This Langflow flow creates an intelligent database query and visualization system that transforms natural language questions into SQL queries and generates custom charts. The flow operates through a two-stage agent architecture that handles both data retrieval and visualization generation. Users can ask questions about their database in plain English, and the system will automatically query the database and create appropriate visual representations of the results.

The first stage uses a SQL specialist agent that receives user questions and database schema information. This agent analyzes the schema to understand the database structure and generates appropriate SQL queries to answer the user's question. The agent has access to a SQL database tool that can execute queries against the configured database. The results are returned in JSON format for further processing by the visualization stage.

The second stage employs a data visualization agent that takes the JSON results from the database query and generates Python code to create charts using Matplotlib. This agent evaluates the data structure and user requirements to determine the most appropriate visualization type. The generated Python code creates charts in base64 format, which are then formatted and displayed to the user. The system requires proper configuration of the database URL, schema definition, and Matplotlib installation to function correctly.

The flow begins with a chat input component that captures user questions in natural language. Prompt templates shape the instructions for both agents, ensuring they understand their specific roles in the pipeline. The system uses structured output formatting to maintain consistent data flow between stages and can be tested thoroughly in Langflow's playground environment.

The flow can be extended significantly using other Langflow components. You could add dataframe operations for advanced data transformations, integrate API requests to pull data from multiple sources, or implement smart routing to direct different question types to specialized processing paths. The system could also incorporate RAG capabilities to answer questions about unstructured documents alongside database queries, or use webhook triggers to automatically generate reports based on external events.