gpt-researcher

<div align="center" id="top"> <img src="https://github.com/assafelovic/gpt-researcher/assets/13554167/20af8286-b386-44a5-9a83-3be1365139c3" alt="Logo" width="80"> #### [](https://gptr.dev) [](https://docs.gptr.dev) [](https://discord.gg/QgZXvJAccX) [](https://badge.fury.io/py/gpt-researcher)  [](https://colab.research.google.com/github/assafelovic/gpt-researcher/blob/master/docs/docs/examples/pip-run.ipynb) [](https://hub.docker.com/r/gptresearcher/gpt-researcher) [](https://twitter.com/assaf_elovic) [English](README.md) | [中文](README-zh_CN.md) | [日本語](README-ja_JP.md) | [한국어](README-ko_KR.md) </div> # 🔎 GPT Researcher **GPT Researcher is an open deep research agent designed for both web and local research on any given task.** The agent produces detailed, factual, and unbiased research reports with citations. GPT Researcher provides a full suite of customization options to create tailor made and domain specific research agents. Inspired by the recent [Plan-and-Solve](https://arxiv.org/abs/2305.04091) and [RAG](https://arxiv.org/abs/2005.11401) papers, GPT Researcher addresses misinformation, speed, determinism, and reliability by offering stable performance and increased speed through parallelized agent work. **Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.** ## Why GPT Researcher? - Objective conclusions for manual research can take weeks, requiring vast resources and time. - LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks. - Current LLMs have token limitations, insufficient for generating long research reports. - Limited web sources in existing services lead to misinformation and shallow results. - Selective web sources can introduce bias into research tasks. ## Demo <a href="https://www.youtube.com/watch?v=f60rlc_QCxE" target="_blank" rel="noopener"> <img src="https://github.com/user-attachments/assets/ac2ec55f-b487-4b3f-ae6f-b8743ad296e4" alt="Demo video" width="800" target="_blank" /> </a> ## Architecture The core idea is to utilize 'planner' and 'execution' agents. The planner generates research questions, while the execution agents gather relevant information. The publisher then aggregates all findings into a comprehensive report. <div align="center"> <img align="center" height="600" src="https://github.com/assafelovic/gpt-researcher/assets/13554167/4ac896fd-63ab-4b77-9688-ff62aafcc527"> </div> Steps: * Create a task-specific agent based on a research query. * Generate questions that collectively form an objective opinion on the task. * Use a crawler agent for gathering information for each question. * Summarize and source-track each resource. * Filter and aggregate summaries into a final research report. ## Tutorials - [How it Works](https://docs.gptr.dev/blog/building-gpt-researcher) - [How to Install](https://www.loom.com/share/04ebffb6ed2a4520a27c3e3addcdde20?sid=da1848e8-b1f1-42d1-93c3-5b0b9c3b24ea) - [Live Demo](https://www.loom.com/share/6a3385db4e8747a1913dd85a7834846f?sid=a740fd5b-2aa3-457e-8fb7-86976f59f9b8) ## Features - 📝 Generate detailed research reports using web and local documents. - 🖼️ Smart image scraping and filtering for reports. - 📜 Generate detailed reports exceeding 2,000 words. - 🌐 Aggregate over 20 sources for objective conclusions. - 🖥️ Frontend available in lightweight (HTML/CSS/JS) and production-ready (NextJS + Tailwind) versions. - 🔍 JavaScript-enabled web scraping. - 📂 Maintains memory and context throughout research. - 📄 Export reports to PDF, Word, and other formats. ## 📖 Documentation See the [Documentation](https://docs.gptr.dev/docs/gpt-researcher/getting-started) for: - Installation and setup guides - Configuration and customization options - How-To examples - Full API references ## ⚙️ Getting Started ### Installation 1. Install Python 3.11 or later. [Guide](https://www.tutorialsteacher.com/python/install-python). 2. Clone the proje