diff --git a/langchain/chains/base.py b/langchain/chains/base.py
index 15827f6577c..53befcff134 100644
--- a/langchain/chains/base.py
+++ b/langchain/chains/base.py
@@ -49,6 +49,10 @@ class Chain(BaseModel, ABC):
         self._validate_outputs(outputs)
         return {**inputs, **outputs}
 
+    def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
+        """Call the chain on all inputs in the list."""
+        return [self(inputs) for inputs in input_list]
+
     def run(self, text: str) -> str:
         """Run text in, text out (if applicable)."""
         if len(self.input_keys) != 1:
diff --git a/langchain/chains/mapreduce.py b/langchain/chains/mapreduce.py
index 8286e49cca1..623b95cdfcd 100644
--- a/langchain/chains/mapreduce.py
+++ b/langchain/chains/mapreduce.py
@@ -60,13 +60,12 @@ class MapReduceChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         # Split the larger text into smaller chunks.
         docs = self.text_splitter.split_text(inputs[self.input_key])
+
         # Now that we have the chunks, we send them to the LLM and track results.
         #  This is the "map" part.
-        summaries = []
-        for d in docs:
-            inputs = {self.map_llm.prompt.input_variables[0]: d}
-            res = self.map_llm.predict(**inputs)
-            summaries.append(res)
+        input_list = [{self.map_llm.prompt.input_variables[0]: d} for d in docs]
+        summary_results = self.map_llm.apply(input_list)
+        summaries = [res[self.map_llm.output_key] for res in summary_results]
 
         # We then need to combine these individual parts into one.
         # This is the reduce part.