# AI Citation Monitor — Which Sources AI Engines Cite (`muhammadafzal/ai-citation-monitor`) Actor Track which sources and domains ChatGPT, Perplexity, Claude, and Gemini cite for your prompts — whether your domain is cited, who dominates the citation share, and how it changes over time. The GEO companion to rank tracking. - **URL**: https://apify.com/muhammadafzal/ai-citation-monitor.md - **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community) - **Categories:** AI, SEO tools, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $40.00 / 1,000 prompt engine checks 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 Monitor — Which Sources AI Engines Cite See **which sources and domains ChatGPT, Perplexity, Claude, and Gemini cite** when they answer your prompts — and whether **your domain** is one of them. This is the citation half of Generative Engine Optimization (GEO): the Rank Tracker tells you *if you're mentioned*; the Citation Monitor tells you *what content AI trusts and links to*, so you know what to create to get cited. Export results, run via API, schedule and monitor runs, or integrate with other tools. --- ### What it does For every **prompt × engine**, the actor records: - **brand_cited** — was your domain cited as a source? - **citation_domains / citation_urls** — every source the answer relied on - **competitor_domains_cited** — which rivals got cited alongside (or instead of) you - **new_domains / lost_domains** — what changed since the last run - **citation_status** — `new`, `gained`, `lost`, `unchanged`, or `absent` for your domain Plus a run **SUMMARY** in the key-value store: - **citation leaderboard** — the domains AI cites most for your topics, with share % - **brand citation rate** — how often your domain is cited - **citation gap domains** — high-authority sources cited ahead of you (your opportunity list) --- ### Input | Field | Type | Description | |---|---|---| | `brandDomain` | string | The domain you want cited (e.g., `notion.so`). | | `competitorDomains` | string[] | Competitor domains to track in citations. | | `prompts` | string[] | Questions to monitor (auto-generated from `industry` if empty). | | `industry` | string | Fallback for prompt generation. | | `trackingId` | string | Stable ID for run-over-run citation history. | | `maxPrompts` | integer | Cap on prompts per run (cost control). | Engines, models, and web search are managed internally (all four engines, web search on) for predictable cost. #### Example input ```json { "brandDomain": "notion.so", "competitorDomains": ["evernote.com", "obsidian.md"], "prompts": [ "What is the best note-taking app for teams?", "Recommend an all-in-one workspace tool." ] } ```` *** ### API key (required) All engines run through **[OpenRouter](https://openrouter.ai)** — set a single environment variable on the actor (Console → Settings → Environment variables, mark **Secret**): ``` OPENROUTER_API_KEY = sk-or-... ``` If the key is missing, checks are reported with an `error` and **not charged**. *** ### Output Each row: ```json { "brand_domain": "notion.so", "engine": "perplexity", "model_used": "perplexity/sonar", "prompt": "What is the best note-taking app for teams?", "brand_cited": true, "brand_cited_urls": ["https://notion.so/product"], "citation_urls": ["https://notion.so/product", "https://zapier.com/blog/..."], "citation_domains": ["notion.so", "zapier.com", "g2.com"], "competitor_domains_cited": ["evernote.com"], "total_citations": 8, "new_domains": ["g2.com"], "lost_domains": ["reddit.com"], "previous_brand_cited": false, "citation_status": "gained", "error": null, "checked_at": "2026-06-08T12:00:00.000Z" } ``` *** ### Pricing (Pay Per Event) | Event | When | |---|---| | `actor-start` | Once per run. | | `prompt-engine-check` | Each successful prompt × engine check. | Failed checks are **not charged**. *** ### Scheduling for trend tracking 1. Run once to seed a baseline. 2. Schedule daily/weekly with the same `trackingId`. 3. Each run diffs cited domains vs the last snapshot (`new_domains` / `lost_domains`) and updates your domain's `citation_status`. History is stored in a named key-value store (`ai-citation-history-`), so multiple sites track independently. *** ### Use cases - **GEO content strategy** — find the exact sources AI cites for your topics, then earn placement on them. - **Citation gap analysis** — see which authoritative domains outrank you and prioritize outreach. - **Competitive monitoring** — track when competitors gain or lose AI citations. - **Pairs with the AI Visibility Rank Tracker** for a full GEO picture: mentions + citations. # Actor input Schema ## `brandDomain` (type: `string`): The domain you want to see cited by AI engines (e.g., 'notion.so'). Each answer's cited sources are checked for this domain. ## `competitorDomains` (type: `array`): Competitor domains to also track in the citations (e.g., 'evernote.com', 'obsidian.md'). Lets you compare citation presence against rivals. ## `prompts` (type: `array`): The questions your buyers ask AI engines (e.g., 'What is the best note-taking app?'). The actor records which sources each engine cites for them. If empty, prompts are auto-generated from 'industry'. ## `industry` (type: `string`): Your category (e.g., 'productivity software'). Used ONLY to auto-generate prompts when you don't supply your own. Ignored if 'prompts' is provided. ## `trackingId` (type: `string`): Optional stable identifier used to store and compare citation history across scheduled runs. Defaults to a slug of your domain. Use distinct IDs to track