> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thedrive.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Cross-Analyze

> Validate and cross-reference across multiple documents

Cross-analyze lets the agent reason **across** 2-5 documents simultaneously. Unlike `/analyze/batch` (which processes documents independently), cross-analyze compares, validates, and reconciles data between documents.

## Use cases

* Validate an invoice against a contract or purchase order
* Check that a report's numbers match a source spreadsheet
* Compare terms across multiple agreements
* Reconcile data from different sources

## Example

<CodeGroup>
  ```python Python theme={null}
  from thedriveai import TheDriveAI

  client = TheDriveAI(api_key="tda_live_...")

  result = client.analyze_cross(
      files=["invoice.pdf", "contract.pdf"],
      document_labels=["invoice", "contract"],
      schema={
          "rates_match": {
              "type": "boolean",
              "description": "Do the hourly rates on the invoice match the contract?"
          },
          "total_valid": {
              "type": "boolean",
              "description": "Does the invoice total equal the sum of line items?"
          },
          "correct_vendor": {
              "type": "boolean",
              "description": "Is the vendor name on the invoice the same as the contracting party?"
          },
      },
  )

  print(result.data)
  # {"rates_match": false, "total_valid": true, "correct_vendor": true}

  print(result.sources["rates_match"])
  # ['[invoice] "Rate: $150.00/hr"', '[contract] "Hourly rate: $125.00"']

  print(result.reasoning["rates_match"])
  # "Invoice hourly rate ($150) differs from contract rate ($125). Discrepancy of $25/hr."

  for doc in result.documents:
      print(f"{doc.label}: {doc.total_pages} pages")
  ```

  ```typescript TypeScript theme={null}
  import { TheDriveAI } from "@thedriveai/sdk";
  import { readFileSync } from "fs";

  const client = new TheDriveAI({ apiKey: "tda_live_..." });

  const result = await client.analyzeCross({
    files: [readFileSync("invoice.pdf"), readFileSync("contract.pdf")],
    documentLabels: ["invoice", "contract"],
    schema: {
      rates_match: { type: "boolean", description: "Do the hourly rates on the invoice match the contract?" },
      total_valid: { type: "boolean", description: "Does the invoice total equal the sum of line items?" },
      correct_vendor: { type: "boolean", description: "Is the vendor name consistent?" },
    },
  });

  console.log(result.data.rates_match);       // false
  console.log(result.sources?.rates_match);   // ['[invoice] "Rate: $150.00/hr"', '[contract] "Hourly rate: $125.00"']
  ```

  ```bash cURL theme={null}
  curl -X POST https://dev.thedrive.ai/api/v1/analyze/cross \
    -H "X-API-Key: tda_live_..." \
    -F files=@invoice.pdf \
    -F files=@contract.pdf \
    -F 'document_labels=["invoice", "contract"]' \
    -F 'schema={
      "rates_match": {"type": "boolean", "description": "Do hourly rates match?"},
      "total_valid": {"type": "boolean", "description": "Does the total equal line item sum?"}
    }'
  ```
</CodeGroup>

Works with Pydantic and Zod too — see [Schemas](/developer/schemas).

## Providing documents

You can mix uploaded files and URLs in a single request:

<CodeGroup>
  ```python Python theme={null}
  result = client.analyze_cross(
      files=["local_invoice.pdf"],
      urls={"contract": "https://example.com/contract.pdf"},
      document_labels=["invoice"],
      schema={...},
  )
  ```

  ```typescript TypeScript theme={null}
  const result = await client.analyzeCross({
    files: [readFileSync("local_invoice.pdf")],
    urls: { contract: "https://example.com/contract.pdf" },
    documentLabels: ["invoice"],
    schema: {...},
  });
  ```
</CodeGroup>

### URL formats

| Format | Example                                                   | Label behavior               |
| ------ | --------------------------------------------------------- | ---------------------------- |
| Array  | `urls=["https://a.com/inv.pdf", "https://b.com/ctr.pdf"]` | Auto-generated from filename |
| Object | `urls={"invoice": "https://a.com/inv.pdf"}`               | Uses the key as the label    |

### Document labels

Each document gets a label used in source citations (`[invoice] "..."`, `[contract] "..."`). Labels are auto-generated from filenames, or you can set them explicitly via `document_labels` (for uploaded files) or URL object keys (for URLs).

## Response

Same structure as `/analyze`, plus per-document metadata:

| Field          | Description                                                         |
| -------------- | ------------------------------------------------------------------- |
| `data`         | Computed answers, type-enforced                                     |
| `confidence`   | Per-field confidence scores (0.0 - 1.0)                             |
| `reasoning`    | Per-field explanation referencing specific documents                |
| `sources`      | Per-field text snippets prefixed with `[doc_label]`                 |
| `documents`    | List of `{label, filename, content_type, total_pages, text_length}` |
| `total_pages`  | Combined page count across all documents                            |
| `credits_used` | Credits consumed                                                    |

## Limits

| Constraint            | Value                                  |
| --------------------- | -------------------------------------- |
| Documents per request | 2 - 5                                  |
| Total combined size   | 100 MB                                 |
| Fields per schema     | 10                                     |
| Timeout               | 5 minutes (use `sync=false` for async) |

## Pricing

5 credits per document + 3 credits per page (minimum 10).