Ag

AgentOS-An Early OS for Coding Agents Memory and Execution and Learning

Hacker News

AgentOS-An Early OS for Coding Agents Memory and Execution and Learning

Hi HN! I'm Jeremy, a solo developer exploring what development tools would look like if designed Specifically FOR AI agents rather than adapted from human tools. This started with AntiGoldfishMode, a CLI to solve the "goldfish memory" problem in AI coding assistants. That success led me to a bigger question: what if we built a complete operating system designed for AI agents? AgentOS is my early exploration of this concept. It's currently a TypeScript-based system with three core engines: Memory Engine: Persistent SQLite-based storage that lets AI agents remember context across sessions. No more losing project knowledge between conversations. Execution Engine: Sandboxed code execution environment where agents can safely test their suggestions before recommending them. Currently supports multiple languages with Docker integration planned. Learning Engine: Basic pattern recognition that tracks what works and what doesn't, building simple predictive models from execution outcomes. Current State: This is very much an early-stage exploration. The core functionality works - agents can store memories, execute code safely, and learn from results. The CLI is functional with commands like agentos memory store, agentos execute, and basic learning capabilities. The Philosophy: Instead of throwing larger context windows at AI agents (the "context window arms race"), I'm exploring purpose-built tools that give them the specific capabilities they need: memory, execution verification, and learning from experience. What's Next: I'm working toward an alpha release and would love feedback from the HN community. Are there specific capabilities you think AI agents need most? What would make them better coding partners? The code is available on GitHub: [ https://github.com/jahboukie/agentos.git ] I'm here to discuss the approach, technical implementation, or what you think the biggest needs are for truly AI-native development tools.

Share card

Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
94%94% 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, started · Missing: reddit linkedin, podcasting, created
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, 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
36%36% 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
27%27% 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
17%17% 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

Similar products

Fa
Fantail SLMs for Coding Agents61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fantail SLMs for Coding Agents

Hacker News1
Sv
Sverklo – repo memory for coding agents33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sverklo – repo memory for coding agents

Hacker News3
Rt
Rta-Smriti – local-first project memory for coding agents33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Rta-Smriti – local-first project memory for coding agents

Hacker News2
Da
Darc – grep-like memory search tool for coding agents61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Darc – grep-like memory search tool for coding agents

Hacker News2
La
LazyAgent – All in one observerbility TUI app for coding agents39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LazyAgent – All in one observerbility TUI app for coding agents

Hacker News6
Sk
Skillmem – local memory for coding agents that stores how, not what34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Skillmem – local memory for coding agents that stores how, not what

Hacker News1
MemoryCustodian
MemoryCustodian93%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Repo-native memory for coding agents

Product Hunt+145Developer Tools
Sl
Slowave – local adaptive memory for coding agents46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Slowave – local adaptive memory for coding agents

Hacker News1
Qarinah
Qarinah19%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Evidence-linked project memory for coding agents

Indie Hackers
VEGA AI
VEGA AI68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI-native OS for learning, training, and execution

Indie Hackerscommitment-full-time