Intercom
The Intercom agent connector is a Python package that equips AI agents to interact with Intercom 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.
Intercom is a customer messaging platform that enables businesses to communicate with customers through chat, email, and in-app messaging. This connector provides access to core Intercom entities including contacts, conversations, companies, teams, admins, tags, and segments for customer support analytics and insights. It also supports creating and updating contacts, creating notes, creating internal articles, creating and updating companies, and creating tags.
Example prompts
The Intercom connector is optimized to handle prompts like these.
- List all contacts in my Intercom workspace
- List all companies in Intercom
- What teams are configured in my workspace?
- Show me all admins in my Intercom account
- List all tags used in Intercom
- Show me all customer segments
- Show me details for a recent contact
- Show me details for a recent company
- Show me details for a recent conversation
- Create a new lead contact named 'Jane Smith' with email [email protected]
- Create an internal article titled 'Onboarding Guide' with instructions for new team members
- Create a company named 'Acme Corp' with company_id 'acme-001'
- Create a tag named 'VIP Customer'
- Update the name of contact {id} to 'John Updated'
- Add a note to contact {id} saying 'Followed up on support request'
- Show me conversations from the last week
- List conversations assigned to team {team_id}
- Show me open conversations
Unsupported prompts
The Intercom connector isn't currently able to handle prompts like these.
- Send a message to a customer
- Delete a conversation
- Delete a contact
- Delete a company
- Assign a conversation to an admin
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Contacts | List, Create, Get, Update, Context Store Search |
| Conversations | List, Get, Context Store Search |
| Companies | List, Create, Get, Update, Context Store Search |
| Teams | List, Get, Context Store Search |
| Admins | List, Get |
| Tags | List, Create, Get |
| Notes | Create |
| Segments | List, Get |
| Internal Articles | Create |
Intercom API docs
See the official Intercom 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 IntercomConnector 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.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
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
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
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
- LangChain
- OpenAI Agents
- FastMCP
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
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
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
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
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.intercom import IntercomConnector
from airbyte_agent_sdk.connectors.intercom.models import IntercomAuthConfig
connector = IntercomConnector(
auth_config=IntercomAuthConfig(
access_token="<Your Intercom API Access Token>"
)
)
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
from langchain_core.tools import tool
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>"
)
)
@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
from agents import Agent, function_tool
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>"
)
)
# 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
from fastmcp import FastMCP
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>"
)
)
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
Authentication
For all authentication options, see the connector's authentication documentation.
Version information
Connector version: 0.1.10