Module airbyte_agent_sdk.connectors.greenhouse

Greenhouse connector for Airbyte SDK.

Auto-generated from OpenAPI specification.

Sub-modules

• airbyte_agent_sdk.connectors.greenhouse.connector
• airbyte_agent_sdk.connectors.greenhouse.connector_model
• airbyte_agent_sdk.connectors.greenhouse.models
• airbyte_agent_sdk.connectors.greenhouse.types

Classes

AirbyteAuthConfig(**data: Any) : Authentication configuration for Airbyte hosted mode execution.

Pass this to the connector's auth_config parameter to use hosted mode, where API credentials are stored securely in Airbyte Cloud.

For hosted mode execution, provide client credentials with either:

• connector_id: Direct connector/source ID (skips lookup)
• workspace_name: Workspace name for connector lookup

Attributes: workspace_name: Workspace name for hosted mode connector lookup organization_id: Optional Airbyte organization ID for multi-org selection airbyte_client_id: Airbyte OAuth client ID (required for hosted mode) airbyte_client_secret: Airbyte OAuth client secret (required for hosted mode) connector_id: Specific connector/source ID (skips lookup if provided)

Examples:

Hosted mode with connector_id (no lookup needed)

connector = GongConnector( auth_config=AirbyteAuthConfig( airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789", connector_id="existing-source-uuid" ) )

Hosted mode with workspace_name (lookup by workspace)

connector = GongConnector( auth_config=AirbyteAuthConfig( workspace_name="user-123", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789" ) )

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

airbyte_client_id: str | None : The type of the None singleton.

airbyte_client_secret: str | None : The type of the None singleton.

connector_id: str | None : The type of the None singleton.

model_config : The type of the None singleton.

organization_id: str | None : The type of the None singleton.

workspace_name: str | None : The type of the None singleton.

AirbyteSearchMeta(**data: Any) : Pagination metadata for search responses.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

cursor: str | None : Cursor for fetching the next page of results.

has_more: bool : Whether more results are available.

model_config : The type of the None singleton.

took_ms: int | None : Time taken to execute the search in milliseconds.

AirbyteSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel
• typing.Generic

Descendants

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[ApplicationsSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[CandidatesSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[DepartmentsSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[JobPostsSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[JobsSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[OffersSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[OfficesSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[SourcesSearchData]
• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult[UsersSearchData]

Class variables

data: list[~D] : List of matching records.

meta: airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchMeta : Pagination metadata.

model_config : The type of the None singleton.

ApplicationsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

CandidatesSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

DepartmentsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

JobPostsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

JobsSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

OffersSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

OfficesSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

SourcesSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

UsersSearchResult(**data: Any) : Result from Airbyte cache search operations with typed records.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• airbyte_agent_sdk.connectors.greenhouse.models.AirbyteSearchResult
• pydantic.main.BaseModel
• typing.Generic

ApplicationsSearchData(**data: Any) : Search result data for applications entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

answers: list[typing.Any] | None : Answers provided in the application.

applied_at: str | None : Timestamp when the candidate applied.

attachments: list[typing.Any] | None : Attachments uploaded with the application.

candidate_id: int | None : Unique identifier for the candidate.

credited_to: dict[str, typing.Any] | None : Information about the employee who credited the application.

current_stage: dict[str, typing.Any] | None : Current stage of the application process.

id: int | None : Unique identifier for the application.

job_post_id: int | None : The type of the None singleton.

jobs: list[typing.Any] | None : Jobs applied for by the candidate.

last_activity_at: str | None : Timestamp of the last activity on the application.

location: str | None : Location related to the application.

model_config : The type of the None singleton.

prospect: bool | None : Status of the application prospect.

prospect_detail: dict[str, typing.Any] | None : Details related to the application prospect.

prospective_department: str | None : Prospective department for the candidate.

prospective_office: str | None : Prospective office for the candidate.

rejected_at: str | None : Timestamp when the application was rejected.

rejection_details: dict[str, typing.Any] | None : Details related to the application rejection.

rejection_reason: dict[str, typing.Any] | None : Reason for the application rejection.

source: dict[str, typing.Any] | None : Source of the application.

status: str | None : Status of the application.

