Intercom authentication

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

This page documents the authentication and configuration options for the Intercom 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
access_token	str	Yes	Your Intercom API Access Token

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": "Intercom",
    "name": "My Intercom Connector",
    "credentials": {
      "access_token": "<Your Intercom API Access Token>"
    },
    "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": "intercom"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

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

Python SDK

The connect() factory returns a fully typed IntercomConnector 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.intercom import IntercomConnector

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

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

@agent.tool_plain
@IntercomConnector.tool_utils
async def intercom_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.intercom import IntercomConnector

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

@tool
@IntercomConnector.tool_utils
async def intercom_execute(entity: str, action: str, params: dict | None = None):
    """Execute Intercom 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.intercom import IntercomConnector

connector = connect("intercom", 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)
@IntercomConnector.tool_utils(framework="openai_agents")
async def intercom_execute(entity: str, action: str, params: dict | None = None):
    """Execute Intercom 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="Intercom Assistant", tools=[intercom_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.intercom import IntercomConnector

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

mcp = FastMCP("Intercom Agent")

@mcp.tool
@IntercomConnector.tool_utils
async def intercom_execute(entity: str, action: str, params: dict | None = None):
    """Execute Intercom 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.intercom import IntercomConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = IntercomConnector(
    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
@IntercomConnector.tool_utils
async def intercom_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.intercom import IntercomConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = IntercomConnector(
    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
@IntercomConnector.tool_utils
async def intercom_execute(entity: str, action: str, params: dict | None = None):
    """Execute Intercom 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.intercom import IntercomConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

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

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.intercom import IntercomConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = IntercomConnector(
    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("Intercom Agent")

@mcp.tool
@IntercomConnector.tool_utils
async def intercom_execute(entity: str, action: str, params: dict | None = None):
    """Execute Intercom 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
access_token	str	Yes	Your Intercom API Access Token

Example request:

from airbyte_agent_sdk.connectors.intercom import IntercomConnector
from airbyte_agent_sdk.connectors.intercom.models import IntercomAuthConfig

connector = IntercomConnector(
    auth_config=IntercomAuthConfig(
        access_token="<Your Intercom API Access Token>"
    )
)