Purpose and audience

Planning and execution agents gather, summarize and track sources, then combine findings into a cited research report.

Evidence 1 ↗ · Evidence 2 ↗

How Jev fits

When TYPESAFE_API_KEY is present, Jev scores passage usefulness for the research question before the LLM reads the context.

Evidence 1 ↗ · Evidence 2 ↗

Using the project

Create GPTResearcher with a query, run conduct_research(), then call write_report() to produce the report.

Evidence 1 ↗

Scope and limits

Missing credentials or failed Jev calls trigger local BM25 filtering. The documented setup separately needs Python 3.12 and research-provider credentials.

Evidence 1 ↗ · Evidence 2 ↗

Repository description

An autonomous agent that conducts deep research on any data using any LLM providers

Project facts

Relationship to Jev
Integration
Evidence
Documented
Language
Python
License
Apache-2.0
Origin
Original repository
Repository status
Not archived
Created
2023-05-12
Last code update
2026-09-26
GitHub stars
29,816
Evidence checked
Sep 30, 2026
Metadata checked
Sep 30, 2026
Check status
Up to date

Stars measure the whole repository, including work unrelated to Jev.

Evidence and scope

Documented records the linked documentation or source. JevHunt has not independently run or benchmarked this project.

Evidence commit: 0957c301ed06c2a5857b834358c7227c739041d4

Discovered through: github-search.

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