Arcade Quickstart
Build AI agents that actually take action.
Most AI apps are stuck in read-only mode. Arcade gives your AI the power to act — send Gmail, update Notion, message in Slack, and more.
In this quickstart, you’ll:
- Install the Arcade client
- Authenticate your first real-world tool
- Run your first tool call using the arcade client
This it’s the foundation to build full agents. Real tools. Real actions. Real fast. Ready to start? Follow the Quickstart below.
Prerequisites
- Create an Arcade account
- Get an Arcade API key and take note, you’ll need it in the next steps.
Install the Arcade client
pip install arcadepy
Instantiate and use the client
Create a new script called example.py
:
from arcadepy import Arcade
# You can also set the `ARCADE_API_KEY` environment variable instead of passing it as a parameter.
client = Arcade(api_key="{arcade_api_key}")
# Arcade needs a unique identifier for your application user (this could be an email address, a UUID, etc).
# In this example, use the email you used to sign up for Arcade.dev:
user_id = "{arcade_user_id}"
# Let's use the `Math.Sqrt` tool from the Arcade Math toolkit to get the square root of a number.
response = client.tools.execute(
tool_name="Math.Sqrt",
input={"a": '625'},
user_id=user_id,
)
print(f"The square root of 625 is {response.output.value}")
# Now, let's use a tool that requires authentication to star a GitHub repository
auth_response = client.tools.authorize(
tool_name="GitHub.SetStarred",
user_id=user_id,
)
if auth_response.status != "completed":
print(f"Click this link to authorize: `{auth_response.url}`. The process will continue once you have authorized the app." )
# Wait for the user to authorize the app
client.auth.wait_for_completion(auth_response.id);
response = client.tools.execute(
tool_name="GitHub.SetStarred",
input={
"owner": "ArcadeAI",
"name": "arcade-ai",
"starred": True,
},
user_id=user_id,
)
print(response.output.value)
Run the code
python3 example.py
> The square root of 625 is 25
> Successfully starred the repository ArcadeAI/arcade-ai
Next Steps
In this simple example, we call the tool methods directly. In your real applications and agents, you’ll likely be letting the LLM decide which tools to call - lean more about using Arcade with Frameworks in the Frameworks section, or how to build your own tools.