🧬 MYND Prompt Phylogeny

Evolutionary Intelligence for Prompt Engineering

    

MYND Prompt Phylogeny — Track the evolutionary lineage of AI prompts across generations

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Part of the MYND AI Ecosystem — A 7-layer architecture for next-generation AI agent systems.

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Overview

phylogeny is the core evolutionary engine repository for MYND Prompt Phylogeny — a system that applies biological phylogenetics to AI prompt engineering. This repository contains the primary backend services, Kubernetes deployment configurations, and database schemas that power prompt lineage tracking, mutation, selection, and fitness evaluation across generations.

Whereas the mynd-prompt-phylogeny repository focuses on the full-stack monorepo with frontend tooling, this repo houses the hardened API server, production database schema, and Kubernetes infrastructure for deploying prompt phylogeny at scale.

Features

• 🌳 Phylogenetic Engine — Core algorithms for building and traversing prompt evolutionary trees
• 🧬 Mutation Operators — Crossover, point mutation, insertion, deletion, and prompt recombination
• 📊 Fitness Evaluation Pipeline — Multi-objective fitness scoring with configurable metrics
• 🗄️ Production Database Schema — Prisma schema optimized for lineage queries with PostgreSQL
• ☸️ Kubernetes Deployment — K8s manifests for scalable, production-grade deployments
• 🔐 Enterprise Auth — JWT authentication with bcrypt password hashing and rate limiting
• 📖 Auto-Generated API Docs — Swagger/OpenAPI documentation via @fastify/swagger-ui
• 🔌 Plugin Architecture — Fastify plugin system for extensible route registration
• 🐳 Docker Compose Stack — One-command local development with all services
• 🧠 Vector Search — ChromaDB integration for semantic prompt retrieval and lineage queries

MYND 7-Layer Architecture

This repository powers Layer 3 — Cognitive Infrastructure of the MYND ecosystem:

Layer 7: Application    → mynd-platform
Layer 6: Skills         → mynd-skill-forge
Layer 5: Decisions      → mynd-decision-paleontology
Layer 4: Knowledge      → mynd-knowledge-paleontology
Layer 3: Cognition  ██→ THIS REPO: Prompt Phylogeny Engine
Layer 2: Models         → mynd-model-holography, mynd-model-arena
Layer 1: Synchronization→ mynd-synchron
Layer 0: Foundation     → mynd-agent-replay

Role: This is the hardened production engine that powers prompt evolution. It provides the APIs consumed by the full-stack client and other MYND layers.

Tech Stack

Component	Technology
Backend	Node.js, Fastify, TypeScript, Prisma ORM
Frontend	Svelte, TypeScript, D3.js, Lucide Svelte
Database	PostgreSQL, ChromaDB (vector store)
Auth	JWT, bcryptjs, @fastify/jwt
API Docs	Swagger / OpenAPI 3.0
Infra	Kubernetes (k8s/), Docker Compose
Security	@fastify/helmet, @fastify/cors, @fastify/rate-limit

Quick Start

Prerequisites

• Node.js 18+
• Docker & Docker Compose
• kubectl (for K8s deployment)
• PostgreSQL 15+

Local Development

# Clone
git clone https://github.com/yethikrishna/phylogeny.git
cd phylogeny

# Environment setup
cp .env.example .env
# Configure DATABASE_URL, JWT_SECRET, REDIS_URL, CHROMADB_URL

# Start services
docker-compose up -d

# Install backend dependencies
cd backend && npm install && npx prisma migrate dev && cd ..

# Install frontend dependencies
cd frontend && npm install && cd ..

# Start dev servers
npm run dev

Kubernetes Deployment

cd k8s
kubectl apply -f namespace.yml
kubectl apply -f deployment.yml
kubectl apply -f service.yml

See DATABASE_SCHEMA.md for schema details.

Project Structure

phylogeny/
├── backend/            # Fastify API server (phylogeny engine)
│   ├── src/
│   │   ├── routes/     # API routes (prompts, evolution, fitness, lineages)
│   │   ├── services/   # Core phylogeny algorithms
│   │   └── lib/        # Prisma, Redis, ChromaDB clients
│   └── prisma/         # Schema & migrations
├── frontend/           # Svelte visualization client
│   └── src/
├── k8s/                # Kubernetes deployment manifests
│   ├── deployment.yml
│   ├── service.yml
│   └── ingress.yml
├── skills/             # Symlinked MYND skill definitions
├── docker-compose.yml  # Local dev stack
├── API.md              # REST API reference
└── DATABASE_SCHEMA.md  # Database schema documentation

Related MYND Projects

This project is part of the larger MYND AI Ecosystem. Explore related projects:

Project	Description
mynd-platform	The core MYND AI orchestration platform
mynd-plan	Hierarchical planning and task decomposition
mynd-agent-replay	Agent execution replay and debugging
mynd-model-arena	Model comparison and benchmarking arena
mynd-synchron	Real-time multi-agent synchronization
mynd-skill-forge	AI skill creation and management platform
mynd-decision-paleontology	Decision forensics for AI agents
mynd-knowledge-paleontology	Knowledge lineage and origin tracking

Contributing

1. Fork and clone the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all existing tests pass
5. Submit a pull request with a clear description

License

MIT License — see LICENSE for details.

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