agentvision 0.7.1

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

AgentVision — Eyes for AI Agents 👁️

Problem: AI coding agents are blind — they write a UI, chart, SVG or PDF and never see the result, shipping breakage they can't perceive.
Result: AgentVision gives them eyes — render → see → report → fix — catching overflow, low contrast, broken images and typos.
So your agent self-corrects before it claims done.

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.

Full-coverage vision. On a large artifact the model gets a downscaled overview plus full-resolution tiles covering it, so fine detail and small text aren't lost to downscaling. It's pixel-based and source-agnostic — the same coverage applies to HTML, a flat image, or a PDF page, not just elements the DOM enumerates.

Match the intent, not just avoid defects

A typo-free, well-laid-out artifact can still be the wrong thing — an infographic that shows the wrong stages, a page missing the panel you asked for, a generated image that ignored half the prompt. Give AgentVision the intent and it grades the render against it, so PASS means "matches what I set out to build," not merely "defect-free":

# Does the render match the thought? (text claims grade deterministically via OCR)
agentvision conform ./infographic.png \
  --brief "launch infographic for AgentVision" \
  --expect 'must: title reads "AgentVision"' \
  --expect 'should: shows 4 stages left to right'

For AI-generated artifacts the fix is a better prompt, not code — so the generative loop generate → see → grade vs intent → refine prompt → regenerate runs until it matches. The image generator is a hook you supply; AgentVision never bundles an image-gen dependency:

agentvision generate --generator mypkg.gen:make_image \
  --brief "minimalist infographic, dark background, no typos" --max-iter 4 -o final.png

See docs/conformance.md. Express intent three ways — a free-text brief (eyes extract the checklist), an explicit checklist (--expect, deterministic), or a reference image (--reference). Claims are must: / should: / nice:.

Eyes → brain: the handoff

In anatomy the eyes are only the afferent half — the retina perceives, the optic nerve carries the signal to the brain, the brain decides, the hand acts, the eyes look again. AgentVision is that afferent pathway for an agent: it perceives and hands a clean signal back to the brain (whatever does your reasoning/planning/memory) — it deliberately doesn't decide for you. Any perception call distills to a Handoff:

agentvision analyze ./page.html --handoff

{ "perceived": "fail", "next_action": "revise", "matches_intent": false,
  "todo": ["[overflow] hero text overflows on the right",
           "[intent/must] a \"Checkout\" button is visible"],
  "open_questions": ["Verify: uses the brand's dark theme"] }

next_action (done / revise / review) drives the brain's loop; todo is the work-list; open_questions is what perception couldn't confirm (never dropped). Available as report.to_handoff(), the MCP perceive_handoff tool, POST /handoff, and a handoff.json per loop iteration — provider- and brain-agnostic. See docs/handoff.md.

Eyes & Brain — AgentVision × Verel

AgentVision is the eyes. It pairs with Verel, the brain — an agent framework where nothing is "done" until a grader returns a verdict. The eyes perceive and grade intent; the brain decides with attestation and compounds only verified work into memory; then the eyes look again.

They ship and version independently (pip install agentvision, pip install verel) yet work in sync: AgentVision plugs into Verel as its verel.senses perception organ — mapped onto a unified verdict bus (vision alongside tests, lint and types), with intent conformance recorded in the brain's memory each iteration. AgentVision stays brain-agnostic; Verel is the reference brain. See docs/handoff.md.

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

Live pages, SPAs & dashboards (polling, websockets, canvas/WebGL):

# localhost dev server, wait for the data to render, freeze animation, machine output
agentvision analyze http://localhost:5173 --allow-local \
  --wait-for "#dashboard" --settle-ms 800 --quiet

Streaming / video / over-time behavior — watch, don't just glance:

# Is the video actually playing? Did loading finish? Are captions on?
agentvision watch https://app.example.com/player --frames 6 --interval-ms 500 \
  --expect 'must: the video is playing'

watch reads deterministic <video> state (currentTime/readyState/captions) + pixel liveness/stall/black-frame detection, then adds a time-aware vision pass. See docs/use-cases/streaming.md.

--nav-wait defaults to load (polling pages never go idle); --freeze (default on) pauses animations + requestAnimationFrame so canvas/WebGL pages capture without hanging; --quiet prints only JSON (logs to stderr, exit codes 0 pass/warn · 2 fail · 3 error).

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())

Drop it into your workflow & your agents

# CI gate (GitHub Action): fails the build on a visual FAIL verdict
- uses: amitpatole/agent-vision@v0.6.1
  with: { source: dist/index.html, command: check, args: --full-page }

• CI / pre-commit / Makefile — shell out; exit codes 0 pass/warn · 2 fail · 3 error, --quiet for JSON-only output. Reusable GitHub Action + pre-commit hook included.
• Your agents — drop integrations/agent-contract.md into the system prompt, use the Claude Code Skill, or the MCP tools (Cursor/Claude/any host).

Full guide: docs/integrations.md.

Documentation

📖 Full docs site: amitpatole.github.io/agent-vision

• Quickstart · The Loop · Conformance · Handoff (eyes→brain) · Streaming / temporal · 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