refactor: Rename LLMAdapter to LLMGateway

cognee-mcp/src/codingagents/coding_rule_associations.py

@@ -4,7 +4,7 @@ from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.infrastructure.databases.vector import get_vector_engine
 
 from cognee.low_level import DataPoint
-from cognee.infrastructure.llm import LLMAdapter
+from cognee.infrastructure.llm import LLMGateway
 from cognee.shared.logging_utils import get_logger
 from cognee.modules.engine.models import NodeSet
 from cognee.tasks.storage import add_data_points, index_graph_edges
@@ -96,12 +96,12 @@ async def add_rule_associations(data: str, rules_nodeset_name: str):
 
     user_context = {"chat": data, "rules": existing_rules}
 
-    user_prompt = LLMAdapter.render_prompt(
+    user_prompt = LLMGateway.render_prompt(
         "coding_rule_association_agent_user.txt", context=user_context
     )
-    system_prompt = LLMAdapter.render_prompt("coding_rule_association_agent_system.txt", context={})
+    system_prompt = LLMGateway.render_prompt("coding_rule_association_agent_system.txt", context={})
 
-    rule_list = await LLMAdapter.acreate_structured_output(
+    rule_list = await LLMGateway.acreate_structured_output(
         text_input=user_prompt, system_prompt=system_prompt, response_model=RuleSet
     )
 

cognee/eval_framework/evaluation/direct_llm_eval_adapter.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel
 from cognee.eval_framework.evaluation.base_eval_adapter import BaseEvalAdapter
 from cognee.eval_framework.eval_config import EvalConfig
 
-from cognee.infrastructure.llm import LLMAdapter
+from cognee.infrastructure.llm import LLMGateway
 
 
 class CorrectnessEvaluation(BaseModel):
@@ -25,10 +25,10 @@ class DirectLLMEvalAdapter(BaseEvalAdapter):
     ) -> Dict[str, Any]:
         args = {"question": question, "answer": answer, "golden_answer": golden_answer}
 
-        user_prompt = LLMAdapter.render_prompt(self.eval_prompt_path, args)
+        user_prompt = LLMGateway.render_prompt(self.eval_prompt_path, args)
-        system_prompt = LLMAdapter.read_query_prompt(self.system_prompt_path)
+        system_prompt = LLMGateway.read_query_prompt(self.system_prompt_path)
 
-        evaluation = await LLMAdapter.acreate_structured_output(
+        evaluation = await LLMGateway.acreate_structured_output(
             text_input=user_prompt,
             system_prompt=system_prompt,
             response_model=CorrectnessEvaluation,

cognee/infrastructure/llm/LLMGateway.py

@@ -4,7 +4,7 @@ from typing import Coroutine
 from cognee.infrastructure.llm import get_llm_config
 
 
-class LLMAdapter:
+class LLMGateway:
     """
     Class handles selection of structured output frameworks and LLM functions.
     Class used as a namespace for LLM related functions, should not be instantiated, all methods are static.

cognee/infrastructure/llm/__init__.py

@@ -11,4 +11,4 @@ from cognee.infrastructure.llm.utils import (
     test_embedding_connection,
 )
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/extraction/extract_categories.py

@@ -1,12 +1,12 @@
 from typing import Type
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 
 async def extract_categories(content: str, response_model: Type[BaseModel]):
-    system_prompt = LLMAdapter.read_query_prompt("classify_content.txt")
+    system_prompt = LLMGateway.read_query_prompt("classify_content.txt")
 
-    llm_output = await LLMAdapter.acreate_structured_output(content, system_prompt, response_model)
+    llm_output = await LLMGateway.acreate_structured_output(content, system_prompt, response_model)
 
     return llm_output

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/extraction/extract_summary.py

@@ -5,7 +5,7 @@ from typing import Type
 from instructor.exceptions import InstructorRetryException
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.shared.data_models import SummarizedCode
 
 logger = get_logger("extract_summary")
@@ -25,9 +25,9 @@ def get_mock_summarized_code():
 
 
 async def extract_summary(content: str, response_model: Type[BaseModel]):
-    system_prompt = LLMAdapter.read_query_prompt("summarize_content.txt")
+    system_prompt = LLMGateway.read_query_prompt("summarize_content.txt")
 
