This version appears to show off a vector store for documents generated from a web crawl (the writer is a vector-store-aaS company)
[1] https://github.com/huggingface/smolagents/tree/main/examples...
I think the biggest one is the goal: HF is to replicate the performance of Deep Research on the GAIA benchmark whereas ours is to teach agentic concepts and show how to build research agents with open-source.
Also, we go into the design in a lot more detail than HF's blog post. On the design side, HF uses code writing and execution as a tool, whereas we use prompt writing and calling as a tool. We do an explicit break down of the query into sub-queries, and sub-sub-queries, etc. whereas HF uses a chain of reasoning to decide what to do next.
I think ours is a better approach for producing a detailed report on an open-ended question, whereas HFs is better for answering a specific, challenging question in short form.