chore: fix review issues

api/core/model_manager.py

@@ -21,6 +21,7 @@ from core.model_runtime.model_providers.__base.speech2text_model import Speech2T
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.provider_manager import ProviderManager
+from core.workflow.utils.generator_timeout import with_first_token_timeout
 from extensions.ext_redis import redis_client
 from models.provider import ProviderType
 from services.enterprise.plugin_manager_service import PluginCredentialType
@@ -109,6 +110,7 @@ class ModelInstance:
         stream: Literal[True] = True,
         user: str | None = None,
         callbacks: list[Callback] | None = None,
+        first_token_timeout: float | None = None,
     ) -> Generator: ...
 
     @overload
@@ -121,6 +123,7 @@ class ModelInstance:
         stream: Literal[False] = False,
         user: str | None = None,
         callbacks: list[Callback] | None = None,
+        first_token_timeout: float | None = None,
     ) -> LLMResult: ...
 
     @overload
@@ -133,6 +136,7 @@ class ModelInstance:
         stream: bool = True,
         user: str | None = None,
         callbacks: list[Callback] | None = None,
+        first_token_timeout: float | None = None,
     ) -> Union[LLMResult, Generator]: ...
 
     def invoke_llm(
@@ -144,6 +148,7 @@ class ModelInstance:
         stream: bool = True,
         user: str | None = None,
         callbacks: list[Callback] | None = None,
+        first_token_timeout: float | None = None,
     ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
@@ -155,26 +160,31 @@ class ModelInstance:
         :param stream: is stream response
         :param user: unique user id
         :param callbacks: callbacks
+        :param first_token_timeout: timeout in seconds for receiving first token (streaming only)
         :return: full response or stream response chunk generator result
         """
         if not isinstance(self.model_type_instance, LargeLanguageModel):
             raise Exception("Model type instance is not LargeLanguageModel")
-        return cast(
+
-            Union[LLMResult, Generator],
+        result = self._round_robin_invoke(
-            self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
-                function=self.model_type_instance.invoke,
+            model=self.model,
-                model=self.model,
+            credentials=self.credentials,
-                credentials=self.credentials,
+            prompt_messages=prompt_messages,
-                prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
-                model_parameters=model_parameters,
+            tools=tools,
-                tools=tools,
+            stop=stop,
-                stop=stop,
+            stream=stream,
-                stream=stream,
+            user=user,
-                user=user,
+            callbacks=callbacks,
-                callbacks=callbacks,
-            ),
         )
 
+        # Apply first token timeout wrapper for streaming responses
+        if stream and first_token_timeout and first_token_timeout > 0 and isinstance(result, Generator):
+            result = with_first_token_timeout(result, first_token_timeout)
+
+        return cast(Union[LLMResult, Generator], result)
+
     def get_llm_num_tokens(
         self, prompt_messages: Sequence[PromptMessage], tools: Sequence[PromptMessageTool] | None = None
     ) -> int:

api/core/workflow/nodes/base/entities.py

@@ -23,10 +23,22 @@ class RetryConfig(BaseModel):
     retry_interval: int = 0  # retry interval in milliseconds
     retry_enabled: bool = False  # whether retry is enabled
 
+    # First token timeout for LLM nodes (milliseconds), 0 means no timeout
+    first_token_timeout: int = 0
+
+    @property
+    def first_token_timeout_seconds(self) -> float:
+        return self.first_token_timeout / 1000
+
     @property
     def retry_interval_seconds(self) -> float:
         return self.retry_interval / 1000
 
+    @property
+    def has_first_token_timeout(self) -> bool:
+        """Check if first token timeout should be applied (retry enabled and timeout > 0)."""
+        return self.retry_enabled and self.first_token_timeout > 0
+
 
 class VariableSelector(BaseModel):
     """

api/core/workflow/nodes/llm/node.py

@@ -237,6 +237,13 @@ class LLMNode(Node[LLMNodeData]):
             )
 
