I Built KIRA: A Local-First AI Agent That Has to Prove Its Work

Disclosure: I am the maker of KIRA Superapp and Orchestrator V1. This is a technical build story, not an independent review. Most AI assistants are good at producing an answer. The harder question is whether they actually did the work they claim to have done. I built KIRA Superapp around that gap. KIRA is an open-source, local-first agent runtime for Apple-silicon Macs. Its orchestration model can run on the machine, working memory stays local, voice can stay local, and actions pass through permission gates. When KIRA uses a tool, the tool result - not the model's confidence - determines whether the task is complete. Its decision layer is Orchestrator V1 , a model built to plan tool use instead of merely producing conversational answers. KIRA should not say a task is complete unless the runtime has evidence from the tool that performed it. TL;DR Local model: Orchestrator V1 runs through MLX on Apple silicon. Local working state: chats, memory, logs, artifacts, and downloaded models stay on the Mac by default. Local voice: Whisper handles speech-to-text and Kokoro ONNX supports text-to-speech. Permission gates: mutating tools ask before changing files, running commands, or calling external services. Result verification: KIRA continues from real tool output instead of declaring success from generated text. Honest networking: web research and configured connectors go online only when those features are used. The code and installation instructions are on GitHub . I want technically specific feedback, including failed installs and broken agent loops - not manufactured praise. More than a chatbot A chatbot receives a prompt and returns text. An agent has a harder job: decide whether it has enough information, choose the right tool, execute it, interpret the result, and decide whether another step is required. KIRA's loop is: User goal -> Orchestrator V1 chooses the next step -> KIRA executes the selected tool -> the real result returns to the runtime -> the model evaluate