Skip to main content

TextFSM Generator is a low‑code, no‑code Python library that transforms simple, readable snippets into TextFSM templates, reducing manual effort and accelerating automation development.

Project description

TextFSM Generator


📖 Overview

TextFSM Generator is a low‑code, no‑code tool and Python library that creates TextFSM templates from simple, English‑readable snippets. It removes the need for manual rule‑writing, streamlines parsing workflows, and helps developers, testers, and citizen developers build consistent, reusable automation patterns with minimal effort


✨ Features

  • 📝 Automatic Template Generation – Build TextFSM templates directly from user‑provided snippets.
  • 📄 Human‑Readable Input – Use plain English snippets instead of complex regex syntax.
  • 🔄 Reusable Templates – Share and reuse templates across teams and projects.
  • Workflow Efficiency – Reduce manual effort and accelerate text parsing tasks.
  • 🤝 Collaboration Ready – Designed for developers, testers, engineers, and QA professionals.

🚀 Benefits

  • Simplifies template creation and reduces errors.
  • Improves consistency across projects and teams.
  • Accelerates development and testing cycles.
  • Enhances maintainability of parsing logic.
  • Makes text parsing scalable and reliable for modern environments.

⚙️ Installation

You can install the textfsmgen package directly from PyPI using pip:

pip install textfsmgen

📦 Dependencies

This project depends on the following Python packages to provide core functionality and seamless integration:

  • textfsm – Template‑based state machine for parsing semi‑structured text.
  • PyYAML – YAML parser and emitter, enabling structured configuration management.

🛣️ Roadmap

  • Template Generation Improvements – Ongoing refinements to TextFSM template creation, deeper integration with parsing libraries, and streamlined developer workflows.
  • 🐳 Containerization – Official Docker images and containerized deployment options for portability, scalability, and simplified setup.
  • 🔄 CI/CD Integration – Automated pipelines for testing, building, and releasing to ensure faster and more reliable delivery.
  • 🤖 Robot Framework Support – Native integration with Robot Framework for keyword‑driven acceptance testing and automation.
  • 🧩 Flexibility & Robustness Verification – Tools to validate template adaptability and resilience across diverse input conditions.
  • 🧪 Testing & QA Strategy – Comprehensive testing methodologies, regression suites, and formalized QA practices for higher reliability.
  • 📊 Metrics & Reporting – Built‑in performance metric analysis, automated reporting, and audit‑ready documentation.
  • 📈 Forecasting & Projections – Estimation tools for usage trends, scalability requirements, and performance forecasting.
  • 🛡️ Quality & Maintenance – Continuous monitoring, static analysis, and long‑term maintainability strategies to ensure code health.
  • 🤖 AI Integration – Advanced AI‑powered capabilities, including:
    • Intelligent template suggestions and auto‑completion.
    • Automated error detection and correction.
    • Adaptive optimization based on usage patterns.
    • Natural language → TextFSM template translation for non‑expert users.
    • Predictive analytics for template performance, reliability, and optimization.
  • 🔍 Testing & Feedback - Early adopters are encouraged to experiment, validate new features, and provide feedback to shape the stable release.

📚 References


🐞 Bugs & Feature Requests

If you encounter a bug or have a feature request, please submit it through the official GitHub Issue Tracker. This helps us track, prioritize, and resolve issues efficiently while keeping all feedback in one place.


📜 License

This project is licensed under the BSD 3‑Clause License, permitting broad use, modification, and redistribution with required attribution and no endorsement.


⚠️ Disclaimer

This package is currently in pre‑beta development. Features, APIs, and dependencies may change before the official 1.x release. While it is functional, please use it with caution in production environments and expect ongoing updates as the project matures.


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

textfsmgen-0.6.2.tar.gz (208.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

textfsmgen-0.6.2-py3-none-any.whl (225.3 kB view details)

Uploaded Python 3

File details

Details for the file textfsmgen-0.6.2.tar.gz.

File metadata

  • Download URL: textfsmgen-0.6.2.tar.gz
  • Upload date:
  • Size: 208.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for textfsmgen-0.6.2.tar.gz
Algorithm Hash digest
SHA256 8b0b04fec32c3a86fecf19d51c3c7b80b1f6f399f2fb2ca4f4b9317f30f51740
MD5 1c8aa1c2c1daaf3542f9a951ac22024e
BLAKE2b-256 402cd3a20d511dbe57cf91d7b58877ea04e8393edfec6f7fc5dc9ac9b9919dd3

See more details on using hashes here.

File details

Details for the file textfsmgen-0.6.2-py3-none-any.whl.

File metadata

  • Download URL: textfsmgen-0.6.2-py3-none-any.whl
  • Upload date:
  • Size: 225.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for textfsmgen-0.6.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e53af63ecfb968aad783c9f0335dc3a4e1fc19e21c3969e89232ea629084a629
MD5 4fd1d5b45e55b931ac4c65a2f8134db3
BLAKE2b-256 60e6e17364b14f52f3865d7810ad6360887df2f133a95194eba888652277c854

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page