RoboCo is licensed under **AGPL-3.0** (see `LICENSE`). Copyright (c) 2026 Renzo Franceschini. Do NOT reintroduce an MIT or other license reference anywhere (README, headers, package metadata) — the project is AGPL.
Contributions require a signed **Contributor License Agreement** (`CLA.md`), automated via the CLA Assistant workflow (`.github/workflows/cla.yml`). The CLA preserves the option to dual-license / offer a commercial edition later; keep copyright assignment language intact. See `CONTRIBUTING.md`.
**RoboCo** is an AI Agentic Company - a virtual organization of 25 AI agents + 1 human CEO, designed to operate as a complete software development workforce. The system implements a structured organizational hierarchy with formal communication protocols, task management, and quality controls.
On a Python workspace, `WorkspaceService` runs `uv sync --extra dev` (not plain `uv sync`) so the clone's `.venv` carries the full gate toolchain (ruff/mypy/xenon/pytest) — the lint/type/complexity tools live in the `dev`**extra**, which plain `uv sync` skips. Without it an agent's `make quality` fails on `ruff: command not found` and the agent can't gate its own work.
The complete task lifecycle is defined in `roboco/foundation/policy/lifecycle.py` (`roboco/enforcement/task_lifecycle.py` is a backwards-compat shim over it):
**In-path PR-review gate** (`awaiting_pr_review`): each assembled PR is reviewed before the PM merges. The cell PM's `submit_up` opens the cell→root PR and the Main PM's `submit_root` opens the root→master PR; both enter `awaiting_pr_review`, where a reviewer `pr_pass`es it on to `awaiting_pm_review` or `pr_fail`s it back to `needs_revision` — the merge-level reject the PM otherwise lacks. Leaf dev tasks and branchless coordination roots skip the gate.
| `awaiting_pm_review` → `completed` | PM roles only |
| `awaiting_pm_review` → `awaiting_ceo_approval` | PM roles only |
| `awaiting_ceo_approval` → `completed/needs_revision/cancelled` | CEO only |
| Any → `cancelled` | PM roles only |
**Unclaim Operation**: Agents can release claimed tasks back to the pool using `unclaim()`. This transitions `claimed` → `pending` and optionally reassigns to another agent.
All tasks follow git workflow. PR is created BEFORE QA review (not after) so QA can review the real PR diff on GitHub and downstream PM/CEO approval chain off a PR that already exists:
Agents do not call the API or per-domain MCP tools directly. They go through two thin MCP servers (`roboco-flow`, `roboco-do`) backed by the server-side **Choreographer** in `roboco/services/gateway/`. The Choreographer composes the existing services (TaskService, JournalService, GitService, etc.) into intent-verb sequences. Tracing, claim-locking, evidence assembly, and remediation hints are all centralized there.
Each agent gets a **spawn manifest** at `/app/tool-manifest.json` listing the verbs its role is allowed to call. The orchestrator builds the manifest from `roboco/services/gateway/role_config.py` and mounts it read-only into the agent container.
Content tools (do_server) — most roles: `commit`, `note`, `say`, `dm`, `evidence`. Auditor is restricted to `note` (scope=reflect) + `evidence`. The `pr_reviewer` posts its change-request on the PR itself (no agent comms). The `prompter` (intake) and `secretary` are restricted to `note` + `evidence` — human-only, no `say`/`dm`/`notify`.
The `next` field tells the agent what to call next; the `remediate` field on errors tells them exactly how to fix and retry. Agents should not guess state — trust the response.
Agent backends are pluggable. `roboco/llm/providers/` defines an `AgentProvider` lifecycle ABC (`base.py`) and a `ProviderRegistry` keyed by `ModelProvider` (`registry.py`), with `ClaudeCodeProvider` (default) and `GrokCliProvider`. The orchestrator resolves a provider at spawn from the agent's `ModelProvider`; when no dedicated provider is registered it falls back to the built-in Claude Code spawn. `ModelProvider` (`roboco/models/base.py`) is `ANTHROPIC` (default), `GROK`, `LOCAL`, `OLLAMA_CLOUD`, `OPENAI` (reserved). The seam is additive: only `GROK` routes through `GrokCliProvider`; Anthropic / Ollama Cloud / self-hosted spawns are unchanged, and every provider gets the same MCP gateway + tool-manifest wiring by construction.
