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MyClone

Build AI-powered digital clones of yourself — powered by RAG, voice, and real-time chat.

License: MIT Python 3.11+ Next.js 15 FastAPI Docker

MyClone is an open-source platform for creating intelligent digital personas. Users ingest data from LinkedIn, Twitter, websites, PDFs, and YouTube — then interact with their AI clone via text chat or real-time voice conversations powered by retrieval-augmented generation (RAG).

Features

  • RAG-Powered Knowledge — LlamaIndex + pgvector for accurate, context-aware responses
  • Real-Time Voice Chat — LiveKit + Deepgram STT + ElevenLabs/Cartesia TTS
  • Multi-Source Ingestion — LinkedIn, Twitter, websites, PDFs, YouTube videos
  • Persistent Memory — Conversations and context survive restarts
  • Embeddable Widget — Drop a script tag on any site to add your clone
  • Multi-Auth — OAuth (LinkedIn, Google) + email/password with email verification
  • Workflow System — Linear assessments and conversational lead qualification
  • Payment Integration — Stripe-powered subscriptions and persona monetization
  • Internationalization — 14 languages supported on public-facing pages

Tech Stack

Layer Technologies
Frontend Next.js 15, React 19, TypeScript, Tailwind CSS v4, shadcn/ui, Zustand, TanStack Query v5
Backend FastAPI, Python 3.11+, SQLAlchemy, Alembic, Poetry/uv
RAG LlamaIndex, OpenAI embeddings, pgvector
Voice LiveKit Cloud, Deepgram (STT), ElevenLabs/Cartesia (TTS)
Database PostgreSQL + pgvector
Message Queue NATS JetStream
Infrastructure Docker, multi-stage builds, docker-compose

Repository Structure

myclone/
├── frontend/           # Next.js 15 app (App Router, Turbopack)
├── backend/            # FastAPI API + workers + RAG system
│   ├── app/            # API routes, auth, ingestion
│   ├── shared/         # Database, RAG, services (used by API + workers)
│   ├── workers/        # Background job processors
│   └── livekit/        # Voice agent integration
├── infra/              # Docker configs, docker-compose
├── scripts/            # Utility scripts
└── docs/               # Project documentation

Quick Start

Prerequisites

1. Clone the Repository

git clone https://github.com/myclone-dev/myclone.git
cd myclone

2. Start the Backend (Docker)

cd backend

# Setup environment
cp .env.example .env
# Edit .env with your API keys (OPENAI_API_KEY at minimum)

# First-time setup: build, start services, run migrations
make setup-local

# Or manually:
docker-compose build
docker-compose up -d
docker-compose exec api alembic -c /app/alembic.ini upgrade head

The API will be available at http://localhost:8001. Interactive docs at http://localhost:8001/docs.

3. Start the Frontend

cd frontend

# Install dependencies
bun install

# Setup environment
cp .env.example .env.local
# Edit .env.local — set NEXT_PUBLIC_API_URL=http://localhost:8001/api

# Start dev server
bun dev

The app will be available at http://localhost:3000.

Architecture Overview

┌──────────────┐     ┌──────────────────┐     ┌──────────────────┐
│   Frontend   │     │   FastAPI (API)   │     │  Voice Agent     │
│   Next.js    │────▶│   REST + SSE      │     │  (LiveKit)       │
└──────────────┘     └────────┬─────────┘     └────────┬─────────┘
                              │                         │
                    ┌─────────▼─────────┐               │
                    │   NATS JetStream  │               │
                    │  (Message Queue)  │               │
                    └─────────┬─────────┘               │
                              │                         │
              ┌───────────────┼─────────────────────────┘
              │               │
    ┌─────────▼──────┐  ┌────▼───────────────┐  ┌───────────────┐
    │  Workers       │  │  PostgreSQL        │  │  OpenAI API   │
    │  - Scraping    │  │  + pgvector        │  │  - GPT-4o     │
    │  - Voice proc  │  │  (Personas, RAG)   │  │  - Embeddings │
    └────────────────┘  └────────────────────┘  └───────────────┘

How it works:

  1. Ingest — Users upload data (LinkedIn, Twitter, websites, PDFs, YouTube). Workers process and chunk the content.
  2. Embed — LlamaIndex creates vector embeddings stored in PostgreSQL + pgvector.
  3. Chat — Text or voice queries trigger RAG retrieval, pulling relevant context for the LLM to generate persona-accurate responses.

Development

Backend Commands

cd backend

# Docker workflow (recommended)
make up                # Start core services
make down              # Stop all services
make logs              # View logs
make migrate           # Run database migrations
make check             # Auto-fix: isort + black + ruff

# Local workflow (uv)
uv sync
uv run uvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

See the Backend README for the full API reference, database management, and troubleshooting.

Frontend Commands

cd frontend

# Development
bun dev               # Start dev server (Turbopack)
bun run build         # Production build
bun start             # Start production server

# Code quality
bun type-check        # TypeScript check
bun lint:fix          # ESLint with auto-fix
bun format            # Prettier formatting

Adding UI Components

cd frontend
bunx shadcn@latest add <component-name>

Never manually create files in src/components/ui/ — always use the shadcn CLI.

Environment Variables

Backend (backend/.env)

Variable Required Description
DATABASE_URL Yes PostgreSQL connection string
OPENAI_API_KEY Yes OpenAI API key for LLM + embeddings
EXPERT_CLONE_API_KEY Yes API authentication key
LIVEKIT_URL For voice LiveKit Cloud WebSocket URL
LIVEKIT_API_KEY For voice LiveKit API key
LIVEKIT_API_SECRET For voice LiveKit API secret
DEEPGRAM_API_KEY For voice Deepgram STT key
ELEVENLABS_API_KEY For voice ElevenLabs TTS key

See backend/.env.example for the full list.

Frontend (frontend/.env.local)

Variable Required Description
NEXT_PUBLIC_API_URL Yes Backend API URL (e.g., http://localhost:8001/api)
NEXT_PUBLIC_APP_URL Yes Frontend URL (e.g., http://localhost:3000)

See frontend/.env.example for the full list.

Contributing

We welcome contributions! Here's how to get started:

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/my-feature
  3. Make your changes with proper testing
  4. Run checks: 
    # Backend
cd backend && make check

# Frontend
cd frontend && bun lint:fix && bun format && bun type-check
  5. Commit your changes: git commit -m 'Add my feature'
  6. Push to your branch: git push origin feature/my-feature
  7. Open a Pull Request

Guidelines

  • Keep PRs focused — one feature or fix per PR
  • Follow existing code style (enforced by linters and pre-commit hooks)
  • Add tests for new functionality where applicable
  • Update documentation if you change public APIs or configuration

Documentation

  • Backend README — API reference, architecture details, database management
  • Backend Docs — API documentation, auth guides, deployment
  • Frontend Docs — Architecture, tech stack, development guide

License

This project is licensed under the MIT License — see the LICENSE file for details.

Support

Built with LlamaIndex, FastAPI, Next.js, and LiveKit

Report Bug | Request Feature

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