Octomind – Plug n Play AI Agents
Homebrew for AI agents. Single binary, zero config.
6 followers
Homebrew for AI agents. Single binary, zero config.
6 followers
Open source AI agent runtime built in Rust. Install one binary, set one API key, run specialist agents in 30 seconds. 39 pre-built agents across 10 domains — dev, devops, security, medical, legal, finance. Like Homebrew: `octomind run developer:rust`. 13+ AI providers, swap mid-session. Adaptive compression saves 72.5% tokens for infinite sessions. Agents extend themselves at runtime via dynamic MCP. Ships with semantic code search, persistent memory, smart file ops. Apache 2.0. No lock-in.
Overclout.com
Hey Product Hunt! I'm Don, maker of Octomind.
I've been building with AI coding agents for the past two years, and the same three problems kept killing my workflow:
Setup fatigue. Every new tool meant 45 minutes of configuring MCP servers, writing system prompts, and wiring dependencies before I could ask my first question.
Context rot. An hour into a session, the agent forgets the architecture decisions we made at the start. You end up repeating yourself constantly, or worse, the agent contradicts its own earlier reasoning.
Vendor lock-in. Hit a rate limit at 2am during a production incident? Too bad — your whole tool is welded to one provider.
Octomind is what I wanted to exist. It's a single Rust binary you install in 30 seconds. You pick a specialist agent from the tap registry (think Homebrew for AI) and you're working. No config files, no dependency hell, no MCP setup.
A few things I'm especially proud of:
- Tap system — 39 specialist agents across 10 categories (developer, devops, security, medical, legal, finance...). One command: `octomind run developer:rust`. Community can publish their own taps via Git.
- Adaptive compression — 72.5% token savings with zero quality loss. Your 4-hour session stays sharp because the runtime intelligently compresses history while preserving key decisions.
- 13+ providers, swap mid-session — Type `/model` and switch from Claude to GPT to DeepSeek to a local Ollama model. No restart, no lost context.
- Dynamic MCP — Agents extend themselves at runtime. They can register new tool servers mid-session without restarting.
The whole thing is Apache 2.0 open source. Every line of code is on GitHub. No telemetry, no cloud dependency, runs fully offline with Ollama.
I'd love to hear:
- What domains would you want a specialist agent for?
- If you've hit context rot or setup fatigue with other tools, what was your breaking point?
Happy to answer any questions about the architecture or the tap system. Let's talk!