qai-consultant

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="assets/brand/qai_logo_horizontal_dark_1680.png">
    <img alt="QAI Consultant" src="assets/brand/qai_logo_horizontal_1680.png" width="360">
  </picture>
</p>

# QAI Consultant

An open-source AI agent that acts as a senior QA Architect — automatically generating a **Test Strategy**, **Risk Register**, **Effort Estimation Report**, and **Test Plan** from a simple project description, plus deterministic **QA Document Quality Review** and **Test Results Analysis** for evaluating what already exists. Also available as an **MCP server** so Claude Code, Claude Desktop, and claude.ai can ground their own QA work in the same standards and numbers.

> 🌐 **Live demo:** [quality-ai-consultant.streamlit.app](https://quality-ai-consultant.streamlit.app)

> 🔌 **New:** QAI Consultant is now also an MCP server — `uvx qai-consultant-mcp`. No API keys, no Pinecone. See [MCP Server](#mcp-server-for-claude-code-claude-desktop-claudeai) below or the [package on PyPI](https://pypi.org/project/qai-consultant-mcp/).

> 🤖 Built with [Claude Code](https://claude.ai/code) by Anthropic.

![CI](https://github.com/gvasile29/qai-consultant/actions/workflows/ci.yml/badge.svg)
![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)
![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)
![Version](https://img.shields.io/badge/version-3.1.4-green.svg)
![PyPI](https://img.shields.io/pypi/v/qai-consultant-mcp?label=qai-consultant-mcp&color=blue)
![Built with Claude](https://img.shields.io/badge/Built%20with-Claude-orange?logo=anthropic)

---

## Screenshots

### Web UI (Streamlit)
![Streamlit Intro](docs/screenshots/streamlit_intro.png)
![Streamlit MCP announcement in sidebar](docs/screenshots/streamlit_mcp_sidebar.png)
![Streamlit Project Discovery dialogue](docs/screenshots/streamlit_dialogue.png)
![Streamlit Risk Register](docs/screenshots/streamlit_risk_register.png)
![Streamlit Effort Estimation](docs/screenshots/streamlit_effort.png)
![Streamlit Test Strategy](docs/screenshots/streamlit_strategy.png)
![Streamlit Test Plan](docs/screenshots/streamlit_test_plan.png)

### CLI
![CLI Banner](docs/screenshots/cli_banner.svg)
![CLI Project Discovery dialogue](docs/screenshots/cli_dialogue.svg)

---

## Quick Start

### Option A — Use the live app (no setup)

👉 [quality-ai-consultant.streamlit.app](https://quality-ai-consultant.streamlit.app)

### Option B — Run locally

```bash
# 1. Clone and install
git clone https://github.com/gvasile29/qai-consultant.git
cd qai-consultant
pip install -r requirements.txt

# 2. Set up API keys
cp .env.example .env
# Edit .env and fill in the 4 keys (see Prerequisites below)

# 3. Build the knowledge base (one-time, pushes to Pinecone)
python src/ingest.py

# 4. Run
python src/cli.py            # Terminal UI
streamlit run src/app.py     # Web UI → http://localhost:8501
```

> 📖 Full installation guide: [INSTALL.md](INSTALL.md)

---

## The Problem

Creating a Test Strategy from scratch is time-consuming and requires deep QA expertise. Most teams either skip it, do it superficially, or spend days researching methodologies.

QAI Consultant eliminates this bottleneck by combining established QA methodologies, industry standards (ISTQB, OWASP, ISO 26262, A-SPICE), and expert knowledge into an AI agent that thinks like a seasoned QA Architect.

---

## Who Is This For?

- **QA Engineers** who need structured guidance on test strategy
- **Engineering Managers** who need effort estimations and resource planning
- **Development teams** without a dedicated QA Architect
- **QA Consultants** who want to accelerate their delivery

---

## What QAI Consultant Generates

From a single 11-question dialogue, QAI Consultant automatically generates **four documents**:

| Document | What it contains |
|---|---|
| ⚠️ **Risk Register** | Risk matrix, likelihood/impact analysis, mitigations per risk |
| 📊 **Effort Estimation Report** | PERT-based breakdown, team capacity analysis, confidence score |
| 📋 **Test Strategy** | ISTQB-aligned strategy tailored to your stack, methodology, and compliance |
| 📝 **Test Plan** | IEEE 829-aligned plan with entry/exit criteria, schedule, and AI tool oversight |

All outputs are saved as Markdown files and available for PDF download.

---

## Knowledge Base

QAI Consultant's recommendations are grounded in real QA standards and methodologies:

- 📘 **ISTQB** — 14 certification syllabuses (CTFL, CTAL-TA, CTAL-TM, CTAL-TAE, CT-AI, and more)
- 🔒 **OWASP** — WSTG v4.2, MASTG, Top 10 2021
- 🚗 **ISO 26262** — Automotive functional safety (ASIL levels, HARA, V&V)
- 🏭 **A-SPICE** — Automotive SPICE process reference model (SWE.4, SWE.5, SWE.6)
- 📋 **IEEE 829** — Test documentation standard
- ⚙️ **ISO/IEC 25010** — Software product quality model
- 🤖 **AI Test Planning** — 17 real-world AI SDLC case studies (2024–2025)
- 🧠 **Expert Knowledge** — Real QA scenarios and lessons learned from practitioners

---

## Prerequisites

QAI Consultant v2.0 runs on cloud APIs — no local GPU or Ollama required.

