## List Verify Jobs

`alpha.verify.list(VerifyListParams**kwargs)  -> SyncPaginatedCursor[VerifyListResponse]`

**get** `/api/alpha/verify`

List Verify jobs with optional filtering and pagination.

Filter by `status`, specific `job_ids`, or creation date range.

### Parameters

- `created_at_on_or_after: Optional[Union[str, datetime, null]]`

  Include items created at or after this timestamp (inclusive)

- `created_at_on_or_before: Optional[Union[str, datetime, null]]`

  Include items created at or before this timestamp (inclusive)

- `expand: Optional[Sequence[str]]`

  Optional fields to include (e.g. `result`).

- `job_ids: Optional[Sequence[str]]`

  Filter by specific job IDs

- `organization_id: Optional[str]`

- `page_size: Optional[int]`

  Number of items per page

- `page_token: Optional[str]`

  Token for pagination

- `project_id: Optional[str]`

- `status: Optional[Literal["CANCELLED", "COMPLETED", "FAILED", 2 more]]`

  Filter by job status

  - `"CANCELLED"`

  - `"COMPLETED"`

  - `"FAILED"`

  - `"PENDING"`

  - `"RUNNING"`

### Returns

- `class VerifyListResponse: …`

  Response for a Verify job.

  - `id: str`

    Unique identifier

  - `configuration: Configuration`

    Verify configuration used for this job

    - `target_pages: Optional[str]`

      Comma-separated page numbers or ranges to analyze (1-based). Omit to analyze all pages. Ignored for non-PDF inputs.

    - `tier: Optional[Literal["agentic", "fast"]]`

      Verify tier: 'fast' runs only the quick deterministic forensic checks (metadata, content integrity, container structure, pixel statistics); 'agentic' (default) runs the full pipeline including the learned detectors and the semantic review pass.

      - `"agentic"`

      - `"fast"`

  - `document_input_type: Literal["file_id", "parse_job_id", "url"]`

    Type of the document input (FILE)

    - `"file_id"`

    - `"parse_job_id"`

    - `"url"`

  - `file_input: str`

    ID of the input file

  - `project_id: str`

    Project this job belongs to

  - `status: Literal["CANCELLED", "COMPLETED", "FAILED", 2 more]`

    Current job status: PENDING, RUNNING, COMPLETED, FAILED, or CANCELLED

    - `"CANCELLED"`

    - `"COMPLETED"`

    - `"FAILED"`

    - `"PENDING"`

    - `"RUNNING"`

  - `user_id: str`

    User who created this job

  - `created_at: Optional[datetime]`

    Creation datetime

  - `error_message: Optional[str]`

    Error message if job failed

  - `result: Optional[Result]`

    Result of a Verify (doctored-document) analysis.

    Raw per-signal detail (evidence list, per-family sub-scores, raw regions,
    forensic heatmaps) is available separately via the job's details endpoint.

    - `detector_version: str`

      Version of the detector that produced the result

    - `overall_score: float`

      Overall doctoring likelihood (0 to 1)

    - `verdict: Literal["AUTHENTIC", "DOCTORED", "LIKELY_DOCTORED", 2 more]`

      Overall verdict for the document

      - `"AUTHENTIC"`

      - `"DOCTORED"`

      - `"LIKELY_DOCTORED"`

      - `"NO_STRONG_SIGNAL"`

      - `"SUSPICIOUS"`

    - `composite_scores: Optional[ResultCompositeScores]`

      Composite scores, each answering one question about the document

      - `ai_generated: Optional[ResultCompositeScoresAIGenerated]`

        Was this content synthesized by a generative model?

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

      - `document_coherence: Optional[ResultCompositeScoresDocumentCoherence]`

        Does the document's content agree with itself (checksums, arithmetic, machine-readable zones)?

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

      - `document_metadata: Optional[ResultCompositeScoresDocumentMetadata]`

        Does the file's provenance / toolchain history look suspicious? Advisory: individually weak workflow-hygiene signals

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

      - `known_fraud: Optional[ResultCompositeScoresKnownFraud]`

        Has this asset (or its template) been seen in fraud before?

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

      - `manually_edited: Optional[ResultCompositeScoresManuallyEdited]`

        Was this document altered after creation (splice, retype, redact, inpaint)?