             # handle invoke result
+            # Get first token timeout from retry config if enabled (convert ms to seconds)
+            first_token_timeout = (
+                self.node_data.retry_config.first_token_timeout_seconds
+                if self.node_data.retry_config.has_first_token_timeout
+                else None
+            )
+
             generator = LLMNode.invoke_llm(
                 node_data_model=self.node_data.model,
                 model_instance=model_instance,
@@ -250,6 +257,7 @@ class LLMNode(Node[LLMNodeData]):
                 node_id=self._node_id,
                 node_type=self.node_type,
                 reasoning_format=self.node_data.reasoning_format,
+                first_token_timeout=first_token_timeout,
             )
 
             structured_output: LLMStructuredOutput | None = None
@@ -367,6 +375,7 @@ class LLMNode(Node[LLMNodeData]):
         node_id: str,
         node_type: NodeType,
         reasoning_format: Literal["separated", "tagged"] = "tagged",
+        first_token_timeout: float | None = None,
     ) -> Generator[NodeEventBase | LLMStructuredOutput, None, None]:
         model_schema = model_instance.model_type_instance.get_model_schema(
             node_data_model.name, model_instance.credentials
@@ -400,6 +409,7 @@ class LLMNode(Node[LLMNodeData]):
                 stop=list(stop or []),
                 stream=True,
                 user=user_id,
+                first_token_timeout=first_token_timeout,
             )
 
         return LLMNode.handle_invoke_result(

api/core/workflow/utils/generator_timeout.py

@@ -0,0 +1,56 @@
+"""
+Generator timeout utilities for workflow nodes.
+
+Provides timeout wrappers for streaming generators, primarily used for
+LLM response streaming where we need to enforce time-to-first-token limits.
+"""
+
+import time
+from collections.abc import Generator
+from typing import TypeVar
+
+T = TypeVar("T")
+
+
+class FirstTokenTimeoutError(Exception):
+    """Raised when a generator fails to yield its first item within the configured timeout."""
+
+    def __init__(self, timeout_ms: int):
+        self.timeout_ms = timeout_ms
+        super().__init__(f"Generator timed out after {timeout_ms}ms without yielding first item")
+
+
+def with_first_token_timeout(
+    generator: Generator[T, None, None],
+    timeout_seconds: float,
+) -> Generator[T, None, None]:
+    """
+    Wrap a generator with first token timeout monitoring.
+
+    Only monitors the time until the FIRST item is yielded.
+    Once the first item arrives, timeout monitoring stops and
+    subsequent items are yielded without timeout checks.
+
+    Args:
+        generator: The source generator to wrap
+        timeout_seconds: Maximum time to wait for first item (in seconds)
+
+    Yields:
+        Items from the source generator
+
+    Raises:
+        FirstTokenTimeoutError: If first item doesn't arrive within timeout
+    """
+    start_time = time.monotonic()
+
+    # Handle first item separately to check timeout only once
+    try:
+        first_item = next(generator)
+        if time.monotonic() - start_time > timeout_seconds:
+            raise FirstTokenTimeoutError(int(timeout_seconds * 1000))
+        yield first_item
+    except StopIteration:
+        return
+
+    # Yield remaining items without timeout checks
+    yield from generator

