# AI Citation Auditor (`franksterino/ai-citation-auditor`) Actor

Verify every citation in AI-generated text: fetches each cited source and returns supported/contradicted/unsupported verdicts with quoted evidence spans. Catches hallucinated citations before you publish.

- **URL**: https://apify.com/franksterino/ai-citation-auditor.md
- **Developed by:** [Dominik Spacek](https://apify.com/franksterino) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 claim verifieds

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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 Auditor

**Verify every citation in AI-generated text against the source it actually cites.**

LLMs routinely produce citations that *look* real — the URL resolves, the paper exists — but the source doesn't say what the text claims it says. In June 2026 KPMG had to pull a flagship report after most of its citations turned out to be fabricated or misattributed. This Actor catches that before you publish.

### What it does

For every citation in your document, the Auditor:

1. **Extracts** claim + source pairs (markdown links, footnotes, DOIs, bare URLs)
2. **Fetches** the cited source — dead links get an automatic archive.org fallback
3. **Reads** the actual page content (boilerplate stripped, PDFs supported)
4. **Judges** whether the source supports the claim, using an LLM that sees *only the fetched source text* — never its own world knowledge

### Output

One row per claim:

| Field | Meaning |
|---|---|
| `verdict` | `supported` / `partially_supported` / `contradicted` / `unsupported` / `uncertain` / `could_not_fetch` |
| `evidence` | **Verbatim quote** from the source backing the verdict — verify it yourself in seconds |
| `confidence` | Judge confidence 0–1 |
| `reasoning` | One-sentence explanation |
| `httpStatus`, `resolvedUrl`, `fromArchive` | Source liveness details |

Plus a `REPORT` in the key-value store with a **citation integrity score (0–100)** and per-verdict summary.

### Input

Three ways to use it:

```json
{ "documentText": "Your markdown or plain text with citations..." }
```

```json
{ "documentUrl": "https://example.com/report.html" }
```

```json
{ "claims": [{ "text": "The Eiffel Tower is 330 m tall.", "source": "https://en.wikipedia.org/wiki/Eiffel_Tower" }] }
```

### What it is not

- **Not an AI-text detector** — it doesn't care who wrote the text.
- **Not a truth oracle** — `supported` means *the cited source says this*, not *this is true*.
- **Never guesses** — unreachable sources are reported as `could_not_fetch`, not judged blind.

### Use cases

- Pre-publish gate for AI-drafted reports, blog posts, whitepapers
- CI check for docs and marketing content
- Auditing research summaries and literature reviews
- Due diligence on any citation-heavy document

Open-source core: [github.com/Franksterino/citeguard](https://github.com/Franksterino/citeguard)

# Actor input Schema

## `documentText` (type: `string`):

Markdown or plain text containing citations (markdown links, footnotes, DOIs, bare URLs). Every citation is extracted and verified against its source.

## `documentUrl` (type: `string`):

Alternatively: URL of a page/markdown file to audit. Its text is fetched, citations extracted and verified.

## `claims` (type: `array`):

Alternatively: explicit pairs to verify, e.g. \[{"text": "claim...", "source": "https://..."}]

## `maxClaims` (type: `integer`):

Safety cap on the number of claims verified in one run.

## Actor input object example

```json
{
  "documentText": "The Eiffel Tower is 330 metres tall ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower)). It is located in Berlin ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower)).",
  "maxClaims": 50
}
```

# 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 = {
    "documentText": "The Eiffel Tower is 330 metres tall ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower)). It is located in Berlin ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower))."
};

// Run the Actor and wait for it to finish
const run = await client.actor("franksterino/ai-citation-auditor").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 = { "documentText": "The Eiffel Tower is 330 metres tall ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower)). It is located in Berlin ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower))." }

# Run the Actor and wait for it to finish
run = client.actor("franksterino/ai-citation-auditor").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 '{
  "documentText": "The Eiffel Tower is 330 metres tall ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower)). It is located in Berlin ([Wikipedia](https://en.wikipedia.org/wiki/Eiffel_Tower))."
}' |
apify call franksterino/ai-citation-auditor --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/BqM989BcsodRVX9xo/builds/Vv7zYTF6KUVx4Z3C4/openapi.json