multiple sites separately. ## `maxPrompts` (type: `integer`): Safety cap on how many prompts are checked per run (cost control). Extra prompts beyond this limit are ignored. ## `platforms` (type: `array`): Internal: which engines to query. Managed by the actor (all four) for predictable cost. ## `models` (type: `object`): Internal: per-engine OpenRouter model slug overrides. Managed by the actor; not user-editable. ## `useWebSearch` (type: `boolean`): Internal: live web search for citation grounding. Managed by the actor (on). ## `webSearchResults` (type: `integer`): Internal: web results per query. Managed by the actor. ## Actor input object example ```json { "brandDomain": "notion.so", "competitorDomains": [ "evernote.com", "obsidian.md" ], "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams." ], "industry": "productivity software", "trackingId": "", "maxPrompts": 25, "platforms": [ "perplexity", "chatgpt", "claude", "gemini" ], "models": {}, "useWebSearch": true, "webSearchResults": 3 } ``` # Actor output Schema ## `results` (type: `string`): Link to the dataset containing all prompt x engine citation records. # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "brandDomain": "notion.so", "competitorDomains": [ "evernote.com", "obsidian.md" ], "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams." ], "industry": "productivity software" }; // Run the Actor and wait for it to finish const run = await client.actor("muhammadafzal/ai-citation-monitor").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "brandDomain": "notion.so", "competitorDomains": [ "evernote.com", "obsidian.md", ], "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams.", ], "industry": "productivity software", } # Run the Actor and wait for it to finish run = client.actor("muhammadafzal/ai-citation-monitor").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 '{ "brandDomain": "notion.so", "competitorDomains": [ "evernote.com", "obsidian.md" ], "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams." ], "industry": "productivity software" }' | apify call muhammadafzal/ai-citation-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhammadafzal/ai-citation-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Citation Monitor — Which Sources AI Engines Cite", "description": "Track which sources and domains ChatGPT, Perplexity, Claude, and Gemini cite for your prompts — whether your domain is cited, who dominates the citation share, and how it changes over time. The GEO companion to rank tracking.", "version": "1.0", "x-build-id": "FGq5ufsQMc392whby" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhammadafzal~ai-citation-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhammadafzal-ai-citation-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/muhammadafzal~ai-citation-monitor/runs": { "post": { "operationId": "runs-sync-muhammadafzal-ai-citation-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/muhammadafzal~ai-citation-monitor/run-sync": { "post": { "operationId": "run-sync-muhammadafzal-ai-citation-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "brandDomain": { "title": "Your Domain", "type": "string", "description": "The domain you want to see cited by AI engines (e.g., 'notion.so'). Each answer's cited sources are checked for this domain.", "default": "notion.so" }, "competitorDomains": { "title": "Competitor Domains", "type": "array", "description": "Competitor domains to also track in the citations (e.g., 'evernote.com', 'obsidian.md'). Lets you compare citation presence against rivals.", "default": [], "items": { "type": "string" } }, "prompts": { "title": "Tracked Prompts", "type": "array", "description": "The questions your buyers ask AI engines (e.g., 'What is the best note-taking app?'). The actor records which sources each engine cites for them. If empty, prompts are auto-generated from 'industry'.", "default": [], "items": { "type": "string" } }, "industry": { "title": "Industry / Category", "type": "string", "description": "Your category (e.g., 'productivity software'). Used ONLY to auto-generate prompts when you don't supply your own. Ignored if 'prompts' is provided.", "default": "productivity software" }, "trackingId": { "title": "Tracking ID", "type": "string", "description": "Optional stable identifier used to store and compare citation history across scheduled runs. Defaults to a slug of your domain. Use distinct IDs to track multiple sites separately.", "default": "" }, "maxPrompts": { "title": "Max Prompts", "minimum": 1, "maximum": 200, "type": "integer", "description": "Safety cap on how many prompts are checked per run (cost control). Extra prompts beyond this limit are ignored.", "default": 25 }, "platforms": { "title": "AI Platforms", "type": "array", "description": "Internal: which engines to query. Managed by the actor (all four) for predictable cost.", "items": { "type": "string" }, "default": [ "perplexity", "chatgpt", "claude", "gemini" ] }, "models": { "title": "Model Overrides", "type": "object", "description": "Internal: per-engine OpenRouter model slug overrides. Managed by the actor; not user-editable.", "default": {} }, "useWebSearch": { "title": "Use Web Search", "type": "boolean", "description": "Internal: live web search for citation grounding. Managed by the actor (on).", "default": true }, "webSearchResults": { "title": "Web Search Results", "minimum": 1, "maximum": 10, "type": "integer", "description": "Internal: web results per query. Managed by the actor.", "default": 3 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```