{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"perplexity-pro","slug":"perplexity-pro","name":"Perplexity Pro","type":"agent","url":"https://www.perplexity.ai/pro","page_url":"https://unfragile.ai/perplexity-pro","categories":["research-search"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"perplexity-pro__cap_0","uri":"capability://search.retrieval.multi.step.agentic.web.search.with.reasoning","name":"multi-step agentic web search with reasoning","description":"Executes iterative web search queries based on chain-of-thought reasoning, where the agent decomposes user queries into sub-questions, performs targeted searches for each, evaluates result relevance, and decides whether additional searches are needed before synthesis. Uses reinforcement learning from human feedback to optimize search query formulation and stopping criteria.","intents":["I need to research a complex topic and get the most current information from multiple sources","I want the AI to ask follow-up questions and dig deeper rather than give me a surface-level answer","I need to understand how different sources contradict or agree on a topic"],"best_for":["researchers and analysts needing current information beyond training data cutoffs","journalists and content creators verifying facts across multiple sources","students and academics conducting literature reviews with source attribution"],"limitations":["Search depth limited by API rate limits and latency — typically 3-7 search iterations per query","Cannot access paywalled or subscription-only content behind authentication walls","Search result quality depends on indexing freshness of underlying search providers (typically 24-48 hour lag for new content)"],"requires":["Active internet connection","Perplexity Pro subscription for advanced reasoning features","JavaScript enabled in browser for real-time result streaming"],"input_types":["natural language queries","follow-up conversational context","file uploads (PDF, DOCX, images) for context-grounded searches"],"output_types":["synthesized text response","inline citations with source URLs","structured source list with relevance scores"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_1","uri":"capability://search.retrieval.inline.source.citation.with.provenance.tracking","name":"inline source citation with provenance tracking","description":"Embeds hyperlinked citations directly within generated text, mapping each claim to specific source URLs with snippet context. Architecture tracks citation provenance through a vector-indexed source database, matching generated text segments to original source passages using semantic similarity and position tracking to ensure citations remain accurate even after paraphrasing.","intents":["I need to verify where specific claims in the answer came from","I want to cite sources properly for academic or professional work","I need to audit the AI's reasoning to check for hallucinations or misattributions"],"best_for":["academic researchers and students requiring proper source attribution","journalists and fact-checkers verifying claim origins","compliance and legal teams auditing AI-generated content for source accuracy"],"limitations":["Citation accuracy depends on source text extraction quality — PDFs with complex layouts may have misaligned citations","Cannot cite sources that are paywalled or require authentication, even if agent accessed them","Citation links may break if source URLs change or content is removed (no archival fallback built-in)"],"requires":["Perplexity Pro subscription","Sources must be publicly accessible via HTTP/HTTPS","JavaScript enabled to render interactive citation popups"],"input_types":["web-indexed sources","uploaded documents (PDF, DOCX, images with OCR)","user-provided source URLs"],"output_types":["HTML with embedded citation links","structured citation metadata (URL, title, access date)","source snippet context on hover/click"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_10","uri":"capability://search.retrieval.source.diversity.and.perspective.balancing","name":"source diversity and perspective balancing","description":"Actively seeks sources representing different perspectives, geographic regions, and expertise domains to ensure balanced coverage. Uses clustering algorithms to identify source categories and ensures the final answer incorporates perspectives from multiple clusters rather than over-weighting sources from a single perspective.","intents":["I want to understand different perspectives on this controversial topic","I want to make sure I'm not just seeing one viewpoint","I want sources from different countries or cultures to understand global perspectives"],"best_for":["researchers studying controversial or politically divisive topics","policy makers seeking balanced evidence","journalists covering contentious issues with multiple stakeholder perspectives"],"limitations":["Perspective detection is heuristic-based — cannot always identify subtle ideological positions","Geographic diversity limited by search index coverage — some regions may be underrepresented","Balancing perspectives may create false equivalence between well-supported and fringe views","Requires sufficient sources representing multiple perspectives — some niche topics may lack diversity"],"requires":["Perplexity Pro subscription","Topic with multiple documented perspectives"],"input_types":["queries about controversial or multi-perspective topics","user-specified perspective categories"],"output_types":["perspective-labeled sources","balanced synthesis acknowledging multiple viewpoints","explicit identification of consensus vs disputed claims"],"categories":["search-retrieval","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_11","uri":"capability://search.retrieval.domain.specific.search.optimization.and.terminology.mapping","name":"domain-specific search optimization