Orb

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

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

Orb is a usage-based billing platform that enables businesses to implement flexible pricing models, track customer usage, and manage subscriptions. This connector provides access to customers, subscriptions, plans, and invoices for billing analytics and customer management.

Example prompts

The Orb connector is optimized to handle prompts like these.

• Show me all my customers in Orb
• List all active subscriptions
• What plans are available?
• Show me recent invoices
• Show me details for a recent customer
• What is the status of a recent subscription?
• Show me the pricing details for a plan
• Confirm the Stripe ID linked to a customer
• What is the payment provider ID for a customer?
• List all invoices for a specific customer
• List all subscriptions for customer XYZ
• Show all active subscriptions for a specific customer
• What subscriptions does customer {external_customer_id} have?
• Pull all invoices from the last month
• Show invoices created after {date}
• List all paid invoices for customer {customer_id}
• What invoices are in draft status?
• Show all issued invoices for subscription {subscription_id}

Unsupported prompts

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

• Create a new customer in Orb
• Update subscription details
• Delete a customer record
• Send an invoice to a customer
• Filter subscriptions by plan name (must filter client-side after listing)
• Pull customers billed for specific products (must examine invoice line_items client-side)

Entities and actions

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

Entity	Actions
Customers	List, Get, Context Store Search
Subscriptions	List, Get, Context Store Search
Plans	List, Get, Context Store Search
Invoices	List, Get, Context Store Search

Orb API docs

See the official Orb 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 OrbConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.orb import OrbConnector

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

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

@agent.tool_plain
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.orb import OrbConnector

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

@tool
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.orb import OrbConnector

connector = connect("orb", 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)
@OrbConnector.tool_utils(framework="openai_agents")
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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="Orb Assistant", tools=[orb_execute])

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.orb import OrbConnector

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

mcp = FastMCP("Orb Agent")

@mcp.tool
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = OrbConnector(
    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
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = OrbConnector(
    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
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

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

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = OrbConnector(
    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("Orb Agent")

@mcp.tool
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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

Pydantic AI

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.connectors.orb.models import OrbAuthConfig

connector = OrbConnector(
    auth_config=OrbAuthConfig(
        api_key="<Your Orb API key>"
    )
)

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

@agent.tool_plain
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain

LangChain

from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.connectors.orb.models import OrbAuthConfig

connector = OrbConnector(
    auth_config=OrbAuthConfig(
        api_key="<Your Orb API key>"
    )
)

@tool
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.connectors.orb.models import OrbAuthConfig

connector = OrbConnector(
    auth_config=OrbAuthConfig(
        api_key="<Your Orb API key>"
    )
)

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

FastMCP

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.orb import OrbConnector
from airbyte_agent_sdk.connectors.orb.models import OrbAuthConfig

connector = OrbConnector(
    auth_config=OrbAuthConfig(
        api_key="<Your Orb API key>"
    )
)

mcp = FastMCP("Orb Agent")

@mcp.tool
@OrbConnector.tool_utils
async def orb_execute(entity: str, action: str, params: dict | None = None):
    """Execute Orb 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: 0.1.9