This SDK allows you to seamlessly integrate the functionalities of Toolbox allowing you to load and use tools defined in the service as standard Python functions within your GenAI applications.

This simplifies integrating external functionalities (like APIs, databases, or custom logic) managed by the Toolbox into your workflows, especially those involving Large Language Models (LLMs).

• Installation
• Quickstart
• Usage
• Loading Tools
  • Load a toolset
  • Load a single tool
• Invoking Tools
• Synchronous Usage
• Use with LangGraph
• Client to Server Authentication
  • When is Client-to-Server Authentication Needed?
  • How it works
  • Configuration
  • Authenticating with Google Cloud Servers
  • Step by Step Guide for Cloud Run
• Authenticating Tools
  • When is Authentication Needed?
  • Supported Authentication Mechanisms
  • Step 1: Configure Tools in Toolbox Service
  • Step 2: Configure SDK Client
    • Provide an ID Token Retriever Function
    • Option A: Add Authentication to a Loaded Tool
    • Option B: Add Authentication While Loading Tools
  • Complete Authentication Example
• Binding Parameter Values
  • Why Bind Parameters?
  • Option A: Binding Parameters to a Loaded Tool
  • Option B: Binding Parameters While Loading Tools
  • Binding Dynamic Values
• Contributing
• License
• Support

pip install toolbox-core

Here's a minimal example to get you started. Ensure your Toolbox service is running and accessible.

import asyncio
from toolbox_core import ToolboxClient

async def main():
    # Replace with the actual URL where your Toolbox service is running
    async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
        weather_tool = await toolbox.load_tool("get_weather")
        result = await weather_tool(location="London")
        print(result)

if __name__ == "__main__":
    asyncio.run(main())

toolbox = ToolboxClient("http://127.0.0.1:5000")
try:
    # ... use toolbox ...
finally:
    await toolbox.close()

Import and initialize a Toolbox client, pointing it to the URL of your running Toolbox service.

from toolbox_core import ToolboxClient

# Replace with your Toolbox service's URL
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:

All interactions for loading and invoking tools happen through this client.

You can load tools individually or in groups (toolsets) as defined in your Toolbox service configuration. Loading a toolset is convenient when working with multiple related functions, while loading a single tool offers more granular control.

A toolset is a collection of related tools. You can load all tools in a toolset or a specific one:

# Load all tools
tools = await toolbox.load_toolset()

# Load a specific toolset
tools = await toolbox.load_toolset("my-toolset")

Loads a specific tool by its unique name. This provides fine-grained control.

tool = await toolbox.load_tool("my-tool")

Once loaded, tools behave like awaitable Python functions. You invoke them using await and pass arguments corresponding to the parameters defined in the tool's configuration within the Toolbox service.

tool = await toolbox.load_tool("my-tool")
result = await tool("foo", bar="baz")

By default, the ToolboxClient and the ToolboxTool objects it produces behave like asynchronous Python functions, requiring the use of await.

If your application primarily uses synchronous code, or you prefer not to manage an asyncio event loop, you can use the synchronous alternatives provided:

• ToolboxSyncClient: The synchronous counterpart to ToolboxClient.
• ToolboxSyncTool: The synchronous counterpart to ToolboxTool.

The ToolboxSyncClient handles communication with the Toolbox service synchronously and produces ToolboxSyncTool instances when you load tools. You do not use the await keyword when interacting with these synchronous versions.

from toolbox_core import ToolboxSyncClient

with ToolboxSyncClient("http://127.0.0.1:5000") as toolbox:
    weather_tool = toolbox.load_tool("get_weather")
    result = weather_tool(location="Paris")
    print(result)

The Toolbox Core SDK integrates smoothly with frameworks like LangGraph, allowing you to incorporate tools managed by the Toolbox service into your agentic workflows.

Here's a conceptual example adapting the official LangGraph tool calling guide:

from toolbox_core import ToolboxClient
from langchain_google_vertexai import ChatVertexAI
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
from langchain.tools import StructuredTool

async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
    tools = await toolbox.load_toolset()
    wrapped_tools = [StructuredTool.from_function(tool, parse_docstring=True) for tool in tools]
    model_with_tools = ChatVertexAI(model="gemini-2.0-flash-001").bind_tools(wrapped_tools)

    def call_model(state: MessagesState):
        messages = state["messages"]
        response = model_with_tools.invoke(messages)
        return {"messages": [response]}

    def should_continue(state: MessagesState):
        messages = state["messages"]
        last_message = messages[-1]
        if last_message.tool_calls:
            return "tools"
        return END

    workflow = StateGraph(MessagesState)

    workflow.add_node("agent", call_model)
    workflow.add_node("tools", ToolNode(wrapped_tools))

    workflow.add_edge(START, "agent")
    workflow.add_conditional_edges("agent", should_continue, ["tools", END])
    workflow.add_edge("tools", "agent")

    app = workflow.compile()

This section describes how to authenticate the ToolboxClient itself when connecting to a Toolbox server instance that requires authentication. This is crucial for securing your Toolbox server endpoint, especially when deployed on platforms like Cloud Run, GKE, or any environment where unauthenticated access is restricted.

This client-to-server authentication ensures that the Toolbox server can verify the identity of the client making the request before any tool is loaded or called. It is different from Authenticating Tools, which deals with providing credentials for specific tools within an already connected Toolbox session.

