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

title, type, weight, description

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

Before you begin

This guide assumes you have already done the following:

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

Step 1: Set up your database

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

1. Connect to postgres using the psql command:

   psql -h 127.0.0.1 -U postgres
   

   Here, postgres denotes the default postgres superuser.

2. 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;
   

3. End the database session:

   \q
   

4. Connect to your database with your new user:

   psql -h 127.0.0.1 -U toolbox_user -d toolbox_db
   

5. 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
   );
   

6. 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');
   

7. 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.

1. 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/v0.4.0/$OS/toolbox
   

2. Make the binary executable:

   chmod +x toolbox
   

3. 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.

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

   ./toolbox --tools_file "tools.yaml"
   

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="Core" lang="bash" >}}

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

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

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

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

1. Install other required dependencies:

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

pip install google-genai {{< /tab >}} {{< tab header="ADK" lang="bash" >}}

pip install google-adk langchain {{< /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 >}} {{< /tabpane >}}

1. Create a new file named hotel_agent.py and copy the following code to create an agent: {{< tabpane persist=header >}} {{< 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(): toolbox_client = ToolboxClient("http://127.0.0.1:5000")

# 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",
        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 >}} {{< tab header="ADK" lang="python" >}} from google.adk.agents import Agent from google.adk.tools.toolbox_tool import ToolboxTool 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

import os

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

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

toolbox_tools = ToolboxTool("http://127.0.0.1:5000")

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', name='hotel_agent', description='A helpful AI assistant.', instruction=prompt, tools=toolbox_tools.get_toolset("my-toolset"), )

session_service = InMemorySessionService() artifacts_service = InMemoryArtifactService() session = 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)

{{< /tab >}} {{< tab header="LangChain" lang="python" >}}

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-1.5-pro") # model = ChatGoogleGenerativeAI(model="gemini-1.5-pro") # model = ChatAnthropic(model="claude-3-5-sonnet-20240620")

# Load the tools from the Toolbox server
client = ToolboxClient("http://127.0.0.1:5000")
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-1.5-pro", vertexai_config={"project": "project-id", "location": "us-central1"}, ) # llm = GoogleGenAI( # api_key=os.getenv("GOOGLE_API_KEY"), # model="gemini-1.5-pro", # ) # llm = Anthropic( # model="claude-3-7-sonnet-latest", # api_key=os.getenv("ANTHROPIC_API_KEY") # )

# Load the tools from the Toolbox server
client = ToolboxClient("http://127.0.0.1:5000")
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 >}} {{< /tabpane >}}

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

{{% tab header="Core" lang="en" %}} To learn more about tool calling with Google GenAI, check out the Google GenAI Documentation. {{% /tab %}} {{% 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 %}} {{< /tabpane >}}

1. Run your agent, and observe the results:

   python hotel_agent.py