review sites, blogs, communities, forums,
social, government, documentation, news).

### Architecture

Identical shared library layout as the rest of the AI Actor ecosystem:

```
src/
├── main.ts                 # Actor.main() entry point
├── types.ts                # Input/record/summary types
├── config/index.ts         # Input validation → strict config
├── analysis/index.ts       # citation extraction + per-domain aggregation
├── report/index.ts         # Final ranked markdown report
└── shared/                 # Reusable library (adapters, engine, parser, citations, …)
```

Pipeline: **validate → build tasks → collect answers (Playwright adapters) → extract & normalize
citations → count per (question, domain) → rank by category**.

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `questions` | string\[] (required) | — | Questions asked on every platform |
| `platforms` | string\[] | all four | `ChatGPT`, `Gemini`, `Claude`, `Perplexity` |
| `topN` | integer | `20` | Domains per ranked category |
| `headless` | boolean | `true` | Headless browser |
| `screenshotOnFailure` | boolean | `true` | Screenshots to KV store on failure |
| `maxRetries` | integer | `2` | Retries per (platform, question) |
| `askTimeoutMs` | integer | `90000` | Max wait per answer |
| `useApifyProxy` | boolean | `true` | Route through Apify Proxy |
| `cookies` | array | `[]` | Session cookies for authenticated platforms |

#### Input example

```json
{ "questions": ["best CRM for startups"] }
```

### Output

#### Dataset record (one per question × domain)

```json
{ "question": "best CRM for startups", "domain": "g2.com", "count": 12, "category": "review", "platforms": ["ChatGPT", "Perplexity"], "urls": ["https://www.g2.com/..."] }
```

#### Key-Value Store artifacts

- `SUMMARY` — ranked top-lists: `topDomains`, `topReviewSites`, `topBlogs`, `topCommunities`,
  `topForums`, `topSocialMedia`, `topGovernmentDomains`, `topDocumentationSites`, `topNews`
- `REPORT` — final ranked markdown report
- `EXPORT_CSV` — flat CSV of all records

### Domain categorization

Domains are normalized to their registrable form (`www.G2.com/x` → `g2.com`, `apple.co.uk` handled)
and classified into: `review`, `blog`, `community`, `forum`, `social`, `government`,
`documentation`, `news`, `ecommerce`, `other`.

### Rate limits & known limitations

- Perplexity is citation-first and most reliable; ChatGPT/Claude/Gemini often need `cookies`.
- Citation coverage depends on whether the engine surfaces links for a given answer.
- Selectors live in `src/shared/adapters/*` and can be updated without touching the pipeline.

### Deployment

```bash
npm install && npm run build && npm test && npm run lint
npx playwright install chromium   # local only; Docker image already has it
apify push
```

Docker base image: `apify/actor-node-playwright-chrome:22`.

### Testing guide

`npm test` runs citation/parser (fixture-based), retry, validation, statistics, adapter (mock page),
engine integration, and analysis tests. Fixtures live in `tests/fixtures/`.

### Troubleshooting

| Symptom | Cause | Fix |
|---------|-------|-----|
| Few/zero citations | Engine returned no links / auth wall | Provide `cookies`, prefer Perplexity |
| `error: captcha` | Bot check | Enable residential proxy, retry |

### Version history

- **1.0.0** — Initial production release.

# Actor input Schema

## `questions` (type: `array`):

Questions to ask each AI platform. Every citation/URL in the answers is extracted and ranked.

## `platforms` (type: `array`):

Which AI answer engines to query. Leave empty to query all four.

## `topN` (type: `integer`):

How many domains to include in each ranked top-list.

## `headless` (type: `boolean`):

Run the browser in headless mode.

## `screenshotOnFailure` (type: `boolean`):

Capture a screenshot to the Key-Value Store when an answer cannot be obtained.

## `maxRetries` (type: `integer`):

Number of retry attempts per (platform, question) on transient failures.

## `askTimeoutMs` (type: `integer`):

Maximum time to wait for a single answer to complete.

## `useApifyProxy` (type: `boolean`):

Route browser traffic through Apify Proxy (residential group preferred).

## `cookies` (type: `array`):

Optional cookies to authenticate platforms that require login.

## `mockMode` (type: `boolean`):

Run fully offline with deterministic synthetic answers (no browser, no auth).

## Actor input object example

```json
{
  "questions": [
    "best CRM for startups"
  ],
  "platforms": [
    "ChatGPT",
    "Gemini",
    "Claude",
    "Perplexity"
  ],
  "topN": 20,
  "headless": true,
  "screenshotOnFailure": true,
  "maxRetries": 2,
  "askTimeoutMs": 90000,
  "useApifyProxy": true,
  "cookies": [],
  "mockMode": false
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset with one record per (question, domain).

## `summary` (type: `string`):

Ranked top-lists by category.

## `report` (type: `string`):

Final ranked citation influence report.

# 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 = {
    "questions": [
        "best CRM for startups"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("muhammad-bilal/ai-citation-finder").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 = { "questions": ["best CRM for startups"] }

# Run the Actor and wait for it to finish
run = client.actor("muhammad-bilal/ai-citation-finder").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "questions": [
    "best CRM for startups"
  ]
}' |
apify call muhammad-bilal/ai-citation-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=muhammad-bilal/ai-citation-finder",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/KfkoHxXznCurqSmcX/builds/dsIlchei1SFK9uOKg/openapi.json