Msty AI
Platform for running local and online models
Allows using AI models locally and offline, or through integration with online providers, via desktop applications and web version. Keeps data stored on the user's device. Integrates RAG system and MCP protocol for APIs.
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RegistrationWhat is Msty AI?
Msty Studio is a platform for executing and managing artificial intelligence models that operates through native applications for Windows, macOS, and Linux, as well as through a web interface accessible from browsers. The system allows working with local models without internet connection or integrating online models from providers such as OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral AI, xAI, Cohere, Alibaba Cloud, Groq, and Open Router.
The platform architecture stores conversations, configurations, and data on the user's device. The default configuration does not require user registration and does not implement telemetry or usage tracking systems. When using local models, operation does not require data transmission to external servers.
Among its technical components is Knowledge Stacks, a Retrieval-Augmented Generation (RAG) system that allows structuring files, folders, notes, and YouTube transcripts into queryable layers through semantic search. It includes automatic processing to remove personally identifiable information before sending data to online models.
The Toolbox module uses the Model Context Protocol (MCP) to connect the platform with APIs, databases, and external tools. This integration enables automating multi-step workflows and establishing real-time connections with external systems.
The platform includes additional capabilities for customizing assistants with specific behaviors and access, assisted content editing, simultaneous comparison of responses between different models, project organization through separate workspaces, token usage and cost metrics, and management of dynamic variables injectable at runtime. The system provides access to updated data through integration with web search providers.
The platform architecture stores conversations, configurations, and data on the user's device. The default configuration does not require user registration and does not implement telemetry or usage tracking systems. When using local models, operation does not require data transmission to external servers.
Among its technical components is Knowledge Stacks, a Retrieval-Augmented Generation (RAG) system that allows structuring files, folders, notes, and YouTube transcripts into queryable layers through semantic search. It includes automatic processing to remove personally identifiable information before sending data to online models.
The Toolbox module uses the Model Context Protocol (MCP) to connect the platform with APIs, databases, and external tools. This integration enables automating multi-step workflows and establishing real-time connections with external systems.
The platform includes additional capabilities for customizing assistants with specific behaviors and access, assisted content editing, simultaneous comparison of responses between different models, project organization through separate workspaces, token usage and cost metrics, and management of dynamic variables injectable at runtime. The system provides access to updated data through integration with web search providers.