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Description
MiniMax Mavis is an advanced AI agent system developed to automate complex workflows through coordinated collaboration between multiple intelligent agents. The platform represents a major evolution of the original MiniMax Agent product and introduces a new multi-agent architecture called Agent Teams. Instead of relying on a single AI assistant, Mavis enables teams of specialized agents to divide responsibilities, execute tasks simultaneously, and collaborate on long-duration projects. The system is designed to support research, software development, knowledge work, planning, content creation, and other business-critical processes. Mavis can maintain progress across extended workflows while reducing the interruptions and context limitations often associated with traditional AI assistants. The platform also integrates with MiniMax’s broader ecosystem of models and services, allowing users to leverage coding, multimodal, and automation capabilities from a single environment. Agent Teams can assign different roles and responsibilities to individual agents, improving efficiency and task specialization. The platform is intended to function as a digital AI assistant capable of handling increasingly sophisticated workflows with minimal supervision. By combining collaborative AI execution with long-context reasoning and automation, MiniMax Mavis helps users complete complex projects faster and more effectively.
Description
oMLX is an MLX server specifically designed for macOS, enhancing the efficiency and speed of local AI operations on Apple Silicon. It caters to the functional dynamics of coding agents by implementing paged SSD KV caching, which enables the persistence of cache blocks on disk; this means that previously accessed prefixes can be retrieved quickly across different requests and even after server restarts, thereby eliminating the need to recompute them from scratch. As a result, the time taken to generate the first token in lengthy contexts can be significantly reduced, dropping from a range of 30 to 90 seconds down to less than five seconds after the initial interaction. The server adeptly manages simultaneous requests through a continuous batching mechanism via mlx-lm’s BatchGenerator, which enhances overall generation throughput without requiring requests to queue up behind a single task. oMLX is capable of simultaneously serving a variety of models, including LLMs, vision-language models, embedding models, and rerankers, utilizing LRU eviction to manage memory constraints effectively. Furthermore, it is compatible with any MLX-format model sourced from Hugging Face, such as Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can also utilize models that are already present in the standard Hugging Face cache, directories associated with LM Studio, or any custom storage locations, ensuring a versatile user experience. This flexibility in model integration enhances the overall usability and practicality of oMLX for developers and researchers alike.
API Access
Has API
API Access
Has API
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Integrations
MiniMax
Anthropic
Claude Code
Cursor
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
Hugging Face
Integrations
MiniMax
Anthropic
Claude Code
Cursor
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
Hugging Face
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
MiniMax
Founded
2023
Country
China
Website
minimax.io
Vendor Details
Company Name
oMLX
Country
United States
Website
omlx.ai/
