The companies will combine Liquid AI’s foundation models with MacPaw’s local inference and memory technologies, beginning with the Eney assistant and potentially expanding the system to Setapp developers.
Published: August 6, 2026
MacPaw has entered a long-term partnership with Liquid AI to develop an artificial intelligence technology stack that can run directly on Mac computers.
The collaboration will combine Liquid AI’s efficient foundation models with two technologies developed by MacPaw: Elix, an on-device inference system, and Mnemos, a local memory layer.
MacPaw’s Eney assistant will be the first product to use the combined system. The companies expect to demonstrate initial results during 2026.
After deploying the technology in Eney, MacPaw plans to extend the infrastructure across its product ecosystem and potentially make it available to developers distributing software through Setapp.
| Category | Confirmed detail |
|---|---|
| Companies | MacPaw and Liquid AI |
| Partnership type | Strategic, long-term technology partnership |
| Announcement date | August 5, 2026 |
| Initial platform | Apple Mac computers |
| First product | Eney AI assistant |
| Model provider | Liquid AI |
| Local inference technology | Elix |
| Local memory technology | Mnemos |
| Primary hardware target | Apple silicon |
| Initial results expected | During 2026 |
| Planned developer platform | Setapp |
| Setapp paying users | More than 150,000 |
MacPaw is initially applying the new AI stack to Eney, its artificial intelligence assistant for macOS.
Eney is designed to allow users to complete tasks across Mac applications through conversational instructions. MacPaw introduced the assistant in 2025 as part of its effort to create a more unified interface for software and workflows.
The company wants Eney to process more tasks directly on the user’s computer instead of sending every request to an external cloud server.
Liquid AI will develop and adapt its foundation models for tasks performed by the assistant. These models will operate through MacPaw’s Elix inference framework on Macs powered by Apple silicon.
Cloud-based models will remain available when MacPaw determines that remote processing is more appropriate for a particular task.
The partnership combines three technical components that perform different functions.
| Technology | Developer | Function |
|---|---|---|
| Liquid Foundation Models | Liquid AI | Provide the underlying language and reasoning capabilities |
| Elix | MacPaw | Runs AI model inference directly on Apple silicon |
| Mnemos | MacPaw | Stores and retrieves contextual information locally |
| Eney | MacPaw | Uses the combined technologies to perform user-facing tasks |
The planned architecture is designed to prioritize local processing while retaining access to cloud-based AI systems.
Local execution can allow supported tasks to continue without an internet connection. It can also reduce the amount of personal information that must leave the computer during processing.
The companies have not stated that every Eney function will operate offline. Some requests may continue to use cloud infrastructure when a remote model offers capabilities that are not available locally.
Elix is MacPaw’s framework for running artificial intelligence models directly on a device.
Inference is the stage at which a trained AI model receives an input and produces an output. In Eney’s case, an input could be a user request to search for information, modify a file or execute a supported action through another application.
Liquid AI will optimize its models for the hardware and tasks involved rather than relying on one general configuration for every device.
The models will then run through Elix on Apple silicon, the chip architecture used across Apple’s current Mac product range.
The partnership is focused on building an integrated system rather than simply connecting Eney to an external AI provider through a cloud application programming interface.
The second major MacPaw technology involved in the project is Mnemos.
Mnemos is intended to give Eney persistent memory across user interactions. This could allow the assistant to retain approved contextual information instead of treating every conversation as an entirely separate session.
The memory layer is being developed to operate locally. MacPaw says this approach is intended to keep personal context stored on the user’s computer.
| Objective | Planned function |
|---|---|
| Context retention | Remember relevant information across interactions |
| Personalization | Use stored context to provide more relevant responses |
| Local storage | Keep supported memory data on the Mac |
| Workflow continuity | Retain information needed for multi-step activities |
| Model support | Provide previous context when the AI processes new requests |
The companies have not disclosed how much information Mnemos will store, how long individual records will be retained or which controls users will receive for reviewing and deleting saved information.
Liquid AI develops foundation models intended to operate efficiently across local devices and other computing environments.
The company was founded by researchers connected to the Massachusetts Institute of Technology. Its technology is designed to reduce the computing resources required to run capable AI models.