CandidatesSearchData(**data: Any) : Search result data for candidates entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

addresses: list[typing.Any] | None : Candidate's addresses

application_ids: list[typing.Any] | None : List of application IDs

applications: list[typing.Any] | None : An array of all applications made by candidates.

attachments: list[typing.Any] | None : Attachments related to the candidate

can_email: bool | None : Indicates if candidate can be emailed

company: str | None : Company where the candidate is associated

coordinator: str | None : Coordinator assigned to the candidate

created_at: str | None : Date and time of creation

custom_fields: dict[str, typing.Any] | None : Custom fields associated with the candidate

educations: list[typing.Any] | None : List of candidate's educations

email_addresses: list[typing.Any] | None : Candidate's email addresses

employments: list[typing.Any] | None : List of candidate's employments

first_name: str | None : Candidate's first name

id: int | None : Candidate's ID

is_private: bool | None : Indicates if the candidate's data is private

keyed_custom_fields: dict[str, typing.Any] | None : Keyed custom fields associated with the candidate

last_activity: str | None : Details of the last activity related to the candidate

last_name: str | None : Candidate's last name

model_config : The type of the None singleton.

phone_numbers: list[typing.Any] | None : Candidate's phone numbers

photo_url: str | None : URL of the candidate's profile photo

recruiter: str | None : Recruiter assigned to the candidate

social_media_addresses: list[typing.Any] | None : Candidate's social media addresses

tags: list[typing.Any] | None : Tags associated with the candidate

title: str | None : Candidate's title (e.g., Mr., Mrs., Dr.)

updated_at: str | None : Date and time of last update

website_addresses: list[typing.Any] | None : List of candidate's website addresses

DepartmentsSearchData(**data: Any) : Search result data for departments entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

child_department_external_ids: list[typing.Any] | None : External IDs of child departments associated with this department.

child_ids: list[typing.Any] | None : Unique IDs of child departments associated with this department.

external_id: str | None : External ID of this department.

id: int | None : Unique ID of this department.

model_config : The type of the None singleton.

name: str | None : Name of the department.

parent_department_external_id: str | None : External ID of the parent department of this department.

parent_id: int | None : Unique ID of the parent department of this department.

GreenhouseAuthConfig(**data: Any) : Harvest API Key Authentication

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

api_key: str : Your Greenhouse Harvest API Key from the Dev Center

model_config : The type of the None singleton.

GreenhouseConnector(auth_config: GreenhouseAuthConfig | AirbyteAuthConfig | BaseModel | None = None, on_token_refresh: Any | None = None) : Type-safe Greenhouse API connector.

Auto-generated from OpenAPI specification with full type safety.

Initialize a new greenhouse connector instance.

Supports both local and hosted execution modes:

• Local mode: Provide connector-specific auth config (e.g., GreenhouseAuthConfig)
• Hosted mode: Provide AirbyteAuthConfig with client credentials and either connector_id or workspace_name

Args: auth_config: Either connector-specific auth config for local mode, or AirbyteAuthConfig for hosted mode on_token_refresh: Optional callback for OAuth2 token refresh persistence. Called with new_tokens dict when tokens are refreshed. Can be sync or async. Example: lambda tokens: save_to_database(tokens) Examples:

Local mode (direct API calls)

connector = GreenhouseConnector(auth_config=GreenhouseAuthConfig(api_key="..."))

Hosted mode with explicit connector_id (no lookup needed)

connector = GreenhouseConnector( auth_config=AirbyteAuthConfig( airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789", connector_id="existing-source-uuid" ) )

Hosted mode with lookup by workspace_name

connector = GreenhouseConnector( auth_config=AirbyteAuthConfig( workspace_name="user-123", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789" ) )

Class variables

connector_name : The type of the None singleton.

connector_version : The type of the None singleton.

sdk_version : The type of the None singleton.

Static methods

create(*, airbyte_config: AirbyteAuthConfig, auth_config: "'GreenhouseAuthConfig'", name: str | None = None, replication_config: dict[str, Any] | None = None, source_template_id: str | None = None) ‑> airbyte_agent_sdk.connectors.greenhouse.connector.GreenhouseConnector : Create a new hosted connector on Airbyte Cloud.

This factory method:

1. Creates a source on Airbyte Cloud with the provided credentials
2. Returns a connector configured with the new connector_id

Args: airbyte_config: Airbyte hosted auth config with client credentials and workspace_name. Optionally include organization_id for multi-org request routing. auth_config: Typed auth config (same as local mode) name: Optional source name (defaults to connector name + workspace_name) replication_config: Optional replication settings dict. Required for connectors with x-airbyte-replication-config (REPLICATION mode sources). source_template_id: Source template ID. Required when organization has multiple source templates for this connector type.

