Daml-LF JSON Encoding

We describe how to decode and encode Daml-LF values as JSON. For each Daml-LF type we explain what JSON inputs we accept (decoding), and what JSON output we produce (encoding).

If you use the JavaScript code generator with TypeScript, the generated types for templates and choices will incorporate the following automatically. You can use this to observe how these rules apply to your templates, or ignore this document and rely on the TypeScript type checker to tell you how to encode data for JSON API correctly.

Codec Library

At the library level, the output format is parameterized by two flags:

encodeDecimalAsString: boolean
encodeInt64AsString: boolean

The suggested defaults for both of these flags is false. If the intended recipient is written in JavaScript, however, note that the JavaScript data model will decode these as numbers, discarding data in some cases; encode-as-String avoids this, as mentioned with respect to JSON.parse below. For that reason, the HTTP JSON API Service uses ``true`` for both flags.

Type-directed Parsing

Note that throughout the document the decoding is type-directed. In other words, the same JSON value can correspond to many Daml-LF values, and a single Daml-LF value can correspond to multiple JSON encodings. This means it is crucial to know the expected type of a JSON-encoded LF value to make sense of it.

For that reason, you should parse the data into appropriate data types (including parsing numbers into appropriate representations) before doing any meaningful manipulations (e.g. comparison for equality).


Contract ids are expressed as their string representation:




Decimals can be expressed as JSON numbers or as JSON strings. JSON strings are accepted using the same format that JSON accepts, and treated them as the equivalent JSON number:


Note that JSON numbers would be enough to represent all Decimals. However, we also accept strings because in many languages (most notably JavaScript) use IEEE Doubles to express JSON numbers, and IEEE Doubles cannot express Daml-LF Decimals correctly. Therefore, we also accept strings so that JavaScript users can use them to specify Decimals that do not fit in IEEE Doubles.

Numbers must be within the bounds of Decimal, [–(10³⁸–1)÷10¹⁰, (10³⁸–1)÷10¹⁰]. Numbers outside those bounds will be rejected. Numbers inside the bounds will always be accepted, using banker’s rounding to fit them within the precision supported by Decimal.

A few valid examples:

42 --> 42
42.0 --> 42
"42" --> 42
9999999999999999999999999999.9999999999 -->
-42 --> -42
"-42" --> -42
0 --> 0
-0 --> 0
0.30000000000000004 --> 0.3
2e3 --> 2000

A few invalid examples:

"  42  "


If encodeDecimalAsString is set, decimals are encoded as strings, using the format -?[0-9]{1,28}(\.[0-9]{1,10})?. If encodeDecimalAsString is not set, they are encoded as JSON numbers, also using the format -?[0-9]{1,28}(\.[0-9]{1,10})?.

Note that the flag encodeDecimalAsString is useful because it lets JavaScript consumers consume Decimals safely with the standard JSON.parse.



Int64, much like Decimal, can be represented as JSON numbers and as strings, with the string representation being [+-]?[0-9]+. The numbers must fall within [-9223372036854775808, 9223372036854775807]. Moreover, if represented as JSON numbers, they must have no fractional part.

A few valid examples:


A few invalid examples:

"   42 "


If encodeInt64AsString is set, Int64s are encoded as strings, using the format -?[0-9]+. If encodeInt64AsString is not set, they are encoded as JSON numbers, also using the format -?[0-9]+.

Note that the flag encodeInt64AsString is useful because it lets JavaScript consumers consume Int64s safely with the standard JSON.parse.



Timestamps are represented as ISO 8601 strings, rendered using the format yyyy-mm-ddThh:mm:ss.ssssssZ:


Parsing is a little bit more flexible and uses the format yyyy-mm-ddThh:mm:ss(\.s+)?Z, i.e. it’s OK to omit the microsecond part partially or entirely, or have more than 6 decimals. Sub-second data beyond microseconds will be dropped. The UTC timezone designator must be included. The rationale behind the inclusion of the timezone designator is minimizing the risk that users pass in local times. Valid examples:


The timestamp must be between the bounds specified by Daml-LF and ISO 8601, [0001-01-01T00:00:00Z, 9999-12-31T23:59:59.999999Z].


> new Date().toISOString()


>>> datetime.datetime.utcnow().isoformat() + 'Z'


import java.time.Instant;
class Main {
    public static void main(String[] args) {
        Instant instant = Instant.now();
        // prints 2019-06-18T09:02:16.652Z


Timestamps are encoded as ISO 8601 strings, rendered using the format yyyy-mm-ddThh:mm:ss[.ssssss]Z.

The sub-second part will be formatted as follows:

  • If no sub-second part is present in the timestamp (i.e. the timestamp represents whole seconds), the sub-second part will be omitted entirely;
  • If the sub-second part does not go beyond milliseconds, the sub-second part will be up to milliseconds, padding with trailing 0s if necessary;
  • Otherwise, the sub-second part will be up to microseconds, padding with trailing 0s if necessary.

In other words, the encoded timestamp will either have no sub-second part, a sub-second part of length 3, or a sub-second part of length 6.