For the MacPaw partnership, Liquid AI will train and fine-tune models for the tasks Eney is expected to perform.
This process may involve selecting model architectures based on the available hardware and optimizing them for specific assistant functions.
Liquid AI has said that its approach begins with choosing an architecture suited to the target device before training the model. The company argues that this allows the final system to use computing resources more efficiently.
MacPaw is not planning to remove cloud-based models completely.
The company intends to use a hybrid architecture in which the system selects between local and remote processing depending on the request.
| Processing type | Planned use |
|---|---|
| On-device model | Core assistant tasks supported by local hardware |
| Local memory | Context retention and retrieval through Mnemos |
| Cloud model | Tasks requiring capabilities better handled remotely |
| Hybrid workflow | Activities involving both local and cloud components |
The companies have not published the exact criteria that will determine when a request is processed locally and when it is transferred to a cloud model.
TechCrunch reported that MacPaw also wants to provide access to cloud models from companies such as Google. This could allow developers to select between MacPaw and Liquid AI’s local infrastructure and external AI services.
The companies have described Eney as the first stage of a broader deployment plan.
MacPaw has not announced a fixed release date for the developer tools.
Setapp is MacPaw’s software marketplace and subscription service for Mac applications.
The platform has more than 150,000 paying users. It provides access to a collection of Mac, iOS and web applications through subscription and individual app purchasing options.
MacPaw plans to place a greater focus on artificial intelligence applications within Setapp.
Once the local processing architecture is finalized, the company wants to allow developers to use the on-device inference system in their own applications.
This could give participating developers access to a shared AI infrastructure without requiring each developer to independently build a complete local model deployment system.
| Planned capability | Status |
|---|---|
| Access to on-device inference | Planned |
| Access to MacPaw’s AI stack | Planned |
| Use of Liquid Foundation Models | Under development |
| Access to selected cloud models | Planned |
| Credit-based AI operations | Being tested |
| Public developer release date | Not announced |
| Developer pricing | Not announced |
MacPaw is experimenting with a credit-based pricing model for AI operations within Setapp.
Under the proposed system, users would receive or purchase credits that could be spent when performing AI-powered tasks.
The number of credits used could vary based on the complexity of the requested operation.
A basic AI action could require fewer credits than a task involving more extensive processing or the use of an external cloud model.
MacPaw has not published credit prices, usage limits or the number of credits that would be included with existing Setapp subscriptions.
MacPaw and Liquid AI are entering an ecosystem in which Apple already provides developers with access to on-device artificial intelligence technologies.
Apple’s developer tools allow supported applications to use models and machine-learning frameworks that operate on Apple hardware.
MacPaw and Liquid AI are attempting to differentiate their system through models optimized for different capabilities, a customizable model layer, persistent local memory and integration with Eney and Setapp.
The companies have not published direct performance comparisons between their planned system and Apple’s local AI technologies.
No benchmark data has been released showing differences in speed, accuracy, memory use or energy consumption.
MacPaw and Liquid AI have announced their technical direction, but several important details remain under development.
The companies have not released final performance measurements for Eney’s new local models.
They have also not disclosed the model sizes, minimum Mac specifications, memory requirements or storage requirements for the finished system.
| Confirmed | Not publicly disclosed |
|---|---|
| Eney will be the first integration | Exact Eney release date |
| Models will run through Elix | Model parameter counts |
| The system will target Apple silicon | Minimum supported Mac hardware |
| Mnemos will provide local memory | Local storage requirements |
| Cloud models will remain available | Rules for cloud routing |
| Initial results are expected in 2026 | Independent performance benchmarks |
| Setapp developer access is planned | Developer pricing |
| Credit-based AI pricing is being tested | Credit values and usage limits |
MacPaw and Liquid AI expect the first results from the partnership during 2026.
The initial milestone will be the implementation of the on-device stack in Eney. A later expansion could bring the same infrastructure to other MacPaw products and independent applications distributed through Setapp.
The companies have not announced when the technology will become broadly available to users or developers.
Until technical specifications and performance results are released, the partnership remains an active infrastructure project rather than a completed on-device AI platform.
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