Show HN: Building a Deep Research Agent Using MCP-Agent

I gotta say, having white blurry blobs of something in the background floating behind white/grey text maybe wasn't the best design-choice out there.

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?

I'm using mcp-agent and have tried the orchestrator workflow pattern[0]. For deep research I'm having mixed results. As far as I can tell, it's not using prompt caching[1] with Anthropic models, nor the gpt-5 responses API[2], which is preferable to the completions API. The many MCP tools from a handful of servers eat up a lot of context. It doesn't report progress, so it'll just spin for minutes at a time without meaningful indication. Mostly it has been high cost and high latency without great grounding in source facts. I like the interface overall, but some of the patterns and examples were convoluted. I'm aware that mcp-agent is being worked on, and I look forward to improvements.

[0]: https://docs.mcp-agent.com/workflows/orchestrator

[1]: https://docs.anthropic.com/en/docs/build-with-claude/prompt-...

[2]: https://platform.openai.com/docs/guides/migrate-to-responses

Great write-up! Gives me a few ideas for a governance bot that I'm working on. Thanks for sharing :)

A good model for planner seems pretty important, what models are best?