Nits

- overwrite -> override
 - enums for error types
 - some nits

backend/onyx/agents/agent_search/core_state.py

@@ -9,7 +9,6 @@ class CoreState(BaseModel):
     This is the core state that is shared across all subgraphs.
     """
 
-    base_question: str = ""
     log_messages: Annotated[list[str], add] = []
 
 
@@ -18,4 +17,4 @@ class SubgraphCoreState(BaseModel):
     This is the core state that is shared across all subgraphs.
     """
 
-    log_messages: Annotated[list[str], add]
+    log_messages: Annotated[list[str], add] = []

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/check_sub_answer.py

@@ -1,8 +1,8 @@
 from datetime import datetime
 from typing import cast
 
+from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
 from langchain_core.runnables.config import RunnableConfig
 
 from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
@@ -12,12 +12,39 @@ from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer
     SubQuestionAnswerCheckUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    binary_string_test,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_POSITIVE_VALUE_STR,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import AgentLLMErrorType
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import SUB_ANSWER_CHECK_PROMPT
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="LLM Timeout Error. The sub-answer will be treated as 'relevant'",
+    rate_limit="LLM Rate Limit Error. The sub-answer will be treated as 'relevant'",
+    general_error="General LLM Error. The sub-answer will be treated as 'relevant'",
+)
 
 
 def check_sub_answer(
@@ -53,14 +80,46 @@ def check_sub_answer(
 
     graph_config = cast(GraphConfig, config["metadata"]["config"])
     fast_llm = graph_config.tooling.fast_llm
-    response = list(
-        fast_llm.stream(
+    agent_error: AgentErrorLoggingFormat | None = None
+    response: BaseMessage | None = None
+    try:
+        response = fast_llm.invoke(
             prompt=msg,
+            timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK,
         )
-    )
 
-    quality_str: str = merge_message_runs(response, chunk_separator="")[0].content
-    answer_quality = "yes" in quality_str.lower()
+    except LLMTimeoutError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - check sub answer")
+
+    except LLMRateLimitError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - check sub answer")
+
+    if agent_error:
+        answer_quality = True
+        log_result = agent_error.error_result
+
+    else:
+        if response:
+            quality_str: str = cast(str, response.content)
+            answer_quality = binary_string_test(
+                text=quality_str, positive_value=AGENT_POSITIVE_VALUE_STR
+            )
+
+        else:
+            answer_quality = True
+            quality_str = "yes - because LLM error"
+
+        log_result = f"Answer quality: {quality_str}"
 
     return SubQuestionAnswerCheckUpdate(
         answer_quality=answer_quality,
@@ -69,7 +128,7 @@ def check_sub_answer(
                 graph_component="initial  - generate individual sub answer",
                 node_name="check sub answer",
                 node_start_time=node_start_time,
-                result=f"Answer quality: {quality_str}",
+                result=log_result,
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/generate_sub_answer.py

@@ -16,6 +16,20 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_sub_question_answer_prompt,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    LLM_ANSWER_ERROR_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import get_answer_citation_ids
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
@@ -30,11 +44,20 @@ from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
+from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import NO_RECOVERED_DOCS
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="LLM Timeout Error. A sub-answer could not be constructed and the sub-question will be ignored.",
+    rate_limit="LLM Rate Limit Error. A sub-answer could not be constructed and the sub-question will be ignored.",
+    general_error="General LLM Error. A sub-answer could not be constructed and the sub-question will be ignored.",
+)
+
 
 def generate_sub_answer(
     state: AnswerQuestionState,
@@ -57,6 +80,8 @@ def generate_sub_answer(
 
     if len(context_docs) == 0:
         answer_str = NO_RECOVERED_DOCS
+        cited_documents: list = []
+        log_results = "No documents retrieved"
         write_custom_event(
             "sub_answers",
             AgentAnswerPiece(
@@ -79,41 +104,67 @@ def generate_sub_answer(
 
         response: list[str | list[str | dict[str, Any]]] = []
         dispatch_timings: list[float] = []
-        for message in fast_llm.stream(
-            prompt=msg,
-        ):
-            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-            content = message.content
-            if not isinstance(content, str):
-                raise ValueError(
-                    f"Expected content to be a string, but got {type(content)}"
+
+        agent_error: AgentErrorLoggingFormat | None = None
+
+        try:
+            for message in fast_llm.stream(
+                prompt=msg,
+                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION,
+            ):
+                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+                content = message.content
+                if not isinstance(content, str):
+                    raise ValueError(
+                        f"Expected content to be a string, but got {type(content)}"
+                    )
+                start_stream_token = datetime.now()
+                write_custom_event(
+                    "sub_answers",
+                    AgentAnswerPiece(
+                        answer_piece=content,
+                        level=level,
+                        level_question_num=question_num,
+                        answer_type="agent_sub_answer",
+                    ),
+                    writer,
                 )
-            start_stream_token = datetime.now()
-            write_custom_event(
-                "sub_answers",
-                AgentAnswerPiece(
-                    answer_piece=content,
-                    level=level,
-                    level_question_num=question_num,
-                    answer_type="agent_sub_answer",
-                ),
-                writer,
-            )
-            end_stream_token = datetime.now()
-            dispatch_timings.append(
-                (end_stream_token - start_stream_token).microseconds
-            )
-            response.append(content)
+                end_stream_token = datetime.now()
+                dispatch_timings.append(
+                    (end_stream_token - start_stream_token).microseconds
+                )
+                response.append(content)
 
-        answer_str = merge_message_runs(response, chunk_separator="")[0].content
-        logger.debug(
-            f"Average dispatch time: {sum(dispatch_timings) / len(dispatch_timings)}"
-        )
+        except LLMTimeoutError:
+            agent_error = AgentErrorLoggingFormat(
+                error_type=AgentLLMErrorType.TIMEOUT,
+                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+                error_result=_llm_node_error_strings.timeout,
+            )
+            logger.error("LLM Timeout Error - generate sub answer")
+        except LLMRateLimitError:
+            agent_error = AgentErrorLoggingFormat(
+                error_type=AgentLLMErrorType.RATE_LIMIT,
+                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+                error_result=_llm_node_error_strings.rate_limit,
+            )
+            logger.error("LLM Rate Limit Error - generate sub answer")
 
-    answer_citation_ids = get_answer_citation_ids(answer_str)
-    cited_documents = [
-        context_docs[id] for id in answer_citation_ids if id < len(context_docs)
-    ]
+        if agent_error:
+            answer_str = LLM_ANSWER_ERROR_MESSAGE
+            cited_documents = []
+            log_results = (
+                agent_error.error_result
+                or "Sub-answer generation failed due to LLM error"
+            )
+
+        else:
+            answer_str = merge_message_runs(response, chunk_separator="")[0].content
+            answer_citation_ids = get_answer_citation_ids(answer_str)
+            cited_documents = [
+                context_docs[id] for id in answer_citation_ids if id < len(context_docs)
+            ]
+            log_results = None
 
     stop_event = StreamStopInfo(
         stop_reason=StreamStopReason.FINISHED,
@@ -131,7 +182,7 @@ def generate_sub_answer(
                 graph_component="initial - generate individual sub answer",
                 node_name="generate sub answer",
                 node_start_time=node_start_time,
-                result="",
+                result=log_results or "",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/states.py

@@ -42,10 +42,8 @@ class SubQuestionRetrievalIngestionUpdate(LoggerUpdate, BaseModel):
 
 
 class SubQuestionAnsweringInput(SubgraphCoreState):
-    question: str = ""
-    question_id: str = (
-        ""  # 0_0 is original question, everything else is <level>_<question_num>.
-    )
+    question: str
+    question_id: str
     # level 0 is original question and first decomposition, level 1 is follow up, etc
     # question_num is a unique number per original question per level.
 

