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Customer-Io

The Customer-Io agent connector is a Python package that equips AI agents to interact with Customer-Io 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 Customer.io App API, providing read access to campaigns, newsletters, segments, messages, activities, sender identities, snippets, collections, reporting webhooks, exports, and transactional message templates. Customer.io is a messaging platform that lets you send automated emails, push notifications, SMS, and in-app messages based on customer behavior. This connector retrieves data from the App API (not the Track API) and requires a Bearer-type App API key generated in your Customer.io workspace settings.

Example prompts​

The Customer-Io connector is optimized to handle prompts like these.

  • List all campaigns in Customer.io
  • Show me all newsletters
  • What segments are defined in my workspace?
  • Get the details of campaign 42
  • List all sender identities
  • Show me all reporting webhooks
  • What snippets do we have?
  • List all collections
  • Show recent activities
  • Create a snippet called 'footer' with content '<p>Thanks!</p>'
  • Update the snippet 'header' to say 'Welcome back!'
  • Create a new collection called 'products'
  • Create a reporting webhook for email events
  • Create a manual segment called 'VIP Customers'
  • Export all customers matching a segment
  • Send a transactional email to user@example.com
  • Send an SMS notification to +15551234567
  • Send a push notification to user 123
  • Trigger broadcast campaign 42
  • List all transactional message templates
  • Get the details of transactional message 5
  • Show the content variants of transactional template 3
  • Update the subject of transactional content 139 in template 3
  • Which campaigns are currently active?
  • Find newsletters sent in the last month
  • What are the most recent email deliveries?
  • Which exports have completed?

Unsupported prompts​

The Customer-Io connector isn't currently able to handle prompts like these.

  • Create a new campaign
  • Delete a segment
  • Delete a snippet
  • Delete a collection
  • Delete a reporting webhook
  • Delete a newsletter
  • Send a newsletter via API
  • Schedule a newsletter via API

Entities and actions​

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

EntityActions
CampaignsList, Get, Context Store Search, Context Store SQL Query
Campaign ActionsList, Get, Context Store Search, Context Store SQL Query, Semantic Search
NewslettersList, Get, Context Store Search, Context Store SQL Query
SegmentsList, Create, Get
MessagesList, Get
ActivitiesList
Sender IdentitiesList, Get
SnippetsList, Create, Update
CollectionsList, Create, Get, Update
Reporting WebhooksList, Create, Get, Update
ExportsList, Create, Get
Transactional MessagesList, Get
Transactional Message ContentsList, Update
Transactional EmailCreate
Transactional SmsCreate
Transactional PushCreate
Transactional Inbox MessageCreate
Broadcast TriggerCreate

Customer-Io API docs​

See the official Customer-Io API reference.

Interfaces​

Use the Customer-Io connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI​

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

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": "customer-io"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "customer-io",
  "entity": "campaigns",
  "action": "list"
}'

Python SDK​

Installation​

uv pip install airbyte-agent-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 CustomerIoConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector

connector = connect("customer-io", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use CustomerIoConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector

connector = connect("customer-io", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@CustomerIoConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="customer_io_inspect",
    docs_tool="customer_io_read_docs",
)
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@CustomerIoConnector.agent_tool(framework="pydantic_ai")
async def customer_io_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@CustomerIoConnector.agent_tool(framework="pydantic_ai")
async def customer_io_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector

connector = connect("customer-io", workspace_name="<your_workspace_name>")

@CustomerIoConnector.agent_tool(
    inspect_tool="customer_io_inspect",
    docs_tool="customer_io_read_docs",
)
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@CustomerIoConnector.agent_tool()
async def customer_io_inspect():
    return await connector.inspect_connector()

@CustomerIoConnector.agent_tool()
async def customer_io_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (customer_io_inspect, customer_io_read_docs, customer_io_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated CustomerIoConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or CustomerIoConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector

connector = connect("customer-io", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = CustomerIoConnector(
    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>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Open source​

In open source mode, you provide API credentials directly to the connector.

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.connectors.customer_io.models import CustomerIoAuthConfig

connector = CustomerIoConnector(
    auth_config=CustomerIoAuthConfig(
        app_api_key="<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use CustomerIoConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.connectors.customer_io.models import CustomerIoAuthConfig

connector = CustomerIoConnector(
    auth_config=CustomerIoAuthConfig(
        app_api_key="<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
    )
)

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

@agent.tool_plain
@CustomerIoConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="customer_io_inspect",
    docs_tool="customer_io_read_docs",
)
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@CustomerIoConnector.agent_tool(framework="pydantic_ai")
async def customer_io_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@CustomerIoConnector.agent_tool(framework="pydantic_ai")
async def customer_io_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.connectors.customer_io.models import CustomerIoAuthConfig

connector = CustomerIoConnector(
    auth_config=CustomerIoAuthConfig(
        app_api_key="<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
    )
)

@CustomerIoConnector.agent_tool(
    inspect_tool="customer_io_inspect",
    docs_tool="customer_io_read_docs",
)
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@CustomerIoConnector.agent_tool()
async def customer_io_inspect():
    return await connector.inspect_connector()

@CustomerIoConnector.agent_tool()
async def customer_io_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (customer_io_inspect, customer_io_read_docs, customer_io_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated CustomerIoConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or CustomerIoConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.customer_io import CustomerIoConnector
from airbyte_agent_sdk.connectors.customer_io.models import CustomerIoAuthConfig

connector = CustomerIoConnector(
    auth_config=CustomerIoAuthConfig(
        app_api_key="<Your Customer.io App API key. Generate one in your workspace settings at Settings > API Credentials > App API Key.
>"
    )
)

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

@agent.tool_plain
@CustomerIoConnector.tool_utils
async def customer_io_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Authentication​

For all authentication options, see the connector's authentication documentation.

IP allow list​

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information​

Connector version: 1.0.0

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