The Daily Agent 📰 - A Smart AI Newspaper Generator

The Daily Agent is an advanced, multi-agent application built with Python, LangChain, and LangGraph that autonomously generates a personalized daily newspaper based on a user's natural language request. It can create a general-interest newspaper with standard sections or a special report focused on specific topics mentioned by the user.

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🎬 Live Demo

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✨ Key Features

• 🗣️ Natural Language Understanding: The agent intelligently parses user requests to handle three scenarios: General Edition, Special Report, and Hybrid requests.
• 🤖 Advanced Multi-Agent Architecture: Built using LangGraph, the system orchestrates a team of specialized agents (Supervisor, Searcher, Summarizer, Creator) to complete its tasks.
• 🧠 Self-Correcting Search: The search agent is designed to be resilient. If its initial search yields poor results, it's prompted to reformulate its query and try again.
• 📊 Robust Structured Output: Uses Pydantic models to enforce a strict, predictable data structure for all information processed by the agent, ensuring reliability.
• ✍️ Dynamic Newspaper Generation: The final agent acts as an "Editor-in-Chief," creatively composing the gathered summaries into a polished, newspaper-style final report.
• 🖥️ Interactive Web Interface: A clean and user-friendly front-end built with Streamlit allows for easy interaction with the agent.

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⚙️ How It Works (Architecture)

The agent's logic is built as a stateful graph using LangGraph. The flow is orchestrated by a supervisor and a series of conditional edges that route the task between specialized nodes.

1. Input Parser: The user's request is first parsed to determine their intent (general, specific, or hybrid) and to create a list of topics to research.
2. Supervisor: The supervisor acts as the main loop controller. It takes one topic from the list and assigns the search_agent its mission.
3. Search Loop (ReAct): The search_agent enters a "Reason and Act" loop, using its search tool to find relevant articles.
4. Summarizer: Once the search is complete, the summarizer node receives the raw search data and formats it into structured ArticleSummary objects.
5. Return to Supervisor: The flow returns to the supervisor to check if there are more topics to process.
6. Newspaper Creator: Once all topics are complete, the final newspaper_creator node composes the human-readable newspaper.

Graph Visualization

graph TD
    A[START] --> B(input_parser);
    B --> C(supervisor);
    C -- Is Work Done? --> D{supervisor_condition};
    D -- No --> E(search_agent);
    D -- Yes --> F(newspaper_creator);
    E -- Tool Needed? --> G{tools_condition};
    G -- Yes --> H(tool_executor);
    H --> E;
    G -- No --> I(summarizer);
    I --> C;
    F --> J[END];

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🛠️ Tech Stack

• Backend: Python
• AI & Orchestration: LangChain, LangGraph
• LLMs: Groq Cloud
• Tools: Tavily Search
• Data Validation: Pydantic
• Frontend: Streamlit

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🚀 Setup and Installation

Follow these steps to run the project locally.

1. Clone the Repository

git clone https://github.com/ChidambaraRaju/langgraph-news-agent
cd langgraph-news-agent

2. Create a Virtual Environment

# Using uv (as you are)
uv venv

# Activate it
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`

3. Install Dependencies

uv add -r requirements.txt

4. Set Up Environment Variables

Create a file named .env in the root of your project folder and add your API keys:

GROQ_API_KEY="YOUR_GROQ_API_KEY_HERE"
TAVILY_API_KEY="YOUR_TAVILY_API_KEY_HERE"

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🏃‍♀️ Usage

To run the web application, execute the following command in your terminal from the project's root directory:

streamlit run app.py

Your web browser will open a new tab with the application running. Simply type your request into the input box and press "Generate Newspaper"!

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📁 Project Structure

/smart_newspaper_agent/
|
|-- agent/
|   |-- __init__.py
|   |-- state.py
|   |-- nodes.py
|   |-- graph.py
|
|-- config.py
|-- app.py
|-- requirements.txt
|-- .env
|-- README.md

📄 License

Distributed under the MIT License. See LICENSE for more information.