Documentation • Tools • Quickstart • Contact Us # Arcade Tool SDK Arcade is a developer platform that lets you build, deploy, and manage tools for AI agents. The Tool SDK makes it easy to create powerful, secure tools that your agents can use to interact with the world.  To learn more, check out our [documentation](https://docs.arcade.dev/home). _Pst. hey, you, give us a star if you like it!_ ## Table of Contents - [Quickstart: Install and call a tool](#quickstart-install-and-call-a-tool) - [Build LLM Tools with Arcade SDK](#build-llm-tools-with-arcade-sdk) - [Tool SDK Installation](#tool-sdk-installation) - [Creating a new Toolkit](#creating-a-new-toolkit) - [Deploy your tools to call with LLMs](#deploy-your-tools-to-call-with-llms) - [Calling your tools](#calling-your-tools) - [LLM API](#llm-api) - [Tools API](#tools-api) - [Integrating with Agent Frameworks](#integrating-with-agent-frameworks) - [Arcade Auth API](#arcade-auth-api) - [Client Libraries](#client-libraries) - [Support and Community](#support-and-community) ## Quickstart: Install and call a tool ```bash # Install the Arcade CLI pip install arcade-ai # Log in to Arcade arcade login # Show what tools are hosted by Arcade arcade show # show what tools are in a toolkit arcade show -T GitHub # look at the definition of a tool arcade show -t GitHub.SetStarred ``` The GitHub.SetStarred tool is hosted by Arcade, so you can call it directly without any additional setup of OAuth or servers. A simple way to test tools, wether hosted by Arcade or not, is to use the `arcade chat` app. ```bash arcade chat ``` This will start a chat with an LLM that can call tools. try calling the GitHub.SetStarred tool with a message like "Star the arcade-ai repo" ``` > arcade chat === Arcade Chat === Chatting with Arcade Engine at https://api.arcade.dev User sam@arcade.dev: star the arcadeai/arcade-ai repo Assistant: Thanks for authorizing the action! Sending your request... Assistant: I have successfully starred the repository arcadeai/arcade-ai for you. ``` If Arcade already hosts the tools you need to build your agent, you can navigate to the [Quickstart](https://docs.arcade.dev/home/quickstart) to learn how to call tools programmatically in Python, Typescript, or HTTP. You can also build your own tools with the SDK and deploy them in one command on Arcade Cloud ## Build LLM Tools with Arcade SDK Arcade provides a tool SDK that allows you to build your own tools and use them in your agentic applications just like the existing tools Arcade provides. This is useful for building new tools, customizing existing tools to fit your needs, combining multiple tools, or building tools that are not yet supported by Arcade. ### Tool SDK Installation **Prerequisites** - **Python 3.10+** and **pip** Now you can install the Tool SDK through pip. 1. **Install the Arcade CLI:** ```bash pip install arcade-ai ``` If you plan on writing evaluations for your tools and the LLMs you use, you will also need to install the `evals` extra. ```bash pip install 'arcade-ai[evals]' ``` 2. **Log in to Arcade:** ```bash arcade login ``` This will prompt you to open a browser and authorize the CLI. It will then save the credentials to your machine typically in `~/.arcade/credentials.json`. If you haven't used the CLI before, you will need to create an account on this page. Now you're ready to build tools with Arcade! ### Creating a New Toolkit Toolkits are the main building blocks of Arcade. They are a collection of tools that are related to a specific service, use case, or agent. Toolkits are created and distributed python packages to facilitate version, dependency, and distribution. To create a new toolkit, you can use the `arcade new` command. This will create a new toolkit in the current directory. 1. **Generate a new toolkit template:** ```bash arcade new ``` ```text Name of the new toolkit?: mytoolkit Description of the toolkit?: myToolkit is a toolkit for ... Github owner username?: mytoolkit Author's email?: user@example.com ``` This will create a new toolkit in the current directory. The generated toolkit includes all the scaffolding you need for a working tool. Look for the `mytoolkit/tool.py` file to customize the behavior of your tool. 2. **Install your new toolkit:** ```bash # make sure you have python installed python --version # install your new toolkit cd mytoolkit make install ``` This will install the toolkit in your local python environment using poetry. The template is meant to be customized so feel free to change anything about the structure, package management, linting, etc. 3. **Show the