Twilio authentication

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

This page documents the authentication and configuration options for the Twilio 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
`account_sid`	`str`	Yes	Your Twilio Account SID (starts with AC)
`auth_token`	`str`	Yes	Your Twilio Auth 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. 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": "Twilio",
    "name": "My Twilio Connector",
    "credentials": {
      "account_sid": "<Your Twilio Account SID (starts with AC)>",
      "auth_token": "<Your Twilio Auth Token>"
    },
    "replication_config": {
      "start_date": "<UTC date and time in the format YYYY-MM-DDTHH:mm:ssZ. Any data before this date will not be replicated.
>"
    }
  }'

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": "twilio"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

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

Python SDK

The connect() factory returns a fully typed TwilioConnector 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)

Pydantic AI
LangChain
OpenAI Agents
FastMCP

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.twilio import TwilioConnector

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

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

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.twilio import TwilioConnector

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

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.twilio import TwilioConnector

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

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Twilio Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.twilio import TwilioConnector

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

mcp = FastMCP("Twilio Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Legacy alternatives

These examples are kept for existing integrations. For new agents, use build_connector_tools above. The legacy TwilioConnector.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.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.twilio import TwilioConnector

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

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

@agent.tool_plain
@TwilioConnector.tool_utils
async def twilio_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.twilio import TwilioConnector

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

@tool
@TwilioConnector.tool_utils
async def twilio_execute(entity: str, action: str, params: dict | None = None):
    """Execute Twilio 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.twilio import TwilioConnector

connector = connect("twilio", 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)
@TwilioConnector.tool_utils(framework="openai_agents")
async def twilio_execute(entity: str, action: str, params: dict | None = None):
    """Execute Twilio 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="Twilio Assistant", tools=[twilio_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.twilio import TwilioConnector

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

mcp = FastMCP("Twilio Agent")

@mcp.tool
@TwilioConnector.tool_utils
async def twilio_execute(entity: str, action: str, params: dict | None = None):
    """Execute Twilio 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 airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.twilio import TwilioConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = TwilioConnector(
    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())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.twilio import TwilioConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = TwilioConnector(
    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="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.twilio import TwilioConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = TwilioConnector(
    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="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Twilio Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.twilio import TwilioConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = TwilioConnector(
    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("Twilio Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

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
`account_sid`	`str`	Yes	Your Twilio Account SID (starts with AC)
`auth_token`	`str`	Yes	Your Twilio Auth Token

Example request:

from airbyte_agent_sdk.connectors.twilio import TwilioConnector
from airbyte_agent_sdk.connectors.twilio.models import TwilioAuthConfig

connector = TwilioConnector(
    auth_config=TwilioAuthConfig(
        account_sid="<Your Twilio Account SID (starts with AC)>",
        auth_token="<Your Twilio Auth Token>"
    )
)

Hosted mode (most cases)​

OAuth​

Token​

Execution​

Legacy alternatives​

Open source mode​

OAuth​

Token​

Hosted mode (most cases)

OAuth

Token

Execution

Legacy alternatives

Open source mode

OAuth

Token