-    llm_output = await LLMAdapter.acreate_structured_output(content, system_prompt, response_model)
+    llm_output = await LLMGateway.acreate_structured_output(content, system_prompt, response_model)
 
     return llm_output
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/extraction/knowledge_graph/extract_content_graph.py

@@ -2,7 +2,7 @@ import os
 from typing import Type
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.config import (
     get_llm_config,
 )
@@ -22,9 +22,9 @@ async def extract_content_graph(content: str, response_model: Type[BaseModel]):
     else:
         base_directory = None
 
-    system_prompt = LLMAdapter.render_prompt(prompt_path, {}, base_directory=base_directory)
+    system_prompt = LLMGateway.render_prompt(prompt_path, {}, base_directory=base_directory)
 
-    content_graph = await LLMAdapter.acreate_structured_output(
+    content_graph = await LLMGateway.acreate_structured_output(
         content, system_prompt, response_model
     )
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py

@@ -11,7 +11,7 @@ from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.ll
     sleep_and_retry_async,
 )
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 
 class AnthropicAdapter(LLMInterface):
@@ -91,7 +91,7 @@ class AnthropicAdapter(LLMInterface):
         if not system_prompt:
             raise InvalidValueError(message="No system prompt path provided.")
 
-        system_prompt = LLMAdapter.read_query_prompt(system_prompt)
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
 
         formatted_prompt = (
             f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py

@@ -9,7 +9,7 @@ from cognee.exceptions import InvalidValueError
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
     LLMInterface,
 )
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
     rate_limit_async,
     sleep_and_retry_async,
@@ -136,7 +136,7 @@ class GeminiAdapter(LLMInterface):
             text_input = "No user input provided."
         if not system_prompt:
             raise InvalidValueError(message="No system prompt path provided.")
-        system_prompt = LLMAdapter.read_query_prompt(system_prompt)
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
 
         formatted_prompt = (
             f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/llm_interface.py

@@ -3,7 +3,7 @@
 from typing import Type, Protocol
 from abc import abstractmethod
 from pydantic import BaseModel
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 
 class LLMInterface(Protocol):
@@ -57,7 +57,7 @@ class LLMInterface(Protocol):
             text_input = "No user input provided."
         if not system_prompt:
             raise ValueError("No system prompt path provided.")
-        system_prompt = LLMAdapter.read_query_prompt(system_prompt)
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
 
         formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py

@@ -8,7 +8,7 @@ from litellm.exceptions import ContentPolicyViolationError
 from instructor.exceptions import InstructorRetryException
 
 from cognee.exceptions import InvalidValueError
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
     LLMInterface,
 )
@@ -326,7 +326,7 @@ class OpenAIAdapter(LLMInterface):
             text_input = "No user input provided."
         if not system_prompt:
             raise InvalidValueError(message="No system prompt path provided.")
-        system_prompt = LLMAdapter.read_query_prompt(system_prompt)
+        system_prompt = LLMGateway.read_query_prompt(system_prompt)
 
         formatted_prompt = (
             f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""

cognee/modules/data/processing/document_types/AudioDocument.py

@@ -1,5 +1,5 @@
 from cognee.modules.chunking.Chunker import Chunker
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 from .Document import Document
 
@@ -8,7 +8,7 @@ class AudioDocument(Document):
     type: str = "audio"
 
     async def create_transcript(self):
-        result = await LLMAdapter.create_transcript(self.raw_data_location)
+        result = await LLMGateway.create_transcript(self.raw_data_location)
         return result.text
 
     async def read(self, chunker_cls: Chunker, max_chunk_size: int):

cognee/modules/data/processing/document_types/ImageDocument.py

@@ -1,4 +1,4 @@
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.chunking.Chunker import Chunker
 
 from .Document import Document
@@ -8,7 +8,7 @@ class ImageDocument(Document):
     type: str = "image"
 
     async def transcribe_image(self):
-        result = await LLMAdapter.transcribe_image(self.raw_data_location)
+        result = await LLMGateway.transcribe_image(self.raw_data_location)
         return result.choices[0].message.content
 
     async def read(self, chunker_cls: Chunker, max_chunk_size: int):

cognee/modules/retrieval/code_retriever.py

@@ -7,7 +7,7 @@ from cognee.shared.logging_utils import get_logger
 from cognee.modules.retrieval.base_retriever import BaseRetriever
 from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.infrastructure.databases.vector import get_vector_engine
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger("CodeRetriever")
 
