Offline-First Android App Architecture with Jetpack Compose, Room, Retrofit & Kotlin Flow (Full Guide)

Modern mobile users expect applications that feel fast, responsive, and reliable at all times  -  even in poor network conditions. Whether a user is traveling through patchy network areas, using airplane mode, or simply facing inconsistent internet speeds, the application should continue to function smoothly. Applications that freeze, show constant spinners, or display “No Internet Connection” screens create friction and frustration.

This is where the offline-first architecture approach comes into play.

An offline-first application is built with the assumption that network availability is not guaranteed. Instead of relying on the network as the primary data source, the app uses local storage as the Single Source of Truth (SSOT). When network connectivity is available, the app performs background synchronization to update local data  -  silently and without blocking the UI.

What Does Offline-First Mean?

The UI layer in Jetpack Compose is state-driven, meaning UI elements automatically react to state changes. When Room updates the database, the Flow stream emits new data, Compose recomposes the screen, and the UI updates  -  with no manual intervention.

In this guide, we will build an article list screen that:

• Shows data immediately using cached records
  
• Silently refreshes data in the background using Retrofit
  
• Works seamlessly offline
  
• Avoids UI jitter, loading indicators, and blocking calls
  
• Updates UI automatically using Flow + StateFlow

High-Level Architecture

‍

Key Architecture Principles

This architecture ensures that data always comes instantly from local storage, improving responsiveness and user trust.

Step-by-Step Implementation

1. Define the Data Models

We keep domain models separate from Room entities to maintain clean layering.

Code

data class Article(
    val id: Int,
    val title: String,
    val content: String
)

@Entity(tableName = "articles")
data class ArticleEntity(
    @PrimaryKey val id: Int,
    val title: String,
    val content: String
)

2. Create the DAO

The DAO exposes a Flow so UI updates automatically when the data changes.

Code

@Dao
interface ArticleDao {

    @Query("SELECT * FROM articles ORDER BY id DESC")
    fun getArticles(): Flow<List<ArticleEntity>>

    @Insert(onConflict = OnConflictStrategy.REPLACE)
    suspend fun insertArticles(articles: List<ArticleEntity>)
}

3. Retrofit API Service

Code

interface ArticleApi {
    @GET("articles")
    suspend fun fetchArticles(): List<Article>
}

4. Repository  -  Core Offline-First Logic

The UI never interacts with the network directly. Only the repository handles synchronization.

Code

class ArticleRepository(
    private val api: ArticleApi,
    private val dao: ArticleDao
) {

    val articles: Flow<List<Article>> =
        dao.getArticles().map { entities ->
            entities.map { Article(it.id, it.title, it.content) }
        }

    suspend fun refresh() {
        try {
            val remoteArticles = api.fetchArticles()
            val entities = remoteArticles.map {
                ArticleEntity(it.id, it.title, it.content)
            }
            dao.insertArticles(entities)
        } catch (e: Exception) {
            // Silent fail  -  cached data stays visible
        }
    }
}

5. ViewModel for UI State

We convert Flow to StateFlow - stable and lifecycle-aware.

Code

class ArticleViewModel(
    private val repository: ArticleRepository
) : ViewModel() {

    val articles = repository.articles.stateIn(
        viewModelScope,
        SharingStarted.WhileSubscribed(5000),
        emptyList()
    )

    init {
        refresh()
    }

    fun refresh() = viewModelScope.launch {
        repository.refresh()
    }
}

6. Jetpack Compose UI

Code

@Composable
fun ArticleScreen(viewModel: ArticleViewModel) {
    val articles by viewModel.articles.collectAsState()

    Column {
        Button(
            onClick = { viewModel.refresh() },
            modifier = Modifier.fillMaxWidth()
        ) {
            Text("Refresh")
        }

        LazyColumn {
            items(articles) { article ->
                ArticleItem(article)
            }
        }
    }
}

@Composable
fun ArticleItem(article: Article) {
    Column(Modifier.padding(16.dp)) {
        Text(article.title, style = MaterialTheme.typography.titleLarge)
        Spacer(Modifier.height(6.dp))
        Text(article.content, style = MaterialTheme.typography.bodyMedium)
    }
}

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Handling Offline State (Optional Enhancements)

Note: Never block UI when offline. The UI should always load from the local DB.

