Prerequisites
  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • newspaper3k package installed

​
Newspaper Tools

Use Newspaper Tools to extract and analyze news articles with AI agents.
1

Install Dependencies

First, install the required packages:
pip install praisonaiagents newspaper3k
2

Import Components

Import the necessary components:
from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
3

Create Agent

Create a news agent:
news_agent = Agent(
    name="NewsAgent",
    role="News Analyst",
    goal="Collect and analyze news articles from various sources.",
    backstory="Expert in news gathering and content analysis.",
    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
    self_reflect=False
)
4

Define Task

Define the news task:
news_task = Task(
    description="Analyze news articles about 'AI developments' from major tech news sources.",
    expected_output="Summary of key AI developments with source articles.",
    agent=news_agent,
    name="ai_news"
)
5

Run Agent

Initialize and run the agent:
agents = PraisonAIAgents(
    agents=[news_agent],
    tasks=[news_task],
    process="sequential"
)
agents.start()

​
Understanding Newspaper Tools

What are Newspaper Tools?

Newspaper Tools provide news article processing capabilities for AI agents:
  • Article extraction
  • Source management
  • Content analysis
  • Trend detection
  • Multi-language support

​
Key Components

News Agent

Create specialized news agents:
Agent(tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics])

News Task

Define news tasks:
Task(description="news_query")

Process Types

Sequential or parallel processing:
process="sequential"

Article Options

Customize article retrieval:
language="en", limit=10

​
Available Functions

from praisonaiagents.tools import get_article
from praisonaiagents.tools import get_news_sources
from praisonaiagents.tools import get_articles_from_source
from praisonaiagents.tools import get_trending_topics

​
Function Details

​
get_article(url: str, language: str = ‘en’)

Extracts and parses news articles:
  • Full text extraction
  • Author detection
  • Date parsing
  • Image extraction
  • NLP processing
# Basic usage
article = get_article("https://example.com/article")

# With language specification
article = get_article(
    "https://lemonde.fr/article",
    language="fr"
)

# Returns: Dict[str, Any]
# Example output:
# {
#   "url": "https://example.com/article",
#   "title": "Breaking News Story",
#   "text": "Full article text...",
#   "authors": ["John Doe", "Jane Smith"],
#   "publish_date": "2025-01-07T05:31:38",
#   "top_image": "https://example.com/image.jpg",
#   "images": ["https://example.com/image1.jpg", ...],
#   "movies": ["https://example.com/video1.mp4", ...],
#   "source_domain": "example.com",
#   "keywords": ["technology", "ai", ...],
#   "summary": "Article summary..."
# }

​
get_news_sources(category: Optional[str] = None, language: str = ‘en’, country: Optional[str] = None)

Gets news sources by category:
  • Category filtering
  • Language support
  • Country filtering
  • Domain information
# Get all sources
sources = get_news_sources()

# Get technology news sources
tech_sources = get_news_sources(
    category="technology",
    language="en",
    country="us"
)

# Returns: List[Dict[str, str]]
# Example output:
# [
#   {
#     "url": "https://techcrunch.com",
#     "domain": "techcrunch.com",
#     "name": "TechCrunch",
#     "category": "technology"
#   },
#   ...
# ]

​
get_articles_from_source(source_url: str, limit: int = 10, language: str = ‘en’)

Gets recent articles from a source:
  • Configurable limit
  • Language support
  • Full article parsing
  • Error handling
# Get latest articles
articles = get_articles_from_source(
    "https://techcrunch.com",
    limit=5
)

# Get articles in specific language
articles = get_articles_from_source(
    "https://lemonde.fr",
    limit=10,
    language="fr"
)

# Returns: List[Dict[str, Any]]
# Each article has the same structure as get_article()
Analyzes trending topics:
  • Cross-source analysis
  • Keyword extraction
  • Customizable sources
  • Topic ranking
# Get trending topics from default sources
topics = get_trending_topics(limit=5)