@@ -41,10 +41,10 @@ class CodeRetriever(BaseRetriever):
             f"Processing query with LLM: '{query[:100]}{'...' if len(query) > 100 else ''}'"
         )
 
-        system_prompt = LLMAdapter.read_query_prompt("codegraph_retriever_system.txt")
+        system_prompt = LLMGateway.read_query_prompt("codegraph_retriever_system.txt")
 
         try:
-            result = await LLMAdapter.acreate_structured_output(
+            result = await LLMGateway.acreate_structured_output(
                 text_input=query,
                 system_prompt=system_prompt,
                 response_model=self.CodeQueryInfo,

cognee/modules/retrieval/graph_completion_cot_retriever.py

@@ -3,7 +3,7 @@ from cognee.shared.logging_utils import get_logger
 
 from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
 from cognee.modules.retrieval.utils.completion import generate_completion
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger()
 
@@ -94,27 +94,27 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
             logger.info(f"Chain-of-thought: round {round_idx} - answer: {answer}")
             if round_idx < max_iter:
                 valid_args = {"query": query, "answer": answer, "context": context}
-                valid_user_prompt = LLMAdapter.render_prompt(
+                valid_user_prompt = LLMGateway.render_prompt(
                     filename=self.validation_user_prompt_path, context=valid_args
                 )
-                valid_system_prompt = LLMAdapter.read_query_prompt(
+                valid_system_prompt = LLMGateway.read_query_prompt(
                     prompt_file_name=self.validation_system_prompt_path
                 )
 
-                reasoning = await LLMAdapter.acreate_structured_output(
+                reasoning = await LLMGateway.acreate_structured_output(
                     text_input=valid_user_prompt,
                     system_prompt=valid_system_prompt,
                     response_model=str,
                 )
                 followup_args = {"query": query, "answer": answer, "reasoning": reasoning}
-                followup_prompt = LLMAdapter.render_prompt(
+                followup_prompt = LLMGateway.render_prompt(
                     filename=self.followup_user_prompt_path, context=followup_args
                 )
-                followup_system = LLMAdapter.read_query_prompt(
+                followup_system = LLMGateway.read_query_prompt(
                     prompt_file_name=self.followup_system_prompt_path
                 )
 
-                followup_question = await LLMAdapter.acreate_structured_output(
+                followup_question = await LLMGateway.acreate_structured_output(
                     text_input=followup_prompt, system_prompt=followup_system, response_model=str
                 )
                 logger.info(

cognee/modules/retrieval/natural_language_retriever.py

@@ -2,7 +2,7 @@ from typing import Any, Optional
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.infrastructure.databases.graph.networkx.adapter import NetworkXAdapter
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.retrieval.base_retriever import BaseRetriever
 from cognee.modules.retrieval.exceptions import SearchTypeNotSupported
 from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
@@ -50,7 +50,7 @@ class NaturalLanguageRetriever(BaseRetriever):
 
     async def _generate_cypher_query(self, query: str, edge_schemas, previous_attempts=None) -> str:
         """Generate a Cypher query using LLM based on natural language query and schema information."""
-        system_prompt = LLMAdapter.render_prompt(
+        system_prompt = LLMGateway.render_prompt(
             self.system_prompt_path,
             context={
                 "edge_schemas": edge_schemas,
@@ -58,7 +58,7 @@ class NaturalLanguageRetriever(BaseRetriever):
             },
         )
 
-        return await LLMAdapter.acreate_structured_output(
+        return await LLMGateway.acreate_structured_output(
             text_input=query,
             system_prompt=system_prompt,
             response_model=str,

cognee/modules/retrieval/utils/completion.py

@@ -1,4 +1,4 @@
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 
 async def generate_completion(
@@ -9,10 +9,10 @@ async def generate_completion(
 ) -> str:
     """Generates a completion using LLM with given context and prompts."""
     args = {"question": query, "context": context}
-    user_prompt = LLMAdapter.render_prompt(user_prompt_path, args)
+    user_prompt = LLMGateway.render_prompt(user_prompt_path, args)
-    system_prompt = LLMAdapter.read_query_prompt(system_prompt_path)
+    system_prompt = LLMGateway.read_query_prompt(system_prompt_path)
 
