Artistic Deep Research

The Art of Automated Intelligence

A next-generation, open-source research agent that transforms raw data into beautiful, comprehensive, and artistic reports.

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🚀 Quick Start · 🏗️ Architecture · 🐞 Report Bug · ✨ Request Feature

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🌟 Overview

Artistic Deep Research is not just another research bot; it's a research artist. Built on top of the powerful LangGraph framework, it automates the tedious process of deep internet research—finding sources, reading content, synthesizing facts—and presents the findings in a stunningly designed HTML report.

Whether you are an analyst, a student, or a curious mind, Artistic Deep Research empowers you to:

• 🔎 Dig Deeper: Iteratively search and reflect on findings to uncover hidden gems.
• 👁️ See Clearly: Visualize complex information through structured, aesthetic reports.
• ⏳ Save Time: Parallel processing across multiple search engines and models.

"Research is seeing what everybody else has seen and thinking what nobody else has thought."

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🚀 Key Features

Feature	Description
🎨 Artistic Reporting	Generates beautiful HTML reports with custom typography, layouts, and your branding.
🧠 Deep Reflection	Uses a "Think Tool" to pause, reflect, and plan the next steps in research, just like a human expert.
⚡ Parallel Execution	Spins up multiple sub-agents to research different aspects of a topic simultaneously.
🔌 Multi-Model Support	Compatible with OpenAI, Anthropic, Google Gemini, and more via init_chat_model.
🌐 Comprehensive Search	Integrates with Tavily, Exa, ArXiv, PubMed, and standard web search.
🛠️ CLI Power	Robust Command Line Interface with rich progress bars and colorful output.

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🧠 Brain Architecture

The Deep Hive architecture mimics a human research team. It is composed of specialized AI nodes working in harmony.

graph TD
    %% Main Flow
    User(👤 User Topic) -->|CLI Start| Supervisor[🧠 Research Supervisor]
    
    subgraph "The Hive"
        Supervisor -->|Delegates Task| Researcher[🕵️ Deep Researcher]
        Researcher -->|Search & Read| Web(🌐 The Internet)
        Web -->|Raw Info| Analyst[🧐 Deep Analyst]
        Analyst -->|Structured Data| Researcher
        
        Researcher -->|Draft Findings| Reviewer[⚖️ Critical Reviewer]
        
        Reviewer -->|Pass| Compiler[📥 Insight Compiler]
        Reviewer -.->|"Fail (Feedback)"| Researcher
    end
    
    Compiler -->|Final Synthesis| ReportGen[🎨 Artistic Report Engine]
    ReportGen --> Output(📄 report.html)
    
    style User fill:#333,stroke:#fff,stroke-width:2px,color:#fff
    style Supervisor fill:#ff69b4,stroke:#333,stroke-width:2px,color:#fff
    style Researcher fill:#4caf50,stroke:#333,stroke-width:2px,color:#fff
    style Reviewer fill:#f44336,stroke:#333,stroke-width:2px,color:#fff
    style ReportGen fill:#2196f3,stroke:#333,stroke-width:2px,color:#fff

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🧪 Research Examples

Artistic Deep Research can handle complex queries across various domains.

⚛️ Physics & Science

Command: python -m Artistic_DeepResearch.cli start --topic "Recent Breakthroughs in Nuclear Fusion 2024-2025"

Output: A detailed breakdown of ITER milestones, private sector investments (Helion, TAE), and Q_plasma achievements.

💰 Market Analysis

Command: python -m Artistic_DeepResearch.cli start --topic "Impact of AI Agents on SaaS Pricing Models"

Output: A comparative analysis of seat-based vs. usage-based pricing, featuring case studies from Salesforce and emerging startups.

🏥 Healthcare

Command: python -m Artistic_DeepResearch.cli start --topic "CRISPR Therapies approved by FDA in 2024"

Output: A timeline of approvals, mechanism of action summaries, and patent landscape analysis.

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🏁 Quick Start

Prerequisites

• Python 3.10+
• API Keys for your preferred Model Provider (e.g., OpenAI, Anthropic) and Search Tool (e.g., Tavily).

Installation

1. Clone the Repository

   git clone https://github.com/Rahulchaube1/ArtisticDeepResearch.git
   cd ArtisticDeepResearch

2. Set Up Environment

   python -m venv .venv
   # Windows
   .\.venv\Scripts\activate
   # Mac/Linux
   source .venv/bin/activate

3. Install Dependencies

   pip install -e .
   # If using requirements file:
   pip install -r requirements.txt

4. Configure API Keys Copy the example environment file and add your keys:

   cp .env.example .env

   Edit .env with your OPENAI_API_KEY, TAVILY_API_KEY, etc.

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🎮 Usage

1. The Command Line Interface (CLI)

The easiest way to run a research task is via our beautiful CLI:

python -m Artistic_DeepResearch.cli start --topic "The Future of Quantum Computing"

Watch as the agent plans, searches, reflects, and finally generates a report.html in your directory.

2. Customizing the Report

You can modify the aesthetic of the generated reports by editing src/Artistic_DeepResearch/report_generator.py. The CSS and HTML templates are fully customizable to match your brand.

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⚙️ Configuration

Artistic Deep Research is highly configurable. You can tweak the behavior in src/Artistic_DeepResearch/configuration.py.

Parameter	Default	Description
max_researcher_iterations	6	How deep the rabbit hole goes. Higher = more detailed.
max_concurrent_research_units	5	Number of parallel agents. Higher = faster but more API usage.
search_api	tavily	The search engine backend (tavily, openai, anthropic).

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🤝 Contributing

We welcome contributions from the community! Whether it's a new "Artistic" theme, a better search tool integration, or a bug fix.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

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

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Built with ❤️ by Rahul Chaube

© 2025 Rahul Chaube. All Rights Reserved.