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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:

Installed Python 3.9+ (including pip and your preferred virtual environment tool for managing dependencies e.g. venv).
Installed PostgreSQL 16+ and the psql client.

Cloud Setup (Optional)

If you plan to use Google Cloud’s Vertex AI with your agent (e.g., using vertexai=True or a Google GenAI model), follow these one-time setup steps for local development:

Install the Google Cloud CLI
Set up Application Default Credentials (ADC)

Set your project and enable Vertex AI

gcloud config set project YOUR_PROJECT_ID
gcloud services enable aiplatform.googleapis.com

Step 1: Set up your database

In this section, we will create a database, insert some data that needs to be accessed by our agent, and create a database user for Toolbox to connect with.

Connect to postgres using the psql command:
```
psql -h 127.0.0.1 -U postgres
```
Here, postgres denotes the default postgres superuser.

{{< notice info >}}

Having trouble connecting?

Password Prompt: If you are prompted for a password for the postgres user and do not know it (or a blank password doesn't work), your PostgreSQL installation might require a password or a different authentication method.
FATAL: role "postgres" does not exist: This error means the default postgres superuser role isn't available under that name on your system.
Connection refused: Ensure your PostgreSQL server is actually running. You can typically check with sudo systemctl status postgresql and start it with sudo systemctl start postgresql on Linux systems.

Common Solution

For password issues or if the postgres role seems inaccessible directly, try switching to the postgres operating system user first. This user often has permission to connect without a password for local connections (this is called peer authentication).

sudo -i -u postgres
psql -h 127.0.0.1

Once you are in the psql shell using this method, you can proceed with the database creation steps below. Afterwards, type \q to exit psql, and then exit to return to your normal user shell.

If desired, once connected to psql as the postgres OS user, you can set a password for the postgres database user using: ALTER USER postgres WITH PASSWORD 'your_chosen_password';. This would allow direct connection with -U postgres and a password next time. {{< /notice >}}

Create a new database and a new user:

{{< notice tip >}} For a real application, it's best to follow the principle of least permission and only grant the privileges your application needs. {{< /notice >}}

  CREATE USER toolbox_user WITH PASSWORD 'my-password';

  CREATE DATABASE toolbox_db;
  GRANT ALL PRIVILEGES ON DATABASE toolbox_db TO toolbox_user;

  ALTER DATABASE toolbox_db OWNER TO toolbox_user;

End the database session:
```
\q
```
(If you used sudo -i -u postgres and then psql, remember you might also need to type exit after \q to leave the postgres user's shell session.)

Connect to your database with your new user:

psql -h 127.0.0.1 -U toolbox_user -d toolbox_db

Create a table using the following command:

CREATE TABLE hotels(
  id            INTEGER NOT NULL PRIMARY KEY,
  name          VARCHAR NOT NULL,
  location      VARCHAR NOT NULL,
  price_tier    VARCHAR NOT NULL,
  checkin_date  DATE    NOT NULL,
  checkout_date DATE    NOT NULL,
  booked        BIT     NOT NULL
);

Insert data into the table.

INSERT INTO hotels(id, name, location, price_tier, checkin_date, checkout_date, booked)
VALUES 
  (1, 'Hilton Basel', 'Basel', 'Luxury', '2024-04-22', '2024-04-20', B'0'),
  (2, 'Marriott Zurich', 'Zurich', 'Upscale', '2024-04-14', '2024-04-21', B'0'),
  (3, 'Hyatt Regency Basel', 'Basel', 'Upper Upscale', '2024-04-02', '2024-04-20', B'0'),
  (4, 'Radisson Blu Lucerne', 'Lucerne', 'Midscale', '2024-04-24', '2024-04-05', B'0'),
  (5, 'Best Western Bern', 'Bern', 'Upper Midscale', '2024-04-23', '2024-04-01', B'0'),
  (6, 'InterContinental Geneva', 'Geneva', 'Luxury', '2024-04-23', '2024-04-28', B'0'),
  (7, 'Sheraton Zurich', 'Zurich', 'Upper Upscale', '2024-04-27', '2024-04-02', B'0'),
  (8, 'Holiday Inn Basel', 'Basel', 'Upper Midscale', '2024-04-24', '2024-04-09', B'0'),
  (9, 'Courtyard Zurich', 'Zurich', 'Upscale', '2024-04-03', '2024-04-13', B'0'),
  (10, 'Comfort Inn Bern', 'Bern', 'Midscale', '2024-04-04', '2024-04-16', B'0');

End the database session:
```
\q
```

Step 2: Install and configure Toolbox

In this section, we will download Toolbox, configure our tools in a tools.yaml, and then run the Toolbox server.

