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GRID — Geometric Resonance Intelligence Driver

CI PyPI Python 3.13+ Ruff License: MIT

Local-First AI · Privacy-First · Production-Ready

Engineering at a Glance

Built primarily by a solo engineer over 6 months — from blank templates to a production-hardened framework with CI, security layers, and full test infrastructure.

What Evidence
Codebase scale 1,490+ Python files, 200K+ lines, 9 distributable wheel packages
Test discipline 4,490+ test functions, ≥75% coverage, 30s performance budget
CI rigor 7-stage GitHub Actions pipeline: secrets scan → lint → smoke → security → test → integration → build
Security depth 3 independent security modules (safety, security, boundaries), sealed-envelope HMAC-SHA256 transfer gate, admission gate with ethical enforcement, zero-trust boundary contracts
API architecture FastAPI + 14-layer middleware chain, dual event bus (Redis + in-memory fallback), circuit breaking, rate limiting
Code quality 0 ruff lint errors across 1,490+ files, strict type hints, conventional commits
Resilience patterns Published separately as GRID-APIGUARD — circuit breaking + rate limiting + retry with 100% test coverage

The architecture is domain-driven with event-driven agentic workflows — not a monolith, not a microservice explosion. Each package has clear boundaries, its own tests, and ships as an independent wheel.

For clients: If you're evaluating this repo as part of a proposal, the CI pipeline, CHANGELOG, and test structure tell the story better than any portfolio page. The code is the portfolio.

GRID is a privacy-first code intelligence framework that helps you understand complex codebases using local AI models. Your code never leaves your machine.

pip install grid-intelligence
grid --help

Or install from source for development:

git clone https://github.com/GRID-INTELLIGENCE/GRID.git
cd GRID
uv sync --group dev --group test
uv run grid --help

For contributor setup see docs/INSTALLATION.md. Environment template: config/environment/.env.example.


Why GRID?

Problem GRID's Answer
Developers spend 40 % of time reading code Semantic analysis reduces onboarding to minutes
Cloud AI tools require sending source code off-machine Everything runs locally — Ollama + ChromaDB
Text search can't answer "why" or "how" 9 Cognition Patterns map structure, flow, and intent
Enterprise tools are expensive Free tier with MIT license

At a Glance

Metric Value
Python 3.13+
Source files 1,490+
Tests 4,490+ test functions
Lint errors 0 (ruff clean)
Coverage ≥ 75 %
RAG precision lift +33–40 %
Architecture DDD + Event-Driven
Package manager uv

Core Capabilities

  • 9 Cognition Patterns — Flow · Spatial · Rhythm · Color · Repetition · Deviation · Cause · Time · Combination
  • Local-First RAG — ChromaDB + Ollama. Optional cloud hybrid (OpenAI / Anthropic / Gemini) via RAG_LLM_MODE=external
  • Cognitive Decision Support — Light of the Seven architecture for bounded rationality
  • Agentic System — Event-driven case management (receptionist → lawyer → executor)
  • Transition Gate — Sealed-envelope HMAC-SHA256 handshake for cross-partition artifact transfers
  • Search with Guardrails — Auth, rate limiting, input sanitization, admin-gated schema routes
  • Unified Fabric — Async event bus and distributed AI Safety bridge
  • Authentication & Billing — JWT + bcrypt, token revocation, tier-based usage tracking

Quick Start (Contributors)

uv sync --group dev --group test   # Creates .venv, installs everything
uv run pytest                      # Run tests
uv run ruff check .                # Lint
uv run python -m grid --help       # CLI

[!IMPORTANT] Use uv add to manage packages — not pip install. See pyproject.toml and uv.lock.

Managing dependencies
uv add <package>                   # Runtime dependency
uv add --group dev <package>       # Dev-only
uv lock                            # Regenerate lockfile
uv sync                            # Sync .venv to lockfile

The .venv/ folder is disposable — delete it and uv sync recreates it.


Project Structure

GRID/
├── src/                       # Source code (9 wheel packages)
│   ├── grid/                  #   Core intelligence engine
│   ├── application/           #   FastAPI (Mothership API)
│   ├── cognitive/             #   Cognitive architecture
│   ├── tools/                 #   RAG, utilities
│   ├── search/                #   Search service + guardrails
│   ├── mycelium/              #   Comprehension frontend
│   ├── infrastructure/        #   Infra adapters
│   ├── unified_fabric/        #   Async event bus + safety bridge
│   └── vection/               #   Motion & perception
├── tests/                     # Test suite (4490+ test functions)
├── safety/                    # GUARDIAN rule engine, PII privacy
├── security/                  # Network interceptor, forensics
├── boundaries/                # Boundary engine, transition gate
├── config/                    # All configuration & structured data
│   ├── deploy/                #   Railway, Render deployment configs
│   ├── contracts/             #   OpenAPI specs
│   └── prompts/               #   Prompt templates
├── schemas/                   # JSON schemas (OpenAPI, telemetry, …)
├── scripts/                   # Build, CI, and demo scripts
├── docs/                      # Documentation (architecture, guides, ADRs)
│   ├── reports/               #   Audit & analysis reports
│   └── checkpoints/           #   Session checkpoints
├── landing/                   # Marketing site (Netlify)
├── frontend/                  # Web client
└── pyproject.toml             # Project config (uv)

