fix: remove trailing newline from lib_agts_search snapshot

- Remove is_ai_generated_graph setting entirely
- Add human_in_the_loop_safe_mode (bool, default True) for HITL blocks
- Add sensitive_action_safe_mode (bool, default False) for sensitive action blocks
- Rename requires_human_review to is_sensitive_action on blocks
- Add has_human_in_the_loop and has_sensitive_action computed properties
- Frontend shows two separate toggles based on block types present
- AI-generated agents set sensitive_action_safe_mode=True by default

autogpt_platform/backend/backend/api/features/admin/execution_analytics_routes.py

@@ -28,7 +28,6 @@ from backend.executor.manager import get_db_async_client
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
-settings = Settings()
 
 
 class ExecutionAnalyticsRequest(BaseModel):
@@ -64,8 +63,6 @@ class ExecutionAnalyticsResult(BaseModel):
     score: Optional[float]
     status: str  # "success", "failed", "skipped"
     error_message: Optional[str] = None
-    started_at: Optional[datetime] = None
-    ended_at: Optional[datetime] = None
 
 
 class ExecutionAnalyticsResponse(BaseModel):
@@ -227,6 +224,11 @@ async def generate_execution_analytics(
     )
 
     try:
+        # Validate model configuration
+        settings = Settings()
+        if not settings.secrets.openai_internal_api_key:
+            raise HTTPException(status_code=500, detail="OpenAI API key not configured")
+
         # Get database client
         db_client = get_db_async_client()
 
@@ -318,8 +320,6 @@ async def generate_execution_analytics(
                     ),
                     status="skipped",
                     error_message=None,  # Not an error - just already processed
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
                 )
             )
 
@@ -349,9 +349,6 @@ async def _process_batch(
 ) -> list[ExecutionAnalyticsResult]:
     """Process a batch of executions concurrently."""
 
-    if not settings.secrets.openai_internal_api_key:
-        raise HTTPException(status_code=500, detail="OpenAI API key not configured")
-
     async def process_single_execution(execution) -> ExecutionAnalyticsResult:
         try:
             # Generate activity status and score using the specified model
@@ -390,8 +387,6 @@ async def _process_batch(
                     score=None,
                     status="skipped",
                     error_message="Activity generation returned None",
-                    started_at=execution.started_at,
-                    ended_at=execution.ended_at,
                 )
 
             # Update the execution stats
@@ -421,8 +416,6 @@ async def _process_batch(
                 summary_text=activity_response["activity_status"],
                 score=activity_response["correctness_score"],
                 status="success",
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
             )
 
         except Exception as e:
@@ -436,8 +429,6 @@ async def _process_batch(
                 score=None,
                 status="failed",
                 error_message=str(e),
-                started_at=execution.started_at,
-                ended_at=execution.ended_at,
             )
 
     # Process all executions in the batch concurrently

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -218,6 +218,7 @@ async def save_agent_to_library(
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
+        sensitive_action_safe_mode=True,
         create_library_agents_for_sub_graphs=False,
     )
 

autogpt_platform/backend/backend/api/features/library/db.py

@@ -401,27 +401,10 @@ async def add_generated_agent_image(
     )
 
 
-def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
-    """
-    Initialize GraphSettings based on graph content.
-
-    Args:
-        graph: The graph to analyze
-
-    Returns:
-        GraphSettings with appropriate human_in_the_loop_safe_mode value
-    """
-    if graph.has_human_in_the_loop:
-        # Graph has HITL blocks - set safe mode to True by default
-        return GraphSettings(human_in_the_loop_safe_mode=True)
-    else:
-        # Graph has no HITL blocks - keep None
-        return GraphSettings(human_in_the_loop_safe_mode=None)
-
-
 async def create_library_agent(
     graph: graph_db.GraphModel,
     user_id: str,
+    sensitive_action_safe_mode: bool = False,
     create_library_agents_for_sub_graphs: bool = True,
 ) -> list[library_model.LibraryAgent]:
     """
@@ -430,6 +413,7 @@ async def create_library_agent(
     Args:
         agent: The agent/Graph to add to the library.
         user_id: The user to whom the agent will be added.
+        sensitive_action_safe_mode: Whether sensitive action blocks require review.
         create_library_agents_for_sub_graphs: If True, creates LibraryAgent records for sub-graphs as well.
 
