genai-toolbox/docs/en/getting-started/local_quickstart.md

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Python Quickstart (Local)	docs	2	How to get started running Toolbox locally with [Python](https://github.com/googleapis/mcp-toolbox-sdk-python), PostgreSQL, and [Agent Development Kit](https://google.github.io/adk-docs/), [LangGraph](https://www.langchain.com/langgraph), [LlamaIndex](https://www.llamaindex.ai/) or [GoogleGenAI](https://pypi.org/project/google-genai/).

Before you begin

This guide assumes you have already done the following:

1. Installed [Python 3.9+][install-python] (including [pip][install-pip] and your preferred virtual environment tool for managing dependencies e.g. [venv][install-venv]).
2. Installed [PostgreSQL 16+ and the psql client][install-postgres].

Cloud Setup (Optional)

{{< snippet "quickstart/shared/cloud_setup.md" "cloud_setup" >}}

Step 1: Set up your database

{{< snippet "quickstart/shared/database_setup.md" "database_setup" >}}

Step 2: Install and configure Toolbox

{{< snippet "quickstart/shared/configure_toolbox.md" "configure_toolbox" >}}

Step 3: Connect your agent to Toolbox

In this section, we will write and run an agent that will load the Tools from Toolbox.

{{< notice tip>}} If you prefer to experiment within a Google Colab environment, you can connect to a local runtime. {{< /notice >}}

1. In a new terminal, install the SDK package.

   {{< tabpane persist=header >}} {{< tab header="ADK" lang="bash" >}}

pip install toolbox-core {{< /tab >}} {{< tab header="Langchain" lang="bash" >}}

pip install toolbox-langchain {{< /tab >}} {{< tab header="LlamaIndex" lang="bash" >}}

pip install toolbox-llamaindex {{< /tab >}} {{< tab header="Core" lang="bash" >}}

pip install toolbox-core {{< /tab >}} {{< /tabpane >}}

1. Install other required dependencies:

   {{< tabpane persist=header >}} {{< tab header="ADK" lang="bash" >}}

pip install google-adk {{< /tab >}} {{< tab header="Langchain" lang="bash" >}}

TODO(developer): replace with correct package if needed

pip install langgraph langchain-google-vertexai

pip install langchain-google-genai

pip install langchain-anthropic

{{< /tab >}} {{< tab header="LlamaIndex" lang="bash" >}}

TODO(developer): replace with correct package if needed

pip install llama-index-llms-google-genai

pip install llama-index-llms-anthropic

{{< /tab >}} {{< tab header="Core" lang="bash" >}}

pip install google-genai {{< /tab >}} {{< /tabpane >}}

1. Create a new file named hotel_agent.py and copy the following code to create an agent: {{< tabpane persist=header >}} {{< tab header="ADK" lang="python" >}} from google.adk.agents import Agent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService from google.genai import types from toolbox_core import ToolboxSyncClient

import asyncio import os

TODO(developer): replace this with your Google API key

os.environ['GOOGLE_API_KEY'] = 'your-api-key'

async def main(): with ToolboxSyncClient("http://127.0.0.1:5000") as toolbox_client:

  prompt = """
    You're a helpful hotel assistant. You handle hotel searching, booking and
    cancellations. When the user searches for a hotel, mention it's name, id,
    location and price tier. Always mention hotel ids while performing any
    searches. This is very important for any operations. For any bookings or
    cancellations, please provide the appropriate confirmation. Be sure to
    update checkin or checkout dates if mentioned by the user.
    Don't ask for confirmations from the user.
  """

  root_agent = Agent(
      model='gemini-2.0-flash-001',
      name='hotel_agent',
      description='A helpful AI assistant.',
      instruction=prompt,
      tools=toolbox_client.load_toolset("my-toolset"),
  )

  session_service = InMemorySessionService()
  artifacts_service = InMemoryArtifactService()
  session = await session_service.create_session(
      state={}, app_name='hotel_agent', user_id='123'
  )
  runner = Runner(
      app_name='hotel_agent',
      agent=root_agent,
      artifact_service=artifacts_service,
      session_service=session_service,
  )

  queries = [
      "Find hotels in Basel with Basel in its name.",
      "Can you book the Hilton Basel for me?",
      "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.",
      "My check in dates would be from April 10, 2024 to April 19, 2024.",
  ]