Download the latest version of Toolbox as a binary:

{{< notice tip >}} Select the correct binary corresponding to your OS and CPU architecture. {{< /notice >}}

export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
curl -O https://storage.googleapis.com/genai-toolbox/v0.10.0/$OS/toolbox

Make the binary executable:
```
chmod +x toolbox
```

Write the following into a tools.yaml file. Be sure to update any fields such as user, password, or database that you may have customized in the previous step.

{{< notice tip >}} In practice, use environment variable replacement with the format ${ENV_NAME} instead of hardcoding your secrets into the configuration file. {{< /notice >}}

sources:
  my-pg-source:
    kind: postgres
    host: 127.0.0.1
    port: 5432
    database: toolbox_db
    user: ${USER_NAME}
    password: ${PASSWORD}
tools:
  search-hotels-by-name:
    kind: postgres-sql
    source: my-pg-source
    description: Search for hotels based on name.
    parameters:
      - name: name
        type: string
        description: The name of the hotel.
    statement: SELECT * FROM hotels WHERE name ILIKE '%' || $1 || '%';
  search-hotels-by-location:
    kind: postgres-sql
    source: my-pg-source
    description: Search for hotels based on location.
    parameters:
      - name: location
        type: string
        description: The location of the hotel.
    statement: SELECT * FROM hotels WHERE location ILIKE '%' || $1 || '%';
  book-hotel:
    kind: postgres-sql
    source: my-pg-source
    description: >-
       Book a hotel by its ID. If the hotel is successfully booked, returns a NULL, raises an error if not.
    parameters:
      - name: hotel_id
        type: string
        description: The ID of the hotel to book.
    statement: UPDATE hotels SET booked = B'1' WHERE id = $1;
  update-hotel:
    kind: postgres-sql
    source: my-pg-source
    description: >-
      Update a hotel's check-in and check-out dates by its ID. Returns a message
      indicating  whether the hotel was successfully updated or not.
    parameters:
      - name: hotel_id
        type: string
        description: The ID of the hotel to update.
      - name: checkin_date
        type: string
        description: The new check-in date of the hotel.
      - name: checkout_date
        type: string
        description: The new check-out date of the hotel.
    statement: >-
      UPDATE hotels SET checkin_date = CAST($2 as date), checkout_date = CAST($3
      as date) WHERE id = $1;
  cancel-hotel:
    kind: postgres-sql
    source: my-pg-source
    description: Cancel a hotel by its ID.
    parameters:
      - name: hotel_id
        type: string
        description: The ID of the hotel to cancel.
    statement: UPDATE hotels SET booked = B'0' WHERE id = $1;
toolsets:
  my-toolset:
    - search-hotels-by-name
    - search-hotels-by-location
    - book-hotel
    - update-hotel
    - cancel-hotel

For more info on tools, check out the Resources section of the docs.

Run the Toolbox server, pointing to the tools.yaml file created earlier:
```
./toolbox --tools-file "tools.yaml"
```
{{< notice note >}} Toolbox enables dynamic reloading by default. To disable, use the --disable-reload flag. {{< /notice >}}

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 >}}

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 >}}

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

TODO(developer): replace with correct package if needed

pip install llama-index-llms-google-genai

pip install llama-index-llms-anthropic

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

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 it's 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 it's 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

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 it's 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())

{{< 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 >}}

Run your agent, and observe the results:
```
python hotel_agent.py
```

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

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Before you begin

Cloud Setup (Optional)

Step 1: Set up your database

Having trouble connecting?

Common Solution

Step 2: Install and configure Toolbox

Step 3: Connect your agent to Toolbox

TODO(developer): replace with correct package if needed

pip install langchain-google-genai

pip install langchain-anthropic

TODO(developer): replace with correct package if needed

pip install llama-index-llms-anthropic

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

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

from langchain_google_genai import ChatGoogleGenerativeAI

from langchain_anthropic import ChatAnthropic

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

from llama_index.llms.anthropic import Anthropic

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