Zendesk-Support authentication

This connector is optimized for AI agents. For the data replication connector, see Zendesk Support.

This page documents the authentication and configuration options for the Zendesk-Support agent connector.

Authentication

Open source execution

In open source mode, you provide API credentials directly to the connector.

OAuth

credentials fields you need:

Field Name	Type	Required	Description
access_token	str	Yes	OAuth 2.0 access token
refresh_token	str	No	OAuth 2.0 refresh token (optional)

Example request:

from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.connectors.zendesk_support.models import ZendeskSupportOauth20AuthConfig

connector = ZendeskSupportConnector(
    auth_config=ZendeskSupportOauth20AuthConfig(
        access_token="<OAuth 2.0 access token>",
        refresh_token="<OAuth 2.0 refresh token (optional)>"
    )
)

Token

credentials fields you need:

Field Name	Type	Required	Description
email	str	Yes	Your Zendesk account email address
api_token	str	Yes	Your Zendesk API token from Admin Center

Example request:

from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.connectors.zendesk_support.models import ZendeskSupportApiTokenAuthConfig

connector = ZendeskSupportConnector(
    auth_config=ZendeskSupportApiTokenAuthConfig(
        email="<Your Zendesk account email address>",
        api_token="<Your Zendesk API token from Admin Center>"
    )
)

Hosted execution

In hosted mode, you first create a connector via the Airbyte Agent API (providing your OAuth or Token credentials), then execute operations using either the Python SDK or API. If you need a step-by-step guide, see the developer quickstart.

OAuth

Create a connector with OAuth credentials.

credentials fields you need:

Field Name	Type	Required	Description
access_token	str	Yes	OAuth 2.0 access token
refresh_token	str	No	OAuth 2.0 refresh token (optional)

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Zendesk-Support",
    "name": "My Zendesk-Support Connector",
    "credentials": {
      "access_token": "<OAuth 2.0 access token>",
      "refresh_token": "<OAuth 2.0 refresh token (optional)>"
    }
  }'

Bring your own OAuth flow

To implement your own OAuth flow, use Airbyte's server-side OAuth API endpoints. For a complete guide, see Build your own OAuth flow.

Step 1: Initiate the OAuth flow

Request a consent URL for your user.

Field Name	Type	Required	Description
workspace_name	string	Yes	Your unique identifier for the workspace
connector_type	string	Yes	The connector type (e.g., "Zendesk-Support")
redirect_url	string	Yes	URL to redirect to after OAuth authorization

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors/oauth/initiate" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Zendesk-Support",
    "redirect_url": "https://yourapp.com/oauth/callback"
  }'

Redirect your user to the consent_url from the response.

Step 2: Handle the callback

After the user authorizes access, Airbyte automatically creates the connector and redirects them to your redirect_url with a connector_id query parameter. You don't need to make a separate API call to create the connector.

https://yourapp.com/oauth/callback?connector_id=<connector_id>

Extract the connector_id from the callback URL and store it for future operations. For error handling and a complete implementation example, see Build your own OAuth flow.

Token

Create a connector with Token credentials.

credentials fields you need:

Field Name	Type	Required	Description
email	str	Yes	Your Zendesk account email address
api_token	str	Yes	Your Zendesk API token from Admin Center

Example request:

curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
  -H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_name": "<WORKSPACE_NAME>",
    "connector_type": "Zendesk-Support",
    "name": "My Zendesk-Support Connector",
    "credentials": {
      "email": "<Your Zendesk account email address>",
      "api_token": "<Your Zendesk API token from Admin Center>"
    }
  }'

Execution

After creating the connector, execute operations using either the Python SDK or API. If your Airbyte client can access multiple organizations, include organization_id in AirbyteAuthConfig and X-Organization-Id in raw API calls.

Python SDK

The connect() factory returns a fully typed ZendeskSupportConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

@tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskSupportConnector.tool_utils(framework="openai_agents")
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Zendesk-Support Assistant", tools=[zendesk_support_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector

connector = connect("zendesk-support", workspace_name="<your_workspace_name>")

mcp = FastMCP("Zendesk-Support Agent")

@mcp.tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment): Pydantic AI

Pydantic AI

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

@tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ZendeskSupportConnector.tool_utils(framework="openai_agents")
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Zendesk-Support Assistant", tools=[zendesk_support_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.zendesk_support import ZendeskSupportConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskSupportConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

mcp = FastMCP("Zendesk-Support Agent")

@mcp.tool
@ZendeskSupportConnector.tool_utils
async def zendesk_support_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Support connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

API

curl -X POST 'https://api.airbyte.ai/api/v1/integrations/connectors/<connector_id>/execute' \
  -H 'Authorization: Bearer <YOUR_BEARER_TOKEN>' \
  -H 'X-Organization-Id: <YOUR_ORGANIZATION_ID>' \
  -H 'Content-Type: application/json' \
  -d '{"entity": "<entity>", "action": "<action>", "params": {}}'

Configuration

The Zendesk-Support connector requires the following configuration variables. These variables are used to construct the base API URL. Pass them via the config parameter when initializing the connector.

Variable	Type	Required	Default	Description
subdomain	string	Yes	your-subdomain	Your Zendesk subdomain