GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance

Core Background & Industry Pain Points Autonomous research agents scrape dozens of pages per run, but writers only need a few useful passages. Embedding similarity conflates “looks alike” with “helps answer,” so noise reaches the writer and every pipeline must budget for an embedding provider. ### Architecture Highlights & Internals v3.7.0 / PyPI 0.16.x routes scraping through Context Filter in select_context(): 50k page cap, 1k/100 chunks, then Jev usefulness scoring when TYPESAFE_API_KEY is set, else local BM25. Jev failures fall back to keyword; report chat reuses the same filter. Retrievers can ship via the gpt_researcher.retrievers entry point. ### Authoritative Benchmarks & Measured Scores On an official 28-task replay with identical sources, Jev kept-passage relevance is 73% vs embeddings 46% (~59% relative lift) at similar cost. Keyword matched or beat embeddings without a cloud key. Reproduce via evals/context_filter/. ### Developer Hands-on Guide Requires Python 3.12+. pip install -U gpt-researcher. Set TYPESAFE_API_KEY for Jev (CONTEXT_FILTER=auto|jev); use CONTEXT_FILTER=embeddings for the old path. See the docs and release notes.