API Reference

Extraction Agents

Copy Markdown

Open in Claude

Open in ChatGPT

Open in Cursor

---

Copy Markdown

View as Markdown

List Extract Agents

extraction_agents.list(ExtractionAgentListParams**kwargs) -> SyncPaginatedCursor[ExtractAgent]

GET/api/v1/beta/extraction-agents

List the extraction agents in a project, newest first.

ParametersExpand Collapse

include_default: Optional[bool]

Whether to include default agents in the results

organization_id: Optional[str]

page_size: Optional[int]

Number of items per page

maximum1000

minimum1

page_token: Optional[str]

Cursor from the previous page’s next_page_token.

project_id: Optional[str]

ReturnsExpand Collapse

class ExtractAgent: …

Schema and configuration for an extraction agent.

id: str

The id of the extraction agent.

formatuuid

config: Config

The configuration parameters for the extraction agent.

chunk_mode: Optional[Literal["PAGE", "SECTION"]]

The mode to use for chunking the document.

One of the following:

"PAGE"

"SECTION"

Deprecatedcitation_bbox: Optional[bool]

Whether to fetch citation bounding boxes for the extraction. Only available in PREMIUM mode. Deprecated: this is now synonymous with cite_sources.

cite_sources: Optional[bool]

Whether to cite sources for the extraction.

confidence_scores: Optional[bool]

Whether to fetch confidence scores for the extraction.

extract_model: Optional[Union[Literal["gemini-2.0-flash", "gemini-2.5-flash", "gemini-2.5-flash-lite", 8 more], str, null]]

The extract model to use for data extraction. If not provided, uses the default for the extraction mode.

One of the following:

Literal["gemini-2.0-flash", "gemini-2.5-flash", "gemini-2.5-flash-lite", 8 more]

Extract model options.

One of the following:

"gemini-2.0-flash"

"gemini-2.5-flash"

"gemini-2.5-flash-lite"

"gemini-2.5-pro"

"openai-gpt-4-1"

"openai-gpt-4-1-mini"

"openai-gpt-4-1-nano"

"openai-gpt-4o"

"openai-gpt-4o-mini"

"openai-gpt-5"

"openai-gpt-5-mini"

str

extraction_mode: Optional[Literal["BALANCED", "FAST", "MULTIMODAL", "PREMIUM"]]

The extraction mode specified (FAST, BALANCED, MULTIMODAL, PREMIUM).

One of the following:

"BALANCED"

"FAST"

"MULTIMODAL"

"PREMIUM"

extraction_target: Optional[Literal["PER_DOC", "PER_PAGE", "PER_TABLE_ROW"]]

The extraction target specified.

One of the following:

"PER_DOC"

"PER_PAGE"

"PER_TABLE_ROW"

high_resolution_mode: Optional[bool]

Whether to use high resolution mode for the extraction.

invalidate_cache: Optional[bool]

Whether to invalidate the cache for the extraction.

multimodal_fast_mode: Optional[bool]

DEPRECATED: Whether to use fast mode for multimodal extraction.

num_pages_context: Optional[int]

Number of pages to pass as context on long document extraction.

minimum1

page_range: Optional[str]

Comma-separated list of page numbers or ranges to extract from (1-based, e.g., ‘1,3,5-7,9’ or ‘1-3,8-10’).

parse_model: Optional[Literal["anthropic-haiku-3.5", "anthropic-haiku-4.5", "anthropic-sonnet-3.5", 22 more]]

Public model names.

One of the following:

"anthropic-haiku-3.5"

"anthropic-haiku-4.5"

"anthropic-sonnet-3.5"

"anthropic-sonnet-3.5-v2"

"anthropic-sonnet-3.7"

"anthropic-sonnet-4.0"

"anthropic-sonnet-4.5"

"gemini-2.0-flash"

"gemini-2.0-flash-lite"

"gemini-2.5-flash"

"gemini-2.5-flash-lite"

"gemini-2.5-pro"

"gemini-3.0-pro"

"gemini-3.1-pro"

"openai-gpt-4-1"

"openai-gpt-4-1-mini"

"openai-gpt-4-1-nano"

"openai-gpt-4o"

"openai-gpt-4o-mini"

"openai-gpt-5"

"openai-gpt-5-mini"

"openai-gpt-5-nano"

"openai-text-embedding-3-large"

"openai-text-embedding-3-small"

"openai-whisper-1"

priority: Optional[Literal["critical", "high", "low", "medium"]]

The priority for the request. This field may be ignored or overwritten depending on the organization tier.

One of the following:

"critical"

"high"

"low"

"medium"

system_prompt: Optional[str]

The system prompt to use for the extraction.

use_reasoning: Optional[bool]

Whether to use reasoning for the extraction.

data_schema: Dict[str, Union[Dict[str, object], List[object], str, 3 more]]

The schema of the data.

One of the following:

Dict[str, object]

List[object]

str

float

bool

name: str

The name of the extraction agent.

project_id: str

The ID of the project that the extraction agent belongs to.

formatuuid

created_at: Optional[datetime]

The creation time of the extraction agent.

formatdate-time

custom_configuration: Optional[Literal["default"]]

Custom configuration type for the extraction agent. Currently supports ‘default’.

updated_at: Optional[datetime]

The last update time of the extraction agent.

formatdate-time

List Extract Agents

Python

HTTP

TypeScript

Python

Go

Java

C#

CLI Tool

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.extraction_agents.list()
page = page.items[0]
print(page.id)

200 example

{
  "items": [
    {
      "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "config": {
        "chunk_mode": "PAGE",
        "citation_bbox": true,
        "cite_sources": true,
        "confidence_scores": true,
        "extract_model": "gemini-2.0-flash",
        "extraction_mode": "BALANCED",
        "extraction_target": "PER_DOC",
        "high_resolution_mode": true,
        "invalidate_cache": true,
        "multimodal_fast_mode": true,
        "num_pages_context": 1,
        "page_range": "page_range",
        "parse_model": "anthropic-haiku-3.5",
        "priority": "critical",
        "system_prompt": "system_prompt",
        "use_reasoning": true
      },
      "data_schema": {
        "foo": {
          "foo": "bar"
        }
      },
      "name": "name",
      "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "created_at": "2019-12-27T18:11:19.117Z",
      "custom_configuration": "default",
      "updated_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "next_page_token": "next_page_token",
  "total_size": 0
}

Returns Examples

200 example

{
  "items": [
    {
      "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "config": {
        "chunk_mode": "PAGE",
        "citation_bbox": true,
        "cite_sources": true,
        "confidence_scores": true,
        "extract_model": "gemini-2.0-flash",
        "extraction_mode": "BALANCED",
        "extraction_target": "PER_DOC",
        "high_resolution_mode": true,
        "invalidate_cache": true,
        "multimodal_fast_mode": true,
        "num_pages_context": 1,
        "page_range": "page_range",
        "parse_model": "anthropic-haiku-3.5",
        "priority": "critical",
        "system_prompt": "system_prompt",
        "use_reasoning": true
      },
      "data_schema": {
        "foo": {
          "foo": "bar"
        }
      },
      "name": "name",
      "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
      "created_at": "2019-12-27T18:11:19.117Z",
      "custom_configuration": "default",
      "updated_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "next_page_token": "next_page_token",
  "total_size": 0
}