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/generate_initial_answer.py

@@ -26,7 +26,18 @@ from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
 from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.operators import (
     dedup_inference_sections,
 )
@@ -42,12 +53,16 @@ from onyx.agents.agent_search.shared_graph_utils.utils import remove_document_ci
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import AgentAnswerPiece
 from onyx.chat.models import ExtendedToolResponse
+from onyx.chat.models import StreamingError
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
 from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
-from onyx.context.search.models import InferenceSection
-from onyx.prompts.agent_search import (
-    INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS,
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION,
 )
+from onyx.context.search.models import InferenceSection
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
+from onyx.prompts.agent_search import INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS
 from onyx.prompts.agent_search import (
     INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
 )
@@ -57,6 +72,12 @@ from onyx.prompts.agent_search import (
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
 from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
 
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="LLM Timeout Error. The initial answer could not be generated.",
+    rate_limit="LLM Rate Limit Error. The initial answer could not be generated.",
+    general_error="General LLM Error. The initial answer could not be generated.",
+)
+
 
 def generate_initial_answer(
     state: SubQuestionRetrievalState,
@@ -224,30 +245,82 @@ def generate_initial_answer(
 
         streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
         dispatch_timings: list[float] = []
-        for message in model.stream(msg):
-            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-            content = message.content
-            if not isinstance(content, str):
-                raise ValueError(
-                    f"Expected content to be a string, but got {type(content)}"
-                )
-            start_stream_token = datetime.now()
 
+        agent_error: AgentErrorLoggingFormat | None = None
+
+        try:
+            for message in model.stream(
+                msg,
+                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION,
+            ):
+                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+                content = message.content
+                if not isinstance(content, str):
+                    raise ValueError(
+                        f"Expected content to be a string, but got {type(content)}"
+                    )
+                start_stream_token = datetime.now()
+
+                write_custom_event(
+                    "initial_agent_answer",
+                    AgentAnswerPiece(
+                        answer_piece=content,
+                        level=0,
+                        level_question_num=0,
+                        answer_type="agent_level_answer",
+                    ),
+                    writer,
+                )
+                end_stream_token = datetime.now()
+                dispatch_timings.append(
+                    (end_stream_token - start_stream_token).microseconds
+                )
+                streamed_tokens.append(content)
+
+        except LLMTimeoutError:
+            agent_error = AgentErrorLoggingFormat(
+                error_type=AgentLLMErrorType.TIMEOUT,
+                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+                error_result=_llm_node_error_strings.timeout,
+            )
+            logger.error("LLM Timeout Error - generate initial answer")
+
+        except LLMRateLimitError:
+            agent_error = AgentErrorLoggingFormat(
+                error_type=AgentLLMErrorType.RATE_LIMIT,
+                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+                error_result=_llm_node_error_strings.rate_limit,
+            )
+            logger.error("LLM Rate Limit Error - generate initial answer")
+
+        if agent_error:
             write_custom_event(
                 "initial_agent_answer",
-                AgentAnswerPiece(
-                    answer_piece=content,
-                    level=0,
-                    level_question_num=0,
-                    answer_type="agent_level_answer",
+                StreamingError(
+                    error=AGENT_LLM_TIMEOUT_MESSAGE,
                 ),
                 writer,
             )
-            end_stream_token = datetime.now()
-            dispatch_timings.append(
-                (end_stream_token - start_stream_token).microseconds
+            return InitialAnswerUpdate(
+                initial_answer=None,
+                error=AgentErrorLoggingFormat(
+                    error_message=agent_error.error_message or "An LLM error occurred",
+                    error_type=agent_error.error_type,
+                    error_result=agent_error.error_result,
+                ),
+                initial_agent_stats=None,
+                generated_sub_questions=sub_questions,
+                agent_base_end_time=None,
+                agent_base_metrics=None,
+                log_messages=[
+                    get_langgraph_node_log_string(
+                        graph_component="initial - generate initial answer",
+                        node_name="generate initial answer",
+                        node_start_time=node_start_time,
+                        result=agent_error.error_result or "An LLM error occurred",
+                    )
+                ],
             )
-            streamed_tokens.append(content)
 
         logger.debug(
             f"Average dispatch time for initial answer: {sum(dispatch_timings) / len(dispatch_timings)}"

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/validate_initial_answer.py

@@ -25,7 +25,7 @@ def validate_initial_answer(
         f"--------{node_start_time}--------Checking for base answer validity - for not set True/False manually"
     )
 
-    verdict = True
+    verdict = True  # not actually required as already streamed out. Refinement will do similar
 
     return InitialAnswerQualityUpdate(
         initial_answer_quality_eval=verdict,

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/nodes/decompose_orig_question.py

@@ -23,6 +23,18 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_history_prompt,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
@@ -33,6 +45,11 @@ from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.chat.models import SubQuestionPiece
 from onyx.configs.agent_configs import AGENT_NUM_DOCS_FOR_DECOMPOSITION
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION,
+)
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH,
 )
@@ -43,6 +60,12 @@ from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="LLM Timeout Error. Sub-questions could not be generated.",
+    rate_limit="LLM Rate Limit Error. Sub-questions could not be generated.",
+    general_error="General LLM Error. Sub-questions could not be generated.",
+)
+
 
 def decompose_orig_question(
     state: SubQuestionRetrievalState,
@@ -112,11 +135,35 @@ def decompose_orig_question(
     )
 
