Project

Archmet

Deterministic software intelligence for architecture governance

Measure architecture. Govern change.

In development

Role: Creator, software architect, deterministic analysis design, optional AI interpretation strategy

Archmet is a deterministic software-intelligence platform for architecture analysis, dependency intelligence, and technical-debt governance across multiple programming languages.

Its analysis engine is designed to produce evidence-backed findings from shared models, metrics, and graph relationships. Archimet is the optional AI layer that explains those findings without replacing deterministic analysis.

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Problem

Architecture drift rarely shows up as a single lint error. The harder problems are structural: coupling hotspots, dependency cycles, layering erosion, N+1 patterns, and slow technical-debt accumulation across services and modules.

I am building Archmet to make those issues measurable and reviewable while keeping the source of truth in deterministic analysis rather than opaque model output.

Approach

The platform is designed around a unified analysis model that can normalize multiple languages into one dependency and architecture view.

Deterministic rules, shared metrics, and framework-aware classification provide the evidence. The AI layer remains optional and is intended to explain verified findings, summarize risk, and support remediation workflows.

  • Deterministic engine first, optional LLM second.
  • One normalized language model instead of separate per-language reports.
  • Evidence-backed findings with metrics, thresholds, and graph paths attached.
  • Self-hosted-first deployment with BYO-LLM as an explicit choice rather than a requirement.

Architecture

The architecture separates language-specific parsers from a shared class and dependency model so findings can be scored consistently across ecosystems.

The analysis model is designed to capture metrics such as WMC, LCOM, CBO, RFC, fan-in/fan-out, and cyclomatic complexity, alongside higher-level detections like god classes, coupling hotspots, circular dependencies, and N+1 query patterns.

The broader platform direction includes a scanner/CLI, CI enforcement, server-side reporting, dashboard views, and the optional Archimet interpretation layer.

In progress

Scanner / CLI

The current platform direction centers on a scanner/CLI as the deterministic analysis entry point for local runs, CI, and report generation.

Planned

CI templates and GitHub Action

Quality-gate automation for pull requests and architecture budgets is part of the delivery surface being shaped around the core engine.

In progress

Server REST API

Persisted reports, async analysis jobs, diffs, and webhook integration belong to the service layer being developed around the analysis engine.

Planned

Dashboard

A dashboard for architecture scores, dependency exploration, trends, and what-if analysis is planned alongside the core engine.

Planned

IDE plugins

Editor integrations for inline diagnostics and local feedback loops fit the product direction, but they remain roadmap-level in this portfolio repository.

Planned

Chat integrations

Slack, Teams, and Zoom Team Chat integrations belong to the enterprise governance roadmap.

In progress

Archimet optional LLM layer

Archimet is the optional AI interpretation layer for grounded explanations, refactoring guidance, and natural-language interaction around deterministic findings.

Key Decisions

Deterministic findings before LLM commentary

The deterministic engine is the source of truth so reports remain reproducible, CI-friendly, and reviewable without trusting model output.

Unified multi-language model

Normalizing multiple ecosystems into one model enables cross-language scoring and governance instead of producing isolated parser-specific reports.

Optional graph and queue infrastructure

Neo4j and Kafka are kept optional so teams can adopt graph exploration or queue-backed analysis jobs without making them mandatory for every deployment.

Validate evidence before explanation

LLM recommendations are intended to stay grounded in already-validated metrics and paths rather than inventing unsupported remediation advice.

Tradeoffs

Higher implementation complexity than lint-only tooling

A calibrated, graph-aware, multi-language engine is more expensive to build and maintain than isolated rule packs, but it targets architecture problems that simpler tooling often misses.

Optional platform services add operational weight

Server, dashboard, graph storage, and queueing broaden the product surface, which creates more deployment choices and more system ownership overhead.

LLM value depends on evidence quality

Keeping the LLM layer optional protects determinism, but it also means the usefulness of natural-language guidance depends entirely on the fidelity of the underlying engine.

In progress

Deterministic architecture analysis

In progress

Active work centers on deterministic parsing, dependency graphs, and architecture-aware rules that produce evidence-backed findings.

Unified multi-language analysis model

In progress

The platform is being shaped around a shared analysis model, with language adapters feeding one governance-oriented view instead of isolated parser reports.

Evidence-backed risk scoring

In progress

Metrics, thresholds, and graph paths are intended to stay attached to each finding so architectural decisions can be reviewed and defended.

Optional LLM interpretation

In progress

Archimet is being developed as an explanation layer that works from verified findings rather than generating them.

Planned

Enterprise governance surface

Planned

The roadmap includes organization-level trends, scaled CI automation, chat integrations, and SSO-aware governance surfaces.

Technology Stack

  • Java
  • Kotlin
  • Gradle
  • Eclipse JDT
  • Kotlin compiler PSI
  • Roslyn
  • Python ast
  • SWC
  • Spring Boot
  • PostgreSQL
  • Redis
  • Neo4j
  • Kafka
  • Next.js
  • Docker
  • Ollama
  • llama.cpp
  • OpenAI-compatible providers

Privacy and deployment

  • Archmet is positioned as self-hosted first so source code and findings can stay inside the team infrastructure.
  • The AI layer is designed around BYO-LLM support, including local model options such as Ollama or llama.cpp when teams want deterministic analysis without mandatory cloud inference.
  • That separation keeps deterministic analysis useful on its own while AI-assisted interpretation remains an explicit deployment choice.

Current Status

Archmet is under active development. This page reflects the current platform direction and separates active work from roadmap items.

Archmet is the product name. Archimet names the optional AI interpretation layer, and the original AMF codename remains in machine-facing identifiers for compatibility.