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I was thinking about something like this for years, few trys before this. Started this
repo last year and I think I got something that usable now.Async framework, mix sync/async python functions, compose them into DAGs, run them, schedule them, persist data between steps or let it flow just in memory.GitHub: https://github.com/walnutgeek/lythonicDocs: https://walnutgeek.github.io/lythonic/PyPI: pip install lythonicIt is dataflow. So theoretically you can compose it with pure functions only. Lythonic requires annotations for params and returns to wire up outputs with inputs. All data saved in sqlite as json for now, and it would work for some amount of data ok.You may use it as task flow keeping params and returns empty and maintaining all data outside of the flow.But practically you may do well with some middle ground, just flow metadata thru, enough to make your function calls reproducible and keep some system of records that you can query reliably.Anyway I will stop rambling ... soon.Python 3.11+
MIT License.
Minimal dependencies: Pydantic, Pyyaml, CroniterPrepping for v0.1. Looking of feedback. v0.0.14 is out. Claude generated reasonable docs. Sorry, I would not be able to do it better. I am working on Web UI and practical E2E example app as well.Thank you.
-Sergey
Show HN: Lythonic – Compose Python functions into data-flow pipelines
A dataflow framework for Python, enabling composition of functions into reproducible pipelines, with a focus on managing data and metadata flow.
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A dataflow framework for Python, enabling composition of functions into reproducible pipelines, with a focus on managing data and metadata flow.
Data orchestration and pipeline management remain critical challenges in modern software development, particularly within data science and machine learning workflows. Lythonic addresses this by providing a Pythonic framework for building data-flow DAGs, enabling developers to compose functions, manage execution, and handle data persistence. The ability to mix sync and async functions simplifies integration with existing codebases. Its focus on reproducibility and system of records through metadata flow is a key differentiator, addressing a common pain point in debugging and auditing complex pipelines. While current data persistence is SQLite-based, indicating early-stage development, the core concept targets a significant market need for robust, scalable, and maintainable data processing infrastructure. The planned Web UI and E2E examples will be crucial for broader adoption.
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What is Lythonic – Compose Python functions into data-flow pipelines?
Lythonic – Compose Python functions into data-flow pipelines is analyzed by our AI as: A dataflow framework for Python, enabling composition of functions into reproducible pipelines, with a focus on managing data and metadata flow.. It focuses on Data orchestration and pipeline management remain critical challenges in modern software development, particularly within data science and machine ...
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When was Lythonic – Compose Python functions into data-flow pipelines publicly launched?
The initial public indexing or launch date for Lythonic – Compose Python functions into data-flow pipelines within our tracked developer communities was recorded on April 14, 2026.
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Which technical categories define Lythonic – Compose Python functions into data-flow pipelines?
Based on metadata extraction, Lythonic – Compose Python functions into data-flow pipelines is categorized under topics such as: Async framework, sync/async python functions, DAGs, data-flow pipelines.
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The original author or development team describes the product as follows: "I was thinking about something like this for years, few trys before this. Started this
repo last year and I think I got something that usable now.Async framework, mix sync/async python functions, ..."
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