     # dispatches custom events for subquestion tokens, adding in subquestion ids.
-    streamed_tokens = dispatch_separated(
-        model.stream(msg),
-        dispatch_subquestion(0, writer),
-        sep_callback=dispatch_subquestion_sep(0, writer),
-    )
+
+    agent_error: AgentErrorLoggingFormat | None = None
+    streamed_tokens: list[BaseMessage_Content] = []
+
+    try:
+        streamed_tokens = dispatch_separated(
+            model.stream(
+                msg,
+                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION,
+            ),
+            dispatch_subquestion(0, writer),
+            sep_callback=dispatch_subquestion_sep(0, writer),
+        )
+    except LLMTimeoutError as e:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - decompose orig question")
+        raise e  # fail loudly on this critical step
+    except LLMRateLimitError as e:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - decompose orig question")
+        raise e
 
     stop_event = StreamStopInfo(
         stop_reason=StreamStopReason.FINISHED,
@@ -125,19 +172,19 @@ def decompose_orig_question(
     )
     write_custom_event("stream_finished", stop_event, writer)
 
-    deomposition_response = merge_content(*streamed_tokens)
+    if agent_error:
+        initial_sub_questions: list[str] = []
+        log_result = agent_error.error_result
+    else:
+        deomposition_response = merge_content(*streamed_tokens)
 
-    # this call should only return strings. Commenting out for efficiency
-    # assert [type(tok) == str for tok in streamed_tokens]
+        list_of_subqs = cast(str, deomposition_response).split("\n")
 
-    # use no-op cast() instead of str() which runs code
-    # list_of_subquestions = clean_and_parse_list_string(cast(str, response))
-    list_of_subqs = cast(str, deomposition_response).split("\n")
-
-    decomp_list: list[str] = [sq.strip() for sq in list_of_subqs if sq.strip() != ""]
+        initial_sub_questions = [sq.strip() for sq in list_of_subqs if sq.strip() != ""]
+        log_result = f"decomposed original question into {len(initial_sub_questions)} subquestions"
 
     return InitialQuestionDecompositionUpdate(
-        initial_sub_questions=decomp_list,
+        initial_sub_questions=initial_sub_questions,
         agent_start_time=agent_start_time,
         agent_refined_start_time=None,
         agent_refined_end_time=None,
@@ -151,7 +198,7 @@ def decompose_orig_question(
                 graph_component="initial - generate sub answers",
                 node_name="decompose original question",
                 node_start_time=node_start_time,
-                result=f"decomposed original question into {len(decomp_list)} subquestions",
+                result=log_result,
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/graph_builder.py

@@ -252,9 +252,7 @@ if __name__ == "__main__":
             db_session, primary_llm, fast_llm, search_request
         )
 
-        inputs = MainInput(
-            base_question=graph_config.inputs.search_request.query, log_messages=[]
-        )
+        inputs = MainInput(log_messages=[])
 
         for thing in compiled_graph.stream(
             input=inputs,

backend/onyx/agents/agent_search/deep_search/main/nodes/compare_answers.py

@@ -1,6 +1,7 @@
 from datetime import datetime
 from typing import cast
 
+from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableConfig
 from langgraph.types import StreamWriter
@@ -10,14 +11,37 @@ from onyx.agents.agent_search.deep_search.main.states import (
 )
 from onyx.agents.agent_search.deep_search.main.states import MainState
 from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import RefinedAnswerImprovement
+from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
 )
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="The LLM timed out, and the answers could not be compared.",
+    rate_limit="The LLM encountered a rate limit, and the answers could not be compared.",
+    general_error="The LLM encountered an error, and the answers could not be compared.",
+)
 
 
 def compare_answers(
@@ -40,15 +64,46 @@ def compare_answers(
 
     msg = [HumanMessage(content=compare_answers_prompt)]
 
+    agent_error: AgentErrorLoggingFormat | None = None
     # Get the rewritten queries in a defined format
     model = graph_config.tooling.fast_llm
-
+    resp: BaseMessage | None = None
+    refined_answer_improvement: bool | None = None
     # no need to stream this
-    resp = model.invoke(msg)
+    try:
+        resp = model.invoke(
+            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
+        )
 
-    refined_answer_improvement = (
-        isinstance(resp.content, str) and "yes" in resp.content.lower()
-    )
+    except LLMTimeoutError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - compare answers")
+        # continue as True in this support step
+    except LLMRateLimitError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - compare answers")
+        # continue as True in this support step
+
+    if agent_error or resp is None:
+        refined_answer_improvement = True
+        if agent_error:
+            log_result = agent_error.error_result
+        else:
+            log_result = "An answer could not be generated."
+
+    else:
+        refined_answer_improvement = (
+            isinstance(resp.content, str) and "yes" in resp.content.lower()
+        )
+        log_result = f"Answer comparison: {refined_answer_improvement}"
 
     write_custom_event(
         "refined_answer_improvement",
@@ -65,7 +120,7 @@ def compare_answers(
                 graph_component="main",
                 node_name="compare answers",
                 node_start_time=node_start_time,
-                result=f"Answer comparison: {refined_answer_improvement}",
+                result=log_result,
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/nodes/create_refined_sub_questions.py

@@ -21,6 +21,18 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_history_prompt,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     format_entity_term_extraction,
@@ -30,10 +42,25 @@ from onyx.agents.agent_search.shared_graph_utils.utils import (
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import StreamingError
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION,
+)
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     REFINEMENT_QUESTION_DECOMPOSITION_PROMPT,
 )
 from onyx.tools.models import ToolCallKickoff
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="The LLM timed out. The sub-questions could not be generated.",
+    rate_limit="The LLM encountered a rate limit. The sub-questions could not be generated.",
+    general_error="The LLM encountered an error. The sub-questions could not be generated.",
+)
 
 
 def create_refined_sub_questions(
@@ -96,29 +123,65 @@ def create_refined_sub_questions(
     # Grader
     model = graph_config.tooling.fast_llm
 
-    streamed_tokens = dispatch_separated(
-        model.stream(msg),
-        dispatch_subquestion(1, writer),
-        sep_callback=dispatch_subquestion_sep(1, writer),
-    )
-    response = merge_content(*streamed_tokens)
+    agent_error: AgentErrorLoggingFormat | None = None
+    streamed_tokens: list[BaseMessage_Content] = []
+    try:
+        streamed_tokens = dispatch_separated(
+            model.stream(
+                msg,
+                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION,
+            ),
+            dispatch_subquestion(1, writer),
+            sep_callback=dispatch_subquestion_sep(1, writer),
+        )
+    except LLMTimeoutError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - create refined sub questions")
 
-    if isinstance(response, str):
-        parsed_response = [q for q in response.split("\n") if q.strip() != ""]
-    else:
-        raise ValueError("LLM response is not a string")
+    except LLMRateLimitError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - create refined sub questions")
 
-    refined_sub_question_dict = {}
-    for sub_question_num, sub_question in enumerate(parsed_response):
-        refined_sub_question = RefinementSubQuestion(
-            sub_question=sub_question,
-            sub_question_id=make_question_id(1, sub_question_num + 1),
-            verified=False,
-            answered=False,
-            answer="",
+    if agent_error:
+        refined_sub_question_dict: dict[int, RefinementSubQuestion] = {}
+        log_result = agent_error.error_result
+        write_custom_event(
+            "refined_sub_question_creation_error",
+            StreamingError(
+                error="Your LLM was not able to create refined sub questions in time and timed out. Please try again.",
+            ),
+            writer,
         )
 
