diff --git a/dataset/src/main/scala/frameless/functions/AggregateFunctions.scala b/dataset/src/main/scala/frameless/functions/AggregateFunctions.scala
index 50721ed70..5aaf6c317 100644
--- a/dataset/src/main/scala/frameless/functions/AggregateFunctions.scala
+++ b/dataset/src/main/scala/frameless/functions/AggregateFunctions.scala
@@ -7,12 +7,15 @@ import org.apache.spark.sql.{functions => untyped}
 
 trait AggregateFunctions {
 
+  private def typedColumnToAggregate[A: TypedEncoder, T](a: TypedColumn[T, A]): TypedAggregate[T, A] =
+    new TypedAggregate[T,A](a.expr)
+
   /** Creates a [[frameless.TypedColumn]] of literal value. If A is to be encoded using an Injection make
     * sure the injection instance is in scope.
     *
     * apache/spark
     */
-  def lit[A: TypedEncoder, T](value: A): TypedColumn[T, A] = frameless.functions.lit(value)
+  def litAggr[A: TypedEncoder, T](value: A): TypedAggregate[T, A] = typedColumnToAggregate(lit(value))
 
   /** Aggregate function: returns the number of items in a group.
     *
diff --git a/dataset/src/test/scala/frameless/SchemaTests.scala b/dataset/src/test/scala/frameless/SchemaTests.scala
index aaef496c2..a08ebcbcd 100644
--- a/dataset/src/test/scala/frameless/SchemaTests.scala
+++ b/dataset/src/test/scala/frameless/SchemaTests.scala
@@ -1,5 +1,6 @@
 package frameless
 
+import frameless.functions.lit
 import frameless.functions.aggregate._
 import org.scalacheck.Prop
 import org.scalacheck.Prop._
diff --git a/dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala b/dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala
index d9aa8ffb7..320dd2d49 100644
--- a/dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala
+++ b/dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala
@@ -168,6 +168,17 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
     check(forAll(prop[Double] _))
   }
 
+  test("litAggr") {
+    def prop[A: TypedEncoder, B: TypedEncoder, C: TypedEncoder](xs: List[A], b: B, c: C): Prop = {
+      val dataset = TypedDataset.create(xs)
+      val (r1, rb, rc, rcount) = dataset.agg(litAggr(1), litAggr(b), litAggr(c), count()).collect().run().head
+      (rcount ?= xs.size.toLong) && (r1 ?= 1) && (rb ?= b) && (rc ?= c)
+    }
+
+    check(forAll(prop[Boolean, Int, String] _))
+    check(forAll(prop[Option[Boolean], Vector[Option[Vector[Char]]], Long] _))
+  }
+
   test("count") {
     def prop[A: TypedEncoder](xs: List[A]): Prop = {
       val dataset = TypedDataset.create(xs)