-    return await LLMAdapter.acreate_structured_output(
+    return await LLMGateway.acreate_structured_output(
         text_input=user_prompt,
         system_prompt=system_prompt,
         response_model=str,
@@ -24,9 +24,9 @@ async def summarize_text(
     prompt_path: str = "summarize_search_results.txt",
 ) -> str:
     """Summarizes text using LLM with the specified prompt."""
-    system_prompt = LLMAdapter.read_query_prompt(prompt_path)
+    system_prompt = LLMGateway.read_query_prompt(prompt_path)
 
-    return await LLMAdapter.acreate_structured_output(
+    return await LLMGateway.acreate_structured_output(
         text_input=text,
         system_prompt=system_prompt,
         response_model=str,

cognee/modules/retrieval/utils/description_to_codepart_search.py

@@ -9,7 +9,7 @@ from cognee.modules.users.methods import get_default_user
 from cognee.modules.users.models import User
 from cognee.shared.utils import send_telemetry
 from cognee.modules.search.methods import search
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger(level=ERROR)
 
@@ -71,7 +71,7 @@ async def code_description_to_code_part(
                 if isinstance(obj, dict) and "description" in obj
             )
 
-            context_from_documents = await LLMAdapter.acreate_structured_output(
+            context_from_documents = await LLMGateway.acreate_structured_output(
                 text_input=f"The retrieved context from documents is {concatenated_descriptions}.",
                 system_prompt="You are a Senior Software Engineer, summarize the context from documents"
                 f" in a way that it is gonna be provided next to codeparts as context"

cognee/modules/search/operations/select_search_type.py

@@ -1,7 +1,7 @@
 from cognee.infrastructure.llm.prompts import read_query_prompt
 from cognee.modules.search.types import SearchType
 from cognee.shared.logging_utils import get_logger
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger("SearchTypeSelector")
 
@@ -24,7 +24,7 @@ async def select_search_type(
     system_prompt = read_query_prompt(system_prompt_path)
 
     try:
-        response = await LLMAdapter.acreate_structured_output(
+        response = await LLMGateway.acreate_structured_output(
             text_input=query,
             system_prompt=system_prompt,
             response_model=str,

cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel
 from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.infrastructure.engine.models import DataPoint
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 
 
@@ -40,7 +40,7 @@ async def chunk_naive_llm_classifier(
         return data_chunks
 
     chunk_classifications = await asyncio.gather(
-        *[LLMAdapter.extract_categories(chunk.text, classification_model) for chunk in data_chunks],
+        *[LLMGateway.extract_categories(chunk.text, classification_model) for chunk in data_chunks],
     )
 
     classification_data_points = []

cognee/tasks/entity_completion/entity_extractors/llm_entity_extractor.py

@@ -6,7 +6,7 @@ from pydantic import BaseModel
 from cognee.infrastructure.entities.BaseEntityExtractor import BaseEntityExtractor
 from cognee.modules.engine.models import Entity
 from cognee.modules.engine.models.EntityType import EntityType
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger("llm_entity_extractor")
 
@@ -50,10 +50,10 @@ class LLMEntityExtractor(BaseEntityExtractor):
         try:
             logger.info(f"Extracting entities from text: {text[:100]}...")
 
-            user_prompt = LLMAdapter.render_prompt(self.user_prompt_template, {"text": text})
+            user_prompt = LLMGateway.render_prompt(self.user_prompt_template, {"text": text})
-            system_prompt = LLMAdapter.read_query_prompt(self.system_prompt_template)
+            system_prompt = LLMGateway.read_query_prompt(self.system_prompt_template)
 
-            response = await LLMAdapter.acreate_structured_output(
+            response = await LLMGateway.acreate_structured_output(
                 text_input=user_prompt,
                 system_prompt=system_prompt,
                 response_model=EntityList,

cognee/tasks/graph/cascade_extract/utils/extract_content_nodes_and_relationship_names.py

