agentvision 0.2.0

Eyes for AI Agents — a machine-graded visual feedback loop coding agents consume to self-correct before claiming done.

AgentVision — Eyes for AI Agents 👁️

Coding agents are blind. They write a UI, a chart, an SVG, a PDF — and never see the rendered result. They reason from source code and stdout, then declare success while the button overflows, the legend is clipped, the contrast fails, or an image 404s. AgentVision gives agents eyes.

AgentVision is a provider-agnostic framework that closes the visual feedback loop for AI coding agents:

render → perceive → report → (agent fixes) → re-render → diff

It is not human-reviewed visual regression (Percy/Applitools/Argos) and not browser automation (browser-use/Playwright). It is a machine-graded visual critique loop an agent consumes to self-correct before claiming done — with a verdict (pass/warn/fail) and actionable, coordinate-grounded issues.

The 60-second pitch

pip install "agentvision[render]"
playwright install chromium     # see `agentvision doctor` if Chromium won't launch
agentvision demo                # no API key required

agentvision demo renders a deliberately broken page, prints a FAIL report (overflow + low-contrast + a 404 image — all DOM/CV-grounded, no LLM key needed), then loops against the fixed version and prints "what changed: 3 issues resolved → PASS." That command is the product.

What makes it trustworthy

Findings are grounded in sources we can actually trust:

• DOM geometry (getBoundingClientRect + scroll offset) — precise element boxes.
• Computed-style contrast (getComputedStyle) — real WCAG ratios, with a confidence flag (it degrades honestly over gradients/images/pseudo-elements rather than lying).
• OCR word boxes (Tesseract) — precise text locations.
• Console / network / 4xx capture — the #1 "looks fine in code, broken live" cause.

A vision LLM (Claude/OpenAI/Gemini) adds semantic critique on top. Its pixel boxes are treated as advisory (bbox_precise: false), never marketed as pixel-accurate.

Many faces, one core

Surface	Who it's for
Library (import agentvision)	Python apps, custom harnesses
CLI (agentvision …)	Any agent that can run a shell command; CI
Claude Code Skill	Claude agents — auto-invokes the loop before claiming done
MCP server (agentvision-mcp)	Cursor, Claude, any MCP-capable host
REST service (agentvision-serve)	Non-MCP / networked / CI agents
Integration recipes	Cursor rules, Aider, generic "agent contract"

⚠️ "Provider-agnostic" describes the API surface, not behavior. The framework can't force a non-Claude agent into the loop — it gives every agent the means. The Claude Code Skill is the one surface that makes an agent use it proactively; MCP is the first-class cross-host path; the recipes cover the rest.

Vision backends

Pluggable and selectable via --backend / AGENTVISION_VISION_BACKEND:

• anthropic (default model claude-haiku-4-5, upgradable to Sonnet/Opus)
• openai, gemini
• local — CV/OCR heuristics only, no API key, no egress (great for CI / air-gapped)

Install

pip install "agentvision[all]"          # everything
pip install "agentvision[render]"       # just rendering + the no-key local loop
pip install "agentvision[render,anthropic]"  # + Claude analysis

System dependencies (Chromium, Tesseract, poppler) and a doctor that checks them:

agentvision doctor          # attempts a real Chromium launch; lists every missing lib
agentvision doctor --fix    # installs the Chromium browser binary

On a bare RHEL/CentOS box, playwright install-deps does not work (apt-only). See docs/quickstart.md for the dnf line, or use the bundled Dockerfile which bakes the deps in.

Usage

# Analyze a file/URL/HTML string and print a structured report
agentvision analyze ./index.html --backend local --json

# Run the self-correcting loop
agentvision loop ./dashboard.html --max-iter 3

# Responsive contact sheet across breakpoints
agentvision sheet ./index.html --breakpoints 375,768,1280,1920

# Visual regression against a named baseline
agentvision baseline ./index.html --name home
agentvision regress  ./index.html --name home

Library:

import asyncio
from agentvision import load_settings
from agentvision.core.loop import LoopSession

async def main():
    settings = load_settings(vision_backend="local")
    session = LoopSession("examples/broken_layout.html", settings=settings)
    result = await session.iterate()
    print(result.report.verdict, [i.message for i in result.report.issues])

asyncio.run(main())

Documentation

• Quickstart · The Loop · Backends · Adapters · Integrations · Vision

What we do not claim (honesty)

• Pixel-accurate vision-model bounding boxes (they're advisory).
• WCAG verdicts on rasterized non-HTML (heuristic only).
• Bit-reproducible screenshots / deterministic LLM reports.
• Uniform provider-agnostic behavior (only the API surface is uniform).

License

MIT © Amit Patole