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🔎 GPT Researcher

GPT Researcher is an autonomous agent designed for comprehensive 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 and RAG 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?

Demo

https://github.com/user-attachments/assets/2cc38f6a-9f66-4644-9e69-a46c40e296d4

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:

Tutorials

Features

📖 Documentation

See the Documentation for:

⚙️ Getting Started

Installation

  1. Install Python 3.11 or later. Guide.

  2. Clone the project and navigate to the directory:

    git clone https://github.com/assafelovic/gpt-researcher.git
    cd gpt-researcher
    
  3. Set up API keys by exporting them or storing them in a .env file.

    export OPENAI_API_KEY={Your OpenAI API Key here}
    export TAVILY_API_KEY={Your Tavily API Key here}
    
  4. Install dependencies and start the server:

    pip install -r requirements.txt
    python -m uvicorn main:app --reload
    

Visit http://localhost:8000 to start.

For other setups (e.g., Poetry or virtual environments), check the Getting Started page.

Run as PIP package

pip install gpt-researcher

Example Usage:

...
from gpt_researcher import GPTResearcher

query = "why is Nvidia stock going up?"
researcher = GPTResearcher(query=query, report_type="research_report")
# Conduct research on the given query
research_result = await researcher.conduct_research()
# Write the report
report = await researcher.write_report()
...

For more examples and configurations, please refer to the PIP documentation page.

Run with Docker

Step 1 - Install Docker

Step 2 - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env'

Step 3 - Within the docker-compose file comment out services that you don't want to run with Docker.

docker-compose up --build

If that doesn't work, try running it without the dash:

docker compose up --build

Step 4 - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes:

Visit localhost:3000 on any browser and enjoy researching!

📄 Research on Local Documents

You can instruct the GPT Researcher to run research tasks based on your local documents. Currently supported file formats are: PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents.

Step 1: Add the env variable DOC_PATH pointing to the folder where your documents are located.

export DOC_PATH="./my-docs"

Step 2:

👪 Multi-Agent Assistant

As AI evolves from prompt engineering and RAG to multi-agent systems, we're excited to introduce our new multi-agent assistant built with LangGraph.

By using LangGraph, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. Inspired by the recent STORM paper, this project showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication.

An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown.

Check it out here or head over to our documentation for more information.

🖥️ Frontend Applications

GPT-Researcher now features an enhanced frontend to improve the user experience and streamline the research process. The frontend offers:

Two deployment options are available:

  1. A lightweight static frontend served by FastAPI
  2. A feature-rich NextJS application for advanced functionality

For detailed setup instructions and more information about the frontend features, please visit our documentation page.

🚀 Contributing

We highly welcome contributions! Please check out contributing if you're interested.

Please check out our roadmap page and reach out to us via our Discord community if you're interested in joining our mission. <a href="https://github.com/assafelovic/gpt-researcher/graphs/contributors"> <img src="https://contrib.rocks/image?repo=assafelovic/gpt-researcher" /> </a>

✉️ Support / Contact us

🛡 Disclaimer

This project, GPT Researcher, is an experimental application and is provided "as-is" without any warranty, express or implied. We are sharing codes for academic purposes under the Apache 2 license. Nothing herein is academic advice, and NOT a recommendation to use in academic or research papers.

Our view on unbiased research claims:

  1. The main goal of GPT Researcher is to reduce incorrect and biased facts. How? We assume that the more sites we scrape the less chances of incorrect data. By scraping multiple sites per research, and choosing the most frequent information, the chances that they are all wrong is extremely low.
  2. We do not aim to eliminate biases; we aim to reduce it as much as possible. We are here as a community to figure out the most effective human/llm interactions.
  3. In research, people also tend towards biases as most have already opinions on the topics they research about. This tool scrapes many opinions and will evenly explain diverse views that a biased person would never have read.

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