An Open-Source, Local-First Agent Framework in Rust

Hacker News

Hey HN, About five months ago, I started thinking seriously about how we run agents — and how little control end users actually have over privacy, data, and compute. Running an LLM locally is great for rewriting emails or answering basic questions, but it doesn’t give you real autonomy. What does give you power is the ability to run full-fledged local agents capable of deep research, coding, reasoning, and tool use — all without sending data to external services. With that vision in mind, I started an open-source project (MIT + Apache-2 licensed) to make building and running agents modular, customizable, and hardware-friendly. Every component of the agent stack can be swapped out: Memory: plug in your own memory backend LLM layer: use local models or cloud APIs interchangeably Execution style: choose between ReAct, CoT, or your own custom executor The goal is to enable real research workflows while maintaining strong performance and developer-friendly usability. The project doesn’t stop at building agents, either — we want to ensure you can run them efficiently on any accelerated hardware, from GPUs to edge devices. Would love feedback from the HN community!

Visit product View on Hacker NewsNov 26, 2025

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

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, efficiently · Missing: supports, reddit linkedin, podcasting

79%79% 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

72%72% 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: month, users · 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 · Strong signals: friendly, efficient, users · Missing: plus, platform, intuitive

42%42% 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

19%19% 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.

Incorrect prediction on native model

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