diff --git a/libs/experimental/langchain_experimental/graph_transformers/llm.py b/libs/experimental/langchain_experimental/graph_transformers/llm.py
index 5b432a94bfc..f9f6fc87926 100644
--- a/libs/experimental/langchain_experimental/graph_transformers/llm.py
+++ b/libs/experimental/langchain_experimental/graph_transformers/llm.py
@@ -13,6 +13,7 @@ from langchain_core.prompts import (
     PromptTemplate,
 )
 from langchain_core.pydantic_v1 import BaseModel, Field, create_model
+from langchain_core.runnables import RunnableConfig
 
 examples = [
     {
@@ -710,13 +711,15 @@ class LLMGraphTransformer:
             prompt = prompt or default_prompt
             self.chain = prompt | structured_llm
 
-    def process_response(self, document: Document) -> GraphDocument:
+    def process_response(
+        self, document: Document, config: Optional[RunnableConfig] = None
+    ) -> GraphDocument:
         """
         Processes a single document, transforming it into a graph document using
         an LLM based on the model's schema and constraints.
         """
         text = document.page_content
-        raw_schema = self.chain.invoke({"input": text})
+        raw_schema = self.chain.invoke({"input": text}, config=config)
         if self._function_call:
             raw_schema = cast(Dict[Any, Any], raw_schema)
             nodes, relationships = _convert_to_graph_document(raw_schema)
@@ -765,7 +768,7 @@ class LLMGraphTransformer:
         return GraphDocument(nodes=nodes, relationships=relationships, source=document)
 
     def convert_to_graph_documents(
-        self, documents: Sequence[Document]
+        self, documents: Sequence[Document], config: Optional[RunnableConfig] = None
     ) -> List[GraphDocument]:
         """Convert a sequence of documents into graph documents.
 
@@ -776,15 +779,17 @@ class LLMGraphTransformer:
         Returns:
             Sequence[GraphDocument]: The transformed documents as graphs.
         """
-        return [self.process_response(document) for document in documents]
+        return [self.process_response(document, config) for document in documents]
 
-    async def aprocess_response(self, document: Document) -> GraphDocument:
+    async def aprocess_response(
+        self, document: Document, config: Optional[RunnableConfig] = None
+    ) -> GraphDocument:
         """
         Asynchronously processes a single document, transforming it into a
         graph document.
         """
         text = document.page_content
-        raw_schema = await self.chain.ainvoke({"input": text})
+        raw_schema = await self.chain.ainvoke({"input": text}, config=config)
         raw_schema = cast(Dict[Any, Any], raw_schema)
         nodes, relationships = _convert_to_graph_document(raw_schema)
 
@@ -811,13 +816,13 @@ class LLMGraphTransformer:
         return GraphDocument(nodes=nodes, relationships=relationships, source=document)
 
     async def aconvert_to_graph_documents(
-        self, documents: Sequence[Document]
+        self, documents: Sequence[Document], config: Optional[RunnableConfig] = None
     ) -> List[GraphDocument]:
         """
         Asynchronously convert a sequence of documents into graph documents.
         """
         tasks = [
-            asyncio.create_task(self.aprocess_response(document))
+            asyncio.create_task(self.aprocess_response(document, config))
             for document in documents
         ]
         results = await asyncio.gather(*tasks)