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Asana

The Asana agent connector is a Python package that equips AI agents to interact with Asana 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.

Asana is a work management platform that helps teams organize, track, and manage projects and tasks. This connector provides access to tasks, projects, workspaces, teams, and users for project tracking, workload analysis, and productivity insights.

Example prompts​

The Asana connector is optimized to handle prompts like these.

  • What tasks are assigned to me this week?
  • List all projects in my workspace
  • Show me the tasks for a recent project
  • Who are the team members in one of my teams?
  • Show me details of my current workspace and its users
  • Create a new task called 'Review Q3 report' in my project
  • Mark the task 'Submit proposal' as completed
  • Update the due date of task X to next Friday
  • Create a new project called 'Product Launch' in my workspace
  • Add a comment on the task saying 'Looks good, approved!'
  • Assign the task to me and set the due date to tomorrow
  • Delete the project 'Old Campaign'
  • Schedule a new team meeting as a task for next Tuesday
  • Add a new team member to my workspace by email
  • Delete the task 'Outdated draft'
  • Summarize my team's workload and task completion rates
  • Find all tasks related to {client_name} across my workspaces
  • Analyze the most active projects in my workspace last month
  • Compare task completion rates between my different teams
  • Identify overdue tasks across all my projects
  • Create a new section called 'In Review' in my project
  • Move a task to the 'Done' section
  • List all tasks in the 'To do' section
  • Rename the 'Backlog' section to 'Icebox'
  • Delete the empty 'Old Section' from the project
  • Create a tag called 'Urgent' in my workspace
  • Tag this task with 'Bug'
  • Remove the 'Low Priority' tag from this task
  • List all tasks tagged 'Release v2'
  • Rename the tag 'WIP' to 'In Progress'
  • Delete the tag 'Deprecated'

Unsupported prompts​

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

  • Move this task to another project

Entities and actions​

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

EntityActions
TasksList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
Project TasksList
Workspace Task SearchList
ProjectsList, Create, Get, Update, Delete, Context Store Search, Context Store SQL Query, Semantic Search
Task ProjectsList
Team ProjectsList
Workspace ProjectsList
WorkspacesList, Get, Context Store Search, Context Store SQL Query
UsersList, Get, Context Store Search, Context Store SQL Query
Workspace UsersList
Team UsersList
TeamsGet, Context Store Search, Context Store SQL Query, Semantic Search
Workspace TeamsList
User TeamsList
AttachmentsList, Get, Download, Context Store Search, Context Store SQL Query
Workspace TagsList, Create
TagsGet, Update, Delete, Context Store Search, Context Store SQL Query
Tag TasksList
Project SectionsList, Create
SectionsGet, Update, Delete, Context Store Search, Context Store SQL Query
Section TasksList, Create
Task SubtasksList
Task DependenciesList
Task DependentsList
Task StoriesCreate
Task TagsCreate, Delete
Workspace MembershipsCreate

Asana API docs​

See the official Asana API reference.

Interfaces​

Use the Asana 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": "asana"
}'

Describe the connector to see its supported entities and actions:

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

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "asana",
  "entity": "tasks",
  "action": "list"
}'

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 AsanaConnector 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.asana import AsanaConnector

connector = connect("asana", 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 AsanaConnector.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.asana import AsanaConnector

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

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

@agent.tool_plain
@AsanaConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="asana_inspect",
    docs_tool="asana_read_docs",
)
async def asana_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@AsanaConnector.agent_tool(framework="pydantic_ai")
async def asana_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.asana import AsanaConnector

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

@AsanaConnector.agent_tool(
    inspect_tool="asana_inspect",
    docs_tool="asana_read_docs",
)
async def asana_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@AsanaConnector.agent_tool()
async def asana_inspect():
    return await connector.inspect_connector()

@AsanaConnector.agent_tool()
async def asana_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 (asana_inspect, asana_read_docs, asana_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 AsanaConnector.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 AsanaConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.asana import AsanaConnector

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

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

@agent.tool_plain
@AsanaConnector.tool_utils
async def asana_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.asana import AsanaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = AsanaConnector(
    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.asana import AsanaConnector
from airbyte_agent_sdk.connectors.asana.models import AsanaPersonalAccessTokenAuthConfig

connector = AsanaConnector(
    auth_config=AsanaPersonalAccessTokenAuthConfig(
        token="<Your Asana Personal Access Token. Generate one at https://app.asana.com/0/my-apps>"
    )
)

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 AsanaConnector.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.asana import AsanaConnector
from airbyte_agent_sdk.connectors.asana.models import AsanaPersonalAccessTokenAuthConfig

connector = AsanaConnector(
    auth_config=AsanaPersonalAccessTokenAuthConfig(
        token="<Your Asana Personal Access Token. Generate one at https://app.asana.com/0/my-apps>"
    )
)

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

@agent.tool_plain
@AsanaConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="asana_inspect",
    docs_tool="asana_read_docs",
)
async def asana_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@AsanaConnector.agent_tool(framework="pydantic_ai")
async def asana_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.asana import AsanaConnector
from airbyte_agent_sdk.connectors.asana.models import AsanaPersonalAccessTokenAuthConfig

connector = AsanaConnector(
    auth_config=AsanaPersonalAccessTokenAuthConfig(
        token="<Your Asana Personal Access Token. Generate one at https://app.asana.com/0/my-apps>"
    )
)

@AsanaConnector.agent_tool(
    inspect_tool="asana_inspect",
    docs_tool="asana_read_docs",
)
async def asana_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@AsanaConnector.agent_tool()
async def asana_inspect():
    return await connector.inspect_connector()

@AsanaConnector.agent_tool()
async def asana_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 (asana_inspect, asana_read_docs, asana_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 AsanaConnector.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 AsanaConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.asana import AsanaConnector
from airbyte_agent_sdk.connectors.asana.models import AsanaPersonalAccessTokenAuthConfig

connector = AsanaConnector(
    auth_config=AsanaPersonalAccessTokenAuthConfig(
        token="<Your Asana Personal Access Token. Generate one at https://app.asana.com/0/my-apps>"
    )
)

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

@agent.tool_plain
@AsanaConnector.tool_utils
async def asana_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: 0.1.21

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