Agent Actors – Plan-Do-Check-Adjust with Parallelized LLM Agent Trees

Hacker News

Hey HN! Been working on this library for architecting stateful LLM agent trees that execute in parallel. Think of it like a AI scheduler for BabyAGIs or AutoGPTs, like: ----- Parent (Plan and Adjust): Chief Revenue Officer / VP Sales Children (Do and Check): 3 Sales Development Representatives; 2 Account Managers; 1 Market Researcher. You can give the CRO a task and it will break it down, distribute it appropriately to its children, and the children will work in parallel on the task. ----- Curious to hear your feedback first HN, we're launching on Twitter tomorrow!

Visit product View on Hacker NewsApr 26, 2023

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

82%82% 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: para · Missing: supports, reddit linkedin, podcasting

60%60% 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

50%50% 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: para · Missing: mobile apps, ios, personal

43%43% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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