
Perplexity Comet
An AI-native browser with Perplexity Assistant built into every tab

About Perplexity Comet
Overview
Perplexity Comet is a Chromium-based browser that combines standard browsing with Perplexity-powered search, page understanding, summarization, and natural-language browser commands. It can work across tabs, help with email and calendar context when connected, and delegate tasks such as comparing products, drafting replies, or organizing research.
Key Features
- Integrates Perplexity search, page Q&A, and summarization directly into the browser
- Chromium foundation keeps familiar browser features and Chrome extension compatibility
- Natural-language commands can reduce friction in tab management and repetitive web tasks
- Useful for research workflows that need citations alongside browser context
- Available across Mac, Windows, iOS, and Android from the official Comet page
Snapshot
Find AI agents by task, not by hype
Capabilities
- Browser use
- Tool use
- Memory
- Multi-step planning
- Human approval
Best for
- Research sessions that span multiple pages and tabs
- Summarizing and querying the page currently in view
- Light browser delegation such as tab organization, shopping comparisons, and email drafting
Not ideal for
- Users who do not want an AI assistant inside their primary browser
- High-risk automated actions without manual review
- Organizations that need mature browser governance but are not on an enterprise setup
What reviewers say
Comet pushes Perplexity from answer engine into browser assistant territory. The product is compelling for research and everyday web delegation, but its agentic browser model makes permission hygiene and human review more important than in ordinary search tools.
Sources
AI character platform for games and interactive apps
AI-assisted novel writing workspace with a living story Codex
Explain anything in videos, slides, and podcasts
Public AI leaderboard and model comparison platform
AI-powered code completion and programming assistant
AI-powered UI component generation from text