Returns: A GreenhouseConnector instance configured in hosted mode

Example:

Create a new hosted connector with API key auth

connector = await GreenhouseConnector.create( airbyte_config=AirbyteAuthConfig( workspace_name="my-workspace", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc", airbyte_client_secret="secret_xyz", ), auth_config=GreenhouseAuthConfig(api_key="..."), )

Use the connector

result = await connector.execute("entity", "list", {})

tool_utils(func: _F | None = None, *, update_docstring: bool = True, max_output_chars: int | None = 100000, framework: FrameworkName | None = None, internal_retries: int = 0, should_internal_retry: Callable[[Exception, tuple[Any, ...], dict[str, Any]], bool] | None = None, exhausted_runtime_failure_message: Callable[[Exception, tuple[Any, ...], dict[str, Any]], str | None] | None = None) ‑> ~_F | Callable[[~_F], ~_F] : Decorator that adds tool utilities like docstring augmentation and output limits.

Composes :func:airbyte_agent_sdk.translation.translate_exceptions for runtime wrapping (sync/async branch + output-size check + framework signal translation + optional internal retry loop), and adds connector-specific docstring augmentation on top of it.

Usage: @mcp.tool() @GreenhouseConnector.tool_utils async def execute(entity: str, action: str, params: dict): ...

@mcp.tool() @GreenhouseConnector.tool_utils(update_docstring=False, max_output_chars=None) async def execute(entity: str, action: str, params: dict): ...

@mcp.tool() @GreenhouseConnector.tool_utils(framework="pydantic_ai", internal_retries=2) async def execute(entity: str, action: str, params: dict): ...

Args: update_docstring: When True, append connector capabilities to doc. max_output_chars: Max serialized output size before raising. Use None to disable. framework: One of "pydantic_ai" | "langchain" | "openai_agents" | "mcp". Defaults to None → auto-detect by attempting each framework's canonical import in order. Explicit always wins. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Default 0. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. should_internal_retry: Optional predicate (error, args, kwargs) -> bool further restricting which retryable errors are safe for this specific tool. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. exhausted_runtime_failure_message: Optional callback (error, args, kwargs) -> str | None. Invoked after internal retries are exhausted OR were skipped via should_internal_retry returning False. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions.

Instance variables

connector_id: str | None : Get the connector/source ID (only available in hosted mode).

Returns: The connector ID if in hosted mode, None if in local mode.

Example: connector = await GreenhouseConnector.create(...) print(f"Created connector: {connector.connector_id}")

Methods

check(self) ‑> airbyte_agent_sdk.connectors.greenhouse.models.GreenhouseCheckResult : Perform a health check to verify connectivity and credentials.

Executes a lightweight list operation (limit=1) to validate that the connector can communicate with the API and credentials are valid.

Returns: GreenhouseCheckResult with status ("healthy" or "unhealthy") and optional error message

Example: result = await connector.check() if result.status == "healthy": print("Connection verified!") else: print(f"Check failed: {result.error}")

close(self) : Close the connector and release resources.

entity_schema(self, entity: str) ‑> dict[str, typing.Any] | None : Get the JSON schema for an entity.

Args: entity: Entity name (e.g., "contacts", "companies")

Returns: JSON schema dict describing the entity structure, or None if not found.

Example: schema = connector.entity_schema("contacts") if schema: print(f"Contact properties: {list(schema.get('properties', {}).keys())}")

execute(self, entity: str, action: "Literal['list', 'get', 'download', 'context_store_search']", params: Mapping[str, Any] | None = None) ‑> Any : Execute an entity operation with full type safety.

This is the recommended interface for blessed connectors as it:

• Uses the same signature as non-blessed connectors
• Provides full IDE autocomplete for entity/action/params
• Makes migration from generic to blessed connectors seamless

Args: entity: Entity name (e.g., "customers") action: Operation action (e.g., "create", "get", "list") params: Operation parameters (typed based on entity+action)

Returns: Typed response based on the operation

Example: customer = await connector.execute( entity="customers", action="get", params={"id": "cus_123"} )

list_entities(self) ‑> list[dict[str, typing.Any]] : Get structured data about available entities, actions, and parameters.

