Linkedin-Ads authentication

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

This page documents the authentication and configuration options for the Linkedin-Ads 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
refresh_token	str	Yes	OAuth 2.0 refresh token for automatic renewal
client_id	str	Yes	OAuth 2.0 application client ID
client_secret	str	Yes	OAuth 2.0 application client secret

Example request:

from airbyte_agent_sdk.connectors.linkedin_ads import LinkedinAdsConnector
from airbyte_agent_sdk.connectors.linkedin_ads.models import LinkedinAdsAuthConfig

connector = LinkedinAdsConnector(
    auth_config=LinkedinAdsAuthConfig(
        refresh_token="<OAuth 2.0 refresh token for automatic renewal>",
        client_id="<OAuth 2.0 application client ID>",
        client_secret="<OAuth 2.0 application client secret>"
    )
)

Token

This authentication method isn't available for this connector.

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
refresh_token	str	Yes	OAuth 2.0 refresh token for automatic renewal
client_id	str	Yes	OAuth 2.0 application client ID
client_secret	str	Yes	OAuth 2.0 application client secret

replication_config fields you need:

Field Name	Type	Required	Description
start_date	str (date)	Yes	UTC date in the format YYYY-MM-DD. Any data before this date will not be replicated.

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": "Linkedin-Ads",
    "name": "My Linkedin-Ads Connector",
    "credentials": {
      "refresh_token": "<OAuth 2.0 refresh token for automatic renewal>",
      "client_id": "<OAuth 2.0 application client ID>",
      "client_secret": "<OAuth 2.0 application client secret>"
    },
    "replication_config": {
      "start_date": "<UTC date in the format YYYY-MM-DD. Any data before this date will not be replicated.>"
    }
  }'

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., "Linkedin-Ads")
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": "Linkedin-Ads",
    "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

This authentication method isn't available for this connector.

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 LinkedinAdsConnector 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.linkedin_ads import LinkedinAdsConnector

connector = connect("linkedin-ads", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_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.linkedin_ads import LinkedinAdsConnector

connector = connect("linkedin-ads", workspace_name="<your_workspace_name>")

@tool
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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.linkedin_ads import LinkedinAdsConnector

connector = connect("linkedin-ads", 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)
@LinkedinAdsConnector.tool_utils(framework="openai_agents")
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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="Linkedin-Ads Assistant", tools=[linkedin_ads_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.linkedin_ads import LinkedinAdsConnector

connector = connect("linkedin-ads", workspace_name="<your_workspace_name>")

mcp = FastMCP("Linkedin-Ads Agent")

@mcp.tool
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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.linkedin_ads import LinkedinAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = LinkedinAdsConnector(
    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
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_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.linkedin_ads import LinkedinAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = LinkedinAdsConnector(
    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
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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.linkedin_ads import LinkedinAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = LinkedinAdsConnector(
    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)
@LinkedinAdsConnector.tool_utils(framework="openai_agents")
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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="Linkedin-Ads Assistant", tools=[linkedin_ads_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.linkedin_ads import LinkedinAdsConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = LinkedinAdsConnector(
    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("Linkedin-Ads Agent")

@mcp.tool
@LinkedinAdsConnector.tool_utils
async def linkedin_ads_execute(entity: str, action: str, params: dict | None = None):
    """Execute Linkedin-Ads 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": {}}'