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MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search

发布时间：2026-04-13来源：MarkTechPost


MiniMax, the AI research company behind the MiniMax omni-modal model stack, has released MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, both to human developers working in a terminal and to AI agents running in tools like Cursor, Claude Code, and OpenCode.




What Problem Is MMX-CLI Solving?



Most large language model (LLM)-based agents today are strong at reading and writing text. They can reason over documents, generate code, and respond to multi-turn instructions. But they have no direct path to generate media — no built-in way to synthesize speech, compose music, render a video, or understand an image without a separate integration layer such as the Model Context Protocol (MCP).



Building those integrations typically requires writing custom API wrappers, configuring server-side tooling, and managing authentication separately from whatever agent framework you are using. MMX-CLI is positioned as an alternative approach: expose all of those capabilities as shell commands that an agent can invoke directly, the same way a developer would from a terminal — with zero MCP glue required.











The Seven Modalities



MMX-CLI wraps MiniMax’s full-modal stack into seven generative command groups —

mmx text

,

mmx image

,

mmx video

,

mmx speech

,

mmx music

,

mmx vision

, and

mmx search

— plus supporting utilities (

mmx auth

,

mmx config

,

mmx quota

,

mmx update

).




  • The

    mmx text

    command supports multi-turn chat, streaming output, system prompts, and JSON output mode. It accepts a

    --model

    flag to target specific MiniMax model variants such as

    MiniMax-M2.7-highspeed

    , with

    MiniMax-M2.7

    as the default.


  • The

    mmx image

    command generates images from text prompts with controls for aspect ratio (

    --aspect-ratio

    ) and batch count (

    --n

    ). It also supports a

    --subject-ref

    parameter for subject reference, which enables character or object consistency across multiple generated images — useful for workflows that require visual continuity.


  • The

    mmx video

    command uses

    MiniMax-Hailuo-2.3

    as its default model, with

    MiniMax-Hailuo-2.3-Fast

    available as an alternative. By default,

    mmx video generate

    submits a job and polls synchronously until the video is ready. Passing

    --async

    or

    --no-wait

    changes this behavior: the command returns a task ID immediately, letting the caller check progress separately via

    mmx video task get --task-id

    . The command also supports a

    --first-frame <path-or-url>

    flag for image-conditioned video generation, where a specific image is used as the opening frame of the output video.


  • The

    mmx speech

    command exposes text-to-speech (TTS) synthesis with more than 30 available voices, speed control, volume and pitch adjustment, subtitle timing data output via

    --subtitles

    , and streaming playback support via pipe to a media player. The default model is

    speech-2.8-hd

    , with

    speech-2.6

    and

    speech-02

    as alternatives. Input is capped at 10,000 characters.


  • The

    mmx music

    command, backed by the

    music-2.5

    model, generates music from a text prompt with fine-grained compositional controls including

    --vocals

    (e.g.

    "warm male baritone"

    ),

    --genre

    ,

    --mood

    ,

    --instruments

    ,

    --tempo

    ,

    --bpm

    ,

    --key

    , and

    --structure

    . The

    --instrumental

    flag generates music without vocals. An

    --aigc-watermark

    flag is also available for embedding an AI-generated content watermark in the output audio.



  • mmx vision

    handles image understanding via a vision-language model (VLM). It accepts a local file path or remote URL — automatically base64-encoding local files — or a pre-uploaded MiniMax file ID. A

    --prompt

    flag lets you ask a specific question about the image; the default prompt is

    "Describe the image."




  • mmx search

    runs a web search query through MiniMax’s own search infrastructure and returns results in text or JSON format.




Technical Architecture



MMX-CLI is written almost entirely in TypeScript (99.8% TS) with strict mode enabled, and uses Bun as the native runtime for development and testing while distributing to npm for compatibility with Node.js 18+ environments. Configuration schema validation uses Zod, and resolution follows a defined precedence order — CLI flags → environment variables →

~/.mmx/config.json

→ defaults — making deployment straightforward in containerized or CI environments. Dual-region support is built into the API client layer, routing Global users to


and CN users to


, switchable via

mmx config set --key region --value cn

.




Key Takeaways




  • MMX-CLI is MiniMax’s official open command-line interface that gives AI agents native access to seven generative modalities — text, image, video, speech, music, vision, and search — without requiring any MCP integration.


  • AI agents running in tools like Cursor, Claude Code, and OpenCode can be set up with two commands and a single natural language instruction, after which the agent learns the full command interface on its own from the bundled SKILL.md documentation.


  • The CLI is designed for programmatic and agent use, with dedicated flags for non-interactive execution, a clean stdout/stderr separation for safe piping, structured exit codes for error handling, and a schema export feature that lets agent frameworks register mmx commands as JSON tool definitions.


  • For AI devs already building agent-based systems, it lowers the integration barrier significantly by consolidating image, video, speech, music, vision, and search generation into a single, well-documented CLI that agents can learn and operate on their own.




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Shobha Kakkar






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Shobha is a data analyst with a proven track record of developing innovative machine-learning solutions that drive business value.














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