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|  |
| Developer(s) | Assaf Elovic and contributors |
|---|---|
| Initial release | 2023 |
| Written in | Python |
| Operating system | Cross-platform |
| Type | Artificial intelligence, Natural language processing, Autonomous agents |
| License | Apache License 2.0 |
| Website | gptr |
GPT Researcher is an open-source autonomous agent designed for comprehensive online research on various tasks. It utilizes large language models (LLMs) to produce detailed, factual, and unbiased research reports, addressing issues of misinformation, speed, determinism, and reliability in AI-driven research.[1]
Features
editGPT Researcher employs a sophisticated architecture involving "planner" and "execution" agents. The planner generates research questions, while execution agents seek relevant information based on these questions. Key features include:[1][2]
- Generation of detailed research reports, outlines, resources, and lesson reports
- Production of long and detailed research reports (over 2,000 words)
- Aggregation of over 20 web sources per research task to form objective and factual conclusions
- Support for over 100 different language models, including GPT, Claude, Gemini, Mistral, and more
- Multiple search provider options, including Tavily, DuckDuckGo, Google, Bing, Serper, SearX, arXiv, and Exa
- Local document analysis capabilities for various file formats (PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word)
- Customizable research focus and output formats
- Export options for research reports to PDF, Word, and other formats
Multi-Agent System
editGPT Researcher incorporates a multi-agent assistant built with LangGraph, inspired by the STORM paper.[3] This system leverages multiple agents with specialized skills to improve the depth and quality of the research process. The multi-agent approach allows for:
- Collaborative research conducted by a team of AI agents
- Comprehensive coverage of topics from planning to publication
- Generation of 5-6 page research reports in multiple formats
Architecture
editThe system is designed to optimize for both cost and performance, using models such as GPT-3.5-turbo and GPT-4-turbo. It follows a parallelized agent workflow, which enhances speed and stability compared to synchronous operations.[2] The architecture consists of several key components:
- Domain-specific agent creation based on research query or task
- Generation of research questions to form an objective opinion
- Crawler agents that scrape online resources for relevant information
- Summarization of scraped resources with source tracking
- Filtering and aggregation of summarized sources to generate the final research report
On average, a research task is completed in approximately 2 minutes, with an estimated cost of $0.005.[1]
Applications
editGPT Researcher is designed for various use cases, including:[1]
- Academic research
- Market analysis
- Strategic planning
- General information gathering
- Lesson planning and educational content creation
It aims to provide unbiased and factual information by aggregating data from multiple sources, making it suitable for individuals, startups, and enterprises requiring informed decision-making.
Development
editGPT Researcher was developed by Assaf Elovic and is maintained as an open-source project on GitHub. It is inspired by recent advancements in AI research methodologies, particularly the Plan-and-Solve[4] and Retrieval-Augmented Generation (RAG)[5] approaches.
The project is continuously evolving, with contributions from a global community of developers. It offers both a lightweight static frontend served by FastAPI and a feature-rich NextJS application for advanced functionality.[6]
Reception
editAs of 2024, GPT Researcher has gained significant attention within the AI community, with over one million downloads reported and hundreds of contributions from developers worldwide.[1] The project has fostered an active community, with a dedicated Discord server for support and collaboration.[7]
Challenges and Limitations
editDespite its capabilities, GPT Researcher faces several challenges:
- As an experimental application, it is provided "as-is" without warranty
- Users are responsible for monitoring and managing their token usage and associated costs
- The tool relies on the accuracy and credibility of its sources, necessitating user verification of results
- Efforts to reduce bias in research are ongoing, as complete elimination of bias is challenging[1]
Future Directions
editThe GPT Researcher project maintains an active roadmap for future development.[8] Potential areas for improvement include:
- Enhanced accuracy and reliability of research outputs
- Expanded capabilities for handling complex research tasks
- Integration of multimodal capabilities for understanding and generating various media types
- Further optimization of the multi-agent system for more efficient collaboration
See also
edit- Large language model
- Natural language processing
- Artificial intelligence
- Autonomous agents
- Retrieval-augmented generation
References
edit- ^ a b c d e f "GPT Researcher". GitHub. Retrieved 2024-09-14.
- ^ a b "GPT Researcher Documentation". GPT Researcher Docs. Retrieved 2024-09-14.
- ^ "Multi-Agent Assistant". GPT Researcher Docs. Retrieved 2024-09-14.
- ^ Yao, Shunyu; Zhao, Dian; Zhang, Ping; Wang, Shuo; Zhang, Kunlun; Wang, Ruihua; Xiao, Daxin; Liu, Tong (2023-05-07). "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models". arXiv. Retrieved 2024-09-14.
- ^ Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike; Yih, Wen-tau; Rocktäschel, Tim; Riedel, Sebastian; Kiela, Douwe (2020-05-22). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks". arXiv. Retrieved 2024-09-14.
- ^ "Frontend Applications". GPT Researcher Docs. Retrieved 2024-09-14.
- ^ "GPT Researcher Discord Community". Discord. Retrieved 2024-09-14.
- ^ "GPT Researcher Roadmap". Trello. Retrieved 2024-09-14.
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