MiniMax, the AI analysis firm behind the MiniMax omni-modal mannequin stack, has launched MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, each to human builders working in a terminal and to AI brokers working in instruments like Cursor, Claude Code, and OpenCode.
What Downside Is MMX-CLI Fixing?
Most giant language mannequin (LLM)-based brokers right now are sturdy at studying and writing textual content. They will purpose over paperwork, generate code, and reply to multi-turn directions. However they don’t have any direct path to generate media — no built-in approach to synthesize speech, compose music, render a video, or perceive a picture and not using a separate integration layer such because the Mannequin Context Protocol (MCP).
Constructing these integrations usually requires writing customized API wrappers, configuring server-side tooling, and managing authentication individually from no matter agent framework you’re utilizing. MMX-CLI is positioned as a substitute strategy: expose all of these capabilities as shell instructions that an agent can invoke straight, the identical method 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 teams — mmx textual content, mmx picture, mmx video, mmx speech, mmx music, mmx imaginative and prescient, and mmx search — plus supporting utilities (mmx auth, mmx config, mmx quota, mmx replace).
- The
mmx textual contentcommand helps multi-turn chat, streaming output, system prompts, and JSON output mode. It accepts a--modelflag to focus on particular MiniMax mannequin variants akin toMiniMax-M2.7-highspeed, withMiniMax-M2.7because the default. - The
mmx picturecommand generates photographs from textual content prompts with controls for facet ratio (--aspect-ratio) and batch rely (--n). It additionally helps a--subject-refparameter for topic reference, which permits character or object consistency throughout a number of generated photographs — helpful for workflows that require visible continuity. - The
mmx videocommand makes use ofMiniMax-Hailuo-2.3as its default mannequin, withMiniMax-Hailuo-2.3-Quickobtainable as a substitute. By default,mmx video generatesubmits a job and polls synchronously till the video is prepared. Passing--asyncor--no-waitmodifications this conduct: the command returns a job ID instantly, letting the caller test progress individually throughmmx video job get --task-id. The command additionally helps a--first-frameflag for image-conditioned video era, the place a selected picture is used because the opening body of the output video. - The
mmx speechcommand exposes text-to-speech (TTS) synthesis with greater than 30 obtainable voices, velocity management, quantity and pitch adjustment, subtitle timing knowledge output through--subtitles, and streaming playback help through pipe to a media participant. The default mannequin isspeech-2.8-hd, withspeech-2.6andspeech-02as alternate options. Enter is capped at 10,000 characters. - The
mmx musiccommand, backed by themusic-2.5mannequin, generates music from a textual content immediate with fine-grained compositional controls together with--vocals(e.g."heat male baritone"),--genre,--mood,--instruments,--tempo,--bpm,--key, and--structure. The--instrumentalflag generates music with out vocals. An--aigc-watermarkflag can be obtainable for embedding an AI-generated content material watermark within the output audio. mmx imaginative and prescienthandles picture understanding through a vision-language mannequin (VLM). It accepts a neighborhood file path or distant URL — robotically base64-encoding native information — or a pre-uploaded MiniMax file ID. A--promptflag enables you to ask a selected query in regards to the picture; the default immediate is"Describe the picture."mmx searchruns an online search question by means of MiniMax’s personal search infrastructure and returns ends in textual content or JSON format.
Technical Structure
MMX-CLI is written virtually fully in TypeScript (99.8% TS) with strict mode enabled, and makes use of Bun because the native runtime for improvement and testing whereas distributing to npm for compatibility with Node.js 18+ environments. Configuration schema validation makes use of Zod, and backbone follows an outlined priority order — CLI flags → atmosphere variables → ~/.mmx/config.json → defaults — making deployment easy in containerized or CI environments. Twin-region help is constructed into the API shopper layer, routing World customers to api.minimax.io and CN customers to api.minimaxi.com, switchable through mmx config set --key area --value cn.
Key Takeaways
- MMX-CLI is MiniMax’s official open command-line interface that offers AI brokers native entry to seven generative modalities — textual content, picture, video, speech, music, imaginative and prescient, and search — with out requiring any MCP integration.
- AI brokers working in instruments like Cursor, Claude Code, and OpenCode might be arrange with two instructions and a single pure language instruction, after which the agent learns the complete command interface by itself from the bundled SKILL.md documentation.
- The CLI is designed for programmatic and agent use, with devoted flags for non-interactive execution, a clear stdout/stderr separation for secure piping, structured exit codes for error dealing with, and a schema export function that lets agent frameworks register mmx instructions as JSON instrument definitions.
- For AI devs already constructing agent-based programs, it lowers the mixing barrier considerably by consolidating picture, video, speech, music, imaginative and prescient, and search era right into a single, well-documented CLI that brokers can be taught and function on their very own.
Try the Repo here. Additionally, be happy to observe us on Twitter and don’t neglect to hitch our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Have to associate with us for selling your GitHub Repo OR Hugging Face Web page OR Product Launch OR Webinar and many others.? Connect with us
Shobha is an information analyst with a confirmed observe report of creating revolutionary machine-learning options that drive enterprise worth.
