Shopify
The Shopify agent connector is a Python package that equips AI agents to interact with Shopify 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.
Shopify is an e-commerce platform that enables businesses to create online stores, manage products, process orders, and handle customer relationships. This connector provides access to Shopify Admin REST API for reading store data including customers, orders, products, inventory, and more.
Example prompts
The Shopify connector is optimized to handle prompts like these.
- List all customers in my Shopify store
- Show me details for a recent customer
- What products do I have in my store?
- List all locations for my store
- Show me inventory levels for a recent location
- Show me all draft orders
- List all custom collections in my store
- Show me details for a recent order
- Show me product variants for a recent product
- Create a new customer in Shopify with email test@example.com
- Update a customer's first name
- Create a new product called 'Summer T-Shirt'
- Update a product title
- Create a draft order for a customer
- Complete a draft order
- Set inventory quantity for a product variant at a location
- Create a basic discount code for 10% off
- Set a metafield on a product
- Create a new page on my store
- Create a new blog post
- Show me orders from the last 30 days
- Show me abandoned checkouts from this week
- What price rules are currently active?
- Show me all pages on my store
- List all blog articles
- Are there any open disputes?
Unsupported prompts
The Shopify connector isn't currently able to handle prompts like these.
- Process a refund
- Send shipping notification to customer
- Modify order line items after creation
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
Shopify API docs
See the official Shopify API reference.
Interfaces
Use the Shopify 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": "shopify"
}'
Describe the connector to see its supported entities and actions:
airbyte-agent connectors describe --json '{
"workspace": "<your_workspace_name>",
"name": "shopify"
}'
Execute an action:
airbyte-agent connectors execute --json '{
"workspace": "<your_workspace_name>",
"name": "shopify",
"entity": "customers",
"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 ShopifyConnector 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)
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
StructuredTool.from_function(
coroutine=tool,
name=tool.__name__,
description=tool.__doc__,
)
for tool in tools.as_list()
]
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]
agent = Agent(name="Shopify Assistant", tools=openai_tools)
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
mcp = FastMCP("Shopify Agent")
for tool in build_connector_tools(connector, framework="mcp").as_list():
mcp.tool(tool)
Legacy alternatives
These examples are kept for existing integrations. For new agents, use build_connector_tools above. The legacy ShopifyConnector.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.
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
@tool
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", 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)
@ShopifyConnector.tool_utils(framework="openai_agents")
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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="Shopify Assistant", tools=[shopify_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
connector = connect("shopify", workspace_name="<your_workspace_name>")
mcp = FastMCP("Shopify Agent")
@mcp.tool
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ShopifyConnector(
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())
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ShopifyConnector(
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="langchain")
langchain_tools = [
StructuredTool.from_function(
coroutine=tool,
name=tool.__name__,
description=tool.__doc__,
)
for tool in tools.as_list()
]
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ShopifyConnector(
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="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]
agent = Agent(name="Shopify Assistant", tools=openai_tools)
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = ShopifyConnector(
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("Shopify Agent")
for tool in build_connector_tools(connector, framework="mcp").as_list():
mcp.tool(tool)
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)
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
StructuredTool.from_function(
coroutine=tool,
name=tool.__name__,
description=tool.__doc__,
)
for tool in tools.as_list()
]
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]
agent = Agent(name="Shopify Assistant", tools=openai_tools)
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
mcp = FastMCP("Shopify Agent")
for tool in build_connector_tools(connector, framework="mcp").as_list():
mcp.tool(tool)
Legacy alternatives
These examples are kept for existing integrations. For new agents, use build_connector_tools above. The legacy ShopifyConnector.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.
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
@tool
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ShopifyConnector.tool_utils(framework="openai_agents")
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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="Shopify Assistant", tools=[shopify_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.shopify import ShopifyConnector
from airbyte_agent_sdk.connectors.shopify.models import ShopifyAccessTokenAuthenticationAuthConfig
connector = ShopifyConnector(
auth_config=ShopifyAccessTokenAuthenticationAuthConfig(
api_key="<Your Shopify Admin API access token>"
)
)
mcp = FastMCP("Shopify Agent")
@mcp.tool
@ShopifyConnector.tool_utils
async def shopify_execute(entity: str, action: str, params: dict | None = None):
"""Execute Shopify 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.
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: 0.1.13