Extract the main article content from any URL as clean Markdown or plain text with metadata, links, and images. No HTML noise, no boilerplate. Feed it directly into your RAG system or LLM context window.
SnapAPI strips navigation, ads, and boilerplate — returning the main article content in the format your LLM needs.
Returns the main article as clean GitHub-flavored Markdown. Headers, bold, lists, tables, and code blocks are preserved — perfect for LLM context windows.
Set format=text for whitespace-normalized plain text. Ideal for sentiment analysis, keyword extraction, and embedding generation.
Returns all hyperlinks in the article as a structured list — internal, external, and citation URLs — for knowledge graph construction.
Returns a deduplicated list of all image URLs in the article. Feed them into your media pipeline or multimodal LLM alongside text content.
Returns page title, description, author, published_at, and Open Graph tags. All from a single API call.
Extracts content after full JavaScript execution — React/Next.js blogs, docs sites, and SPA articles are all supported without extra configuration.
Replace YOUR_API_KEY with the key from your dashboard.
# Extract article as Markdown curl -G "https://api.snapapi.pics/v1/extract" --data-urlencode "url=https://example.com/article" -H "Authorization: Bearer YOUR_API_KEY" # Returns: { markdown, text, title, author, description, links, images } # Plain text format (for embeddings) curl -G "https://api.snapapi.pics/v1/extract" --data-urlencode "url=https://example.com/article" -d "format=text" -H "Authorization: Bearer YOUR_API_KEY"
import SnapAPI from 'snapapi-js'; const client = new SnapAPI('YOUR_API_KEY'); // Extract for RAG pipeline const article = await client.extract.fetch({ url: 'https://example.com/article', }); // Feed into OpenAI const response = await openai.chat.completions.create({ model: 'gpt-4o', messages: [{ role: 'user', content: `Summarize: ${article.markdown}` }], });
from snapapi import SnapAPI from openai import OpenAI snap = SnapAPI("YOUR_API_KEY") oai = OpenAI() article = snap.extract.fetch(url="https://example.com/article") response = oai.chat.completions.create( model="gpt-4o", messages=[{ "role": "user", "content": f"Summarize: {article['markdown']}" }] ) print(response.choices[0].message.content)
import "github.com/Sleywill/snapapi-go" client := snapapi.New("YOUR_API_KEY") result, err := client.Extract.Fetch(snapapi.ExtractOptions{ URL: "https://example.com/article", }) if err != nil { panic(err) } fmt.Println(result.Title) fmt.Println(result.Markdown)
SnapAPI’s extraction endpoint is purpose-built for LLM pipelines that need clean, structured text.
Feed real-time web content into your RAG pipeline. Extract articles as Markdown, split by headers, embed, and store in your vector database — all from one API response.
Give your LLM agent the ability to read any webpage. Pass the extracted Markdown directly in the context window without HTML noise or token waste.
Extract article text from thousands of URLs for sentiment analysis, entity extraction, or automatic summarization pipelines. Clean input = better model output.
Automatically extract and summarize articles for your newsletter or digest. SnapAPI returns author, published date, and description alongside the full Markdown body.
All plans include every capability. No feature gates.