Aden A self-healing agent framework that refactors its own logic

Hacker News

I’ve spent the last year watching agentic workflows break the moment a schema changes or an LLM returns a slightly unexpected format. Traditional "chains" are too brittle for production, so I built Aden to move away from static, hardcoded logic. Aden is an infrastructure layer that uses a "Queen Bee" coding agent to transform your natural language goals into a recursive graph of worker agents. When an agent fails or hits an edge case, the framework doesn't just throw an error; it captures the failure trace, uses the coding agent to refactor the underlying node logic or connection graph, and redeploys the fix in real-time. It’s built to be MCP-native with 100+ connectors and includes "Agentic P&L" guardrails to prevent token-burn from runaway loops. You can try it out by cloning the repo and running the quickstart script to build your first autonomous swarm. contact@adenhq.com

Visit product View on Hacker NewsJan 27, 2026

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

90%90% 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

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

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% 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 · Missing: plus, platform, intuitive

35%35% 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

24%24% 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.

Correct prediction on native model

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