feat: upgrade vLLM to 0.19.1

- Bump vllm[flashinfer] to 0.19.1 in Dockerfile
- Add OpenAIServingRender (new required dependency in 0.19.x serving layer)
- Pass openai_serving_render to all four serving class constructors
- Remove log_error_stack param (removed upstream in 0.19.x)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Dockerfile

@@ -10,7 +10,7 @@ RUN ldconfig /usr/local/cuda-12.9/compat/
 
 # Install vLLM with FlashInfer - use CUDA 12.9 PyTorch wheels
 RUN uv pip install --system "packaging>=24.2" && \
-    uv pip install --system "vllm[flashinfer]==0.18.1" --extra-index-url https://download.pytorch.org/whl/cu129
+    uv pip install --system "vllm[flashinfer]==0.19.1" --extra-index-url https://download.pytorch.org/whl/cu129
 
 # Install additional Python dependencies (after vLLM to avoid PyTorch version conflicts)
 COPY builder/requirements.txt /requirements.txt

src/engine.py

@@ -19,6 +19,7 @@ from vllm.entrypoints.openai.models.protocol import BaseModelPath, LoRAModulePat
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest, ResponsesResponse
 from vllm.entrypoints.openai.responses.serving import OpenAIServingResponses
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 
 from constants import DEFAULT_BATCH_SIZE, DEFAULT_BATCH_SIZE_GROWTH_FACTOR, DEFAULT_MAX_CONCURRENCY, DEFAULT_MIN_BATCH_SIZE
 from engine_args import get_engine_args
@@ -246,16 +247,33 @@ class OpenAIvLLMEngine(vLLMEngine):
             lora_modules=self.lora_adapters,
         )
         await self.serving_models.init_static_loras()
-        
+
         # Get chat template from vLLM tokenizer if available
         chat_template = None
         if self.tokenizer and hasattr(self.tokenizer, 'tokenizer'):
             chat_template = self.tokenizer.tokenizer.chat_template
-        
+
+        self.openai_serving_render = OpenAIServingRender(
+            model_config=self.llm.model_config,
+            renderer=self.llm.renderer,
+            io_processor=self.llm.io_processor,
+            model_registry=self.serving_models.registry,
+            request_logger=None,
+            chat_template=chat_template,
+            chat_template_content_format="auto",
+            trust_request_chat_template=os.getenv('TRUST_REQUEST_CHAT_TEMPLATE', 'false').lower() == 'true',
+            enable_auto_tools=os.getenv('ENABLE_AUTO_TOOL_CHOICE', 'false').lower() == 'true',
+            exclude_tools_when_tool_choice_none=os.getenv('EXCLUDE_TOOLS_WHEN_TOOL_CHOICE_NONE', 'false').lower() == 'true',
+            tool_parser=os.getenv('TOOL_CALL_PARSER', "") or None,
+            reasoning_parser=os.getenv('REASONING_PARSER', "") or None,
+            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
+        )
+
         self.chat_engine = OpenAIServingChat(
-            engine_client=self.llm, 
+            engine_client=self.llm,
             models=self.serving_models,
             response_role=self.response_role,
+            openai_serving_render=self.openai_serving_render,
             request_logger=None,
             chat_template=chat_template,
             chat_template_content_format="auto",
@@ -268,20 +286,20 @@ class OpenAIvLLMEngine(vLLMEngine):
             enable_prompt_tokens_details=os.getenv('ENABLE_PROMPT_TOKENS_DETAILS', 'false').lower() == 'true',
             enable_force_include_usage=os.getenv('ENABLE_FORCE_INCLUDE_USAGE', 'false').lower() == 'true',
             enable_log_outputs=os.getenv('ENABLE_LOG_OUTPUTS', 'false').lower() == 'true',
-            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
         )
         self.completion_engine = OpenAIServingCompletion(
             engine_client=self.llm,
             models=self.serving_models,
+            openai_serving_render=self.openai_serving_render,
             request_logger=None,
             return_tokens_as_token_ids=os.getenv('RETURN_TOKENS_AS_TOKEN_IDS', 'false').lower() == 'true',
             enable_prompt_tokens_details=os.getenv('ENABLE_PROMPT_TOKENS_DETAILS', 'false').lower() == 'true',
             enable_force_include_usage=os.getenv('ENABLE_FORCE_INCLUDE_USAGE', 'false').lower() == 'true',
-            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
         )
         self.responses_engine = OpenAIServingResponses(
             engine_client=self.llm,
             models=self.serving_models,
+            openai_serving_render=self.openai_serving_render,
             request_logger=None,
             chat_template=chat_template,
             chat_template_content_format="auto",
@@ -293,12 +311,12 @@ class OpenAIvLLMEngine(vLLMEngine):
             enable_prompt_tokens_details=os.getenv('ENABLE_PROMPT_TOKENS_DETAILS', 'false').lower() == 'true',
             enable_force_include_usage=os.getenv('ENABLE_FORCE_INCLUDE_USAGE', 'false').lower() == 'true',
             enable_log_outputs=os.getenv('ENABLE_LOG_OUTPUTS', 'false').lower() == 'true',
-            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
         )
         self.messages_engine = AnthropicServingMessages(
             engine_client=self.llm,
             models=self.serving_models,
             response_role=self.response_role,
+            openai_serving_render=self.openai_serving_render,
             request_logger=None,
             chat_template=chat_template,
             chat_template_content_format="auto",