FastAPI is a Python workable choice for marketplace. GreatCTO auto-detects both — adds the marketplace archetype overlay, wires marketplace-specific gates, and runs 83 specialist agents around your existing FastAPI workflow.
GreatCTO reads your pyproject.toml / requirements.txt and detects fastapi + marketplace archetype from signals: imports, file structure, env vars, README hints.
Attaches the marketplace archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for FastAPI-style projects.
qa-engineer runs mypy / ruff / pytest --cov; security-officer scans for SQL injection patterns common in ORMs (SQLAlchemy, Django ORM); performance-engineer profiles async patterns for I/O contention.
Bugs you've hit before in other FastAPI projects (connection-pool exhaustion, ORM N+1 queries, retry storms) — the agent's Step 0 includes the prior detection order. MTTR drops 94 % on second occurrence (methodology).
$ cd my-fastapi-app && npx great-cto init ✓ scanning manifests… found pyproject.toml ✓ stack: fastapi (Python) ✓ archetype: marketplace ⚠ archetype + stack combo is unusual — review overlay manually ✓ 83 agents ready $ /start "add marketplace feature" ▸ architect drafting ARCH-marketplace.md… ▸ pm decomposing into beads tasks… ⚐ gate:plan — your approval needed
Approve → 3 senior-devs run in parallel worktrees → 5 reviewers fan out in parallel → gate:ship → deploy. One real run walked stage-by-stage: /proof.
This is the shape of what senior-dev drafts for "marketplace feature" — auth first, schema validation, and the audit line the marketplace reviewer requires before gate:ship opens.
# app/routers/marketplace.py — drafted by senior-dev, reviewed by 5 agents
from fastapi import APIRouter, Depends
from app.auth import require_user # security-officer: auth before handler
from app.audit import audit_log # gate:marketplace: every decision logged
router = APIRouter(prefix="/marketplace")
@router.post("/")
async def create(payload: MarketplaceIn, user=Depends(require_user)):
result = await handle(payload, user) # qa-engineer: pydantic schema enforced
await audit_log(who=user.id, what="marketplace feature", confidence=result.confidence)
return resultmarketplace overlay.Two-sided platforms with Stripe Connect payouts.
Seller onboarding with KYC and 1099-K reporting.
Dispute mediation with escrow hold-and-release.
FastAPI (Python) is not a typical fit for marketplace. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the marketplace archetype page for the typical stack list and decide if your case is the right tool / right archetype.
No black-box "AI does it all" loop. GreatCTO is a deterministic state machine — 8 stages, 22 nodes, 2 human gates. Every node maps to a real agent on GitHub. Inspect the state machine →
$ npx great-cto init
Free, MIT, runs locally. Built as a Claude Code plugin — install with one command.
Eight stages, two human gates, four memory layers. Why this exact shape, and what I tried that didn't work.
One run, one feature, from prompt to merged PR. Time, cost, and gate-by-gate breakdown — no marketing math.
Regex vs LLM-based archetype detection, the false-positive count, and why I keep rejecting the obvious fix.
The bottleneck in agentic SDLC isn't model quality — it's process governance. Here's the state machine that closes the gap.