🤖 Perplexity AI Search & Multi-Turn Chat Scraper (Live Citations & Trust Scores)

🚀 The #1 Most Complete, Low-Latency Real-Time Perplexity AI Web Search, Structured Citations, Trust Scores, Multi-Turn Conversation Memory & Search Focus Filtering Actor on Apify.

Looking for instant AI reasoning backed by real-time web citations, domain trust levels, and conversational thread memory without paying $20/user/month for individual subscriptions or navigating rate limits?

The Perplexity AI Search & Multi-Turn Chat Scraper combines live web indexing with state-of-the-art conversational AI models to deliver structured answers backed by verified source citations, publication snippets, domain trust scores, and conversational thread memory.

Pass the returned context_uuid into subsequent requests to maintain context across multi-turn conversational chat threads (is_followup: true).

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🌟 Institutional Comparison: Why Choose Perplexity AI Scraper?

Market Feature	Official Perplexity API	Tavily AI Search	Exa.ai Search	🤖 Perplexity AI Scraper
Pricing per 1k Searches	💸 $5.00 + Token Markup ($6–$14)	💸 $8.00 – $16.00	💸 $5.00 – $15.00	⚡ Pay-Per-Event ($0.003 / search)
Real-Time Web Citations	✅ Yes	✅ Yes	⚠️ Snippets Only	✅ Live Verified URLs & Trust Scores
Multi-Turn Thread Memory	⚠️ Custom Setup	❌ No Memory	❌ No Memory	✅ Built-in context_uuid Memory
Search Focus Modes	⚠️ Limited	⚠️ General / News	⚠️ Auto Only	✅ Internet, Academic, Writing, YouTube, Reddit
AI Model Selection	⚠️ Sonar Only	❌ Fixed LLM	❌ Fixed LLM	✅ Turbo, Sonar, GPT-4o, Claude 3.5 Sonnet
Sub-100ms Autocomplete	❌ No	❌ No	❌ No	✅ Sub-100ms Instant Suggest Mode
Domain Trust Ratings	❌ No	❌ No	❌ No	✅ 1-3 Domain Trust Scores & Reasons

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⚡ Comprehensive Feature Breakdown

1. 🔍 Real-Time AI Web Search & Live Citations (AI_SEARCH)

Submit any natural language question or research prompt to get a comprehensive, factual AI answer with direct clickable web citations, source titles, publication snippets, and trust ratings.

2. 💬 Multi-Turn Conversation Thread Memory (context_uuid)

Pass the context_uuid returned from your initial request into follow-up requests to continue chatting seamlessly within the same conversational context (is_followup: true). No database or state management needed on your end.

3. 🎯 Search Focus Filtering (focus)

Target your query to specific domains and knowledge bases:

• internet: Global public web pages (Default).
• academic: Scholarly articles, arXiv, PubMed, and peer-reviewed journals.
• writing: Direct text and code generation without web browsing.
• youtube: Search video transcripts and timestamped descriptions.
• reddit: Search real community discussions and opinions on Reddit.

4. ⚡ AI Model Selection (model)

Select your preferred AI reasoning engine:

• turbo: Ultra-fast real-time concise synthesis model.
• sonar: Deep web reasoning with dense citations.
• gpt4o: OpenAI GPT-4o multimodal reasoning with web browsing.
• claude35sonnet: Anthropic Claude 3.5 Sonnet advanced comprehension.

5. 🔍 Instant Autocomplete & Search Suggestions (SUGGEST)

Switch mode to SUGGEST to get sub-100ms keyword autocomplete and live navigation suggestions for search bars and input widgets.

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📥 Input Parameters & Schema

Parameter	Type	Required	Default	Options / Valid Values	Description
mode	string	No	AI_SEARCH	AI_SEARCH, SUGGEST	Primary Operation Mode: Full AI search & reasoning or instant keyword autocomplete.
query	string	Yes	—	Any search query or prompt	The question or research prompt to search and answer.
focus	string	No	internet	internet, academic, writing, youtube, reddit	Target domain or knowledge base for search indexing.
model	string	No	turbo	turbo, sonar, gpt4o, claude35sonnet	Preferred AI reasoning model.
context_uuid	string	No	null	UUID string (e.g. 3b180c85-...)	Pass previous context_uuid to continue the conversation in the same thread.

