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golang-dependency-injection Comprehensive guide for dependency injection (DI) in Golang. Covers why DI matters (testability, loose coupling, separation of concerns, lifecycle management), manual constructor injection, and DI library comparison (google/wire, uber-go/dig, uber-go/fx, samber/do). Use this skill when designing service architecture, setting up dependency injection, refactoring tightly coupled code, managing singletons or service factories, or when the user asks about inversion of control, service containers, or wiring dependencies in Go. false MIT Designed for Claude Code or similar AI coding agents, and for projects using Golang.
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Persona: You are a Go software architect. You guide teams toward testable, loosely coupled designs — you choose the simplest DI approach that solves the problem, and you never over-engineer.

Modes:

  • Design mode (new project, new service, or adding a service to an existing DI setup): assess the existing dependency graph and lifecycle needs; recommend manual injection or a library from the decision table; then generate the wiring code.
  • Refactor mode (existing coupled code): use up to 3 parallel sub-agents — Agent 1 identifies global variables and init() service setup, Agent 2 maps concrete type dependencies that should become interfaces, Agent 3 locates service-locator anti-patterns (container passed as argument) — then consolidate findings and propose a migration plan.

Community default. A company skill that explicitly supersedes skill takes precedence.

Dependency Injection in Go

Dependency injection (DI) means passing dependencies to a component rather than having it create or find them. In Go, this is how you build testable, loosely coupled applications — your services declare what they need, and the caller (or container) provides it.

This skill is not exhaustive. When using a DI library (google/wire), refer to the library's official documentation and code examples for current API signatures.

For interface-based design foundations (accept interfaces, return structs)

Best Practices Summary

  1. Dependencies MUST be injected via constructors — NEVER use global variables or init() for service setup
  2. Small projects (< 10 services) SHOULD use manual constructor injection — no library needed
  3. Interfaces MUST be defined where consumed, not where implemented — accept interfaces, return structs
  4. NEVER use global registries or package-level service locators
  5. The DI container MUST only exist at the composition root (main() or app startup) — NEVER pass the container as a dependency
  6. Prefer lazy initialization — only create services when first requested
  7. Use singletons for stateful services (DB connections, caches) and transients for stateless ones
  8. Mock at the interface boundary — DI makes this trivial
  9. Keep the dependency graph shallow — deep chains signal design problems
  10. Choose the right DI library for your project size and team — see the decision table below

Why Dependency Injection?

Problem without DI How DI solves it
Functions create their own dependencies Dependencies are injected — swap implementations freely
Testing requires real databases, APIs Pass mock implementations in tests
Changing one component breaks others Loose coupling via interfaces — components don't know each other's internals
Services initialized everywhere Centralized container manages lifecycle (singleton, factory, lazy)
All services loaded at startup Lazy loading — services created only when first requested
Global state and init() functions Explicit wiring at startup — predictable, debuggable

DI shines in applications with many interconnected services — HTTP servers, microservices, CLI tools with plugins. For a small script with 2-3 functions, manual wiring is fine. Don't over-engineer.

Manual Constructor Injection (No Library)

For small projects, pass dependencies through constructors. See Manual DI examples for a complete application example.

// ✓ Good — explicit dependencies, testable
type UserService struct {
    db     UserStore
    mailer Mailer
    logger *slog.Logger
}

func NewUserService(db UserStore, mailer Mailer, logger *slog.Logger) *UserService {
    return &UserService{db: db, mailer: mailer, logger: logger}
}

// main.go — manual wiring
func main() {
    logger := slog.Default()
    db := postgres.NewUserStore(connStr)
    mailer := smtp.NewMailer(smtpAddr)
    userSvc := NewUserService(db, mailer, logger)
    orderSvc := NewOrderService(db, logger)
    api := NewAPI(userSvc, orderSvc, logger)
    api.ListenAndServe(":8080")
}
// ✗ Bad — hardcoded dependencies, untestable
type UserService struct {
    db *sql.DB
}

func NewUserService() *UserService {
    db, _ := sql.Open("postgres", os.Getenv("DATABASE_URL")) // hidden dependency
    return &UserService{db: db}
}

Manual DI breaks down when:

  • You have 15+ services with cross-dependencies
  • You need lifecycle management (health checks, graceful shutdown)
  • You want lazy initialization or scoped containers
  • Wiring order becomes fragile and hard to maintain

DI Library Comparison

Go has three main approaches to DI libraries:

Decision Table

Criteria Manual google/wire
Project size Small (< 10 services) Medium-Large
Type safety Compile-time Compile-time (codegen)
Code generation None Required (wire_gen.go)
Reflection None None
API style N/A Provider sets + build tags
Lazy loading Manual N/A (all eager)
Singletons Manual Built-in
Transient/factory Manual Manual
Scopes/modules Manual Provider sets
Health checks Manual Manual
Graceful shutdown Manual Manual
Container cloning N/A N/A
Debugging Print statements Compile errors
Go version Any Any
Learning curve None Medium

Quick Comparison: Same App, Four Ways

The dependency graph: Config -> Database -> UserStore -> UserService -> API

Manual:

cfg := NewConfig()
db := NewDatabase(cfg)
store := NewUserStore(db)
svc := NewUserService(store)
api := NewAPI(svc)
api.Run()
// No automatic shutdown, health checks, or lazy loading

google/wire:

// wire.go — then run: wire ./...
func InitializeAPI() (*API, error) {
    wire.Build(NewConfig, NewDatabase, NewUserStore, NewUserService, NewAPI)
    return nil, nil
}
// No shutdown or health check support

Testing with DI

DI makes testing straightforward — inject mocks instead of real implementations:

// Define a mock
type MockUserStore struct {
    users map[string]*User
}

func (m *MockUserStore) FindByID(ctx context.Context, id string) (*User, error) {
    u, ok := m.users[id]
    if !ok {
        return nil, ErrNotFound
    }
    return u, nil
}

// Test with manual injection
func TestUserService_GetUser(t *testing.T) {
    mock := &MockUserStore{
        users: map[string]*User{"1": {ID: "1", Name: "Alice"}},
    }
    svc := NewUserService(mock, nil, slog.Default())

    user, err := svc.GetUser(context.Background(), "1")
    if err != nil {
        t.Fatalf("unexpected error: %v", err)
    }
    if user.Name != "Alice" {
        t.Errorf("got %q, want %q", user.Name, "Alice")
    }
}

This is particularly useful for integration tests where you want most services to be real but need to mock a specific boundary (database, external API, mailer).

When to Adopt a DI Library

Signal Action
< 10 services, simple dependencies Stay with manual constructor injection
10-20 services, some cross-cutting concerns Consider a DI library
20+ services, lifecycle management needed Strongly recommended
Need health checks, graceful shutdown Use a library with built-in lifecycle support
Team unfamiliar with DI concepts Start manual, migrate incrementally

Common Mistakes

Mistake Fix
Global variables as dependencies Pass through constructors or DI container
init() for service setup Explicit initialization in main() or container
Depending on concrete types Accept interfaces at consumption boundaries
Passing the container everywhere (service locator) Inject specific dependencies, not the container
Deep dependency chains (A->B->C->D->E) Flatten — most services should depend on repositories and config directly
Creating a new container per request One container per application; use scopes for request-level isolation

References