Use Arcade tools with LangGraph

Use LangGraph with Arcade

In this guide, let’s explore how to integrate Arcade into your LangGraph application. Follow the step-by-step instructions below. For complete working examples, see our Python and JavaScript examples.

Prerequisites

Obtain an Arcade API key

Set up your environment

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

Python

Terminal


pip install langchain-arcade langchain-openai langgraph

Configure API keys

Provide your Arcade and OpenAI . You can store them in environment variables or directly in your code:

Need an Arcade ? Visit the Get an API key page to create one.

Python


import os
 
arcade_api_key = os.environ.get("ARCADE_API_KEY", "YOUR_ARCADE_API_KEY")
openai_api_key = os.environ.get("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")

Create and manage Arcade tools

Python

Use the ArcadeToolManager to retrieve specific tools or entire Servers:

Python


from langchain_arcade import ArcadeToolManager
 
manager = ArcadeToolManager(api_key=arcade_api_key)
 
# Fetch the "ScrapeUrl" tool from the "Firecrawl" MCP Server
tools = manager.get_tools(tools=["Firecrawl.ScrapeUrl"])
print(manager.tools)
 
# Get all tools from the "Gmail" MCP Server
tools = manager.get_tools(toolkits=["Gmail"])
print(manager.tools)

Set up the language model and memory

Create an AI model and bind your . Use MemorySaver for checkpointing:

Python


from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
 
model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)
bound_model = model.bind_tools(tools)
 
memory = MemorySaver()

Create a ReAct-style agent

Use the prebuilt ReAct from LangGraph to handle your Arcade :

Python


from langgraph.prebuilt import create_react_agent
 
graph = create_react_agent(model=bound_model, tools=tools, checkpointer=memory)

Provide configuration and user query

Supply a basic config dictionary and a user query. Notice that user_id is required for authorization:

Python


config = {
    "configurable": {
        "thread_id": "1",
        "user_id": "{arcade_user_id}"
    }
}
user_input = {
    "messages": [
        ("user", "List any new and important emails in my inbox.")
    ]
}

Stream the response

Stream the assistant’s output. If the tool requires authorization, the will ask the user to authorize the .

Python


from langgraph.errors import NodeInterrupt
 
try:
    for chunk in graph.stream(user_input, config, stream_mode="values"):
        chunk["messages"][-1].pretty_print()
except NodeInterrupt as exc:
    print(f"\nNodeInterrupt occurred: {exc}")
    print("Please authorize the tool or update the request, then re-run.")

Tips for selecting tools

Relevance: Pick only the you need. Avoid using all tools at once.
Avoid conflicts: Be mindful of duplicate or overlapping functionality.

Next steps

Now that you have integrated Arcade tools into your LangGraph , you can:

Experiment with different Servers, such as “Math” or “Search.”
Customize the ’s prompts for specific tasks.
Try out other language models and compare their performance.

Enjoy exploring Arcade and building powerful AI-enabled Python applications!