rishabhcli/QAgent

Self-healing QA agent: an autonomous multi-agent loop (Tester -> Triage -> Fixer -> Verifier) that runs E2E tests, detects bugs, patches + redeploys them, and learns from past fixes. Best ...

2 stars · 0 forks · TypeScript Topics: redis, nextjs, multi-agent, openai, browser-automation, self-healing, autonomous-agents, ai-agents, e2e-testing, qa-automation, llm, browserbase, stagehand, wandb-weave

README

QAgent - Self-Healing QA Agent A self-improving QA agent that automatically tests web applications, identifies bugs, applies fixes, and verifies the fixes – all without human intervention. Overview QAgent is a multi-agent system that creates a closed-loop for automated bug detection and fixing: ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ TESTER │───▶│ TRIAGE │───▶│ FIXER │───▶│ VERIFIER │ │ Agent │ │ Agent │ │ Agent │ │ Agent │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ │ ┌──────────────┐ │ │ │ Redis │◀────────────────┘ │ │ (Knowledge │ │ │ Base) │ │ └──────────────┘ │ │ ▼ ▼ ┌─────────────────────────────────────────────────────────┐ │ W&B Weave (Observability) │ └─────────────────────────────────────────────────────────┘

Why QAgent?

Continuous Testing: Runs E2E tests like a QA engineer, simulating real user flows Automatic Bug Fixing: Doesn't just report bugs – it fixes them and redeploys Self-Improvement: Learns from past bugs to diagnose and fix faster over time Measurable Impact: Track pass rates, time-to-fix, and iterations to prove improvement

Quick Start

Clone the repository

Install dependencies

Set up environment

Edit .env.local with your API keys

Run development server (demo app)

Run the QAgent agent

Start the Marimo dashboard

Technology Purpose

Browserbase + Stagehand AI-powered browser automation for E2E testing

Vercel Instant deployment after fixes

Redis Vector knowledge base for learning from past bugs

W&B Weave Tracing and evaluation of agent runs

Custom Orchestrator (ADK/A2A-compatible) Multi-agent workflow coordination (ADK integration planned)

Marimo Interactive analytics dashboard

Next.js Demo application

OpenAI LLM for patch generation

Documentation

File Purpose

CLAUDE.md Agent configuration, tech stack, workflow rules

TASKS.md Phase-scoped task tracker

docs/PRD.md Product Requirements Document

docs/DESIGN.md System design and data structures

docs/ARCHITECTURE.md Architecture Decision Records

prompts/ralph-loop.md Development workflow prompts

Project Structure QAgent/ ├── .claude/ │ └── skills/ # Domain-specific knowledge modules │ ├── browserbase-stagehand/ │ ├── redis-vectorstore/ │ ├── vercel-deployment/ │ ├── wandb-weave/ │ ├── google-adk/ │ ├── marimo-dashboards/ │ └── qagent-agents/ ├── agents/ # Agent implementations │ ├── tester/ │ ├── triage/ │ ├── fixer/ │ ├── verifier/ │ └── orchestrator/ ├── app/ # Next.js demo app ├── dashboard/ # Marimo analytics ├── docs/ # Documentation ├── lib/ # Shared libraries ├── prompts/ # Workflow prompts └── tests/ # Test suites

How It Works The QAgent Loop

Test - Tester Agent runs E2E tests using Browserbase/Stagehand Detect - Failures are captured with screenshots, DOM state, logs Diagnose - Triage Agent analyzes the failure and queries Redis for similar issues Fix - Fixer Agent generates a patch using LLM + past fix patterns Deploy - Verifier Agent applies the patch and deploys via Vercel Verify - Tests are re-run to confirm the fix works Learn - Successful fixes are stored in Redis for future reference Repeat - Loop continues until all tests pass

Self-Improvement

Knowledge Base: Every bug and fix is stored with embeddings for semantic search Pattern Learning: Similar bugs are fixed faster using past solutions TraceTriage: Agent failures are analyzed to improve prompts and workflows RedTeam: Adversarial tests continuously harden the system

For AI Agents

Start every session by reading CLAUDE.md Check current work in TASKS.md Follow the Ralph Loop workflow for iterative development Load skills from .claude/skills/ as needed

Development

Install dependencies

Run demo app

Run agent

Run tests

Run E2E tests

Lint and format

Build

BROWSERBASE_API_KEY - Browserbase API key OPENAI_API_KEY - OpenAI API key REDIS_URL - Redis connection string VERCEL_TOKEN - Vercel API token WANDB_API_KEY - Weights & Biases API key GOOGLE_CLOUD_PROJECT - Google Cloud project (reserved for ADK/A2A integration)

Getting Started See the Quick Start section above for setup instructions. Once running, connect a GitHub repository through the dashboard and start your first QAgent run. References

QAgent Paper - Agentic patching framework Stagehand - AI browser automation Browserbase - Cloud browsers W&B Weave - LLM observability Google ADK - Planned orchestration framework Marimo - Reactive notebooks

License See LICENSE file

source