ByteDance releases a big move: Deep Research project DeerFlow is officially open source

When AI learns to "think deeply", the research paradigm is subverted

In today's information explosion, from students writing papers to companies doing market analysis, "efficient research" has always been a rigid demand. However, traditional research is time-consuming and laborious, and ordinary people can only touch the surface. In 2025, with the launch of Deep Research by OpenAI and the open source DeerFlow by ByteDance , AI has finally entered a new era of "deep research" - they can not only automatically search and integrate massive amounts of information, but also reason, verify, and generate structured reports like human experts. This AI-driven research revolution is reshaping the underlying logic of scientific research, business, and even personal growth.

1. The Essence of Deep Research: From “Information Retrieval” to “Cognitive Enhancement”

1. What is Deep Research?

Deep Research is an AI-driven systematic research framework , the core of which is: •  Multi-step reasoning : breaking down complex problems into executable sub-tasks and advancing step by step (such as academic papers need to go through literature review → hypothesis proposal → data verification) •  Multi-source information integration : processing heterogeneous data such as text, PDF, images, databases, etc. at the same time, breaking through the limitations of a single information source •  Structured output : generating professional reports with references, charts and logical frameworks, rather than fragmented answers

2. Why do we need Deep Research?

Pain points of traditional research: •  Low efficiency : Manual retrieval takes hours to days, AI can reduce the time to minutes •  Lack of depth : Humans are susceptible to cognitive biases, AI can cross-verify information and discover hidden connections •  High cost : Professional research relies on teamwork, AI enables a single person to complete complex topics

2. OpenAI Deep Research: An “AI Assistant” for Expert-Level Research

1. Core Competencies

•  Model architecture : Based on the o3 model , optimized for web browsing and reasoning, supporting dynamic adjustment of research direction •  Functional highlights : • Real-time online search (including academic papers, industry reports) • Processing of complex formats such as PDF, tables, codes, etc. • Generate 10,000-word reports with precise citations (citations can be traced back to the original paragraph) •  Performance : In the "Ultimate Human Test" covering 100+ subjects, the accuracy rate reached 26.6%, setting a new industry record

2. Usage scenarios

3. Limitations

•  Reliance on public data : cannot access paid databases •  Risk of illusion : logical loopholes may appear in complex fields and require manual review •  Usage threshold : need to subscribe to Pro version

3. DeerFlow: A research alternative in the open source community

Project Positioning

The multi-agent collaboration framework , open sourced by ByteDance , has the following features: •  Open source and free : complete code + web page, supports local deployment •  Modular design : can freely combine search, crawler, code execution and other tool chains •  Chinese-friendly : natively supports Chinese research scenarios and adapts to local data sources

DeerFlow core architecture

• Multi-agent architecture: Based on the LangGraph architecture, a modular multi-agent system design is adopted. The system consists of roles such as coordinator, planner, research team and report generator. The coordinator manages the life cycle of the research process, the planner is responsible for task decomposition and generation of research plans, the research team includes researchers, code analysts, etc., who are responsible for specific information collection and technical tasks, and the report generator organizes the research results into reports.

• Open source framework based on LangStack: Built on the open source framework of LangChain and LangGraph, the code structure is clear and the logic is concise, which lowers the learning threshold.

DeerFlow main features

• Dynamic task iteration: It can automatically generate and optimize task plans according to research needs to ensure that the research process is efficient and flexible.

• Multi-tool integration: supports web search, Arxiv academic resource retrieval, crawler and Python code execution, providing strong support for scientific researchers in academic literature collection and analysis.

• Multimodal content generation: It can not only generate in-depth research reports, but also support the generation of podcast scripts, PPT and other diversified content to meet the needs of different scenarios.

• MCP seamless integration: By combining with ByteDance’s internal MCP (Model Control Platform), higher automation and accuracy can be achieved.

• LLM integration: supports integration of most models through litellm, supports open source models such as Qwen, is compatible with OpenAI's API interface, and the multi-layer LLM system is suitable for tasks of different complexity.

• Human-computer collaboration: supports interactive modification of research plans using natural language, and also supports automatic acceptance of research plans; supports Notion-like block editing, allowing AI optimization, including AI-assisted polishing, sentence shortening and expansion.

Advantages and Challenges

•  Advantages :  • Zero cost to start 

• Support enterprise private data access to meet compliance requirements

•  Challenges : • Tool chain debugging requires technical foundation • Multi-agent collaboration efficiency needs to be optimized

4. OpenAI vs ByteDance: A Comparison of the Two Major Solutions

5. Future Outlook: The Era of Human-Machine Collaboration in AI Research

1. Technology trends : • Multimodal research (analysis video, experimental video) • Real-time collaboration (AI generates first draft → human feedback → iterative optimization)
   
   
2. Industry impact : • Scientific research: shorten the paper cycle and lower the threshold for interdisciplinary research • Education: AI tutors guide students to complete research projects • Enterprises: build exclusive knowledge bases and analysis systems at low cost
   
   
   

Conclusion: Let AI become your "super brain"

Whether it is OpenAI's Deep Research or ByteDance's DeerFlow , they are redefining "research" - evolving from time-consuming and laborious information collection to intelligence-driven cognitive upgrades. Just as Deep Research has shown its potential in the "ultimate human test" and the chain reaction that DeerFlow has triggered in the open source community, this revolution has just begun.

DeerFlow GitHub repository address

https://github.com/bytedance/deer-flow