Linkedin-Ads
The Linkedin-Ads agent connector is a Python package that equips AI agents to interact with Linkedin-Ads through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.
Connector for the LinkedIn Ads Marketing API. Provides access to ad accounts, campaigns, campaign groups, creatives, conversions, and ad analytics data. Supports OAuth 2.0 and direct access token authentication. Use this connector to retrieve advertising performance metrics, manage campaign structures, and monitor creative assets across your LinkedIn advertising accounts.
Example prompts
The Linkedin-Ads connector is optimized to handle prompts like these.
- List all my LinkedIn ad accounts
- Show me all campaigns in my ad account
- List all campaign groups
- Show me the creatives for my campaigns
- List all conversions configured for my ad accounts
- Show me account users for my LinkedIn ads accounts
- Which campaigns have the highest click-through rate?
- What is the total ad spend across all campaigns this month?
- Show me campaigns with status ACTIVE
- Which creatives have the most impressions?
- Compare campaign performance by cost type
Unsupported prompts
The Linkedin-Ads connector isn't currently able to handle prompts like these.
- Create a new campaign
- Update campaign budgets
- Delete an ad creative
- Pause a campaign
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Accounts | List, Get, Context Store Search |
| Account Users | List, Context Store Search |
| Campaigns | List, Get, Context Store Search |
| Campaign Groups | List, Get, Context Store Search |
| Creatives | List, Get, Context Store Search |
| Conversions | List, Get, Context Store Search |
| Ad Campaign Analytics | List, Context Store Search |
| Ad Creative Analytics | List, Context Store Search |
Linkedin-Ads API docs
See the official Linkedin-Ads API reference.
SDK installation
uv pip install airbyte-agent-sdk
SDK usage
Connectors can run in hosted or open source mode.
Hosted
In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead.
If your Airbyte client can access multiple organizations, also set organization_id.
This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.
The connect() factory returns a fully typed LinkedinAdsConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
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
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
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
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
- LangChain
- OpenAI Agents
- FastMCP
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
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
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
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
Open source
In open source mode, you provide API credentials directly to the connector.
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
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>"
)
)
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
from langchain_core.tools import tool
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>"
)
)
@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
from agents import Agent, function_tool
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>"
)
)
# 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
from fastmcp import FastMCP
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>"
)
)
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
Authentication
For all authentication options, see the connector's authentication documentation.
Version information
Connector version: 1.0.5