Klaviyo authentication

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

This page documents the authentication and configuration options for the Klaviyo agent connector.

Authentication

Open source execution

In open source mode, you 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
api_key	str	Yes	Your Klaviyo private API key

Example request:

from airbyte_agent_sdk.connectors.klaviyo import KlaviyoConnector
from airbyte_agent_sdk.connectors.klaviyo.models import KlaviyoAuthConfig

connector = KlaviyoConnector(
    auth_config=KlaviyoAuthConfig(
        api_key="<Your Klaviyo private API key>"
    )
)

Hosted execution

In hosted mode, you first create a connector via the Airbyte 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 hosted execution tutorial.

OAuth

This authentication method isn't available for this connector.

Bring your own OAuth flow

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
api_key	str	Yes	Your Klaviyo private API key

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": "Klaviyo",
    "name": "My Klaviyo Connector",
    "credentials": {
      "api_key": "<Your Klaviyo private API key>"
    }
  }'

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 KlaviyoConnector 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.klaviyo import KlaviyoConnector

connector = connect("klaviyo", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@KlaviyoConnector.tool_utils
async def klaviyo_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.klaviyo import KlaviyoConnector

connector = connect("klaviyo", workspace_name="<your_workspace_name>")

@tool
@KlaviyoConnector.tool_utils
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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.klaviyo import KlaviyoConnector

connector = connect("klaviyo", 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)
@KlaviyoConnector.tool_utils(framework="openai_agents")
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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="Klaviyo Assistant", tools=[klaviyo_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.klaviyo import KlaviyoConnector

connector = connect("klaviyo", workspace_name="<your_workspace_name>")

mcp = FastMCP("Klaviyo Agent")

@mcp.tool
@KlaviyoConnector.tool_utils
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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.klaviyo import KlaviyoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = KlaviyoConnector(
    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
@KlaviyoConnector.tool_utils
async def klaviyo_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.klaviyo import KlaviyoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = KlaviyoConnector(
    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
@KlaviyoConnector.tool_utils
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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.klaviyo import KlaviyoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = KlaviyoConnector(
    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)
@KlaviyoConnector.tool_utils(framework="openai_agents")
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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="Klaviyo Assistant", tools=[klaviyo_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.klaviyo import KlaviyoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = KlaviyoConnector(
    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("Klaviyo Agent")

@mcp.tool
@KlaviyoConnector.tool_utils
async def klaviyo_execute(entity: str, action: str, params: dict | None = None):
    """Execute Klaviyo 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": {}}'