     Returns:
@@ -465,7 +449,10 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            _initialize_graph_settings(graph_entry).model_dump()
+                            GraphSettings.from_graph(
+                                graph_entry,
+                                sensitive_action_safe_mode=sensitive_action_safe_mode,
+                            ).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -627,33 +614,6 @@ async def update_library_agent(
         raise DatabaseError("Failed to update library agent") from e
 
 
-async def update_library_agent_settings(
-    user_id: str,
-    agent_id: str,
-    settings: GraphSettings,
-) -> library_model.LibraryAgent:
-    """
-    Updates the settings for a specific LibraryAgent.
-
-    Args:
-        user_id: The owner of the LibraryAgent.
-        agent_id: The ID of the LibraryAgent to update.
-        settings: New GraphSettings to apply.
-
-    Returns:
-        The updated LibraryAgent.
-
-    Raises:
-        NotFoundError: If the specified LibraryAgent does not exist.
-        DatabaseError: If there's an error in the update operation.
-    """
-    return await update_library_agent(
-        library_agent_id=agent_id,
-        user_id=user_id,
-        settings=settings,
-    )
-
-
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
@@ -838,7 +798,7 @@ async def add_store_agent_to_library(
                 "isCreatedByUser": False,
                 "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    _initialize_graph_settings(graph_model).model_dump()
+                    GraphSettings.from_graph(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(
@@ -1228,8 +1188,14 @@ async def fork_library_agent(
         )
         new_graph = await on_graph_activate(new_graph, user_id=user_id)
 
-        # Create a library agent for the new graph
-        return (await create_library_agent(new_graph, user_id))[0]
+        # Create a library agent for the new graph, preserving sensitive_action_safe_mode
+        return (
+            await create_library_agent(
+                new_graph,
+                user_id,
+                sensitive_action_safe_mode=original_agent.settings.sensitive_action_safe_mode,
+            )
+        )[0]
     except prisma.errors.PrismaError as e:
         logger.error(f"Database error cloning library agent: {e}")
         raise DatabaseError("Failed to fork library agent") from e

autogpt_platform/backend/backend/api/features/library/model.py

@@ -73,6 +73,12 @@ class LibraryAgent(pydantic.BaseModel):
     has_external_trigger: bool = pydantic.Field(
         description="Whether the agent has an external trigger (e.g. webhook) node"
     )
+    has_human_in_the_loop: bool = pydantic.Field(
+        description="Whether the agent has human-in-the-loop blocks"
+    )
+    has_sensitive_action: bool = pydantic.Field(
+        description="Whether the agent has sensitive action blocks"
+    )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
     # Indicates whether there's a new output (based on recent runs)
@@ -180,6 +186,8 @@ class LibraryAgent(pydantic.BaseModel):
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
             has_external_trigger=graph.has_external_trigger,
+            has_human_in_the_loop=graph.has_human_in_the_loop,
+            has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
             can_access_graph=can_access_graph,

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -52,6 +52,8 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -75,6 +77,8 @@ async def test_get_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -150,6 +154,8 @@ async def test_get_favorite_library_agents_success(
                 output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
+                has_human_in_the_loop=False,
+                has_sensitive_action=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
                 recommended_schedule_cron=None,
                 new_output=False,
@@ -218,6 +224,8 @@ def test_add_agent_to_library_success(
         output_schema={"type": "object", "properties": {}},
         credentials_input_schema={"type": "object", "properties": {}},
         has_external_trigger=False,
+        has_human_in_the_loop=False,
+        has_sensitive_action=False,
         status=library_model.LibraryAgentStatus.COMPLETED,
         new_output=False,
         can_access_graph=True,

autogpt_platform/backend/backend/api/features/v1.py

@@ -761,10 +761,8 @@ async def create_new_graph(
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
     graph.validate_graph(for_run=False)
 
