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:

pip install streamlit agno ipywidgets

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:

import streamlit as st
from agno.run.response import RunEvent, RunResponse

async def as_stream(response):
    async for chunk in response:
        if isinstance(chunk, RunResponse) and isinstance(chunk.content, str):
            if chunk.event == RunEvent.run_response:
                yield chunk.content

def apply_styles():
    st.markdown("""
  <style>
  hr.divider {
  background-color: white;
  margin: 0;
  }
  </style>
  <hr class='divider' />""", unsafe_allow_html=True)

Setup credentials

Set your Anthropic API key as an environment variable:

export ANTHROPIC_API_KEY="your_api_key_here"

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:

from utils import apply_styles

import streamlit as st
from textwrap import dedent

from agno.models.anthropic import Claude
from agno.agent import Agent
from agno.tools.mcp import MCPTools
from agno.storage.json import JsonStorage
from agno.run.response import RunEvent, RunResponse
from mcp.client.stdio import stdio_client, StdioServerParameters

from mcp import ClientSession

import asyncio
import threading
from queue import Queue

Define the agent streaming function

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

async def stream_clickhouse_agent(message):
    env = {
            "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
            "CLICKHOUSE_PORT": "8443",
            "CLICKHOUSE_USER": "demo",
            "CLICKHOUSE_PASSWORD": "",
            "CLICKHOUSE_SECURE": "true"
        }
    
    server_params = StdioServerParameters(
        command="uv",
        args=[
        'run',
        '--with', 'mcp-clickhouse',
        '--python', '3.13',
        'mcp-clickhouse'
        ],
        env=env
    )
    
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            mcp_tools = MCPTools(timeout_seconds=60, session=session)
            await mcp_tools.initialize()
            agent = Agent(
                model=Claude(id="claude-3-5-sonnet-20240620"),
                tools=[mcp_tools],
                instructions=dedent("""\
                    You are a ClickHouse assistant. Help users query and understand data using ClickHouse.
                    - Run SQL queries using the ClickHouse MCP tool
                    - Present results in markdown tables when relevant
                    - Keep output concise, useful, and well-formatted
                """),
                markdown=True,
                show_tool_calls=True,
                storage=JsonStorage(dir_path="tmp/team_sessions_json"),
                add_datetime_to_instructions=True, 
                add_history_to_messages=True,
            )
            chunks = await agent.arun(message, stream=True)
            async for chunk in chunks:
                if isinstance(chunk, RunResponse) and chunk.event == RunEvent.run_response:
                    yield chunk.content

Add synchronous wrapper functions

Add helper functions to handle async streaming in Streamlit:

def run_agent_query_sync(message):
    queue = Queue()
    def run():
        asyncio.run(_agent_stream_to_queue(message, queue))
        queue.put(None)  # Sentinel to end stream
    threading.Thread(target=run, daemon=True).start()
    while True:
        chunk = queue.get()
        if chunk is None:
            break
        yield chunk

async def _agent_stream_to_queue(message, queue):
    async for chunk in stream_clickhouse_agent(message):
        queue.put(chunk)

Create the Streamlit interface

Add the Streamlit UI components and chat functionality:

st.title("A ClickHouse-backed AI agent")

if st.button("💬 New Chat"):
  st.session_state.messages = []
  st.rerun()

apply_styles()

if "messages" not in st.session_state:
  st.session_state.messages = []

for message in st.session_state.messages:
  with st.chat_message(message["role"]):
    st.markdown(message["content"])

if prompt := st.chat_input("What is up?"):
  st.session_state.messages.append({"role": "user", "content": prompt})
  with st.chat_message("user"):
    st.markdown(prompt)
  with st.chat_message("assistant"):
    response = st.write_stream(run_agent_query_sync(prompt))
  st.session_state.messages.append({"role": "assistant", "content": response})

Run the application

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

uv run \
  --with streamlit \
  --with agno \
  --with anthropic \
  --with mcp \
  streamlit run app.py --server.headless true

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.