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. 💙   submitted by   /u/Effective-Ad2060 [link]   [comments]