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Show HN: QAgent – a self-healing QA agent that fixes bugs and opens PRs

QAgent, a self-healing QA agent that automatically tests web applications, identifies bugs, applies fixes, and verifies fixes without human intervention, has been released as an open-source project on GitHub. The multi-agent system uses Browserbase and Stagehand for AI-powered browser automation, Redis as a vector knowledge base for learning from past bugs, and Vercel for instant deployment after fixes, with the goal of creating a closed-loop for automated bug detection and fixing.

read3 min views1 publishedJul 22, 2026
Show HN: QAgent – a self-healing QA agent that fixes bugs and opens PRs
Image: source

A self-improving QA agent that automatically tests web applications, identifies bugs, applies fixes, and verifies the fixes – all without human intervention.

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)                  │
└─────────────────────────────────────────────────────────┘

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

git clone https://github.com/rishabhcli/QAgent.git
cd QAgent

pnpm install

cp .env.example .env.local

pnpm dev

pnpm run agent

marimo run dashboard/app.py
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
File Purpose

TASKS.mddocs/PRD.mddocs/DESIGN.mddocs/ARCHITECTURE.mdprompts/ralph-loop.md

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

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

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

Start every session by readingCLAUDE.mdCheck current work inTASKS.mdFollow the Ralph Loop workflow for iterative developmentLoad skills from.claude/skills/

as needed

pnpm install

pnpm dev

pnpm run agent

pnpm test

pnpm run test:e2e

pnpm lint && pnpm format

pnpm build

See .env.example for required environment variables:

BROWSERBASE_API_KEY

  • Browserbase API keyOPENAI_API_KEY

  • OpenAI API keyREDIS_URL

  • Redis connection stringVERCEL_TOKEN

  • Vercel API tokenWANDB_API_KEY

  • Weights & Biases API keyGOOGLE_CLOUD_PROJECT

  • Google Cloud project (reserved for ADK/A2A integration)

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

QAgent Paper- Agentic patching frameworkStagehand- AI browser automationBrowserbase- Cloud browsersW&B Weave- LLM observabilityGoogle ADK- Planned orchestration frameworkMarimo- Reactive notebooks

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