Quality Manager Intelligence Agent

An autonomous agent that continuously monitors the GenAI, AI Agents, software testing, and developer tools landscape — then delivers structured, actionable intelligence reports tailored for QA Managers and Tech Project Managers in the high-tech industry.

---

What It Does

Capability	Details
Web intelligence	Polls 30+ curated RSS feeds, GitHub Trending, Arxiv papers, and web sources every 6 hours
AI-powered analysis	Uses OpenAI GPT-4o-mini to summarise every article and extract QA-specific insights
Trend detection	Clusters articles into trends, calculates momentum scores, and raises alerts for rapidly accelerating themes
Self-expansion	Discovers new information sources autonomously by mining article pages for RSS feeds and querying the LLM for recommendations
Rich reports	Generates beautiful HTML + Markdown reports organised by category, relevance score, trend landscape
Notifications	Sends alerts via Email (SMTP) and Slack when high-momentum trends are detected

---

Quick Start

Mac / Linux

git clone <repo-url> && cd qa-intelligence-agent
bash setup.sh          # creates .venv, installs deps, prompts for API key
python3 main.py run    # first run – generates a report

Windows

git clone <repo-url> && cd qa-intelligence-agent
setup.bat              :: creates .venv, installs deps, creates run.bat
run.bat run            :: first run – generates a report

Windows note: run.bat sets PYTHONNOUSERSITE=1 automatically. This prevents Windows Store Python stub modules from shadowing venv packages (a known issue on Windows 10/11 when the Windows Store Python app is installed). If you run python main.py run directly without this flag and get an ImportError, add set PYTHONNOUSERSITE=1 to your terminal session first.

Manual setup (any OS)

cd qa-intelligence-agent
cp .env.example .env
# Edit .env – set OPENAI_API_KEY=sk-...

python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt
python main.py run

4. Start the scheduler (runs every 6 hours)

python main.py schedule

5. View reports

Reports are saved to ./reports/ as HTML files. Open in any browser.

---

CLI Commands

python main.py run                   # Single run and exit
python main.py schedule              # Start recurring scheduler
python main.py schedule --interval 3 # Override interval (hours)
python main.py report                # Generate report from existing data
python main.py status                # Show run history
python main.py sources               # List all registered sources

---

Docker Deployment

cp .env.example .env     # Fill in OPENAI_API_KEY
docker compose up -d     # Start in background
docker compose logs -f   # Watch logs

# One-shot run (useful in CI pipelines)
docker compose --profile run-once up qa-agent-run-once

---

Configuration

All settings are via environment variables (.env file):

Variable	Default	Description
OPENAI_API_KEY	required	OpenAI API key
OPENAI_MODEL	gpt-4o	Model for summarisation and trend analysis (gpt-4o-mini for lower cost)
GITHUB_TOKEN	optional	GitHub PAT (raises API rate limit from 60 → 5000 req/hr)
SCHEDULE_INTERVAL_HOURS	6	How often the agent runs
MIN_RELEVANCE_SCORE	40	Minimum score (0–100) to include an article in reports (lower = more articles, higher OpenAI cost)
NOTIFY_EMAIL	optional	Email address to send alert notifications to
SLACK_BOT_TOKEN	optional	Slack bot token for channel notifications
SLACK_CHANNEL	#qa-intelligence	Slack channel to post to

---

Report Structure

Each generated report contains:

1. Executive Stats – Articles collected, trends detected, alerts count
2. 🚨 Alerts – Trends requiring immediate attention (momentum ≥ threshold)
3. ⭐ Top 10 Articles – Highest relevance score, with AI summary + insights + QA relevance
4. 📂 Articles by Category – All articles grouped by: GenAI, AI Agents, QA & Testing, DevOps, Tools, Project Management
5. 📈 Trend Landscape – All detected trends with momentum bars and descriptions

---

Monitored Sources (default)

Category	Sources
GenAI & LLMs	OpenAI Blog, Anthropic, Google AI Blog, Hugging Face, The Batch (DeepLearning.AI)
AI Agents	LangChain Blog, Simon Willison's Blog, Import AI
QA & Testing	Ministry of Testing, Test Automation University, Google Testing Blog, Applitools Blog
DevOps	Martin Fowler, The New Stack, DevOps.com
Tools	Hacker News, GitHub Blog, Dev.to, GitHub Trending
Academic	Arxiv (AI Agents, Software Testing + LLMs, GenAI Test Automation, RAG, Multi-Agent Systems)

New sources are discovered automatically every run and added to the database.

---

Architecture

qa-intelligence-agent/
├── src/
│   ├── agent/           # CoreAgent orchestrator, TrendAnalyzer, SourceDiscoverer
│   ├── collectors/      # RSS, GitHub, Arxiv, Web scrapers
│   ├── processors/      # RelevanceScorer, Summarizer, ContentProcessor
│   ├── storage/         # SQLAlchemy models + repositories (SQLite/PostgreSQL)
│   ├── reports/         # HTML/Markdown report generator + Jinja2 templates
│   ├── notifications/   # Email (SMTP) + Slack notifier
│   ├── scheduler/       # APScheduler wrapper
│   └── config/          # Settings (pydantic-settings) + sources.yaml
├── tests/               # Pytest suite (one class per file)
├── reports/             # Generated HTML reports
├── data/                # SQLite database
└── main.py              # CLI entry point

Execution Cycle

CoreAgent.run()
  ├── 1. Load active sources from DB
  ├── 2. Collect   → RSS + GitHub + Arxiv + Web scrapers
  ├── 3. Process   → Score relevance (fast) → AI summarise (high-score items only)
  ├── 4. Trends    → LLM detects themes across recent articles
  ├── 5. Discover  → Mine pages + ask LLM for new source recommendations
  ├── 6. Report    → Generate HTML + Markdown
  └── 7. Notify    → Email + Slack alerts for high-momentum trends

---

Running Tests

pytest                        # Run all tests with coverage
pytest -m "not integration"   # Skip tests that need network
pytest tests/test_storage/    # Run a specific module

---

Adding New Sources

Option A – Edit src/config/sources.yaml:

rss_feeds:
  - name: "New Blog"
    url: "https://newblog.com/feed"
    category: "genai"
    relevance_boost: 10

Option B – The agent will discover them automatically based on article links and LLM recommendations.

---

Extending the Agent

To add a new collector (e.g. LinkedIn, YouTube, Jira changelog):

1. Create src/collectors/my_collector.py with a class following the same interface as RSSCollector
2. Register the call in CoreAgent._collect()
3. Add tests in tests/test_collectors/test_my_collector.py

---

License

MIT