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📥 Input Examples

Example 1: Basic AI Web Search & Citations

{
  "mode": "AI_SEARCH",
  "query": "What is Quantum Computing? Explain briefly.",
  "focus": "internet",
  "model": "turbo"
}

Example 2: Academic Paper Research (arXiv / PubMed)

{
  "mode": "AI_SEARCH",
  "query": "Recent breakthroughs in room-temperature superconductors",
  "focus": "academic",
  "model": "sonar"
}

Example 3: Multi-Turn Conversational Follow-Up (Same Thread)

{
  "mode": "AI_SEARCH",
  "query": "What are the primary applications in cryptography?",
  "context_uuid": "3b180c85-11bf-4e76-9908-0fc0c2ac47cc",
  "model": "turbo"
}

Example 4: Instant Keyword Autocomplete

{
  "mode": "SUGGEST",
  "query": "Artificial Intelligence"
}

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📤 Output Dataset Examples

1. Real-Time AI Search with Live Web Citations:

{
  "mode": "AI_SEARCH_RESULT",
  "success": true,
  "query": "What is Quantum Computing? Explain briefly.",
  "context_uuid": "3b180c85-11bf-4e76-9908-0fc0c2ac47cc",
  "backend_uuid": "7e760172-2526-4dbd-a1c5-9f04b8805f52",
  "is_followup": false,
  "model": "turbo",
  "search_focus": "internet",
  "answer": "Quantum computing is a multidisciplinary field comprising aspects of computer science, physics, and mathematics that utilizes quantum mechanics to solve complex problems faster than classical computers. Rather than using traditional bits (which represent either a 0 or a 1), quantum computers encode information using quantum bits, or qubits, which can exist in a multidimensional state of superposition and entanglement.",
  "sources_count": 8,
  "sources": [
    {
      "title": "Quantum Computing Fundamentals - IBM Research",
      "url": "https://www.ibm.com/quantum/what-is-quantum-computing",
      "snippet": "Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers.",
      "trust_level": 2,
      "trust_description": "High authority technological source"
    },
    {
      "title": "What is Quantum Computing? - MIT Technology Review",
      "url": "https://www.technologyreview.com/topic/quantum-computing/",
      "snippet": "An introduction to quantum systems, qubits, and quantum supremacy.",
      "trust_level": 3,
      "trust_description": "Top-tier academic & technology publication"
    }
  ],
  "related_queries_count": 3,
  "related_queries": [
    "How do quantum computers differ from classical computers?",
    "What are the practical applications of quantum computing in 2026?",
    "What are the leading quantum computing companies?"
  ]
}

---

💻 Multi-Language Code Integration Suite

Python (apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "mode": "AI_SEARCH",
    "query": "What are the key advancements in AI models in 2026?",
    "focus": "internet",
    "model": "turbo"
}

# Run the Actor and wait for it to finish
run = client.actor("dev00/perplexity-ai-search-api").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Answer:\n{item['answer']}\n")
    print(f"Citations Count: {item['sources_count']}")
    for s in item['sources']:
        print(f" - [{s['title']}]({s['url']})")

JavaScript / Node.js (apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: 'YOUR_APIFY_TOKEN',
});

const input = {
    mode: "AI_SEARCH",
    query: "Compare Vector Databases: Pinecone vs Milvus vs Qdrant",
    focus: "internet",
    model: "claude35sonnet"
};

const run = await client.actor("dev00/perplexity-ai-search-api").call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log("AI Answer:", items[0].answer);
console.log("Sources:", items[0].sources);

---

💰 Pay-Per-Event (PPR) Pricing

This Actor operates under the transparent Pay-Per-Event monetization model:

• Price: $0.003 per AI search query / conversation turn ($3.00 per 1,000 queries).
• No Monthly Minimums: Only pay for the exact volume of AI searches you execute.
• Includes Multi-Turn Thread Memory: Zero additional charge for continuing conversation threads via context_uuid.

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🎯 High-Growth Industry Use Cases

1. AI Agents & Autonomous Research Assistants: Connect directly to AI agent frameworks (LangChain, LlamaIndex, AutoGPT, CrewAI) for real-time web verification and live citation synthesis.
2. Customer Support & Conversational Chatbots: Power customer-facing chatbots with up-to-date product knowledge and conversational thread memory (context_uuid).
3. Academic & Technical Research Tools: Search across scholarly papers (arXiv, PubMed) and summarize dense peer-reviewed literature.
4. Market Sentiment & Reddit Opinion Mining: Extract real consumer feedback, product reviews, and community sentiment using the focus="reddit" mode.
5. Video Intelligence & Lecture Extraction: Pull summarized insights and timestamps from YouTube video transcripts using focus="youtube".

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❓ Frequently Asked Questions (FAQ)

Q: How does conversational thread memory work?

When you run the Actor, the output includes a context_uuid. Simply pass this context_uuid into your subsequent Actor runs, and the engine automatically links the conversation context together (is_followup: true).

Q: How does this differ from standard LLMs like ChatGPT?

Standard LLMs suffer from fixed training cutoff dates and lack citations. This Actor performs live web queries in real-time and returns structured citations with URLs, snippets, and trust scores.

Q: What are the differences between the Search Focus modes?

• internet: General global web pages.
• academic: Scholarly articles, arXiv, PubMed, and journals.
• writing: Direct text/code generation without web search.
• youtube: Video transcripts and timestamps.
• reddit: User discussions and community sentiment on Reddit.

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🏷️ Keywords & SEO Tags

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