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genai-toolbox/docs/en/samples/bigquery/local_quickstart.md
Anubhav Dhawan 927ef3c508 docs: Add toolbox-core examples to quickstart guide (#642) 
## Description
This PR enhances our documentation by integrating `toolbox-core` code
snippets into the quickstart examples on the docsite.

## Context
Previously, our client-facing documentation primarily showcased agent
implementations using third-party frameworks like LangChain, LlamaIndex,
and ADK. This created a documentation gap, as there were no baseline
examples demonstrating how to use our foundational `toolbox-core` SDK
directly. Users who wanted to build a custom integration without a
specific framework had no direct reference in the guides.

## Changes
This PR introduces a new "Core" tab to all relevant code examples within
the auth services and local quickstart (with BigQuery) documentations.

---------

Co-authored-by: Yuan <45984206+Yuan325@users.noreply.github.com>
2025-06-10 18:30:39 +05:30

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---
title: "Quickstart (Local with BigQuery)"
type: docs
weight: 1
description: >
How to get started running Toolbox locally with Python, BigQuery, and
LangGraph, LlamaIndex, or ADK.
---
[![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/samples/bigquery/colab_quickstart_bigquery.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 and configured the [Google Cloud SDK (gcloud CLI)][install-gcloud].
1. Authenticated with Google Cloud for Application Default Credentials (ADC):
```bash
gcloud auth login --update-adc
```
1. Set your default Google Cloud project (replace `YOUR_PROJECT_ID` with your actual project ID):
```bash
gcloud config set project YOUR_PROJECT_ID
export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID
```
Toolbox and the client libraries will use this project for BigQuery, unless overridden in configurations.
1. [Enabled the BigQuery API][enable-bq-api] in your Google Cloud project.
1. Installed the BigQuery client library for Python:
```bash
pip install google-cloud-bigquery
```
1. Completed setup for usage with an LLM model such as
{{< tabpane text=true persist=header >}}
{{% tab header="Core" lang="en" %}}
- [langchain-vertexai](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm/#setup) package.
- [langchain-google-genai](https://python.langchain.com/docs/integrations/chat/google_generative_ai/#setup) package.
- [langchain-anthropic](https://python.langchain.com/docs/integrations/chat/anthropic/#setup) package.
{{% /tab %}}
{{% tab header="LangChain" lang="en" %}}
- [langchain-vertexai](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm/#setup) package.
- [langchain-google-genai](https://python.langchain.com/docs/integrations/chat/google_generative_ai/#setup) package.
- [langchain-anthropic](https://python.langchain.com/docs/integrations/chat/anthropic/#setup) package.
{{% /tab %}}
{{% tab header="LlamaIndex" lang="en" %}}
- [llama-index-llms-google-genai](https://pypi.org/project/llama-index-llms-google-genai/) package.
- [llama-index-llms-anthropic](https://docs.llamaindex.ai/en/stable/examples/llm/anthropic) package.
{{% /tab %}}
{{% tab header="ADK" lang="en" %}}
- [google-adk](https://pypi.org/project/google-adk/) package.
{{% /tab %}}
{{< /tabpane >}}
[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-gcloud]: https://cloud.google.com/sdk/docs/install
[enable-bq-api]: https://cloud.google.com/bigquery/docs/quickstarts/query-public-dataset-console#before-you-begin
## Step 1: Set up your BigQuery Dataset and Table
In this section, we will create a BigQuery dataset and a table, then insert some data that needs to be accessed by our agent. BigQuery operations are performed against your configured Google Cloud project.
1. Create a new BigQuery dataset (replace `YOUR_DATASET_NAME` with your desired dataset name, e.g., `toolbox_ds`, and optionally specify a location like `US` or `EU`):
```bash
export BQ_DATASET_NAME="YOUR_DATASET_NAME" # e.g., toolbox_ds
export BQ_LOCATION="US" # e.g., US, EU, asia-northeast1
bq --location=$BQ_LOCATION mk $BQ_DATASET_NAME
```
You can also do this through the [Google Cloud Console](https://console.cloud.google.com/bigquery).
{{< notice tip >}}
For a real application, ensure that the service account or user running Toolbox has the necessary IAM permissions (e.g., BigQuery Data Editor, BigQuery User) on the dataset or project. For this local quickstart with user credentials, your own permissions will apply.
{{< /notice >}}
1. The hotels table needs to be defined in your new dataset for use with the bq query command. First, create a file named `create_hotels_table.sql` with the following content:
