u/Divyansh3021

Hey everyone,

​I recently finished building a Model Context Protocol (MCP) index containing roughly 3 million arXiv papers. My goal was to make it easier to connect local and cloud LLMs directly to a massive corpus of ML and STEM research to help reduce hallucinated citations and improve research workflows.

​The index is live, but before I open it up broadly, I want to make sure the retrieval quality actually holds up against highly niche, complex queries (especially for obscure math, hyper-specific domains, or newer architectures).

​I’m looking for a small group of folks around 10 to try it out, try to break the retrieval system, and give me brutal feedback on the relevance of the fetched papers.

​If you want to stress-test it with your own LLM setup and see how it performs with your daily research queries, let me know in the comments or shoot me a DM and I’ll send you the connection details!

Thanks!

Hey everyone,

​I recently finished building a Model Context Protocol (MCP) index containing roughly 3 million arXiv papers. My goal was to make it easier to connect local and cloud LLMs directly to a massive corpus of ML and STEM research to help reduce hallucinated citations and improve research workflows.

​The index is live, but before I open it up broadly, I want to make sure the retrieval quality actually holds up against highly niche, complex queries (especially for obscure math, hyper-specific domains, or newer architectures).

​I’m looking for a small group of folks (around 20) to try it out, try to break the retrieval system, and give me brutal feedback on the relevance of the fetched papers.

​If you want to stress-test it with your own LLM setup and see how it performs with your daily research queries, let me know in the comments or shoot me a DM and I’ll send you the connection details!

Thanks!

One thing I’m learning while building Ninelayer:

The hardest part of agent infrastructure is not retrieval.

It is trust.

When a coding agent searches the web, it may find 10 relevant pages. But relevance is not enough.

The agent needs to know:
- Is this source official?
- Is it current?
- Does it match the user’s framework version?
- Is there a GitHub issue or release note that changes the answer?
- Can the final response cite the source?

That is the problem we’re working on.

Not “more search results for agents.”

Better evidence for agents.

Hi there, yesterday I was running a simple experiment. I gave Claude Code a simple task to create a Conversational Agent with 2 tools using Langchain, it created the agent but with using 2 deprecated classes, which in result gave me depreciation warnings. Then I ran the test again but gave Claude access to my MCP server called NineLayer. Claude didn't hallucinated this time and gave me an updated and clean code.

Here are some screenshots of it and github repo for it: https://github.com/Divyansh3021/ninelayer-experiments/tree/master/experiments/01-langchain-conversational-agent

I am running some more experiments, if you guys have any particular test in mind where you are getting annoyed by Stale Context, send me the issues and I'll run them against NineLayer to see if it is really solving the issue or not.

Thanks!

Without NineLayer

With NineLayer

Hi there, recently I was experimenting with my product NineLayer, it is a search engine that provides fresh context to coding agents.

In a recent test I gave Claude Code a task to build a simple task to create a simple Conversational Agent having 2 simple tools.

In the run where Claude didn't use Ninelayer, it used AgentExecutor and create_openai_tools_agent—which immediately threw a wall of LangChainDeprecationWarnings in my terminal. But with Ninelayer providing real-time context, it actually caught the LangGraph v1 update and skipped straight to the new create_agent API, completely avoiding the deprecation trap.

I am attaching supporting screenshots.

I am actively running more experiments, if you guys have more such experiments which you want me to test, let me know!

Thanks!

without using NineLayer

with using NineLayer

So I have been building my product for past couple of months now, the idea is to build a "Search Engine for AI Agents", currently most of the AI Agents still use the search layer that was built for them which is wrong in many ways, the results are SEO corrupted, they are given whole pages instead of targeted section of those, stale responses are some issues to start with. To solve this problem I am building NineLayer, now there are some existing products in the market also like Tavily and Exa but they are also expensive and their responses have hallucinations too.

I have been trying to get my early users for NineLayer and I have been failing to do so, I have tried posting on X, in different communities, I have started posting on Reddit also, some traction here but still not enough. Tried LinkedIn but the people there are not that much into trying new products or be seen as an early user.

It'll help me a lot if you guys can share some tips and tricks for getting users.

I'll be attaching platform link so that you guys can have better understanding.

Thanks!

It's a very happening journey to create your own product. While working on NineLayer with the goal to create a search engine for AI Agents.

Recently we ran a Freshstack benchmark are compared NineLayer woth Exa and Tavily, here are the results:

Answer quality came in at 4.30/5, competitive, not perfect, but look at the cost: $0.0017 per query.

That’s literally 5× cheaper than Tavily ($0.0082) and Exa ($0.0076).

We are daily shipping features, rolling out bug fixes as we move along. And part of the journey is to get feedback from early users.

So here I am, asking to the devs out there for their honest feedback about NineLayer.

I'll be attaching the links in comment.

Thanks again!

Hey guys, So I have been working on an idea, the idea is to build a search engine for AI Agents. Currently agents use the internet that was originally created for humans to consume not by Language Models, so it has lots of content repeatability, it provides whole pages of content instead of specific targeted sections, hammering the model's context length and in turn our token cost goes up. The current solutions like Exa and Tavily are good but they are super expensive, like for a person having a $20/month subscription, taking a $30/month agent search subscription doesn't make any sense. So that's where my product comes into picture, it's called NineLayer. Currently the product is in its early stages, I need the community help here to improve this. Any feedback on the product will be a huge help.

I'll be attaching the link in comments.

Thanks!