Typeform
The Typeform agent connector is a Python package that equips AI agents to interact with Typeform 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 Typeform API. Provides access to forms, form responses, webhooks, workspaces, images, and themes. Supports listing and retrieving typeform resources for survey and form management workflows.
Example prompts
The Typeform connector is optimized to handle prompts like these.
- List all my typeforms
- Show me the responses for my latest form
- What workspaces do I have?
- List all themes in my account
- Get the details of a specific form
- Which forms received the most responses last month?
- Find responses submitted in the last week
- What forms were created this year?
- Show me all forms in a specific workspace
Unsupported prompts
The Typeform connector isn't currently able to handle prompts like these.
- Create a new typeform
- Delete a form response
- Update form settings
- Send a webhook notification
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Forms | List, Get, Context Store Search |
| Responses | List, Context Store Search |
| Webhooks | List, Context Store Search |
| Workspaces | List, Context Store Search |
| Images | List, Context Store Search |
| Themes | List, Context Store Search |
Typeform API docs
See the official Typeform 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 TypeformConnector 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.typeform import TypeformConnector
connector = connect("typeform", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@TypeformConnector.tool_utils
async def typeform_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.typeform import TypeformConnector
connector = connect("typeform", workspace_name="<your_workspace_name>")
@tool
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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.typeform import TypeformConnector
connector = connect("typeform", 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)
@TypeformConnector.tool_utils(framework="openai_agents")
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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="Typeform Assistant", tools=[typeform_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.typeform import TypeformConnector
connector = connect("typeform", workspace_name="<your_workspace_name>")
mcp = FastMCP("Typeform Agent")
@mcp.tool
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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.typeform import TypeformConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = TypeformConnector(
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
@TypeformConnector.tool_utils
async def typeform_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.typeform import TypeformConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = TypeformConnector(
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
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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.typeform import TypeformConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = TypeformConnector(
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)
@TypeformConnector.tool_utils(framework="openai_agents")
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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="Typeform Assistant", tools=[typeform_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.typeform import TypeformConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = TypeformConnector(
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("Typeform Agent")
@mcp.tool
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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.typeform import TypeformConnector
from airbyte_agent_sdk.connectors.typeform.models import TypeformAuthConfig
connector = TypeformConnector(
auth_config=TypeformAuthConfig(
access_token="<Personal access token from your Typeform account settings>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@TypeformConnector.tool_utils
async def typeform_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.typeform import TypeformConnector
from airbyte_agent_sdk.connectors.typeform.models import TypeformAuthConfig
connector = TypeformConnector(
auth_config=TypeformAuthConfig(
access_token="<Personal access token from your Typeform account settings>"
)
)
@tool
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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.typeform import TypeformConnector
from airbyte_agent_sdk.connectors.typeform.models import TypeformAuthConfig
connector = TypeformConnector(
auth_config=TypeformAuthConfig(
access_token="<Personal access token from your Typeform account settings>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@TypeformConnector.tool_utils(framework="openai_agents")
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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="Typeform Assistant", tools=[typeform_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.typeform import TypeformConnector
from airbyte_agent_sdk.connectors.typeform.models import TypeformAuthConfig
connector = TypeformConnector(
auth_config=TypeformAuthConfig(
access_token="<Personal access token from your Typeform account settings>"
)
)
mcp = FastMCP("Typeform Agent")
@mcp.tool
@TypeformConnector.tool_utils
async def typeform_execute(entity: str, action: str, params: dict | None = None):
"""Execute Typeform 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: 1.0.4