Custom Auth Flow with CrewAI

In this guide, we will explore how to create a custom auth flow that will be performed before executing Arcade tools within your CrewAI team.

The ArcadeToolManager’s built-in authorization and execution flows work well for many typical use cases. However, some scenarios call for a tailored approach. By implementing a custom auth flow, you gain flexibility in handling tool authorization. If your use case calls for a unique interface, additional approval steps, or specialized error handling, then this guide is for you.

Prerequisites

Obtain an Arcade API key

Set up your environment

Install the required package, and ensure your environment variables are set with your Arcade and OpenAI :

Terminal


pip install crewai-arcade

Configure API keys

Provide your Arcade and OpenAI . You can store them in environment variables like so:

Terminal


export ARCADE_API_KEY="your_arcade_api_key"
export OPENAI_API_KEY="your_openai_api_key"

Define your custom auth flow

The custom auth flow defined in the following code snippet is a function that will be called whenever CrewAI needs to call a .

Python


from typing import Any
 
from crewai_arcade import ArcadeToolManager
 
USER_ID = "{arcade_user_id}"
 
def custom_auth_flow(
    manager: ArcadeToolManager, tool_name: str, **tool_input: dict[str, Any]
) -> Any:
    """Custom auth flow for the ArcadeToolManager
 
    This function is called when CrewAI needs to call a tool that requires authorization.
    Authorization is handled before executing the tool.
    This function overrides the ArcadeToolManager's default auth flow performed by ArcadeToolManager.authorize_tool
    """
    # Get authorization status
    auth_response = manager.authorize(tool_name, USER_ID)
 
    # If the user is not authorized for the tool,
    # then we need to handle the authorization before executing the tool
    if not manager.is_authorized(auth_response.id):
        print(f"Authorization required for tool: '{tool_name}' with inputs:")
        for input_name, input_value in tool_input.items():
            print(f"  {input_name}: {input_value}")
        # Handle authorization
        print(f"\nTo authorize, visit: {auth_response.url}")
        # Block until the user has completed the authorization
        auth_response = manager.wait_for_auth(auth_response)
 
        # Ensure authorization completed successfully
        if not manager.is_authorized(auth_response.id):
            raise ValueError(f"Authorization failed for {tool_name}. URL: {auth_response.url}")
    else:
        print(f"Authorization already granted for tool: '{tool_name}' with inputs:")
        for input_name, input_value in tool_input.items():
            print(f"  {input_name}: {input_value}")
 
 
def tool_manager_callback(tool_manager: ArcadeToolManager, tool_name: str, **tool_input: dict[str, Any]) -> Any:
    """Tool executor callback with custom auth flow for the ArcadeToolManager
 
    ArcadeToolManager's default executor handles authorization and tool execution.
    This function overrides the default executor to handle authorization in a custom way and then executes the tool.
    """
    custom_auth_flow(tool_manager, tool_name, **tool_input)
    return tool_manager.execute_tool(USER_ID, tool_name, **tool_input)

Get Arcade tools

You can now provide the manager callback to the ArcadeToolManager upon initialization:

Python


# Provide the tool manager callback to the ArcadeToolManager
manager = ArcadeToolManager(executor=tool_manager_callback)
 
# Retrieve the provided tools and/or MCP Servers as CrewAI StructuredTools.
tools = manager.get_tools(tools=["Gmail.ListEmails"], toolkits=["Slack"])

Use tools in your CrewAI agent team

Create a Crew that uses your tools with the custom auth flow. When the is called, your tool manager callback will be called to handle the authorization and then the tool will be executed.

Python


from crewai import Agent, Crew, Task
from crewai.llm import LLM
 
crew_agent = Agent(
    role="Main Agent",
    backstory="You are a helpful assistant",
    goal="Help the user with their requests",
    tools=tools,
    allow_delegation=False,
    verbose=True,
    llm=LLM(model="gpt-4o"),
)
 
task = Task(
    description="Get the 5 most recent emails from the user's inbox and summarize them and recommend a response for each.",
    expected_output="A bulleted list with a one sentence summary of each email and a recommended response to the email.",
    agent=crew_agent,
    tools=crew_agent.tools,
)
 
crew = Crew(
    agents=[crew_agent],
    tasks=[task],
    verbose=True,
    memory=True,
)
 
result = crew.kickoff()
 
print("\n\n\n ------------ Result ------------ \n\n\n")
print(result)

Next steps

Now you’re ready to integrate Arcade tools with a custom auth flow into your own CrewAI team.