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AI Use Cases · 10 Feb, 2025

Deep Research In AI: Comparing Open-source And Closed-source Solutions

Deep Research applications are transforming how we automate tasks, analyze data, and generate reports, significantly reducing time and effort.

newmindIstanbul10 FEB, 20258 MIN READ
Deep Research In AI: Comparing Open-source And Closed-source Solutions

Deep Research In AI: Comparing Open-source And Closed-source Solutions

  • Deep Research applications are transforming how we automate tasks, analyze data, and generate reports, significantly reducing time and effort.

  • Large Language Models (LLMs) power these tools, enhancing efficiency across various domains.

  • Solutions vary from open-source projects that prioritize transparency and community-driven development to closed-source options from major companies offering advanced capabilities.

  • Understanding these choices helps in selecting the right tool for research and business needs.

Navigating Deep Research: Open-Source Vs. Closed-Source Applications

Open-Source Deep Research Applications

Open-source applications provide transparency, flexibility, and community-driven development. Some notable examples include:

  • Open-R1 by Hugging Face: Open-R1 aims to reproduce and build upon the DeepSeek-R1 pipeline, focusing on replicating benchmarks presented by OpenAI. It supports model training and evaluation, synthetic data generation, and code-native agents for improved performance (Programming Language: Python; Framework: Smolagents).

  • Automated-AI-Web-Researcher-Ollama: Automated-AI-Web-Researcher Leverages locally run LLMs through Ollama for automated online research. It performs structured research, breaks down queries into focused areas, investigates each area via web searching and scraping, and compiles findings (Programming Language: Python; Framework: Ollama).

  • Open Deep Research by btahir: This is an open-source alternative to Gemini Deep Research, generating AI-powered reports from web search results. It uses the Bing Search API or Google Custom Search, JinaAI for content extraction, and supports multiple AI platforms and models (Programming Language: TypeScript; Framework: Next.js).

  • Open Deep Research by nickscamara: A clone of OpenAI's Deep Research experiment, using Firecrawl's extract and search with a reasoning model. It features advanced routing, AI SDK with multi-LLM support, data persistence, and authentication (Programming Language: TypeScript; Framework: Next.js).

  • OpenDeep Researcher by mshumer: This is an AI research agent utilizing Claude 3.5-haiku and SERPAPI for comprehensive research. It breaks down research into subtopics, generates individual reports, and combines them into a final report with feedback from a "boss" persona (Programming Language: Python; Framework: Jupyter Notebook).

  • Deep-Research by dzhng: An iterative research agent that generates search queries, scrapes websites, and processes information using AI reasoning models. It dynamically optimizes queries, uses Firecrawl for web scraping, and OpenAI's 03-mini model for reasoning (Programming Language: Python).

  • STORM by stanford-oval: An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations. It conducts internet-based research, generates outlines and articles, supports various language models and retrieval modules. Co-STORM, a collaborative version, introduces dynamic mind mapping (Programming Language: Python).

  • node-Deep Research by jina-ai: Keeps searching, reading web pages, and reasoning until it finds the answer or exceeds the token budget. It uses Gemini/OpenAI for reasoning, Jina Reader for searching, provides a Web Server API, supports Docker deployment, and utilizes Brave search engine (Programming Language: TypeScript; Framework: Node.js).

  • Report mAlstro: Generates comprehensive reports on any topic, following a workflow similar to Google's Gemini Deep Research. It combines planning, parallel web research, and structured writing with human oversight (Programming Language: Python).

  • Autogen: A clone of the Deep Research agent using Agentchat in Autogen, featuring a multi-agent system for complex research tasks. It includes agents for web searches, information analysis, quality verification, and summary creation (Programming Language: Python).

Closed-Source Deep Research Applications

Closed-source applications are often developed by large companies with significant resources and expertise. They may offer advanced features but lack the transparency and community-driven development of open-source options. Some prominent examples include:

  • Gemini by Google: Google's most capable AI, designed to be multimodal and handle various forms of information. It offers features like Deep Research, transforming prompts into research plans, browsing sites, and creating reports (Programming Language: Python).

  • OpenAI's Deep Research: A paid tool synthesizing information from multiple sites into cited reports, leveraging OpenAI's 03 LLM. Designed for intensive knowledge work, it provides thorough, precise research with citations and summaries (Programming Language: Python).

