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Polos: Open-source runtime for AI agents with sandbox and durable exec

Hacker News

Polos: Open-source runtime for AI agents with sandbox and durable exec

Hi HN, I'm Neha. I spent years at Google building infrastructure that handled billions of events at 99.999% reliability. When I started building AI agents, I was surprised at how much production plumbing you're expected to own yourself. The agent itself is the easy part. The hard part is everything around it: where does it execute safely? What happens when it fails midway through a workflow? How do you trigger it from your existing tools? How do you even know what it did? I kept stitching together Docker, a workflow engine, a notification layer, and custom retry logic. Every team I talked to was doing the same thing. So I built Polos - an open-source runtime that handles the production layer so you just write the agent. What it does: - Sandboxed execution: agents run sensitive operations inside managed Docker containers with built-in tools for file I/O, bash, and web search. You don't manage the sandbox or its lifecycle, Polos does. Will support more sandboxes like E2B in the future. - Slack integration: @mention an agent in Slack, get responses in thread. Trigger workflows from Slack, receive notifications, collect input. Agents become part of your team's existing workflow. - Durable workflows: if an agent fails mid-run, it resumes from the exact step that failed. Built-in prompt caching with 60-80% cost savings on retries. - Observability: OpenTelemetry tracing for every step, tool call, and decision. - LLM agnostic: works with OpenAI, Anthropic, Google, or any provider via Vercel AI SDK and LiteLLM. The stack is Rust orchestrator (Axum + Tokio + PostgreSQL), Python and TypeScript SDKs, and Vite UI. You can install and run a durable, sandboxed agent in under 5 minutes: ``` curl -fsSL https://install.polos.dev/install.sh | bash npx create-polos cd my-project && polos dev ``` Here's a 3-min demo of a coding agent that picks up a GitHub issue, fixes the code in a sandbox, and submits a PR: https://www.youtube.com/watch?v=KYVBpdZ_5eM Happy to discuss technical decisions and more: why Rust for the orchestrator, how durable execution works without a DAG, and the sandbox lifecycle model. GitHub: https://github.com/polos-dev/polos

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
97%97% 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: started · 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: exist, existing, ide · Missing: https docs, excited, just released
58%58% 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: google, way · Missing: mobile apps, ios, personal
41%41% 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
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
19%19% predicted probability of success on AppSumo, 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.

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