Guides · By AnyCap Team
How to add vision capabilities to an AI agent
Most AI agents work with text and code, but they cannot see unless you give them a visual capability surface.
AnyCap adds image understanding and video analysis so the agent can inspect screenshots, review designs, summarize demos, and reason about visual evidence inside the same workflow.
Vision capabilities usually show up as two commands: image read for still images and video read for temporal analysis. Both return structured text that an agent can reason over, summarize, or feed into follow-up actions.
What you need
- An AI agent that can run shell commands, such as Claude Code, Cursor, or Codex
- Node.js 18+ for skills.sh and npm install support
- A browser for the one-time login flow
- Images or videos to analyze, as public URLs or local files
Install the AnyCap skill
This installs the AnyCap skill so your agent can discover image and video analysis without improvising the workflow from scratch. The skill explains commands, setup, and the situations where vision should be used.
# For Claude Code
npx -y skills add anycap-ai/anycap -a claude-code -y
# For Cursor
npx -y skills add anycap-ai/anycap -a cursor -yInstall the AnyCap CLI
curl -fsSL https://anycap.ai/install.sh | shOr use npm install -g @anycap/cli. The CLI is the runtime surface that executes image and video reads after the skill tells the agent how to call them.
Log in
This authenticates the CLI once so the agent can use visual understanding together with other AnyCap capabilities in the same session.
anycap loginUse image understanding
The command returns structured details about visible text, objects, layout, and context. Focused prompts usually make the output much more useful for real product work.
# Analyze an image from a URL
anycap actions image-read --url https://example.com/screenshot.png
# Analyze a local screenshot with a specific question
anycap actions image-read --file ./screenshot.png --instruction "What accessibility issues do you see?"Use video analysis
Video analysis returns scene-level structure, key moments, and temporal relationships, which makes it useful for demos, user recordings, and competitive analysis.
# Analyze a video
anycap actions video-read --url https://example.com/demo.mp4
# Analyze a local recording with a focused prompt
anycap actions video-read --file ./recording.mp4 --instruction "List each feature shown in order"Combine vision in agent workflows
With vision installed, your agent can combine visual input with writing, coding, and planning tasks. That is where the capability becomes more than a captioning tool.
UI review workflow
Look at this screenshot and identify any UI issuesVideo summary workflow
Watch this demo video and write release notesCombined generation plus vision
Generate a hero image, then analyze it for brand consistencyThe agent can orchestrate upload, analysis, interpretation, and follow-up actions without forcing the user to manage each step manually.
Where vision adds the most value
- UI and QA review
- Have the agent inspect screenshots for layout regressions, accessibility problems, text overflow, or visual bugs before a release.
- Design and brand review
- Ask the agent to compare a mockup against brand guidelines, extract visible text, or summarize the hierarchy and composition of a layout.
- Video understanding
- Feed a product demo, user recording, or ad creative into the agent so it can summarize scenes, extract key moments, and turn the analysis into notes or tickets.
How agents use vision output well
Vision features are most useful when the analysis becomes part of a larger workflow rather than a one-off caption. For example, an agent can read a screenshot, identify accessibility issues, and then open code files to propose a fix based on what it found.
The same applies to video. A scene-by-scene summary becomes more valuable when the agent turns it into release notes, a QA checklist, or a list of missing product explanations. The capability is not just about describing visuals. It is about helping the agent make decisions with visual evidence.
In practice, focused prompts produce better results than generic ones. Asking 'What is in this image?' is useful, but asking 'What onboarding issues would block a first-time user in this screenshot?' gives the model a sharper evaluation frame.
Common setup and usage mistakes
- Passing a local path to --url
- --url is for files already reachable on the web. Local screenshots and recordings go through --file, which uploads them automatically before the read runs.
- Using generic prompts for complex reviews
- Broad prompts get broad answers. A targeted question about accessibility, information hierarchy, or feature order produces more actionable output.
- Treating vision as a standalone task
- The highest leverage comes when the agent uses the visual analysis to drive the next step, such as drafting notes, filing bugs, or making code changes.
FAQ
What is the difference between image read and video read?
Image read analyzes a single visual frame and returns structured details such as objects, visible text, layout, and context. Video read adds temporal understanding, so the output includes scenes, actions, sequence, and key moments over time.
What image and video formats are supported?
Image workflows commonly support formats such as JPEG, PNG, WebP, and GIF, while video workflows commonly support MP4, WebM, and MOV. Pass a stable URL with --url, or a local file with --file.
Can vision capabilities work with locally stored files?
Yes. Pass the path with --file and the CLI uploads it before the analysis runs. Image read accepts up to 10 images per request; video read accepts one local video up to 100 MB.
What are good first use cases for vision in an agent?
Strong early use cases include screenshot QA, accessibility review, extracting information from UI mockups, summarizing product demos, and comparing visuals against design or brand expectations.
CLI version and updating · 0.6.5
Current release checked October 21, 2026: 0.6.5. Use the update command for your installation method, then verify the version and login in the same environment that runs your agent.
Direct binary installation
anycap updatenpm installation
npm install -g @anycap/cli@latestanycap --version
anycap statusVersion 0.6.5 retries transient downloads, recovers an expired video link by querying the same task, and saves multiple image outputs to numbered files. After a video download failure, resume the existing task instead of generating again.
anycap video tasks wait <task-id> -o result.mp4Dated examples retain the CLI version, output and charge from the original run. Updating the CLI does not revalidate an old result. Check the live model schema and current pricing before a new billed task.