Keep in mind that it's not fully "fair", since these public dataset are often documented in the internet so already present in pre-training of the models underneath (Claude Sonnet 4.5 in this case)
(Ryadh from ClickHouse here)
Your comment is spot-on.
This the main challenge with Agentic Analytics and there are known limitations. It is also where we are orienting our investments atm.
Our own experience running internal agents taught us that the best remediation comes from providing the LLMs with the maximum and most accurate context possible. Robust evaluations are also critical to measure accuracy, detect regressions, and improve. But there is no silver bullet.
SOTA LLMs are increasingly better at generating SQL and notoriously bad with math and numbers in general. Combining them with powerful querying capabilities bridges that gap and makes the overall experience an useful one.
IMO, we'll always have to deal with the stochastic nature of these models and hallucinations, which calls for caution and requires raising awareness within the user base. What I found watching our users internally is that, while it's not magical, it allows users to request data more often, and compounds in data-driven decision-making, assuming the users are trained to interpret the interactions
Interestingly, LibreChat has a broad range of applications already and we'll continue to support them. The investment area we want to tackle in priority is around the analytics use-case specifically.In that space, I don't see an SSO-tax scheme unfolding tbh, it's really about better visualizations, semantic layers and anything that can improve the quality of the insights produced on top of analytics data
It's a fair concern, and I understand where you are coming from. What I can say is that it's not our first rodeo incorporating another OSS product in our family. I tried to summarize it in the post:
> "This proven playbook is the same one that we applied when joining forces with PeerDB to provide our ClickPipes CDC capabilities, and HyperDX, which became the UX of our observability product, ClickStack."
If you research both instances above, the result is that these projects got more traction and adoption overall.
I hope this helps! and thank you for using LibreChat
It actually comes from our own experience at ClickHouse.
We deployed this stack internally 8 months ago, and since very few people here have touched our legacy BI systems :)
I have never seen an adoption curve like this one tbh. It's obviously not perfect and can hallucinate sometimes, which can be tricky, but with the right approach and awareness in place, the value it delivers is massive. What really happens is that more users get access to data instantly, and as a result, we make better, data-driven, decisions overall.
My favourite use-case: our sales and support folks systematically ask DWAINE (our dwh agent) to produce a report before important meetings with customers, something along the lines of: "I'm meeting with <customer_name> for a QBR, what do I need to know?". This will pull usage data, support interactions, billing, and many other dimensions, and you can guess that the quality of the conversation is greatly improved.
Ryadh from ClickHouse here, happy to answers questions if folks have any.
So, why this move ?
Basically, we noticed that the existing "agentic" open-source ecosystem is primarily focused on developer tools and SDKs, as developers are the early adopters who build the foundation for emerging technologies. Current projects provide frameworks, orchestration, and integrations The idea behind the Agentic Data Stack is a higher-level integration to provide a composable software stack for agentic analytics that users can setup quicky, with room for customization.
Congrats on Tablespace! it's good to see innovation in that space and I always love to see ClickBench mentioned in the wild :) (I work for ClickHouse).
I'm curious to hear your take about the tradeoffs that the HTAP model introduces? any impact on ingest times or query throughput for example?
The readme contains a lot of the implementation details:
"We use three different tables with different levels of detail: planes_mercator contains 100% of the data, planes_mercator_sample10 contains 10% of the data, and planes_mercator_sample100 contains 1% of the data. The loading starts with a 1% sample to provide instant response even while rendering the whole world. After loading the first level of detail, it continues to the next level of 10%, and then it continues with 100% of the data. This gives a nice effect of progressive loading."
Feature differentiation is actually a pretty interesting topic for o11y. You can do many things with an OLAP store but you need to be aware of the differences with of the shelf solutions, I try to summarize it here:
https://clickhouse.com/blog/the-state-of-sql-based-observabi...
I hope this helps! I'd love to hear your opinion about it
Another important dimension I'd consider as well is the broader ecosystem of integrations. In OSS, it's a byproduct of the success of the main project but an often overlooked aspect when choosing a solution.
ChDB is the in-process version for Python. You can try clickhouse-local if you want a CLI experience, or clickhouse-client against a clickhouse-server for the server experience