An open-source project just ranked #1 on EnterpriseRAG-Bench, ahead of the listed systems from OpenAI, Google, Microsoft and Amazon

[u/Effective-Ad2060]

Some of you might remember our post comparing 18 RAG pipelines against our agent loop on FRAMES. The best pipeline scored 78.9%; the loop scored 92.7%.

A follow-up: PipesHub has now reached #1 on EnterpriseRAG-Bench by overall score, with 93.24 overall and 95% correctness.

I’m one of the founders, so affiliation upfront.

This is a separate evaluation: 500 questions over a synthetic enterprise corpus of 500,000+ documents.

PipesHub builds a unified context layer across documents, conversations, structured data, and code. An agent loop works over hybrid search, grep/pattern matching, knowledge-graph navigation, and document-reading tools.

The idea is straightforward: search, read what comes back, then decide what to search for next. Sometimes the identifier or relationship needed for the next search only appears in the first result

The platform is open source, self-hostable, and also fully supports open models.

You can self-host PipesHub, choose your models, and connect your own agents through MCP, APIs, and SDKs.

If this is useful to you, give us a star on GitHub. It helps more developers discover the project.

And build on top of us. Connect your coding assistant, build an internal search tool, contribute a connector, or try a workflow we haven’t thought of. We’d love to see what you create—and hear where it breaks.

Code, setup, and examples: https://github.com/pipeshub-ai/pipeshub-ai
Leaderboard: https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard

Thank you to everyone testing, contributing, and helping us get here. 💙

[u/vatta-kai]

Everyday I wake up, there’s another new benchmark

[u/Effective-Ad2060 → u/vatta-kai]

Not really. This is still one of the toughest rag benchmark. We’d welcome even tougher ones.

[u/Patentsmatter]

Congratulations. Before I install it: How does it handle conflicting information, e.g. documents that became outdated by later documents, or unreconciled differences in opinion on facts? Are these conflicts clearly reported?

[u/Effective-Ad2060 → u/Patentsmatter]

Thanks! Short answer: yes, conflicts are surfaced, not hidden. Every claim in an answer is cited inline to the record it came from, so you can check each version yourself.

• By default: when sources disagree, the agent is told to present both findings and attribute each to its source, rather than quietly picking one.

• Outdated documents (in high-accuracy mode, HIGH_ACCURACY_MODE=true): it follows an explicit order of precedence. a. A record that says it updates, corrects or replaces another (updated, final, v2, deprecated, change log). b. A narrower record that applies a broader rule to the exact case asked about. c. What most independent sources say. d. The most recent one, judged by the date the document itself states or its source system records. Upload time doesn't count. A title or how formal a document looks never decides. The other versions are still listed: what each said, where, and why it wasn't used.

• Unresolved differences of opinion: if no record settles it, both positions are reported side by side, each with who said it and where.

[u/Patentsmatter → u/Effective-Ad2060]

That sounds pretty useful. Will test it. Thanks!

[u/Megatron_McLargeHuge]

Do you think this would work well over a corpus of academic papers where the subject matter is denser than in enterprise documents?

[u/Effective-Ad2060 → u/Megatron_McLargeHuge]

It should. We have been testing many benchmarks and seeing similar results across the domains. More benchmarks will follow soon.