Returns a list of entity descriptions with:

• entity_name: Name of the entity (e.g., "contacts", "deals")
• description: Entity description from the first endpoint
• available_actions: List of actions (e.g., ["list", "get", "create"])
• parameters: Dict mapping action -> list of parameter dicts

Example: entities = connector.list_entities() for entity in entities: print(f"{entity['entity_name']}: {entity['available_actions']}")

JobPostsSearchData(**data: Any) : Search result data for job_posts entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

active: bool | None : Flag indicating if the job post is active or not.

content: str | None : Content or description of the job post.

created_at: str | None : Date and time when the job post was created.

demographic_question_set_id: int | None : ID of the demographic question set associated with the job post.

external: bool | None : Flag indicating if the job post is external or not.

first_published_at: str | None : Date and time when the job post was first published.

id: int | None : Unique identifier of the job post.

internal: bool | None : Flag indicating if the job post is internal or not.

internal_content: str | None : Internal content or description of the job post.

job_id: int | None : ID of the job associated with the job post.

live: bool | None : Flag indicating if the job post is live or not.

location: dict[str, typing.Any] | None : Details about the job post location.

model_config : The type of the None singleton.

questions: list[typing.Any] | None : List of questions related to the job post.

title: str | None : Title or headline of the job post.

updated_at: str | None : Date and time when the job post was last updated.

JobsSearchData(**data: Any) : Search result data for jobs entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

closed_at: str | None : The date and time the job was closed

confidential: bool | None : Indicates if the job details are confidential

copied_from_id: int | None : The ID of the job from which this job was copied

created_at: str | None : The date and time the job was created

custom_fields: dict[str, typing.Any] | None : Custom fields related to the job

departments: list[typing.Any] | None : Departments associated with the job

hiring_team: dict[str, typing.Any] | None : Members of the hiring team for the job

id: int | None : Unique ID of the job

is_template: bool | None : Indicates if the job is a template

keyed_custom_fields: dict[str, typing.Any] | None : Keyed custom fields related to the job

model_config : The type of the None singleton.

name: str | None : Name of the job

notes: str | None : Additional notes or comments about the job

offices: list[typing.Any] | None : Offices associated with the job

opened_at: str | None : The date and time the job was opened

openings: list[typing.Any] | None : Openings associated with the job

requisition_id: str | None : ID associated with the job requisition

status: str | None : Current status of the job

updated_at: str | None : The date and time the job was last updated

OffersSearchData(**data: Any) : Search result data for offers entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

application_id: int | None : Unique identifier for the application associated with the offer

candidate_id: int | None : Unique identifier for the candidate associated with the offer

created_at: str | None : Timestamp indicating when the offer was created

custom_fields: dict[str, typing.Any] | None : Additional custom fields related to the offer

id: int | None : Unique identifier for the offer

job_id: int | None : Unique identifier for the job associated with the offer

keyed_custom_fields: dict[str, typing.Any] | None : Keyed custom fields associated with the offer

model_config : The type of the None singleton.

opening: dict[str, typing.Any] | None : Details about the job opening

resolved_at: str | None : Timestamp indicating when the offer was resolved

sent_at: str | None : Timestamp indicating when the offer was sent

starts_at: str | None : Timestamp indicating when the offer starts

status: str | None : Status of the offer

updated_at: str | None : Timestamp indicating when the offer was last updated

version: int | None : Version of the offer data

OfficesSearchData(**data: Any) : Search result data for offices entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

child_ids: list[typing.Any] | None : IDs of child offices associated with this office

child_office_external_ids: list[typing.Any] | None : External IDs of child offices associated with this office

external_id: str | None : Unique identifier for this office in the external system

id: int | None : Unique identifier for this office in the API system

location: dict[str, typing.Any] | None : Location details of this office

model_config : The type of the None singleton.

name: str | None : Name of the office

parent_id: int | None : ID of the parent office, if this office is a branch office

parent_office_external_id: str | None : External ID of the parent office in the external system

primary_contact_user_id: int | None : User ID of the primary contact person for this office

SourcesSearchData(**data: Any) : Search result data for sources entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

id: int | None : The unique identifier for the source.

model_config : The type of the None singleton.

name: str | None : The name of the source.

type_: dict[str, typing.Any] | None : Type of the data source

UsersSearchData(**data: Any) : Search result data for users entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors (in MRO)

• pydantic.main.BaseModel

Class variables

created_at: str | None : The date and time when the user account was created.

departments: list[typing.Any] | None : List of departments associated with users

disabled: bool | None : Indicates whether the user account is disabled.

emails: list[typing.Any] | None : Email addresses of the users

employee_id: str | None : Employee identifier for the user.

first_name: str | None : The first name of the user.

id: int | None : Unique identifier for the user.

last_name: str | None : The last name of the user.

linked_candidate_ids: list[typing.Any] | None : IDs of candidates linked to the user.

model_config : The type of the None singleton.

name: str | None : The full name of the user.

offices: list[typing.Any] | None : List of office locations where users are based

primary_email_address: str | None : The primary email address of the user.

site_admin: bool | None : Indicates whether the user is a site administrator.

updated_at: str | None : The date and time when the user account was last updated.