Comfy Nodekit – build/serialize ComfyUI workflows in Python

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Hello HN! I made Comfy Nodekit, a small library to make it easier to build and serialize ComfyUI workflows directly in Python. If you’ve ever tried to hand-craft large ComfyUI graphs or maintain JSON workflows by hand, you probably know how messy it gets once you reach a few dozen nodes. We needed a better way to generate and modify graphs programmatically, so we built this. Comfy Nodekit lets you create graphs with typed node factories, then export them back to the exact JSON format ComfyUI expects. It also introspects your running ComfyUI server to generate Python bindings for your custom nodes automatically, so you don’t lose compatibility when your setup changes. We’re using this internally at Katalist to generate complex workflows with hundreds of nodes on the fly, things that would be impossible to manage and version. Highlights * Works with custom nodes (via /object_info) * Type-safe graph composition + JSON export * MIT license * No runtime overhead - just Python + JSON Why not just use ComfyScript or raw JSON? Because we wanted a Python-first, typed model that stays in sync with our ComfyUI server and is focused purely on composition, not on adding another runtime layer. Happy to answer questions what kinds of workflows we're building for our image generation task.

Visit product View on Hacker NewsOct 23, 2025

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

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