You need four API keys in a `.env` file (all have free tiers):

| Key | Where to get it |
|-----|----------------|
| `MISTRAL_API_KEY` | [console.mistral.ai](https://console.mistral.ai/) → API Keys |
| `OPENROUTER_API_KEY` | [openrouter.ai/keys](https://openrouter.ai/keys) |
| `PINECONE_API_KEY` | [pinecone.io](https://www.pinecone.io/) → API Keys |
| `PINECONE_INDEX_NAME` | Name of your Pinecone index (e.g. `qai-consultant`, dimensions: 384, metric: cosine) |

```bash
cp .env.example .env
# Edit .env and fill in all four values
```

---

## Architecture

![QAI Consultant Architecture](docs/screenshots/architecture.svg)

## How It Works

```
You describe your project (11 questions)
        ↓
QAI retrieves relevant knowledge from Pinecone (parallel RAG, 3 threads)
        ↓
QAI analyzes risks from your context → Risk Register (Mistral API)
        ↓
QAI estimates effort using PERT + industry benchmarks → Effort Report
        ↓
QAI generates a Test Strategy backed by QA standards → Test Strategy (Mistral API)
        ↓
QAI generates an IEEE 829-aligned Test Plan → Test Plan (Mistral API)
        ↓
Four documents ready for Markdown + PDF download
```

LLM calls use **Mistral API** as the primary provider, with **OpenRouter** as automatic fallback.

---

## Interfaces

### Web UI (Browser — recommended)

```bash
streamlit run src/app.py
```

Or use the **live hosted version**: [quality-ai-consultant.streamlit.app](https://quality-ai-consultant.streamlit.app)

### CLI (Terminal)

```bash
python src/cli.py
```

### MCP Server (for Claude Code, Claude Desktop, claude.ai)

QAI Consultant is also available as a local, fully keyless MCP server —
`qai-consultant-mcp`. No Pinecone, no Mistral/OpenRouter API keys: it runs a
local embedding index over the same knowledge base and exposes deterministic
QA effort estimation, so your own AI coding assistant can ground its QA
planning directly, no separate LLM call needed.

![qai-consultant-mcp answering a retrieve_qa_knowledge call in MCP Inspector](assets/demo/qai-consultant-mcp-demo.gif)

```bash
uvx qai-consultant-mcp
```

**Claude Code:**

```bash
claude mcp add qai-consultant -- uvx qai-consultant-mcp
```

**Claude Desktop** (`claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "qai-consultant": {
      "command": "uvx",
      "args": ["qai-consultant-mcp"]
    }
  }
}
```

**Tools:**

| Tool | What it does |
|---|---|
| `retrieve_qa_knowledge` | Grounding chunks from the KB (ISTQB, OWASP, IEEE, ISO, EU AI Act), filterable by category |
| `list_kb_sources` | Every document in the KB, grouped by category |
| `estimate_qa_effort` | Deterministic PERT-based effort estimate (no LLM narrative — you write your own from the numbers) |
| `review_qa_document` | Deterministic 0–100 quality score for an existing Test Plan/Strategy/test case list across six ISTQB/IEEE-829 dimensions, with findings + KB citations |
| `analyze_test_results` | Deterministic health metrics from JUnit XML/CSV test execution data — flaky tests, ever-failing tests, slowest tests, failure clustering |

**Prompts:** `qa_project_interview` (the same 11-question intake this app uses), `risk_register_structure`, `test_strategy_structure`, `test_plan_structure` — each grounds the client's generation in `retrieve_qa_knowledge` with `[Source N]` citations.

**Privacy:** usage telemetry is off by default. Set `QAI_TELEMETRY=1` to opt in; even then, only tool name/success/duration/category and an anonymous install ID are sent — never your query text or project details.

---

## Feedback Loop

After each generation, QAI asks: *"Was this strategy useful?"*

- **Yes** → strategy saved to `knowledge_base/generated_strategies/` and included in the next re-ingestion
- **Partially** → strategy saved with your improvement notes
- **No** → discarded

This creates a **feedback loop** where QAI learns from validated real-world outputs over time.

---

## Roadmap

- **v0.1** ✅ Core agent + CLI + Streamlit Web UI
- **v0.2** ✅ Feedback loop — validated strategies grow the knowledge base
- **v0.3** ✅ Risk Register — automatic risk analysis alongside Test Strategy
- **v0.4** ✅ Effort Estimation Report — PERT-based with team capacity analysis
- **v0.5** ✅ Auto re-ingest — file watcher + incremental ingest + manifest tracking
- **v0.6** ✅ Confidence level algorithm — score-based (0-100): PERT spread + capacity gap + data quality + multiplier magnitude
- **v1.0** ✅ MVP — error handling, input validation, logging, full documentation, tests, Apache 2.0 license
- **v2.0** ✅ Cloud migration — Ollama → Mistral API + OpenRouter fallback; ChromaDB → Pinecone; deployed to Streamlit Cloud
- **v2.0.1** ✅ Stability — 27 bugs fixed: PERT normalization, template application, PDF caching, session state, filename sanitization, RAG fallback, per-step exception isolation
- **v2.0.2** ✅ Stability — release-gate evals (esti