api/tests/unit_tests/core/workflow/nodes/llm/test_first_token_timeout.py

@@ -0,0 +1,395 @@
+"""Tests for LLM Node first token timeout retry functionality."""
+
+import time
+from collections.abc import Generator
+from unittest import mock
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import AssistantPromptMessage
+from core.workflow.nodes.base.entities import RetryConfig
+from core.workflow.utils.generator_timeout import FirstTokenTimeoutError, with_first_token_timeout
+
+
+class TestRetryConfigFirstTokenTimeout:
+    """Test cases for RetryConfig first token timeout fields."""
+
+    def test_default_values(self):
+        """Test that first token timeout fields have correct default values."""
+        config = RetryConfig()
+
+        assert config.first_token_timeout == 0
+        assert config.has_first_token_timeout is False
+
+    def test_has_first_token_timeout_when_retry_enabled_and_positive(self):
+        """Test has_first_token_timeout returns True when retry enabled with positive timeout."""
+        config = RetryConfig(
+            retry_enabled=True,
+            first_token_timeout=3000,  # 3000ms = 3s
+        )
+
+        assert config.has_first_token_timeout is True
+        assert config.first_token_timeout_seconds == 3.0
+
+    def test_has_first_token_timeout_when_retry_disabled(self):
+        """Test has_first_token_timeout returns False when retry is disabled."""
+        config = RetryConfig(
+            retry_enabled=False,
+            first_token_timeout=60,
+        )
+
+        assert config.has_first_token_timeout is False
+
+    def test_has_first_token_timeout_when_zero_timeout(self):
+        """Test has_first_token_timeout returns False when timeout is 0."""
+        config = RetryConfig(
+            retry_enabled=True,
+            first_token_timeout=0,
+        )
+
+        assert config.has_first_token_timeout is False
+
+    def test_backward_compatibility(self):
+        """Test that existing workflows without first_token_timeout work correctly."""
+        old_config_data = {
+            "max_retries": 3,
+            "retry_interval": 1000,
+            "retry_enabled": True,
+        }
+
+        config = RetryConfig.model_validate(old_config_data)
+
+        assert config.max_retries == 3
+        assert config.retry_interval == 1000
+        assert config.retry_enabled is True
+        assert config.first_token_timeout == 0
+        # has_first_token_timeout is False because timeout is 0
+        assert config.has_first_token_timeout is False
+
+    def test_full_config_serialization(self):
+        """Test that full config can be serialized and deserialized."""
+        config = RetryConfig(
+            max_retries=5,
+            retry_interval=2000,
+            retry_enabled=True,
+            first_token_timeout=120,
+        )
+
+        config_dict = config.model_dump()
+        restored_config = RetryConfig.model_validate(config_dict)
+
+        assert restored_config.max_retries == 5
+        assert restored_config.retry_interval == 2000
+        assert restored_config.retry_enabled is True
+        assert restored_config.first_token_timeout == 120
+        assert restored_config.has_first_token_timeout is True
+
+
+class TestWithFirstTokenTimeout:
+    """Test cases for with_first_token_timeout function."""
+
+    @staticmethod
+    def _create_mock_chunk(text: str = "test") -> LLMResultChunk:
+        """Helper to create a mock LLMResultChunk."""
+        return LLMResultChunk(
+            model="test-model",
+            prompt_messages=[],
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+            ),
+        )
+
+    def test_first_token_arrives_within_timeout(self):
+        """Test that chunks are yielded normally when first token arrives in time."""
+
+        def mock_generator() -> Generator[LLMResultChunk, None, None]:
+            yield self._create_mock_chunk("Hello")
+            yield self._create_mock_chunk(" world")
+
+        wrapped = with_first_token_timeout(mock_generator(), timeout_seconds=10)
+        chunks = list(wrapped)
+
+        assert len(chunks) == 2
+
+    def test_first_token_timeout_raises_error(self, monkeypatch):
+        """Test that timeout error is raised when first token doesn't arrive in time."""
+        call_count = 0
+
+        def mock_monotonic():
+            nonlocal call_count
+            call_count += 1
+            # First call: start_time = 0
+            # Second call (when checking): current_time = 11 (exceeds 10 second timeout)
+            if call_count == 1:
+                return 0.0
+            return 11.0
+
+        monkeypatch.setattr(time, "monotonic", mock_monotonic)
+
+        def slow_generator() -> Generator[LLMResultChunk, None, None]:
+            # This chunk arrives "after timeout"
+            yield self._create_mock_chunk("Late token")
+
+        wrapped = with_first_token_timeout(slow_generator(), timeout_seconds=10)
+
+        with pytest.raises(FirstTokenTimeoutError) as exc_info:
+            list(wrapped)
+
+        # Error message shows milliseconds (10 seconds = 10000ms)
