24K Labs Entity Extractor (Regex)

x402

24K Labs Entity Extractor (Regex) is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).

Extracts and deduplicates emails, URLs, IPv4 addresses, @mentions, and #hashtags from free text, returning each entity type with occurrence counts.

Price / call$0.002

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StatusUnknown

How this service works

Pull deduped lists of emails, URLs, IPv4 addresses, @mentions and #hashtags out of free text, with counts.

Output

A structured response containing deduplicated lists for each entity type found in the input text (emails, URLs, IPv4 addresses, @mentions, #hashtags), each accompanied by an occurrence count indicating how many times that entity appeared in the original text.

Example use cases

Mining leads from scraped web content

“I just scraped a bunch of raw HTML and text from a marketing page — can you extract every email address and URL from it, deduplicated, with counts so I know which ones appear most often?”

Parsing IPs and handles from server logs

“Here's a chunk of server log output — pull out all IPv4 addresses and any @mentions or #hashtags that appear in it, and tell me how many times each one shows up.”

Analyzing hashtags in social posts

“I have a batch of raw social media posts pasted together in one blob — extract every #hashtag and @mention from them with deduplication and frequency counts so I can see which ones are trending.”

Technical details

Endpoint

POSThttps://api.24klabs.ai/api/entity-extract-regex

When to prefer

Choose this endpoint when you need fast, lightweight extraction of common structured entities (emails, URLs, IPs, social handles, hashtags) from free text using pattern matching — no ML inference needed. Ideal when you don't need semantic understanding, just reliable regex-based identification and deduplication with counts. Prefer over NLP-based NER tools when speed and cost matter and entity types are well-defined.

Side effects

Empty or whitespace-only input returns empty entity lists
Malformed or non-standard formats may not be captured by regex patterns (e.g. internationalized email domains)
Very large text inputs may hit payload size limits
Obfuscated entities (e.g. 'user [at] example [dot] com') will not be extracted
Non-public IPv6 addresses are not covered by the IPv4-specific extractor

Input

Schema · Request body

Field	Type	Description
text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "emails": [
      "bob@acme.io"
    ]
  }
}

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How this service works

Pull deduped lists of emails, URLs, IPv4 addresses, @mentions and #hashtags out of free text, with counts.

Output

Example use cases

Mining leads from scraped web content

“I just scraped a bunch of raw HTML and text from a marketing page — can you extract every email address and URL from it, deduplicated, with counts so I know which ones appear most often?”

Parsing IPs and handles from server logs

“Here's a chunk of server log output — pull out all IPv4 addresses and any @mentions or #hashtags that appear in it, and tell me how many times each one shows up.”

Analyzing hashtags in social posts

Technical details

Endpoint

POSThttps://api.24klabs.ai/api/entity-extract-regex

When to prefer

Side effects

Empty or whitespace-only input returns empty entity lists
Malformed or non-standard formats may not be captured by regex patterns (e.g. internationalized email domains)
Very large text inputs may hit payload size limits
Obfuscated entities (e.g. 'user [at] example [dot] com') will not be extracted
Non-public IPv6 addresses are not covered by the IPv4-specific extractor

Input

Schema · Request body

Field	Type	Description
text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "emails": [
      "bob@acme.io"
    ]
  }
}