  • Genspark: Genspark offers AI-driven search and research services by integrating multiple models. It creates custom pages called Sparkpages, consolidating web knowledge into a single unit. It aims to provide a cleaner, more informative digital landscape by eliminating spam, ads, and biased content. It utilizes a multi-agent framework for personalized and relevant research experiences (Programming Language: Python).

Table of Deep Research Applications

The Table of Deep Research Applications presents a detailed comparison of various open-source and closed-source AI research tools. It outlines essential aspects such as application type, provider, key features, programming language, and framework, offering a clear overview to help users identify the most suitable solution for their research needs.

Tool Name Type Provider Key Features Programming Language Framework
Open-R1 Open Source Hugging Face Model training/evaluation, synthetic data generation, code-native agents Python Smolagents
Automated-AI-Web-Researcher-Ollama Open Source TheBlewish Automated research planning, web searching/scraping, research summary generation Python Ollama
Open Deep Research (btahir) Open Source btahir Flexible web search, time-based filtering, multiple export formats TypeScript Next.js
Open Deep Research (nickscamara) Open Source nickscamara Firecrawl integration, advanced routing, multi-provider AI SDK TypeScript Next.js
OpenDeep Researcher Open Source mshumer Subtopic breakdown, multiple search rounds, individual reports, feedback system Python Jupyter Notebook
Deep-Research Open Source dzhng Dynamic query optimization, Firecrawl web scraping, 03-mini reasoning Python -
STORM Open Source stanford-oval Citation generation, multi-perspective analysis, VectorRM integration, dynamic mind mapping Python Node.js
Report mAlstro Open Source langchain-ai Research planning, parallel web research, structured writing, customizable models and prompts Python -
Autogen Deep Research Open Source - Multi-agent system, web searches, information analysis, quality verification, summary creation Python -
Gemini Closed Source Google Multimodal processing, TPU optimization, research planning, comprehensive reports Python -
OpenAI Deep Research Closed Source OpenAI Research planning, comprehensive reports, medical research capabilities Python -
Genspark Closed Source Genspark Multi-source synthesis, Sparkpages generation, content filtering, multi-agent system Python -

Our Perspective

The rise of agentic AI is transforming the landscape of Deep Research tools. AI agents are now capable of automating complex research tasks, analyzing vast amounts of information, and generating comprehensive reports, leading to expanded use cases across various domains. This shift underscores the evolving nature of knowledge processing, where the power no longer resides solely in knowledge itself but in how effectively it is processed and delivered.

We champion the use of agentic-based systems in Deep Research because they excel at efficiently navigating and synthesizing information from diverse sources. This approach aligns with the current emphasis on delivering knowledge in a readily accessible and actionable format. By automating the research process, agentic AI empowers users to focus on extracting insights and applying knowledge to decision-making, ultimately driving innovation and progress.

One key insight in the development of these systems is the advantage of using code over JSON for agent actions. Code actions are more concise, enable tool reuse, and can reduce the number of LLM calls—thereby lowering operational costs. Moreover, since large language models are often trained extensively on code, they are generally more efficient at generating actions in code, making this approach more scalable and aligned with the strengths of today’s AI systems.

Key Takeaways

  • Deep Research tools powered by LLMs are revolutionizing how we automate tasks, analyze data, and generate reports, significantly reducing time and effort across industries.

  • Open-source and closed-source solutions offer different strengths: open-source emphasizes transparency and community-driven development, while closed-source typically provides advanced features backed by large companies.

  • Open-source tools like Open-R1, STORM, and Autogen showcase flexible, customizable, and community-enhanced approaches to Deep Research with transparent architecture and multi-agent systems.

  • Closed-source solutions such as Gemini, OpenAI Deep Research, and Genspark offer robust, commercial-grade capabilities for scalable and precise research but lack the transparency of open-source counterparts.

  • Agentic AI is a driving force in Deep Research, enabling tools to automate complex workflows, synthesize diverse data sources, and deliver insights in actionable formats.

  • Using code instead of JSON for agent actions improves efficiency by enabling tool reuse, reducing LLM calls, and better aligning with how models are trained—making code-based agents more scalable and cost-effective.

  • Choosing the right Deep Research tool depends on specific needs such as transparency, customization, computational resources, or feature depth, making this comparison essential for researchers and organizations.

References

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