+        assert "10000ms" in str(exc_info.value)
+
+    def test_no_timeout_check_after_first_token(self, monkeypatch):
+        """Test that subsequent chunks are not subject to timeout after first token received."""
+        call_count = 0
+
+        def mock_monotonic():
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return 0.0  # start_time
+            elif call_count == 2:
+                return 5.0  # first token arrives at 5s (within 10s timeout)
+            else:
+                # Subsequent calls simulate long delays for remaining chunks
+                # These should NOT trigger timeout because first token already received
+                return 100.0 + call_count
+
+        monkeypatch.setattr(time, "monotonic", mock_monotonic)
+
+        def generator_with_slow_subsequent_chunks() -> Generator[LLMResultChunk, None, None]:
+            yield self._create_mock_chunk("First")
+            yield self._create_mock_chunk("Second")
+            yield self._create_mock_chunk("Third")
+
+        wrapped = with_first_token_timeout(
+            generator_with_slow_subsequent_chunks(),
+            timeout_seconds=10,
+        )
+
+        # Should not raise, even though "time" passes beyond timeout after first token
+        chunks = list(wrapped)
+        assert len(chunks) == 3
+
+    def test_empty_generator_no_error(self):
+        """Test that empty generator doesn't raise timeout error (no chunks to check)."""
+
+        def empty_generator() -> Generator[LLMResultChunk, None, None]:
+            return
+            yield  # unreachable, but makes this a generator
+
+        wrapped = with_first_token_timeout(empty_generator(), timeout_seconds=10)
+        chunks = list(wrapped)
+
+        assert chunks == []
+
+    def test_exact_timeout_boundary(self, monkeypatch):
+        """Test behavior at exact timeout boundary (should not raise when equal)."""
+        call_count = 0
+
+        def mock_monotonic():
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return 0.0
+            # Exactly at boundary: current_time - start_time = 10, timeout_seconds = 10
+            # Since we check > not >=, this should NOT raise
+            return 10.0
+
+        monkeypatch.setattr(time, "monotonic", mock_monotonic)
+
+        def generator() -> Generator[LLMResultChunk, None, None]:
+            yield self._create_mock_chunk("Token at boundary")
+
+        wrapped = with_first_token_timeout(generator(), timeout_seconds=10)
+
+        # Should not raise because 10 is not > 10
+        chunks = list(wrapped)
+        assert len(chunks) == 1
+
+    def test_just_over_timeout_boundary(self, monkeypatch):
+        """Test behavior just over timeout boundary (should raise)."""
+        call_count = 0
+
+        def mock_monotonic():
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return 0.0
+            # Just over boundary
+            return 10.001
+
+        monkeypatch.setattr(time, "monotonic", mock_monotonic)
+
+        def generator() -> Generator[LLMResultChunk, None, None]:
+            yield self._create_mock_chunk("Late token")
+
+        wrapped = with_first_token_timeout(generator(), timeout_seconds=10)
+
+        with pytest.raises(FirstTokenTimeoutError):
+            list(wrapped)
+
+
+class TestLLMNodeInvokeLLMWithTimeout:
+    """Test cases for LLMNode.invoke_llm with first_token_timeout parameter."""
+
+    def test_invoke_llm_without_timeout(self):
+        """Test invoke_llm works normally when first_token_timeout is None."""
+        from core.workflow.nodes.llm.node import LLMNode
+
+        with mock.patch.object(LLMNode, "handle_invoke_result") as mock_handle:
+            mock_handle.return_value = iter([])
+
+            # Mock model_instance.invoke_llm to return empty generator
+            mock_model_instance = mock.MagicMock()
+            mock_model_instance.invoke_llm.return_value = iter([])
+            mock_model_instance.model_type_instance.get_model_schema.return_value = mock.MagicMock()
+
+            mock_node_data_model = mock.MagicMock()
+            mock_node_data_model.completion_params = {}
+
+            result = LLMNode.invoke_llm(
+                node_data_model=mock_node_data_model,
+                model_instance=mock_model_instance,
+                prompt_messages=[],
+                user_id="test-user",
+                structured_output_enabled=False,
+                structured_output=None,
+                file_saver=mock.MagicMock(),
+                file_outputs=[],
+                node_id="test-node",
+                node_type=mock.MagicMock(),
+                first_token_timeout=None,  # No timeout
+            )
+
+            list(result)  # Consume generator
+            mock_handle.assert_called_once()
+
+    def test_invoke_llm_with_timeout_passes_to_model_instance(self):
+        """Test invoke_llm passes first_token_timeout to model_instance.invoke_llm."""
+        from core.workflow.nodes.llm.node import LLMNode
+
+        with mock.patch.object(LLMNode, "handle_invoke_result") as mock_handle:
+            mock_handle.return_value = iter([])
