Mr. Graph – A graph definition and execution library for Python

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What: Mr. Graph is a python library designed to make composing graphs of sync and async functions easy! Use google style docstrings to automagically create dataclasses and chain together function calls into graphs. Why: I like the design of Dagster, but not the latency. For apps and systems engineering, sometimes I want to compose a graph out of regular python functions. I don;t need all the heavy machinery that comes with a full workflow engine. Current features: - Use with either async or sync functions - Uses google style doc strings to name return values. - Creates dataclasses for each function's output. - Can infer pipelines from input and output signatures - All directed acyclic graph layouts supported. linear, fan-in, fan- out. Future Features: - Better examples for use with async calls (like LLMs) - Splitting dataclasses, better error handling, logging improvements. This is under active development. Any feedback, interest, or contributions are appreciated. Thanks! github link: https://github.com/mcminis1/mr-graph

Visit product View on Hacker NewsMar 15, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, apps · Missing: agents, macos, agent

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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