Skip to main content
Skip to main content
Edit this page

How to build a ClickHouse-backed AI Agent with Streamlit

In this guide you'll learn how to build a web-based AI agent using Streamlit that can interact with ClickHouse's SQL playground using ClickHouse's MCP Server and Agno.

Example application

This example creates a full web application that provides a chat interface for querying ClickHouse data. You can find the source code for this example in the examples repository.

Prerequisites​

  • You'll need to have Python installed on your system. You'll need to have uv installed
  • You'll need an Anthropic API key, or API key from another LLM provider

You can run the following steps to create your Streamlit application.

Install libraries​

Install the required libraries by running the following commands:

Create utilities file​

Create a utils.py file with two utility functions. The first is an asynchronous function generator for handling stream responses from the Agno agent. The second is a function for applying styles to the Streamlit application:

Setup credentials​

Set your Anthropic API key as an environment variable:

Using another LLM provider

If you don't have an Anthropic API key, and want to use another LLM provider, you can find the instructions for setting up your credentials in the Agno "Integrations" docs

Import required libraries​

Start by creating your main Streamlit application file (e.g., app.py) and add the imports:

Define the agent streaming function​

Add the main agent function that connects to ClickHouse's SQL playground and streams responses:

Add synchronous wrapper functions​

Add helper functions to handle async streaming in Streamlit:

Create the Streamlit interface​

Add the Streamlit UI components and chat functionality:

Run the application​

To start your ClickHouse AI agent web application you can run the following command from your terminal:

This will open your web browser and navigate to http://localhost:8501 where you can interact with your AI agent and ask it questions about the example datasets available in ClickHouse's SQL playground.