```sql
CREATE TABLE IF NOT EXISTS `YOUR_PROJECT_ID.YOUR_DATASET_NAME.hotels` (
id INT64 NOT NULL,
name STRING NOT NULL,
location STRING NOT NULL,
price_tier STRING NOT NULL,
checkin_date DATE NOT NULL,
checkout_date DATE NOT NULL,
booked BOOLEAN NOT NULL
);
```
> **Note:** Replace `YOUR_PROJECT_ID` and `YOUR_DATASET_NAME` in the SQL with your actual project ID and dataset name.
Then run the command below to execute the sql query:
```bash
bq query --project_id=$GOOGLE_CLOUD_PROJECT --dataset_id=$BQ_DATASET_NAME --use_legacy_sql=false < create_hotels_table.sql
```
1. Next, populate the hotels table with some initial data. To do this, create a file named `insert_hotels_data.sql` and add the following SQL INSERT statement to it.
```sql
INSERT INTO `YOUR_PROJECT_ID.YOUR_DATASET_NAME.hotels` (id, name, location, price_tier, checkin_date, checkout_date, booked)
VALUES
(1, 'Hilton Basel', 'Basel', 'Luxury', '2024-04-20', '2024-04-22', FALSE),
(2, 'Marriott Zurich', 'Zurich', 'Upscale', '2024-04-14', '2024-04-21', FALSE),
(3, 'Hyatt Regency Basel', 'Basel', 'Upper Upscale', '2024-04-02', '2024-04-20', FALSE),
(4, 'Radisson Blu Lucerne', 'Lucerne', 'Midscale', '2024-04-05', '2024-04-24', FALSE),
(5, 'Best Western Bern', 'Bern', 'Upper Midscale', '2024-04-01', '2024-04-23', FALSE),
(6, 'InterContinental Geneva', 'Geneva', 'Luxury', '2024-04-23', '2024-04-28', FALSE),
(7, 'Sheraton Zurich', 'Zurich', 'Upper Upscale', '2024-04-02', '2024-04-27', FALSE),
(8, 'Holiday Inn Basel', 'Basel', 'Upper Midscale', '2024-04-09', '2024-04-24', FALSE),
(9, 'Courtyard Zurich', 'Zurich', 'Upscale', '2024-04-03', '2024-04-13', FALSE),
(10, 'Comfort Inn Bern', 'Bern', 'Midscale', '2024-04-04', '2024-04-16', FALSE);
```
> **Note:** Replace `YOUR_PROJECT_ID` and `YOUR_DATASET_NAME` in the SQL with your actual project ID and dataset name.
Then run the command below to execute the sql query:
```bash
bq query --project_id=$GOOGLE_CLOUD_PROJECT --dataset_id=$BQ_DATASET_NAME --use_legacy_sql=false < insert_hotels_data.sql
```
## Step 2: Install and configure Toolbox
In this section, we will download Toolbox, configure our tools in a `tools.yaml` to use BigQuery, and then run the Toolbox server.
1. Download the latest version of Toolbox as a binary:
{{< notice tip >}}
Select the
[correct binary](https://github.com/googleapis/genai-toolbox/releases)
corresponding to your OS and CPU architecture.
{{< /notice >}}
<!-- {x-release-please-start-version} -->
```bash
export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
curl -O https://storage.googleapis.com/genai-toolbox/v0.7.0/$OS/toolbox
```
<!-- {x-release-please-end} -->
1. Make the binary executable:
```bash
chmod +x toolbox
```
1. Write the following into a `tools.yaml` file. You must replace the `YOUR_PROJECT_ID` and `YOUR_DATASET_NAME` placeholder in the config with your actual BigQuery project and dataset name. The `location` field is optional; if not specified, it defaults to 'us'. The table name `hotels` is used directly in the statements.
{{< notice tip >}}
Authentication with BigQuery is handled via Application Default Credentials (ADC). Ensure you have run `gcloud auth application-default login`.
{{< /notice >}}
```yaml
sources:
my-bigquery-source:
kind: bigquery
project: YOUR_PROJECT_ID
location: us
tools:
search-hotels-by-name:
kind: bigquery-sql
source: my-bigquery-source
description: Search for hotels based on name.
parameters:
- name: name
type: string
description: The name of the hotel.
statement: SELECT * FROM `YOUR_DATASET_NAME.hotels` WHERE LOWER(name) LIKE LOWER(CONCAT('%', @name, '%'));
search-hotels-by-location:
kind: bigquery-sql
source: my-bigquery-source
description: Search for hotels based on location.
parameters:
- name: location
type: string
description: The location of the hotel.
statement: SELECT * FROM `YOUR_DATASET_NAME.hotels` WHERE LOWER(location) LIKE LOWER(CONCAT('%', @location, '%'));
book-hotel:
kind: bigquery-sql
source: my-bigquery-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: integer
description: The ID of the hotel to book.
statement: UPDATE `YOUR_DATASET_NAME.hotels` SET booked = TRUE WHERE id = @hotel_id;
update-hotel:
kind: bigquery-sql
source: my-bigquery-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: 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.
- name: hotel_id
type: integer
description: The ID of the hotel to update.
statement: >-
UPDATE `YOUR_DATASET_NAME.hotels` SET checkin_date = PARSE_DATE('%Y-%m-%d', @checkin_date), checkout_date = PARSE_DATE('%Y-%m-%d', @checkout_date) WHERE id = @hotel_id;
cancel-hotel:
kind: bigquery-sql
source: my-bigquery-source
description: Cancel a hotel by its ID.
parameters:
- name: hotel_id
