LedgerMind is an offline-first finance agent that transforms messy bank CSVs into clear budgets, actionable insights, and transparent AI explanations.
It combines:
Explain Signal → every prediction comes with reasoning.
Learning Loop → the agent retrains on your feedback in real time.
Planner → generates actionable steps like categorizing unknowns, setting budgets, or exporting reports.
Offline-first privacy → powered by open-source LLMs (gpt-oss:20b), all running locally.
💡 What Inspired Me
I grew up with parents who taught me the importance of budgeting. Any time they lent me money, they’d ask me to make a budget to keep track of it. At first, this was valuable — but over time, it became tedious, especially since I didn’t know Excel well.
That experience stuck with me. I wanted a tool that made budgeting simple, transparent, and adaptive without needing to become an Excel wizard. LedgerMind is my answer to that problem: a smart agent that learns your habits, explains its reasoning, and keeps your finances private.
📚 What I Learned
How to integrate local open-source LLMs into a production-grade workflow.
The value of explainability — users trust AI more when they can see why it makes decisions.
How to blend rules, machine learning, and LLMs into a single cohesive system.
Security best practices: cookie-based auth, CSRF protection, RBAC.
Frontend/Backend orchestration with FastAPI, React, Tailwind, and Docker.
🛠️ How I Built It
Backend: FastAPI + SQLAlchemy with Postgres/SQLite.
Routers for ingestion, charts, ML, reports, budgets, rules, and planner.
Real-time ML feedback loop using partial_fit.
Frontend: Vite + React + Tailwind.
Unified ChatDock for natural language queries.
Panels for charts, budgets, unknowns, rules, and planner.
ML/LLM:
Transaction classification with incremental learning.
NL → SQL grounding for transaction queries.
Explanations via gpt-oss:20b (Ollama/vLLM).
Infra: Docker Compose for one-click dev/prod setup.
⚡ Challenges I Faced
Running large local models efficiently (prompt trimming, context management).
Blending rules + ML + LLM into suggestions without duplicates.
UI polish: building a clean, intuitive UX under hackathon deadlines.
Security: implementing cookie auth + CSRF correctly across frontend and backend.
Branding: designing a logo and cinematic visuals that look professional.
✨ Closing Thought
LedgerMind grew out of a personal frustration with tedious budgeting and became a demonstration of what open-source AI can achieve. It’s a tool that gets smarter with every click, explains its reasoning, and keeps your finances private — just the kind of agent I wish I had growing up.
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