What's New

v2.8.0 — Admission Gate & Entity Persistence (March 2026)

  • Admission Gate — Top-of-stack ethical participation enforcement with 6 sequential gates, 3-tier penalty escalation, and entity attribution
  • Entity Persistence — SQLite-backed entity state with async CRUD and startup hydration
  • Frontend Dashboard — Admission page with live stats, policy billboard, bannered entities table
  • REST API — 7 endpoints under /admission/* with admin-protected penalty management
Earlier releases

v2.7.0 — Security Hardening & CI Hygiene: search guardrails, transition gate, API attack surface guardrails, CI audit gates, cross-platform path traversal protection

v2.6.x — Mycelium Frontend, wheel packaging, version/changelog CI gate

v2.5.0 — Environmental Intelligence (Le Chatelier homeostatic middleware), Round Table multi-agent facilitator

v2.4.0 — 664 → 0 lint errors, StrEnum modernization (122 classes)

v2.3.0 — Ruff formatter consolidation, GUARDIAN engine hardening

January 2026 — Auth & billing, advanced RAG (+33-40 % precision), Unified Fabric, Databricks scaffold


Development Workflow

CI Pipeline (GitHub Actions)

Job Purpose
Secrets Scan Version/changelog gate, no-debug gate, secret detection
Lint ruff check + ruff format --check
Smoke Test Fast sanity checks
Security Scan Attack surface & guardrail tests
Test Unit + integration + API tests with coverage
Build Package Validates all 9 wheel packages build cleanly
Integration Tests Cross-module integration

Quality Gates

Before merging to main:

  1. uv run ruff check . — zero errors
  2. uv run pytest tests/ -q --tb=short — all passing
  3. pyproject.toml version matches top CHANGELOG.md entry
  4. No DEBUG=true or ENABLE_DEV_TOKEN in production paths
  5. No untracked files in src/ or tests/

Tooling

Tool Command
Ruff (lint + format) uv run ruff check . / uv run ruff format .
Mypy (types) uv run mypy src/grid/ src/application/
Pytest (tests) uv run pytest tests/

Privacy & External LLM

GRID is local-first by default. No data leaves your machine unless you explicitly opt in.

Variable Purpose
RAG_LLM_MODE=external Switch RAG to external API
RAG_LLM_PROVIDER openai, anthropic, or gemini
OPENAI_API_KEY / ANTHROPIC_API_KEY / GEMINI_API_KEY Provider key

When using external providers, prompts and responses are sent to the chosen provider.


The Story

GRID began in November 2025 as blank templates and empty journals. By December it became a system — domain-driven architecture, security foundations, and a core principle:

When the environment is noisy, separate signal from noise, compress it into a structured core, and keep moving.

There's a state in the pattern engine called MIST — "high confidence that we don't know." That epistemic humility, inspired by Carl Jung, shapes everything we build.

Nov 2025 → First commit. Blank templates.
Dec 2025 → Architecture cleanup. Security foundation. DDD.
Jan 2026 → Cognitive layer. RAG optimization. Production hardening.
Feb 2026 → 540+ files. Environmental Intelligence. Mycelium. v2.6+.
Mar 2026 → 1270+ files. Admission gate. Entity persistence. v2.8.0.
Apr 2026 → 1490+ files. 4490+ tests. TS 6.0, strict asyncio, CI green.

GRID is built by someone who cares about doing things right — principled, not perfect.


Documentation

Area Link
What Can I Do? docs/WHAT_CAN_I_DO.md
Architecture docs/ARCHITECTURE.md
Security docs/security/SECURITY_ARCHITECTURE.md
Skills & RAG docs/SKILLS_RAG_QUICKSTART.md
Agentic System docs/AGENTIC_SYSTEM_USAGE.md
Event Architecture docs/EVENT_DRIVEN_ARCHITECTURE.md
Pipeline Runbook docs/release/pipeline-runbook.md
Contributing CONTRIBUTING.md

FAQ

What is GRID? — A privacy-first, local-first tool that helps you understand any codebase using local AI models.

How is this different from Copilot? — Copilot helps you write code. GRID helps you understand code.

Does my code stay local? — Yes. Zero network requests, no API keys needed, works offline.

Languages supported? — Python, JS/TS, Java, Go, Rust, C/C++, Ruby, PHP, C#.

System requirements? — 8 GB RAM minimum (16 GB recommended), Python 3.13+, Windows / Mac / Linux.

What are the 9 Cognition Patterns? — Flow, Spatial, Rhythm, Color, Repetition, Deviation, Cause, Time, Combination — a unique framework for understanding complex systems.

Is the architecture original? — Yes. While inspired by seL4, Fuchsia, and cognitive science, the 9 Cognition Patterns and Geometric Resonance metaphor are proprietary innovations.

License

MIT — see LICENSE.

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