# erikrose/parsimonious

The fastest pure-Python PEG parser I can muster

Repository: https://github.com/erikrose/parsimonious
Canonical: https://ross.abutalabs.com/products/parsimonious
Language: Python
License: MIT
License Family: permissive
Last push: 2025-11-12T01:18:06+00:00

## Health v2 (maintenance only)
Score: 55/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 51, release rhythm 35, longevity 100
- inputs: {"age_days": 5325, "days_push": 295, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1916, forks 138 (observed 2026-08-28T04:05:53.778512+00:00)

## What it is
Parsimonious is a pure-Python parsing library based on parsing expression grammars (PEGs), letting you define grammars in a simplified EBNF notation and produce parse trees. It emphasizes speed, low memory use, readable grammars, and good error reporting, with a NodeVisitor API for walking resulting trees.

## Use cases
- parse a custom DSL in python
- build a parser for a config file format like ini
- write a wiki markup to HTML converter
- define a grammar in EBNF and get an AST
- implement a packrat parser without C extensions
- parse structured text with arbitrary lookahead

## When to choose
- you need a pure-Python parser with no compiled dependencies
- you want readable, extensible grammars in EBNF-like notation
- you need arbitrary-lookahead PEG parsing with good error messages
- you want to separate grammar recognition from tree transformation

## When to avoid
- you need maximum parsing speed and can accept C-based tools like Lark or ANTLR
- you need left-recursive grammar support
- you are parsing very large inputs where memory is critical

## Facets
- artifact type: library
- maturity: active
- function: parser
- domain: parsers, programming-languages, developer-tools
- platform: python, cross-platform
- tags: peg, packrat, grammar, ebnf, syntax-tree, pure-python

## Member repositories
- erikrose/parsimonious (main) score 55

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.778512+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 2026-08-30T03:10:10.579858+00:00, confidence not recorded.
  - readme: https://github.com/erikrose/parsimonious (fetched 2026-08-28T04:05:53.778512+00:00, sha 086ee6d09bb0)
  - registry_pypi: https://pypi.org/pypi/parsimonious/json (fetched 2026-08-29T10:49:31.987776+00:00, sha ce249e3b8ea4)
- Data as of 2026-08-30T08:39:29.467469+00:00.