# xiaomi-miloco **Repository Path**: mirrors_trending/xiaomi-miloco ## Basic Information - **Project Name**: xiaomi-miloco - **Description**: Xiaomi Miloco - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-11-19 - **Last Updated**: 2026-07-11 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
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Xiaomi's open-source AI solution for the future of whole-home intelligence. It uses the video and audio from Mi Home cameras as a full-modal perception gateway, the in-house MiMo large model as its intelligent brain, and runs as an Agent plugin on top of [OpenClaw](https://openclaw.ai) to orchestrate whole-home devices for a proactive, intelligent experience. Miloco 2.0 perceives what happens at home, makes proactive decisions and controls devices based on common sense, breaks down "vague and long-term" goals into trackable household tasks, recognizes family members, and—drawing on home memory—delivers personalized service to each member: querying and controlling devices, tuning the home to each member's comfort, or offering useful reminders at the right moment. ## What's New - **2026-07-03** — Release v2026.7.3: adds event-feedback packaging and a conversational first-run setup, proactively initiated on fresh installs; plus improvements to in-dashboard model-config management, perception stability (false-"person" detection guarding), camera lifecycle, and CLI diagnostics. - **2026-06-18** — Miloco 2.0 officially released: re-architected as an OpenClaw plugin, adding general common sense, identity recognition, home memory, household tasks, proactive intelligence, and a home dashboard. See [Core Features](#core-features) below. ## Core Features - **General Common Sense** — No preset rules required. Built-in common sense automatically detects hazards and raises tiered alerts (e.g. a child playing with knives, an elderly person falling). - **Identity Recognition** — Fuses identity signals such as faces and body posture, with the large model recognizing family members. Supports proactively registering new members and identity-based personalized operations. - **Home Memory** — Distills long-term habits and preferences of family members from perception and interaction, used as a reference when the Agent makes proactive decisions. Stable long-term habits can also trigger proactive reminders or be promoted into automatically executed household tasks. - **Household Tasks** — Upgrades from single "condition-triggered rules" to complex, long-running household tasks: conditional automation ("turn on the lights when someone enters"), scheduled reminders ("remind me to take medicine every day"), habit tracking ("exercise half an hour daily"), and more. Once triggered, the Agent understands the intent and executes autonomously. - **Proactive Intelligence** — Built on the four foundational capabilities—general common sense, identity recognition, home memory, and household tasks—the system observes, reasons, and intervenes at the right time like a butler with common sense who knows the family and can plan ahead, getting things done before the user even asks. - **Home Dashboard** — A user-facing web dashboard for viewing a real-time overview of the home, Mi Home devices, family members and profiles, and the history of past events. > [!TIP] > **Raise your own Miloco.** Its out-of-the-box behavior won't always match your taste—just tell Miloco through OpenClaw (e.g. "don't remind me when the place is messy"), and it remembers your preference and adjusts what it does proactively. Every remark "raises" a Miloco that's tuned to your home, and it knows you better the longer you live with it. ## Prerequisites - **Hardware**: ≥ 4GB RAM and ≥ 256GB storage recommended, running 24/7. A Mac mini is recommended. - **Operating System**: macOS / Linux (run under WSL on Windows). - **OpenClaw** — Miloco runs as a plugin on top of it, so [install it](https://openclaw.ai) first with version ≥ 2026.5.2. - **Xiaomi account** + devices already added to Mi Home. - **Multimodal large model API key** — [Xiaomi MiMo](https://platform.xiaomimimo.com) is recommended: MiMo-v2.5 for perception, MiMo-v2.5-pro for the Agent (configured in OpenClaw). > [!CAUTION] > **Cost note**: Miloco 2.0's perception and Agent rely primarily on cloud-based large models and will incur ongoing API usage costs—keep an eye on your usage. You can review token consumption on the "Models" page of the home dashboard. ## Install ### Option 1: Install via the Agent (recommended) Send the following instruction to OpenClaw to complete the installation automatically: ```text Please install the Miloco plugin for me: https://raw.githubusercontent.com/XiaoMi/xiaomi-miloco/main/scripts/install-guide.md ``` ### Option 2: One-line command-line install ```bash curl -LsSf https://github.com/XiaoMi/xiaomi-miloco/releases/latest/download/install.sh | bash ``` ### Option 3: Build from source From the project root, run: ```bash bash scripts/install.sh --dev # build from source (scripts/build.sh), then install locally ``` --- ### Windows (WSL) Whichever method you choose above, native Windows is not supported—install and run everything inside [WSL](https://learn.microsoft.com/en-us/windows/wsl/install). > [!IMPORTANT] > **Local camera streaming requires extra WSL networking setup.** The dashboard's live "right now at home" view pulls camera streams over the LAN, and WSL's default NAT mode blocks the UDP packets cameras send—so the feed won't load until you enable mirrored networking and allow the Hyper-V firewall. 1. **On Windows** — Add the following to `%USERPROFILE%\.wslconfig` (i.e. `C:\Users\
## Acknowledgements
Miloco stands on the shoulders of the following open-source projects:
- [OpenClaw](https://openclaw.ai) — AI Agent runtime and plugin platform
- [jMuxer](https://github.com/samirkumardas/jmuxer) (MIT) — real-time video stream muxing for the home dashboard
- [BGE / bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) (BAAI, MIT) — text embedding model
- [Silero VAD](https://github.com/snakers4/silero-vad) (Silero Team, MIT) — voice activity detection, gating the perceived speech field
## License
See [LICENSE.md](LICENSE.md) for the full license terms.
**Important notice**: This project is for non-commercial use only. Without written authorization from Xiaomi Inc., it may not be used to develop applications (apps), web services, or other forms of software.