-    # The return value of the create graph & library function is intentionally not used here,
-    # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
-    await library_db.create_library_agent(graph, user_id=user_id)
+    await library_db.create_library_agent(graph, user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -888,21 +886,19 @@ async def set_graph_active_version(
 async def _update_library_agent_version_and_settings(
     user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
-    # Keep the library agent up to date with the new active version
     library = await library_db.update_agent_version_in_library(
         user_id, agent_graph.id, agent_graph.version
     )
-    # If the graph has HITL node, initialize the setting if it's not already set.
-    if (
-        agent_graph.has_human_in_the_loop
-        and library.settings.human_in_the_loop_safe_mode is None
-    ):
-        await library_db.update_library_agent_settings(
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await library_db.update_library_agent(
+            library_agent_id=library.id,
             user_id=user_id,
-            agent_id=library.id,
-            settings=library.settings.model_copy(
-                update={"human_in_the_loop_safe_mode": True}
-            ),
+            settings=updated_settings,
         )
     return library
 
@@ -919,21 +915,18 @@ async def update_graph_settings(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> GraphSettings:
     """Update graph settings for the user's library agent."""
-    # Get the library agent for this graph
     library_agent = await library_db.get_library_agent_by_graph_id(
         graph_id=graph_id, user_id=user_id
     )
     if not library_agent:
         raise HTTPException(404, f"Graph #{graph_id} not found in user's library")
 
-    # Update the library agent settings
-    updated_agent = await library_db.update_library_agent_settings(
+    updated_agent = await library_db.update_library_agent(
+        library_agent_id=library_agent.id,
         user_id=user_id,
-        agent_id=library_agent.id,
         settings=settings,
     )
 
-    # Return the updated settings
     return GraphSettings.model_validate(updated_agent.settings)
 
 

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -84,7 +84,7 @@ class HITLReviewHelper:
             Exception: If review creation or status update fails
         """
         # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.safe_mode:
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
                 f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -104,7 +104,7 @@ class HumanInTheLoopBlock(Block):
         execution_context: ExecutionContext,
         **_kwargs,
     ) -> BlockOutput:
-        if not execution_context.safe_mode:
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -242,7 +242,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -343,7 +343,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -409,7 +409,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -471,7 +471,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -535,7 +535,7 @@ async def test_smart_decision_maker_parameter_validation():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -658,7 +658,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -730,7 +730,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -786,7 +786,7 @@ async def test_smart_decision_maker_raw_response_conversion():
         outputs = {}
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 
@@ -905,7 +905,7 @@ async def test_smart_decision_maker_agent_mode():
         # Create a mock execution context
 
         mock_execution_context = ExecutionContext(
-            safe_mode=False,
+            human_in_the_loop_safe_mode=False,
         )
 
         # Create a mock execution processor for agent mode tests
@@ -1027,7 +1027,7 @@ async def test_smart_decision_maker_traditional_mode_default():
 
         # Create execution context
 
-        mock_execution_context = ExecutionContext(safe_mode=False)
+        mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
 
         # Create a mock execution processor for tests
 

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -386,7 +386,7 @@ async def test_output_yielding_with_dynamic_fields():
             outputs = {}
             from backend.data.execution import ExecutionContext
 
-            mock_execution_context = ExecutionContext(safe_mode=False)
+            mock_execution_context = ExecutionContext(human_in_the_loop_safe_mode=False)
             mock_execution_processor = MagicMock()
 
             async for output_name, output_value in block.run(
@@ -609,7 +609,9 @@ async def test_validation_errors_dont_pollute_conversation():
                 outputs = {}
                 from backend.data.execution import ExecutionContext
 
-                mock_execution_context = ExecutionContext(safe_mode=False)
+                mock_execution_context = ExecutionContext(
+                    human_in_the_loop_safe_mode=False
+                )
 
