A codec specifies how to map from and to raw values that are sent over the network. Raw values, which are natively supported by client/server interpreters, include Strings, byte arrays, Files and multiparts.

There are built-in codecs for most common types such as String, Int etc. Codecs are usually defined as implicit values and resolved implicitly when they are referenced.

For example, a query[Int]("quantity") specifies an input parameter which corresponds to the quantity query parameter and will be mapped as an Int. There’s an implicit Codec[Int] value that is referenced by the query method (which is defined in the tapir package).

In a server setting, if the value cannot be parsed as an int, a decoding failure is reported, and the endpoint won’t match the request, or a 400 Bad Request response is returned (depending on configuration).

Optional and multiple parameters

Some inputs/outputs allow optional, or multiple parameters:

  • path segments are always required
  • query and header values can be optional or multiple (repeated query parameters/headers)
  • bodies can be optional, but not multiple

In general, optional parameters are represented as Option values, and multiple parameters as List values. For example, header[Option[String]]("X-Auth-Token") describes an optional header. An input described as query[List[String]]("color") allows multiple occurences of the color query parameter, with all values gathered into a list.

Note that only textual bodies can be optional (optional binary/streaming bodies aren’t supported). That’s because a body cannot be missing - there’s always some body. This is unlike e.g. a query parameter: for which a value can be present, a value can be empty (but defined!), or the parameter might be missing altogether - which corresponds to a None. That’s why only strict (non-streaming) textual bodies, which are empty (""), will be considered as an empty value (None), if the codec allows optional values.

Implementation note

To support optional and multiple parameters, inputs/outputs don’t require implicit Codec values (which represent only mandatory values), but CodecForOptional and CodecForMany implicit values.

A CodecForOptional can be used in a context which allows optional values. Given a Codec[T], instances of both CodecForOptional[T] and CodecForOptional[Option[T]] will be generated (that’s also the way to add support for custom optional types). The first one will require a value, and report a decoding failure if a value is missing. The second will properly map to an Option, depending if the value is present or not.


A codec also contains the schema of the mapped type. This schema information is used when generating documentation. For primitive types, the schema values are built-in, and include values such as Schema.SString, Schema.SArray, Schema.SBinary etc.

The schema is left unchanged when mapping over a codec, as the underlying representation of the value doesn’t change.

When codecs are derived for complex types, e.g. for json mapping, schemas are looked up through implicit SchemaFor[T] values. See json support for more details.

Tapir supports schema generation for coproduct types of the box. In order to extend openApi schema representation 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 discriminator may look like:

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)

Media types

Codecs carry an additional type parameter, which specifies the media type. Some built-in media types include text/plain, application/json and multipart/form-data. Custom media types can be added by creating an implementation of the tapir.MediaType trait.

Thanks to codec being parametrised by media types, it is possible to have a Codec[MyCaseClass, TextPlain, _] which specifies how to serialize a case class to plain text, and a different Codec[MyCaseClass, Json, _], which specifies how to serialize a case class to json. Both can be implicitly available without implicit resolution conflicts.

Different media types can be used in different contexts. When defining a path, query or header parameter, only a codec with the TextPlain media type can be used. However, for bodies, any media types is allowed. For example, the input/output described by jsonBody[T] requires a json codec.

Custom types

Support for custom types can be added by writing a codec from scratch, or mapping over an existing codec. However, custom types can also be supported by mapping over inputs/outputs, not codecs. When to use one and the other?

In general, codecs should be used when translating between raw values and “application-primitives”. Codecs also allow the decoding process to result in an error, or to complete successfully. 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

Additionally, if a type is supported by a codec, it can be used in multiple contexts, such as query parameters, headers, bodies, etc. Mapped inputs by construction have a fixed context.

On the other hand, when building composite types out of many values, or when an isomorphic representation of a type is needed, but only for a single input/output/endpoint, mapping over an input/output is the simpler solution. Note that while codecs can report errors during decoding, mapping over inputs/outputs doesn’t have this possibility.


While codecs support reporting decoding failures, this is not meant as a validation solution, as it only works on single values, while validation often involves multiple combined values.

Decoding failures should be reported when the input is in an incorrect low-level format, when parsing a “raw value” fails. In other words, decoding failures should be reported for format failures, not business validation errors.

Any validation should be done as part of the “business logic” methods provided to the server interpreters. In case validation fails, the result can be an error, which is one of the mappings defined in an endpoint (the E in Endpoint[I, E, O, S]).