  for query in queries:
      content = types.Content(role='user', parts=[types.Part(text=query)])
      events = runner.run(session_id=session.id,
                          user_id='123', new_message=content)

      responses = (
        part.text
        for event in events
        for part in event.content.parts
        if part.text is not None
      )

      for text in responses:
        print(text)

asyncio.run(main()) {{< /tab >}} {{< tab header="LangChain" lang="python" >}} import asyncio

from langgraph.prebuilt import create_react_agent

TODO(developer): replace this with another import if needed

from langchain_google_vertexai import ChatVertexAI

from langchain_google_genai import ChatGoogleGenerativeAI

from langchain_anthropic import ChatAnthropic

from langgraph.checkpoint.memory import MemorySaver

from toolbox_langchain import ToolboxClient

prompt = """ You're a helpful hotel assistant. You handle hotel searching, booking and cancellations. When the user searches for a hotel, mention it's name, id, location and price tier. Always mention hotel ids while performing any searches. This is very important for any operations. For any bookings or cancellations, please provide the appropriate confirmation. Be sure to update checkin or checkout dates if mentioned by the user. Don't ask for confirmations from the user. """

queries = [ "Find hotels in Basel with Basel in its name.", "Can you book the Hilton Basel for me?", "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.", "My check in dates would be from April 10, 2024 to April 19, 2024.", ]

async def run_application(): # TODO(developer): replace this with another model if needed model = ChatVertexAI(model_name="gemini-2.0-flash-001") # model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001") # model = ChatAnthropic(model="claude-3-5-sonnet-20240620")

# Load the tools from the Toolbox server
async with ToolboxClient("http://127.0.0.1:5000") as client:
    tools = await client.aload_toolset()

    agent = create_react_agent(model, tools, checkpointer=MemorySaver())

    config = {"configurable": {"thread_id": "thread-1"}}
    for query in queries:
        inputs = {"messages": [("user", prompt + query)]}
        response = agent.invoke(inputs, stream_mode="values", config=config)
        print(response["messages"][-1].content)

asyncio.run(run_application()) {{< /tab >}} {{< tab header="LlamaIndex" lang="python" >}} import asyncio import os

from llama_index.core.agent.workflow import AgentWorkflow

from llama_index.core.workflow import Context

TODO(developer): replace this with another import if needed

from llama_index.llms.google_genai import GoogleGenAI

from llama_index.llms.anthropic import Anthropic

from toolbox_llamaindex import ToolboxClient

prompt = """ You're a helpful hotel assistant. You handle hotel searching, booking and cancellations. When the user searches for a hotel, mention it's name, id, location and price tier. Always mention hotel ids while performing any searches. This is very important for any operations. For any bookings or cancellations, please provide the appropriate confirmation. Be sure to update checkin or checkout dates if mentioned by the user. Don't ask for confirmations from the user. """

queries = [ "Find hotels in Basel with Basel in its name.", "Can you book the Hilton Basel for me?", "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.", "My check in dates would be from April 10, 2024 to April 19, 2024.", ]

async def run_application(): # TODO(developer): replace this with another model if needed llm = GoogleGenAI( model="gemini-2.0-flash-001", vertexai_config={"project": "project-id", "location": "us-central1"}, ) # llm = GoogleGenAI( # api_key=os.getenv("GOOGLE_API_KEY"), # model="gemini-2.0-flash-001", # ) # llm = Anthropic( # model="claude-3-7-sonnet-latest", # api_key=os.getenv("ANTHROPIC_API_KEY") # )

