Project Title

deep-research — AI-powered Research Assistant for Iterative Deep Dives into Any Topic

Overview

Deep-research is an AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. This project aims to provide a simple implementation of a deep research agent that can refine its research direction over time and deep dive into a topic, with a focus on keeping the codebase under 500 lines of code for easy understanding and extension.

Key Features

Iterative Research: Performs deep research by iteratively generating search queries, processing results, and diving deeper based on findings.
Intelligent Query Generation: Uses LLMs to generate targeted search queries based on research goals and previous findings.
Depth & Breadth Control: Configurable parameters to control how wide (breadth) and deep (depth) the research goes.
Smart Follow-up: Generates follow-up questions to better understand research needs.
Comprehensive Reports: Produces detailed markdown reports with findings and sources.
Concurrent Processing: Handles multiple searches and result processing in parallel for efficiency.

Use Cases

Researchers and academics needing to conduct in-depth research on specific topics.
Journalists and writers looking to gather comprehensive information for articles or reports.
Businesses and analysts seeking to understand market trends or competitor analysis.

Advantages

Simplicity: The project is designed to be easy to understand and build upon, with a codebase under 500 lines of code.
Flexibility: Configurable parameters allow for customization of research depth and breadth.
Efficiency: Concurrent processing enables handling multiple searches and results in parallel.
Iterative Improvement: The system refines its research direction over time based on findings and user input.

Limitations / Considerations

API Dependencies: Requires API keys for Firecrawl and OpenAI, which may involve costs or limitations on usage.
Complexity of Setup: The setup process involves configuring environment variables and potentially using Docker, which may be complex for some users.
Customization: While the project is designed to be simple, additional customization may require a deeper understanding of the underlying technologies.

Haystack: An open-source NLP framework for building search systems, which differs in its focus on document retrieval rather than iterative research.
SerpAPI: A Python library for accessing search engine results programmatically, which provides a simpler interface for web scraping but lacks the iterative research capabilities of deep-research.
LangChain: A framework for building LLM-powered applications, which offers more complex features but may be overkill for simple research tasks.

Basic Information

GitHub: deep-research
Stars: 17,673
License: Unknown
Last Commit: 2025-09-08

Requirements:

Node.js environment
API keys for Firecrawl API (for web search and content extraction)
API keys for OpenAI API (for o3 mini model)

📊 Project Information

Project Name: deep-research
GitHub URL: https://github.com/dzhng/deep-research
Programming Language: TypeScript
⭐ Stars: 17,673
🍴 Forks: 1,826
📅 Created: 2025-02-04
🔄 Last Updated: 2025-09-08

🏷️ Project Topics

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deep-research

Project Description