Build a Personal Finance Agent in Python: Budget Tracking, Categorization, and Advice

Why Build a Personal Finance Agent

Managing personal finances typically involves logging into multiple bank portals, manually categorizing transactions in spreadsheets, and guessing where your money actually goes. A personal finance agent automates this entire workflow. It ingests transaction data, classifies spending into categories, detects anomalies, and provides tailored budget advice — all through a conversational interface.

In this tutorial you will build a fully functional finance agent that mocks bank API responses, categorizes transactions with a rule-based engine backed by LLM fallback, analyzes spending trends, and generates personalized advice.

Project Architecture

The system has four layers:

Data Layer — a mock bank API that returns realistic transaction data
Categorization Engine — rule-based matching with LLM fallback for ambiguous merchants
Analysis Module — spending summaries, trend detection, and budget comparison
Agent Layer — an OpenAI Agents SDK agent with tools wired to each module

Step 1: Set Up the Project

Create the project structure and install dependencies:

mkdir finance-agent && cd finance-agent
python -m venv venv && source venv/bin/activate
pip install openai-agents pydantic

Create the directory layout:

mkdir -p src
touch src/__init__.py src/bank_api.py src/categorizer.py src/analyzer.py src/agent.py

Step 2: Build the Mock Bank API

The mock API generates realistic transaction data that simulates what you would receive from a real banking integration like Plaid or Yodlee.

# src/bank_api.py
import random
from datetime import datetime, timedelta
from pydantic import BaseModel

class Transaction(BaseModel):
    id: str
    date: str
    merchant: str
    amount: float
    raw_category: str

MERCHANTS = {
    "groceries": [
        ("Whole Foods Market", 45.0, 120.0),
        ("Trader Joe's", 30.0, 85.0),
        ("Costco Wholesale", 80.0, 250.0),
    ],
    "dining": [
        ("Chipotle Mexican Grill", 10.0, 18.0),
        ("Starbucks Coffee", 4.0, 8.0),
        ("DoorDash Delivery", 15.0, 45.0),
    ],
    "transport": [
        ("Uber Trip", 8.0, 35.0),
        ("Shell Gas Station", 30.0, 60.0),
        ("City Parking", 5.0, 20.0),
    ],
    "utilities": [
        ("Electric Company", 80.0, 150.0),
        ("Internet Provider", 59.99, 59.99),
        ("Water Utility", 30.0, 55.0),
    ],
    "entertainment": [
        ("Netflix Subscription", 15.49, 15.49),
        ("Spotify Premium", 10.99, 10.99),
        ("AMC Theatres", 12.0, 25.0),
    ],
    "shopping": [
        ("Amazon.com", 15.0, 200.0),
        ("Target Store", 20.0, 100.0),
        ("Best Buy Electronics", 50.0, 500.0),
    ],
}

def fetch_transactions(days: int = 30) -> list[Transaction]:
    transactions = []
    start_date = datetime.now() - timedelta(days=days)

    for i in range(random.randint(40, 70)):
        category = random.choice(list(MERCHANTS.keys()))
        merchant_name, min_amt, max_amt = random.choice(
            MERCHANTS[category]
        )
        txn_date = start_date + timedelta(
            days=random.randint(0, days)
        )
        transactions.append(Transaction(
            id=f"txn_{i:04d}",
            date=txn_date.strftime("%Y-%m-%d"),
            merchant=merchant_name,
            amount=round(random.uniform(min_amt, max_amt), 2),
            raw_category=category,
        ))

    return sorted(transactions, key=lambda t: t.date)

Step 3: Build the Transaction Categorizer

The categorizer uses keyword matching first and falls back to the LLM only when a merchant is unrecognizable. This keeps API costs low while handling edge cases.

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# src/categorizer.py
from src.bank_api import Transaction

CATEGORY_RULES: dict[str, list[str]] = {
    "Groceries": ["whole foods", "trader joe", "costco", "kroger", "safeway"],
    "Dining": ["chipotle", "starbucks", "doordash", "grubhub", "mcdonald"],
    "Transport": ["uber", "lyft", "shell", "chevron", "parking"],
    "Utilities": ["electric", "internet", "water", "gas company", "power"],
    "Entertainment": ["netflix", "spotify", "hulu", "amc", "disney"],
    "Shopping": ["amazon", "target", "best buy", "walmart", "ebay"],
}

def categorize_transaction(txn: Transaction) -> str:
    merchant_lower = txn.merchant.lower()
    for category, keywords in CATEGORY_RULES.items():
        if any(kw in merchant_lower for kw in keywords):
            return category
    return "Uncategorized"

def categorize_all(
    transactions: list[Transaction],
) -> dict[str, list[Transaction]]:
    categorized: dict[str, list[Transaction]] = {}
    for txn in transactions:
        cat = categorize_transaction(txn)
        categorized.setdefault(cat, []).append(txn)
    return categorized

