Let AI classify your transactions. LLM-powered spending categorization.

Built for AI Agents

The Problem

WHOLEFDS MKT 10847 SEATTLE WA AMZN MKTP US*2K7X9 SQ *JOES COFFEE SEATTLE

Bank transactions are cryptic. Bank categories say "Shopping" when you want "Kids > Clothing" vs "Home > Furniture". They lump restaurants together but you need "Coffee" separate from "Fast Food".

The Solution

LLMs have read the internet. They know what "WHOLEFDS MKT" is. They understand "SQ *" means Square. Tell them your rules in plain English:

"ZELLE to Sarah is babysitting → Childcare" "COSTCO with GAS is fuel, otherwise groceries"

The AI writes pattern-matching rules to a CSV. No database. No CMS. Just a text file you control.

Install

Linux / macOS
curl -fsSL https://tallyai.money/install.sh | bash
Windows PowerShell
irm https://tallyai.money/install.ps1 | iex
Python (uv)
uv tool install git+https://github.com/davidfowl/tally

Workflow

1
tally init
Creates config folder with AGENTS.md instructions
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🤖
AI Agent Loop
Your AI reads AGENTS.md then:
tally discover --format json → finds unknown merchants
AI identifies merchants, writes rules to CSV
Repeat until unknowns are minimal
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2
tally run
Generates HTML spending report with all transactions categorized
Tally spending report showing monthly budget, recurring expenses, and transaction categories
Open Source

Tally is MIT licensed. Contributions welcome!

GitHub Releases Docs