automerge-swift-codable

To encode and decode Swift Codable models to Automerge documents, you use AutomergeEncoder to write models into a document and AutomergeDecoder to read them back, bridging high-level Codable usage with the core API.

Yes, you can use AutomergeText for collaborative text fields and Counter for concurrent counters when encoding Swift Codable models, integrating these specific Automerge types directly within your schema strategies.

The Codable layer bridges high-level Swift model serialization to Automerge documents, whereas the core API provides lower-level direct access. You can adopt hybrid patterns between them to optimize for performance and schema evolution.

You persist and sync complex Codable models across sessions and devices by applying AutomergeEncoder and AutomergeDecoder to encode your Swift data structures directly into Automerge documents for synchronization.

You should choose a hybrid pattern between Codable and the core API when you need to balance high-level Codable model encoding with performance optimizations that require direct core API workflows within Automerge documents.

Yes, the Automerge Swift Codable layer supports schema evolution by providing options for schema strategies, allowing your complex Codable models to evolve while being encoded and decoded within Automerge documents.