Track spending in plain language, import bank statements and receipts, ask questions about your money. Your data stays as plain text files on your machine. Works with any LLM, including local ones. All changes are versioned with git.
demo.mp4
Download for macOS (Apple Silicon)
I spent $45 at Whole Foods yesterday
The agent figures out the details and adds it to your ledger as a proper double-entry transaction.
Here is my March bank statement, please add missing transactions: ~/Downloads/statement-march.pdf
Add transaction from this receipt: ~/Desktop/receipt.jpg
Drop a PDF bank statement, an invoice, or a photo of a paper receipt into the chat. The agent extracts the text, pulls out every transaction, and adds them to your ledger. The original file is archived in your workspace so you can refer back to it later.
How much did I spend on food this month?
What's my net worth?
Show me all transactions from my last trip to Italy
The agent checks your ledger and gives you a clear answer. Type @ to point it at a specific account, payee, or tag.
I'm visiting Italy from April 10th to 15th. Tag all transactions during this period with the 'trip_italy' tag.
My food budget is $600/month, please notify me when I get close.
Tell the agent once — it remembers and applies it when it matters.
Which subscriptions can I cancel?
Create a skill that compares my spending this month to the previous month and shows where I spent more.
Skills are reusable playbooks the agent follows step by step, like monthly reviews or subscription audits. Install them from the plugin marketplace or create your own.
What did you change today?
Undo the last change.
Every modification is auto-committed to a local git repo. Review your history anytime, roll back mistakes, or push to a private repo for backup.
- Download Accountant24 for Apple Silicon Macs and install it.
- Launch Accountant24.
- Connect your LLM provider.
- Pick your favorite model and start chatting.
Want your financial data to never leave your machine? Run a local model with Ollama.
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Pull a Gemma 4 or any other model:
ollama pull gemma4:31b # requires ~24 GB+ RAM -
In Settings → Providers, connect Ollama. The app detects all your local Ollama models and configures them automatically.
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Pick your Gemma 4 model and start chatting.
| Accountant24 | YNAB / Monarch | Actual Budget | hledger CLI | |
|---|---|---|---|---|
| Data format | Plain text (hledger) | Proprietary cloud | SQLite | Plain text (hledger) |
| Data location | Your machine | Their servers | Your machine | Your machine |
| Input method | Natural language | Manual entry | Manual entry | Manual entry |
| AI | Built-in, learns over time | — | — | — |
| Memory | Persistent | — | — | — |
| Skills | Built-in + custom | — | — | — |
| Version control | Git, built-in | — | — | Via manual setup |
| Accounting | Double-entry bookkeeping | Envelope budgeting | Envelope budgeting | Double-entry bookkeeping |
| Multi-currency | Yes | — | — | Yes |
| Bank sync | — | Yes | Yes (via add-ons) | — |
| Interface | Desktop app | Web + mobile | Web + desktop | CLI |
| Price | Free | $100–$180/yr | Free | Free |
| Lock-in | None (plain text) | High | Medium | None (plain text) |
For years, I managed my personal finances with YNAB. It worked — but every transaction was a manual chore. I'd fall behind, dread catching up, and put it off for weeks at a time.
One weekend I started playing with hledger and Claude Code, just to see if an agent could handle the bookkeeping for me. I wrote a couple of small skills, and to my surprise, it actually worked — and it was genuinely fun to use.
So I kept going. I packed everything into a standalone agent, tuned it for this one job, and started using it every day. I'm honestly happier with it than any tool I've used for my money before.
If it works this well for me, maybe it'll work for you too. That's why I'm releasing it as an open source project.
Each piece of this puzzle does one thing really well.
hledger is a mature accounting engine with proper double-entry bookkeeping. It's fast, reliable, and stores everything in plain text files you fully own. The catch: it has a steep learning curve — journal syntax, report commands, filter expressions. Not something most people want to deal with.
LLMs are great at understanding what you mean in plain language. But they hallucinate numbers and can't do accounting on their own. Left alone with a ledger, they'd quietly corrupt your books.
pi is the agent framework that glues everything together — sessions, tool execution, LLM communication. It's small, well-designed, and easy to extend.
git tracks every change to plain text files — which is exactly what a ledger is. The agent commits your changes as you go, so mistakes are easy to undo, and pushing to a private remote gives you backups.
Put them together and each piece covers the other's weakness. You speak naturally, the LLM figures out what you mean, hledger keeps the math honest, git makes every change traceable, and pi orchestrates the whole thing. That's the combination I ended up with after trying a few alternatives — and it's been working well ever since.
Accountant24 wouldn't exist without these projects:
- pi by Mario Zechner — a minimal but powerful framework for building AI agents.
- hledger by Simon Michael — the accounting engine that makes proper double-entry bookkeeping possible.
Both are remarkable pieces of software.
This is a personal project I use every day, and I'd love to hear from anyone else using it. Bug reports, ideas, feedback, pull requests — all welcome. If you're planning a bigger change, open an issue first so we can talk it through.
Accountant24 has a marketplace for plugins. Browse what the community has published and install a plugin right from Settings → Plugins in the app. Listing is automatic and nobody reviews the plugins, so install only plugins you trust. To build and publish a plugin of your own, see Create a plugin.