Enhancements you may add:

But remember: UI data source must remain the local database.
Network status should only control user messaging, not core logic.

Why This Architecture Scales Well

This pattern is used in production apps like YouTube, Google Drive, Medium, WhatsApp, Slack, etc.

Performance Notes

• Use immutable Kotlin data classes to avoid unnecessary recompositions.
  
• For large lists or infinite scrolling, introduce Paging 3.
  
• Use collectAsStateWithLifecycle() to avoid lifecycle issues.
  
• For conflict resolution in offline editing, adopt:
  
  • Last Write Wins, or
    
  • Server-driven versioning depending on use case.

Sync Strategies in Offline-First Systems

Not all synchronization needs are the same, and how your app syncs data depends on whether users only consume data or also modify it.

1. Read-Only Apps (Easiest Case)

Examples:

• News apps
  
• Blog readers
  
• Cryptocurrency price tickers
  

Sync behavior:

• Fetch data periodically
  
• Replace old data with the latest version
  
• No need to handle conflicts
  

This is exactly the approach in our sample.

2. Read-Write Apps (More Complex)

Examples:

• Note-taking apps (Notion, Evernote)
  
• Task managers (Todoist)
  
• Messaging apps
  

In such apps, data originates from both:

• The user (local writes)
  
• The remote database server
  

Two common conflict strategies:

If designing collaborative editing apps, consider:

• Version numbers
  
• Operation logs
  
• CRDTs (Conflict-Free Replicated Data Types)
  

This depends on scale and domain complexity.

Batching & Throttling Network Calls

Syncing every change instantly can:

• Drain battery
  
• Waste bandwidth
  
• Hammer your backend
  

Use strategies like:

• Sync only when app becomes active
  
• Sync on a fixed interval (e.g., every 15 minutes)
  
• Sync when the user manually triggers refresh
  
• Sync on network reconnect event
  

Android provides WorkManager for reliable background scheduling.

Example periodic sync request:

Code

val request = PeriodicWorkRequestBuilder<SyncWorker>(15, TimeUnit.MINUTES)
    .setConstraints(
        Constraints.Builder()
            .setRequiredNetworkType(NetworkType.CONNECTED)
            .build()
    )
    .build()

WorkManager.getInstance(context).enqueueUniquePeriodicWork(
    "article_sync",
    ExistingPeriodicWorkPolicy.KEEP,
    request
)

This maintains offline-first stability without constant retries.

Handling Errors Gracefully

Offline-first apps should not display errors just because network is unavailable.
However, real failures (e.g., server errors) still need meaningful UI feedback.

Good UI Messaging Examples:

• "Showing saved content (Last updated: 12:45 PM)"
  
• "Unable to refresh. Data may be outdated."
  
• "Error: no internet"
  

The app should never remove cached content when errors occur.

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Ensuring Data Integrity (Avoid Corrupt Cache)

Over time, cached data must remain valid and consistent. Techniques:

Code

val migration_1_2 = object : Migration(1, 2) {
    override fun migrate(database: SupportSQLiteDatabase) {
        database.execSQL("ALTER TABLE articles ADD COLUMN author TEXT DEFAULT ''")
    }
}

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Conclusion

Implementing offline-first behavior is not just a technical choice  -  it is a user experience multiplier. Users trust apps that behave consistently and do not depend on external conditions like network stability.

By combining:

• Room is the local cache
  
• Retrofit for network access
  
• Kotlin Flow & StateFlow for reactive updates
  
• ViewModel for lifecycle-aware UI state
  
• Jetpack Compose for declarative UI rendering
  

You achieve an application architecture that is:

• Smooth
  
• Stable
  
• Offline-friendly
  
• Scalable to real-world production systems
  

The best part: this architecture is simple  -  and elegant.