diff --git a/libs/checkpoint/langgraph/store/base/__init__.py b/libs/checkpoint/langgraph/store/base/__init__.py
index ef6729d83..f1353d052 100644
--- a/libs/checkpoint/langgraph/store/base/__init__.py
+++ b/libs/checkpoint/langgraph/store/base/__init__.py
@@ -227,8 +227,8 @@ class InvalidNamespaceError(ValueError):
     """Provided namespace is invalid."""
 
 
-class EmbeddingConfig(TypedDict, total=False):
-    """Configuration for vector embeddings in PostgreSQL store."""
+class IndexConfig(TypedDict, total=False):
+    """Configuration for indexing documents for semantic search in the store."""
 
     dims: int
     """Number of dimensions in the embedding vectors.
@@ -245,12 +245,6 @@ class EmbeddingConfig(TypedDict, total=False):
 
     embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc]
     """Optional function to generate embeddings from text."""
-    aembed: Optional[AEmbeddingsFunc]
-    """Optional asynchronous function to generate embeddings from text.
-    
-    Provide for asynchronous embedding generation if you do not provide
-    an Embeddings object.
-    """
 
     text_fields: Optional[list[str]]
     """Fields to extract text from for embedding generation.
diff --git a/libs/checkpoint/langgraph/store/base/_embed.py b/libs/checkpoint/langgraph/store/base/_embed.py
index a04b4f138..a6c88e1f6 100644
--- a/libs/checkpoint/langgraph/store/base/_embed.py
+++ b/libs/checkpoint/langgraph/store/base/_embed.py
@@ -29,8 +29,6 @@ Similar to EmbeddingsFunc, but returns an awaitable that resolves to the embeddi
 
 def ensure_embeddings(
     embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, None],
-    *,
-    aembed: Optional[AEmbeddingsFunc] = None,
 ) -> Embeddings:
     """Ensure that an embedding function conforms to LangChain's Embeddings interface.
 
@@ -42,9 +40,6 @@ def ensure_embeddings(
         embed: Either an existing Embeddings instance, or a function that converts
             text to embeddings. If the function is async, it will be used for both
             sync and async operations.
-        aembed: Optional async function for embeddings. If provided, it will be used
-            for async operations while the sync function is used for sync operations.
-            Must be None if embed is async.
 
     Returns:
         An Embeddings instance that wraps the provided function(s).
@@ -56,14 +51,12 @@ def ensure_embeddings(
         >>> embeddings = ensure_embeddings(my_embed_fn)
         >>> # Wrap an async function
         >>> embeddings = ensure_embeddings(my_async_fn)
-        >>> # Provide both sync and async implementations
-        >>> embeddings = ensure_embeddings(my_embed_fn, aembed=my_async_fn)
     """
-    if embed is None and aembed is None:
-        raise ValueError("embed or aembed must be provided")
+    if embed is None:
+        raise ValueError("embed must be provided")
     if isinstance(embed, Embeddings):
         return embed
-    return EmbeddingsLambda(embed, afunc=aembed)
+    return EmbeddingsLambda(embed)
 
 
 class EmbeddingsLambda(Embeddings):
@@ -97,18 +90,11 @@ class EmbeddingsLambda(Embeddings):
     def __init__(
         self,
         func: Union[EmbeddingsFunc, AEmbeddingsFunc, None],
-        afunc: Optional[AEmbeddingsFunc] = None,
     ) -> None:
         if _is_async_callable(func):
-            if afunc is not None:
-                raise ValueError(
-                    "afunc must be None if func is async. The async func will be used for both sync and async operations."
-                )
             self.afunc = func
         else:
             self.func = func
-            if afunc is not None:
-                self.afunc = afunc
 
     def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Embed a list of texts into vectors.
diff --git a/libs/checkpoint/langgraph/store/memory/__init__.py b/libs/checkpoint/langgraph/store/memory/__init__.py
index 7af33962a..2d246eadd 100644
--- a/libs/checkpoint/langgraph/store/memory/__init__.py
+++ b/libs/checkpoint/langgraph/store/memory/__init__.py
@@ -41,7 +41,7 @@ from langchain_core.embeddings import Embeddings
 
 from langgraph.store.base import (
     BaseStore,
-    EmbeddingConfig,
+    IndexConfig,
     GetOp,
     Item,
     ListNamespacesOp,
@@ -101,7 +101,7 @@ class InMemoryStore(BaseStore):
         "_vectors",
     )
 
-    def __init__(self, embedding_config: Optional[EmbeddingConfig] = None) -> None:
+    def __init__(self, embedding_config: Optional[IndexConfig] = None) -> None:
         self._data: dict[tuple[str, ...], dict[str, Item]] = defaultdict(dict)
         # [ns][key][path]
         self.inmem_store: dict[tuple[str, ...], dict[str, dict[str, list[float]]]] = (