Persona: You are a Go engineer who prefers declarative collection transforms over manual loops. You reach for lo to eliminate boilerplate, but you know when the stdlib is enough and when to upgrade to lop, lom, or loi.

samber/lo — Functional Utilities for Go

Lodash-inspired, generics-first utility library with 500+ type-safe helpers for slices, maps, strings, math, channels, tuples, and concurrency. Zero external dependencies. Immutable by default.

Official Resources:

• github.com/samber/lo
• lo.samber.dev
• pkg.go.dev/github.com/samber/lo

This skill is not exhaustive. Please refer to library documentation and code examples for more information. Context7 can help as a discoverability platform.

Why samber/lo

Go's stdlib slices and maps packages cover ~10 basic helpers (sort, contains, keys). Everything else — Map, Filter, Reduce, GroupBy, Chunk, Flatten, Zip — requires manual for-loops. lo fills this gap:

• Type-safe generics — no interface{} casts, no reflection, compile-time checking, no interface boxing overhead
• Immutable by default — returns new collections, safe for concurrent reads, easier to reason about
• Composable — functions take and return slices/maps, so they chain without wrapper types
• Zero dependencies — only Go stdlib, no transitive dependency risk
• Progressive complexity — start with lo, upgrade to lop/lom/loi only when profiling demands it
• Error variants — most functions have Err suffixes (MapErr, FilterErr, ReduceErr) that stop on first error

Installation

go get github.com/samber/lo

Package	Import	Alias	Go version
Core (immutable)	github.com/samber/lo	lo	1.18+
Parallel	github.com/samber/lo/parallel	lop	1.18+
Mutable	github.com/samber/lo/mutable	lom	1.18+
Iterator	github.com/samber/lo/it	loi	1.23+
SIMD (experimental)	github.com/samber/lo/exp/simd	—	1.25+ (amd64 only)

Choose the Right Package

Start with lo. Move to other packages only when profiling shows a bottleneck or when lazy evaluation is explicitly needed.

Package	Use when	Trade-off
lo	Default for all transforms	Allocates new collections (safe, predictable)
lop	CPU-bound work on large datasets (1000+ items)	Goroutine overhead; not for I/O or small slices
lom	Hot path confirmed by pprof -alloc_objects	Mutates input — caller must understand side effects
loi	Large datasets with chained transforms (Go 1.23+)	Lazy evaluation saves memory but adds iterator complexity
simd	Numeric bulk ops after benchmarking (experimental)	Unstable API, may break between versions

Key rules:

• lop is for CPU parallelism, not I/O concurrency — for I/O fan-out, use errgroup instead
• lom breaks immutability — only use when allocation pressure is measured, never assumed
• loi eliminates intermediate allocations in chains like Map → Filter → Take by evaluating lazily
• For reactive/streaming pipelines over infinite event streams, → see samber/cc-skills-golang@golang-samber-ro skill + samber/ro package

For detailed package comparison and decision flowchart, see Package Guide.

Core Patterns

Transform a slice

// ✓ lo — declarative, type-safe
names := lo.Map(users, func(u User, _ int) string {
    return u.Name
})

// ✗ Manual — boilerplate, error-prone
names := make([]string, 0, len(users))
for _, u := range users {
    names = append(names, u.Name)
}

Filter + Reduce

total := lo.Reduce(
    lo.Filter(orders, func(o Order, _ int) bool {
        return o.Status == "paid"
    }),
    func(sum float64, o Order, _ int) float64 {
        return sum + o.Amount
    },
    0,
)

GroupBy

byStatus := lo.GroupBy(tasks, func(t Task, _ int) string {
    return t.Status
})
// map[string][]Task{"open": [...], "closed": [...]}

Error variant — stop on first error

results, err := lo.MapErr(urls, func(url string, _ int) (Response, error) {
    return http.Get(url)
})

Common Mistakes

Mistake	Why it fails	Fix
Using lo.Contains when slices.Contains exists	Unnecessary dependency for a stdlib-covered op	Prefer slices.Contains, slices.Sort, maps.Keys since Go 1.21+
Using lop.Map on 10 items	Goroutine creation overhead exceeds transform cost	Use lo.Map — lop benefits start at ~1000+ items for CPU-bound work
Assuming lo.Filter modifies the input	lo is immutable by default — it returns a new slice	Use lom.Filter if you explicitly need in-place mutation
Using lo.Must in production code paths	Must panics on error — fine in tests and init, dangerous in request handlers	Use the non-Must variant and handle the error
Chaining many eager transforms on large data	Each step allocates an intermediate slice	Use loi (lazy iterators) to avoid intermediate allocations

Best Practices

1. Prefer stdlib when available — slices.Contains, slices.Sort, maps.Keys carry no dependency. Use lo for transforms the stdlib doesn't offer (Map, Filter, Reduce, GroupBy, Chunk, Flatten)
2. Compose lo functions — chain lo.Filter → lo.Map → lo.GroupBy instead of writing nested loops. Each function is a building block
3. Profile before optimizing — switch from lo to lom/lop only after go tool pprof confirms allocation or CPU as the bottleneck
4. Use error variants — prefer lo.MapErr over lo.Map + manual error collection. Error variants stop early and propagate cleanly
5. Use lo.Must only in tests and init — in production, handle errors explicitly

Quick Reference

Function	What it does
lo.Map	Transform each element
lo.Filter / lo.Reject	Keep / remove elements matching predicate
lo.Reduce	Fold elements into a single value
lo.ForEach	Side-effect iteration
lo.GroupBy	Group elements by key
lo.Chunk	Split into fixed-size batches
lo.Flatten	Flatten nested slices one level
lo.Uniq / lo.UniqBy	Remove duplicates
lo.Find / lo.FindOrElse	First match or default
lo.Contains / lo.Every / lo.Some	Membership tests
lo.Keys / lo.Values	Extract map keys or values
lo.PickBy / lo.OmitBy	Filter map entries
lo.Zip2 / lo.Unzip2	Pair/unpair two slices
lo.Range / lo.RangeFrom	Generate number sequences
lo.Ternary / lo.If	Inline conditionals
lo.ToPtr / lo.FromPtr	Pointer helpers
lo.Must / lo.Try	Panic-on-error / recover-as-bool
lo.Async / lo.Attempt	Async execution / retry with backoff
lo.Debounce / lo.Throttle	Rate limiting
lo.ChannelDispatcher	Fan-out to multiple channels

For the complete function catalog (300+ functions), see API Reference.

For composition patterns, stdlib interop, and iterator pipelines, see Advanced Patterns.

If you encounter a bug or unexpected behavior in samber/lo, open an issue at github.com/samber/lo/issues.

Cross-References

• → See samber/cc-skills-golang@golang-samber-ro skill for reactive/streaming pipelines over infinite event streams (samber/ro package)
• → See samber/cc-skills-golang@golang-samber-mo skill for monadic types (Option, Result, Either) that compose with lo transforms
• → See samber/cc-skills-golang@golang-data-structures skill for choosing the right underlying data structure
• → See samber/cc-skills-golang@golang-performance skill for profiling methodology before switching to lom/lop