                 # Create a proper mock execution processor for agent mode
                 from collections import defaultdict

autogpt_platform/backend/backend/data/analytics.py

@@ -104,7 +104,7 @@ async def get_accuracy_trends_and_alerts(
             AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
             {user_filter}
         GROUP BY DATE(e."createdAt")
-        HAVING COUNT(*) >= 1  -- Include all days with at least 1 execution
+        HAVING COUNT(*) >= 3  -- Need at least 3 executions per day
     ),
     trends AS (
         SELECT 

autogpt_platform/backend/backend/data/block.py

@@ -474,7 +474,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.requires_human_review: bool = False
+        self.is_sensitive_action: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -637,8 +637,9 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             - should_pause: True if execution should be paused for review
             - input_data_to_use: The input data to use (may be modified by reviewer)
         """
-        # Skip review if not required or safe mode is disabled
-        if not self.requires_human_review or not execution_context.safe_mode:
+        if not (
+            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
+        ):
             return False, input_data
 
         from backend.blocks.helpers.review import HITLReviewHelper

autogpt_platform/backend/backend/data/execution.py

@@ -81,7 +81,8 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    safe_mode: bool = True
+    human_in_the_loop_safe_mode: bool = True
+    sensitive_action_safe_mode: bool = False
     user_timezone: str = "UTC"
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None
@@ -153,14 +154,8 @@ class GraphExecutionMeta(BaseDbModel):
     nodes_input_masks: Optional[dict[str, BlockInput]]
     preset_id: Optional[str]
     status: ExecutionStatus
-    started_at: Optional[datetime] = Field(
-        None,
-        description="When execution started running. Null if not yet started (QUEUED).",
-    )
-    ended_at: Optional[datetime] = Field(
-        None,
-        description="When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW).",
-    )
+    started_at: datetime
+    ended_at: datetime
     is_shared: bool = False
     share_token: Optional[str] = None
 
@@ -235,8 +230,10 @@ class GraphExecutionMeta(BaseDbModel):
 
     @staticmethod
     def from_db(_graph_exec: AgentGraphExecution):
-        start_time = _graph_exec.startedAt
-        end_time = _graph_exec.endedAt
+        now = datetime.now(timezone.utc)
+        # TODO: make started_at and ended_at optional
+        start_time = _graph_exec.startedAt or _graph_exec.createdAt
+        end_time = _graph_exec.updatedAt or now
 
         try:
             stats = GraphExecutionStats.model_validate(_graph_exec.stats)
@@ -906,14 +903,6 @@ async def update_graph_execution_stats(
 
     if status:
         update_data["executionStatus"] = status
-        # Set endedAt when execution reaches a terminal status
-        terminal_statuses = [
-            ExecutionStatus.COMPLETED,
-            ExecutionStatus.FAILED,
-            ExecutionStatus.TERMINATED,
-        ]
-        if status in terminal_statuses:
-            update_data["endedAt"] = datetime.now(tz=timezone.utc)
 
     where_clause: AgentGraphExecutionWhereInput = {"id": graph_exec_id}
 

autogpt_platform/backend/backend/data/graph.py

@@ -62,7 +62,23 @@ logger = logging.getLogger(__name__)
 
 
 class GraphSettings(BaseModel):
-    human_in_the_loop_safe_mode: bool | None = None
+    human_in_the_loop_safe_mode: bool = True
+    sensitive_action_safe_mode: bool = False
+
+    @classmethod
+    def from_graph(
+        cls,
+        graph: "GraphModel",
+        hitl_safe_mode: bool | None = None,
+        sensitive_action_safe_mode: bool = False,
+    ) -> "GraphSettings":
+        # Default to True if not explicitly set
+        if hitl_safe_mode is None:
+            hitl_safe_mode = True
+        return cls(
+            human_in_the_loop_safe_mode=hitl_safe_mode,
+            sensitive_action_safe_mode=sensitive_action_safe_mode,
+        )
 
 
 class Link(BaseDbModel):
@@ -244,10 +260,14 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if (
-                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-                or node.block.requires_human_review
-            )
+            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+        )
+
+    @computed_field
+    @property
+    def has_sensitive_action(self) -> bool:
+        return any(
+            node.block_id for node in self.nodes if node.block.is_sensitive_action
         )
 