# Load the tools from the Toolbox server
async with ToolboxClient("http://127.0.0.1:5000") as client:
    tools = await client.aload_toolset()

    agent = AgentWorkflow.from_tools_or_functions(
        tools,
        llm=llm,
        system_prompt=prompt,
    )
    ctx = Context(agent)
    for query in queries:
        response = await agent.run(user_msg=query, ctx=ctx)
        print(f"---- {query} ----")
        print(str(response))

asyncio.run(run_application()) {{< /tab >}} {{< tab header="Core" lang="python" >}} import asyncio

from google import genai from google.genai.types import ( Content, FunctionDeclaration, GenerateContentConfig, Part, Tool, )

from toolbox_core import ToolboxClient

prompt = """ You're a helpful hotel assistant. You handle hotel searching, booking and cancellations. When the user searches for a hotel, mention it's name, id, location and price tier. Always mention hotel id while performing any searches. This is very important for any operations. For any bookings or cancellations, please provide the appropriate confirmation. Be sure to update checkin or checkout dates if mentioned by the user. Don't ask for confirmations from the user. """

queries = [ "Find hotels in Basel with Basel in its name.", "Please book the hotel Hilton Basel for me.", "This is too expensive. Please cancel it.", "Please book Hyatt Regency for me", "My check in dates for my booking would be from April 10, 2024 to April 19, 2024.", ]

async def run_application(): async with ToolboxClient("http://127.0.0.1:5000") as toolbox_client:

    # The toolbox_tools list contains Python callables (functions/methods) designed for LLM tool-use
    # integration. While this example uses Google's genai client, these callables can be adapted for
    # various function-calling or agent frameworks. For easier integration with supported frameworks
    # (https://github.com/googleapis/mcp-toolbox-python-sdk/tree/main/packages), use the
    # provided wrapper packages, which handle framework-specific boilerplate.
    toolbox_tools = await toolbox_client.load_toolset("my-toolset")
    genai_client = genai.Client(
        vertexai=True, project="project-id", location="us-central1"
    )

    genai_tools = [
        Tool(
            function_declarations=[
                FunctionDeclaration.from_callable_with_api_option(callable=tool)
            ]
        )
        for tool in toolbox_tools
    ]
    history = []
    for query in queries:
        user_prompt_content = Content(
            role="user",
            parts=[Part.from_text(text=query)],
        )
        history.append(user_prompt_content)

        response = genai_client.models.generate_content(
            model="gemini-2.0-flash-001",
            contents=history,
            config=GenerateContentConfig(
                system_instruction=prompt,
                tools=genai_tools,
            ),
        )
        history.append(response.candidates[0].content)
        function_response_parts = []
        for function_call in response.function_calls:
            fn_name = function_call.name
            # The tools are sorted alphabetically
            if fn_name == "search-hotels-by-name":
                function_result = await toolbox_tools[3](**function_call.args)
            elif fn_name == "search-hotels-by-location":
                function_result = await toolbox_tools[2](**function_call.args)
            elif fn_name == "book-hotel":
                function_result = await toolbox_tools[0](**function_call.args)
            elif fn_name == "update-hotel":
                function_result = await toolbox_tools[4](**function_call.args)
            elif fn_name == "cancel-hotel":
                function_result = await toolbox_tools[1](**function_call.args)
            else:
                raise ValueError("Function name not present.")
            function_response = {"result": function_result}
            function_response_part = Part.from_function_response(
                name=function_call.name,
                response=function_response,
            )
            function_response_parts.append(function_response_part)

        if function_response_parts:
            tool_response_content = Content(role="tool", parts=function_response_parts)
            history.append(tool_response_content)

        response2 = genai_client.models.generate_content(
            model="gemini-2.0-flash-001",
            contents=history,
            config=GenerateContentConfig(
                tools=genai_tools,
            ),
        )
        final_model_response_content = response2.candidates[0].content
        history.append(final_model_response_content)
        print(response2.text)

asyncio.run(run_application())

{{< /tab >}} {{< /tabpane >}}

{{< tabpane text=true persist=header >}}

{{% tab header="ADK" lang="en" %}} To learn more about Agent Development Kit, check out the ADK documentation. {{% /tab %}} {{% tab header="Langchain" lang="en" %}} To learn more about Agents in LangChain, check out the LangGraph Agent documentation. {{% /tab %}} {{% tab header="LlamaIndex" lang="en" %}} To learn more about Agents in LlamaIndex, check out the LlamaIndex AgentWorkflow documentation. {{% /tab %}} {{% tab header="Core" lang="en" %}} To learn more about tool calling with Google GenAI, check out the Google GenAI Documentation. {{% /tab %}} {{< /tabpane >}}

1. Run your agent, and observe the results:

   python hotel_agent.py
   

{{< notice info >}} For more information, visit the Python SDK repo. {{</ notice >}}