GPT Researcher

Overview

GPT Researcher automates the entire research process. Instead of hours of manual searching and reading, it plans research sub-questions, searches multiple sources, reads and extracts information, identifies consensus and conflicts, and compiles everything into a structured, cited report.

How It Works

1. Input a question — Any research topic
2. Plan sub-queries — Decomposes into focused questions
3. Multi-source search — Searches 20+ sources per sub-query
4. Synthesize — Cross-references findings, produces cited report

Use Cases

• Market research — Analyze markets, competitors, and trends
• Due diligence — Research companies and technologies
• Academic research — Literature reviews and topic overviews
• Competitive analysis — Compare products, pricing, positioning

Getting Started

from gpt_researcher import GPTResearcher

researcher = GPTResearcher(
    query="Compare the top 5 AI agent frameworks for enterprise use",
    report_type="research_report"
)
report = await researcher.conduct_research()

Example

Query: "Current state of AI agent memory solutions?"

Report (2500 words):
1. Introduction — Memory is the most-requested capability...
2. Solutions — mem0 (51k stars), Zep, MemGPT, LangChain memory
3. Approaches — Vector stores, knowledge graphs, hybrid
4. Recommendations per use case

Sources: 47 consulted, 22 cited

Alternatives

• Deer Flow — ByteDance’s research superagent
• Open Deep Research — Firecrawl-powered research
• Perplexity — AI search engine (not open source)