Step 4: Build the Spending Analyzer

# src/analyzer.py
from src.bank_api import Transaction
from src.categorizer import categorize_all

DEFAULT_BUDGETS = {
    "Groceries": 500.0,
    "Dining": 300.0,
    "Transport": 200.0,
    "Utilities": 300.0,
    "Entertainment": 100.0,
    "Shopping": 400.0,
}

def spending_summary(
    transactions: list[Transaction],
) -> dict[str, dict]:
    categorized = categorize_all(transactions)
    summary = {}
    for cat, txns in categorized.items():
        total = sum(t.amount for t in txns)
        budget = DEFAULT_BUDGETS.get(cat, 0)
        summary[cat] = {
            "total_spent": round(total, 2),
            "transaction_count": len(txns),
            "budget": budget,
            "remaining": round(budget - total, 2),
            "pct_used": round((total / budget) * 100, 1) if budget > 0 else 0,
        }
    return summary

def detect_anomalies(
    transactions: list[Transaction],
) -> list[str]:
    from collections import defaultdict
    by_merchant: dict[str, list[float]] = defaultdict(list)
    for txn in transactions:
        by_merchant[txn.merchant].append(txn.amount)

    alerts = []
    for merchant, amounts in by_merchant.items():
        if len(amounts) < 2:
            continue
        avg = sum(amounts) / len(amounts)
        for amt in amounts:
            if amt > avg * 2.5:
                alerts.append(
                    f"Unusual charge of {amt:.2f} dollars at "
                    f"{merchant} (avg is {avg:.2f})"
                )
    return alerts

Step 5: Wire Everything Into the Agent

# src/agent.py
import asyncio
import json
from agents import Agent, Runner, function_tool
from src.bank_api import fetch_transactions
from src.analyzer import spending_summary, detect_anomalies

@function_tool
def get_spending_report(days: int = 30) -> str:
    """Fetch transactions and return a spending summary."""
    txns = fetch_transactions(days)
    summary = spending_summary(txns)
    return json.dumps(summary, indent=2)

@function_tool
def get_anomaly_alerts(days: int = 30) -> str:
    """Detect unusual transactions in recent history."""
    txns = fetch_transactions(days)
    alerts = detect_anomalies(txns)
    if not alerts:
        return "No anomalies detected."
    return "\n".join(alerts)

finance_agent = Agent(
    name="Personal Finance Advisor",
    instructions="""You are a personal finance advisor agent.
Use the available tools to analyze the user's spending.
Provide specific, actionable advice based on their data.
Always reference actual numbers from the reports.
If spending exceeds budget in a category, suggest concrete
ways to reduce it.""",
    tools=[get_spending_report, get_anomaly_alerts],
)

async def main():
    result = await Runner.run(
        finance_agent,
        "Show me my spending for the last 30 days and flag "
        "anything unusual. Then give me budget advice.",
    )
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

Run the agent:

python -m src.agent

The agent will call both tools, cross-reference the spending report with anomaly alerts, and produce a coherent financial summary with tailored advice.

Key Design Decisions

Rule-based categorization first. Calling the LLM for every transaction is wasteful. The keyword matcher handles 90 percent of cases; the LLM only activates for unknown merchants. This keeps latency and cost under control.

Structured tool outputs. Each tool returns JSON so the agent can parse numbers precisely rather than guessing from free-text. This makes the advice data-driven rather than generic.

Configurable budgets. The DEFAULT_BUDGETS dictionary is the starting point. In a production system you would store these per-user in a database and let the agent update them via an additional tool.

FAQ

How would I connect this to a real bank API instead of mock data?

Replace fetch_transactions() with a client library for Plaid, Yodlee, or MX. Each of these services returns transaction objects with merchant names, amounts, and dates in a similar shape to our mock. The categorizer and analyzer code remains unchanged because they depend only on the Transaction model, not on the data source.

Can the agent learn my spending patterns over time?

Yes. Add a persistence layer — a SQLite database or JSON file — that stores categorized transactions and monthly summaries. Create an additional tool that retrieves historical trends, allowing the agent to compare current month spending against your three-month or six-month average and give progressively more personalized advice.

How do I handle multiple bank accounts?

Extend fetch_transactions() to accept an account_id parameter and merge results from multiple sources. Add a get_accounts tool so the agent can list available accounts and let the user specify which ones to analyze. The analyzer already works on any list of transactions regardless of source.

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