Octomind

Octomind is an open-source runtime for installing and orchestrating specialized AI agents, positioning itself as 'Homebrew for AI agents.' Instead of assembling an agent from raw prompts, tools, and model calls, users run a single command like `octomind run [specialist:domain]` to pull down a pre-configured specialist that already ships with the right model, system prompt, tool permissions, and evaluation harness for its domain. The catalog covers 116 specialists across 28 domains, including lawyers, doctors, engineers, security researchers, and other knowledge-worker archetypes. The runtime is distributed as a single binary via Homebrew, Cargo, or direct download, and speaks to 20+ model providers (OpenAI, Anthropic, DeepSeek, Ollama, and others) so teams can mix hosted and local inference. Two features stand out for long-running work: adaptive context compression, which the project claims reduces token spend by roughly 72.5 percent while keeping quality stable across 4+ hour sessions, and hard spending caps enforced per-request and per-session so runaway loops cannot burn a budget. Workflows chain specialists into automated pipelines, which lets teams compose narrow experts (e.g. threat-modeler then code-fixer then PR-writer) instead of asking one generalist to do it all. Octomind Cloud is the hosted companion for teams that would rather not run the binary themselves. It suits engineering-oriented teams who want reproducible agent installs, provider portability, and cost control without gluing together LangChain, an eval harness, and a budget guard by hand.