
GPT Researcher
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Open-source autonomous research agent that performs deep investigations across web and local documents to generate detailed reports with citations.
Overview
GPT Researcher is a powerful, open-source AI agent designed for autonomous deep research. Using a multi-agent architecture, it breaks a complex question into subtopics, searches the web and/or your uploaded documents, summarizes key findings with citations, and produces a structured, exportable report.
It supports “deep research” mode for recursive exploration of subtopics, custom prompts for tone and structure, integration with vector databases, and multiple report output formats. Ideal for analysts, academics, journalists, founders, or anyone needing to generate longform, fact-rich content fast.
Inputs:
A single research query or question
Optional: Local documents (PDF, DOCX, MD, etc.) via
DOC_PATHOptional: Custom prompt templates for tone, length, or format
Optional: Vector store (e.g. Chroma, FAISS) for document recall
Optional: API keys (OpenAI, Tavily, Serper, Browserless, etc.)
Outputs:
Structured longform research report (2000+ words)
Citations for all referenced sources
Exportable formats: PDF, DOCX, Markdown, HTML
Optional: intermediate results (
.json,log.txt)Optional: charts/images pulled from web sources
Setup Instructions:
Install dependencies
Requires Python 3.11+git clone https://github.com/assafelovic/gpt-researcher.git cd gpt-researcher pip install .Set your API keys
export OPENAI_API_KEY=your_key export TAVILY_API_KEY=your_key(Optional) Add local docs folder
export DOC_PATH="./docs"Run the agent in code:
from gpt_researcher import GPTResearcher async def run(): researcher = GPTResearcher( query="How is climate change affecting Arctic wildlife?", report_type="deep" ) await researcher.conduct_research() report = await researcher.write_report() print(report)Or run from CLI:
gpt-researcher --query "How is AI transforming education?" --report_type deepPricing:
Free & open-source
Requires API usage from providers (OpenAI, Tavily, Serper, etc.)
No licensing cost or paid tier
Demo Screens

Agent Architecture
Capabilities
Research Planning
Breaks complex queries into logical sub-questions to guide agent workflows.
Web Search & Scraping
Gathers data from 20+ real-time sources using Tavily or Serper to extract relevant articles.
Document Parsing
Extracts and summarizes content from local files like PDFs or Word docs.
Deep Research Loop
Recursively answers subquestions by spawning new planning & scraping cycles for depth.
Summarization & Citation
Writes detailed summaries of each source and attaches citations automatically.
Report Generation
Aggregates research into structured outputs with options for PDF, Word, or HTML formatting.
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