Build an AI Agent Expense Tracker with Streamlit, FastAPI, Function Calling & Google Sheets
Last Updated on July 20, 2026 by Editorial Team
Author(s): A.Venkatesh
Originally published on Towards AI.
Introduction
Most expense tracker applications require users to manually fill out forms:
The article then walks through building a production-ready AI Expense Tracker that accepts natural-language requests and uses an AI agent to orchestrate CRUD operations via function calling. It outlines the overall project components (Streamlit UI, FastAPI backend, AI agent, and Google Sheets storage), explains the system architecture, and details each implementation step: creating REST endpoints, encapsulating Google Sheets access in a database layer, building an intent-understanding agent with a system prompt, enabling function calling with defined tools, and supporting multi-step tool execution for tasks like deleting the most recent expense. The tutorial continues with building an interactive Streamlit dashboard, adding Plotly-based analytics with dynamic filters, generating LLM-driven financial insights, designing a modern UX, and deploying the integrated frontend/backend on Streamlit Community Cloud. Finally, it covers key challenges encountered, summarizes what the project teaches about agentic AI and tool use, and concludes with guidance and resources (including a live demo and GitHub repository).
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