Octomind – cloud AI agents
AI agents that outlive your laptop — no server, no setup
43 followers
AI agents that outlive your laptop — no server, no setup
43 followers
Run AI agents in the cloud — pick a machine, tell it what to do, close your laptop. Sessions resume from any device. Per-second billing, 21 models built in, no API keys. Solved 24/25 benchmark tasks — ahead of Claude Code and Codex.
Products used by Octomind – cloud AI agents
Explore the tech stack and tools that power Octomind – cloud AI agents. See what products Octomind – cloud AI agents uses for development, design, marketing, analytics, and more.
Engineering & Development 2
Engineering & Development 2
OpenRouter Model FusionRun many models side by side and fuse the best answer
5.0 (41 reviews)
In the early days of Octomind, before we had our own model gateway, OpenRouter was the shortcut that let me build the agent instead of wrangling API keys from a dozen providers. One key, every model, switch mid-session – that was the thesis I was trying to prove, and OpenRouter made it trivial to test. That experience is what became the Octomind Hub: same idea, taken further – curated roster, published per-token prices, visible caps, an OpenAI-compatible endpoint, and the whole thing is open source so you can self-host it. Our provider library (octolib) still supports OpenRouter alongside 20+ others; the runtime is provider-agnostic, zero lock-in. OpenRouter was the first step in that journey, and it's still in the stack.
GitHubHow people build software
5.0 (645 reviews)
Our entire stack – 113 repos, Apache 2.0, the runtime, the code search, the memory layer, the filesystem MCP, all of it – lives on GitHub. The tap registry is a GitHub repo too: community-built specialist agents submitted as TOML manifests, 108 of them across 27 domains, all flowing in through Issues and Discussions. We ship a GitHub Action that runs Octomind agents in CI for code review on every PR, and the benchmark we cite in the launch is fully reproducible and hosted there – a benchmark you can't re-run is marketing. Even our AI team member Ava has her own account and commits alongside the humans. GitHub isn't infrastructure we use, it's where the project actually happens.
LLMs 1
LLMs 1
Hugging FaceThe AI community building the future.
5.0 (90 reviews)
HuggingFace's open embedding models are the reason Octomind is efficient instead of just convenient. Our code search runs their models locally via fastembed – the agent finds the exact symbol by meaning instead of grep-reading its way through a codebase and burning tokens on every re-read. We looked at key-gated providers like OpenAI and Voyage, but Octomind is Apache 2.0 – you can't ship an open source runtime with embedded API keys to a paid service, and that key sprawl is exactly the problem Octomind exists to solve. fastembed handles the CPU inference, HuggingFace provides the model ecosystem, and the whole thing runs on any machine with no keys, no accounts, no billing. That's how we spent 8M input tokens where a competitor spent 30M on the same task with the same model, and solved 5 more. We're now training our own model for better task routing, but the embedding foundation came from here.