LabOps Agent 🔬

Slack Agent for Good — Slack Agent Builder Challenge 2026 (Salesforce/Devpost)

Predict. Alert. Act. All from Slack.

LabOps Agent is a Slack-native AI agent that predicts reagent stockouts in clinical laboratories before they happen, based on historical demand patterns by test type — and enables lab staff to act directly from Slack without switching to external LIMS systems.

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The Problem

Clinical laboratories run on reagents. When a critical reagent runs out mid-operation, testing stops. Current solutions (Quartzy, Scispot) only send passive threshold alerts after stock is already low. No product today predicts stockouts based on test-type demand patterns.

Example: TSH demand spikes every autumn in Argentina (Mar–May, 2.5x ratio). A lab with 680 units, at a projected demand of ~147 units/day, runs out in ~6 days — inside the 7-day reorder window. Without prediction, they find out when the analyzer throws an error.

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What LabOps Agent Does

1. Predicts stockouts using Prophet, calibrated with patterns derived from anonymized demand analysis in Argentine clinical labs
2. Alerts lab staff in #labops-alerts with interactive Block Kit messages — before the stockout happens
3. Acts — staff orders reagents, assigns tasks, and updates inventory without leaving Slack

Why LabOps Agent is Different

No existing product (Quartzy, Scispot, Benchling) combines:

• Prediction by test type — TSH spikes in autumn (Mar–May), Hemograma is stable; each reagent gets its own Prophet model
• Native Slack agent — not a webhook or Zapier bridge; Bolt Python with Socket Mode, interactive Block Kit buttons, and modals
• Domain expertise — built by someone with 4 years of B2B KAM experience in clinical diagnostics (Argentina), not a generic inventory template

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Technologies Used

Technology	How It's Used	Platform
MCP Server	Exposes 4 lab tools: get_inventory, get_forecast, create_order, update_canvas	Anthropic/Slack
Claude Tool-Use Agent	LLM selects and invokes MCP tools via agent_router.py on every @mention	Anthropic
Slack Channel History API	Queries #labops-alerts message history for past reagent incidents (works with the bot token)	Slack
Claude API Summarization	Generates natural language summaries of reagent alert history	Anthropic

Design decision note: search.messages (Slack Search API) was evaluated but replaced by conversations_replies (Channel History API) for thread history retrieval. search.messages requires a workspace user token (xoxp-) which is not available in standard bot-token deployments; the bot token returns empty results. Thread-scoped history via conversations_replies provides the relevant alert context without requiring a user token.

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MCP Server

LabOps Agent exposes a real MCP Server using the official Anthropic MCP Python SDK:

# Run MCP Server (stdio transport)
cd backend
python mcp_server.py

Tools available:

• get_inventory — Query current reagent stock
• get_forecast — Prophet demand forecast
• create_order — Create reagent order
• update_canvas — Update inventory Canvas

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Architecture

┌─────────────────────────────────────────────────────┐
│                  SLACK WORKSPACE                     │
│   #labops-alerts  │  Canvas Inventario  │  App Home  │
└──────────────────┬──────────────────────────────────┘
                   │ Bolt Python (Socket Mode)
┌──────────────────▼──────────────────────────────────┐
│                FASTAPI BACKEND                       │
│  ┌─────────────────┐   ┌──────────────────────────┐ │
│  │   MCP SERVER    │   │   PREDICTION ENGINE      │ │
│  │ get_inventory   │   │   Prophet + seasonality  │ │
│  │ get_forecast    │   │   params from 414K B2B   │ │
│  │ create_order    │   │   CV MAPE 8-11% (synth)  │ │
│  │ update_canvas   │   └──────────────────────────┘ │
│  └─────────────────┘                                 │
└─────────────────────────────────┬───────────────────┘
                                  │
┌─────────────────────────────────▼───────────────────┐
│                    SUPABASE                          │
│  inventory │ demand_history │ orders │ alerts_log    │
└─────────────────────────────────────────────────────┘
                                  │
┌─────────────────────────────────▼───────────────────┐
│                  CLAUDE API                          │
│  Natural language explanations of predictions        │
└─────────────────────────────────────────────────────┘

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Demo Flow (3 minutes)

0:00–0:45 — Agent detects TSH will run out in ~6 days (demand forecast exceeds stock)

0:45–1:30 — Alert fires in #labops-alerts with Block Kit buttons:

• 📊 Ver proyección
• 🛒 Ordenar reactivo
• 👤 Asignar al equipo

1:30–2:15 — User clicks "Ordenar reactivo" → modal opens → one click confirms → Canvas auto-updates

2:15–3:00 — Agent explains WHY TSH is at risk (seasonal demand pattern) → Claude API summarizes past incidents

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Try it Live

Demo video (≤3 min): Watch on YouTube — full demo with voiceover narration. Live deploy: https://labops-agent.onrender.com (Render persistent host — Socket Mode + FastAPI co-hosted) Sandbox workspace: https://labopsespacio.slack.com (invite-only sandbox).