@@ -1,7 +1,7 @@
 from typing import List, Tuple
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.root_dir import get_absolute_path
 
 
@@ -32,15 +32,15 @@ async def extract_content_nodes_and_relationship_names(
         }
 
         base_directory = get_absolute_path("./tasks/graph/cascade_extract/prompts")
-        text_input = LLMAdapter.render_prompt(
+        text_input = LLMGateway.render_prompt(
             "extract_graph_relationship_names_prompt_input.txt",
             context,
             base_directory=base_directory,
         )
-        system_prompt = LLMAdapter.read_query_prompt(
+        system_prompt = LLMGateway.read_query_prompt(
             "extract_graph_relationship_names_prompt_system.txt", base_directory=base_directory
         )
-        response = await LLMAdapter.acreate_structured_output(
+        response = await LLMGateway.acreate_structured_output(
             text_input=text_input,
             system_prompt=system_prompt,
             response_model=PotentialNodesAndRelationshipNames,

cognee/tasks/graph/cascade_extract/utils/extract_edge_triplets.py

@@ -1,6 +1,6 @@
 from typing import List
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.shared.data_models import KnowledgeGraph
 from cognee.root_dir import get_absolute_path
 
@@ -26,13 +26,13 @@ async def extract_edge_triplets(
         }
 
         base_directory = get_absolute_path("./tasks/graph/cascade_extract/prompts")
-        text_input = LLMAdapter.render_prompt(
+        text_input = LLMGateway.render_prompt(
             "extract_graph_edge_triplets_prompt_input.txt", context, base_directory=base_directory
         )
-        system_prompt = LLMAdapter.read_query_prompt(
+        system_prompt = LLMGateway.read_query_prompt(
             "extract_graph_edge_triplets_prompt_system.txt", base_directory=base_directory
         )
-        extracted_graph = await LLMAdapter.acreate_structured_output(
+        extracted_graph = await LLMGateway.acreate_structured_output(
             text_input=text_input, system_prompt=system_prompt, response_model=KnowledgeGraph
         )
 

cognee/tasks/graph/cascade_extract/utils/extract_nodes.py

@@ -1,7 +1,7 @@
 from typing import List
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.root_dir import get_absolute_path
 
 
@@ -24,13 +24,13 @@ async def extract_nodes(text: str, n_rounds: int = 2) -> List[str]:
             "text": text,
         }
         base_directory = get_absolute_path("./tasks/graph/cascade_extract/prompts")
-        text_input = LLMAdapter.render_prompt(
+        text_input = LLMGateway.render_prompt(
             "extract_graph_nodes_prompt_input.txt", context, base_directory=base_directory
         )
-        system_prompt = LLMAdapter.read_query_prompt(
+        system_prompt = LLMGateway.read_query_prompt(
             "extract_graph_nodes_prompt_system.txt", base_directory=base_directory
         )
-        response = await LLMAdapter.acreate_structured_output(
+        response = await LLMGateway.acreate_structured_output(
             text_input=text_input, system_prompt=system_prompt, response_model=PotentialNodes
         )
 

cognee/tasks/graph/extract_graph_from_code.py

@@ -2,7 +2,7 @@ import asyncio
 from typing import Type, List
 from pydantic import BaseModel
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
 from cognee.tasks.storage import add_data_points
 
@@ -18,7 +18,7 @@ async def extract_graph_from_code(
         - Graph nodes are stored using the `add_data_points` function for later retrieval or analysis.
     """
     chunk_graphs = await asyncio.gather(
-        *[LLMAdapter.extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
+        *[LLMGateway.extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
     )
 
     for chunk_index, chunk in enumerate(data_chunks):

cognee/tasks/graph/extract_graph_from_data.py

@@ -11,7 +11,7 @@ from cognee.modules.graph.utils import (
     retrieve_existing_edges,
 )
 from cognee.shared.data_models import KnowledgeGraph
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 
 async def integrate_chunk_graphs(
@@ -56,7 +56,7 @@ async def extract_graph_from_data(
     Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model.
     """
     chunk_graphs = await asyncio.gather(
-        *[LLMAdapter.extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
+        *[LLMGateway.extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
     )
 
