textX
Meta-language for DSL implementation inspired by Xtext
Decision gist · record as of 2026-08-14
Yes. textX is actively maintained, has no known vulnerabilities, low install friction, and is well-suited for anyone building a textual DSL in Python. The framework is production-stable (Development Status 5), has been in development since 2014, and is appropriate for research, commercial, and open-source use under the MIT license.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; Arpeggio must be installed as a runtime dependency.
- Low install friction with only two runtime dependencies (Arpeggio and importlib-metadata).
- The package is actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary projects.
last release 2026-07-08 (37 days) · last repo commit 2026-08-02 · 852 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 299,747 downloads/mo, #7,853 on PyPI
Alternatives
Verify before relying
from textx import metamodel_from_str
grammar = """Model: items*=Item; Item: 'item' name=ID;"""
mm = metamodel_from_str(grammar)
model = mm.model_from_str("item foo item bar")- Whether textX's PEG parser approach handles all grammar patterns needed for your specific DSL use case
- Performance characteristics when parsing large or deeply nested models
What it is and what it does
textX is a Python framework for implementing Domain-Specific Languages by writing a grammar definition. From that grammar, it automatically generates a parser and meta-model (abstract syntax tree) that can parse and instantiate models conforming to your language. It uses a PEG parser (via Arpeggio) with unlimited lookahead and no grammar ambiguities, following the design principles of Xtext but implemented entirely in Python.
You define your DSL syntax as a textX grammar, optionally provide custom Python classes for specific rules, and then use the generated meta-model to parse text into Python object graphs. This is useful when you need to build support for a new textual language, file format, or configuration syntax without writing a parser from scratch.
Use it for
- Build a custom configuration file format with validation and programmatic access to parsed settings
- Create a domain-specific language for modeling (e.g., state machines, workflows, or diagrams) and interpret or transform the models
- Parse and process existing textual file formats or languages where you want programmatic control over the abstract syntax
- Implement a code generation tool that reads a textual specification and produces output in another language
- Define a query or expression language for a specialized application domain
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
textX is actively maintained, has no known vulnerabilities, low install friction, and is well-suited for anyone building a textual DSL in Python. The framework is production-stable (Development Status 5), has been in development since 2014, and is appropriate for research, commercial, and open-source use under the MIT license.
Install
textx on PyPI
Before you install
Low install friction with only two runtime dependencies (Arpeggio and importlib-metadata). The package is actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.8 or later; Arpeggio must be installed as a runtime dependency.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary projects.
Quickstart
from textx import metamodel_from_str
grammar = """Model: items*=Item; Item: 'item' name=ID;"""
mm = metamodel_from_str(grammar)
model = mm.model_from_str("item foo item bar")Verify before relying
- Whether textX's PEG parser approach handles all grammar patterns needed for your specific DSL use case
- Performance characteristics when parsing large or deeply nested models
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesArpeggioimportlib-metadata |
| Maintenance | Actively maintained 37 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 299,747 / month, #7,853 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: CompilersTopic :: Software Development :: InterpretersTopic :: Software Development :: Libraries :: Python Modules |
Evidence: textx-4.4.0-py3-none-any.whl
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