Moadim.io

Moadim is an open-source loop engine for AI agents that runs scheduled agent routines against repositories. It is most useful when teams need a practical AI workflow layer rather than another dashboard to babysit. The product page and repository describe a system built for people who already work in code, terminals, and docs. It keeps the core loop close to the repo, makes setup visible, and gives builders a way to test the idea without a sales call. The way it works is simple: you define a loop with a prompt, a schedule, and an agent, then Moadim runs Claude, Codex, Hermes, Pi, or a NanoClaw task in an isolated workbench on each tick. That matters for builders because the handoff between an AI assistant and the actual project is where many experiments break. Moadim keeps that handoff explicit. You can see what is installed, what command runs, where the output lands, and which parts need review before they affect production work. The best users are developers who want recurring agent jobs, small teams managing repo maintenance, and AI builders experimenting with loop engineering. They get the most value when they already have repeatable jobs, model-serving needs, metadata tasks, or agent development steps that happen often enough to deserve a repeatable workflow. A solo developer can use it for a local project, while a small team can standardize the same flow across shared repos or operating runbooks. Key features include Git-compatible routines, agent-agnostic execution, multi-runner support, local daemon operation, REST endpoints, MCP access, Swagger UI, tmux-backed workbenches, and unlimited routines or crons. These are not vague AI promises; they are concrete workflow pieces that can be checked against the source material. The public docs show the install path and examples, while the product pages describe the intended use cases and limits. That makes the listing safer to evaluate than a tool that only offers a landing-page claim. Pricing is currently best treated as free open-source software via cargo, cargo-binstall, or npm; any paid model or cloud runner used by an agent remains separate. If you use paid infrastructure, hosted APIs, cloud projects, or commercial models around it, those separate services can still create cost. The tool itself should be evaluated on whether it reduces repeated setup time, manual tagging work, local inference friction, or agent-operation overhead in your own workflow. What stands out is the agent loop model. Instead of asking a model to remember recurring work, Moadim turns the routine into a versioned, reviewable loop that can survive reboots and be changed through normal repo workflows. OpenTools lists Moadim for builders who want to compare real AI infrastructure and workflow tools, not just chat interfaces. If your team wants a tool that can be inspected, installed, and tested against a real repository or media workflow, this is a practical candidate to put in a short evaluation batch. Start with the official docs, run the smallest safe example, and then decide whether it belongs in your daily development or operations loop.