| Feature | laminate | serde_with | eserde | figment | config |
|---|---|---|---|---|---|
| Type coercion | 4 levels, graduated | Per-field macros | No | No | Basic |
| Multi-error | Yes (partial success) | No | Yes (all-Err) | No | No |
| Type detection | 16+ types, confidence | No | No | No | No |
| Source hints | CSV/JSON/Env | No | No | Provider merging | Provider merging |
| Diagnostics | Risk-leveled | No | Error list | No | No |
| Domain packs | 6 domains | No | No | No | No |
| AI/LLM adapters | Anthropic/OpenAI/Ollama | No | No | No | No |
| Schema inference | Yes | No | No | No | No |
| Derive macro | Yes (7 attributes) | Yes (macros) | Yes (derive) | No | No |
| Streaming/SSE | Yes | No | No | No | No |
When to use Laminate
- You're consuming external data (APIs, CSV, config) and need forgiving parsing
- You want to know WHAT was coerced, not just that it worked
- You need type detection on unknown data
- You're building AI/LLM applications with multi-provider support
- You need domain-specific parsing (medical, financial, logistics)
When NOT to use Laminate
- You control both producer and consumer — use serde directly
- You need maximum deserialization performance with zero overhead — use serde
- You only need per-field customization — serde_with is simpler
- You only need configuration merging — figment or config is more focused