README + deployment guide now show three ways to run RoboCo — pull the
pre-built images (docker-compose.registry.yml), build from source, or the
local-dev flow — instead of only the dev path. Add the ROBOCO_REGISTRY /
ROBOCO_VERSION knobs and the required ROBOCO_ENCRYPTION_KEY (which was
missing) to .env.example, and fix a stale piragi reference there.
usage.md drops the unmeasured per-agent RAM ceiling and records the figures
measured on the live stack: RAM is low and Ollama-dominated, storage is the
real cost (images share the agent-base layer). Idle numbers — peak under an
active task is best read live with docker stats.
A documentation audit against the code surfaced several stale claims:
- Agent count: the roster is 19 AI agents (the UX/UI cell has two devs,
ux-dev-1 + ux-dev-2), not 18 / a single UX dev. Fixed in README,
CLAUDE.md, base.md, and docs/ux_ui.
- API: domain routes are mounted under /api, not /api/v1 (the /api/v1
prefix is the agent gateway only); dropped the non-existent /api/v1/test
group; fixed the orchestrator-status path in deployment.md.
- Quick Start uvicorn target is roboco.api.app:app (the api package
deliberately does not export app).
- Verb table: the developer PR verb is open_pr (renamed from
submit_for_qa); the lifecycle's canonical module is
foundation/policy/lifecycle.py (enforcement/task_lifecycle.py is a shim).
- Backend team stack: vector store is PostgreSQL + pgvector (via piragi),
not Qdrant; mypy targets roboco/, not src/.
- .env.example: replaced the phantom Qdrant/OpenAI blocks with the real
Ollama/RAG settings.
Renn F