A Multi-Agent RAG System for Auditable Token Distribution

Project Type: Final Year Project (FYP) Technologies: React, TypeScript, TailwindCSS, Supabase, Node.js, Express, ZK-SNARK (Groth16), Circom, Solidity (ERC20), Mock API (Swagger UI), FastAPI

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Table of Contents

1. Project Overview
2. Core Features
3. Architecture
4. Demo Flow
5. Installation & Setup
6. Future Work
7. License

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Project Overview

This project implements a multi-agent RAG (Retrieval-Augmented Generation) system integrated with zero-knowledge proofs (ZKP) to enable auditable and privacy-preserving token distribution. The system allows users to prove their eligibility for a token-based subsidy (or scholarship) without revealing sensitive income data.

Key goals:

• Showcase technical strength in full-stack development, ZKP, and multi-agent AI frameworks.
• Provide a privacy-preserving, auditable workflow for conditional token distribution.
• Deliver a reusable system suitable for educational or NGO contexts.

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Core Features

• Front-end:

  • React + TypeScript + Vite
  • TailwindCSS styling and Lucide React icons
  • Role-based pages: Citizen, Admin, ZK Demo
  • Real-time ZK proof display and status badges

• Back-end:

  • Node.js + Express servers for Mock LHDN API and ZK circuit service
  • Supabase for user authentication and database storage
  • Row-Level Security (RLS) ensures users can only access their own profiles

• Zero-Knowledge Proof (ZKP):

  • Groth16 SNARKs implemented in Circom
  • Income classification without revealing exact income
  • Proof verification via backend service

• Token Distribution (Planned / Minimal Demo):

  • ERC20 / claim contract for token issuance
  • End-to-end demonstration of claim after ZKP verification
  • Auditable workflow recorded on blockchain or local testnet

• Multi-Agent / RAG System (Planned):

  • AI agents can analyze policy documents and assist eligibility scoring
  • Supports Retrieval-Augmented Generation for automated decision-making

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Architecture

Key Notes:

• ZKP allows users to prove income bracket eligibility without revealing exact figures.
• Mock LHDN API simulates a trusted income source for the proof system.
• Multi-agent RAG system planned to automate policy scoring (AI integration).

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Demo Flow

1. Register / Login using Supabase Auth
2. Fill Profile (name, date of birth, gender, wallet address, household info)
3. IC Verification → Backend retrieves income data from Mock LHDN
4. ZK Proof Generation → Generates proof for income bracket (e.g., B1-T2)
5. View Eligibility → Verified bracket shown in front-end without revealing actual income
6. Token Claim (Demo) → Send claim transaction to local/testnet contract (optional)

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Installation & Setup

Prerequisites:

• Node.js >= 20
• npm >= 9
• Docker (optional, for local DB)

Clone the repository:

git clone https://github.com/BLTC-520/gov-subsidy-platform.git
cd gov-subsidy-platform

Install dependencies:

# Frontend
cd frontend
npm install
npm run dev

# Backend (Mock LHDN + ZK service)
cd ../backend/mock-lhdn-api
npm install
npm start

cd ../zk-service
npm install
npm start

root -> ./startallservices.sh

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Future Work

• Integrate AI / RAG for automatic policy analysis and eligibility scoring
• Expand blockchain integration for real-world auditable token distribution (will use Foundry)
• Deploy system for real scholarship / subsidy pilot projects

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License

MIT License