Lyzr's low-code multi-agent automation framework

Hacker News

Hi everyone! We’ve been experimenting with ways to make it easier to build multi-agent systems, somewhere between LangGraph’s orchestration flexibility and AutoGen’s ease of use. Lyzr Automata is our open-source framework that lets you build, connect, and run AI agents in Python, with just a few lines of code. You can define agents as simple Python classes, chain them using the Automata graph, and run them locally or via a hosted orchestrator. It’s designed to be simple, extensible, and self-contained.

Visit product View on Hacker NewsOct 22, 2025

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

78%78% 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 · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · 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

26%26% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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