[title]

[the full markdown content]

2. The CONTENT must include:

   • TL;DR (2-3 sentences)
   • Why use Reuters data? (2-3 use cases)
   • What data can you extract? (list of fields)
   • The extraction approach (why raw HTTP+HTML is fragile, why data API is better)
   • Quick start with AlterLab Extract API (Python and cURL examples, plus a third for batch/async)
   • Define your schema (show JSON schema parameter, explain validation)
   • Handle pagination and scale (batching, rate limits, async jobs)
   • Key takeaways (concise summary)

3. Must include internal links naturally:

   • AlterLab pricing in cost/scale section
   • Extract API docs in quick start
   • Getting started guide in intro

4. Enhanced code blocks with title and line highlighting for:

   • Primary Python example (with title="extract_reuters-com.py" and line highlighting {5-12})
   • cURL example (title="Terminal")
   • Third example: batch/async usage (we'll choose a Python async example)

5. At least 2 infographic components (as HTML blocks):

   • Stats Grid
   • Step Flow
   • TryIt Block (we'll use all three to be safe, but at least two are required)

6. Content safety disclaimer near the top: 'This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.'

7. Length: 1500-3000 words.

8. Avoid banned phrases and structures (as per the Herald identity rules, but note: this is a blog post, not an email. However, the hard rules for Herald are for email generation. The blog post has its own constraints, but we should still avoid fluff and be direct.)

9. Emphasize 'data API' framing, not 'scraper'.

10. Show structured JSON output in at least one example.

Let's plan the content:

TITLE: Must contain 'Reuters Data API' or 'structured reuters data' We'll use: "Reuters Data API: Extract Structured JSON in 2026"

EXCERPT: 150-160 chars, include 'reuters data api' Example: "Learn how to extract structured JSON from Reuters using AlterLab's data API. Get headline, author, date & more in typed output — no parsing needed."

CATEGORY: tutorials

TAGS: We need 3-5 from the list. Choose relevant ones: Data Extraction, APIs, Data Pipelines, Python, Anti-Bot (since AlterLab handles anti-bot)

SEO_TITLE: "Reuters Data API — Extract Structured JSON | AlterLab"

SEO_DESCRIPTION: max 155 chars, targeting 'reuters data api' Example: "Extract structured Reuters data via API with AlterLab. Get JSON output for headline, author, date & more. Pay-as-you-go, no minimums."

FAQ: exactly 3 items.

Q1: Is there an official Reuters data API? A: Reuters offers limited official APIs primarily for financial data. For general news article extraction, AlterLab provides a compliant data API that structures public Reuters content into typed JSON without requiring custom parsers.

Q2: What Reuters data can I extract with AlterLab? A: You can extract publicly available news fields including headline, author, published date, source, and summary. Define your desired schema to receive validated JSON output — AlterLab handles the extraction and typing.

Q3: How much does Reuters data extraction cost? A: AlterLab charges per extraction based on complexity, with costs clamped between $0.001 and $0.50 per call. There are no minimums, and credits never expire. See pricing for details.

Now, the CONTENT (markdown):

We'll break it down as per required sections.

Important: Start the CONTENT with the TL;DR.

Let's write:

TL;DR: To get structured Reuters data via API, define a JSON schema for the fields you need (e.g., headline, author, date), then call AlterLab's Extract API with the URL and schema. You'll receive typed JSON output — no HTML parsing required. This approach handles anti-bot measures and scales for data pipelines.

Why use Reuters data?

• Train AI models on real-time news sentiment and events
• Build competitive intelligence dashboards tracking industry mentions
• Enrich internal datasets with timely external news for analytics

What data can you extract? Reuters news pages contain structured information we can extract:

• headline: The main title of the article
• author: The journalist or byline
• published_date: When the article was published (ISO 8601 format)
• source: Typically "Reuters" or a specific bureau
• summary: A brief overview of the article's content All fields are publicly available on article pages. Define these in your JSON schema to get typed output.

The extraction approach Raw HTTP requests to Reuters return HTML that requires fragile parsing with regex or CSS selectors — which break when the site updates. AlterLab's data API abstracts this away: you specify the schema, and we handle retrieval, anti-bot bypass, and structuring into validated JSON. This is more reliable for production pipelines.

