We benchmarked 18 RAG pipelines against an agent loop on Google's FRAMES. The best pipeline hit 78.9%. The agent loop hit 92.7%.

Hybrid search, reranking, query decomposition, and query expansion are often treated as must-haves for good RAG. We wanted to see how much each actually helped, so we tested them. Same model, same embeddings, same documents, across all 824 multi-hop questions in FRAMES.

We built 18 pipeline variants. The best one scored 78.9%. Our agent loop (with retrieval tools) that could read the results and search again scored 92.7%—roughly the same as giving the model the right articles upfront.

The reranker results might surprise you. A small reranker dropped our best pipeline’s accuracy by 9 percentage points, while a larger one barely helped. I’d already suspected reranking wouldn’t help much here, but wanted to test that assumption.

Another thing we noticed: models sometimes fill in gaps from memory, even when you explicitly tell them to stick to the retrieved documents. Those answers can still be full of citations. We ended up checking every correct answer against what the system had actually read.

Here’s the write-up if you’re interested:
Agentic RAG vs. traditional RAG on FRAMES

Full disclosure: I work on PipesHub, which is open source. The benchmark code and runbook are in the repo: https://github.com/pipeshub-ai/pipeshub-ai/tree/frames