Bluemarz (Open Source AI Agent Orchestration)

Hacker News

Today I'm happy to launch Bluemarz. Bluemarz is an open-source, stateless framework for deploying and managing multi-agent AI systems. It supports multiple LLMs (OpenAI, Claude, Gemini) and enables dynamic multi-agent collaboration, reusable tools, and Retrieval-Augmented Generation (RAG). We’re excited to share Bluemarz with the community—feedback and contributions welcome! https://github.com/StartADAM/bluemarz/

Visit product View on Hacker NewsNov 24, 2024

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6points

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

80%80% 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: supports, gemini · Missing: reddit linkedin, podcasting, created

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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 · Missing: mobile apps, ios, personal

45%45% 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

29%29% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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