Clawrity

Multi-channel AI business intelligence agent. Enterprise clients interact via Slack (or Teams) and get data-grounded answers, daily digests, budget recommendations, ROI forecasts, and competitor/sector intelligence — all specific to their business data.

---

Architecture

Built on the OpenClaw pattern:

• ProtocolAdapter — normalises messages from any channel (Slack, Teams, etc.)
• SOUL.md — per-client personality, rules, and business context
• HEARTBEAT.md — autonomous daily digest scheduling

All intelligence lives in the Clawrity backend. OpenClaw layer has zero business logic.

Tech Stack

Component	Tool
Language	Python 3.11
API Framework	FastAPI + uvicorn
LLM	Groq API — llama-3.3-70b-versatile
Embeddings	sentence-transformers all-MiniLM-L6-v2 (CPU, 384d)
Database	PostgreSQL + pgvector
Channel (dev)	Slack Bolt SDK (Socket Mode)
Channel (demo)	Microsoft Teams Bot Framework SDK
Scheduler	APScheduler AsyncIOScheduler
Web Search	Tavily API + DuckDuckGo fallback
Forecasting	Prophet

Quick Start

1. Prerequisites

• Python 3.11+
• Docker & Docker Compose
• Groq API key (free: https://console.groq.com)
• Tavily API key (free: https://app.tavily.com)

2. Environment Setup

cp .env.example .env
# Fill in your API keys in .env

3. Start PostgreSQL + pgvector

docker compose up -d postgres

4. Install Dependencies

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

5. Download Kaggle Datasets

Download these two datasets and place them in data/raw/:

1. Global Superstore: https://kaggle.com/datasets/apoorvaappz/global-super-store-dataset
2. Marketing Campaign Performance: https://kaggle.com/datasets/manishabhatt22/marketing-campaign-performance-dataset

mkdir -p data/raw data/processed
# Place downloaded files in data/raw/

6. Seed Demo Data

python scripts/seed_demo_data.py --client_id acme_corp \
  --superstore data/raw/Global_Superstore2.csv \
  --marketing data/raw/marketing_campaign_dataset.csv

7. Run RAG Pipeline

python scripts/run_rag_pipeline.py --client_id acme_corp

8. Start the API

uvicorn main:app --reload --port 8000

---

Slack Bot Setup (Socket Mode)

Step 1: Create Slack App

1. Go to https://api.slack.com/apps
2. Click Create New App → From scratch
3. Name it Clawrity and select your workspace

Step 2: Enable Socket Mode

1. In the left sidebar, click Socket Mode
2. Toggle Enable Socket Mode to ON
3. Click Generate Token — name it clawrity-socket
4. Copy the xapp-... token → paste into .env as SLACK_APP_TOKEN

Step 3: Configure Bot Token

1. Go to OAuth & Permissions
2. Under Bot Token Scopes, add:
   • app_mentions:read
   • chat:write
   • channels:history
   • channels:read
3. Click Install to Workspace
4. Copy the xoxb-... token → paste into .env as SLACK_BOT_TOKEN

Step 4: Enable Events

1. Go to Event Subscriptions
2. Toggle Enable Events to ON (no Request URL needed in Socket Mode)
3. Under Subscribe to bot events, add:
   • app_mention
   • message.channels
4. Click Save Changes

Step 5: Get Signing Secret

1. Go to Basic Information
2. Under App Credentials, copy Signing Secret
3. Paste into .env as SLACK_SIGNING_SECRET

Step 6: Invite Bot to Channel

In Slack, go to your desired channel and type:

/invite @Clawrity

---

API Endpoints

Method	Path	Description
POST	/chat	Send message → get AI response
POST	/slack/events	Slack webhook fallback
POST	/compare	Side-by-side RAG vs no-RAG
POST	/forecast/run/{client_id}	Trigger Prophet forecasting
GET	/forecast/{client_id}/{branch}	Get cached forecast
GET	/admin/stats/{client_id}	RAG monitoring stats
GET	/health	System status

Adding a New Client

1. Create config/clients/client_newclient.yaml (copy from client_acme.yaml)
2. Create soul/newclient_soul.md
3. Create heartbeat/newclient_heartbeat.md
4. Place data in data/raw/ and run seed + RAG scripts
5. Restart — zero code changes required

---

Project Structure

clawrity/
├── main.py                         # FastAPI application
├── config/                         # Configuration
│   ├── settings.py                 # pydantic-settings from .env
│   ├── client_loader.py            # YAML client config loader
│   └── clients/client_acme.yaml    # Per-client config
├── soul/                           # Per-client personality
│   ├── soul_loader.py
│   └── acme_soul.md
├── heartbeat/                      # Autonomous digest scheduling
│   ├── heartbeat_loader.py
│   ├── scheduler.py
│   └── acme_heartbeat.md
├── agents/                         # AI agents
│   ├── gen_agent.py                # Response generation
│   ├── qa_agent.py                 # Quality assurance
│   ├── orchestrator.py             # Pipeline coordinator
│   └── scout_agent.py              # Competitor intelligence
├── skills/                         # Capabilities
│   ├── postgres_connector.py       # DB connection pool
│   ├── nl_to_sql.py                # Natural language → SQL
│   └── web_search.py               # Tavily + DuckDuckGo
├── channels/                       # Message channels
│   ├── protocol_adapter.py         # OpenClaw normalisation
│   ├── slack_handler.py            # Slack Socket Mode
│   └── teams_handler.py            # Teams stub
├── rag/                            # Retrieval-augmented generation
│   ├── preprocessor.py
│   ├── chunker.py
│   ├── vector_store.py
│   ├── retriever.py
│   ├── evaluator.py
│   └── monitoring.py
├── forecasting/
│   └── prophet_engine.py
├── connectors/
│   ├── base_connector.py
│   └── csv_connector.py
├── etl/
│   └── normaliser.py
└── scripts/
    ├── seed_demo_data.py
    └── run_rag_pipeline.py