Autonomous AI Agents for data analysis and visualization

A multi-agent system where user can request for analysis over a SQL database using a prompt. A data analysis agent returns the analyzed data and a data visualization agent generates a visualization. User can specify the type of visualization (line, bar, scatter, multi line, etc.). The agents are controlled through a simple command-line interface and the generated visualizations follows a pre-defined design template.

Some examples

The example uses the chinook sqlite database taken from here.

User prompt	Generated visualization
Tabulate the annual sales in USA, United Kingdom, and Canada. Visualization type: Stacked bar chart.
Tabulate the sales of rock, metal and heavy metal genre music year wise in the last 5 years. Visualization type: Multi Line.

Environment and AI agent framework used

• Ubuntu 24.04 (WSL2)
• Python 3.12
• LangChain
• GPT-5 (Open AI API key required)

Set up

1. Download the database.

mkdir database
cd database/
wget https://www.timestored.com/data/sample/chinook.db
wget https://www.timestored.com/data/sample/sakila.db
cd ..

2. Create a virtual environment (recommended).

python -m venv agent
source agent/bin/activate

3. Install LangChain and other dependencies.

pip install langchain  langgraph  langchain-community
pip install -U "langchain[openai]"
pip install matplotlib pandas numpy seaborn

4. Set up the OpenAI API key.

export OPENAI_API_KEY="[INSERT YOUR API KEY]"
curl https://api.openai.com/v1/models   -H "Authorization: Bearer $OPENAI_API_KEY"

How to use the agent?

Run python main.py.

• Available users: admin, client1, client2, client3
• Available databases: chinook, northwind_small, sakila
• Refer database_config.py for details on users and databases. The user "admin" has access to all databases.
• Example use-case:
  • user: admin
  • database: chinook
  • question: "Tabulate the sales of artist AC/DC in the last 5 years. Visualization type: Line."
• The analyzed data will be displayed on the command-line interface and the generated visualization will be stored inside visualization/ directory.

Run automated test

Some sample automated test scripts are provided to check the functionality and correctness of the agents. For example:

• Run a test using the following command.
  python -m test.test5
• First it will display the user prompt and the expected correct analysis.
• Then the agents will return its analysed data and generate the visualization (stored in visualization/).

How to add new database?

1. Download it inside database/ directory.

cd database/
wget https://www.timestored.com/data/sample northwind_small.sqlite

2. Update database_config.py with DATABASES and USER_DB_ACCESS dictionary.

How to change the visualization template?

Just modify the visualization/company_template.py script with your design color and font choice.