style: apply black and isort formatting, remove unused imports

Signed-off-by: Mihai Criveti <crivetimihai@gmail.com>

Author: crivetimihai

agent_runtimes/langchain_agent/agent_langchain.py

@@ -75,11 +75,8 @@ def create_llm(config: AgentConfig) -> BaseChatModel:
     }
 
     if provider not in providers:
-        raise ValueError(
-            f"Unsupported LLM provider: {provider}. "
-            f"Supported providers: {', '.join(providers.keys())}"
-        )
-    
+        raise ValueError(f"Unsupported LLM provider: {provider}. " f"Supported providers: {', '.join(providers.keys())}")
+
     return providers[provider](config, common_args)
 
 
@@ -89,11 +86,7 @@ def _create_openai_llm(config: AgentConfig, common_args: Dict[str, Any]) -> Base
     if not config.openai_api_key:
         raise ValueError("OPENAI_API_KEY is required for OpenAI provider")
 
-    openai_args = {
-        "model": config.default_model, 
-        "api_key": config.openai_api_key, 
-        **common_args
-    }
+    openai_args = {"model": config.default_model, "api_key": config.openai_api_key, **common_args}
 
     if config.openai_base_url:
         openai_args["base_url"] = config.openai_base_url
@@ -106,45 +99,26 @@ def _create_openai_llm(config: AgentConfig, common_args: Dict[str, Any]) -> Base
 def _create_azure_llm(config: AgentConfig, common_args: Dict[str, Any]) -> BaseChatModel:
     """Create Azure OpenAI LLM instance."""
 
-    required_fields = [
-        config.azure_openai_api_key,
-        config.azure_openai_endpoint,
-        config.azure_deployment_name
-    ]
+    required_fields = [config.azure_openai_api_key, config.azure_openai_endpoint, config.azure_deployment_name]
 
     if not all(required_fields):
-        raise ValueError(
-            "Azure OpenAI requires AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and AZURE_DEPLOYMENT_NAME"
-        )
-    
+        raise ValueError("Azure OpenAI requires AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and AZURE_DEPLOYMENT_NAME")
+
     return AzureChatOpenAI(
-        api_key=config.azure_openai_api_key,
-        azure_endpoint=config.azure_openai_endpoint,
-        api_version=config.azure_openai_api_version,
-        azure_deployment=config.azure_deployment_name,
-        **common_args
+        api_key=config.azure_openai_api_key, azure_endpoint=config.azure_openai_endpoint, api_version=config.azure_openai_api_version, azure_deployment=config.azure_deployment_name, **common_args
     )
 
 
 def _create_bedrock_llm(config: AgentConfig, common_args: Dict[str, Any]) -> BaseChatModel:
     """Create AWS Bedrock LLM instance."""
 
     if BedrockChat is None:
-        raise ImportError(
-            "langchain-aws is required for Bedrock support. "
-            "Install with: pip install langchain-aws"
-        )
-    
-    required_fields = [
-        config.aws_access_key_id,
-        config.aws_secret_access_key,
-        config.bedrock_model_id
-    ]
+        raise ImportError("langchain-aws is required for Bedrock support. " "Install with: pip install langchain-aws")
+
+    required_fields = [config.aws_access_key_id, config.aws_secret_access_key, config.bedrock_model_id]
 
     if not all(required_fields):
-        raise ValueError(
-            "AWS Bedrock requires AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and BEDROCK_MODEL_ID"
-        )
+        raise ValueError("AWS Bedrock requires AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and BEDROCK_MODEL_ID")
 
     return BedrockChat(
         model_id=config.bedrock_model_id,
@@ -157,37 +131,23 @@ def _create_bedrock_llm(config: AgentConfig, common_args: Dict[str, Any]) -> Bas
 def _create_ollama_llm(config: AgentConfig, common_args: Dict[str, Any]) -> BaseChatModel:
     """Create OLLAMA LLM instance."""
     if ChatOllama is None:
-        raise ImportError(
-            "langchain-community is required for OLLAMA support. "
-            "Install with: pip install langchain-community"
-        )
-    
+        raise ImportError("langchain-community is required for OLLAMA support. " "Install with: pip install langchain-community")
+
     if not config.ollama_model:
         raise ValueError("OLLAMA_MODEL is required for OLLAMA provider")
-    
-    return ChatOllama(
-        model=config.ollama_model, 
-        base_url=config.ollama_base_url, 
-        **common_args
-    )
+
+    return ChatOllama(model=config.ollama_model, base_url=config.ollama_base_url, **common_args)
 
 
 def _create_anthropic_llm(config: AgentConfig, common_args: Dict[str, Any]) -> BaseChatModel:
     """Create Anthropic LLM instance."""
     if ChatAnthropic is None:
-        raise ImportError(
-            "langchain-anthropic is required for Anthropic support. "
-            "Install with: pip install langchain-anthropic"
-        )
-    
+        raise ImportError("langchain-anthropic is required for Anthropic support. " "Install with: pip install langchain-anthropic")
+
     if not config.anthropic_api_key:
         raise ValueError("ANTHROPIC_API_KEY is required for Anthropic provider")
 
-    return ChatAnthropic(
-        model=config.default_model,
-        api_key=config.anthropic_api_key,
-        **common_args
-    )
+    return ChatAnthropic(model=config.default_model, api_key=config.anthropic_api_key, **common_args)
 
 
 class MCPTool(BaseTool):
@@ -371,12 +331,7 @@ def is_initialized(self) -> bool:
     async def check_readiness(self) -> bool:
         """Check if agent is ready to handle requests"""
         try:
-            return (
-                self._initialized
-                and self.agent_executor is not None
-                and len(self.tools) >= 0  # Allow 0 tools for testing
-                and await self.test_gateway_connection()
-            )
+            return self._initialized and self.agent_executor is not None and len(self.tools) >= 0 and await self.test_gateway_connection()  # Allow 0 tools for testing
         except Exception:
             return False
 
@@ -428,9 +383,7 @@ async def run_async(
                     chat_history.append(SystemMessage(content=msg["content"]))
 
             # Run the agent
-            result = await self.agent_executor.ainvoke(
-                {"input": input_text, "chat_history": chat_history, "tool_names": [tool.name for tool in self.tools]}
-            )
+            result = await self.agent_executor.ainvoke({"input": input_text, "chat_history": chat_history, "tool_names": [tool.name for tool in self.tools]})
 
             return result["output"]