Reproduce the live deploy (judge-reachable, chart renders)

Slack fetches forecast-chart images from its own servers, so the backend must be reachable over public HTTPS. Socket Mode also needs a persistent host, so this deploys to Render/Railway/Fly — not Vercel serverless (see Deployment).

1. Join the Slack Developer Program and create a Developer Sandbox workspace.
2. Create a new app from slack-manifest.json, install it, and copy the Bot + App tokens.
3. Deploy with the included render.yaml blueprint (one persistent web service hosts both the chart endpoint and the Socket Mode websocket).
4. After the first deploy, set BACKEND_URL=https://labops-agent.onrender.com (or your service URL) and redeploy so the forecast chart renders in Slack.

Local + tunnel (for recording the demo on your machine):

docker-compose up --build                 # backend + slack client + DB
cloudflared tunnel --url http://localhost:8000   # public HTTPS for the chart
# set BACKEND_URL=https://<random>.trycloudflare.com in .env, restart slack_client

Without a public BACKEND_URL, the forecast still shows as native Block Kit fields — the chart image is simply omitted, never broken.

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Setup Instructions

Prerequisites

• Python 3.11+
• Supabase account
• Slack Developer Sandbox (via Slack Developer Program)
• Anthropic API key

1. Clone and install

git clone https://github.com/Marianooss/labops-agent
cd labops-agent
pip install -r requirements.txt

2. Configure environment

cp .env.example .env
# Fill in your keys:
# SUPABASE_URL, SUPABASE_KEY
# SLACK_BOT_TOKEN, SLACK_APP_TOKEN, SLACK_SIGNING_SECRET
# SLACK_USER_TOKEN (optional — only for Slack Search API, not used in current implementation)
# ANTHROPIC_API_KEY
# LABOPS_ALERTS_CHANNEL=#labops-alerts

3. Set up Supabase

# Run in Supabase SQL Editor:
# 1. data/create_tables.sql
# 2. data/seed_data.sql

4. Create Slack App (from manifest)

1. Go to api.slack.com/apps → Create New App
2. Select From an app manifest
3. Choose your Developer Sandbox workspace
4. Paste the contents of slack-manifest.json
5. Click Create
6. Go to OAuth & Permissions → Install to Workspace
7. Copy the Bot User OAuth Token and App-Level Token into your .env

Manifest includes all required scopes: chat:write, channels:read, channels:history, groups:read, groups:history, im:write, users:read, app_mentions:read, canvases:read, canvases:write.

5. One-Click Docker Setup (recommended for judges)

# Clone and start everything (PostgreSQL + backend + slack client + auto-seed)
git clone https://github.com/Marianooss/labops-agent
cd labops-agent
docker-compose up --build

# Wait ~30s for DB init, then test:
curl "http://localhost:8000/alert/trigger?reagent_name=TSH"

This runs the full stack locally without needing a cloud Supabase account:

• db — PostgreSQL with auto-created schema and seed data
• backend — FastAPI on http://localhost:8000
• slack_client — Bolt Python Socket Mode (waits gracefully if Slack tokens are missing)

To enable Slack integration, copy .env.example to .env and fill in SLACK_BOT_TOKEN, SLACK_APP_TOKEN, and SLACK_SIGNING_SECRET before running docker-compose up.

6. Local Setup without Docker (one-script)

# Single command — starts backend + Slack client + seeds DB
python scripts/start_local.py

Or manually in separate terminals:

# Terminal 1: FastAPI backend
cd backend && uvicorn main:app --reload --port 8000

# Terminal 2: Slack agent
cd backend && python slack_client.py

Note: Backend commands must be run from the backend/ directory because modules use relative imports.

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Deployment (Render / Railway / Fly)

Why not Vercel? The agent uses Slack Socket Mode, which holds a persistent websocket. Vercel serverless functions are short-lived and cannot keep that connection open. (If you must use Vercel, you would have to switch to HTTP Events mode and expose /slack/events — a different integration path this repo does not use.) Instead, deploy to a persistent host.

The included render.yaml provisions one web service that hosts both:

1. the FastAPI HTTP app (serves the public /chart/forecast/{reagent} PNG that Slack image blocks fetch — needs public HTTPS), and
2. the Socket Mode websocket, started in a background thread via RUN_SOCKET_MODE=1 (see backend/main.py startup).