     @property

autogpt_platform/backend/backend/executor/utils.py

@@ -873,11 +873,8 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
-            safe_mode=(
-                settings.human_in_the_loop_safe_mode
-                if settings.human_in_the_loop_safe_mode is not None
-                else True
-            ),
+            human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
+            sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),

autogpt_platform/backend/backend/executor/utils_test.py

@@ -386,6 +386,7 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
+    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
@@ -651,6 +652,7 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_user.timezone = "UTC"
     mock_settings = mocker.MagicMock()
     mock_settings.human_in_the_loop_safe_mode = True
+    mock_settings.sensitive_action_safe_mode = False
 
     mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
     mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -96,9 +96,9 @@ jina_credentials = APIKeyCredentials(
 )
 unreal_credentials = APIKeyCredentials(
     id="66f20754-1b81-48e4-91d0-f4f0dd82145f",
-    provider="unreal_speech",
+    provider="unreal",
     api_key=SecretStr(settings.secrets.unreal_speech_api_key),
-    title="Use Credits for Unreal Speech",
+    title="Use Credits for Unreal",
     expires_at=None,
 )
 open_router_credentials = APIKeyCredentials(
@@ -216,14 +216,6 @@ webshare_proxy_credentials = UserPasswordCredentials(
     title="Use Credits for Webshare Proxy",
 )
 
-openweathermap_credentials = APIKeyCredentials(
-    id="8b3d4e5f-6a7b-8c9d-0e1f-2a3b4c5d6e7f",
-    provider="openweathermap",
-    api_key=SecretStr(settings.secrets.openweathermap_api_key),
-    title="Use Credits for OpenWeatherMap",
-    expires_at=None,
-)
-
 DEFAULT_CREDENTIALS = [
     ollama_credentials,
     revid_credentials,
@@ -251,7 +243,6 @@ DEFAULT_CREDENTIALS = [
     llama_api_credentials,
     v0_credentials,
     webshare_proxy_credentials,
-    openweathermap_credentials,
 ]
 
 SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
@@ -355,17 +346,11 @@ class IntegrationCredentialsStore:
             all_credentials.append(zerobounce_credentials)
         if settings.secrets.google_maps_api_key:
             all_credentials.append(google_maps_credentials)
-        if settings.secrets.llama_api_key:
-            all_credentials.append(llama_api_credentials)
-        if settings.secrets.v0_api_key:
-            all_credentials.append(v0_credentials)
         if (
             settings.secrets.webshare_proxy_username
             and settings.secrets.webshare_proxy_password
         ):
             all_credentials.append(webshare_proxy_credentials)
-        if settings.secrets.openweathermap_api_key:
-            all_credentials.append(openweathermap_credentials)
         return all_credentials
 
     async def get_creds_by_id(

autogpt_platform/backend/backend/monitoring/late_execution_monitor.py

@@ -60,10 +60,8 @@ class LateExecutionMonitor:
         if not all_late_executions:
             return "No late executions detected."
 
-        # Sort by started time (oldest first), with None values (unstarted) first
-        all_late_executions.sort(
-            key=lambda x: x.started_at or datetime.min.replace(tzinfo=timezone.utc)
-        )
+        # Sort by created time (oldest first)
+        all_late_executions.sort(key=lambda x: x.started_at)
 
         num_total_late = len(all_late_executions)
         num_queued = len(queued_late_executions)
@@ -76,7 +74,7 @@ class LateExecutionMonitor:
         was_truncated = num_total_late > tuncate_size
 
         late_execution_details = [
-            f"* `Execution ID: {exec.id}, Graph ID: {exec.graph_id}v{exec.graph_version}, User ID: {exec.user_id}, Status: {exec.status}, Started At: {exec.started_at.isoformat() if exec.started_at else 'Not started'}`"
+            f"* `Execution ID: {exec.id}, Graph ID: {exec.graph_id}v{exec.graph_version}, User ID: {exec.user_id}, Status: {exec.status}, Created At: {exec.started_at.isoformat()}`"
             for exec in truncated_executions
         ]
 

autogpt_platform/backend/migrations/20260113170839_add_ended_at_to_agent_graph_execution/migration.sql

