Custom types

To support a custom type, you’ll need to provide an implicit Codec for that type.

This can be done by writing a codec from scratch, mapping over an existing codec, or automatically deriving one. Which of these approaches can be taken, depends on the context in which the codec will be used.

Providing an implicit codec

To create a custom codec, you can either directly implement the Codec trait, which requires to provide the following information:

  • encode and rawDecode methods
  • codec meta-data (CodecMeta) consisting of:
    • schema of the type (for documentation)
    • validator for the type
    • codec format (text/plain, application/json etc.)
    • type of the raw value, to which data is serialised (String, Int etc.)

This might be quite a lot of work, that’s why it’s usually easier to map over an existing codec. To do that, you’ll need to provide two mappings:

  • an encode method which encodes the custom type into the base type
  • a decode method which decodes the base type into the custom type, optionally reporting decode errors (the return type is a DecodeResult)

For example, to support a custom id type:

def decode(s: String): DecodeResult[MyId] = MyId.parse(s) match {
  case Success(v) => DecodeResult.Value(v)
  case Failure(f) => DecodeResult.Error(s, f)
def encode(id: MyId): String = id.toString

implicit val myIdCodec: Codec[MyId, TextPlain, _] = Codec.stringPlainCodecUtf8
Note that inputs/outputs can also be mapped over. However, this kind of mapping is always an isomorphism, doesn’t allow any validation or reporting decode errors. Hence, it should be used only for grouping inputs or outputs from a tuple into a custom type.

Automatically deriving codecs

In some cases, codecs can be automatically derived:

  • for supported json libraries
  • for urlencoded and multipart forms

Automatic codec derivation usually requires other implicits, such as:

  • json encoders/decoders from the json library
  • codecs for individual form fields
  • schema of the custom type, through the Schema[T] implicit

Schema derivation

For case classes types, Schema[_] values are derived automatically using Magnolia, given that schemas are defined for all of the case class’s fields. It is possible to configure the automatic derivation to use snake-case, kebab-case or a custom field naming policy, by providing an implicit tapir.generic.Configuration value:

implicit val customConfiguration: Configuration =

Alternatively, Schema[_] values can be defined by hand, either for whole case classes, or only for some of its fields. For example, here we state that the schema for MyCustomType is a String:

implicit val schemaForMyCustomType: SchemaFor[MyCustomType] = Schema(Schema.SString)

If you have a case class which contains some non-standard types (other than strings, number, other case classes, collections), you only need to provide the schema for the non-standard types. Using these schemas, the rest will be derived automatically.

Sealed traits / coproducts

Tapir supports schema generation for coproduct types (sealed trait hierarchies) of the box, but they need to be defined by hand (as implicit values). To properly reflect the schema in OpenAPI documentation, a discriminator object can be specified.

For example, given following coproduct:

sealed trait Entity{
  def kind: String
case class Person(firstName:String, lastName:String) extends Entity {
  def kind: String = "person"
case class Organization(name: String) extends Entity {
  def kind: String = "org"  

The schema may look like this:

val sPerson = implicitly[SchemaFor[Person]]
val sOrganization = implicitly[SchemaFor[Organization]]
implicit val sEntity: SchemaFor[Entity] = 
    SchemaFor.oneOf[Entity, String](_.kind, _.toString)("person" -> sPerson, "org" -> sOrganization)

Customising derived schemas

In some cases, it might be desirable to customise the derived schemas, e.g. to add a description to a particular field of a case class. This can be done by looking up an implicit instance of the Derived[Schema[T]] type, and assigning it to an implicit schema. When such an implicit Schmea[T] is in scope will have higher priority than the built-in low-priority conversion from Derived[Schema[T]] to Schema[T].

Schemas for products/coproducts (case classes and case class families) can be traversed and modified using .modifyUnsafe. The “unsafe” prefix comes from the fact that the method takes a list of fields, traverses the schema to find the referenced one; the correctness of this specification is not checked.

To traverse colletions, using the "each" field name.

For example:

case class Basket(fruits: List[FruitAmount])
case class FruitAmount(fruit: String, amount: Int)
implicit val customBasketSchema: Schema[Basket] = implicitly[Derived[Schema[Basket]]].value
      .modifyUnsafe("fruits", "each", "amount")(_.description("How many fruits?"))

Schema for cats datatypes

The tapir-cats module contains Schema[_] instances for some cats datatypes. See the tapir.codec.cats.TapirCodecCats trait or import sttp.tapir.codec.cats._ to bring the implicit values into scope.