XiaomiMiMo/MiMo-Code

MiMoCode MiMo Code: Where Models and Agents Co-Evolve 中文 | English Website | Blog MiMoCode is a terminal-native AI coding assistant. It can read and write code, run commands, manage Git, and use a persistent memory system to keep a deep understanding of your project across sessions while continuously improving itself. MiMo Desktop Beta Invitation : Apply for the international beta Xiaomi MiMo Desktop Beta Xiaomi MiMo Desktop is built for real-world work: one conversation can handle office work, design, coding, and multimodal creation, while multiple conversations can divide work and collaborate. The desktop app is powered by MiMo Code as its core engine , bringing its terminal-native intelligence to desktop workflows. Smart orchestration : evaluates task type and cost, then dynamically selects models, frameworks, and tools; complex tasks can run across multiple agents in parallel. Continuous iteration : drag in files in multiple formats, create PPTs, webpages, 3D assets, and apps, preview and operate on results in-session, edit selected regions precisely, and roll back versions. Controlled long-task cost : routes between standard and flagship models, edits only the required regions, and supports up to 99% same-session and 95% cross-session cache hit rates. Browser control : opens webpages, retrieves information, fills forms, and checks key interactions after generating webpages. Computer control (international version only) : reads the screen and operates mouse and keyboard across apps; Record & Replay lets you reuse recorded workflows with natural language. After beta approval, users can access limited-time, limited-quantity trials of the new MiMo-X-Pro-Preview and MiMo-X-Flash-Preview models. MiMoCode supports connecting to any mainstream LLM provider API. Quick Start # One-line install (macOS / Linux) curl -fsSL https://mimo.xiaomi.com/install | bash # One-line install (Windows PowerShell) powershell -ep Bypass -c " irm https://mimo.xiaomi.com/install.ps1 | iex " # Or install via npm (all platforms) npm install -g @mimo-ai/cli # Run mimo The first launch guides you through configuration automatically. Supported options: Xiaomi MiMo Platform — OAuth login Codex (ChatGPT Pro/Plus) — OpenAI OAuth login Import from Claude Code — migrate existing authentication in one step Provider list — connect catalog providers by API key, or OAuth where supported (e.g. xAI/Grok) Custom Provider — add any OpenAI-compatible API in the TUI WSL: clipboard issues If you encounter garbled text when copying on WSL, install xsel : sudo apt install xsel macOS: rendering issues in the default terminal MiMoCode does not support the built-in macOS Terminal (Terminal.app). If the interface is misaligned, flickers, or has other rendering issues, use iTerm2 or the VS Code integrated terminal instead: brew install --cask iterm2 TUI lag and visual animation issues If the TUI lags when run directly over SSH, render it locally and run only the MiMoCode server on the remote host. Start the server from the remote project directory: # Remote host mimo serve --port 4096 # Local host: create the SSH port forward ssh -N -L 4096:127.0.0.1:4096 user@remote-host # Local host: connect from another terminal mimo attach http://127.0.0.1:4096 If decorative animation is causing the lag, run /vivid , or configure Vivid visuals in the ctrl+p command palette, to switch between Vivid and Minimal visuals as needed. Windows: garbled CJK (Chinese/Japanese/Korean) output in the shell On Windows with a non-UTF-8 system locale (e.g. zh-CN, whose active code page is 936/GBK), command output containing CJK characters may appear garbled (mojibake). MiMoCode forces UTF-8 output for spawned PowerShell/cmd subprocesses. If you still encounter garbled output in cases this does not yet cover, enable Windows' system-wide UTF-8 support: Settings → Time & language → Language & region → Administrative language settings → Change system locale → check "Beta: Use Unicode UTF-8 for worldwide language support" → reboot. This switches the active code page (ACP) to UTF-8 (65001) for all programs, so subprocesses no longer inherit the legacy code page. Note it is a system-wide Beta toggle and may cause some older non-Unicode programs to display incorrectly, so treat it as a workaround. MiMo Ecosystem Beyond MiMoCode, Xiaomi MiMo models also work in other agents and coding tools like Cursor, Cline, and Zed. awesome-mimo-agent collects setup guides for using MiMo in those tools — worth a look if you want to try MiMo elsewhere. Contributions welcome: open a PR to add your own setup. Core Features Multiple Agents Agent Description build Default. Full tool permissions for development plan Read-only analysis mode for code exploration and solution design compose Orchestration mode for specs-driven development and skill-driven workflows Press Tab to switch between primary agents. Subagents are created by the system as needed. After the first message the mode locks: Build and Plan can still switch between each other, but Compose is isolated once entered — keeping the skill/tool set fixed from session start significantly improves tool-call reliability. For frontier models (Fable/Sol-class), the recommended way to run compose-style work is the build agent with the /compose-next skill — see Compose Mode . Persistent Memory Cross-session memory powered by SQLite FTS5 full-text search: Project memory ( MEMORY.md ) — persistent project knowledge, rules, and architecture decisions Session checkpoint ( checkpoint.md ) — structured state snapshots maintained automatically by the checkpoint-writer subagent Scratch notes ( notes.md ) — temporary note area for agents Task progress ( tasks/<id>/progress.md ) — per-task logs Memory is injected automatically when a session resumes, so the agent does not need to relearn project context. Intelligent Context Management Automatic checkpoints — decides when to save session state based on the model context window Context reconstruction — when context approaches the limit, rebuilds it from the latest checkpoint, project memory, task progress, and retained recent messages so the agent can continue the current task Budgeted injection — uses a token budget to control how much checkpoint, memory, and notes content enters context, with importance ranking Adjustable compaction point — /context-limit (or compaction.max_context ) makes a model compact earlier than its own window, per model Compacting earlier than the model window ( /context-limit ) Compaction normally fires just below the model's context window. Run /context-limit to pick a smaller working budget for the current model — 200K / 300K / 500K / 1M or a custom value — stored per model as compaction.max_context : { "compaction" : { "max_context" : { "openai/gpt-5.6" : " 272K " , // token count, "300K", "1M", or "50%" of the window "anthropic/*" : " 300K " // wildcards allowed, longest pattern wins } } } The value is always clamped to what the provider actually accepts, so it can only lower the compaction point, never raise it. 0 restores the model's own window. Why you might want it: Cost tiers. OpenAI prices GPT-5.6 prompts above 272K input at 2x input and 1.5x output for the whole request. The advertised window is not always what you get. The same model can have a different usable window depending on how you reach it — a ChatGPT/Codex subscription, a direct API key, or a reseller such as OpenRouter — so a catalog figure of 1M does not mean your route serves 1M. Quality and latency. Very long contexts are slower and, past a point, not better. mimo models <provider> prints, per model, the window MiMoCode resolved and the token count where it will compact. The prompt footer uses that same number as its denominator ( 33.0K/260K↓ (13%) — the ↓ means a budget is in force), and /status breaks it down. Task Tracking A tree-shaped task system ( T1 , T1.1 , T1.