AI research assistants are getting smarter, but turning that reasoning into something usable is still hard.
Real research involves more than text:...
For further actions, you may consider blocking this person and/or reporting abuse
Whoa, amazing detailed tutorial Anmol, thanks for sharing!
thanks Nathan! It's insanely long but also shows a lot of useful patterns.
Interesting, I read through, and I'm trying to understand the difference between Tako and Tavily if they are both search?
Hey Steve - Tako has a custom index of licensed + large public datasets that aren't easily accessible on the web. For example, Tako has global public and private company metrics from S&P Global, website traffic from SimilarWeb, sports stats from SportRadar, public opinion polling from YouGov, and hundreds of other sources. Web + structured data knowledge graph is a great complement to producing highly accurate, real-time research.
they may look same on the surface but Tavily is for pulling relevant info from the open web so the agent can read and summarize it -- while Tako is more like searching structured datasets/visualizations and can return embeddable interactive charts (via MCP Apps)
If you are wondering where Tako gets context from: the agent uses the LLM to turn the user question into a few atomic data questions, then calls their MCP knowledge_search with those queries (covered in the backend section).
Nice, this looks fun to build!
Super detailed and clear way to get visually compelling research working - thanks Anmol!
thanks for reading! 🙌
Really cool way to orchestrate Tako and Tavily to produce compelling outputs!
Wow, this is quite good. Bookmarking this one as I think I have a use case for it.
Hey Fliin, awesome. I'd love to get your feedback once you start building. This example is open source, so feel free to build on top of it!
Thanks for the article — the overall approach is interesting, especially the MCP + chart embedding angle.
That said, I’m a bit confused by the implementation details in the repo. The example relies on
useCopilotAction, which the official CopilotKit docs now clearly mark as deprecated, with guidance to use the newer hooks instead.Given that this post was published very recently (and the repo was updated recently as well), could you, or Nathan clarify:
useCopilotActionstill recommended in practice despite being marked deprecated?Right now there’s a real mismatch between the docs, blog posts, and example repos, which makes it hard to know which patterns are safe to adopt in new projects. Some clarity here would save developers a lot of time.