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

Related MCP server: checkyourself

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

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 (estimate integrity + RAG metrics), 5 estimation/validation defects fixed, session-state crash fix, narrative duplication/truncation fixes, per-step generation isolation from LLM outages

• v2.5.0 ✅ In-app Release Notes — sidebar panel + one-time "what's new" banner

• v2.5.1 ✅ Knowledge base — new evaluation_audit/ pillar: process/test maturity models, audit methodology, security/compliance audit, real public failure case studies

• v2.5.2 ✅ EU AI Act Article 50 transparency patch — sidebar AI-interaction notice + visible "AI-generated content" label on every generated document

• v2.6.0 ✅ EU AI Act knowledge base pillar — risk tiers, provider/deployer obligations, Article 50 transparency, Articles 9-15 testing implications, conformity assessment, timeline

• v3.0.0 ✅ MCP server MVP — local, keyless qai-consultant-mcp (standards-grounded retrieval + deterministic effort estimation), in-app announcement, and machine-readable AI-generated marking (EU AI Act Article 50(2))

• v3.1.0 ✅ Evaluation Package — QA Document Quality Review (deterministic ISTQB/IEEE-829 rubric scoring an existing Test Plan/Strategy/test case list, with an optional AI narrative) and Test Results Analysis (flaky/ever-failing/slowest/failure-clustering metrics from JUnit XML/CSV, grounding the Risk Register in real execution data); available in the web app, CLI (--review, --results), and the MCP server (review_qa_document, analyze_test_results)

• v3.1.1 ✅ Visit counter — a running total of app visits now shows in the sidebar, persisted in Pinecone so it survives redeploys

• v3.1.2 ✅ Fix — the 3.1.1 visit counter never actually incremented (Pinecone rejected its all-zero placeholder vector); now works correctly

• v3.1.3 ✅ Fix — the visit counter's label was in Romanian ("vizite") instead of English; now reads "visits" to match the rest of the app's UI copy

• v3.1.4 ✅ Added the mcp-name marker to README_MCP.md (PyPI long description) — a prerequisite for listing qai-consultant-mcp in the official Anthropic MCP registry; no functional change

• v3.1.5 ✅ Fix — qai-consultant-mcp failed to start (ModuleNotFoundError: mcp.server.fastmcp) after the upstream mcp SDK's breaking 2.0.0 release removed the FastMCP module the server depends on; mcp is now pinned to >=1.8.0,<2.0.0

• v3.1.6 ✅ Fix — qai-consultant-mcp could fail to attach in Claude Desktop on a cold cache (a client-side handshake timeout, since the server used to fully embed the whole knowledge base before responding to initialize); the full index build is now lazy, deferred until the first real request

• v3.2 ✅ CI quality gates completion — a separate, always-green-by-construction nightly workflow exercising real Pinecone/Mistral/OpenRouter contract tests, isolated from the blocking PR checks

• v3.3 ✅ Adopted the EU's official AI-generated-content icon (Code of Practice, AI Act Article 50(4)) in the Streamlit sidebar and all generated-document PDF exports, reinforcing the existing text/metadata disclosure

• v3.3.1 ✅ Fix — qai-consultant-mcp could intermittently fail to attach in Claude Desktop because 4 of its 6 runtime dependencies had loose version bounds, letting uv re-resolve and reinstall on any unrelated upstream release; all dependencies are now exact-pinned

• v3.4 ✅ App visual redesign ("Calibration Bench") — token-based color/typography system (IBM Plex fonts, no font CDN) and a reusable "Signal Ledger" score/severity component, applied to Document Review, Effort confidence, Results Analysis, Risk Register, and the Project Discovery question list

• v3.4.1 ✅ Distribution links (official MCP registry, Glama, Awesome MCP Servers) now shown in the app's own MCP panel, not just the READMEs; landing page redesign started (Phase 1 of 3 — see the CHANGELOG)

• v4.0 Remote MCP + distribution — hosted server connectable from claude.ai, registry submissions

Contributing

QAI Consultant is built by the QA community, for the QA community.

Contributions are welcome:

• 📚 Add new knowledge sources to knowledge_base/

• 🧠 Share expert knowledge using the prompts in knowledge_base/expert_knowledge/

• 🐛 Report bugs or suggest features via GitHub Issues

• 🔧 Submit pull requests

See CONTRIBUTING.md for detailed guidelines.

Troubleshooting

📖 Full troubleshooting guide: INSTALL.md