# Swift Assist: Broadening Development Possibilities with Xcode 26
On Monday, during the WWDC25 keynote, Apple presented an exciting upgrade concerning Swift Assist, a function aimed at improving the development journey in Xcode. Craig Federighi emphasized that developers can now incorporate their chosen Large Language Models (LLMs) directly into Xcode, signifying a major enhancement of the tool’s functionality.
## What is Swift Assist?
Swift Assist was first revealed at last year’s WWDC but has only recently become available in the Xcode 26 beta. Apple defines Swift Assist as a supporter for developers, intended to simplify coding tasks and enable them to concentrate on more intricate issues and solutions. The feature integrates smoothly into Xcode and is aware of the latest software development kits (SDKs) and Swift language enhancements, guaranteeing that developers have access to the most up-to-date coding resources.
As per Apple, Swift Assist employs a robust cloud-based model, emphasizing privacy and security. Developers’ code is processed to fulfill requests but is not stored on servers, and Apple does not utilize this information to train machine learning models.
## What is the New Swift Assist?
Apple has provided more details about its vision for Swift Assist in Xcode 26, rolling out several fresh features. The refined Swift Assist comprises:
– **Integrated Predictive Code Completion**: Improving the coding experience by foreseeing developers’ requirements.
– **Native ChatGPT Integration**: Offering limited complimentary usage, enabling developers to utilize conversational AI for coding support.
– **Support for Third-Party Providers**: Developers can now integrate a variety of LLMs, including local models, by simply inputting an API key.
By default, developers can activate ChatGPT with a few clicks, subject to a daily request limit. Those with a ChatGPT Plus subscription can log in or use their API key to bypass these restrictions.
The adaptability of Xcode 26 permits developers to install other providers, such as Anthropic, by inputting an API key, thereby accessing leading models in the coding arena. Developers can also choose which models to showcase and designate favorites for easier retrieval.
Furthermore, local models are available. Developers utilizing tools like Ollama or LM Studio can access models operating directly on their devices, offering a personalized coding environment. They can add multiple providers and transition between them effortlessly within Xcode’s Coding Assistant.
## Conclusion
The advancement of Swift Assist aligns Xcode with the contemporary landscape of AI tools, highlighting a model-agnostic, customizable, and modular strategy. This adaptability could markedly enhance the allure of Xcode in a multi-model environment, addressing the varied needs of developers.
As Apple persists in its innovation, the incorporation of LLMs into Xcode signifies a hopeful progression in making coding more efficient and accessible. Developers are invited to explore these new features and share their insights on employing LLMs in their coding workflows.
Read More