title	Connect Arcade to LLM (Python)
description	Learn how to connect Arcade to your LLM in Python

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Connect Arcade to your LLM with Python

Arcade tools work alongside an LLM. To make that work, you need a small piece of glue code called a "harness." The harness orchestrates the back-and-forth between the user, the model, and the tools. In this guide, you'll build one so you can wire Arcade into your LLM-powered app.

Integrate Arcade's tool-calling capabilities into an application that uses an LLM in Python.

</GuideOverview.Outcomes>

<GuideOverview.Prerequisites>

</GuideOverview.Prerequisites>

<GuideOverview.YouWillLearn>

Setup an agentic loop
Add Arcade tools to your agentic loop
Implement a multi-turn conversation loop

</GuideOverview.YouWillLearn>

Create a new project and install the dependencies

In your terminal, run the following command to create a new uv project

mkdir arcade-llm-example
cd arcade-llm-example
uv init

Create a new virtual environment and activate it:

uv venv
source .venv/bin/activate

Install the dependencies:

uv add arcadepy openai python-dotenv

Your directory should now look like this:

arcade-llm-example/
├── .git/
├── .gitignore
├── python-version
├── .venv/
├── main.py
├── pyproject.toml
├── main.py
├── README.md
└── uv.lock

Instantiate and use the clients

Create a new file called .env and add your Arcade API key, as well as your OpenAI API key:

ARCADE_API_KEY=YOUR_ARCADE_API_KEY
ARCADE_USER_ID=YOUR_ARCADE_USER_ID
OPENROUTER_API_KEY=YOUR_OPENROUTER_API_KEY
OPENROUTER_MODEL=YOUR_OPENROUTER_MODEL

The `ARCADE_USER_ID` is the email address you used to sign up for Arcade. When your app is ready for production, you can set this dynamically based on your app's auth system. Learn more about how to achieve secure auth in production [here](/guides/user-facing-agents/secure-auth-production). In this example, you're using OpenRouter to access the model, as it makes it straightforward to use any model from multiple providers with a single API.

OpenRouter is compliant with the OpenAI API specification, so you can use it with any OpenAI-compatible library.

If you don't know which model to use, try one of these:

anthropic/claude-haiku-4.5
deepseek/deepseek-v3.2
google/gemini-3-flash-preview
google/gemini-2.5-flash-lite
openai/gpt-4o-mini

Open the main.py file in your editor of choice, and replace the contents with the following:

from arcadepy import Arcade
from openai import OpenAI
from dotenv import load_dotenv
import json
import os

load_dotenv()

arcade_client = Arcade()
arcade_user_id = os.getenv("ARCADE_USER_ID")
llm_client = OpenAI(
    api_key=os.getenv("OPENROUTER_API_KEY"),
    base_url="https://openrouter.ai/api/v1"
)

Select and retrieve the tools from Arcade

In this example, you're implementing a multi-tool agent that can retrieve and send emails, as well as send messages to Slack. While a harness can expose a broad catalog of tools to the LLM, it's best to limit that set to what's relevant for the task to keep the model efficient.

# Define the tools for the agent to use
tool_catalog = [
    "Gmail.ListEmails",
    "Gmail.SendEmail",
    "Slack.SendMessage",
    "Slack.WhoAmI"
]

# Get the tool definitions from the Arcade API
tool_definitions = []
for tool in tool_catalog:
    tool_definitions.append(arcade_client.tools.formatted.get(name=tool, format="openai"))

Write a helper function that handles tool authorization and execution

The model can use any tool you give it, and some tools require permission before they work. When this happens, you can either involve the model in the permission step or handle it behind the scenes and continue as if the tool were already authorized. In this guide, authorization happens outside the model so it can act as if the tool is already available. It's like ordering a coffee: after you place your order, the barista handles payment behind the counter instead of explaining every step of card verification and receipts. The customer (and the model) gets the result without having to think about any of the intermediate steps.

