AlliHat vs Doop: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AlliHat and Doop — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AlliHat
AlliHat
A Safari sidebar extension that integrates Claude, ChatGPT, Gemini and Apple's on-device model for page-aware Q&A and browsing assistance.
Key features
- Multi-Model Integration: Connects and switches between Claude, ChatGPT, Gemini, and Apple's on-device model directly from a Safari sidebar so users can compare outputs or choose a local/private model.
- Page-Aware Question Answering: Understands the content of the active webpage and answers questions in context without leaving the page, enabling fast fact checks and summaries tied to the current content.
- Highlight-to-Answer: Convert selected text into immediate queries or summaries — highlight any passage to get concise explanations, clarifications, or rewritten text.
- Automated Browsing Mode: Can follow links and browse pages on the user's behalf to gather information, synthesize findings, and produce consolidated summaries or action items.
- Inline Sidebar UI: Persistent, non-intrusive sidebar interface inside Safari that provides continuous access to assistants while keeping the browsing context visible.
- Privacy-Focused Option: Supports Apple's on-device AI for local processing when users prefer to keep data off cloud models, offering a privacy-friendly workflow.
- Integrates multiple providers: Claude, ChatGPT, Gemini, plus Apple's on-device model(s)
- Safari sidebar UI surfacing model responses alongside pages
- Page-aware question answering (use current page content as context)
- Highlight text to get instant, contextual answers or summaries
- Automated browsing mode that can follow links and collect context
- Option to leverage on-device models for local processing / privacy
- Supports switching between model providers within the sidebar
Best for
- Web Research and Summarization: Quickly summarize long articles or research pages and obtain concise takeaways without switching apps or copying content.
- Fact-Checking and Source-Based Q&A: Ask targeted questions about a page's content (e.g., verify claims, extract dates or figures) with answers grounded in the current page.
- Drafting Responses with Context: Compose email replies, comments, or notes that reference specific webpage content by using the sidebar to pull relevant passages and craft text.
- Information Aggregation: Use automated browsing to traverse related links, compile data from multiple pages, and produce a single consolidated report or summary.
- Model Comparison and Tuning: Compare outputs from Claude, ChatGPT, Gemini, and on-device models side-by-side to choose the best phrasing or perspective for a task.
- Quick Clarification while Reading: Highlight confusing paragraphs or technical passages to receive immediate, plain-language explanations without disrupting reading flow.
- Research and summarization of web pages without leaving the browser
- Quick inline answers by highlighting page text
- Assisted browsing where the tool follows links and aggregates information
- Content drafting or editing with contextual page references
- Customer support or knowledge work that needs page-specific LLM assistance
Doop
Kevin Goedecke
Open-source infinite design canvas where humans and AI agents design together live, with agents joining through a built-in MCP server.
Key features
- Agent-Native MCP Canvas: Agents connect over an HTTP MCP endpoint with a single command and one browser OAuth approval, then edit the canvas as you, attributed and accountable, with no API keys handed over.
- Streaming Frames: Every section an agent writes renders on the canvas the moment it lands, so you watch the design arrive rather than waiting on a spinner.
- Comments as Tasks: A note left anywhere on the canvas becomes a task the right agent picks up, works on, and replies to with a screenshot, turning feedback directly into the backlog.
- Agent Self-Review: A built-in headless renderer gives agents screenshots of their own frames so they judge fit, spacing and contrast like a senior designer and correct issues before handoff.
- Shared Canvas Memory: Tasks, decisions and comments live on the canvas rather than in one agent's context, so any agent that joins later plugs into the same state and continues.
- Learned Taste Profile: Casual feedback such as 'rounder corners' or 'keep it to the blue' is distilled into a persistent taste profile applied to every new frame and inherited by every agent.
- Live Export URLs: Each frame is a URL that can be embedded in a doc, a post or an og:image and re-renders whenever the design changes, so shared assets never go stale.
- Reference and URL Import: Paste screenshots to have agents distill palette, type and mood into a written brief, or paste a public URL to land an editable snapshot of your existing page on the canvas for side-by-side variants.
Best for
- Agent-Assisted Landing Pages: Steering Claude Code or Codex through hero, pricing and footer frames on one canvas and watching each render live.
- Design Review Loops: Leaving contrast or spacing notes on a frame and letting an agent apply the fix and return a screenshot without a synchronous handoff.
- Redesign Comparison: Importing an existing public page as an editable snapshot so agent-generated variants sit next to the original instead of replacing it blind.
- Team Design Sessions: Multiple people and multiple agents working the same canvas, each seeing what the others' agents are doing in real time.
- Style Consistency: Building a canvas taste profile once so every subsequent frame and every new agent inherits the same corner radius, palette and type decisions.
- Always-Fresh Shared Assets: Embedding live frame URLs in documentation or social posts so the shared image updates automatically when the design changes.