tools in the template Toolkit:** ```bash # show the tools in Mytoolkit arcade show --local -T Mytoolkit # show the definition of a tool arcade show --local -t Mytoolkit.SayHello # show all tools installed in your local python environment arcade show --local ``` Now you can edit the `mytoolkit/tool.py` file to customize the behavior of your tool. Next, you can host your tools to call with LLMs by deploying your toolkit to Arcade Cloud. ### Deploy your tools to call with LLMs To make your tools in the toolkit available to call with LLMs, you can deploy your toolkit to Arcade Cloud. The `worker.toml` file created in the directory where you ran `arcade new` will be used to deploy your toolkit. In that directory, run the following command to deploy your toolkit: ```bash # from inside the mytoolkit dir cd ../ arcade deploy ``` This command will package your toolkit and deploy it as a worker instance in Arcade's cloud infrastructure: ``` [11:52:44] Deploying 'demo-worker...' ⠦ Deploying 1 workers Changed Packages ┏━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Added ┃ Removed ┃ Updated ┃ No Changes ┃ ┡━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━┩ │ Mytoolkit│ │ │ │ └──────────┴─────────┴─────────┴────────────┘ [11:53:13] ✅ Worker 'demo-worker' deployed successfully. ``` You can manage your deployed workers with the following commands: ```bash # List all workers (both local and cloud-deployed) arcade worker list # Remove a deployed worker arcade worker rm demo-worker ``` Once deployed, your toolkit is immediately available through the Arcade platform. You can now call your tools through the playground, LLM API, or Tools API without any additional setup. For local development and testing when running the Arcade Engine locally or tunneling to it, you can use `arcade serve` to host your toolkit locally and connect it to the Arcade Engine. If you are running the Arcade Engine locally, go to localhost:9099 (or other local address) and add the worker address in the "workers" page. ## Calling your tools Arcade provides multiple ways to use your tools with various agent frameworks. Depending on your use case, you can choose the best method for your application. ### LLM API The LLM API provides the simplest way to integrate Arcade tools into your application. It extends the standard OpenAI API with additional capabilities: ```python import os from openai import OpenAI prompt = "Say hello to Sam" api_key = os.environ["ARCADE_API_KEY"] openai = OpenAI( base_url="https://api.arcade.dev/v1", api_key=api_key, ) response = openai.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}, ], tools=["Mytoolkit.SayHello"], tool_choice="generate", user="user@example.com" ) print(response.choices[0].message.content) ``` When a user hasn't authorized a service, the API seamlessly returns an authorization link in the response: ``` Please authorize the tool by visiting: https://some.auth.url.arcade.will.generate.for.you... ``` All you need to do is show the url to the user, and from then on, the user will never have to do this again. All future requests will use the authorized token. After authorization, the same API call returns the completed action: ``` Hello Sam! ``` ### Tools API Use the Tools API when you want to integrate Arcade's runtime for tool calling into an agent framework (like LangChain or LangGraph), or if you're using your own approach and want to call Arcade tools or tools you've built with the Arcade Tool SDK. Here's an example of how to use the Tools API to call a tool directly without a framework: ```python import os from arcadepy import Arcade client = Arcade(api_key=os.environ["ARCADE_API_KEY"]) # Start the authorization process for Slack auth_response = client.tools.authorize( tool_name="Mytoolkit.SayHello", user_id="user@example.com", ) # If the tool is not already authorized, prompt the user to authenticate if auth_response.status != "completed": print("Please authorize by visiting:") print(auth_response.url) client.auth.wait_for_completion(auth_response) # Execute the tool to send a Slack message after authorization tool_input = { "username": "sam", "message": "I'll be late to the meeting" } response = client.tools.execute( tool_name="Mytoolkit.SayHello", input=tool_input, user_id="user@example.com", ) print(response) ``` ### Integrating with Agent Frameworks You can also use the Tools API with a framework like LangChain or LangGraph. Currently Arcade provides ease-of-use integrations for the following frameworks: - LangChain/Langgraph - CrewAI - LlamaIndex (coming soon) Here's an example of how to use the