Granola
The Granola agent connector is a Python package that equips AI agents to interact with Granola 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.
The Granola API connector provides read access to meeting notes from Granola, an AI-powered meeting notes platform. This connector integrates with the Granola Enterprise API to list and retrieve notes, including summaries, transcripts, attendees, and calendar event details. Requires an Enterprise plan API key.
Example prompts
The Granola connector is optimized to handle prompts like these.
- List all meeting notes from Granola
- Show me recent meeting notes
- Get the details of a specific note
- List notes created in the last week
- Find meeting notes from last month
- Which meetings had the most attendees?
- Show me notes that mention budget reviews
- What meetings happened this quarter?
Unsupported prompts
The Granola connector isn't currently able to handle prompts like these.
- Create a new meeting note
- Delete a meeting note
- Update an existing note
- Share a note with someone
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Notes | List, Get, Context Store Search |
Granola API docs
See the official Granola 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 GranolaConnector 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.granola import GranolaConnector
connector = connect("granola", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@GranolaConnector.tool_utils
async def granola_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.granola import GranolaConnector
connector = connect("granola", workspace_name="<your_workspace_name>")
@tool
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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.granola import GranolaConnector
connector = connect("granola", 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)
@GranolaConnector.tool_utils(framework="openai_agents")
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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="Granola Assistant", tools=[granola_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.granola import GranolaConnector
connector = connect("granola", workspace_name="<your_workspace_name>")
mcp = FastMCP("Granola Agent")
@mcp.tool
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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.granola import GranolaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = GranolaConnector(
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
@GranolaConnector.tool_utils
async def granola_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.granola import GranolaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = GranolaConnector(
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
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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.granola import GranolaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = GranolaConnector(
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)
@GranolaConnector.tool_utils(framework="openai_agents")
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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="Granola Assistant", tools=[granola_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.granola import GranolaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = GranolaConnector(
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("Granola Agent")
@mcp.tool
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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.granola import GranolaConnector
from airbyte_agent_sdk.connectors.granola.models import GranolaAuthConfig
connector = GranolaConnector(
auth_config=GranolaAuthConfig(
api_key="<Granola Enterprise API key generated from Settings > Workspaces > API tab>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@GranolaConnector.tool_utils
async def granola_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.granola import GranolaConnector
from airbyte_agent_sdk.connectors.granola.models import GranolaAuthConfig
connector = GranolaConnector(
auth_config=GranolaAuthConfig(
api_key="<Granola Enterprise API key generated from Settings > Workspaces > API tab>"
)
)
@tool
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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.granola import GranolaConnector
from airbyte_agent_sdk.connectors.granola.models import GranolaAuthConfig
connector = GranolaConnector(
auth_config=GranolaAuthConfig(
api_key="<Granola Enterprise API key generated from Settings > Workspaces > API tab>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@GranolaConnector.tool_utils(framework="openai_agents")
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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="Granola Assistant", tools=[granola_execute])
FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.granola import GranolaConnector
from airbyte_agent_sdk.connectors.granola.models import GranolaAuthConfig
connector = GranolaConnector(
auth_config=GranolaAuthConfig(
api_key="<Granola Enterprise API key generated from Settings > Workspaces > API tab>"
)
)
mcp = FastMCP("Granola Agent")
@mcp.tool
@GranolaConnector.tool_utils
async def granola_execute(entity: str, action: str, params: dict | None = None):
"""Execute Granola 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: 1.0.6