# Helper function to authorize and run any tool
def authorize_and_run_tool(tool_name: str, input: str):
    # Start the authorization process
    auth_response = arcade_client.tools.authorize(
        tool_name=tool_name,
        user_id=arcade_user_id,
    )

    # If the authorization is not completed, print the authorization URL and wait for the user to authorize the app.
    # Tools that do not require authorization will have the status "completed" already.
    if auth_response.status != "completed":
        print(f"Click this link to authorize {tool_name}: {auth_response.url}. The process will continue once you have authorized the app.")
        arcade_client.auth.wait_for_completion(auth_response.id)

    # Parse the input
    input_json = json.loads(input)

    # Run the tool
    result = arcade_client.tools.execute(
        tool_name=tool_name,
        input=input_json,
        user_id=arcade_user_id,
    )

    # Return the tool output to the caller as a JSON string
    return json.dumps(result.output.value)

This helper function adapts to any tool in the catalog and will make sure that the authorization requirements are met before executing the tool. For more complex agentic patterns, this is generally the best place to handle interruptions that may require user interaction, such as when the tool requires a user to approve a request, or to provide additional context.

Write a helper function that handles the LLM's invocation

There are many orchestration patterns that can be used to handle the LLM invocation. A common pattern is a ReAct architecture, where the user prompt will result in a loop of messages between the LLM and the tools, until the LLM provides a final response (no tool calls). This is the pattern we will implement in this example.

To avoid the risk of infinite loops, limit the number of turns (in this case, a maximum of 5). This is a parameter that you can tune to your needs. Set it to a value that is high enough to allow the LLM to complete its task but low enough to prevent infinite loops.

def invoke_llm(
    history: list[dict],
    model: str = "google/gemini-2.5-flash",
    max_turns: int = 5,
    tools: list[dict] = None,
    tool_choice: str = "auto",
) -> list[dict]:
    """
    Multi-turn LLM invocation that processes the conversation until
    the assistant provides a final response (no tool calls).

    Returns the updated conversation history.
    """
    turns = 0

    while turns < max_turns:
        turns += 1

        response = llm_client.chat.completions.create(
            model=model,
            messages=history,
            tools=tools,
            tool_choice=tool_choice,
        )

        assistant_message = response.choices[0].message

        if assistant_message.tool_calls:
            for tool_call in assistant_message.tool_calls:
                tool_name = tool_call.function.name
                tool_args = tool_call.function.arguments
                print(f"🛠️ Harness: Calling {tool_name} with input {tool_args}")
                tool_result = authorize_and_run_tool(tool_name, tool_args)
                print(f"🛠️ Harness: Tool call {tool_name} completed")
                history.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": tool_result,
                })

            continue

        else:
            history.append({
                "role": "assistant",
                "content": assistant_message.content,
            })

        break

    return history

These two helper functions form the core of your agentic loop. Notice that authorization is handled outside the agentic context, and the tool execution is passed back to the LLM in every case. Depending on your needs, you may want to handle tool orchestration within the harness and pass only the final result of multiple tool calls to the LLM.

Write the main agentic loop

Now that you've written the helper functions, write an agentic loop that interacts with the user. The core pieces of this loop are:

Initialize the conversation history with the system prompt
Get the user input and add it to the conversation history
Invoke the LLM with the conversation history, tools, and tool choice
Repeat from step 2 until the user decides to stop the conversation

def chat():
    """Interactive multi-turn chat session."""
    print("Chat started. Type 'quit' or 'exit' to end the session.\n")

    # Initialize the conversation history with the system prompt
    history: list[dict] = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]

    while True:
        try:
            user_input = input("😎 You: ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        if user_input.lower() in ("quit", "exit"):
            print("Goodbye!")
            break

        # Add user message to history
        history.append({"role": "user", "content": user_input})

        # Get LLM response
        history = invoke_llm(
            history, tools=tool_definitions)

        # Print the latest assistant response
        assistant_response = history[-1]["content"]
        print(f"\n🤖 Assistant: {assistant_response}\n")


if __name__ == "__main__":
    chat()

Run the code

It's time to run the code and see it in action. Run the following command to start the chat:

uv run main.py

With the selection of tools above, you should be able to get the agent to effectively complete the following prompts:

"Please send a message to the #general channel on Slack greeting everyone with a haiku about agents."
"Please write a poem about multi-tool orchestration and send it to the #general channel on Slack, also send it to me in an email."
"Please summarize my latest 5 emails, then send me a DM on Slack with the summary."

Next Steps

Learn more about using Arcade with a framework or MCP client.
Learn more about how to build your own MCP Servers.

