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ADK-Studio – a visual builder for creating AI agent workflows with Rust

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ADK-Studio – a visual builder for creating AI agent workflows with Rust

Hi HN, I’ve been working on ADK-Rust, an open-source framework for building and deploying AI agents in Rust. The motivation came from building agent systems where performance, safety, and predictable behavior mattered more than rapid prototyping. Most agent frameworks and workflow tools today are Python- or JS-first and tend to be runtime-heavy when taken to production. Recently, I added ADK-Studio — a visual, low-code environment for building AI agent workflows on top of ADK-Rust. You can think of ADK-Studio as a Rust-native alternative to tools like n8n, but focused specifically on AI agents: - Visual, drag-and-drop workflow design (sequential, parallel, loop, router agents) - Tool integration (functions, MCP servers, browser automation, search) - Real-time execution with SSE streaming and event traces - Code generation: visual workflows compile down to production Rust code - Build and run agents as native executables directly from the studio The goal is to let people prototype agent workflows visually, then ship them as fast, memory-safe Rust binaries instead of long-running JS/Python services. Making AI Agents with ADK Studio is super simple: 1. ADK-Studio install: `cargo install adk-studio` 2. Start ADK Studio server: `adk-studio --port 6000` 3. Open in browser: open http://localhost:6000 I would really appreciate feedback from folks building agent systems, workflow engines, or AI inference infrastructure — especially around design tradeoffs vs existing tools like n8n. Project site: https://adk-rust.com GitHub: https://github.com/zavora-ai/adk-rust Best James

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, builder · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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