---
title: "Python Quickstart (Local)"
type: docs
weight: 2
description: >
  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/).
---

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/genai-toolbox/blob/main/docs/en/getting-started/colab_quickstart.ipynb)

## 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]).
1. Installed [PostgreSQL 16+ and the `psql` client][install-postgres].

### 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:

1. [Install the Google Cloud CLI](https://cloud.google.com/sdk/docs/install)
1. [Set up Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment)
1. Set your project and enable Vertex AI

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

[install-python]: https://wiki.python.org/moin/BeginnersGuide/Download
[install-pip]: https://pip.pypa.io/en/stable/installation/
[install-venv]: https://packaging.python.org/en/latest/tutorials/installing-packages/#creating-virtual-environments
[install-postgres]: https://www.postgresql.org/download/

## 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.

1. Connect to postgres using the `psql` command:

    ```bash
    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.

<br/>

#### **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).

```bash
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 >}}

1. 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 >}}

    ```sql
      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;
    ```

1. End the database session:

    ```bash
    \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.)

1. Connect to your database with your new user:

    ```bash
    psql -h 127.0.0.1 -U toolbox_user -d toolbox_db
    ```

1. Create a table using the following command:

    ```sql
    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
    );
    ```

1. Insert data into the table.

    ```sql
    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');
    ```

1. End the database session:

    ```bash
    \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.

1. Download the latest version of Toolbox as a binary:

    {{< notice tip >}}
  Select the
  [correct binary](https://github.com/googleapis/genai-toolbox/releases)
  corresponding to your OS and CPU architecture.
    {{< /notice >}}
    <!-- {x-release-please-start-version} -->
    ```bash
    export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
    curl -O https://storage.googleapis.com/genai-toolbox/v0.9.0/$OS/toolbox
    ```
    <!-- {x-release-please-end} -->

1. Make the binary executable:

    ```bash
    chmod +x toolbox
    ```

1. 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 >}}

    ```yaml
    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.

1. Run the Toolbox server, pointing to the `tools.yaml` file created earlier:

    ```bash
    ./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](https://research.google.com/colaboratory/local-runtimes.html).
{{< /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 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

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
    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())

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

    {{< tabpane text=true persist=header >}}
{{% tab header="ADK" lang="en" %}}
To learn more about Agent Development Kit, check out the [ADK
documentation.](https://google.github.io/adk-docs/)
{{% /tab %}}
{{% tab header="Langchain" lang="en" %}}
To learn more about Agents in LangChain, check out the [LangGraph Agent
documentation.](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)
{{% /tab %}}
{{% tab header="LlamaIndex" lang="en" %}}
To learn more about Agents in LlamaIndex, check out the [LlamaIndex
AgentWorkflow
documentation.](https://docs.llamaindex.ai/en/stable/examples/agent/agent_workflow_basic/)
{{% /tab %}}
{{% tab header="Core" lang="en" %}}
To learn more about tool calling with Google GenAI, check out the
[Google GenAI
Documentation](https://github.com/googleapis/python-genai?tab=readme-ov-file#manually-declare-and-invoke-a-function-for-function-calling).
{{% /tab %}}
{{< /tabpane >}}

1. Run your agent, and observe the results:

    ```sh
    python hotel_agent.py
    ```

{{< notice info >}}
For more information, visit the [Python SDK repo](https://github.com/googleapis/mcp-toolbox-sdk-python).
{{</ notice >}}