-        refined_sub_question_dict[sub_question_num + 1] = refined_sub_question
+    else:
+        response = merge_content(*streamed_tokens)
+
+        if isinstance(response, str):
+            parsed_response = [q for q in response.split("\n") if q.strip() != ""]
+        else:
+            raise ValueError("LLM response is not a string")
+
+        refined_sub_question_dict = {}
+        for sub_question_num, sub_question in enumerate(parsed_response):
+            refined_sub_question = RefinementSubQuestion(
+                sub_question=sub_question,
+                sub_question_id=make_question_id(1, sub_question_num + 1),
+                verified=False,
+                answered=False,
+                answer="",
+            )
+
+            refined_sub_question_dict[sub_question_num + 1] = refined_sub_question
+
+        log_result = f"Created {len(refined_sub_question_dict)} refined sub questions"
 
     return RefinedQuestionDecompositionUpdate(
         refined_sub_questions=refined_sub_question_dict,
@@ -128,7 +191,7 @@ def create_refined_sub_questions(
                 graph_component="main",
                 node_name="create refined sub questions",
                 node_start_time=node_start_time,
-                result=f"Created {len(refined_sub_question_dict)} refined sub questions",
+                result=log_result,
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/nodes/decide_refinement_need.py

@@ -26,6 +26,19 @@ def decide_refinement_need(
 
     decision = True  # TODO: just for current testing purposes
 
+    if state.error:
+        return RequireRefinemenEvalUpdate(
+            require_refined_answer_eval=False,
+            log_messages=[
+                get_langgraph_node_log_string(
+                    graph_component="main",
+                    node_name="decide refinement need",
+                    node_start_time=node_start_time,
+                    result="Timeout Error",
+                )
+            ],
+        )
+
     log_messages = [
         get_langgraph_node_log_string(
             graph_component="main",

backend/onyx/agents/agent_search/deep_search/main/nodes/extract_entities_terms.py

@@ -21,6 +21,9 @@ from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION,
+)
 from onyx.configs.constants import NUM_EXPLORATORY_DOCS
 from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT
 from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE
@@ -81,6 +84,7 @@ def extract_entities_terms(
     # Grader
     llm_response = fast_llm.invoke(
         prompt=msg,
+        timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION,
     )
 
     cleaned_response = (

backend/onyx/agents/agent_search/deep_search/main/nodes/generate_refined_answer.py

@@ -11,7 +11,6 @@ from onyx.agents.agent_search.deep_search.main.models import (
     AgentRefinedMetrics,
 )
 from onyx.agents.agent_search.deep_search.main.operations import get_query_info
-from onyx.agents.agent_search.deep_search.main.operations import logger
 from onyx.agents.agent_search.deep_search.main.states import MainState
 from onyx.agents.agent_search.deep_search.main.states import (
     RefinedAnswerUpdate,
@@ -23,7 +22,18 @@ from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
 from onyx.agents.agent_search.shared_graph_utils.models import InferenceSection
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
 from onyx.agents.agent_search.shared_graph_utils.operators import (
     dedup_inference_sections,
@@ -43,8 +53,14 @@ from onyx.agents.agent_search.shared_graph_utils.utils import (
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import AgentAnswerPiece
 from onyx.chat.models import ExtendedToolResponse
+from onyx.chat.models import StreamingError
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
 from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION,
+)
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
 )
@@ -56,6 +72,15 @@ from onyx.prompts.agent_search import (
 )
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
 from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="The LLM timed out. The refined answer could not be generated.",
+    rate_limit="The LLM encountered a rate limit. The refined answer could not be generated.",
+    general_error="The LLM encountered an error. The refined answer could not be generated.",
+)
 
 
 def generate_refined_answer(
@@ -231,28 +256,80 @@ def generate_refined_answer(
 
     streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
     dispatch_timings: list[float] = []
-    for message in model.stream(msg):
-        # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-        content = message.content
-        if not isinstance(content, str):
-            raise ValueError(
-                f"Expected content to be a string, but got {type(content)}"
-            )
+    agent_error: AgentErrorLoggingFormat | None = None
 
-        start_stream_token = datetime.now()
+    try:
+        for message in model.stream(
+            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION
+        ):
+            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+            content = message.content
+            if not isinstance(content, str):
+                raise ValueError(
+                    f"Expected content to be a string, but got {type(content)}"
+                )
+
+            start_stream_token = datetime.now()
+            write_custom_event(
+                "refined_agent_answer",
+                AgentAnswerPiece(
+                    answer_piece=content,
+                    level=1,
+                    level_question_num=0,
+                    answer_type="agent_level_answer",
+                ),
+                writer,
+            )
+            end_stream_token = datetime.now()
+            dispatch_timings.append(
+                (end_stream_token - start_stream_token).microseconds
+            )
+            streamed_tokens.append(content)
+
+    except LLMTimeoutError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - generate refined answer")
+
+    except LLMRateLimitError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - generate refined answer")
+
+    if agent_error:
         write_custom_event(
-            "refined_agent_answer",
-            AgentAnswerPiece(
-                answer_piece=content,
-                level=1,
-                level_question_num=0,
-                answer_type="agent_level_answer",
+            "initial_agent_answer",
+            StreamingError(
+                error=AGENT_LLM_TIMEOUT_MESSAGE,
             ),
             writer,
         )
-        end_stream_token = datetime.now()
-        dispatch_timings.append((end_stream_token - start_stream_token).microseconds)
-        streamed_tokens.append(content)
+
+        return RefinedAnswerUpdate(
+            refined_answer=None,
+            refined_answer_quality=False,  # TODO: replace this with the actual check value
+            refined_agent_stats=None,
+            agent_refined_end_time=None,
+            agent_refined_metrics=AgentRefinedMetrics(
+                refined_doc_boost_factor=0.0,
+                refined_question_boost_factor=0.0,
+                duration_s=None,
+            ),
+            log_messages=[
+                get_langgraph_node_log_string(
+                    graph_component="main",
+                    node_name="generate refined answer",
+                    node_start_time=node_start_time,
+                    result=agent_error.error_result or "An LLM error occurred",
+                )
+            ],
+        )
 
     logger.debug(
         f"Average dispatch time for refined answer: {sum(dispatch_timings) / len(dispatch_timings)}"
@@ -266,49 +343,6 @@ def generate_refined_answer(
         revision_question_efficiency=revision_question_efficiency,
     )
 