+
+            mock_model_instance = mock.MagicMock()
+            mock_model_instance.invoke_llm.return_value = iter([])
+            mock_model_instance.model_type_instance.get_model_schema.return_value = mock.MagicMock()
+
+            mock_node_data_model = mock.MagicMock()
+            mock_node_data_model.completion_params = {}
+
+            result = LLMNode.invoke_llm(
+                node_data_model=mock_node_data_model,
+                model_instance=mock_model_instance,
+                prompt_messages=[],
+                user_id="test-user",
+                structured_output_enabled=False,
+                structured_output=None,
+                file_saver=mock.MagicMock(),
+                file_outputs=[],
+                node_id="test-node",
+                node_type=mock.MagicMock(),
+                first_token_timeout=60,  # With timeout
+            )
+
+            list(result)  # Consume generator
+
+            # Verify model_instance.invoke_llm was called with first_token_timeout
+            mock_model_instance.invoke_llm.assert_called_once()
+            call_kwargs = mock_model_instance.invoke_llm.call_args.kwargs
+            assert call_kwargs.get("first_token_timeout") == 60
+
+    def test_invoke_llm_with_zero_timeout_passes_zero(self):
+        """Test invoke_llm passes zero timeout to model_instance."""
+        from core.workflow.nodes.llm.node import LLMNode
+
+        with mock.patch.object(LLMNode, "handle_invoke_result") as mock_handle:
+            mock_handle.return_value = iter([])
+
+            mock_model_instance = mock.MagicMock()
+            mock_model_instance.invoke_llm.return_value = iter([])
+            mock_model_instance.model_type_instance.get_model_schema.return_value = mock.MagicMock()
+
+            mock_node_data_model = mock.MagicMock()
+            mock_node_data_model.completion_params = {}
+
+            result = LLMNode.invoke_llm(
+                node_data_model=mock_node_data_model,
+                model_instance=mock_model_instance,
+                prompt_messages=[],
+                user_id="test-user",
+                structured_output_enabled=False,
+                structured_output=None,
+                file_saver=mock.MagicMock(),
+                file_outputs=[],
+                node_id="test-node",
+                node_type=mock.MagicMock(),
+                first_token_timeout=0,  # Zero timeout
+            )
+
+            list(result)  # Consume generator
+
+            # Verify model_instance.invoke_llm was called with zero timeout
+            mock_model_instance.invoke_llm.assert_called_once()
+            call_kwargs = mock_model_instance.invoke_llm.call_args.kwargs
+            assert call_kwargs.get("first_token_timeout") == 0
+
+
+class TestRetryConfigIntegration:
+    """Integration tests for RetryConfig with LLM node data."""
+
+    def test_retry_config_in_node_data(self):
+        """Test RetryConfig can be properly configured in LLMNodeData."""
+        from core.model_runtime.entities.llm_entities import LLMMode
+        from core.workflow.nodes.llm.entities import ContextConfig, LLMNodeData, ModelConfig
+
+        node_data = LLMNodeData(
+            title="Test LLM",
+            model=ModelConfig(
+                provider="openai",
+                name="gpt-4",
+                mode=LLMMode.CHAT,
+                completion_params={},
+            ),
+            prompt_template=[],
+            context=ContextConfig(enabled=False),
+            structured_output_enabled=False,
+            retry_config=RetryConfig(
+                max_retries=3,
+                retry_interval=1000,
+                retry_enabled=True,
+                first_token_timeout=3000,  # 3000ms = 3s
+            ),
+        )
+
+        assert node_data.retry_config.max_retries == 3
+        assert node_data.retry_config.retry_enabled is True
+        assert node_data.retry_config.first_token_timeout == 3000
+        assert node_data.retry_config.first_token_timeout_seconds == 3.0
+        assert node_data.retry_config.has_first_token_timeout is True
+
+    def test_default_retry_config_in_node_data(self):
+        """Test default RetryConfig in LLMNodeData."""
+        from core.model_runtime.entities.llm_entities import LLMMode
+        from core.workflow.nodes.llm.entities import ContextConfig, LLMNodeData, ModelConfig
+
+        node_data = LLMNodeData(
+            title="Test LLM",
+            model=ModelConfig(
+                provider="openai",
+                name="gpt-4",
+                mode=LLMMode.CHAT,
+                completion_params={},
+            ),
+            prompt_template=[],
+            context=ContextConfig(enabled=False),
+            structured_output_enabled=False,
+        )
+
+        # Should have default RetryConfig
+        assert node_data.retry_config.max_retries == 0
+        assert node_data.retry_config.retry_enabled is False
+        assert node_data.retry_config.first_token_timeout == 0
+        assert node_data.retry_config.has_first_token_timeout is False

