[buryat]

I just wrote a tool for reducing logs for LLM analysis (https://github.com/ascii766164696D/log-mcp)

Lots of logs contain non-interesting information so it easily pollutes the context. Instead, my approach has a TF-IDF classifier + a BERT model on GPU for classifying log lines further to reduce the number of logs that should be then fed to a LLM model. The total size of the models is 50MB and the classifier is written in Rust so it allows achieve >1M lines/sec for classifying. And it finds interesting cases that can be missed by simple grepping

I trained it on ~90GB of logs and provide scripts to retrain the models (https://github.com/ascii766164696D/log-mcp/tree/main/scripts)

It's meant to be used with Claude Code CLI so it could use these tools instead of trying to read the log files

[aluzzardi]

Mendral co-founder here and author of the post.

This is an interesting approach. I definitely agree with the problem statement: if the LLM has to filter by error/fatal because of context window constraints, it will miss crucial information.

We took a different approach: we have a main agent (opus 4.6) dispatching "log research" jobs to sub agents (haiku 4.5 which is fast/cheap). The sub agent reads a whole bunch of logs and returns only the relevant parts to the parent agent.

This is exactly how coding agents (e.g. Claude Code) do it as well. Except instead of having sub agents use grep/read/tail, they use plain SQL.

[buryat]

yeah, I saw Claude Code doing lots of grepping/find and was curious if that approach might miss something in the log lines or if loading small portion of interesting log lines into the context could help. I find frequently that just looking at ERROR/WARN lines is not enough since some might not actually be errors and some other skipped log lines might have something to look into.

And I just wanted to try MCP tooling tbh hehe Took me 2 days to create this to be honest

[synergy20]

did you create the subagent yourself?claude's agent never called haiku in my case

[jcgrillo]

Do you think it could do anything interesting with a highly compressed representation? CLP can apparently achieve 169x compression ratio:

https://github.com/y-scope/clp

https://www.uber.com/blog/reducing-logging-cost-by-two-order...

[buryat]

interesting approach, thanks for directing me!

Since the classifier would need to have access to the whole log message I was looking into how search is organized for the CLP compression and see that:

> First, recall that CLP-compressed logs are searchable–a user query will first be directed to dictionary searches, and only matching log messages will be decompressed.

so then yeah it can be combined with a classifier as they get decompressed to get a filtered view at only log lines that should be interesting.

The toughest part is still figuring out what does "interesting" actually mean in this context and without domain knowledge of the logs it would be difficult to capture everything. But I think it's still better than going through all the logs post searching.

[ManuelKiessling]

https://github.com/dx-tooling/platform-problem-monitoring-co... could have a useful approach, too: it finds patterns in log lines and gives you a summary in the sense of „these 500 lines are all technically different, but they are all saying the same“.

[buryat]

the patter matcher is interesting to also collapse log lines and compare that between runs, thank you!

In my tool I was going more of a premise that it's frequently difficult to even say what you're looking for so I wanted to have some step after reading logs to say what should be actually analyzed further which naturally requires to have some model

[shad42]

very interesting, curious if there is any downside to running this at scale (compute?)