GPT Researcher¶

What it is¶

GPT Researcher is an autonomous agent designed for comprehensive online research on any given topic. It plans the research, browses the web, and synthesizes a final report with citations. It uses a "master-agent" and "research-agent" pattern to break down complex queries.

What problem it solves¶

It automates the time-consuming process of manual research, gathering information from multiple sources and producing high-quality, grounded summaries. It specifically addresses LLM hallucinations by grounding every claim in a retrieved web source.

Where it fits in the stack¶

Category: Agent / Research Automation

The Research Process¶

GPT Researcher follows a structured 3-step autonomous workflow: 1. Plan: Generates a set of research questions that form an objective plan for the topic. 2. Research: For each question, it triggers a research agent to scrape 20+ web sources for relevant information. 3. Report: Aggregates all findings, filters out duplicates, and synthesizes a final Markdown report with full citations.

Typical use cases¶

Market Research: Analyzing industry trends and competitor offerings.
Technical Deep Dives: Researching new frameworks or hardware specifications.
Academic/Legal Preparation: Gathering sources and summaries for specific inquiries.
Daily Intelligence: Generating briefings on evolving news topics.

When to use it¶

Exhaustive Research: When you need to gather information from dozens of sources simultaneously and summarize them into a single report.
Fact-Checking: To verify information against current web data and receive a cited bibliography.
Automated Summarization: When you need to create comprehensive, long-form reports on complex topics without manual browsing.

When not to use it¶

Real-Time Fact Retrieval: For single-shot questions (e.g., "What is the capital of France?"), standard RAG or search tools are faster and cheaper.
Creative Writing: It is optimized for factual synthesis, not creative or conversational tasks.
Strict Latency Limits: Because it performs multi-source scraping and analysis, reports can take minutes to generate.

Getting started¶

Installation¶

pip install gpt-researcher

Environment Setup¶

export OPENAI_API_KEY='your-key'
export TAVILY_API_KEY='your-key'

Basic Usage¶

Run a simple research task using the Python library to generate a report.

CLI examples¶

# Run a quick research report on a topic
python -m gpt_researcher.cli "What is the future of solid-state batteries?" --report_type research_report

# Generate a detailed, in-depth report (takes longer)
python -m gpt_researcher.cli "Impact of AI on software engineering 2026" --report_type detailed_report --tone analytical

# Conduct research with a specific source domain filter
python -m gpt_researcher.cli "Latest SpaceX launches" --report_type research_report --query_domains spacex.com,nasa.gov

API examples¶

from gpt_researcher import GPTResearcher
import asyncio

async def main():
    # 1. Initialize the researcher
    researcher = GPTResearcher(query="Future of home-office automation 2026", report_type="research_report")

    # 2. Conduct research
    await researcher.conduct_research()

    # 3. Write the final report
    report = await researcher.write_report()
    print(report)

if __name__ == "__main__":
    asyncio.run(main())

Strengths¶

High Recall: Scrapes dozens of sources per task, far exceeding standard "search" tools.
Citation-First: Every report includes a comprehensive bibliography of the sources used.
Customizable: Allows defining specific "research tasks" and report formats (PDF, Markdown, etc.).

Limitations¶

Cost: Scraping and synthesizing many sources can consume significant LLM tokens.
Speed: A thorough research task can take several minutes to complete.
Quality: Dependent on the quality of search results and the LLM used for synthesis.

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-05-29
Confidence: high