@@ -1,8 +0,0 @@
--- AlterTable
-ALTER TABLE "AgentGraphExecution" ADD COLUMN "endedAt" TIMESTAMP(3);
-
--- Set endedAt to updatedAt for existing records with terminal status only
-UPDATE "AgentGraphExecution"
-SET "endedAt" = "updatedAt"
-WHERE "endedAt" IS NULL
-  AND "executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED');

autogpt_platform/backend/schema.prisma

@@ -450,7 +450,6 @@ model AgentGraphExecution {
   createdAt DateTime  @default(now())
   updatedAt DateTime? @updatedAt
   startedAt DateTime?
-  endedAt   DateTime?
 
   isDeleted Boolean @default(false)
 

autogpt_platform/backend/snapshots/grph_single

@@ -11,6 +11,7 @@
   "forked_from_version": null,
   "has_external_trigger": false,
   "has_human_in_the_loop": false,
+  "has_sensitive_action": false,
   "id": "graph-123",
   "input_schema": {
     "properties": {},

autogpt_platform/backend/snapshots/grphs_all

@@ -11,6 +11,7 @@
     "forked_from_version": null,
     "has_external_trigger": false,
     "has_human_in_the_loop": false,
+    "has_sensitive_action": false,
     "id": "graph-123",
     "input_schema": {
       "properties": {},

autogpt_platform/backend/snapshots/lib_agts_search

@@ -27,6 +27,8 @@
         "properties": {}
       },
       "has_external_trigger": false,
+      "has_human_in_the_loop": false,
+      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": true,
@@ -34,7 +36,8 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": null
+        "human_in_the_loop_safe_mode": true,
+        "sensitive_action_safe_mode": false
       },
       "marketplace_listing": null
     },
@@ -65,6 +68,8 @@
         "properties": {}
       },
       "has_external_trigger": false,
+      "has_human_in_the_loop": false,
+      "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
       "can_access_graph": false,
@@ -72,7 +77,8 @@
       "is_favorite": false,
       "recommended_schedule_cron": null,
       "settings": {
-        "human_in_the_loop_safe_mode": null
+        "human_in_the_loop_safe_mode": true,
+        "sensitive_action_safe_mode": false
       },
       "marketplace_listing": null
     }

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/components/AnalyticsResultsTable.tsx

@@ -51,8 +51,6 @@ export function AnalyticsResultsTable({ results }: Props) {
       "Execution ID",
       "Status",
       "Score",
-      "Started At",
-      "Ended At",
       "Summary Text",
       "Error Message",
     ];
@@ -64,8 +62,6 @@ export function AnalyticsResultsTable({ results }: Props) {
       result.exec_id,
       result.status,
       result.score?.toString() || "",
-      result.started_at ? new Date(result.started_at).toLocaleString() : "",
-      result.ended_at ? new Date(result.ended_at).toLocaleString() : "",
       `"${(result.summary_text || "").replace(/"/g, '""')}"`, // Escape quotes in summary
       `"${(result.error_message || "").replace(/"/g, '""')}"`, // Escape quotes in error
     ]);
@@ -252,13 +248,15 @@ export function AnalyticsResultsTable({ results }: Props) {
                         )}
                       </td>
                       <td className="px-4 py-3">
-                        <Button
-                          variant="ghost"
-                          size="small"
-                          onClick={() => toggleRowExpansion(result.exec_id)}
-                        >
-                          <EyeIcon size={16} />
-                        </Button>
+                        {(result.summary_text || result.error_message) && (
+                          <Button
+                            variant="ghost"
+                            size="small"
+                            onClick={() => toggleRowExpansion(result.exec_id)}
+                          >
+                            <EyeIcon size={16} />
+                          </Button>
+                        )}
                       </td>
                     </tr>
 
