GPT Researcher vs WrenAI
WrenAI leads on trust: 85.7/100 (Grade A) against 63.0/100 (Grade C), a 22.7-point gap. GPT Researcher leads on transparency and adoption, and rests on broader evidence; WrenAI leads on supply-chain integrity and provenance. Note: WrenAI's evidence coverage is thinner (coverage B vs A) — its score rests on fewer independent signal types.
An autonomous agent that conducts deep research on any data using any LLM providers
Choose GPT Researcher if transparency and adoption matter most.
- +2.9Adoption: 6.9k weekly downloads against 121
- +0.6Transparency: OSSF Scorecard 6.2 against 5.5
- A vs BEvidence coverage: 4 of 5 independent signal types, against 3
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.
Choose WrenAI if supply-chain integrity and provenance matter most.
- +8.9Safety / Integrity: 94% of recent commits signed, against 49%
- +7.2Identity / Provenance: package provenance attested, against none
- +4.3Maintenance: last push 1d ago, against 24d ago
Where they differ
An independent, evidence-based trust comparison of GPT Researcher and WrenAI, two Research & Data projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.
Full evidence
| Signal | GPT Researcherassafelovic/gpt-researcher | WrenAICanner/WrenAI |
|---|---|---|
| HVTrust score | 63.0 | 85.7 |
| Evidence grade | C | A |
| Coverage grade | A | B |
| Overall rank | #598 | #89 |
| Rank in Research & Data | #13 | #4 |
| GitHub stars | 30.4k | 18.0k |
| Last updated | 24d ago | 1d ago |
| Build provenance | No | Yes |
| OSSF Scorecard | 6.2 / 10 | 5.5 / 10 |
| License | Apache-2.0 | NOASSERTION |
| Downloads | 7k/wk | 121/wk |
| Trust dimensions (points earned) | ||
| Safety / integrity / 25 | 10.2 | 19.1 |
| Identity & provenance / 18 | 10.8 | 18.0 |
| Transparency / 17 | 13.8 | 13.2 |
| Maintenance / 20 | 14.2 | 18.5 |
| Adoption / 20 | 15.9 | 13.0 |
| Runtime capability surface (full matrix) | ||
| MCP server | Implemented | Implemented |
| External providers | 5 — Firecrawl, Google Gemini, Multi-provider (LiteLLM), … | 3 — Amazon Bedrock, Anthropic, Postgres |
| Requires API keys | Yes | No |
| Plugin surface | plugins | plugins |
| Provenance drift | Unknown | Match |
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →