GPT Researcher

What GPT Researcher is

GPT Researcher is an application and package for turning a question into a research report. It separates planning, source gathering and writing, which makes it easier to understand where an answer came from than treating a long generated response as one unexplained operation.

Its context filter decides which passages reach the report writer. That matters because finding a source is only part of research: the system also has to select useful evidence and preserve its meaning. A citation is a starting point for checking a claim, not automatic proof of accuracy.

How it fits into a workflow

Choose a bounded research question, inspect the resulting sources, and compare the report's claims with those sources. If you supply local documents, check which provider receives their content. Keep the original question and configuration so that differences between runs can be interpreted.

Examples described as illustrative or proposed trials are editorial suggestions. Documented real builds, when available, are linked separately below.

Requirements, cost and limitations

Current main-branch requirements specify Python 3.12 or later. The documented default uses OpenAI for models and Tavily for search; supported alternatives change the accounts and setup required. Running the client locally does not keep supplied documents local when a hosted model processes them. No universal RAM or GPU minimum was established in the reviewed documentation.

As checked on 30 October 2026, the root LICENSE is Apache-2.0, although the package metadata still says MIT. Keep that upstream discrepancy in view when adopting a version. Model and search usage, hosting and optional services are separate operating costs. Published benchmark bills describe particular runs and are not a quote for a new report.

Maintenance and version context

2026-09-26: v3.7.0 release documents Python 3.12+, context-filter changes and scraper/retriever fixes.

When to choose it and what to compare

The focused report workflow makes this a useful learning option for research automation. Its published context-filter evaluation also shows how a builder can test a specific design decision. Those limited results do not establish that every report topic, configuration or model will perform similarly.

Focused source review 2026-10-30: Current repository, license, setup, permissions and published implementation evidence; not independently executed. Documentation review, not hands-on product testing.

Sources and evidence

Product facts are based on the following published sources, checked 2026-10-30. Workflow suggestions and comparisons are editorial interpretation; PrisAIm has not independently benchmarked this product.