{"repo":"mihirrd/logslim","free":true,"listed":false,"github":"https://github.com/mihirrd/logslim","clone":"git clone https://github.com/mihirrd/logslim.git","description":"A smart Log ingestion tool that compresses log files losslessly using template extraction and Parquet storage, then lets you replay or query the original logs by time window or pattern.","language":"Java","stars":14,"topics":["compression","java","log","next","observability","cli","duckdb","parquet"],"license":"MIT","category":"analytics","readme_excerpt":"LogSlim Structured, queryable logs your AI agent can actually read — losslessly, at up to 80% less storage. LogSlim turns unstructured log streams into structured templates + parameters automatically — no regex, no grok, no SDK. That structure is the point: it lets an AI agent investigate a service by reasoning over a few dozen templates and their distributions instead of drowning in (and paying for) millions of raw lines. LogSlim exposes this to agents through a built-in MCP server . As a side effect of separating templates from parameters, the same data stores in compressed Parquet at up to 80% less space — and every original line is still exactly reconstructable. Lossless. Sits in front of your existing storage. No agent, no SDK changes, no vendor lock-in. --- --- Why structure, not raw lines A busy service emits millions of log lines an hour. You cannot hand those to an LLM agent during an incident: - it doesn't fit a context window, - it costs a fortune per glance, and - the one line that matters is buried among thousands of near-duplicates (the \"lost in the middle\" problem). LogSlim collapses that firehose into templates + counts + per-slot distributions , losslessly . An agent then investigates the way a human SRE does — overview → anomaly → drill → raw — and pulls exact log lines only for the narrow window it actually needs. On the included incident.log (13,728 lines of a real DB-pool-exhaustion cascade), the parser extracts 91 templates, the top 15 of which cover 90%","default_branch":null,"files":null,"tree":[],"storefront":"/r/mihirrd","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mihirrd/logslim/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}