-    logger.debug(f"\n\n---INITIAL ANSWER ---\n\n Answer:\n Agent: {initial_answer}")
-    logger.debug("-" * 10)
-    logger.debug(f"\n\n---REVISED AGENT ANSWER ---\n\n Answer:\n Agent: {answer}")
-
-    logger.debug("-" * 100)
-
-    if state.initial_agent_stats:
-        initial_doc_boost_factor = state.initial_agent_stats.agent_effectiveness.get(
-            "utilized_chunk_ratio", "--"
-        )
-        initial_support_boost_factor = (
-            state.initial_agent_stats.agent_effectiveness.get("support_ratio", "--")
-        )
-        num_initial_verified_docs = state.initial_agent_stats.original_question.get(
-            "num_verified_documents", "--"
-        )
-        initial_verified_docs_avg_score = (
-            state.initial_agent_stats.original_question.get("verified_avg_score", "--")
-        )
-        initial_sub_questions_verified_docs = (
-            state.initial_agent_stats.sub_questions.get("num_verified_documents", "--")
-        )
-
-        logger.debug("INITIAL AGENT STATS")
-        logger.debug(f"Document Boost Factor: {initial_doc_boost_factor}")
-        logger.debug(f"Support Boost Factor: {initial_support_boost_factor}")
-        logger.debug(f"Originally Verified Docs: {num_initial_verified_docs}")
-        logger.debug(
-            f"Originally Verified Docs Avg Score: {initial_verified_docs_avg_score}"
-        )
-        logger.debug(
-            f"Sub-Questions Verified Docs: {initial_sub_questions_verified_docs}"
-        )
-    if refined_agent_stats:
-        logger.debug("-" * 10)
-        logger.debug("REFINED AGENT STATS")
-        logger.debug(
-            f"Revision Doc Factor: {refined_agent_stats.revision_doc_efficiency}"
-        )
-        logger.debug(
-            f"Revision Question Factor: {refined_agent_stats.revision_question_efficiency}"
-        )
-
     agent_refined_end_time = datetime.now()
     if state.agent_refined_start_time:
         agent_refined_duration = (

backend/onyx/agents/agent_search/deep_search/main/states.py

@@ -17,6 +17,7 @@ from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
 from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
 from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
 from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
 from onyx.agents.agent_search.shared_graph_utils.models import (
     EntityRelationshipTermExtraction,
 )
@@ -76,6 +77,7 @@ class InitialAnswerUpdate(LoggerUpdate):
     """
 
     initial_answer: str | None = None
+    error: AgentErrorLoggingFormat | None = None
     initial_agent_stats: InitialAgentResultStats | None = None
     generated_sub_questions: list[str] = []
     agent_base_end_time: datetime | None = None
@@ -88,6 +90,7 @@ class RefinedAnswerUpdate(RefinedAgentEndStats, LoggerUpdate):
     """
 
     refined_answer: str | None = None
+    error: AgentErrorLoggingFormat | None = None
     refined_agent_stats: RefinedAgentStats | None = None
     refined_answer_quality: bool = False
 

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/expand_queries.py

@@ -16,14 +16,40 @@ from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states impor
     QueryExpansionUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION,
+)
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     QUERY_REWRITING_PROMPT,
 )
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="Query rewriting failed due to LLM timeout - the original question will be used.",
+    rate_limit="Query rewriting failed due to LLM rate limit - the original question will be used.",
+    general_error="Query rewriting failed due to LLM error - the original question will be used.",
+)
 
 
 def expand_queries(
@@ -54,13 +80,43 @@ def expand_queries(
         )
     ]
 
-    llm_response_list = dispatch_separated(
-        llm.stream(prompt=msg), dispatch_subquery(level, question_num, writer)
-    )
+    agent_error: AgentErrorLoggingFormat | None = None
+    llm_response_list: list[BaseMessage_Content] = []
 
-    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
+    try:
+        llm_response_list = dispatch_separated(
+            llm.stream(
+                prompt=msg,
+                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION,
+            ),
+            dispatch_subquery(level, question_num, writer),
+        )
+    except LLMTimeoutError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - expand queries")
 
-    rewritten_queries = llm_response.split("\n")
+    except LLMRateLimitError:
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - expand queries")
+    # use subquestion as query if query generation fails
+    if agent_error:
+        llm_response = ""
+        rewritten_queries = [question]
+        log_result = agent_error.error_result
+    else:
+        llm_response = merge_message_runs(llm_response_list, chunk_separator="")[
+            0
+        ].content
+        rewritten_queries = llm_response.split("\n")
+        log_result = f"Number of expanded queries: {len(rewritten_queries)}"
 
     return QueryExpansionUpdate(
         expanded_queries=rewritten_queries,
@@ -69,7 +125,7 @@ def expand_queries(
                 graph_component="shared - expanded retrieval",
                 node_name="expand queries",
                 node_start_time=node_start_time,
-                result=f"Number of expanded queries: {len(rewritten_queries)}",
+                result=log_result,
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/verify_documents.py

@@ -1,5 +1,6 @@
 from typing import cast
 
+from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables.config import RunnableConfig
 
@@ -10,12 +11,41 @@ from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states impor
     DocVerificationUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    binary_string_test,
+)
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_RATELIMIT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_LLM_TIMEOUT_MESSAGE,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AGENT_POSITIVE_VALUE_STR,
+)
+from onyx.agents.agent_search.shared_graph_utils.constants import (
+    AgentLLMErrorType,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLoggingFormat
+from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
+from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     DOCUMENT_VERIFICATION_PROMPT,
 )
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_llm_node_error_strings = LLMNodeErrorStrings(
+    timeout="The LLM timed out. The document could not be verified. The document will be treated as 'relevant'",
+    rate_limit="The LLM encountered a rate limit. The document could not be verified. The document will be treated as 'relevant'",
+    general_error="The LLM encountered an error. The document could not be verified. The document will be treated as 'relevant'",
+)
 
 
 def verify_documents(
@@ -26,7 +56,7 @@ def verify_documents(
 
     Args:
         state (DocVerificationInput): The current state
-        config (RunnableConfig): Configuration containing ProSearchConfig
+        config (RunnableConfig): Configuration containing AgentSearchConfig
 
     Updates:
         verified_documents: list[InferenceSection]
@@ -51,11 +81,42 @@ def verify_documents(
         )
     ]
 
-    response = fast_llm.invoke(msg)
+    agent_error: AgentErrorLoggingFormat | None = None
+    response: BaseMessage | None = None
 
