Giselle – open-source visual editor for building AI workflows

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GitHub: https://github.com/giselles-ai/giselle Giselle is an open source tool for designing and running AI workflows using a visual node-based editor. Apache 2.0 licensed. Key features: - Drag-and-drop workflow builder for chaining AI apps - Multi-model support (OpenAI, Anthropic, Google Gemini) in one canvas - GitHub integration for automating issues, PRs, and code reviews - Built-in knowledge store with vector search - Self-hostable with Docker or run locally We built this because we needed a way to compose multiple AI models for complex tasks without writing glue code. The visual editor makes it easy to see the flow and debug when things go wrong. Tech stack: - Frontend: Next.js, TypeScript, Tailwind CSS - Visual Node Editor: React Flow, Zustand, Tiptap - AI: Vercel AI SDK, AI Gateway - Database: PostgreSQL with Drizzle ORM - Auth & Storage: Supabase - Background Jobs: Trigger.dev (for long-running agent tasks) - Analytics: PostHog Happy to answer questions about the architecture or implementation.

Visit product View on Hacker NewsDec 30, 2025

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

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% 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: apps, google, 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 · Strong signals: reviews, host, builder · Missing: plus, platform, intuitive

37%37% 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

22%22% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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