diff --git a/.semversioner/next-release/patch-20260211214912747264.json b/.semversioner/next-release/patch-20260211214912747264.json
new file mode 100644
index 0000000000..e65444da75
--- /dev/null
+++ b/.semversioner/next-release/patch-20260211214912747264.json
@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Add async iterator support to InputReader and use it in load_input_documents and load_update_documents workflows."
+}
diff --git a/packages/graphrag-input/graphrag_input/input_reader.py b/packages/graphrag-input/graphrag_input/input_reader.py
index be95168336..ae840eb8f2 100644
--- a/packages/graphrag-input/graphrag_input/input_reader.py
+++ b/packages/graphrag-input/graphrag_input/input_reader.py
@@ -11,6 +11,8 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
+    from collections.abc import AsyncIterator
+
     from graphrag_storage import Storage
 
     from graphrag_input.text_document import TextDocument
@@ -33,34 +35,44 @@ def __init__(
         self._file_pattern = file_pattern
 
     async def read_files(self) -> list[TextDocument]:
-        """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed."""
+        """Load all files from storage and return them as a single list."""
+        return [doc async for doc in self]
+
+    def __aiter__(self) -> AsyncIterator[TextDocument]:
+        """Return the async iterator, enabling `async for doc in reader`."""
+        return self._iterate_files()
+
+    async def _iterate_files(self) -> AsyncIterator[TextDocument]:
+        """Async generator that yields documents one at a time as files are loaded."""
         files = list(self._storage.find(re.compile(self._file_pattern)))
         if len(files) == 0:
             msg = f"No {self._file_pattern} matches found in storage"
             logger.warning(msg)
-            files = []
+            return
 
-        documents: list[TextDocument] = []
+        file_count = len(files)
+        doc_count = 0
 
         for file in files:
             try:
-                documents.extend(await self.read_file(file))
+                for doc in await self.read_file(file):
+                    doc_count += 1
+                    yield doc
             except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
                 logger.warning("Warning! Error loading file %s. Skipping...", file)
                 logger.warning("Error: %s", e)
 
         logger.info(
             "Found %d %s files, loading %d",
-            len(files),
+            file_count,
             self._file_pattern,
-            len(documents),
+            doc_count,
         )
-        total_files_log = (
-            f"Total number of unfiltered {self._file_pattern} rows: {len(documents)}"
+        logger.info(
+            "Total number of unfiltered %s rows: %d",
+            self._file_pattern,
+            doc_count,
         )
-        logger.info(total_files_log)
-
-        return documents
 
     @abstractmethod
     async def read_file(self, path: str) -> list[TextDocument]:
diff --git a/packages/graphrag/graphrag/index/workflows/load_input_documents.py b/packages/graphrag/graphrag/index/workflows/load_input_documents.py
index 8e27ed0a2a..26166bb279 100644
--- a/packages/graphrag/graphrag/index/workflows/load_input_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/load_input_documents.py
@@ -4,6 +4,7 @@
 """A module containing run_workflow method definition."""
 
 import logging
+from dataclasses import asdict
 
 import pandas as pd
 from graphrag_input import InputReader, create_input_reader
@@ -39,8 +40,9 @@ async def run_workflow(
 
 async def load_input_documents(input_reader: InputReader) -> pd.DataFrame:
     """Load and parse input documents into a standard format."""
-    output = pd.DataFrame(await input_reader.read_files())
-    output["human_readable_id"] = output.index
-    if "raw_data" not in output.columns:
-        output["raw_data"] = pd.Series(dtype="object")
-    return output
+    documents = [asdict(doc) async for doc in input_reader]
+    documents = pd.DataFrame(documents)
+    documents["human_readable_id"] = documents.index
+    if "raw_data" not in documents.columns:
+        documents["raw_data"] = pd.Series(dtype="object")
+    return documents
diff --git a/packages/graphrag/graphrag/index/workflows/load_update_documents.py b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
index a61a228493..38104fdbf5 100644
--- a/packages/graphrag/graphrag/index/workflows/load_update_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
@@ -4,6 +4,7 @@
 """A module containing run_workflow method definition."""
 
 import logging
+from dataclasses import asdict
 
 import pandas as pd
 from graphrag_input.input_reader import InputReader
@@ -50,7 +51,8 @@ async def load_update_documents(
     previous_table_provider: TableProvider,
 ) -> pd.DataFrame:
     """Load and parse update-only input documents into a standard format."""
-    input_documents = pd.DataFrame(await input_reader.read_files())
+    input_documents = [asdict(doc) async for doc in input_reader]
+    input_documents = pd.DataFrame(input_documents)
     input_documents["human_readable_id"] = input_documents.index
     if "raw_data" not in input_documents.columns:
         input_documents["raw_data"] = pd.Series(dtype="object")