and terminology mapping","description":"Recognizes domain-specific terminology and automatically maps between common terms, technical jargon, and alternative phrasings within specialized fields (e.g., medical, legal, technical). Uses domain-specific knowledge bases to expand queries with relevant synonyms and related concepts, improving search precision for expert users while remaining accessible to non-experts. Adapts search strategy based on detected domain.","intents":["I'm searching in a specialized field and want the AI to understand domain terminology","I want to search using lay terms but get results using technical terminology","I need to understand how different fields refer to the same concept"],"best_for":["domain experts (medical, legal, technical) conducting specialized research","non-experts trying to understand specialized topics","teams bridging communication between technical and non-technical stakeholders"],"limitations":["Domain detection may fail for interdisciplinary queries that span multiple fields","Terminology mapping is incomplete for emerging fields or very specialized subdomains","False positive domain detection may apply wrong terminology mapping","Domain-specific knowledge bases may be outdated for rapidly evolving fields"],"requires":["Perplexity Pro subscription","Query that contains sufficient domain signals for detection"],"input_types":["natural language query with domain-specific or lay terminology"],"output_types":["domain-optimized search results","terminology mapping showing synonyms and related terms","explanation of domain-specific concepts","results in both technical and lay language"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_2","uri":"capability://data.processing.analysis.document.and.image.upload.with.context.grounded.search","name":"document and image upload with context-grounded search","description":"Accepts file uploads (PDF, DOCX, images) and uses OCR/text extraction to embed document content into the search context, enabling the agent to ground web searches in user-provided materials. Architecture extracts embeddings from uploaded content and uses them as semantic anchors to bias search query generation toward related topics and to validate whether web results are consistent with provided documents.","intents":["I have a research paper and want to find related recent work or contradicting viewpoints","I want to analyze an image or document and get the AI to search for similar or related content","I need to fact-check claims in a document I've uploaded against current web sources"],"best_for":["researchers comparing their work against the current literature landscape","content creators analyzing competitor materials and finding gaps","compliance teams validating claims in documents against authoritative sources"],"limitations":["OCR accuracy limited for scanned PDFs with poor image quality or non-Latin scripts — typically 85-95% accuracy","File size limits: typically 10-50MB per file depending on format","Image uploads limited to common formats (JPEG, PNG, WebP) — vector graphics and CAD files not supported","Context window limits mean very large documents may be truncated or summarized before search"],"requires":["Perplexity Pro subscription","File upload must be under size limit (varies by format)","Supported formats: PDF, DOCX, PPTX, images (JPEG, PNG, WebP, GIF)"],"input_types":["PDF documents","Microsoft Office files (DOCX, PPTX)","images (JPEG, PNG, WebP, GIF)","scanned documents (OCR-processed)"],"output_types":["synthesized analysis with web search results","comparison matrix showing document claims vs web sources","extracted key concepts from document with related web results"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_3","uri":"capability://planning.reasoning.real.time.result.streaming.with.progressive.synthesis","name":"real-time result streaming with progressive synthesis","description":"Streams search results and intermediate reasoning steps to the user in real-time as the agent executes, rather than waiting for all searches to complete before responding. Uses server-sent events (SSE) to push partial results, reasoning traces, and citation data incrementally, allowing users to see the agent's thought process and stop early if they have enough information.","intents":["I want to see the agent's reasoning as it happens, not wait for a complete answer","I want to stop the search early if I see the answer is going in the wrong direction","I want to understand why the agent is asking certain follow-up questions"],"best_for":["users with low patience for latency who want to see progress","researchers who want to redirect the agent mid-search based on intermediate results","developers building on Perplexity API who need streaming response handling"],"limitations":["Streaming adds ~50-100ms latency per chunk due to serialization overhead","Cannot easily backtrack or modify reasoning once streamed — user must start new query","Browser compatibility required for SSE — older IE versions not supported","Mobile networks with high latency may experience choppy streaming"],"requires":["Modern browser with SSE support (all modern browsers)","Stable internet connection (streaming breaks on connection loss)","JavaScript enabled for real-time UI updates"],"input_types":["user query","streaming control signals (pause, stop)"],"output_types":["streamed text chunks","reasoning step indicators","citation metadata (streamed as available)","source list updates (incremental)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_4","uri":"capability://memory.knowledge.conversational.context.persistence.with.multi.turn.reasoning","name":"conversational context persistence with multi-turn reasoning","description":"Maintains conversation history across multiple turns, using prior exchanges to inform search strategy and answer synthesis. Architecture stores embeddings