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

      - `recapture: Optional[ResultCompositeScoresRecapture]`

        Was the document captured through a channel that destroys forensic evidence (photo of a screen, print-then-rescan)?

        - `applicable: Optional[bool]`

          Whether the checks feeding this composite ran on this document. When false the document was not checked for this — not cleared of it

        - `score: Optional[float]`

          Score (0 to 1); null when the composite was not applicable

    - `confidence: Optional[float]`

      Confidence in the verdict (0 to 1): how firmly the detected signals support the verdict bucket, independent of the doctoring likelihood itself

    - `error: Optional[str]`

      Error detail when the analysis could not complete

    - `page_count: Optional[int]`

      Number of analysed pages (1 for images/docx)

    - `page_dimensions: Optional[List[ResultPageDimension]]`

      Rendered pixel size per page, so region bboxes can be scaled onto the page

      - `height: int`

        Rendered page height in pixels

      - `page: int`

        0-based page index (0 for standalone images)

      - `width: int`

        Rendered page width in pixels

    - `reasoning: Optional[str]`

      Explanation of the verdict

    - `suspect_regions: Optional[List[ResultSuspectRegion]]`

      Regions that led to the suspected fraud, ranked most-suspect first, each with an explanation of what makes it suspect

      - `bbox: List[int]`

        Region bounding box as [x, y, w, h] in page-render pixels

      - `explanation: str`

        Human-readable explanation of what makes this region suspect

      - `kind: str`

        Kind of anomaly detected in this region

      - `page: int`

        0-based page index (0 for standalone images)

      - `score: float`

        Suspicion score for this region (0 to 1)

      - `source: str`

        Detector that flagged this region

      - `primary: Optional[bool]`

        Whether this region is part of the small set of decisive evidence behind the verdict — the boxes a reviewer should look at first

      - `review: Optional[str]`

        Automated reviewer verdict for this region (confirmed, dismissed, unsure, or empty). A dismissed region can still be surfaced when it is the only place to look; this label says how to read it

    - `synthetic_score: Optional[float]`

      Likelihood (0 to 1) that the document is wholly generated or fabricated rather than a capture of a real document. Null for jobs completed before this score was introduced

    - `tampering_score: Optional[float]`

      Likelihood (0 to 1) that a real captured document was locally edited — a genuine capture with regions altered after the fact. Null for jobs completed before this score was introduced

  - `transaction_id: Optional[str]`

    Idempotency key

  - `updated_at: Optional[datetime]`

    Update datetime

### Example

```python
import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
page = client.alpha.verify.list()
page = page.items[0]
print(page.id)
```

#### Response

```json
{
  "items": [
    {
      "id": "id",
      "configuration": {
        "target_pages": "1,3,5-7",
        "tier": "agentic"
      },
      "document_input_type": "file_id",
      "file_input": "file_input",
      "project_id": "project_id",
      "status": "CANCELLED",
      "user_id": "user_id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "error_message": "error_message",
      "result": {
        "detector_version": "detector_version",
        "overall_score": 0,
        "verdict": "AUTHENTIC",
        "composite_scores": {
          "ai_generated": {
            "applicable": true,
            "score": 0
          },
          "document_coherence": {
            "applicable": true,
            "score": 0
          },
          "document_metadata": {
            "applicable": true,
            "score": 0
          },
          "known_fraud": {
            "applicable": true,
            "score": 0
          },
          "manually_edited": {
            "applicable": true,
            "score": 0
          },
          "recapture": {
            "applicable": true,
            "score": 0
          }
        },
        "confidence": 0,
        "error": "error",
        "page_count": 1,
        "page_dimensions": [
          {
            "height": 0,
            "page": 0,
            "width": 0
          }
        ],
        "reasoning": "reasoning",
        "suspect_regions": [
          {
            "bbox": [
              0,
              0,
              0,
              0
            ],
            "explanation": "explanation",
            "kind": "kind",
            "page": 0,
            "score": 0,
            "source": "source",
            "primary": true,
            "review": "review"
          }
        ],
        "synthetic_score": 0,
        "tampering_score": 0
      },
      "transaction_id": "transaction_id",
      "updated_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "next_page_token": "next_page_token",
  "total_size": 0
}
```