Tools API with LangChain/Langgraph: ```python import os from langchain_arcade import ToolManager from langchain_openai import ChatOpenAI from langgraph.checkpoint.memory import MemorySaver from langgraph.prebuilt import create_react_agent # 1) Set API keys (place your real keys in env variables or directly below) 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") # 2) Create an ToolManager and fetch/add tools/toolkits manager = ToolManager(api_key=arcade_api_key) # Tool names follow the format "ToolkitName.ToolName" tools = manager.init_tools(tools=["Web.ScrapeUrl"]) print(manager.tools) # Get all tools from a toolkit tools = manager.init_tools(toolkits=["github"]) print(manager.tools) # add a tool manager.add_tool("Search.SearchGoogle") print(manager.tools) # add a toolkit manager.add_toolkit("Search") print(manager.tools) # 3) Get StructuredTool objects for langchain lc_tools = manager.to_langchain() # 4) Create a ChatOpenAI model and bind the Arcade tools. model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key) bound_model = model.bind_tools(lc_tools) # 5) Use MemorySaver for checkpointing. memory = MemorySaver() # 6) Create a ReAct-style agent from the prebuilt function. graph = create_react_agent(model=bound_model, tools=lc_tools, checkpointer=memory) # 7) Provide basic config and a user query. # Note: user_id is required for the tool to be authorized config = {"configurable": {"thread_id": "1", "user_id": "user@example.com"}} user_input = {"messages": [("user", "star the arcadeai/arcade-ai repo on github")]} # 8) Stream the agent's output. If the tool is unauthorized, it may trigger interrupts for chunk in graph.stream(user_input, config, stream_mode="values"): chunk["messages"][-1].pretty_print() # if we were interrupted, we can check for interrupts in state current_state = graph.get_state(config) if current_state.tasks: for task in current_state.tasks: if hasattr(task, "interrupts"): for interrupt in task.interrupts: print(interrupt.value) ``` The last message may result in an authorization prompt. If so, the user will need to authorize the tool by visiting the url in the response. Once authorized, running the same script will return the completed action since the tool will be authorized for that user. ### Arcade Auth API The Auth API provides the lowest-level integration with Arcade, for when you only need Arcade's authentication capabilities. This API is ideal for: - Framework developers building their own agent systems - Applications with existing tool execution mechanisms - Developers who need fine-grained control over LLM interactions and tool execution With the Auth API, Arcade handles all the complex authentication tasks (OAuth flow management, link creation, token storage, refresh cycles), while you retain complete control over how you interact with LLMs and execute tools. ```python from arcadepy import Arcade from google.oauth2.credentials import Credentials from googleapiclient.discovery import build client = Arcade() # Get this user UNIQUE ID from a trusted source, # like your database or user management system user_id = "user@example.com" # Start the authorization process response = client.auth.start( user_id=user_id, provider="google", scopes=["https://www.googleapis.com/auth/gmail.readonly"], ) if response.status != "completed": print("Please complete the authorization challenge in your browser:") print(response.url) # Wait for the authorization to complete auth_response = client.auth.wait_for_completion(response) # Use the authorized token in your own tool execution logic token = auth_response.context.token # Example: Using the token with your own Gmail API implementation credentials = Credentials(token=token) gmail_service = build('gmail', 'v1', credentials=credentials) emails = gmail_service.users().messages().list(userId='me').execute() ``` ## Client Libraries - **[ArcadeAI/arcade-py](https://github.com/ArcadeAI/arcade-py):** The Python client for interacting with Arcade. - **[ArcadeAI/arcade-js](https://github.com/ArcadeAI/arcade-js):** The JavaScript client for interacting with Arcade. - **[ArcadeAI/arcade-go](https://github.com/ArcadeAI/arcade-go):** (coming soon) The Go client for interacting with Arcade. ## Support and Community - **Discord:** Join our [Discord community](https://discord.com/invite/GUZEMpEZ9p) for real-time support and discussions. - **GitHub:** Contribute or report issues on the [Arcade GitHub repository](https://github.com/ArcadeAI/arcade-ai). - **Documentation:** Find in-depth guides and API references at [Arcade Documentation](https://docs.arcade.dev).