---
title: "Newspaper Agent"
description: "News article extraction tools for AI agents."
icon: "newspaper"
---

<Note>
  **Prerequisites**
  - Python 3.10 or higher
  - PraisonAI Agents package installed
  - `newspaper3k` package installed
</Note>

## Newspaper Tools

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

<Steps>
  <Step title="Install Dependencies">
    First, install the required packages:
    ```bash
    pip install praisonaiagents newspaper3k
    ```
  </Step>

  <Step title="Import Components">
    Import the necessary components:
    ```python
    from praisonaiagents import Agent, Task, PraisonAIAgents
    from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
    ```
  </Step>

  <Step title="Create Agent">
    Create a news agent:
    ```python
    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
    )
    ```
  </Step>

  <Step title="Define Task">
    Define the news task:
    ```python
    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"
    )
    ```
  </Step>

  <Step title="Run Agent">
    Initialize and run the agent:
    ```python
    agents = PraisonAIAgents(
        agents=[news_agent],
        tasks=[news_task],
        process="sequential"
    )
    agents.start()
    ```
  </Step>
</Steps>

## Understanding Newspaper Tools

<Card title="What are Newspaper Tools?" icon="question">
  Newspaper Tools provide news article processing capabilities for AI agents:
  - Article extraction
  - Source management
  - Content analysis
  - Trend detection
  - Multi-language support
</Card>

## Key Components

<CardGroup cols={2}>
  <Card title="News Agent" icon="user-robot">
    Create specialized news agents:
    ```python
    Agent(tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics])
    ```
  </Card>
  <Card title="News Task" icon="list-check">
    Define news tasks:
    ```python
    Task(description="news_query")
    ```
  </Card>
  <Card title="Process Types" icon="arrows-split-up-and-left">
    Sequential or parallel processing:
    ```python
    process="sequential"
    ```
  </Card>
  <Card title="Article Options" icon="sliders">
    Customize article retrieval:
    ```python
    language="en", limit=10
    ```
  </Card>
</CardGroup>

## Available Functions

```python
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

```python
# 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

```python
# 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

```python
# 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()
```

### get_trending_topics(sources: Optional[List[str]] = None, limit: int = 10, language: str = 'en')

Analyzes trending topics:
- Cross-source analysis
- Keyword extraction
- Customizable sources
- Topic ranking

```python
# 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

```python
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

```python
# 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

<AccordionGroup>
  <Accordion title="Agent Configuration">
    Configure agents with clear news focus:
    ```python
    Agent(
        name="NewsAnalyst",
        role="News Specialist",
        goal="Analyze news content effectively",
        tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics]
    )
    ```
  </Accordion>

  <Accordion title="Task Definition">
    Define specific news objectives:
    ```python
    Task(
        description="Analyze tech news from major sources for AI developments",
        expected_output="Trend analysis with key findings"
    )
    ```
  </Accordion>
</AccordionGroup>

## Common Patterns

### News Monitoring
```python
# 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

```python
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:
```python
# 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']}")
```

2. Trend Analysis:
```python
# Analyze trending topics
topics = get_trending_topics(limit=10)
print("Current hot topics:")
for i, topic in enumerate(topics, 1):
    print(f"{i}. {topic}")
```

3. Content Aggregation:
```python
# 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"])