MacPaw has entered a long-term partnership with Liquid AI to build a local artificial intelligence stack for macOS, beginning with the Ukrainian software company’s Eney assistant and potentially expanding to developers distributing apps through Setapp.
Announced on August 5, the collaboration will combine Liquid AI’s compact foundation models with MacPaw’s own inference and memory technologies. The goal is to move more AI processing from remote data centers onto Apple silicon, allowing supported features to respond faster, work without a constant internet connection and keep more personal information on the user’s Mac.
MacPaw said the first results are expected in Eney later in 2026. The companies are also designing the underlying components as shared infrastructure that could eventually be offered to thousands of Mac developers through Setapp. No public date has been set for that wider developer rollout.
Eney Becomes the Test Bed
Eney, introduced by MacPaw as a proactive assistant for macOS, will serve as the first production environment for the joint work. Rather than relying entirely on a cloud model for every request, the planned system will decide which tasks can be handled locally and which still require more powerful remote models.
That hybrid design matters because desktop assistants may need access to files, application activity, preferences and conversation history. Sending all of that context to an external server creates additional privacy, latency and connectivity concerns. Local processing can reduce those dependencies, although smaller on-device models remain constrained by available memory and compute power.
MacPaw founder and CEO Oleksandr Kosovan described the company’s direction in clear terms: “We believe intelligence should live where people work: private by design, fast by default.”
Cloud systems will not disappear from Eney. MacPaw says heavier tasks can still be routed remotely when a cloud model is more suitable. The company is building a split architecture rather than presenting local inference as a complete replacement for hosted AI.
Elix Handles Local Inference
The inference layer is called Elix. MacPaw describes it as a Mac-native engine built on MLX, Apple’s machine-learning framework for Apple silicon. It is being tuned with techniques such as speculative decoding, quantization and key-value cache optimization to improve model speed and efficiency on Mac hardware.
Liquid AI will develop and fine-tune Liquid Foundation Models for the tasks Eney is expected to perform. The startup designs models around the hardware and operating conditions in which they will run, rather than treating deployment as a final optimization step.
Liquid AI co-founder and CEO Ramin Hasani said the partnership would bring “efficient, private, on-device LFMs to millions of Mac users.”
Its broader edge strategy is visible in LEAP, a developer platform created to deploy models on phones, laptops, vehicles and other local hardware. Liquid AI has said LEAP can integrate supported foundation models into mobile applications with a small amount of code, reflecting its effort to make local deployment accessible beyond specialist inference teams.
Mnemos Adds Persistent Memory
The second MacPaw component, Mnemos, is a local-first memory layer intended to retain useful context across interactions. Instead of treating every request as an isolated prompt, it can help an assistant remember relevant information and use it in later tasks.
Persistent memory is central to MacPaw’s vision for Eney because an assistant operating across applications must understand more than the current chat. It may need to connect a file opened earlier, a recurring workflow and a preference established days before. MacPaw says Mnemos is being designed as an on-device knowledge base, reducing the need to keep that context in a hosted service.
MacPaw has not yet detailed the user controls, retention settings or developer-facing permissions that will govern the final system.
Setapp Could Become an AI Platform
The partnership extends beyond a single assistant. MacPaw plans to make the models, inference framework and memory layer reusable across its product ecosystem. The longer-term opportunity is to expose those building blocks to independent Mac developers through Setapp.
Setapp currently offers more than 250 apps and has over 150,000 paying users. MacPaw has been repositioning the subscription service from an app catalog into a broader development and distribution platform for AI software.
Its existing AI Gateway gives developers a single API for models from providers including OpenAI, Anthropic, Google and xAI. The gateway handles provider routing, authentication and Setapp’s credit system, reducing the need for each app maker to manage separate integrations and user API keys.
Adding local inference would create another route. Developers could run suitable tasks directly on a Mac, call a cloud model for more demanding work, or combine both within the same application. That could support document classification, summarization, transcription, code assistance and other features involving private data.
MacPaw is also experimenting with credit-based AI plans in Setapp, where usage depends on the number and complexity of operations. How local processing will be priced, metered or shared with developers has not been disclosed.
A Different Route From Cloud-Only AI
The announcement arrives as software companies look for ways to control the cost and privacy implications of AI features. Cloud models remain stronger for many complex jobs, but every remote request can add server expense, network delay and data-handling obligations.
On-device models change that calculation. Once downloaded, they can perform repeated tasks without paying an external provider for every inference and continue operating without connectivity. The trade-off is that developers must account for hardware differences, model size, updates and performance limits across supported Macs.
Apple already provides developers with access to on-device intelligence through its own frameworks. MacPaw is positioning Elix and Liquid AI’s models as a complementary stack with Mac-specific optimization, custom adapters, persistent memory and a connection to Setapp’s cloud gateway.
The Rollout Questions
For now, the partnership is an infrastructure commitment rather than a finished developer product. Eney is scheduled to show the first results later this year, while availability through Setapp remains a future step. MacPaw has not announced supported Mac models, minimum memory requirements, SDK pricing, developer revenue terms or a firm release schedule.
Those details will determine whether the system becomes a practical foundation for mainstream Mac apps or remains limited to selected partners and MacPaw’s own products. Still, the direction is clear: MacPaw wants Setapp to offer developers both local and cloud AI through one commercial ecosystem.
If the companies deliver that architecture, app makers could gain a simpler way to build private, offline-capable features without assembling a separate inference engine, memory system, model pipeline and billing layer. The immediate test will be Eney. The larger test will be whether MacPaw can turn that internal stack into infrastructure other Mac developers can reliably ship.
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