     # Note: Filter edges with missing source or target nodes

cognee/tasks/graph/infer_data_ontology.py

@@ -27,7 +27,7 @@ from cognee.modules.data.methods.add_model_class_to_graph import (
 from cognee.tasks.graph.models import NodeModel, GraphOntology
 from cognee.shared.data_models import KnowledgeGraph
 from cognee.modules.engine.utils import generate_node_id, generate_node_name
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 
 logger = get_logger("task:infer_data_ontology")
 
@@ -53,9 +53,9 @@ async def extract_ontology(content: str, response_model: Type[BaseModel]):
         The structured ontology extracted from the content.
     """
 
-    system_prompt = LLMAdapter.read_query_prompt("extract_ontology.txt")
+    system_prompt = LLMGateway.read_query_prompt("extract_ontology.txt")
 
-    ontology = await LLMAdapter.acreate_structured_output(content, system_prompt, response_model)
+    ontology = await LLMGateway.acreate_structured_output(content, system_prompt, response_model)
 
     return ontology
 

cognee/tasks/summarization/summarize_code.py

@@ -3,7 +3,7 @@ from typing import AsyncGenerator, Union
 from uuid import uuid5
 
 from cognee.infrastructure.engine import DataPoint
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from .models import CodeSummary
 
 
@@ -16,7 +16,7 @@ async def summarize_code(
     code_data_points = [file for file in code_graph_nodes if hasattr(file, "source_code")]
 
     file_summaries = await asyncio.gather(
-        *[LLMAdapter.extract_code_summary(file.source_code) for file in code_data_points]
+        *[LLMGateway.extract_code_summary(file.source_code) for file in code_data_points]
     )
 
     file_summaries_map = {

cognee/tasks/summarization/summarize_text.py

@@ -4,7 +4,7 @@ from uuid import uuid5
 from pydantic import BaseModel
 
 from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.cognify.config import get_cognify_config
 from .models import TextSummary
 
@@ -43,7 +43,7 @@ async def summarize_text(
         summarization_model = cognee_config.summarization_model
 
     chunk_summaries = await asyncio.gather(
-        *[LLMAdapter.extract_summary(chunk.text, summarization_model) for chunk in data_chunks]
+        *[LLMGateway.extract_summary(chunk.text, summarization_model) for chunk in data_chunks]
     )
 
     summaries = [

examples/python/agentic_reasoning_procurement_example.py

@@ -3,7 +3,7 @@ import logging
 import cognee
 import asyncio
 
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from dotenv import load_dotenv
 from cognee.api.v1.search import SearchType
 from cognee.modules.engine.models import NodeSet
@@ -185,7 +185,7 @@ async def run_procurement_example():
     print(research_information)
     print("\nPassing research to LLM for final procurement recommendation...\n")
 
-    final_decision = await LLMAdapter.acreate_structured_output(
+    final_decision = await LLMGateway.acreate_structured_output(
         text_input=research_information,
         system_prompt="""You are a procurement decision assistant. Use the provided QA pairs that were collected through a research phase. Recommend the best vendor,
          based on pricing, delivery, warranty, policy fit, and past performance. Be concise and justify your choice with evidence.

examples/python/graphiti_example.py

@@ -12,7 +12,7 @@ from cognee.tasks.temporal_awareness.index_graphiti_objects import (
 )
 from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
 from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
-from cognee.infrastructure.llm.LLMAdapter import LLMAdapter
+from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.modules.users.methods import get_default_user
 
 text_list = [
@@ -59,10 +59,10 @@ async def main():
         "context": context,
     }
 
-    user_prompt = LLMAdapter.render_prompt("graph_context_for_question.txt", args)
+    user_prompt = LLMGateway.render_prompt("graph_context_for_question.txt", args)
-    system_prompt = LLMAdapter.read_query_prompt("answer_simple_question_restricted.txt")
+    system_prompt = LLMGateway.read_query_prompt("answer_simple_question_restricted.txt")
 
-    computed_answer = await LLMAdapter.acreate_structured_output(
+    computed_answer = await LLMGateway.acreate_structured_output(
         text_input=user_prompt,
         system_prompt=system_prompt,
         response_model=str,