of previous queries and answers in a session-scoped vector index, enabling the agent to recognize topic continuity, avoid redundant searches, and build on prior reasoning without requiring users to re-explain context.","intents":["I want to ask follow-up questions without re-explaining my original query","I want the AI to remember what we discussed earlier and use that context","I want to refine my search iteratively based on previous results"],"best_for":["researchers conducting deep dives into topics over multiple queries","analysts building comprehensive reports through iterative questioning","students exploring topics with progressive refinement"],"limitations":["Context window limits mean very long conversations (50+ turns) may lose early context","Context persistence is session-scoped — conversations do not persist across browser sessions by default","Large context windows add latency — each new query must re-encode prior conversation","No built-in conversation branching — cannot easily explore alternative reasoning paths"],"requires":["Perplexity Pro subscription","Active session (conversation lost if browser tab closed)","JavaScript enabled for session state management"],"input_types":["natural language follow-up queries","references to prior conversation turns","implicit context (pronouns, topic references)"],"output_types":["contextually-aware answers","search queries informed by prior context","explicit references to prior turns when relevant"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_5","uri":"capability://search.retrieval.source.credibility.scoring.and.conflict.detection","name":"source credibility scoring and conflict detection","description":"Evaluates source reliability using a multi-factor scoring system that considers domain authority, author credentials, publication date, and citation patterns within the source. Detects when multiple sources contradict each other and surfaces these conflicts explicitly, allowing users to understand disagreement in the literature rather than seeing a false consensus.","intents":["I want to know which sources are most authoritative on this topic","I want to understand where experts disagree on this question","I want to identify potentially unreliable or biased sources in the results"],"best_for":["researchers and academics evaluating source quality","journalists fact-checking claims and identifying expert consensus","policy makers understanding disagreement among authoritative sources"],"limitations":["Credibility scoring is heuristic-based — cannot detect sophisticated misinformation or coordinated disinformation campaigns","Author credential verification relies on public data — cannot verify credentials for pseudonymous or private authors","Conflict detection limited to explicit contradictions — subtle disagreements or different framings may be missed","Domain authority scores may be outdated if sources have recently changed ownership or editorial direction"],"requires":["Perplexity Pro subscription","Sources must be publicly indexed with metadata available"],"input_types":["web search results","source URLs and metadata"],"output_types":["credibility scores (0-100 scale)","conflict matrices showing disagreement between sources","source reliability indicators (badges, warnings)","explanation of credibility factors"],"categories":["search-retrieval","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_6","uri":"capability://planning.reasoning.query.expansion.and.clarification.with.user.feedback","name":"query expansion and clarification with user feedback","description":"Detects ambiguous or under-specified queries and generates clarifying questions before executing searches. Uses semantic analysis to identify multiple interpretations of a query and presents them to the user, allowing explicit disambiguation before the agent commits to a search strategy. Incorporates user feedback to refine search direction mid-conversation.","intents":["I'm not sure what I'm looking for — I want the AI to help me clarify my question","My query could mean multiple things — I want to pick the right interpretation","I want to redirect the search if the AI is interpreting my question wrong"],"best_for":["users with vague or exploratory information needs","researchers refining research questions iteratively","non-experts searching for technical topics who may not know the right terminology"],"limitations":["Clarification questions add latency — typically 1-2 seconds before search begins","Cannot disambiguate queries where all interpretations are equally valid — may present too many options","Relies on user feedback to refine — if user doesn't respond to clarifications, agent falls back to default interpretation","May over-clarify simple queries, creating friction for experienced users"],"requires":["Perplexity Pro subscription","User willingness to answer clarifying questions"],"input_types":["ambiguous natural language queries","user feedback on clarification options"],"output_types":["clarifying questions","interpretation options with examples","refined search queries based on user selection"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_7","uri":"capability://data.processing.analysis.comparative.analysis.and.synthesis.across.sources","name":"comparative analysis and synthesis across sources","description":"Automatically identifies common themes, disagreements, and evidence patterns across multiple sources, then synthesizes them into a structured comparison. Uses natural language processing to extract claims, evidence, and reasoning from each source, then aligns them semantically to create comparison matrices that show how different sources approach the same question.","intents":["I want to compare how different experts or sources approach this question","I want to see the evidence for and against a particular claim","I want to understand the consensus view and