Zendesk-Chat authentication

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

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

Hosted mode (most cases)

In hosted mode, create the connector through the Airbyte Agent CLI or API, then execute operations using the CLI, Python SDK, or API. If you need a step-by-step guide, see the developer quickstart.

OAuth

Use the CLI for hosted OAuth connector creation when possible. It opens the hosted setup flow and avoids passing connector secrets through the command line:

airbyte-agent login
airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat"
}'

For API-first use cases, create a connector with OAuth credentials directly.

credentials fields you need:

Field Name	Type	Required	Description
`access_token`	`str`	Yes	Your Zendesk Chat OAuth 2.0 access token

replication_config fields you need:

Field Name	Type	Required	Description
`start_date`	`str (date-time)`	Yes	The date from which to start replicating data, in the format YYYY-MM-DDT00:00:00Z.

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-Chat",
    "name": "My Zendesk-Chat Connector",
    "credentials": {
      "access_token": "<Your Zendesk Chat OAuth 2.0 access token>"
    },
    "replication_config": {
      "start_date": "<The date from which to start replicating data, in the format YYYY-MM-DDT00:00:00Z.>"
    }
  }'

Token

This authentication method isn't available for this connector.

Execution

After creating the connector, execute operations using the CLI, Python SDK, or API. If your Airbyte client can access multiple organizations, set the default organization with airbyte-agent organizations use, include organization_id in AirbyteAuthConfig, or include X-Organization-Id in raw API calls.

CLI

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "zendesk-chat",
  "entity": "<entity>",
  "action": "<action>",
  "params": {}
}'

Python SDK

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

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Zendesk-Chat Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

mcp = FastMCP("Zendesk-Chat Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Legacy alternatives

These examples are kept for existing integrations. For new agents, use build_connector_tools above. The legacy ZendeskChatConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

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

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

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

@tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Chat 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
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

connector = connect("zendesk-chat", 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)
@ZendeskChatConnector.tool_utils(framework="openai_agents")
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Chat 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-Chat Assistant", tools=[zendesk_chat_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector

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

mcp = FastMCP("Zendesk-Chat Agent")

@mcp.tool
@ZendeskChatConnector.tool_utils
async def zendesk_chat_execute(entity: str, action: str, params: dict | None = None):
    """Execute Zendesk-Chat 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
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskChatConnector(
    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>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskChatConnector(
    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>"
    )
)

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskChatConnector(
    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>"
    )
)

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Zendesk-Chat Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ZendeskChatConnector(
    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-Chat Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

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": {}}'

Open source mode

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

OAuth

credentials fields you need:

Field Name	Type	Required	Description
`access_token`	`str`	Yes	Your Zendesk Chat OAuth 2.0 access token

Example request:

from airbyte_agent_sdk.connectors.zendesk_chat import ZendeskChatConnector
from airbyte_agent_sdk.connectors.zendesk_chat.models import ZendeskChatAuthConfig

connector = ZendeskChatConnector(
    auth_config=ZendeskChatAuthConfig(
        access_token="<Your Zendesk Chat OAuth 2.0 access token>"
    )
)

Token

This authentication method isn't available for this connector.

Configuration

The Zendesk-Chat connector also needs these configuration values to construct the base API URL.

Hosted CLI: airbyte-agent connectors create doesn't currently accept these configuration fields directly. For hosted connectors that need these values, create the connector with the hosted API replication_config, then use the CLI for describe and execute operations after creation.
Hosted API: pass these values in the connector creation replication_config.
Open source mode: provide these values with your local connector setup so the connector can build the correct API base URL.

Variable	Type	Required	Default	Description
`subdomain`	`string`	Yes	your-subdomain	Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL)

Hosted mode (most cases)​

OAuth​

Token​

Execution​

Legacy alternatives​

Open source mode​

OAuth​

Token​

Configuration​

Hosted mode (most cases)

OAuth

Token

Execution

Legacy alternatives

Open source mode

OAuth

Token

Configuration