@@ -266,44 +264,6 @@ export function AnalyticsResultsTable({ results }: Props) {
                       <tr>
                         <td colSpan={7} className="bg-gray-50 px-4 py-3">
                           <div className="space-y-3">
-                            {/* Timestamps section */}
-                            <div className="grid grid-cols-2 gap-4 border-b border-gray-200 pb-3">
-                              <div>
-                                <Text
-                                  variant="body"
-                                  className="text-xs font-medium text-gray-600"
-                                >
-                                  Started At:
-                                </Text>
-                                <Text
-                                  variant="body"
-                                  className="text-sm text-gray-700"
-                                >
-                                  {result.started_at
-                                    ? new Date(
-                                        result.started_at,
-                                      ).toLocaleString()
-                                    : "—"}
-                                </Text>
-                              </div>
-                              <div>
-                                <Text
-                                  variant="body"
-                                  className="text-xs font-medium text-gray-600"
-                                >
-                                  Ended At:
-                                </Text>
-                                <Text
-                                  variant="body"
-                                  className="text-sm text-gray-700"
-                                >
-                                  {result.ended_at
-                                    ? new Date(result.ended_at).toLocaleString()
-                                    : "—"}
-                                </Text>
-                              </div>
-                            </div>
-
                             {result.summary_text && (
                               <div>
                                 <Text

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/components/ExecutionAnalyticsForm.tsx

@@ -541,19 +541,7 @@ export function ExecutionAnalyticsForm() {
       {/* Accuracy Trends Display */}
       {trendsData && (
         <div className="space-y-4">
-          <div className="flex items-start justify-between">
-            <h3 className="text-lg font-semibold">Execution Accuracy Trends</h3>
-            <div className="rounded-md bg-blue-50 px-3 py-2 text-xs text-blue-700">
-              <p className="font-medium">
-                Chart Filters (matches monitoring system):
-              </p>
-              <ul className="mt-1 list-inside list-disc space-y-1">
-                <li>Only days with ≥1 execution with correctness score</li>
-                <li>Last 30 days</li>
-                <li>Averages calculated from scored executions only</li>
-              </ul>
-            </div>
-          </div>
+          <h3 className="text-lg font-semibold">Execution Accuracy Trends</h3>
 
           {/* Alert Section */}
           {trendsData.alert && (

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/AgentSettingsModal/AgentSettingsModal.tsx

@@ -31,10 +31,18 @@ export function AgentSettingsModal({
     }
   }
 
-  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
-    useAgentSafeMode(agent);
+  const {
+    currentHITLSafeMode,
+    showHITLToggle,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
+    isPending,
+    shouldShowToggle,
+  } = useAgentSafeMode(agent);
 
-  if (!hasHITLBlocks) return null;
+  if (!shouldShowToggle) return null;
 
   return (
     <Dialog
@@ -57,23 +65,48 @@ export function AgentSettingsModal({
       )}
       <Dialog.Content>
         <div className="space-y-6">
-          <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-            <div className="flex w-full items-start justify-between gap-4">
-              <div className="flex-1">
-                <Text variant="large-semibold">Require human approval</Text>
-                <Text variant="large" className="mt-1 text-zinc-900">
-                  The agent will pause and wait for your review before
-                  continuing
-                </Text>
+          {showHITLToggle && (
+            <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+              <div className="flex w-full items-start justify-between gap-4">
+                <div className="flex-1">
+                  <Text variant="large-semibold">
+                    Human-in-the-loop approval
+                  </Text>
+                  <Text variant="large" className="mt-1 text-zinc-900">
+                    The agent will pause at human-in-the-loop blocks and wait
+                    for your review before continuing
+                  </Text>
+                </div>
+                <Switch
+                  checked={currentHITLSafeMode || false}
+                  onCheckedChange={handleHITLToggle}
+                  disabled={isPending}
+                  className="mt-1"
+                />
               </div>
-              <Switch
-                checked={currentSafeMode || false}
-                onCheckedChange={handleToggle}
-                disabled={isPending}
-                className="mt-1"
-              />
             </div>
-          </div>
+          )}
+          {showSensitiveActionToggle && (
+            <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+              <div className="flex w-full items-start justify-between gap-4">
+                <div className="flex-1">
+                  <Text variant="large-semibold">
+                    Sensitive action approval
+                  </Text>
+                  <Text variant="large" className="mt-1 text-zinc-900">
+                    The agent will pause at sensitive action blocks and wait for
+                    your review before continuing
+                  </Text>
+                </div>
+                <Switch
+                  checked={currentSensitiveActionSafeMode}
+                  onCheckedChange={handleSensitiveActionToggle}
+                  disabled={isPending}
+                  className="mt-1"
+                />
+              </div>
+            </div>
+          )}
         </div>
       </Dialog.Content>
     </Dialog>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedSettingsView/SelectedSettingsView.tsx

