We’re not a coding tool. We’re the context layer for agents.

Not quite “better RAG for code”. The core idea is agentic discovery plus semantic search. Instead of static chunks pushed into context, the agent can dynamically traverse docs, follow links, grep for exact identifiers, and request only the relevant pieces on demand.

No manual chunking. We index with multiple strategies (hierarchical docs structure, symbol boundaries, semantic splitting) so the agent can jump into the right part without guessing chunk edges.

Context is selective. The agent retrieves minimal snippets and can fetch more iteratively as it reasons, rather than preloading large chunks. We benchmark this using exact match evaluations on real agent tasks: correctness, reduced hallucination, and fewer round trips.

hey, what error does it throw?

Nia is focused on external context rather than learning the patterns inside your own codebase. Cursor and IDE-native tools are better for local project structure today. Where Nia helps is when the agent needs ground truth from outside your repo. For example, you can index React Native docs, libraries you depend on, API references or Stack for your backend and let the agent search and validate against those sources directly instead of losing context between prompts.

going to open source it soon :)

Serena is great for semantic code editing and symbol-level retrieval on your own codebase. It gives the agent IDE-like capabilities inside the repo. Nia focuses on a different layer. We target external context: remote code, docs, packages, APIs, research, enterprise knowledge, etc

w nia the agent can dynamically search, traverse, and validate information outside the local project so it never hallucinates against out-of-date or incomplete sources.

augment is a coding agent. nia is an external context engine for coding agents that improves their code output quality

Sure, but Augment’s main value add is their context engine, and imo they do it really well. If all they had to do was launch an MCP for their context engine product to compete, I think the comparison is still worth exploring.

yeah, their mcp is to provide better context of your own codebase. not external information.

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

I started out with coding agents specifically because it came from personal pain of how horrible they are with providing up to date context.

hey! i use multiple DBs but the primary ones are turbopuffer and chroma for package search. they are really great

re local, I do local for certain companies!

Nice. Huge congrats on the launch!!!

glad to hear that!