Bluesky Scraper Pro - Posts, Profiles, Analytics and AI Insights

Short description (Apify Store)

High-performance Bluesky scraper powered by AT Protocol. Collect posts, profiles, followers, search results, and full threads with optional AI sentiment, topic extraction, summary, and viral potential scoring.

Long description (Apify Store)

Bluesky Scraper Pro is an Apify actor designed for fast, structured extraction of public Bluesky data without browser automation. It uses AT Protocol endpoints for lower cost and higher speed, making it a practical choice for analysts, growth teams, and builders who need reliable social data pipelines.

The actor supports seven scraping modes in one workflow: profile posts, profile lookup, keyword search, post search, follower list, following list, and full thread extraction. Results are normalized into consistent JSON records and enriched with optional AI analysis using Groq and OpenAI. Each run also generates a Markdown output artifact (OUTPUT_MARKDOWN) for LLM workflows and reporting.

Features

• Seven scrape modes in one actor
• Cursor-based pagination across supported endpoints
• Built-in 0.5 second request pacing for safer collection
• AI enrichment with provider fallback (Groq -> OpenAI or OpenAI -> Groq)
• Dataset output + Markdown output for LLM ingestion
• Pay-per-event pricing model ready for Store monetization

Use cases

• Influencer research: evaluate account activity, content style, and engagement trends
• Brand monitoring: track mentions, narrative changes, and sentiment movement
• Trend spotting: discover recurring topics and emerging conversations
• Competitor analysis: compare account growth patterns and content strategy

Input example

{
  "mode": "profile_posts",
  "handles": ["atproto.com", "bsky.app"],
  "query": "",
  "postUri": "",
  "maxResults": 100,
  "includeReplies": true,
  "enableAI": true,
  "aiProvider": "groq",
  "aiApiKey": "",
  "proxyConfiguration": {}
}

Output example

{
  "did": "did:plc:example123",
  "handle": "atproto.com",
  "text": "This is an example post body from Bluesky.",
  "createdAt": "2026-04-29T12:01:00.000Z",
  "likeCount": 45,
  "repostCount": 11,
  "replyCount": 7,
  "sentiment": "positive",
  "key_topics": ["atproto", "developer-tools", "social-media"],
  "summary": "Short summary generated from post content and engagement context.",
  "viral_potential_score": 78,
  "markdown_version": "### 1. @atproto.com\\n\\nText: This is an example post body from Bluesky.",
  "mode": "profile_posts",
  "uri": "at://did:plc:example123/app.bsky.feed.post/abc123",
  "cid": "bafyreih...",
  "displayName": "AT Protocol",
  "avatar": "https://...",
  "indexedAt": "2026-04-29T12:01:02.000Z",
  "labels": [],
  "lang": ["en"],
  "isReply": false,
  "isRepost": false,
  "sourceValue": "atproto.com",
  "sentiment_score": 0.66
}

Pricing

This actor is configured for Pay-per-event pricing:

• Event: scrapeCompleted
• Price: $1.49 per 1,000 results

The actor attempts to emit usage charges in 1,000-result blocks.

Installation and usage

$apify run

For cloud deployment:

$apify push

FAQ

Does this actor require browser automation?

No. It uses AT Protocol HTTP endpoints, which are faster and cheaper for this use case.

Can I run without AI enrichment?

Yes. Set enableAI to false to skip enrichment and collect raw structured data only.

Which AI provider is used first?

The order is controlled by aiProvider. The actor automatically tries the other provider as fallback when possible.

Why does the Actor fail with "requires at least one handle"?

Some modes (profile_posts, follower_list, etc.) require you to fill in the "handles" field. Always use the generated input form in the Apify Console and fill the required fields for your chosen mode.

Why do keyword_search and post_search sometimes return no results?

Bluesky's public search API (app.bsky.feed.searchPosts) has restrictions on unauthenticated requests and pagination. These modes return empty results gracefully with a warning. Profile and thread modes work fully without any login.

Is the output suitable for LLM workflows?

Yes. The actor saves JSON records to dataset and a Markdown artifact to OUTPUT_MARKDOWN for direct LLM ingestion.

Is this ready for Apify Store publishing?

Yes. The actor includes Store-friendly metadata, input schema, pricing configuration, and production-oriented output structure.