**Grok runtime.**`GROK` agents run xAI's official `grok` CLI (model `grok-build`) authenticated by a **SuperGrok subscription**, not a metered API key — so a Grok workforce can't stall mid-task on out-of-credits. The host `~/.grok/auth.json` is mounted **read-only** into each agent (`GrokCliProvider._append_grok_auth_mount`; `ROBOCO_HOST_GROK_DIR` is the host mount source, set up once with `grok login`). It reaches parity with the Claude path by construction: same MCP gateway + manifest, per-role tool-removal and git-operation deny rules, a prompt-injection guard on the task prompt, headless tool auto-approval, and per-agent token/cost capture from the grok session store. It covers both one-shot delivery roles and the interactive Intake (Prompter) and Secretary chats (per-turn `grok -p` with session resume).
**Token auto-refresh.** The grok access token has a fixed ~6h server-set TTL and the CLI cannot refresh it headlessly — on an expired token it hangs forever at an interactive login prompt. The orchestrator mints a fresh token from the offline-access refresh token (xAI's OIDC `refresh_token` grant) before expiry and rewrites the shared `auth.json` in place (`roboco/llm/providers/grok_auth.py``refresh_if_stale`, run once per dispatch tick; the orchestrator's `~/.grok` mount is read-write so it can rewrite it). As a backstop the agent entrypoint runs `python -m roboco.llm.providers.grok_auth --check` and refuses to start (exit 78) on a missing/expired token instead of hanging.
## Self-Healing & Feature Flags
**Self-healing CI loop (default-off).** RoboCo can watch its own repository's CI (a single named workflow) and, on a detected regression, open a fix task that is held out of dispatch until the CEO approves it (it terminates at `awaiting_ceo_approval`), then dispatch it through the normal delivery flow. It is dormant by default and armed by `ROBOCO_SELF_HEAL_ENABLED` plus a second opt-in `ROBOCO_SELF_HEAL_ORIGINATE_ENABLED`; origination is bounded by `ROBOCO_SELF_HEAL_MAX_OPEN_TASKS` / `_MAX_PER_CYCLE` so it can't flood the backlog. It never auto-merges or self-deploys (`roboco/services/self_heal_engine.py`).
**Feature flags / company-in-a-box.** Env-gated, default-off subsystems toggle from the panel's Settings → Feature Flags card (`panel/src/components/settings/feature-flags-card.tsx`) instead of hand-editing env: web research (`ROBOCO_RESEARCH_ENABLED`), the strategy engine (`ROBOCO_STRATEGY_ENGINE_ENABLED`), pitch provisioning (`ROBOCO_PROVISIONING_*`), external / internal PR review, and the self-heal flags above. A toggle persists in the settings store and takes effect on the next backend restart; an unset flag falls back to its environment / config default.
The system runs as Docker Compose services. All Dockerfiles live under `docker/` at the project root; every service uses `context: .` plus `dockerfile: docker/<name>.Dockerfile`.
| `/ws/system` | Operator/system-wide stream (no per-agent keying) — the rate-limit lifecycle (`RATE_LIMIT_HIT` / `RATE_LIMIT_LIFTED`) and live usage (`USAGE_SNAPSHOT`, pushed to the usage dashboard) |
Server-side events reach these sockets through `roboco/api/websocket_bridge.py`, which subscribes to the `StreamEventBus` and forwards each event to the matching connections. To add a new live event: define an `EventType` (dotted value), publish it to the bus, add a `_handle_*` forwarder in `websocket_bridge`, and consume it on the panel via the `useWebSocket("/<endpoint>", …)` hook — do not stand up a parallel endpoint or client stack.
- **Provider rate limits** are tracked in Redis (`RateLimitStateTracker`, `roboco/services/gateway/`). On a provider 429 an agent calls `i_am_blocked(reason="rate_limited")`; the spawn gate then **queues** (never drops) further work for that provider, and a background probe-and-resume loop in the orchestrator clears the limit and revives parked agents when it lifts.
- **Token usage** is captured per agent session from the Claude Code transcript via the SDK server's `/usage/sync` (hook → orchestrator finalize → `agent_spawn_sessions` → `daily_usage_rollups` → dashboard). Cost uses provider-aware pricing in `roboco/billing/pricing.py` (Anthropic priced; local/Ollama intentionally `$0`). The token sweep also publishes `USAGE_SNAPSHOT` to `/ws/system`, so the dashboard's "Token Usage & Cost" panel updates live and falls back to HTTP polling when the stream is down.
The organizational structure, communication matrix, role descriptions, and access-control model are documented inline above and in the published `docs/` tree (per-area `README.md` files, `usage.md`, `deployment.md`).