Represented using their string representation, without any additional quotes:



Represented as empty object {}. Note that in JavaScript {} !== {}; however, null would be ambiguous; for the type Optional Unit, null decodes to None, but {} decodes to Some ().

Additionally, we think that this is the least confusing encoding for Unit since unit is conceptually an empty record. We do not want to imply that Unit is used similarly to null in JavaScript or None in Python.


Represented as an ISO 8601 date rendered using the format yyyy-mm-dd:


The dates must be between the bounds specified by Daml-LF and ISO 8601, [0001-01-01, 9999-12-31].


Represented as strings.


Represented as booleans.



Records can be represented in two ways. As objects:

{ f₁: v₁, ..., fₙ: vₙ }

And as arrays:

[ v₁, ..., vₙ ]

Note that Daml-LF record fields are ordered. So if we have

record Foo = {f1: Int64, f2: Bool}

when representing the record as an array the user must specify the fields in order:

[42, true]

The motivation for the array format for records is to allow specifying tuple types closer to what it looks like in Daml. Note that a Daml tuple, i.e. (42, True), will be compiled to a Daml-LF record Tuple2 { _1 = 42, _2 = True }.


Records are always encoded as objects.


Lists are represented as

[v₁, ..., vₙ]


TextMaps are represented as objects:

{ k₁: v₁, ..., kₙ: vₙ }


GenMaps are represented as lists of pairs:

[ [k₁, v₁], [kₙ, vₙ] ]

Order does not matter. However, any duplicate keys will cause the map to be treated as invalid.



Optionals are encoded using null if the value is None, and with the value itself if it’s Some. However, this alone does not let us encode nested optionals unambiguously. Therefore, nested Optionals are encoded using an empty list for None, and a list with one element for Some. Note that after the top-level Optional, all the nested ones must be represented using the list notation.

A few examples, using the form

JSON  -->  Daml-LF  :  Expected Daml-LF type

to make clear what the target Daml-LF type is:

null    -->  None                  : Optional Int64
null    -->  None                  : Optional (Optional Int64)
42      -->  Some 42               : Optional Int64
[]      -->  Some None             : Optional (Optional Int64)
[42]    -->  Some (Some 42)        : Optional (Optional Int64)
[[]]    -->  Some (Some None)      : Optional (Optional (Optional Int64))
[[42]]  -->  Some (Some (Some 42)) : Optional (Optional (Optional Int64))

Finally, if Optional values appear in records, they can be omitted to represent None. Given Daml-LF types

record Depth1 = { foo: Optional Int64 }
record Depth2 = { foo: Optional (Optional Int64) }

We have

{ }              -->  Depth1 { foo: None }            :  Depth1
{ }              -->  Depth2 { foo: None }            :  Depth2
{ foo: 42 }      -->  Depth1 { foo: Some 42 }         :  Depth1
{ foo: [42] }    -->  Depth2 { foo: Some (Some 42) }  :  Depth2
{ foo: null }    -->  Depth1 { foo: None }            :  Depth1
{ foo: null }    -->  Depth2 { foo: None }            :  Depth2
{ foo: [] }      -->  Depth2 { foo: Some None }       :  Depth2

Note that the shortcut for records and Optional fields does not apply to Map (which are also represented as objects), since Map relies on absence of key to determine what keys are present in the Map to begin with. Nor does it apply to the [f₁, ..., fₙ] record form; Depth1 None in the array notation must be written as [null].

Type variables may appear in the Daml-LF language, but are always resolved before deciding on a JSON encoding. So, for example, even though Oa doesn’t appear to contain a nested Optional, it may contain a nested Optional by virtue of substituting the type variable a:

record Oa a = { foo: Optional a }

{ foo: 42 }     -->  Oa { foo: Some 42 }        : Oa Int
{ }             -->  Oa { foo: None }           : Oa Int
{ foo: [] }     -->  Oa { foo: Some None }      : Oa (Optional Int)
{ foo: [42] }   -->  Oa { foo: Some (Some 42) } : Oa (Optional Int)

In other words, the correct JSON encoding for any LF value is the one you get when you have eliminated all type variables.


Encoded as described above, never applying the shortcut for None record fields; e.g. { foo: None } will always encode as { foo: null }.


Variants are expressed as

{ tag: constructor, value: argument }

For example, if we have

variant Foo = Bar Int64 | Baz Unit | Quux (Optional Int64)

These are all valid JSON encodings for values of type Foo:

{"tag": "Bar", "value": 42}
{"tag": "Baz", "value": {}}
{"tag": "Quux", "value": null}
{"tag": "Quux", "value": 42}

Note that Daml data types with named fields are compiled by factoring out the record. So for example if we have

data Foo = Bar {f1: Int64, f2: Bool} | Baz

We’ll get in Daml-LF

record Foo.Bar = {f1: Int64, f2: Bool}
variant Foo = Bar Foo.Bar | Baz Unit

and then, from JSON

{"tag": "Bar", "value": {"f1": 42, "f2": true}}
{"tag": "Baz", "value": {}}

This can be encoded and used in TypeScript, including exhaustiveness checking; see a type refinement example.


Enums are represented as strings. So if we have

enum Foo = Bar | Baz

There are exactly two valid JSON values for Foo, “Bar” and “Baz”.