Monday

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

The Monday agent connector is a Python package that equips AI agents to interact with Monday 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 Monday.com platform API. Monday.com is a work operating system that enables teams to build workflows for project management, CRM, software development, and more. This connector provides read access to boards, items, users, teams, tags, updates, workspaces, and activity logs via the Monday.com GraphQL API (v2).

Example prompts

The Monday connector is optimized to handle prompts like these.

List all users in the Monday.com account
Show me all boards
Get the details of board 18395979459
List all teams
Show me all tags
List recent updates
Which boards were updated in the last week?
Find all items assigned to a specific group
What are the most active boards by update count?
Show me all users who are admins
List items with their column values from a specific board

Unsupported prompts

The Monday connector isn't currently able to handle prompts like these.

Create a new board
Delete an item
Update a column value
Add a new user to the account
Create a webhook subscription

Entities and actions

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

Entity	Actions
Users	List, Get, Context Store Search
Boards	List, Get, Context Store Search
Items	List, Get, Context Store Search
Teams	List, Get, Context Store Search
Tags	List, Context Store Search
Updates	List, Get, Context Store Search
Workspaces	List, Get, Context Store Search
Activity Logs	List, Context Store Search

Monday API docs

See the official Monday 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 MondayConnector 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.monday import MondayConnector

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

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

@agent.tool_plain
@MondayConnector.tool_utils
async def monday_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.monday import MondayConnector

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

@tool
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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.monday import MondayConnector

connector = connect("monday", 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)
@MondayConnector.tool_utils(framework="openai_agents")
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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="Monday Assistant", tools=[monday_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.monday import MondayConnector

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

mcp = FastMCP("Monday Agent")

@mcp.tool
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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.monday import MondayConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = MondayConnector(
    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
@MondayConnector.tool_utils
async def monday_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.monday import MondayConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = MondayConnector(
    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
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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.monday import MondayConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = MondayConnector(
    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)
@MondayConnector.tool_utils(framework="openai_agents")
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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="Monday Assistant", tools=[monday_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.monday import MondayConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = MondayConnector(
    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("Monday Agent")

@mcp.tool
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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.monday import MondayConnector
from airbyte_agent_sdk.connectors.monday.models import MondayApiTokenAuthenticationAuthConfig

connector = MondayConnector(
    auth_config=MondayApiTokenAuthenticationAuthConfig(
        api_key="<Your Monday.com personal API token>"
    )
)

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

@agent.tool_plain
@MondayConnector.tool_utils
async def monday_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.monday import MondayConnector
from airbyte_agent_sdk.connectors.monday.models import MondayApiTokenAuthenticationAuthConfig

connector = MondayConnector(
    auth_config=MondayApiTokenAuthenticationAuthConfig(
        api_key="<Your Monday.com personal API token>"
    )
)

@tool
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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.monday import MondayConnector
from airbyte_agent_sdk.connectors.monday.models import MondayApiTokenAuthenticationAuthConfig

connector = MondayConnector(
    auth_config=MondayApiTokenAuthenticationAuthConfig(
        api_key="<Your Monday.com personal API token>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@MondayConnector.tool_utils(framework="openai_agents")
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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="Monday Assistant", tools=[monday_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.monday import MondayConnector
from airbyte_agent_sdk.connectors.monday.models import MondayApiTokenAuthenticationAuthConfig

connector = MondayConnector(
    auth_config=MondayApiTokenAuthenticationAuthConfig(
        api_key="<Your Monday.com personal API token>"
    )
)

mcp = FastMCP("Monday Agent")

@mcp.tool
@MondayConnector.tool_utils
async def monday_execute(entity: str, action: str, params: dict | None = None):
    """Execute Monday 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

Example prompts​

Unsupported prompts​

Entities and actions​

Monday API docs​

SDK installation​

SDK usage​

Hosted​

Open source​

Authentication​

Version information​