ProdMind

Local-First AI Engineering Intelligence System for Vibecoded Software

ProdMind is a graph-native engineering intelligence platform designed to analyze, compress, persist, and reason about large AI-generated codebases using deterministic memory systems, semantic dependency graphs, and incremental architectural intelligence.

Instead of treating a repository as temporary prompt context, ProdMind treats software as a persistent evolving system.

It builds:

• structural memory,
• semantic graph intelligence,
• dependency-aware snapshots,
• architectural retrieval systems,
• incremental change intelligence,
• and AI-ready compressed repository context.

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Why ProdMind Exists

AI-generated software scales faster than human architectural understanding.

Modern vibecoding workflows using:

• Claude Code
• Cursor
• Gemini
• Antigravity
• Copilot
• autonomous coding agents

can generate production-scale repositories rapidly — but architectural integrity collapses over time.

The major problems:

• context window fragmentation
• hidden dependency chains
• architectural drift
• hallucinated integrations
• silent coupling growth
• invisible blast radius
• unstable scaling patterns
• repeated AI regressions

ProdMind solves this by creating a persistent graph-memory intelligence layer for software systems.

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Current System Status

Completed Systems

Phase 1 — Monorepo Foundation & Infrastructure

• Turborepo + pnpm monorepo
• Strict TypeScript governance
• Shared contracts architecture
• Hono backend scaffold
• Vite + React frontend scaffold
• CI workflow
• Runtime config governance
• Workspace package boundaries

Phase 2 — Database & Graph Memory Foundation

• Drizzle ORM + libSQL/SQLite
• 8 production-grade graph tables
• Repository abstraction layer
• Snapshot lifecycle state machine
• Transaction-safe persistence
• Graph traversal engine
• Blast radius analysis
• Cycle detection
• WAL mode optimization
• Foreign-key enforcement
• Immutable snapshot governance

Phase 3 — ZIP Ingestion & File Processing Pipeline

• ZIP upload pipeline
• Secure extraction engine
• ZIP-slip protection
• Ignore-rule sanitization
• File classification engine
• Secret detection engine
• SHA-256 hashing pipeline
• Repository manifest generation
• Incremental diffing support
• TypeScript AST parsing
• JSX/TSX support
• Worker-thread execution
• Full ingestion orchestration
• Upload API integration
• Graph persistence integration

Phase 4 — Graph Construction & Memory Engine (Completed)

• Dependency graph construction
• Import/export resolution
• Graph normalization
• Snapshot memory activation
• Context compression engine
• Repository/module/file/symbol summaries
• Incremental diff intelligence
• Snapshot evolution tracking
• Graph persistence pipeline
• Semantic graph intelligence
• Architectural boundary detection
• Graph metrics engine
• Memory retrieval systems
• Graph validation & integrity analysis
• Large-scale graph verification

Phase 5 — AI Orchestration & Deterministic Execution (Completed)

• 5.0 — AI orchestration engine foundation (step composition, workflow lifecycle, cancellation, tracing)
• 5.1 — Provider layer foundation (contracts, config, errors, health, timeout, rate limiting)
• 5.2 — Deterministic context assembly engine (assembly, budgeting, compression, dedup, normalization, slicing)
• 5.3 — Deterministic structured prompt execution system (envelopes, execution pipeline, analysis, tracing)
• 5.4 — Runtime layer (budgeting, capabilities, health, isolation, lifecycle, retries, sandbox, scheduling, telemetry)
• 5.5 — Provider adapters (OpenAI, Anthropic, Gemini, Local; governance, validation, replay, selection, secrets, fingerprinting)
• 5.6 — Deterministic DAG orchestration runtime (execution contracts, graph/scheduler, replay/provenance, governance/isolation, planner/AI bridge, stress validation)

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Core Capabilities

Repository Ingestion

Upload repository ZIP files for deterministic structural analysis.

AST-Based Structural Intelligence

Extract:

• imports
• exports
• symbols
• interfaces
• async patterns
• dependency relationships

without executing user code.

Persistent Graph Memory

Every upload becomes a queryable immutable architectural snapshot.

Incremental Intelligence

Only modified graph regions are recomputed between snapshots.

Dependency Traversal Engine

Deterministic BFS-based traversal for:

• dependency analysis
• blast radius analysis
• impact propagation
• architectural exploration

Context Compression Engine

Multi-layer repository compression:

• repository summaries
• module summaries
• file summaries
• symbol summaries

optimized for future AI retrieval efficiency.

Semantic Graph Intelligence (In Progress)

Planned semantic capabilities:

• service boundary detection
• domain clustering
• architectural ownership mapping
• coupling analysis
• infrastructure vs business-logic separation

Snapshot Evolution Tracking

Track repository evolution across uploads:

• changed graph regions
• modified dependency chains
• architectural drift
• incremental recomputation

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Architecture

Monorepo Structure

apps/
  web/            → React frontend
  server/         → Hono API server

packages/
  ai/             → AI orchestration layer
  contracts/      → Zod schemas + DTO contracts
  core/           → Runtime infrastructure
  db/             → Graph persistence + repositories
  parser/         → ZIP ingestion + AST intelligence
  shared/         → Shared utilities/constants

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Tech Stack

Backend

• TypeScript
• Hono
• Drizzle ORM
• SQLite / libSQL
• Zod
• tsup

Frontend

• React
• Vite
• Zustand
• TailwindCSS
• shadcn/ui

Infrastructure

• Turborepo
• pnpm workspaces
• Vitest
• GitHub Actions

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Engineering Principles

• Local-first architecture
• Deterministic graph generation
• Immutable snapshot history
• Contracts-first development
• Strong package boundaries
• No user-code execution
• Hallucination-resistant workflows
• Incremental recomputation
• Transaction-safe persistence
• AI as orchestration, not source-of-truth

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Current Architectural Status

Stable & Production-Grade

• Database layer
• Snapshot lifecycle
• Repository persistence
• Graph traversal engine
• ZIP ingestion
• AST parsing pipeline
• Compression engine
• Incremental diffing

Active Focus

Phase 6 planning:

• engineering risk intelligence
• graph-aware chat system
• local LLM routing
• retrieval-augmented AI analysis
• system integration hardening

Not Yet Started

• Engineering risk intelligence
• Graph-aware chat system
• Local LLM routing
• Retrieval-augmented AI analysis

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Development

Install

pnpm install

Run Development

pnpm dev

Typecheck

pnpm typecheck

Lint

pnpm lint

Test

pnpm test

Build

pnpm build

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Long-Term Vision

ProdMind aims to become a persistent engineering intelligence layer capable of:

• understanding massive repositories,
• preserving architectural memory,
• analyzing dependency evolution,
• preventing AI-generated architectural collapse,
• enabling staff-engineer-grade AI reasoning,
• and powering deterministic AI engineering workflows.

The goal is not another coding assistant.

The goal is an architectural cognition system for AI-native software engineering.

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Target Users

• Vibecoders
• AI-first developers
• Indie hackers
• Startup engineering teams
• Rapid prototyping teams
• Staff engineers using AI augmentation
• Engineering reviewers
• Autonomous coding workflow operators

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Current State of Development

ProdMind is actively under heavy architectural development.

The foundational ingestion, persistence, traversal, compression, and snapshot systems are operational.

The project has completed AI orchestration infrastructure and is evolving toward:

• engineering risk intelligence,
• graph-aware chat systems,
• local LLM routing,
• retrieval-augmented AI analysis,
• and production system integration hardening.

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License

Private / In Active Development