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

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Python Quickstart (Local)	docs	2	How to get started running MCP 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 3.10+ (including pip and your preferred virtual environment tool for managing dependencies e.g. venv).
2. Installed PostgreSQL 16+ and the psql client.

Cloud Setup (Optional)

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

Step 1: Set up your database

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

Step 2: Install and configure MCP Toolbox

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

Step 3: Connect your agent to MCP Toolbox

In this section, we will write and run an agent that will load the Tools from MCP 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 google-adk[toolbox] {{< /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" >}}

No other dependencies required for 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 the agent: {{< tabpane persist=header >}} {{% tab header="ADK" text=true %}}

2. Create a new agent project. This will create a new directory named my_agent with a file agent.py.

   adk create my_agent
   

   
   

3. Update my_agent/agent.py with the following content to connect to MCP Toolbox:

   {{< regionInclude "quickstart/python/adk/quickstart.py" "quickstart" >}}
   

   
   

4. Create a .env file with your Google API key:

   echo 'GOOGLE_API_KEY="YOUR_API_KEY"' > my_agent/.env
   

{{% /tab %}} {{% tab header="LangChain" text=true %}} Create a new file named agent.py and copy the following code:

{{< include "quickstart/python/langchain/quickstart.py" >}}

{{% /tab %}} {{% tab header="LlamaIndex" text=true %}} Create a new file named agent.py and copy the following code:

{{< include "quickstart/python/llamaindex/quickstart.py" >}}

{{% /tab %}} {{% tab header="Core" text=true %}} Create a new file named agent.py and copy the following code:

{{< include "quickstart/python/core/quickstart.py" >}}

{{% /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 >}}

4. Run your agent, and observe the results:

   {{< tabpane persist=header >}} {{% tab header="ADK" text=true %}} Run your agent locally for testing:

adk run my_agent

Alternatively, serve it via a web interface:

adk web --port 8000

For more information, refer to the ADK documentation on Running Agents and Deploying to Cloud.

{{% /tab %}} {{< tab header="Langchain" lang="bash" >}} python agent.py {{< /tab >}} {{< tab header="LlamaIndex" lang="bash" >}} python agent.py {{< /tab >}} {{< tab header="Core" lang="bash" >}} python agent.py {{< /tab >}} {{< /tabpane >}}

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