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

🔌 New: QAI Consultant is now also an MCP server — uvx qai-consultant-mcp. No API keys, no Pinecone. See MCP Server below or the package on PyPI.

🤖 Built with Claude Code by Anthropic.

Screenshots

Web UI (Streamlit)

CLI

Quick Start

Option A — Use the live app (no setup)

👉 quality-ai-consultant.streamlit.app

Option B — Run locally

# 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

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:

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):

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

Architecture

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)

streamlit run src/app.py

Or use the live hosted version: quality-ai-consultant.streamlit.app

CLI (Terminal)

python src/cli.py

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

Listed on the official MCP registry (io.github.gvasile29/qai-consultant-mcp), Glama, and Awesome MCP Servers.

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.

uvx qai-consultant-mcp

Claude Code:

claude mcp add qai-consultant -- uvx qai-consultant-mcp

Claude Desktop (claude_desktop_config.json):

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

Tools:

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