RAGFlow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capa
Compare RAGFlow
How does it stack up against its Memory & Knowledge neighbours?
Pick any agent to compare →Promising trust profile, but some evidence still deserves review.
Is RAGFlow safe? RAGFlow scores 72.0/100 (Grade B), ranked #125 of 1308 tracked open-source AI agent projects, on evidence coverage A (4 of 5 independent signal types). The public evidence: no package-provenance attestation found; OSSF Scorecard rates its supply-chain practices 5.5/10; 100% of recent commits are signed; last pushed 2026-08-19. Every point is earned from checkable signals — never paid placement. How scoring works →
Ranked neighbours in Memory & Knowledge
Quick Trust Read
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade B reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage A is separate — it grades how many independent signal types back the score (4 of 5), so a high score on thin evidence stays visible. Full methodology →
Rank Trend
Activity & Reach
Analysis
HVTrust Dimensions vs Memory & Knowledge
72.0 / 100 · 100.0% confidenceRAGFlow Memory & Knowledge average (44 agents)
Activity Inputs
98.4 / 100Supply Chain Trust
Is RAGFlow safe?
Does RAGFlow publish package provenance?
Does RAGFlow have an OpenSSF Scorecard?
Is RAGFlow actively maintained?
What license does RAGFlow use?
Are RAGFlow's commits signed?
Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.
AI agent surface
MCP, providers, tool surface
These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →
- Amazon Bedrock
- Anthropic
- Cohere
- Google Gemini
- Groq
- Mistral AI
- Multi-provider (LiteLLM)
- OpenAI
- Postgres
- Replicate
- Tavily
- browser
- search
- MCP signal live
- External deps live
- Tool / plugin surface live
- Package provenance drift live
Detected changes to RAGFlow's runtime surface and supply-chain posture, from daily public-signal snapshots. A change here means our detectors see something different — a genuinely changed capability, or better evidence of an existing one.
Maintain RAGFlow?
For maintainers
HVTrust scores RAGFlow from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.
Reputation Timeline
Signal history
Embed Badge Badge guide for maintainers →
For maintainers
[](https://hvtracker.net/agents/ragflow)
<a href="https://hvtracker.net/agents/ragflow"><img src="https://hvtracker.net/badge/ragflow.svg" alt="HVTrust"></a>
Other agents in Memory & Knowledge
GitHub REST API (repo, commits, stars, forks, license) · PyPI / pypistats (downloads, provenance) · OpenSSF Scorecard CLI · Algolia HN Search API
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.3 · Raw JSON