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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.

EntityActions
CustomersList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
OrdersList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
ProductsList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
Product VariantsList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
Product ImagesList, Get, Context Store Search, Context Store SQL Query
Abandoned CheckoutsList, Context Store Search, Context Store SQL Query
LocationsList, Get, Context Store Search, Context Store SQL Query
Inventory LevelsList, Context Store Search, Context Store SQL Query
Inventory ItemsList, Get, Context Store Search, Context Store SQL Query
ShopGet, Context Store Search, Context Store SQL Query
Price RulesList, Get, Context Store Search, Context Store SQL Query
Discount CodesList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
Custom CollectionsList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
Smart CollectionsList, Get, Context Store Search, Context Store SQL Query
CollectsList, Get, Context Store Search, Context Store SQL Query
Draft OrdersList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
FulfillmentsList, Get, Context Store Search, Context Store SQL Query
Order RefundsList, Get, Context Store Search, Context Store SQL Query
TransactionsList, Get
Tender TransactionsList, Context Store Search, Context Store SQL Query
CountriesList, Get, Context Store Search, Context Store SQL Query
Metafield ShopsList, Get, Context Store Search, Context Store SQL Query
Metafield CustomersList, Context Store Search, Context Store SQL Query
Metafield ProductsList, Context Store Search, Context Store SQL Query
Metafield OrdersList, Context Store Search, Context Store SQL Query
Metafield Draft OrdersList, Context Store Search, Context Store SQL Query
Metafield LocationsList, Context Store Search, Context Store SQL Query
Metafield Product VariantsList, Context Store Search, Context Store SQL Query
Metafield Smart CollectionsList, Context Store Search, Context Store SQL Query
Metafield Product ImagesList, Context Store Search, Context Store SQL Query
Customer AddressList, Get
Fulfillment OrdersList, Get, Context Store Search, Context Store SQL Query
PagesList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
BlogsList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
ArticlesList, Get, Create, Update, Delete, Context Store Search, Context Store SQL Query
Balance TransactionsList, Context Store Search, Context Store SQL Query
DisputesList, Get, Context Store Search, Context Store SQL Query
Metafield PagesList, Context Store Search, Context Store SQL Query
Metafield BlogsList, Context Store Search, Context Store SQL Query
Metafield ArticlesList, Context Store Search, Context Store SQL Query
Draft Order CompleteUpdate
Inventory SetCreate
Inventory AdjustCreate
MetafieldsCreate, Delete

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)

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.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())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use ShopifyConnector.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.shopify import ShopifyConnector

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

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

@agent.tool_plain
@ShopifyConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="shopify_inspect",
    docs_tool="shopify_read_docs",
)
async def shopify_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@ShopifyConnector.agent_tool(framework="pydantic_ai")
async def shopify_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.shopify import ShopifyConnector

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

@ShopifyConnector.agent_tool(
    inspect_tool="shopify_inspect",
    docs_tool="shopify_read_docs",
)
async def shopify_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ShopifyConnector.agent_tool()
async def shopify_inspect():
    return await connector.inspect_connector()

@ShopifyConnector.agent_tool()
async def shopify_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 (shopify_inspect, shopify_read_docs, shopify_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 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. For new code, use build_connector_tools or ShopifyConnector.agent_tool above.

Pydantic AI
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 {})

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.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())
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.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>"
    ),
    shop="<Your Shopify store name (e.g., 'my-store' from my-store.myshopify.com)>"
)

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 ShopifyConnector.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.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>"
    ),
    shop="<Your Shopify store name (e.g., 'my-store' from my-store.myshopify.com)>"
)

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

@agent.tool_plain
@ShopifyConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="shopify_inspect",
    docs_tool="shopify_read_docs",
)
async def shopify_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

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

@agent.tool_plain
@ShopifyConnector.agent_tool(framework="pydantic_ai")
async def shopify_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.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>"
    ),
    shop="<Your Shopify store name (e.g., 'my-store' from my-store.myshopify.com)>"
)

@ShopifyConnector.agent_tool(
    inspect_tool="shopify_inspect",
    docs_tool="shopify_read_docs",
)
async def shopify_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@ShopifyConnector.agent_tool()
async def shopify_inspect():
    return await connector.inspect_connector()

@ShopifyConnector.agent_tool()
async def shopify_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 (shopify_inspect, shopify_read_docs, shopify_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 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. For new code, use build_connector_tools or ShopifyConnector.agent_tool above.

Pydantic AI
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>"
    ),
    shop="<Your Shopify store name (e.g., 'my-store' from my-store.myshopify.com)>"
)

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 {})

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: 0.1.14

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