Quick start with AlterLab Extract API First, get started with the AlterLab client. Then, define your schema and call the extract endpoint.

Here's a Python example:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "headline": {
      "type": "string",
      "description": "The headline field"
    },
    "author": {
      "type": "string",
      "description": "The author field"
    },
    "published_date": {
      "type": "string",
      "description": "The published date field"
    },
    "source": {
      "type": "string",
      "description": "The source field"
    },
    "summary": {
      "type": "string",
      "description": "The summary field"
    }
  }
}

result = client.extract(
    url="https://www.reuters.com/business/",
    schema=schema,
)
print(result.data)

And the equivalent cURL request:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.reuters.com/business/",
    "schema": {
      "type": "object",
      "properties": {
        "headline": {"type": "string"},
        "author": {"type": "string"},
        "published_date": {"type": "string"},
        "source": {"type": "string"},
        "summary": {"type": "string"}
      }
    }
  }'

Full Extract API reference

Define your schema The schema parameter uses JSON Schema draft-07. AlterLab validates the extracted data against this schema, ensuring types and required fields. For example, if you mark headline as required and it's missing, the API returns an error. This guarantees your pipeline receives predictable, typed JSON — eliminating downstream parsing errors.

Example output from the above call:

{
  "headline": "Global stocks rise as inflation data surprises",
  "author": "John Doe",
  "published_date": "2026-03-15T08:30:00Z",
  "source": "Reuters",
  "summary": "Stock markets gained Tuesday after U.S. inflation data came in below forecasts..."
}

Handle pagination and scale For extracting multiple articles (e.g., from a Reuters section page), batch requests and handle pagination:

• Use the section URL (e.g., https://www.reuters.com/business/) to get a list of article links
• Extract each article URL from the listing page (AlterLab can extract links too with a schema for url fields)
• Process articles in parallel with async jobs to stay within rate limits
• See pricing for volume discounts — costs scale linearly with extraction complexity, not volume.

Here's a Python async example for batch processing:

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

# First, get article URLs from a section page
listing_schema = {
  "type": "object",
  "properties": {
    "articles": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "url": {"type": "string", "format": "uri"}
        }
      }
    }
  }
}

listing_result = client.extract(
    url="https://www.reuters.com/business/",
    schema=listing_schema
)

# Then extract data from each article concurrently
async def extract_article(url):
    schema = {
      "type": "object",
      "properties": {
        "headline": {"type": "string"},
        "author": {"type": "string"},
        "published_date": {"type": "string"},
        "source": {"type": "string"},
        "summary": {"type": "string"}
      }
    }
    return client.extract(url=url, schema=schema)

# In practice, use asyncio.gather with semaphore for rate limiting
# This example shows the concept; adjust for your volume and rate limits

Key takeaways

• AlterLab's Extract API turns unstructured Reuters HTML into typed JSON via schema definition
• You get reliable, validated output without maintaining fragile parsers
• Focus on your data pipeline, not anti-bot evasion or HTML changes
• Start with a free tier, pay only for what you extract

Now, we must include the infographic components. We'll place them appropriately.

Let's plan:

• After the TL;DR, we can put the Steps infographic to show the process.
• After the Quick start section, we can put the Stats Grid to show performance.
• At the end, we can put the TryIt block as an invitation.

However, note: the instructions say to embed as HTML blocks in the markdown.

We'll use:

We must also include the disclaimer near the top. Let's put it right after the TL;DR or at the very beginning of the CONTENT? The instruction: "Include a disclaimer near the top"

We'll put it after the TL;DR and before the first section.

Example:

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Now, let's write the full CONTENT in markdown.

Note: We must count words to be 1500-3000. We'll aim for around 2000.

Important: Mention AlterLab naturally at most 4 times (not counting code examples). We'll mention it in: - TL;DR (once) - Why use Reuters data? (maybe not, but we can in the extraction approach) - The extraction approach (once) - Quick start (we are showing code, so the mention in code doesn't count? The rule says "not counting code examples") - Define your schema (maybe) - Handle pagination and scale (we mentioned pricing link, but not AlterLab by name? We should mention the service) - Key takeaways (once)

Let's count: TL;DR: "AlterLab's Extract API" -> 1 Extraction approach: "Alter