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KodeHold - AI-powered coding orchestrator with conscious team methodology, design document lifecycle, ADRs, persistent memory (ICM), and multi-LLM support

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⚠️ WORK IN PROGRESS — EXPERIMENTAL KodeHold is a personal experiment and not production-ready. Everything may change. Use at your own risk — expect breaking changes, unexpected behavior, and gaps in documentation. If it breaks, you keep both pieces.

KodeHold — /ˈkəʊd həʊld/ → "Code Hold". Koden er i gode hænder.

AI-powered coding orchestrator — conscious team-based software engineering with structured design documents, persistent memory, lifecycle gates, and multi-LLM support.

KodeHold simulates a disciplined software organization where specialized AI agents collaborate under a Director to produce high-quality code. Every project is centered on a living design document that is continuously reviewed and updated throughout development.

Philosophy

  • Design-first — no code without an approved design document
  • Separation of concerns — distinct teams for design, implementation, review, testing, memory, and front-line support
  • Token-conscious — every operation evaluated for token cost
  • Persistent memory — full project context preserved across sessions via Graphify knowledge graph queries (graphify query) and opencode-mem (search_memories/add_memory)
  • Traceable decisions — all architecture decisions recorded as ADRs (Nygaard format)
  • LLM-agnostic — bring your own model; second-opinion cross-check supported via different providers
  • Gate-driven lifecycle — every state transition validated by automated gates; no skipping steps
  • Design doc discipline — all agents read the design doc before work and update it after

Architecture

Director                        ← orchestrator, lifecycle gates, delegation, second opinions
├── Architects   — design documents, ADRs, technology evaluation
├── Engineers    — implementation, refactoring, bug fixes
├── Testers      — testing, verification, regression suites
├── Reviewers    — code review, design review, second opinion coordination
├── Scribes      — documentation, opencode-mem, knowledge extraction
└── FLS          — front line support, triage, hotfix, escalation

Lifecycle

INIT → ACTIVE → REVIEW → CLOSED ↔ REOPEN
State Description
INIT Design doc created, ADRs drafted, project scoped
ACTIVE Implementation — Engineers → Testers → Reviewers (sequential, enforced)
REVIEW Final gate — Team Meeting reviews all work across all 6 teams
CLOSED Complete, context stored in opencode-mem and design docs
REOPEN Resurrected for new features or bugfixes

Each transition runs automated gates via scripts/gate.sh. Agents check state before work and refuse if in the wrong phase. The ACTIVE→REVIEW gate enforces that Testers complete before Reviewers begin (.testers_done marker).

Design Doc Discipline

All agents follow read-before, update-after:

  1. Read the relevant design document section before starting any work
  2. Update the design doc after completing work — bump version, changelog, and relevant sections
  3. The Director verifies the design doc is current before any gate transition

Teams

Team Trigger Sequence
Architects Design, ADR, architecture, design review —
Engineers Implementation, code, feature, bugfix, refactor —
Testers Test, verify, regression, QA Must finish before Reviewers
Reviewers Review, code review, design review Must run after Tests pass
Scribes Memory (opencode-mem), documentation, changelog —
FLS Support, hotfix, triage, minor change —

Workspaces

Managed projects live in workspaces/<name>/:

  • python3 scripts/workspace.py init <name> — create a new project
  • python3 scripts/workspace.py list — list all projects with state
  • python3 scripts/workspace.py gate <name> <transition> — run gate on a project
  • python3 scripts/workspace.py deploy-ready <name> — check if CLOSED

Second Opinion

For critical decisions, the Director can request a second opinion from a different AI provider (e.g., Claude if primary is Codex). Falls back to user prompt via /models or /connect if no secondary provider is configured.

Quick Start

# Prerequisites: OpenCode
git clone https://github.com/Acharnite/kodehold.git
cd kodehold
opencode

The Director agent loads automatically as the default agent. Start by reading AGENTS.md for the full protocol and delegation table.

Documentation

Path Description
AGENTS.md Quick reference — states, delegation table, gates, shipping gate
docs/design/README.md Main design document — full architecture, lifecycle, constraints
docs/adr/ Architecture Decision Records (ADR-0001 through ADR-0025)
.opencode/agents/director.md Director agent — full orchestrator protocol
.opencode/agents/ Team agent definitions (7 agents)
scripts/gate.py Lifecycle gate automation (5 transitions)
scripts/ship.py Shipping gate automation (7 checks: version → changelog → todo → tests → git → branch)
scripts/workspace.py Workspace project management
tests/run.sh Test suite — 80+ tests across smoke, init, integration

Requirements

  • OpenCode — agent framework
  • Ollama or another LLM provider

Python Environment

KodeHold's scripts (scripts/*.py) and test suite are written in Python. To set up a virtual environment:

# Create and activate a venv
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run tests
python3 -m pytest tests/ -v

Dependencies (see requirements.txt):

Package Used by
pytest Test suite

Note: .venv/ is in .gitignore — it won't be committed.

All configs and functions are in English for token efficiency.

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KodeHold - AI-powered coding orchestrator with conscious team methodology, design document lifecycle, ADRs, persistent memory (ICM), and multi-LLM support

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