# PradeepTeam

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Pradeep Sahu](https://cerebralvalley.ai/u/sahupra1357)
- **GitHub:** https://github.com/sahupra1357/ResturantAgent
- **Demo video:** https://youtu.be/hXcVo551Ueo
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/825

Restaurant Ops Agents is a real-time, multi-agent operating system for an 80-seat bistro. A simulated dining floor
(3D and 2D) and an online-order page generate orders. Six agents react to every order on one event bus:

| Agent | What it does during service |
| --- | --- |
| **Order** | Parses mods and allergies, allergen-checks every item, routes it to a kitchen station and fires courses; asks to throttle online orders when the kitchen is slammed |
| **Inventory** | Depletes stock lot by lot (first-expiring first, FEFO), flags low stock against tonight's forecast and lots near expiry, quarantines on a cooler failure |
| **Forecasting** | Revises tonight's covers every 15 service minutes from what is actually seated; forecasts tomorrow with P10/P50/P90 bands (LightGBM quantile models) |
| **Procurement** | Compares supplier quotes, drafts purchase orders (MILP optimizer), negotiates with supplier vendor agents, learns supplier reliability, re-plans after a rejection |
| **Menu** | "86 salmon after 4 more", specials that use at-risk stock (margin and allergens checked), menu engineering and price suggestions |
| **Prep & staffing** | Prep list from the forecast, mise en place burn-down, calls a cook in when a station stays behind |

**Design principle: numbers come from models and rules; judgment comes from agents.** Agents call deterministic
tools for every number (forecasts, quotes, stock, margins). A LangGraph coordinator runs every proposal through a
constraint checker (shelf life, minimum order, budget, station capacity, allergens) and resolves cross-agent
conflicts, for example blocking a salmon special while salmon is short. Anything above a threshold goes to the
manager as an approval interrupt with a safe default on timeout. The manager sees every decision and its reason live,
and approves, edits or rejects with one click. The hot path never waits on a model: an order is parsed,
allergen-checked and routed in under a millisecond.

Around the agents sits the rest of a real restaurant:
- **Front of house:** bookings and a waitlist.
- **Payments:** checks, splits, tips, gift cards and loyalty, delivery by zone, and Stripe-ready card payments.
- **Guests:** profiles with persistent allergies.
- **Food safety:** HACCP checklist, temperature log and food donations.
- **Kitchen:** kitchen display and printed station chits.
- **Purchasing:** POs sent to suppliers as PDF by e-mail.
- **Back office:** day close with a double-entry journal, backups with a restore drill, crash recovery from snapshots
  or the event log, role-based sign-in with an audit log, an admin Setup screen and a POS history importer.

**Disruptions you can trigger live:**
- **Party of 12:** the grill wait jumps and online ready-time quotes stretch.
- **Salmon short:** an 86, a replacement special and a fallback PO that re-plans after a rejection.
- **Cooler failure:** dairy quarantined and the affected dishes 86'd.
- **Heatwave:** the dish mix shifts toward cold dishes.
- **Forecast miss:** tonight is revised and tomorrow's order adjusted.

**Measured, not claimed.** KPIs include order-to-station latency, quote accuracy, ticket times, 86s caught before a
guest ordered, waste, food cost, and auto-executed vs approved decisions. A batch simulator compares the agents with
a fixed-par baseline over a week. Every service can be replayed from its event log. 138 backend tests pass.

**Stack:**
- **Backend:** Python, FastAPI, LangGraph and LangChain, LightGBM, a MILP optimizer, SQLite, and optional Redis
  Streams.
- **Frontend:** React (Vite) with a Three.js floor.
- **Meta:** the Meta Model API (Muse Spark).

## More from Meta Global AI Developer Hackathon

- [Spark](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/822)
- [alamazing](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/823)
- [Biopilot Bench](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/824)
- [SnowMAN](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/826)
- [VALLUVAM](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/827)
- [ComArtist](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/828)

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Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/825. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt