AI-Agents.News_Reader_Agent

# 📰 News Reader AI Agent 🚀

[![Python 3.13+](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/)
[![CrewAI](https://img.shields.io/badge/Framework-CrewAI-red.svg)](https://www.crewai.com/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A powerful multi-agent system built with **CrewAI** that automates the process of discovering, summarizing, and curating daily news reports on specific topics.

---

## 🔄 Workflow

The system employs a sequential multi-agent orchestration to transform a simple topic into a professional news briefing.

```mermaid
graph TD
    %% Styling Definitions
    classDef agent node font-weight:bold
    classDef hunter fill:#f9f,stroke:#333,stroke-width:2px,color:#000
    classDef summarizer fill:#bbf,stroke:#333,stroke-width:2px,color:#000
    classDef curator fill:#dfd,stroke:#333,stroke-width:2px,color:#000
    classDef file fill:#fff,stroke:#333,stroke-dasharray: 5 5,color:#000

    Start((Input: Topic)) --> Hunter

    subgraph "Phase 1: Discovery"
        Hunter[news_hunter_agent]:::hunter -->|SerperDev & Scrape| ContentHarvest[(content_harvest.md)]:::file
    end

    ContentHarvest --> Summarizer

    subgraph "Phase 2: Analysis"
        Summarizer[summarizer_agent]:::summarizer -->|Multi-tier Summarization| Summary[(summary.md)]:::file
    end

    Summary --> Curator

    subgraph "Phase 3: Editorial"
        Curator[curator_agent]:::curator -->|Final Assembly| FinalReport[(final_report.md)]:::file
    end

    FinalReport --> End((Final News Briefing))

    %% Additional styling
    style Start fill:#f5f5f5,stroke:#333
    style End fill:#f5f5f5,stroke:#333
```

---

## 🌟 Key Features

- **Multi-Agent Orchestration**: Specialized agents for high-quality news delivery.
- **Automated Web Search**: Uses **SerperDev** to find the most relevant and credible news articles.
- **Intelligent Web Scraping**: High-performance scraping using **Playwright** and **BeautifulSoup** to extract clean content from article pages.
- **Hierarchical Summarization**: Generates multi-tier summaries (Headline, Executive, and Comprehensive) for different reader needs.
- **Publication-Ready Reports**: Assembles a professional final report in Markdown format.

---

## 🏗️ Architecture

### 🤖 Agents

1.  **Senior News Intelligence Specialist (`news_hunter_agent`)**:
    *   **Goal**: Discover and collect relevant, credible news articles.
    *   **Tools**: `SerperDevTool`, `scrape_tool`.
2.  **Expert News Analyst (`summarizer_agent`)**:
    *   **Goal**: Transform raw articles into clear, multi-level summaries.
    *   **Tools**: `scrape_tool`.
3.  **Senior News Editor (`curator_agent`)**:
    *   **Goal**: Curate summaries into a cohesive, professional narrative.

### 📋 Tasks

- **Content Harvesting**: Searching for articles, filtering by credibility/relevance, and extracting full text.
- **Multi-Tier Summarization**: Creating tweet-style headlines, executive summaries, and detailed analysis.
- **Final Report Assembly**: Combining all work into a premium daily briefing.

---

## 📁 Project Structure

```text
.
├── config/
│   ├── agents.yaml      # Agent roles and backstories
│   └── tasks.yaml       # Task definitions and process flow
├── output/              # Generated markdown reports
├── .env                 # Environment variables (API Keys)
├── main.py              # Entry point & CrewAI orchestration
├── tools.py             # Custom scraping & search tools
└── pyproject.toml       # Project dependencies & metadata
```

---

## 🛠️ Setup & Installation

### Prerequisites

- **Python**: 3.13 or higher.
- **uv**: A fast Python package manager (recommended).
- **Playwright**: For web scraping (will be installed automatically via dependencies).

### 1. Clone the repository

```bash
git clone <repository-url>
cd AI-Agents.News_Reader_Agent
```

### 2. Install dependencies

Using `uv`:

```bash
uv sync
```

### 3. Environment variables

Copy the example environment file and fill in your API keys:

```bash
cp .env.example .env
```

Fill in the following:

- `OPENAI_API_KEY`: Your OpenAI API key (for models like `o4-mini-2025-04-16`).
- `SERPER_API_KEY`: Your Serper.dev API key for web search.

### 4. Install Playwright browsers

```bash
uv run playwright install chromium
```

---

## 🚀 Usage

Run the agent simply by executing:

```bash
uv run python main.py
```

By default, the agent searches for **"Trends of Korea-Japan couples"**. You can modify the input topic in `main.py`:

```python
result = NewsReaderAgent().crew().kickoff(inputs={"topic": "Your Topic Here"})
```

---

## 📈 Output

Generated reports are saved in the `output/` directory:

- `content_harvest.md`: Raw collected articles and metadata.
- `summary.md`: Multi-tier summaries for each article.
- `final_report.md`: The curated, publication-ready news briefing.

---

## 🧰 Tech Stack

- **Framework**: [CrewAI](https://www.crewai.com/)
- **LLM**: OpenAI (o4-mini-2025-04-16)
- **Search**: [Serper.dev](https://serper.dev/)
- **Scraping**: [Playwright](https://playwright.dev/python/), [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/)
- **Project Management**: [uv](https://github.com/astral-sh/uv)

---