where experts diverge"],"best_for":["researchers conducting literature reviews and meta-analyses","policy analysts synthesizing evidence from multiple sources","students writing comparative essays or research papers"],"limitations":["Comparison accuracy depends on source clarity — poorly written or ambiguous sources may be misaligned","Cannot compare sources in different languages without translation (which adds latency and error)","Comparison matrices may oversimplify nuanced positions into binary agree/disagree","Requires sufficient sources (typically 3+) to create meaningful comparisons"],"requires":["Perplexity Pro subscription","Multiple sources addressing the same question"],"input_types":["web search results","uploaded documents","user-specified comparison dimensions"],"output_types":["comparison matrices","Venn diagrams of agreement/disagreement","synthesis of common themes","evidence summaries for each position"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_8","uri":"capability://data.processing.analysis.temporal.analysis.and.trend.detection","name":"temporal analysis and trend detection","description":"Analyzes how information, claims, and evidence have evolved over time by searching for historical versions of topics and tracking how expert consensus or source positions have changed. Uses publication dates and temporal metadata to create timelines showing when claims emerged, gained acceptance, or were refuted.","intents":["I want to understand how expert opinion on this topic has evolved","I want to see when a claim first appeared and how it spread","I want to know if this is a recent development or a long-standing issue"],"best_for":["historians and researchers studying how knowledge evolves","journalists tracking how narratives change over time","analysts identifying emerging trends or paradigm shifts"],"limitations":["Temporal analysis limited by search index freshness — typically cannot see changes older than 10-15 years","Requires sufficient historical sources — some topics may not have adequate historical coverage","Timeline accuracy depends on source dating — some sources may have incorrect or missing publication dates","Cannot detect claims that existed before the web era (pre-1995)"],"requires":["Perplexity Pro subscription","Topic with sufficient historical coverage in indexed sources"],"input_types":["queries with temporal dimensions (e.g., 'how has X changed since Y')","explicit time range specifications"],"output_types":["timelines of claim emergence and evolution","historical comparison of expert positions","trend analysis showing adoption or decline of ideas","source citations with publication dates"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__cap_9","uri":"capability://planning.reasoning.follow.up.question.generation.with.knowledge.gap.detection","name":"follow-up question generation with knowledge gap detection","description":"Identifies gaps in the current answer or areas where additional information would strengthen the response, then suggests follow-up questions the user might want to ask. Uses semantic analysis to detect when the answer addresses only part of a broader topic or when important related questions remain unanswered.","intents":["I want suggestions for what to ask next to deepen my understanding","I want to know what important questions I haven't asked yet","I want to explore related topics that might be relevant"],"best_for":["exploratory researchers discovering new topics","students learning about unfamiliar subjects","analysts conducting comprehensive investigations"],"limitations":["Follow-up suggestions are heuristic-based — may suggest questions already answered implicitly","Cannot detect domain-specific gaps without explicit knowledge of the field","May suggest too many follow-ups, creating decision paralysis","Suggestions are generic — not personalized to user's specific goals or expertise level"],"requires":["Perplexity Pro subscription"],"input_types":["current query and answer","conversation history"],"output_types":["suggested follow-up questions","explanation of why each question is relevant","estimated search time for each follow-up"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"perplexity-pro__headline","uri":"capability://research.search.ai.research.agent.with.advanced.multi.step.reasoning","name":"ai research agent with advanced multi-step reasoning","description":"Perplexity Pro is an advanced AI research agent that performs deep web searches, analyzes sources, and generates comprehensive answers with inline citations, making it ideal for thorough research tasks.","intents":["best AI research agent","AI tool for deep web searches","comprehensive answer generator for research","AI agent for source analysis","best tool for academic research"],"best_for":["academic researchers","students","professionals needing in-depth analysis"],"limitations":[],"requires":[],"input_types":["text queries","file uploads"],"output_types":["text answers","inline 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hour lag for new content)","Citation accuracy depends on source text extraction quality — PDFs with complex layouts may have misaligned citations","Cannot cite sources that are paywalled or require authentication, even if agent accessed them","Citation links may break if source URLs change or content is removed (no archival fallback built-in)","Perspective detection is heuristic-based — cannot always identify subtle ideological positions","Geographic diversity limited by search index coverage — some regions may be underrepresented","Balancing perspectives may create false equivalence between well-supported and fringe views","Requires sufficient sources representing multiple perspectives — some niche topics may lack diversity","builder identity is not verified yet","no observed match outcomes 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