It is an LLM-based autonomous agent designed to conduct deep local and web research on any topic and generate detailed, factual, and unbiased long-form reports with citations.
It is an LLM-based autonomous agent designed to conduct deep local and web research on any topic and generate detailed, factual, and unbiased long-form reports with citations. GPT Researcher utilizes a combination of “planner” and “execution” agents to perform research tasks. The planner generates research questions, while the execution agents gather relevant information from both local and web sources. The findings are then aggregated into a comprehensive report by a publisher agent. The system is built to address challenges such as misinformation, speed, determinism, and reliability by leveraging parallelized agent workflows and advanced AI techniques inspired by recent research papers like Plan-and-Solve and RAG.
GPT Researcher supports a wide range of file formats for local research, including PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents. It also features a multi-agent assistant built with LangGraph, which enhances the depth and quality of research by enabling multiple specialized agents to collaborate on a single task. The average output is a 5-6 page research report available in multiple formats such as PDF, Docx, and Markdown.
The project is open-source and provides a full suite of customization options, allowing users to create domain-specific research agents. It can be deployed using Docker or run as a PIP package, with detailed setup instructions available in the documentation. GPT Researcher also includes an enhanced frontend to improve user experience and streamline the research process. The project is experimental and provided “as-is” under the Apache 2 license, with no warranties or guarantees. Contributions are welcome, and users are encouraged to join the community via Discord for further engagement.
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