Moira

AI-native SDLC cockpit — governed orchestration layer above best-of-breed agent backends.

One repo. This is the whole Moira product: orchestrator/ (Python sidecar) + cockpit/ (React/TS) + src-tauri/ (desktop shell). The AI SDLC framework content (intents, requirements, specs, agents, skills) and any target application code live in separate repositories that Moira reads/writes as a workspace — they are not part of this repo.

Moira drives AI agents across the software development lifecycle (intent → requirements → design → code → QA → deploy) with human quality gates, git-native decision provenance, and model-agnostic execution. It does not re-implement an agent harness — it orchestrates pluggable frontier backends (Claude Code CLI, OpenAI Codex CLI, direct API) and adds the governance, traceability, and cockpit layer on top.

Status: v0.1 (in development)

End-to-end on a real project (CSL Driver): shape specs via Discovery skills → guided/visual pipeline runs → human gates → tamper-evident git-native audit → report & traceability. Built & verified (83 unit tests):

• orchestrator/ — dependency-free DAG engine + gates (auto/hybrid/human/off) + pluggable backends (mock/claude_code/litellm) + audit with tamper-evident hash chain + pluggable persistence (SQLite / PostgreSQL / git mirror) + HTTP API. Drives AI SDLC skills for discovery (single + chained).
• cockpit/ — React + TS + Vite cockpit: Overview (mission control), Runs (+ run metrics, report, context orbit), Inbox (pending-decision gates), a modern pipeline editor, Discovery, Files, Traceability (list + graph + provenance orbit), reusable UI primitives, profile menu. Plus a mobile gate inbox (/m).
• src-tauri/ — Tauri v2 desktop shell (spawns the Python sidecar). Needs cargo tauri + webkit2gtk.

Getting started

New here? Read USER_GUIDE.md — how to run Moira, load/create an AI SDLC repo, create a workspace, define agents, build pipelines, and run them.

Run the cockpit

# web cockpit (no Tauri needed) — builds frontend, serves it + API on one origin
./run-cockpit.sh                 # -> http://127.0.0.1:8765

# dev mode (hot reload): two terminals
python3 orchestrator/moira_api.py --repo ../ai-sdlc      # API on :8765
npm --prefix cockpit run dev                              # UI on :5173 (proxies /api)

# desktop shell (needs tauri-cli + webkit2gtk)
cargo tauri dev

Architecture

Tauri Shell (Rust) + React UI   ← cockpit (web or desktop) + mobile gate inbox (/m)
        │ HTTP
Python orchestration sidecar    ← own DAG engine, gates, audit (hash-chain),
        │ delegates each node to    pluggable persistence (SQLite/Postgres/git)
Execution layer (pluggable)     ← Claude Code CLI · LiteLLM (frontier/local) · Codex CLI

Key decisions:

• ADR-002 — own dependency-free DAG engine (LangGraph deferred)
• ADR-003 — LiteLLM for model-agnostic routing (frontier-first, local as anti-lock-in)
• ADR-004 — DEV execution is delegated, not re-implemented
• ADR-005 — pluggable run/audit persistence (primary store + export sinks)

Repository layout

orchestrator/   Python sidecar — DAG engine, gates, audit (hash chain), pluggable
                persistence (SQLite/Postgres/git), HTTP API, backends (mock/claude_code/litellm)
cockpit/        React + TypeScript + Vite frontend (+ mobile gate inbox)
src-tauri/      Tauri v2 desktop shell (spawns the sidecar)
docs/           Marketing landing pages (PL + EN)

See CONTRIBUTING.md for how to run, test and build.

Source of truth

Project context, intents, requirements, specs, ADRs, standards live in a separate AI SDLC repo that you point a workspace at (e.g. --repo /path/to/ai-sdlc).

Why build-own

Hycom owns the tooling: no per-seat license fees, full control, on-prem. GitLab Duo and exAI Cloud are reference designs, not vendors we pay.

License

Apache License 2.0 — see LICENSE and NOTICE. © 2026 Hycom S.A.