StripeAgentToolkit
This notebook provides a quick overview for getting started with Stripe's agent toolkit.
You can read more about StripeAgentToolkit
in Stripe's launch blog or on the project's PyPi page.
Overviewโ
Integration detailsโ
Class | Package | Serializable | JS Support | Package latest |
---|---|---|---|---|
StripeAgentToolkit | stripe-agent-toolkit | โ | โ |
Setupโ
This externally-managed package is hosted out of the stripe-agent-toolkit
project, which is managed by Stripe's team.
You can install it, along with langgraph for the following examples, with pip
:
%pip install --quiet -U langgraph stripe-agent-toolkit
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m24.2[0m[39;49m -> [0m[32;49m24.3.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
Credentialsโ
In addition to installing the package, you will need to configure the integration with your Stripe account's secret key, which is available in your Stripe Dashboard.
import getpass
import os
if not os.environ.get("STRIPE_SECRET_KEY"):
os.environ["STRIPE_SECRET_KEY"] = getpass.getpass("STRIPE API key:\n")
It's also helpful (but not needed) to set up LangSmith for best-in-class observability:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
Instantiationโ
Here we show how to create an instance of the Stripe Toolkit
from stripe_agent_toolkit.crewai.toolkit import StripeAgentToolkit
stripe_agent_toolkit = StripeAgentToolkit(
secret_key=os.getenv("STRIPE_SECRET_KEY"),
configuration={
"actions": {
"payment_links": {
"create": True,
},
}
},
)
Agentโ
Here's how to use the toolkit to create a basic agent in langgraph:
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
llm = ChatAnthropic(
model="claude-3-5-sonnet-20240620",
)
langgraph_agent_executor = create_react_agent(llm, stripe_agent_toolkit.get_tools())
input_state = {
"messages": """
Create a payment link for a new product called 'test' with a price
of $100. Come up with a funny description about buy bots,
maybe a haiku.
""",
}
output_state = langgraph_agent_executor.invoke(input_state)
print(output_state["messages"][-1].content)
Relatedโ
- Tool conceptual guide
- Tool how-to guides