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🚀 Try Live Demo • 📂 Documentation • ☁️ Deploy Now
Demo Credentials: [email protected] • demo123
WealthFam provides a holistic view of your financial health. From daily expense tracking to complex investment management and AI-powered intelligence, WealthFam is built to help families refine their finances and achieve long-term growth.
Deploy WealthFam to your preferred cloud platform with just one click:
Try WealthFam without installing: https://wealthfam.onrender.com
Demo credentials: [email protected] / demo123 (or register your own!)
⚠️ Note: This runs on a free instance. It may spin down after inactivity (takes ~50s to wake up). Data resets frequently.
📖 Deployment: See
/deployment for all configuration files and platform-specific guides📚 Documentation: Complete guides in
/docs - Deployment Guide | Quick Deploy | Checklist
The easiest way to run WealthFam is using our pre-built Docker image. You don't need to build anything yourself!
Create a directory for your data and configuration:
mkdir wealthfam && cd wealthfam
Create a docker-compose.yml file:
version: '3.8'
services:
wealthfam:
image: wglabz/wealthfam:latest
container_name: wealthfam
restart: unless-stopped
ports:
- "80:80"
volumes:
- ./data:/data
environment:
- DATABASE_URL=duckdb:////data/family_finance_v3.duckdb
Run the application:
docker-compose up -d
Open http://localhost in your browser.
Clone the repository:
git clone https://github.com/your-org/wealthfam.git
cd wealthfam
Setup Backend:
cd backend
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn app.main:app --reload
Setup Frontend:
cd ../frontend
npm install
npm run dev
WealthFam is designed with data privacy at its core. By leveraging DuckDB, your financial data remains in a local, high-performance database, ensuring you have full control over your intelligence.
WealthFam: Refine Your Finances
WealthFam features a sophisticated, multi-channel ingestion pipeline designed to minimize manual entry while maintaining 100% data accuracy through smart deduplication and noise reduction.
The following diagram illustrates this enhanced end-to-end lifecycle:
graph TD
%% Source Layer
subgraph "1. Ingestion Channels"
direction TB
A["<b>Mobile App</b><br/>(SMS Ingestion)"]
B["<b>Email Tracker</b><br/>(IMAP Sync)"]
C["<b>Web Dashboard</b><br/>(Manual Upload)"]
end
%% Safety Layer
subgraph "2. Safety & Noise Reduction"
direction TB
HASH[MD5 Content Hash]
DEDUP{Deduplication<br/>Check}
IGN{Ignore Patterns<br/>Filter}
A & B & C --> HASH
HASH --> DEDUP
DEDUP -->|Unique| IGN
DEDUP -->|Duplicate| SKP[Skip / Log]
end
%% Intelligence Layer
subgraph "3. Intelligence Engine"
direction LR
DET{Format<br/>Detector}
IGN -->|Valid| DET
subgraph "Parsing Logic"
P1[Static Bank<br/>Parsers]
P2[Learned<br/>Patterns]
P3[AI / LLM<br/>Parser]
end
DET -->|Bank / UPI / CC| P1 & P2 & P3
DET -->|CAS PDF| MF[MF CAS Parser]
end
%% Storage & Persistence Layer
subgraph "4. Validation & Storage"
P1 & P2 & P3 --> TXN(Parsed Transaction)
MF --> PORT(Portfolio Engine)
TXN --> RULE{Rule Engine}
RULE -->|High Confidence| LEDG[<b>Confirmed Ledger</b>]
RULE -->|Low Confidence| TRIA[<b>Review Triage</b>]
PORT -->|XIRR / Performance| HOLD[<b>MF Holdings</b>]
end
%% Learning Loop
P1 & P2 & P3 -.->|Failure| UNP[<b>Training Area</b>]
UNP -.->|Manual Labeling| P2
UNP -.->|Discard & Ignore| IGN
TRIA -.->|Discard & Ignore| IGN
%% Manual Review Path
TRIA -->|User Approval| LEDG
%% Styling
style LEDG fill:#1b5e20,color:#fff,stroke:#2e7d32,stroke-width:2px
style TRIA fill:#ff8f00,color:#fff,stroke:#ef6c00,stroke-width:2px
style UNP fill:#c62828,color:#fff,stroke:#b71c1c,stroke-width:2px
style HOLD fill:#1565c0,color:#fff,stroke:#0d47a1,stroke-width:2px
style DEDUP fill:#eceff1,stroke:#455a64
style IGN fill:#eceff1,stroke:#455a64
style SKP fill:#f5f5f5,stroke:#9e9e9e,stroke-dasharray: 5 5
Content type
Image
Digest
sha256:e0fba445e…
Size
499 MB
Last updated
about 1 month ago
docker pull wglabz/wealthfam