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.       --- ## Screenshots ### Web UI (Streamlit)        ### CLI   --- ## 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  ## 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.  ```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 (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 - **v3.2** 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](CONTRIBUTING.md) for detailed guidelines. --- ## Troubleshooting | Problem | Solution | |---|---| | "Missing required secret: 'MISTRAL_API_KEY'" | Add your key to `.env` or Streamlit Cloud secrets | | "Missing required secret: 'PINECONE_API_KEY'" | Add your Pinecone key to `.env` | | "Knowledge base is empty" | Run `python src/ingest.py` to push documents to Pinecone | | "Both Mistral API and OpenRouter are unavailable" | Check API keys are valid and have credits | > 📖 Full troubleshooting guide: [INSTALL.md](INSTALL.md#troubleshooting)