argus

argus

An agentic QA framework that authors, generates, triages, and self-heals Playwright tests for any web app, usable from Claude Code/Desktop as an MCP server or from CI as a CLI.

Category
Visit Server

README

πŸ‘οΈ Argus β€” Agentic QA Framework

An agentic QA framework that authors, generates, triages, and self-heals Playwright tests for any web app β€” usable from Claude Code/Desktop as an MCP server or from CI as a CLI β€” with the whole loop running as a deployment gate in GitHub Actions.

CI

[!NOTE] 🚧 Early development. Build progress is tracked as milestones M0 β†’ M4 (see Roadmap).


Why

Test suites are expensive to write and brittle to maintain. Argus puts a Claude agent in the loop to do the slow parts: explore an app and write real Playwright tests, then β€” when the UI drifts β€” diagnose the failure and open a fix PR, while still refusing to paper over genuine bugs.

The idea: one core, two consumers

Argus defines its QA tools once and exposes them twice.

                     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                     β”‚   @argus/core                β”‚
                     β”‚   Agent loop (Claude)        β”‚   Anthropic Messages API + tool use
                     β”‚   + single Tool Registry     β”‚   browser Β· dom Β· fs Β· playwright Β· git
                     β””β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜
                             β”‚               β”‚
              β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”         β”Œβ”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
              β”‚ @argus/mcp      β”‚         β”‚ @argus/cli       β”‚
              β”‚ MCP server      β”‚         β”‚ npx argus ...    β”‚
              β”‚ (Claude Desktop)β”‚         β”‚ (used in CI)     β”‚
              β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜         β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

The loop: four behaviors

Stage Input The agent… Output
Author Plain-English intent compiles intent into a structured test plan *.plan.json
Generate A URL explores the app, writes specs with assertions tests/*.spec.ts
Triage A failed run classifies real bug vs DOM drift vs flake root-cause report
Heal A drift verdict rewrites the locator, verifies green, opens a PR a pull request

Quickstart

pnpm install
cp .env.example .env   # add your ANTHROPIC_API_KEY
pnpm build

Watch the agent run (E2E)

The agent loop is live. Point it at the bundled demo app and watch it explore β€” navigate, snapshot the DOM, read data-testids, and click through the login β†’ cart flow:

pnpm --filter @argus/core exec playwright install chromium   # one-time
pnpm --filter @argus/sample-shop dev                          # terminal 1 β†’ http://localhost:3100
node --env-file=.env packages/cli/dist/index.js smoke http://localhost:3100/login   # terminal 2

It prints a step-by-step trace and a token/cost line (~$0.05–0.15 per run on the fast model). Requires a real Anthropic API key (a Max subscription doesn't fund the API).

Or have it write a test and run it green:

node --env-file=.env packages/cli/dist/index.js generate http://localhost:3100/login --run

It explores the app, writes tests/generated/login.spec.ts, and runs it against sample-shop (3 passed, 0 failed). Defaults to Opus for quality; add --model claude-haiku-4-5 for ~10Β’ runs.

Full CLI / MCP usage docs land with milestones M2 and M4.

Repo layout

argus/
β”œβ”€ packages/
β”‚  β”œβ”€ core/   # agent loop, tool registry, Claude client, prompts
β”‚  β”œβ”€ mcp/    # MCP server wrapping the registry
β”‚  └─ cli/    # `argus author|generate|triage|heal`
β”œβ”€ apps/
β”‚  └─ sample-shop/   # Next.js demo target (login + products + cart)
└─ tests/     # generated Playwright specs land here

Roadmap

  • M0 β€” Foundations Β· monorepo, tooling, CI stub
  • M1 β€” Core + sample-shop + Generate Β· the first "AI writes real tests" moment
  • M2 β€” CLI + GitHub Actions gate Β· failing tests block deployment
  • M3 β€” Triage + Heal Β· self-healing PRs
  • M4 β€” MCP server + polish Β· drive Argus from Claude Desktop; demo GIFs

License

MIT Β© Piyush Pathak

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured