Assaf Elovic

license

Apache-2.0(OSI)

source

public

core features withheld

no

GPT Researcher is Apache-2.0 with its full source public and no proprietary tier.

• https://github.com/assafelovic/gpt-researcher recorded 2026-08-13

  Apache-2.0 license on the repo and repeated in the README; full source including frontend and Dockerfile/Dockerfile.fullstack; 29.0k stars, 3.9k forks; no paid or enterprise tier

• https://raw.githubusercontent.com/assafelovic/gpt-researcher/main/LICENSE recorded 2026-09-29

  The repository LICENSE file is the unmodified Apache text, headed "Apache License" and "Version 2.0, January 2004", with the standard "APPENDIX: How to apply the Apache License to your work."

• https://raw.githubusercontent.com/assafelovic/gpt-researcher/main/README.md recorded 2026-09-29

  The README tells readers to clone the repository ("git clone https://github.com/assafelovic/gpt-researcher.git"), states the code is shared "under the Apache 2 license", and lists no paid or enterprise tier; the optional Jev filter says "Nothing is required." and falls back to local keyword ranking.

Adoption is measured by PyPI downloads of gpt-researcher, the project's one declared distribution artifact.

• https://pypistats.org/api/packages/gpt-researcher/recent recorded 2026-08-13

  80,472 downloads in the trailing 30 days for gpt-researcher

Solid, focused deep-research agent with no headline GAIA or benchmark number, so this reads from its feature set. It is narrower than general coding and multi-tool agents, which keeps it mid-tier.

• https://github.com/assafelovic/gpt-researcher recorded 2026-08-13

  deep research agent over web and local sources with cited multi-source reports, frontend and Docker deployment included; no benchmark figure published

• https://raw.githubusercontent.com/assafelovic/gpt-researcher/main/README.md recorded 2026-09-29

  The README describes a deep research agent producing "detailed, factual, and unbiased research reports with citations", a "tree-like exploration pattern" for deep research, and "multi-agent assistants built with" LangGraph and AG2; no GAIA or agent benchmark is published.