You'll need this type of authentication if your Toolbox server is configured to deny unauthenticated requests. For example:

• Your Toolbox server is deployed on Cloud Run and configured to "Require authentication."
• Your server is behind an Identity-Aware Proxy (IAP) or a similar authentication layer.
• You have custom authentication middleware on your self-hosted Toolbox server.

Without proper client authentication in these scenarios, attempts to connect or make calls (like load_tool) will likely fail with Unauthorized errors.

The ToolboxClient (and ToolboxSyncClient) allows you to specify functions (or coroutines for the async client) that dynamically generate HTTP headers for every request sent to the Toolbox server. The most common use case is to add an Authorization header with a bearer token (e.g., a Google ID token).

These header-generating functions are called just before each request, ensuring that fresh credentials or header values can be used.

You can configure these dynamic headers as seen below:

from toolbox_core import ToolboxClient

async with ToolboxClient("toolbox-url", client_headers={"header1": header1_getter, "header2": header2_getter, ...}) as client:
    # Use client
    pass

For Toolbox servers hosted on Google Cloud (e.g., Cloud Run) and requiring Google ID token authentication, the helper module auth_methods provides utility functions.

1. Configure Permissions: Grant the roles/run.invoker IAM role on the Cloud Run service to the principal. This could be your user account email or a service account.

2. Configure Credentials

   • Local Development: Set up ADC.
   • Google Cloud Environments: When running within Google Cloud (e.g., Compute Engine, GKE, another Cloud Run service, Cloud Functions), ADC is typically configured automatically, using the environment's default service account.

3. Connect to the Toolbox Server

   from toolbox_core import auth_methods
   
   auth_token_provider = auth_methods.aget_google_id_token # can also use sync method
   async with ToolboxClient(
       URL,
       client_headers={"Authorization": auth_token_provider},
   ) as client:
       tools = await client.load_toolset()
   
       # Now, you can use the client as usual.

Tools can be configured within the Toolbox service to require authentication, ensuring only authorized users or applications can invoke them, especially when accessing sensitive data.

Authentication is configured per-tool within the Toolbox service itself. If a tool you intend to use is marked as requiring authentication in the service, you must configure the SDK client to provide the necessary credentials (currently Oauth2 tokens) when invoking that specific tool.

The Toolbox service enables secure tool usage through Authenticated Parameters. For detailed information on how these mechanisms work within the Toolbox service and how to configure them, please refer to Toolbox Service Documentation - Authenticated Parameters

First, ensure the target tool(s) are configured correctly in the Toolbox service to require authentication. Refer to the Toolbox Service Documentation - Authenticated Parameters for instructions.

Your application needs a way to obtain the required Oauth2 token for the authenticated user. The SDK requires you to provide a function capable of retrieving this token when the tool is invoked.

You must provide the SDK with a function (sync or async) that returns the necessary token when called. The implementation depends on your application's authentication flow (e.g., retrieving a stored token, initiating an OAuth flow).

async def get_auth_token():
    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
    # This example just returns a placeholder. Replace with your actual token retrieval.
    return "YOUR_ID_TOKEN" # Placeholder

You can add the token retriever function to a tool object after it has been loaded. This modifies the specific tool instance.

async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
    tool = await toolbox.load_tool("my-tool")

    auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)  # Single token

    # OR

    multi_auth_tool = tool.add_auth_token_getters({
        "my_auth_1": get_auth_token_1,
        "my_auth_2": get_auth_token_2,
    })  # Multiple tokens

You can provide the token retriever(s) directly during the load_tool or load_toolset calls. This applies the authentication configuration only to the tools loaded in that specific call, without modifying the original tool objects if they were loaded previously.

auth_tool = await toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})

# OR

auth_tools = await toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})

import asyncio
from toolbox_core import ToolboxClient

async def get_auth_token():
    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
    # This example just returns a placeholder. Replace with your actual token retrieval.
    return "YOUR_ID_TOKEN" # Placeholder

async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
    tool = await toolbox.load_tool("my-tool")

    auth_tool = tool.add_auth_token_getters({"my_auth": get_auth_token})
    result = auth_tool(input="some input")
    print(result)

The SDK allows you to pre-set, or "bind", values for specific tool parameters before the tool is invoked or even passed to an LLM. These bound values are fixed and will not be requested or modified by the LLM during tool use.

• Protecting sensitive information: API keys, secrets, etc.
• Enforcing consistency: Ensuring specific values for certain parameters.
• Pre-filling known data: Providing defaults or context.

Bind values to a tool object after it has been loaded. This modifies the specific tool instance.

async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
    tool = await toolbox.load_tool("my-tool")

    bound_tool = tool.bind_param("param", "value")

    # OR

    bound_tool = tool.bind_params({"param": "value"})

Specify bound parameters directly when loading tools. This applies the binding only to the tools loaded in that specific call.

bound_tool = await toolbox.load_tool("my-tool", bound_params={"param": "value"})

# OR

bound_tools = await toolbox.load_toolset(bound_params={"param": "value"})

Instead of a static value, you can bind a parameter to a synchronous or asynchronous function. This function will be called each time the tool is invoked to dynamically determine the parameter's value at runtime.

async def get_dynamic_value():
    # Logic to determine the value
    return "dynamic_value"

dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)

Contributions are welcome! Please refer to the DEVELOPER.md file for guidelines on how to set up a development environment and run tests.

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

If you encounter issues or have questions, check the existing GitHub Issues for the main Toolbox project.