type: integer
description: The ID of the hotel to cancel.
statement: UPDATE `YOUR_DATASET_NAME.hotels` SET booked = FALSE WHERE id = @hotel_id;
```
**Important Note on `toolsets`**: The `tools.yaml` content above does not include a `toolsets` section. The Python agent examples in Step 3 (e.g., `await toolbox_client.load_toolset("my-toolset")`) rely on a toolset named `my-toolset`. To make those examples work, you will need to add a `toolsets` section to your `tools.yaml` file, for example:
```yaml
# Add this to your tools.yaml if using load_toolset("my-toolset")
# Ensure it's at the same indentation level as 'sources:' and 'tools:'
toolsets:
my-toolset:
- search-hotels-by-name
- search-hotels-by-location
- book-hotel
- update-hotel
- cancel-hotel
```
Alternatively, you can modify the agent code to load tools individually (e.g., using `await toolbox_client.load_tool("search-hotels-by-name")`).
For more info on tools, check out the [Resources](../../resources/) section of the docs.
1. Run the Toolbox server, pointing to the `tools.yaml` file created earlier:
```bash
./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](https://research.google.com/colaboratory/local-runtimes.html).
{{< /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="Langchain" lang="bash" >}}
pip install toolbox-langchain
{{< /tab >}}
{{< tab header="LlamaIndex" lang="bash" >}}
pip install toolbox-llamaindex
{{< /tab >}}
{{< tab header="ADK" lang="bash" >}}
pip install google-adk
{{< /tab >}}
{{< /tabpane >}}
1. Install other required dependencies:
{{< tabpane persist=header >}}
{{< tab header="Core" 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="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="ADK" lang="bash" >}}
pip install toolbox-core
{{< /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():
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 >}}
{{< 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 main():
# 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
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 = await agent.ainvoke(inputs, stream_mode="values", config=config)
print(response["messages"][-1].content)
asyncio.run(main())
{{< /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 main():
# TODO(developer): replace this with another model if needed
llm = GoogleGenAI(
model="gemini-2.0-flash-001",
vertexai_config={"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
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.arun(user_msg=query, ctx=ctx)
print(f"---- {query} ----")
print(str(response))
asyncio.run(main())
{{< /tab >}}
{{< 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 # For constructing message content
from toolbox_core import ToolboxSyncClient
import os
os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'
# TODO(developer): Replace 'YOUR_PROJECT_ID' with your Google Cloud Project ID.
os.environ['GOOGLE_CLOUD_PROJECT'] = 'YOUR_PROJECT_ID'
# TODO(developer): Replace 'us-central1' with your Google Cloud Location (region).
os.environ['GOOGLE_CLOUD_LOCATION'] = 'us-central1'
# --- Load Tools from Toolbox ---
# TODO(developer): Ensure the Toolbox server is running at http://127.0.0.1:5000
with ToolboxSyncClient("http://127.0.0.1:5000") as toolbox_client:
# TODO(developer): Replace "my-toolset" with the actual ID of your toolset as configured in your MCP Toolbox server.
agent_toolset = toolbox_client.load_toolset("my-toolset")
# --- Define the Agent's Prompt ---
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.
"""
# --- Configure the Agent ---
root_agent = Agent(
model='gemini-2.0-flash-001',
name='hotel_agent',
description='A helpful AI assistant that can search and book hotels.',
instruction=prompt,
tools=agent_toolset, # Pass the loaded toolset
)
# --- Initialize Services for Running the Agent ---
session_service = InMemorySessionService()
artifacts_service = InMemoryArtifactService()
# Create a new session for the interaction.
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,
)
# --- Define Queries and Run the Agent ---
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 >}}
{{< /tabpane >}}
{{< tabpane text=true persist=header >}}
{{% tab header="Core" lang="en" %}}
To learn more about the Core SDK, check out the [Toolbox Core SDK documentation.](https://github.com/googleapis/genai-toolbox/tree/main/sdks/toolbox-core)
{{% /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="ADK" lang="en" %}}
To learn more about Agents in ADK, check out the [ADK documentation.](https://google.github.io/adk-docs/)
{{% /tab %}}
{{< /tabpane >}}
1. Run your agent, and observe the results:
```sh
python hotel_agent.py
```
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