# logpai/logparser

A machine learning toolkit for log parsing [ICSE'19, DSN'16]

Repository: https://github.com/logpai/logparser
Canonical: https://ross.abutalabs.com/products/logparser
Language: Python
License: NOASSERTION
License Family: other
Topics: log, log-mining, log-analysis, log-parser, log-parsing, anomaly-detection, benchmark
Last push: 2025-06-10T23:50:49+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 26, release rhythm 8, longevity 100
- inputs: {"age_days": 4046, "days_push": 449, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1987, forks 578 (observed 2026-08-28T04:06:02.862378+00:00)

## What it is
Logparser is a Python machine learning toolkit and benchmark suite for automated log parsing. It extracts event templates from unstructured log messages and converts raw logs into structured event sequences.

## Use cases
- parse raw log messages into structured event templates
- extract message templates from unstructured system logs
- cluster log messages into event types
- benchmark log parsing algorithms on standard datasets
- preprocess logs for anomaly detection pipelines
- convert semi-structured logs into structured events for analytics

## When to choose
- you need to structure raw log data for downstream log analytics or anomaly detection
- you want to compare or evaluate multiple log parsing algorithms
- you are doing research on log mining or template extraction
- you work with common log formats like HDFS, BGL, or HPC logs

## When to avoid
- you need real-time log ingestion and storage rather than offline parsing
- your logs already have well-defined structured formats like JSON
- you need a full log management platform with dashboards and alerting
- you require a permissively licensed dependency, as the license is non-standard

## Facets
- artifact type: library
- maturity: active
- function: parser, machine-learning, logging, benchmarking, data-science
- domain: developer-tools, data-science, analytics, monitoring
- platform: python, cross-platform
- tags: log-parsing, log-mining, log-analysis, template-extraction, anomaly-detection, benchmark

## Member repositories
- logpai/logparser (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:02.862378+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:03:00.646325+00:00, confidence not recorded.
  - readme: https://github.com/logpai/logparser (fetched 2026-08-28T04:06:02.862378+00:00, sha 912b11f80b86)
- Data as of 2026-08-30T08:39:29.467469+00:00.