Home
LangChain
Use Arcade AI Tools with LangChain

Use LangChain with Arcade

In this guide, we'll walk through how to use Arcade AI tools with LangChain to build powerful AI applications.

Prerequisites

  • Set up Arcade AI

  • Install the required packages:

    pip install arcade-ai langchain-openai langchain-arcade

Import the necessary packages

Begin by importing the required libraries:

import os
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_arcade import ArcadeToolManager
from langchain_openai import ChatOpenAI

Set up API keys

Ensure your environment variables ARCADE_API_KEY and OPENAI_API_KEY are set with your actual API keys.

arcade_api_key = os.environ["ARCADE_API_KEY"]
openai_api_key = os.environ["OPENAI_API_KEY"]

Initialize the Arcade Tool Manager

The ArcadeToolManager helps you fetch and manage tools from Arcade AI. Initialize it with your Arcade AI API key:

manager = ArcadeToolManager(api_key=arcade_api_key)

Retrieve specific tools

Fetch specific tools by specifying their names. Tool names follow the format ToolkitName.ToolName:

tools = manager.get_tools(tools=["Web.ScrapeUrl"])
print(manager.tools)

You may notice that "" and "." are used interchangeably in the tool names. This is due to the fact that some language models do not allow "." in tool names. Given this, Arcade allows you to use either "" or "." in tool names interchangeably.

Output:

['Web_ScrapeUrl']

This retrieves the ScrapeUrl tool from the Web toolkit.

Retrieve a Toolkit

You can initialize all tools from a specific toolkit:

# Clear existing tools and initialize new ones from the "Search" toolkit
manager.init_tools(toolkits=["Search"])
print(manager.tools)

Output:

['Search_SearchGoogle']

This replaces the current tools with those from the Search toolkit.

Add more tools from other toolkits

Use get_tools to add additional tools without clearing existing ones:

# Add tools from the "Math" toolkit
tools = manager.get_tools(toolkits=["Math"])
print(manager.tools)

Output:

['Search_SearchGoogle', 'Math_Add', 'Math_Divide', 'Math_Multiply', 'Math_Sqrt', 'Math_Subtract', 'Math_SumList', 'Math_SumRange']

Now you have several mathematical tools alongside your search tool.

Create the LLM and Agent

Define your agent and wrap it with an executor for processing:

prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(api_key=openai_api_key)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

Execute the agent with example queries

Try out your agent with sample inputs:

agent_executor.invoke({"input": "Lookup Seymour Cray on Google"})

Output:

> Entering new AgentExecutor chain...

Invoking: `Search_SearchGoogle` with `{'query': 'Seymour Cray'}`

[Search results about Seymour Cray]

> Finished chain.
agent_executor.invoke({"input": "What is 1234567890 * 9876543210?"})

Output:

> Entering new AgentExecutor chain...

Invoking: `Math_Multiply` with `{'a': 1234567890, 'b': 9876543210}`

12193263111263526900

> Finished chain.

Tips for Selecting Tools

  • Relevance: Include only the tools necessary for your agent's tasks to optimize performance.
  • Avoid Conflicts: Be cautious of tools with overlapping functionalities that might cause ambiguity.

Next Steps

Now that you've set up an agent with Arcade AI tools, you can:

  • Explore more complex queries and tasks.
  • Integrate additional toolkits to expand capabilities.
  • Customize the agent's prompt for specific behaviors.
  • Experiment with different language models for varied performance.
Get a token for a userAuthorize LangChain Tools