Example code

**main.py** (full file)

```python filename="main.py" from arcadepy import Arcade from dotenv import load_dotenv from openai import OpenAI import json import os

load_dotenv()

arcade_client = Arcade() arcade_user_id = os.getenv("ARCADE_USER_ID") llm_client = OpenAI( base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENROUTER_API_KEY"), )

Define the tools to use in the agent

tool_catalog = [ "Gmail.ListEmails", "Gmail.SendEmail", "Slack.SendMessage", "Slack.WhoAmI" ]

Get the tool definitions from the Arcade API to expose them to the LLM

tool_definitions = [] for tool in tool_catalog: tool_definitions.append(arcade_client.tools.formatted.get(name=tool, format="openai"))

Helper function to authorize and run any tool

def authorize_and_run_tool(tool_name: str, input: str): # Start the authorization process auth_response = arcade_client.tools.authorize( tool_name=tool_name, user_id=arcade_user_id, )

# If the authorization is not completed, print the authorization URL and wait for the user to authorize the app.
# Tools that do not require authorization will have the status "completed" already.
if auth_response.status != "completed":
    print(f"Click this link to authorize {tool_name}: {auth_response.url}. The process will continue once you have authorized the app.")
    arcade_client.auth.wait_for_completion(auth_response.id)

# Parse the input
input_json = json.loads(input)

# Run the tool
result = arcade_client.tools.execute(
    tool_name=tool_name,
    input=input_json,
    user_id=arcade_user_id,
)

# Return the tool output to the caller as a JSON string
return json.dumps(result.output.value)

def invoke_llm( history: list[dict], model: str = "google/gemini-2.5-flash", max_turns: int = 5, tools: list[dict] = None, tool_choice: str = "auto", ) -> list[dict]: """ Multi-turn LLM invocation that processes the conversation until the assistant provides a final response (no tool calls).

Returns the updated conversation history.
"""
turns = 0

while turns < max_turns:
    turns += 1

    response = llm_client.chat.completions.create(
        model=model,
        messages=history,
        tools=tools,
        tool_choice=tool_choice,
    )

    assistant_message = response.choices[0].message

    if assistant_message.tool_calls:
        for tool_call in assistant_message.tool_calls:
            tool_name = tool_call.function.name
            tool_args = tool_call.function.arguments
            print(f"🛠️ Harness: Calling {tool_name} with input {tool_args}")
            tool_result = authorize_and_run_tool(tool_name, tool_args)
            print(f"🛠️ Harness: Tool call {tool_name} completed")
            history.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": tool_result,
            })

        continue

    else:
        history.append({
            "role": "assistant",
            "content": assistant_message.content,
        })

    break

return history

def chat(): """Interactive multi-turn chat session.""" print("Chat started. Type 'quit' or 'exit' to end the session.\n")

history: list[dict] = [
    {"role": "system", "content": "You are a helpful assistant."}
]

while True:
    try:
        user_input = input("😎 You: ").strip()
    except (EOFError, KeyboardInterrupt):
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    if user_input.lower() in ("quit", "exit"):
        print("Goodbye!")
        break

    # Add user message to history
    history.append({"role": "user", "content": user_input})

    # Get LLM response
    history = invoke_llm(
        history, tools=tool_definitions)

    # Print the latest assistant response
    assistant_response = history[-1]["content"]
    print(f"\n🤖 Assistant: {assistant_response}\n")

if name == "main": chat()

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Connect Arcade to your LLM with Python

Create a new project and install the dependencies

Instantiate and use the clients

Select and retrieve the tools from Arcade

Write a helper function that handles tool authorization and execution

Write a helper function that handles the LLM's invocation

Write the main agentic loop

Run the code

Next Steps

Example code

Define the tools to use in the agent

Get the tool definitions from the Arcade API to expose them to the LLM

Helper function to authorize and run any tool

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Connect Arcade to your LLM with Python

Create a new project and install the dependencies

Instantiate and use the clients

Select and retrieve the tools from Arcade

Write a helper function that handles tool authorization and execution

Write a helper function that handles the LLM's invocation

Write the main agentic loop

Run the code

Next Steps

Example code

Define the tools to use in the agent

Get the tool definitions from the Arcade API to expose them to the LLM

Helper function to authorize and run any tool