looks cool, what's the largest codebase you have tested it on?

OpenEmr and Istio. https://gitlab.com/rhobimd-oss/shebe/-/blob/main/docs/Perfor...

thank you haha!

The goal here is not to replace Cursor’s own local codebase indexing. Cursor already does that part well. What Nia focuses on is external context. It lets agents pull in accurate information from remote sources like docs, packages, APIs, and broader knowledge bases

That’s what GP is saying. This is the Docs feature of Cursor. It covers external docs/arbitrary web content.

`@Docs` — will show a bunch of pre-indexed Docs, and you can add whatever you want and it’ll show up in the list. You can see the state of Docs indexing in Cursor Settings.

The UX leaves a bit to be desired, but that’s a problem Cursor seems to have in general.

yeah ux is pretty bad and overall functionality. it still relies on a static retrieval layer and limited index scope.

+ as I mentioned above there are many more use cases than just coding.Think docs, APIs, research, knowledge bases, even personal or enterprise data sources the agent needs to explore and validate dynamically.

As an AI user (claude code, rovo, github copilot) I have come across this. In code it didnt build something right where it needed to use up to date docs. Luckily those people have now made an MCP but I had to wait. For a different project I may be SOL. Suprised this isnt solved, well done for taking it on.

From a business point of view I am not sure how you get traction without being 10x better than what Cursor can produce tomorrow. If you are successful the coding agents will copy your idea and then people being lazy and using what works have no inventive to switch.

I am not trying to discourage. More like encourage you to figure out how you get that elusive moat that all startups seek.

As a user I am excited to try it soon. Got something in mind that this should make easier.

thanks! will be waiting for ur feedback

great question!

For large and active codebases, we avoid full reindexing. Nia tracks diffs and file level changes, so background workers only reindex what actually changed. We are also building “inline agents” that watch pull requests or recent commits and proactively update the index ahead of your agent queries.

Local vs upstream divergence is a real scenario. Today Nia prioritizes providing external context to your coding agents: packages, provider docs, SDK versions, internal wikis, etc. We can still reconcile with your local code if you point the agent at your local workspace (cursor and claude code already provide that path). We look at file paths, symbol names and usage references to map local edits to known context. In cases where the delta is large, we surface both the local version and the latest indexed version so the agent understands what changed.

Your FAQ says you don’t store code. But this answer sounds like you do? Even if you’re storing as an embedding that’s still storage. Which is it?

We don’t store your code or any proprietary local content on our servers. When we say “external context” we mean public or user-approved remote sources like docs, packages or APIs. Those are indexed on our side. Your private project code stays local

hey! knowledge graphs are also used at runtime but paired with other techniques, since graphs are only useful for relationship queries.

Not exactly just RAG. The shift is agentic discovery paired with semantic search.

Also, most of the coding agents still combine RAG and agentic search. See cursor blog about how semantic search helps them understand and navigate massive codebases: https://cursor.com/blog/semsearch

Wouldn’t call it just RAG though. Agentic discovery and semantic search are the way to go right now, so Nia combines both approaches. For example, you can dynamically search through a documentation tree or grep for specific things.

We call it agentic RAG. The retriever is an agent. It’s still RAG

Which would be much better than the techniques used in 2023. As context windows increase, combining them becomes even easier.

There are a lot of ways of how you can interpret agentic rag, pure rag, etc

same here, tried both context7 and ref tools.

Hi! I'm the developer of ref.tools. Would love to know what you're searching that you couldn't find, very occasionally things are missing from the index. Drop me a line at matt@ref.tools

Also FYI a bunch of search quality improvements dropped this week so you might want to try again. :)

- deep research agent to enrich and give more context - support for both documentation and entire codebases (both private and public)

hmm how are you accessing private codebases?

and can you explain more on how you use the "entire codebase"?