Customer-Io authentication
This page documents the authentication and configuration options for the Customer-Io 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
This authentication method isn't available for this connector.
Token
Create a connector with Token credentials.
credentials fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
app_api_key | str | Yes | Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key. |
replication_config fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
start_date | str (date-time) | Yes | UTC date and time in the format YYYY-MM-DDTHH:mm:ssZ from which to start replicating data. |
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": "Customer-Io",
"name": "My Customer-Io Connector",
"credentials": {
"app_api_key": "<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
},
"replication_config": {
"start_date": "<UTC date and time in the format YYYY-MM-DDTHH:mm:ssZ from which to start replicating data.>"
}
}'
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": "customer-io"
}'
Describe the connector to see its supported entities and actions:
airbyte-agent connectors describe --json '{
"workspace": "<your_workspace_name>",
"name": "customer-io"
}'
Execute an action:
airbyte-agent connectors execute --json '{
"workspace": "<your_workspace_name>",
"name": "customer-io",
"entity": "<entity>",
"action": "<action>",
"params": {}
}'
Python SDK
The connect() factory returns a fully typed CustomerIoConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
connector = connect("customer-io", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
connector = connect("customer-io", workspace_name="<your_workspace_name>")
@tool
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
connector = connect("customer-io", 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)
@CustomerIoConnector.tool_utils(framework="openai_agents")
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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="Customer-Io Assistant", tools=[customer_io_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
connector = connect("customer-io", workspace_name="<your_workspace_name>")
mcp = FastMCP("Customer-Io Agent")
@mcp.tool
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = CustomerIoConnector(
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
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = CustomerIoConnector(
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
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = CustomerIoConnector(
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)
@CustomerIoConnector.tool_utils(framework="openai_agents")
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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="Customer-Io Assistant", tools=[customer_io_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = CustomerIoConnector(
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("Customer-Io Agent")
@mcp.tool
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
"""Execute Customer-Io 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": {}}'
Open source mode
In open source mode, provide API credentials directly to the connector.
OAuth
This authentication method isn't available for this connector.
Token
credentials fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
app_api_key | str | Yes | Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key. |
Example request:
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.connectors.customer_io.models import CustomerIoAuthConfig
connector = CustomerIoConnector(
auth_config=CustomerIoAuthConfig(
app_api_key="<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
)
)