-    verified_documents = []
-    if isinstance(response.content, str) and "yes" in response.content.lower():
-        verified_documents.append(retrieved_document_to_verify)
+    try:
+        response = fast_llm.invoke(
+            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
+        )
+
+    except LLMTimeoutError:
+        # In this case, we decide to continue and don't raise an error, as
+        # little harm in letting some docs through that are less relevant.
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.TIMEOUT,
+            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
+            error_result=_llm_node_error_strings.timeout,
+        )
+        logger.error("LLM Timeout Error - verify documents")
+    except LLMRateLimitError:
+        # In this case, we decide to continue and don't raise an error, as
+        # little harm in letting some docs through that are less relevant.
+        agent_error = AgentErrorLoggingFormat(
+            error_type=AgentLLMErrorType.RATE_LIMIT,
+            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
+            error_result=_llm_node_error_strings.rate_limit,
+        )
+        logger.error("LLM Rate Limit Error - verify documents")
+
+    if agent_error or response is None:
+        verified_documents = [retrieved_document_to_verify]
+
+    else:
+        verified_documents = []
+        if isinstance(response.content, str) and binary_string_test(
+            text=response.content, positive_value=AGENT_POSITIVE_VALUE_STR
+        ):
+            verified_documents.append(retrieved_document_to_verify)
 
     return DocVerificationUpdate(
         verified_documents=verified_documents,

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/states.py

@@ -21,9 +21,13 @@ from onyx.context.search.models import InferenceSection
 
 
 class ExpandedRetrievalInput(SubgraphCoreState):
-    question: str = ""
-    base_search: bool = False
+    # exception from 'no default value'for LangGraph input states
+    # Here, sub_question_id default Nonoe implies usage for the
+    # original question. This is sometimes needed for nested sub-graphs
+
     sub_question_id: str | None = None
+    question: str
+    base_search: bool
 
 
 ## Update/Return States
@@ -88,4 +92,4 @@ class DocVerificationInput(ExpandedRetrievalInput):
 
 
 class RetrievalInput(ExpandedRetrievalInput):
-    query_to_retrieve: str = ""
+    query_to_retrieve: str

backend/onyx/agents/agent_search/run_graph.py

@@ -12,7 +12,7 @@ from onyx.agents.agent_search.deep_search.main.graph_builder import (
     main_graph_builder as main_graph_builder_a,
 )
 from onyx.agents.agent_search.deep_search.main.states import (
-    MainInput as MainInput_a,
+    MainInput as MainInput,
 )
 from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
@@ -21,6 +21,7 @@ from onyx.chat.models import AnswerPacket
 from onyx.chat.models import AnswerStream
 from onyx.chat.models import ExtendedToolResponse
 from onyx.chat.models import RefinedAnswerImprovement
+from onyx.chat.models import StreamingError
 from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import SubQueryPiece
 from onyx.chat.models import SubQuestionPiece
@@ -33,6 +34,7 @@ from onyx.llm.factory import get_default_llms
 from onyx.tools.tool_runner import ToolCallKickoff
 from onyx.utils.logger import setup_logger
 
+
 logger = setup_logger()
 
 _COMPILED_GRAPH: CompiledStateGraph | None = None
@@ -72,13 +74,15 @@ def _parse_agent_event(
             return cast(AnswerPacket, event["data"])
         elif event["name"] == "refined_answer_improvement":
             return cast(RefinedAnswerImprovement, event["data"])
+        elif event["name"] == "refined_sub_question_creation_error":
+            return cast(StreamingError, event["data"])
     return None
 
 
 def manage_sync_streaming(
     compiled_graph: CompiledStateGraph,
     config: GraphConfig,
-    graph_input: BasicInput | MainInput_a,
+    graph_input: BasicInput | MainInput,
 ) -> Iterable[StreamEvent]:
     message_id = config.persistence.message_id if config.persistence else None
     for event in compiled_graph.stream(
@@ -92,7 +96,7 @@ def manage_sync_streaming(
 def run_graph(
     compiled_graph: CompiledStateGraph,
     config: GraphConfig,
-    input: BasicInput | MainInput_a,
+    input: BasicInput | MainInput,
 ) -> AnswerStream:
     config.behavior.perform_initial_search_decomposition = (
         INITIAL_SEARCH_DECOMPOSITION_ENABLED
@@ -123,9 +127,7 @@ def run_main_graph(
 ) -> AnswerStream:
     compiled_graph = load_compiled_graph()
 
-    input = MainInput_a(
-        base_question=config.inputs.search_request.query, log_messages=[]
-    )
+    input = MainInput(log_messages=[])
 
     # Agent search is not a Tool per se, but this is helpful for the frontend
     yield ToolCallKickoff(
@@ -172,9 +174,7 @@ if __name__ == "__main__":
             # search_request.persona = get_persona_by_id(1, None, db_session)
             # config.perform_initial_search_path_decision = False
             config.behavior.perform_initial_search_decomposition = True
-            input = MainInput_a(
-                base_question=config.inputs.search_request.query, log_messages=[]
-            )
+            input = MainInput(log_messages=[])
 
             tool_responses: list = []
             for output in run_graph(compiled_graph, config, input):

backend/onyx/agents/agent_search/shared_graph_utils/agent_prompt_ops.py

@@ -150,3 +150,17 @@ def get_prompt_enrichment_components(
         history=history,
         date_str=date_str,
     )
+
+
+def binary_string_test(text: str, positive_value: str = "yes") -> bool:
+    """
+    Tests if a string contains a positive value (case-insensitive).
+
+    Args:
+        text: The string to test
+        positive_value: The value to look for (defaults to "yes")
+
+    Returns:
+        True if the positive value is found in the text
+    """
+    return positive_value.lower() in text.lower()

backend/onyx/agents/agent_search/shared_graph_utils/constants.py

@@ -0,0 +1,17 @@
+from enum import Enum
+
+AGENT_LLM_TIMEOUT_MESSAGE = "The agent timed out. Please try again."
+AGENT_LLM_ERROR_MESSAGE = "The agent encountered an error. Please try again."
+AGENT_LLM_RATELIMIT_MESSAGE = (
+    "The agent encountered a rate limit error. Please try again."
+)
+LLM_ANSWER_ERROR_MESSAGE = "The question was not answered due to an LLM error."
+
+AGENT_POSITIVE_VALUE_STR = "yes"
+AGENT_NEGATIVE_VALUE_STR = "no"
+
+
+class AgentLLMErrorType(str, Enum):
+    TIMEOUT = "timeout"
+    RATE_LIMIT = "rate_limit"
+    GENERAL_ERROR = "general_error"

backend/onyx/agents/agent_search/shared_graph_utils/models.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 from pydantic import BaseModel
 
 from onyx.agents.agent_search.deep_search.main.models import (
@@ -56,6 +58,12 @@ class InitialAgentResultStats(BaseModel):
     agent_effectiveness: dict[str, float | int | None]
 
