diff --git a/libs/langchain/langchain/schema/runnable/base.py b/libs/langchain/langchain/schema/runnable/base.py
index 7631c54dd30..b477ecdaada 100644
--- a/libs/langchain/langchain/schema/runnable/base.py
+++ b/libs/langchain/langchain/schema/runnable/base.py
@@ -217,9 +217,10 @@ class Runnable(Generic[Input, Output], ABC):
         """
         return RunnableBinding(bound=self, kwargs=kwargs)
 
-    def each(self) -> Runnable[List[Input], List[Output]]:
+    def map(self) -> Runnable[List[Input], List[Output]]:
         """
-        Wrap a Runnable to run it on each element of the input sequence.
+        Return a new Runnable that maps a list of inputs to a list of outputs,
+        by calling invoke() with each input.
         """
         return RunnableEach(bound=self)
 
@@ -1384,7 +1385,7 @@ class RunnableEach(Serializable, Runnable[List[Input], List[Output]]):
     def lc_namespace(self) -> List[str]:
         return self.__class__.__module__.split(".")[:-1]
 
-    def each(self) -> RunnableEach[Input, Output]:  # type: ignore[override]
+    def map(self) -> RunnableEach[Input, Output]:  # type: ignore[override]
         return self
 
     def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]:
diff --git a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr
index 44793ce3a03..fe06e568f3c 100644
--- a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr
+++ b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr
@@ -563,10 +563,9 @@
           "lc": 1,
           "type": "constructor",
           "id": [
-            "langchain",
-            "output_parsers",
-            "list",
-            "CommaSeparatedListOutputParser"
+            "runnable",
+            "test_runnable",
+            "FakeSplitIntoListParser"
           ],
           "kwargs": {}
         }
diff --git a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py
index cb5c0147250..c6630d7a6d6 100644
--- a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py
+++ b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py
@@ -21,7 +21,6 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
-from langchain.pydantic_v1 import ValidationError
 from langchain.schema.document import Document
 from langchain.schema.messages import (
     AIMessage,
@@ -29,7 +28,7 @@ from langchain.schema.messages import (
     HumanMessage,
     SystemMessage,
 )
-from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.output_parser import BaseOutputParser, StrOutputParser
 from langchain.schema.retriever import BaseRetriever
 from langchain.schema.runnable import (
     RouterRunnable,
@@ -1090,6 +1089,24 @@ async def test_llm_with_fallbacks(
     assert dumps(runnable, pretty=True) == snapshot
 
 
+class FakeSplitIntoListParser(BaseOutputParser[List[str]]):
+    """Parse the output of an LLM call to a comma-separated list."""
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    def get_format_instructions(self) -> str:
+        return (
+            "Your response should be a list of comma separated values, "
+            "eg: `foo, bar, baz`"
+        )
+
+    def parse(self, text: str) -> List[str]:
+        """Parse the output of an LLM call."""
+        return text.strip().split(", ")
+
+
 def test_each(snapshot: SnapshotAssertion) -> None:
     prompt = (
         SystemMessagePromptTemplate.from_template("You are a nice assistant.")
@@ -1098,7 +1115,7 @@ def test_each(snapshot: SnapshotAssertion) -> None:
     first_llm = FakeStreamingListLLM(responses=["first item, second item, third item"])
     second_llm = FakeStreamingListLLM(responses=["this", "is", "a", "test"])
 
-    chain = prompt | first_llm | CommaSeparatedListOutputParser() | second_llm.each()
+    chain = prompt | first_llm | FakeSplitIntoListParser() | second_llm.map()
 
     assert dumps(chain, pretty=True) == snapshot
     output = chain.invoke({"question": "What up"})