Readit Newshttps://huggingface.co/spaces/Ayanami0730/DeepResearch-Leade...
[1] https://docs.searxng.org/admin/installation-docker.html#inst...
None the less, I tried to find the actual APIs/service/software used for the "search" part, as I've found that to be the hardest to actually get right (at least for as-local-as-possible usage) for my own "Deep Research Agent".
I've experimented with Brave's search API which worked OK, but seems pricey for agent usage. Currently experimenting with using my own (local) YaCy instance right now, which actually gives me higher quality artifacts at the end, as there are no rate-limits and the model can do hundreds of search calls without me worrying about the cost. But it isn't very quick at picking up some stuff like news and more, otherwise works OK too.
What is the author doing here for the actual searching? Anyone else have any other ideas/approaches to this?
So the core idea is the Deep Orchestrator is pretty unopinionated on what to use for searching, as long as it is exposed over MCP. I tried with a basic fetch server that's one of the reference MCP servers (with a single tool called `fetch`), and also tried with Brave.
I think the folks at Jina wrote some really good stuff on the actual search part: https://jina.ai/news/a-practical-guide-to-implementing-deeps... -- and how to do page/url ranking over the course of the flow. My recommendation would be to do all that in an MCP server itself. That keeps the "deep orchestrator" architecture fairly clean, and you can plug in increasingly sophisticated search techniques over time.
We're looking for thoughts and feedback so will be happy to take any questions here! And as the post shows, it's really easy to try this out -- would be grateful for you to try deploying an agent as an MCP server and get your impressions!