 
+class AgentErrorLoggingFormat(BaseModel):
+    error_message: str
+    error_type: str
+    error_result: str | None = None
+
+
 class RefinedAgentStats(BaseModel):
     revision_doc_efficiency: float | None
     revision_question_efficiency: float | None
@@ -126,3 +134,12 @@ class AgentPromptEnrichmentComponents(BaseModel):
     persona_prompts: PersonaPromptExpressions
     history: str
     date_str: str
+
+
+class LLMNodeErrorStrings(BaseModel):
+    timeout: str = "LLM Timeout Error"
+    rate_limit: str = "LLM Rate Limit Error"
+    general_error: str = "General LLM Error"
+
+
+BaseMessage_Content = str | list[str | dict[str, Any]]

backend/onyx/agents/agent_search/shared_graph_utils/utils.py

@@ -20,6 +20,7 @@ from onyx.agents.agent_search.models import GraphInputs
 from onyx.agents.agent_search.models import GraphPersistence
 from onyx.agents.agent_search.models import GraphSearchConfig
 from onyx.agents.agent_search.models import GraphTooling
+from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
 from onyx.agents.agent_search.shared_graph_utils.models import (
     EntityRelationshipTermExtraction,
 )
@@ -34,6 +35,9 @@ from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.chat.prompt_builder.answer_prompt_builder import AnswerPromptBuilder
+from onyx.configs.agent_configs import (
+    AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION,
+)
 from onyx.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from onyx.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from onyx.configs.constants import DEFAULT_PERSONA_ID
@@ -46,6 +50,8 @@ from onyx.context.search.models import SearchRequest
 from onyx.db.engine import get_session_context_manager
 from onyx.db.persona import get_persona_by_id
 from onyx.db.persona import Persona
+from onyx.llm.chat_llm import LLMRateLimitError
+from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.llm.interfaces import LLM
 from onyx.prompts.agent_search import (
     ASSISTANT_SYSTEM_PROMPT_DEFAULT,
@@ -65,8 +71,9 @@ from onyx.tools.tool_implementations.search.search_tool import (
 from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
 from onyx.tools.tool_implementations.search.search_tool import SearchTool
 from onyx.tools.utils import explicit_tool_calling_supported
+from onyx.utils.logger import setup_logger
 
-BaseMessage_Content = str | list[str | dict[str, Any]]
+logger = setup_logger()
 
 
 # Post-processing
@@ -372,8 +379,24 @@ def summarize_history(
         )
     )
 
-    history_response = llm.invoke(history_context_prompt)
+    try:
+        history_response = llm.invoke(
+            history_context_prompt,
+            timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION,
+        )
+    except LLMTimeoutError:
+        logger.error("LLM Timeout Error - summarize history")
+        return (
+            history  # this is what is done at this point anyway, so we default to this
+        )
+    except LLMRateLimitError:
+        logger.error("LLM Rate Limit Error - summarize history")
+        return (
+            history  # this is what is done at this point anyway, so we default to this
+        )
+
     assert isinstance(history_response.content, str)
+
     return history_response.content
 
 

backend/onyx/configs/agent_configs.py

@@ -13,6 +13,21 @@ AGENT_DEFAULT_MIN_ORIG_QUESTION_DOCS = 3
 AGENT_DEFAULT_MAX_ANSWER_CONTEXT_DOCS = 10
 AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH = 2000
 
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION = 30  # in seconds
+
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION = 10  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION = 25  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION = 4  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION = 3  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION = 8  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION = 12  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK = 8  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION = 25  # in seconds
+
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION = 6  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION = 25  # in seconds
+AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS = 8  # in seconds
+
 #####
 # Agent Configs
 #####
@@ -77,4 +92,76 @@ AGENT_MAX_STATIC_HISTORY_WORD_LENGTH = int(
     or AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH
 )  # 2000
 
+
+AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION
+)  # 25
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
+)  # 3
+
+AGENT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION
+)  # 30
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION
+)  # 8
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION
+)  # 12
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION
+)  # 25
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION
+)  # 25
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK
+)  # 8
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION
+)  # 6
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION
+)  # 1
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION
+)  # 4
+
+
+AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS = int(
+    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS")
+    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
+)  # 8
+
+
 GRAPH_VERSION_NAME: str = "a"

backend/onyx/llm/chat_llm.py

@@ -50,6 +50,18 @@ litellm.telemetry = False
 _LLM_PROMPT_LONG_TERM_LOG_CATEGORY = "llm_prompt"
 
 
+class LLMTimeoutError(Exception):
+    """
+    Exception raised when an LLM call times out.
+    """
+
+
+class LLMRateLimitError(Exception):
+    """
+    Exception raised when an LLM call is rate limited.
+    """
+
+
 def _base_msg_to_role(msg: BaseMessage) -> str:
     if isinstance(msg, HumanMessage) or isinstance(msg, HumanMessageChunk):
         return "user"
@@ -380,6 +392,7 @@ class DefaultMultiLLM(LLM):
         tool_choice: ToolChoiceOptions | None,
         stream: bool,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> litellm.ModelResponse | litellm.CustomStreamWrapper:
         # litellm doesn't accept LangChain BaseMessage objects, so we need to convert them
         # to a dict representation
@@ -405,7 +418,7 @@ class DefaultMultiLLM(LLM):
                 stream=stream,
                 # model params
                 temperature=0,
-                timeout=self._timeout,
+                timeout=timeout_override or self._timeout,
                 # For now, we don't support parallel tool calls
                 # NOTE: we can't pass this in if tools are not specified
                 # or else OpenAI throws an error
@@ -424,6 +437,12 @@ class DefaultMultiLLM(LLM):
         except Exception as e:
             self._record_error(processed_prompt, e)
             # for break pointing
+            if isinstance(e, litellm.Timeout):
+                raise LLMTimeoutError(e)
+
+            elif isinstance(e, litellm.RateLimitError):
+                raise LLMRateLimitError(e)
+
             raise e
 
     @property
@@ -444,6 +463,7 @@ class DefaultMultiLLM(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> BaseMessage:
         if LOG_DANSWER_MODEL_INTERACTIONS:
             self.log_model_configs()
@@ -451,7 +471,12 @@ class DefaultMultiLLM(LLM):
         response = cast(
             litellm.ModelResponse,
             self._completion(
-                prompt, tools, tool_choice, False, structured_response_format
+                prompt=prompt,
+                tools=tools,
+                tool_choice=tool_choice,
+                stream=False,
+                structured_response_format=structured_response_format,
+                timeout_override=timeout_override,
             ),
         )
         choice = response.choices[0]
@@ -469,19 +494,31 @@ class DefaultMultiLLM(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         if LOG_DANSWER_MODEL_INTERACTIONS:
             self.log_model_configs()
 
         if DISABLE_LITELLM_STREAMING:
-            yield self.invoke(prompt, tools, tool_choice, structured_response_format)
+            yield self.invoke(
+                prompt,
+                tools,
+                tool_choice,
+                structured_response_format,
+                timeout_override,
+            )
             return
 
         output = None
         response = cast(
             litellm.CustomStreamWrapper,
             self._completion(
-                prompt, tools, tool_choice, True, structured_response_format
+                prompt=prompt,
+                tools=tools,
+                tool_choice=tool_choice,
+                stream=True,
+                structured_response_format=structured_response_format,
+                timeout_override=timeout_override,
             ),
         )
         try:

backend/onyx/llm/custom_llm.py

@@ -81,6 +81,7 @@ class CustomModelServer(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> BaseMessage:
         return self._execute(prompt)
 
