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Clickup-Api

The Clickup-Api agent connector is a Python package that equips AI agents to interact with Clickup-Api 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.

ClickUp is a productivity platform that provides project management, task tracking, docs, goals, and time tracking for teams. This connector provides access to workspaces, spaces, folders, lists, tasks (including workspace-wide search), comments, goals, views, time tracking, members, and docs.

Example prompts​

The Clickup-Api connector is optimized to handle prompts like these.

  • List all workspaces I have access to
  • Show me the spaces in my workspace
  • List the folders in a space
  • Show me the lists in a folder
  • Get the tasks in a list
  • Get details for a specific task
  • Search for tasks containing 'bug' across my workspace
  • Find all urgent priority tasks in my workspace
  • Show me tasks assigned to a specific user
  • List comments on a task
  • Get threaded replies on a comment
  • Create a comment on a task
  • Update a comment to mark it resolved
  • List all goals in my workspace
  • Get details for a specific goal
  • Show me all workspace-level views
  • Get tasks matching a saved view
  • List time entries for my workspace this week
  • Get details for a specific time entry
  • Show me the members assigned to a task
  • List all docs in my workspace
  • Get details for a specific doc
  • What tasks are overdue in my workspace?
  • Which tasks were updated in the last 24 hours?
  • Show me all high-priority tasks across all projects
  • How much time has been tracked this week?
  • What are the most commented tasks?

Unsupported prompts​

The Clickup-Api connector isn't currently able to handle prompts like these.

  • Delete a task
  • Delete a comment
  • Delete a goal

Entities and actions​

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

EntityActions
UserGet, Context Store Search, Context Store SQL Query
TeamsList, Context Store Search, Context Store SQL Query
SpacesList, Get, Context Store Search, Context Store SQL Query
FoldersList, Get, Context Store Search, Context Store SQL Query
ListsList, Get, Context Store Search, Context Store SQL Query
TasksList, Get, Search, Context Store Search, Context Store SQL Query
CommentsList, Create, Get, Update, Context Store Search, Context Store SQL Query
GoalsList, Get, Context Store Search, Context Store SQL Query
ViewsList, Get
View TasksList
Time TrackingList, Get, Context Store Search, Context Store SQL Query
MembersList
DocsList, Get

Clickup-Api API docs​

See the official Clickup-Api API reference.

Interfaces​

Use the Clickup-Api 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": "clickup-api"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "clickup-api",
  "entity": "user",
  "action": "get"
}'

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 ClickupApiConnector 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.clickup_api import ClickupApiConnector

connector = connect("clickup-api", 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 ClickupApiConnector.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.clickup_api import ClickupApiConnector

connector = connect("clickup-api", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@ClickupApiConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="clickup_api_inspect",
    docs_tool="clickup_api_read_docs",
)
async def clickup_api_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@ClickupApiConnector.agent_tool(framework="pydantic_ai")
async def clickup_api_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.clickup_api import ClickupApiConnector

connector = connect("clickup-api", workspace_name="<your_workspace_name>")

@ClickupApiConnector.agent_tool(
    inspect_tool="clickup_api_inspect",
    docs_tool="clickup_api_read_docs",
)
async def clickup_api_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ClickupApiConnector.agent_tool()
async def clickup_api_inspect():
    return await connector.inspect_connector()

@ClickupApiConnector.agent_tool()
async def clickup_api_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 (clickup_api_inspect, clickup_api_read_docs, clickup_api_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 ClickupApiConnector.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 ClickupApiConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.clickup_api import ClickupApiConnector

connector = connect("clickup-api", workspace_name="<your_workspace_name>")

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

@agent.tool_plain
@ClickupApiConnector.tool_utils
async def clickup_api_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.clickup_api import ClickupApiConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ClickupApiConnector(
    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.clickup_api import ClickupApiConnector
from airbyte_agent_sdk.connectors.clickup_api.models import ClickupApiAuthConfig

connector = ClickupApiConnector(
    auth_config=ClickupApiAuthConfig(
        api_key="<Your ClickUp personal API token>"
    )
)

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 ClickupApiConnector.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.clickup_api import ClickupApiConnector
from airbyte_agent_sdk.connectors.clickup_api.models import ClickupApiAuthConfig

connector = ClickupApiConnector(
    auth_config=ClickupApiAuthConfig(
        api_key="<Your ClickUp personal API token>"
    )
)

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

@agent.tool_plain
@ClickupApiConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="clickup_api_inspect",
    docs_tool="clickup_api_read_docs",
)
async def clickup_api_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@ClickupApiConnector.agent_tool(framework="pydantic_ai")
async def clickup_api_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.clickup_api import ClickupApiConnector
from airbyte_agent_sdk.connectors.clickup_api.models import ClickupApiAuthConfig

connector = ClickupApiConnector(
    auth_config=ClickupApiAuthConfig(
        api_key="<Your ClickUp personal API token>"
    )
)

@ClickupApiConnector.agent_tool(
    inspect_tool="clickup_api_inspect",
    docs_tool="clickup_api_read_docs",
)
async def clickup_api_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ClickupApiConnector.agent_tool()
async def clickup_api_inspect():
    return await connector.inspect_connector()

@ClickupApiConnector.agent_tool()
async def clickup_api_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 (clickup_api_inspect, clickup_api_read_docs, clickup_api_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 ClickupApiConnector.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 ClickupApiConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.clickup_api import ClickupApiConnector
from airbyte_agent_sdk.connectors.clickup_api.models import ClickupApiAuthConfig

connector = ClickupApiConnector(
    auth_config=ClickupApiAuthConfig(
        api_key="<Your ClickUp personal API token>"
    )
)

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

@agent.tool_plain
@ClickupApiConnector.tool_utils
async def clickup_api_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: 0.1.5

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