# itamarst/eliot

Eliot: the logging system that tells you *why* it happened

Repository: https://github.com/itamarst/eliot
Canonical: https://ross.abutalabs.com/products/eliot
Homepage: https://eliot.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, logging, logging-library, tracing, causality, causality-analysis, causation, twisted, journald, elasticsearch, asyncio, scientific-computing, dask, numpy
Last push: 2026-05-07T23:47:45+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 81, release rhythm 83, longevity 100
- inputs: {"age_days": 4529, "days_push": 118, "days_rel": 118, "gap_med": 0, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1187, forks 74 (observed 2026-08-28T04:03:55.307734+00:00)

## What it is
Eliot is a Python structured logging library that records causal chains of actions, showing what happened and why. It supports distributed tracing, asyncio/Trio/Twisted, and scientific computing with NumPy and Dask.

## Use cases
- debug why an error happened in my python app
- trace causality across distributed services
- structured logging for asyncio applications
- log actions in dask and numpy scientific workflows
- find performance bottlenecks with action-based logs
- correlate sentry errors with operation traces

## When to choose
- you need causal, action-based logs rather than flat log lines
- you use asyncio, Trio, Twisted, or Dask and want built-in support
- you want structured JSON logs suitable for Elasticsearch/Logstash aggregation

## When to avoid
- you only need simple human-readable log messages
- you want an all-in-one log aggregation and storage solution
- you're outside the Python ecosystem

## Facets
- artifact type: library
- maturity: active
- function: logging, tracing, monitoring
- domain: developer-tools, backend, data-science, microservices
- platform: python, cross-platform
- tags: structured-logging, causal-tracing, asyncio, twisted, dask, scientific-computing, json-logging

## Member repositories
- itamarst/eliot (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.307734+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 2026-08-30T06:23:42.423272+00:00, confidence not recorded.
  - readme: https://github.com/itamarst/eliot (fetched 2026-08-28T04:03:55.307734+00:00, sha 589eaffd9c59)
  - registry_pypi: https://pypi.org/pypi/eliot/json (fetched 2026-08-29T12:31:25.451953+00:00, sha 845cc4dde985)
- Data as of 2026-08-30T08:39:29.467469+00:00.