web/app/components/workflow/nodes/_base/components/retry/retry-on-panel.tsx

@@ -9,6 +9,10 @@ import Split from '@/app/components/workflow/nodes/_base/components/split'
 import { useRetryConfig } from './hooks'
 import s from './style.module.css'
 
+// Nodes that support first token timeout configuration
+// These nodes internally call LLM and have streaming response characteristics
+const LLM_RELATED_NODE_TYPES = ['llm', 'agent', 'parameter-extractor', 'question-classifier']
+
 type RetryOnPanelProps = Pick<Node, 'id' | 'data'>
 const RetryOnPanel = ({
   id,
@@ -16,10 +20,14 @@ const RetryOnPanel = ({
 }: RetryOnPanelProps) => {
   const { t } = useTranslation()
   const { handleRetryConfigChange } = useRetryConfig(id)
-  const { retry_config } = data
+  const { retry_config, type } = data
+
+  // Check if this is an LLM-related node that supports first token timeout
+  const isLLMRelatedNode = LLM_RELATED_NODE_TYPES.includes(type)
 
   const handleRetryEnabledChange = (value: boolean) => {
     handleRetryConfigChange({
+      ...retry_config,
       retry_enabled: value,
       max_retries: retry_config?.max_retries || 3,
       retry_interval: retry_config?.retry_interval || 1000,
@@ -32,6 +40,7 @@ const RetryOnPanel = ({
     else if (value < 1)
       value = 1
     handleRetryConfigChange({
+      ...retry_config,
       retry_enabled: true,
       max_retries: value,
       retry_interval: retry_config?.retry_interval || 1000,
@@ -44,12 +53,27 @@ const RetryOnPanel = ({
     else if (value < 100)
       value = 100
     handleRetryConfigChange({
+      ...retry_config,
       retry_enabled: true,
       max_retries: retry_config?.max_retries || 3,
       retry_interval: value,
     })
   }
 
+  const handleFirstTokenTimeoutChange = (value: number) => {
+    if (value > 60000)
+      value = 60000
+    else if (value < 0)
+      value = 0
+    handleRetryConfigChange({
+      ...retry_config,
+      retry_enabled: true,
+      max_retries: retry_config?.max_retries || 3,
+      retry_interval: retry_config?.retry_interval || 1000,
+      first_token_timeout: value,
+    })
+  }
+
   return (
     <>
       <div className="pt-2">
@@ -62,54 +86,76 @@ const RetryOnPanel = ({
             onChange={v => handleRetryEnabledChange(v)}
           />
         </div>
-        {
+        {retry_config?.retry_enabled && (
-          retry_config?.retry_enabled && (
+          <div className="px-4 pb-2">
-            <div className="px-4 pb-2">
+            <div className="mb-1 flex w-full items-center">
-              <div className="mb-1 flex w-full items-center">
+              <div className="system-xs-medium-uppercase mr-2 grow text-text-secondary">{t('nodes.common.retry.maxRetries', { ns: 'workflow' })}</div>
-                <div className="system-xs-medium-uppercase mr-2 grow text-text-secondary">{t('nodes.common.retry.maxRetries', { ns: 'workflow' })}</div>
+              <Slider
+                className="mr-3 w-[108px]"
+                value={retry_config?.max_retries || 3}
+                onChange={handleMaxRetriesChange}
+                min={1}
+                max={10}
+              />
+              <Input
+                type="number"
+                wrapperClassName="w-[100px]"
+                value={retry_config?.max_retries || 3}
+                onChange={e =>
+                  handleMaxRetriesChange(Number.parseInt(e.currentTarget.value, 10) || 3)}
+                min={1}
+                max={10}
+                unit={t('nodes.common.retry.times', { ns: 'workflow' }) || ''}
+                className={s.input}
+              />
+            </div>
+            <div className="mb-1 flex w-full items-center">
+              <div className="system-xs-medium-uppercase mr-2 grow text-text-secondary">{t('nodes.common.retry.retryInterval', { ns: 'workflow' })}</div>
+              <Slider
+                className="mr-3 w-[108px]"
+                value={retry_config?.retry_interval || 1000}
+                onChange={handleRetryIntervalChange}
+                min={100}
+                max={5000}
+              />
+              <Input
+                type="number"
+                wrapperClassName="w-[100px]"
+                value={retry_config?.retry_interval || 1000}
+                onChange={e =>
+                  handleRetryIntervalChange(Number.parseInt(e.currentTarget.value, 10) || 1000)}
+                min={100}
+                max={5000}
+                unit={t('nodes.common.retry.ms', { ns: 'workflow' }) || ''}
+                className={s.input}
+              />
+            </div>
+            {/* First token timeout - only for LLM-related nodes */}
+            {isLLMRelatedNode && (
+              <div className="flex w-full items-center">
+                <div className="system-xs-medium-uppercase mr-2 grow text-text-secondary">{t('nodes.common.retry.firstTokenTimeout', { ns: 'workflow' })}</div>
                 <Slider
                   className="mr-3 w-[108px]"
-                  value={retry_config?.max_retries || 3}
+                  value={retry_config?.first_token_timeout ?? 3000}
-                  onChange={handleMaxRetriesChange}
+                  onChange={handleFirstTokenTimeoutChange}
-                  min={1}
+                  min={0}
-                  max={10}
+                  max={60000}
                 />
                 <Input
                   type="number"
                   wrapperClassName="w-[100px]"
-                  value={retry_config?.max_retries || 3}
+                  value={retry_config?.first_token_timeout ?? 3000}
                   onChange={e =>
-                    handleMaxRetriesChange(Number.parseInt(e.currentTarget.value, 10) || 3)}
+                    handleFirstTokenTimeoutChange(Number.parseInt(e.currentTarget.value, 10) || 0)}
-                  min={1}
+                  min={0}
-                  max={10}
+                  max={60000}
-                  unit={t('nodes.common.retry.times', { ns: 'workflow' }) || ''}
-                  className={s.input}
-                />
-              </div>
-              <div className="flex items-center">
-                <div className="system-xs-medium-uppercase mr-2 grow text-text-secondary">{t('nodes.common.retry.retryInterval', { ns: 'workflow' })}</div>
-                <Slider
-                  className="mr-3 w-[108px]"
-                  value={retry_config?.retry_interval || 1000}
-                  onChange={handleRetryIntervalChange}
-                  min={100}
-                  max={5000}
-                />
-                <Input
-                  type="number"
-                  wrapperClassName="w-[100px]"
-                  value={retry_config?.retry_interval || 1000}
-                  onChange={e =>
-                    handleRetryIntervalChange(Number.parseInt(e.currentTarget.value, 10) || 1000)}
-                  min={100}
-                  max={5000}
                   unit={t('nodes.common.retry.ms', { ns: 'workflow' }) || ''}
                   className={s.input}
                 />
               </div>
-            </div>
+            )}
-          )
+          </div>
-        }
+        )}
       </div>
       <Split className="mx-4 mt-2" />
     </>