Render → New → Blueprint → select this repo → fill the secret env vars
→ after first deploy, set `BACKEND_URL=https://labops-agent.onrender.com` → redeploy

Railway and Fly.io work identically with the same Dockerfile (it honors the host-injected $PORT).

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Project Structure

labops-agent/
├── backend/
│   ├── main.py           # FastAPI entry point
│   ├── mcp_server.py     # MCP tools (4 lab tools)
│   ├── prediction.py     # Prophet demand forecasting
│   ├── slack_client.py   # Bolt Python + event handlers
│   ├── database.py       # Dual backend: Supabase or PostgreSQL
│   ├── claude_client.py  # Claude API wrapper
│   └── blocks_loader.py  # Block Kit / Canvas template loader
├── blocks/
│   ├── alert.json        # Stockout alert Block Kit template
│   ├── modal_order.json  # Order reagent modal template
│   └── canvas.json       # Inventory canvas template
├── data/
│   ├── create_tables.sql # Database schema
│   └── seed_data.sql     # Demo data (DEMO badge)
├── docs/
│   ├── architecture.md   # Technical architecture
│   ├── impact.md         # Impact metrics
│   └── demo_script.md    # 3-minute demo script
├── scripts/
│   ├── init_db.py        # Auto-seed PostgreSQL on Docker startup
│   ├── start_local.py    # One-script local startup (backend + slack + seed)
│   ├── holdout_backtest.py   # Monthly hold-out backtest (illustrative)
│   └── cross_validation.py   # Rolling-origin CV (headline accuracy metric)
├── notebooks/
│   ├── cv_metrics.json       # Rolling-origin CV results (real, reproducible)
│   ├── holdout_metrics.json  # Monthly hold-out results (caveated)
│   └── prophet_metrics.json  # Consolidated metrics summary
├── tests/
│   ├── test_mcp.py       # MCP tool unit tests
│   ├── test_prediction.py # Prophet engine tests
│   └── test_integration.py # Bolt handler integration tests
├── models/               # Prophet serialized models (.pkl)
├── render.yaml           # Render Blueprint (persistent host + Socket Mode)
├── docker-compose.yml    # One-click local stack
├── Dockerfile            # Backend container
├── LICENSE               # MIT License
├── AGENTS.md             # Development operating system
├── BIBLE.md              # Immutable declarations
└── CLAUDE.md             # Claude Code instructions

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UiPath Components Used

None — this project uses Slack platform APIs (Channel History API, Canvas API) for messaging and surfaces, Anthropic MCP Server for tool exposure, and Claude API for natural language generation.

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Demo Screenshots

Extracted from the final demo video (YouTube).

Block Kit Alert

Real-time Block Kit alert fired when Prophet predicts a stockout within the reorder lead time window.

Order Modal

Pre-filled order modal with model-suggested quantity and supplier dropdown — one click creates the order in Supabase.

Demand Forecast Chart

Prophet-generated forecast chart with 80% confidence interval, rendered live and embedded in the Slack thread.

Canvas Update

Inventory Canvas updates in real time after an order is confirmed.

Verified numbers from live deploy:

• TSH demand: ~144 u/día (hábil), ~79 u/día (fin de semana) — Prophet forecast, output real /alert/trigger, out-of-peak (Jul-Sep)
• Stockout projection: 6 días con 680 u stock (projected_stockout_date: 2026-07-08)
• Forecast chart: renders from https://labops-agent.onrender.com/chart/forecast/TSH
• Canvas: persists via lab_config table in Supabase

Data & Privacy

All data in this project is synthetic and clearly labeled with DEMO badges. No real patient data, no PHI. The prediction model's seasonal parameters (peak months, peak/trough ratios per test type) were derived from analysis of 414,289 real B2B demand records (Labmedicina, 2025-2026, anonymized — raw data not included, see data/DATASET_PROVENANCE.md); Prophet trains on a synthetic daily series encoding those parameters. Reported CV metrics (MAPE ~8-11%) are a synthetic-benchmark validation of the pipeline, not a real-consumption accuracy claim — see docs/architecture.md.

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License

MIT License — see LICENSE

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Builder

Mariano Adrian Oss · DevelopOss · Buenos Aires, Argentina

B2B KAM in clinical diagnostics (4 years) + AI Builder. This project applies real insider knowledge of clinical laboratory operations to a problem that existing software hasn't solved: uninterrupted diagnostic access for vulnerable patients inside the tools labs already use daily.

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LabOps Agent · Slack Agent Builder Challenge 2026 · Track: Slack Agent for Good