# Get topics from specific sources
topics = get_trending_topics(
    sources=[
        "https://techcrunch.com",
        "https://wired.com"
    ],
    limit=10,
    language="en"
)

# Returns: List[str]
# Example: ["artificial intelligence", "climate change", ...]

​
Examples

​
Basic News Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics

# Create news agent
news_agent = Agent(
    name="NewsExpert",
    role="News Analyst",
    goal="Gather and analyze news content efficiently.",
    backstory="Expert in news analysis and content curation.",
    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
    self_reflect=False
)

# Define news task
news_task = Task(
    description="Analyze tech news trends.",
    expected_output="Tech news analysis report.",
    agent=news_agent,
    name="tech_news_analysis"
)

# Run agent
agents = PraisonAIAgents(
    agents=[news_agent],
    tasks=[news_task],
    process="sequential"
)
agents.start()

​
Advanced News Analysis with Multiple Agents

# Create news collection agent
collector_agent = Agent(
    name="Collector",
    role="News Collector",
    goal="Gather comprehensive news coverage.",
    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
    self_reflect=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Content Analyst",
    goal="Analyze news content and identify trends.",
    backstory="Expert in news analysis and trend identification.",
    self_reflect=False
)

# Define tasks
collection_task = Task(
    description="Collect news articles about renewable energy developments.",
    agent=collector_agent,
    name="energy_news"
)

analysis_task = Task(
    description="Analyze the articles and identify key trends and breakthroughs.",
    agent=analysis_agent,
    name="news_analysis"
)

# Run agents
agents = PraisonAIAgents(
    agents=[collector_agent, analysis_agent],
    tasks=[collection_task, analysis_task],
    process="sequential"
)
agents.start()

​
Best Practices

​
Common Patterns

​
News Monitoring

# News monitor agent
monitor = Agent(
    name="Monitor",
    role="News Monitor",
    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
)

# Analysis agent
analyst = Agent(
    name="Analyst",
    role="News Analyst"
)

# Define tasks
monitor_task = Task(
    description="Monitor tech news sources",
    agent=monitor
)

analysis_task = Task(
    description="Analyze news trends",
    agent=analyst
)

# Run workflow
agents = PraisonAIAgents(
    agents=[monitor, analyst],
    tasks=[monitor_task, analysis_task]
)

​
Example Agent Configuration

from praisonaiagents import Agent
from praisonaiagents.tools import (
    get_article, get_news_sources,
    get_articles_from_source, get_trending_topics
)

agent = Agent(
    name="NewsAnalyzer",
    description="An agent that analyzes news articles",
    tools=[
        get_article, get_news_sources,
        get_articles_from_source, get_trending_topics
    ]
)

​
Dependencies

The newspaper tools require the following Python packages:
  • newspaper3k: For article extraction and parsing
  • nltk: For NLP processing (automatically installed with newspaper3k)
These will be automatically installed when needed.

​
Error Handling

All functions include comprehensive error handling:
  • Network errors
  • Parsing errors
  • Language errors
  • Source availability errors
Errors are handled consistently:
  • Success cases return the expected data type
  • Error cases return a dict with an “error” key
  • All errors are logged for debugging

​
Common Use Cases

  1. News Monitoring:
# Monitor tech news
sources = get_news_sources(category="technology")
for source in sources:
    articles = get_articles_from_source(
        source["url"],
        limit=5
    )
    for article in articles:
        if "ai" in article.get("keywords", []):
            print(f"AI article found: {article['title']}")
  1. Trend Analysis:
# Analyze trending topics
topics = get_trending_topics(limit=10)
print("Current hot topics:")
for i, topic in enumerate(topics, 1):
    print(f"{i}. {topic}")
  1. Content Aggregation:
# Aggregate news from multiple sources
def aggregate_news(categories):
    all_articles = []
    for category in categories:
        sources = get_news_sources(category=category)
        for source in sources[:3]:  # Top 3 sources per category
            articles = get_articles_from_source(
                source["url"],
                limit=3
            )
            all_articles.extend(articles)
    return all_articles

news = aggregate_news(["technology", "business", "science"])