web/app/components/workflow/nodes/_base/components/retry/types.ts

@@ -2,4 +2,6 @@ export type WorkflowRetryConfig = {
   max_retries: number
   retry_interval: number
   retry_enabled: boolean
+  // First token timeout for LLM nodes (seconds), 0 means no timeout
+  first_token_timeout?: number
 }

web/i18n/en-US/workflow.json

@@ -433,6 +433,7 @@
   "nodes.common.memory.windowSize": "Window Size",
   "nodes.common.outputVars": "Output Variables",
   "nodes.common.pluginNotInstalled": "Plugin is not installed",
+  "nodes.common.retry.firstTokenTimeout": "First token timeout",
   "nodes.common.retry.maxRetries": "max retries",
   "nodes.common.retry.ms": "ms",
   "nodes.common.retry.retries": "{{num}} Retries",
@@ -444,6 +445,8 @@
   "nodes.common.retry.retrySuccessful": "Retry successful",
   "nodes.common.retry.retryTimes": "Retry {{times}} times on failure",
   "nodes.common.retry.retrying": "Retrying...",
+  "nodes.common.retry.seconds": "s",
+  "nodes.common.retry.timeoutDuration": "Timeout duration",
   "nodes.common.retry.times": "times",
   "nodes.common.typeSwitch.input": "Input value",
   "nodes.common.typeSwitch.variable": "Use variable",

web/i18n/zh-Hans/workflow.json

@@ -433,6 +433,7 @@
   "nodes.common.memory.windowSize": "记忆窗口",
   "nodes.common.outputVars": "输出变量",
   "nodes.common.pluginNotInstalled": "插件未安装",
+  "nodes.common.retry.firstTokenTimeout": "首个 Token 超时",
   "nodes.common.retry.maxRetries": "最大重试次数",
   "nodes.common.retry.ms": "毫秒",
   "nodes.common.retry.retries": "{{num}} 重试次数",
@@ -444,6 +445,8 @@
   "nodes.common.retry.retrySuccessful": "重试成功",
   "nodes.common.retry.retryTimes": "失败时重试 {{times}} 次",
   "nodes.common.retry.retrying": "重试中...",
+  "nodes.common.retry.seconds": "秒",
+  "nodes.common.retry.timeoutDuration": "超时时长",
   "nodes.common.retry.times": "次",
   "nodes.common.typeSwitch.input": "输入值",
   "nodes.common.typeSwitch.variable": "使用变量",