@@ -90,5 +91,6 @@ class CustomModelServer(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         yield self._execute(prompt)

backend/onyx/llm/interfaces.py

@@ -90,12 +90,13 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> BaseMessage:
         self._precall(prompt)
         # TODO add a postcall to log model outputs independent of concrete class
         # implementation
         return self._invoke_implementation(
-            prompt, tools, tool_choice, structured_response_format
+            prompt, tools, tool_choice, structured_response_format, timeout_override
         )
 
     @abc.abstractmethod
@@ -105,6 +106,7 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> BaseMessage:
         raise NotImplementedError
 
@@ -114,12 +116,13 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         self._precall(prompt)
         # TODO add a postcall to log model outputs independent of concrete class
         # implementation
         messages = self._stream_implementation(
-            prompt, tools, tool_choice, structured_response_format
+            prompt, tools, tool_choice, structured_response_format, timeout_override
         )
 
         tokens = []
@@ -138,5 +141,6 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
+        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         raise NotImplementedError

backend/onyx/prompts/agent_search.py

@@ -5,8 +5,6 @@ UNKNOWN_ANSWER = "I do not have enough information to answer this question."
 NO_RECOVERED_DOCS = "No relevant information recovered"
 YES = "yes"
 NO = "no"
-
-
 # Framing/Support/Template Prompts
 HISTORY_FRAMING_PROMPT = f"""
 For more context, here is the history of the conversation so far that preceded this question:

web/src/app/chat/ChatPage.tsx

@@ -1121,6 +1121,7 @@ export function ChatPage({
         "Continue Generating (pick up exactly where you left off)",
     });
   };
+  const [uncaughtError, setUncaughtError] = useState<string | null>(null);
 
   const onSubmit = async ({
     messageIdToResend,
@@ -1549,8 +1550,23 @@ export function ChatPage({
                 }
               );
             } else if (Object.hasOwn(packet, "error")) {
-              error = (packet as StreamingError).error;
-              stackTrace = (packet as StreamingError).stack_trace;
+              if (
+                sub_questions.length > 0 &&
+                sub_questions
+                  .filter((q) => q.level === 0)
+                  .every((q) => q.is_stopped === true)
+              ) {
+                setUncaughtError((packet as StreamingError).error);
+                updateChatState("input");
+                setAgenticGenerating(false);
+                setAlternativeGeneratingAssistant(null);
+                setSubmittedMessage("");
+                return;
+                // throw new Error((packet as StreamingError).error);
+              } else {
+                error = (packet as StreamingError).error;
+                stackTrace = (packet as StreamingError).stack_trace;
+              }
             } else if (Object.hasOwn(packet, "message_id")) {
               finalMessage = packet as BackendMessage;
             } else if (Object.hasOwn(packet, "stop_reason")) {
@@ -2039,6 +2055,7 @@ export function ChatPage({
         }
 
         const data = await response.json();
+
         router.push(data.redirect_url);
       } catch (error) {
         console.error("Error seeding chat from Slack:", error);
@@ -2633,6 +2650,7 @@ export function ChatPage({
                                     {message.sub_questions &&
                                     message.sub_questions.length > 0 ? (
                                       <AgenticMessage
+                                        error={uncaughtError}
                                         docSidebarToggled={
                                           documentSidebarToggled &&
                                           (selectedMessageForDocDisplay ==

web/src/app/chat/message/AgenticMessage.tsx

@@ -80,6 +80,7 @@ export const AgenticMessage = ({
   agenticDocs,
   secondLevelSubquestions,
   toggleDocDisplay,
+  error,
 }: {
   docSidebarToggled?: boolean;
   isImprovement?: boolean | null;
@@ -110,6 +111,7 @@ export const AgenticMessage = ({
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument?: (document: OnyxDocument) => void;
   toggleDocDisplay?: (agentic: boolean) => void;
+  error?: string | null;
 }) => {
   const [noShowingMessage, setNoShowingMessage] = useState(isComplete);
 
@@ -483,11 +485,28 @@ export const AgenticMessage = ({
                           ) : (
                             content
                           )}
+                          {error && (
+                            <p className="mt-2 text-red-700 text-sm my-auto">
+                              {error}
+                            </p>
+                          )}
                         </div>
                       </div>
                     </>
-                  ) : isComplete ? null : (
-                    <></>
+                  ) : isComplete ? (
+                    error && (
+                      <p className="mt-2 mx-4 text-red-700 text-sm my-auto">
+                        {error}
+                      </p>
+                    )
+                  ) : (
+                    <>
+                      {error && (
+                        <p className="mt-2 mx-4 text-red-700 text-sm my-auto">
+                          {error}
+                        </p>
+                      )}
+                    </>
                   )}
                   {handleFeedback &&
                     (isActive ? (

web/src/app/chat/message/Messages.tsx

@@ -185,6 +185,7 @@ export const AIMessage = ({
   setPresentingDocument,
   index,
   toggledDocumentSidebar,
+  removePadding,
 }: {
   index?: number;
   shared?: boolean;
@@ -213,6 +214,7 @@ export const AIMessage = ({
   overriddenModel?: string;
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument?: (document: OnyxDocument) => void;
+  removePadding?: boolean;
 }) => {
   const toolCallGenerating = toolCall && !toolCall.tool_result;
 
@@ -398,7 +400,9 @@ export const AIMessage = ({
     <div
       id={isComplete ? "onyx-ai-message" : undefined}
       ref={trackedElementRef}
-      className={`py-5 ml-4 lg:px-5 relative flex `}
+      className={`py-5  ml-4 lg:px-5 relative flex
+        
+        ${removePadding && "!pl-24 -mt-12"}`}
     >
       <div
         className={`mx-auto ${
@@ -407,11 +411,13 @@ export const AIMessage = ({
       >
         <div className={`lg:mr-12 ${!shared && "mobile:ml-0 md:ml-8"}`}>
           <div className="flex">
-            <AssistantIcon
-              className="mobile:hidden"
-              size={24}
-              assistant={alternativeAssistant || currentPersona}
-            />
+            {!removePadding && (
+              <AssistantIcon
+                className="mobile:hidden"
+                size={24}
+                assistant={alternativeAssistant || currentPersona}
+              />
+            )}
 
             <div className="w-full">
               <div className="max-w-message-max break-words">
@@ -588,7 +594,8 @@ export const AIMessage = ({
                     )}
                   </div>
 
-                  {handleFeedback &&
+                  {!removePadding &&
+                    handleFeedback &&
                     (isActive ? (
                       <div
                         className={`