@@ -13,8 +13,16 @@ interface Props {
 }
 
 export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
-  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
-    useAgentSafeMode(agent);
+  const {
+    currentHITLSafeMode,
+    showHITLToggle,
+    handleHITLToggle,
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
+    isPending,
+    shouldShowToggle,
+  } = useAgentSafeMode(agent);
 
   return (
     <SelectedViewLayout agent={agent}>
@@ -34,24 +42,51 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
         </div>
 
         <div className={`${AGENT_LIBRARY_SECTION_PADDING_X} space-y-6`}>
-          {hasHITLBlocks ? (
-            <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
-              <div className="flex w-full items-start justify-between gap-4">
-                <div className="flex-1">
-                  <Text variant="large-semibold">Require human approval</Text>
-                  <Text variant="large" className="mt-1 text-zinc-900">
-                    The agent will pause and wait for your review before
-                    continuing
-                  </Text>
+          {shouldShowToggle ? (
+            <>
+              {showHITLToggle && (
+                <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+                  <div className="flex w-full items-start justify-between gap-4">
+                    <div className="flex-1">
+                      <Text variant="large-semibold">
+                        Human-in-the-loop approval
+                      </Text>
+                      <Text variant="large" className="mt-1 text-zinc-900">
+                        The agent will pause at human-in-the-loop blocks and
+                        wait for your review before continuing
+                      </Text>
+                    </div>
+                    <Switch
+                      checked={currentHITLSafeMode || false}
+                      onCheckedChange={handleHITLToggle}
+                      disabled={isPending}
+                      className="mt-1"
+                    />
+                  </div>
                 </div>
-                <Switch
-                  checked={currentSafeMode || false}
-                  onCheckedChange={handleToggle}
-                  disabled={isPending}
-                  className="mt-1"
-                />
-              </div>
-            </div>
+              )}
+              {showSensitiveActionToggle && (
+                <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+                  <div className="flex w-full items-start justify-between gap-4">
+                    <div className="flex-1">
+                      <Text variant="large-semibold">
+                        Sensitive action approval
+                      </Text>
+                      <Text variant="large" className="mt-1 text-zinc-900">
+                        The agent will pause at sensitive action blocks and wait
+                        for your review before continuing
+                      </Text>
+                    </div>
+                    <Switch
+                      checked={currentSensitiveActionSafeMode}
+                      onCheckedChange={handleSensitiveActionToggle}
+                      disabled={isPending}
+                      className="mt-1"
+                    />
+                  </div>
+                </div>
+              )}
+            </>
           ) : (
             <div className="rounded-xl border border-zinc-100 bg-white p-6">
               <Text variant="body" className="text-muted-foreground">

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/OldAgentLibraryView.tsx

@@ -173,9 +173,8 @@ export function OldAgentLibraryView() {
     if (agentRuns.length > 0) {
       // select latest run
       const latestRun = agentRuns.reduce((latest, current) => {
-        if (!latest.started_at && !current.started_at) return latest;
-        if (!latest.started_at) return current;
-        if (!current.started_at) return latest;
+        if (latest.started_at && !current.started_at) return current;
+        else if (!latest.started_at) return latest;
         return latest.started_at > current.started_at ? latest : current;
       }, agentRuns[0]);
       selectRun(latestRun.id as GraphExecutionID);

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-runs-selector-list.tsx

@@ -184,11 +184,9 @@ export function AgentRunsSelectorList({
                     ))}
                   {agentPresets.length > 0 && <Separator className="my-1" />}
                   {agentRuns
-                    .toSorted((a, b) => {
-                      const aTime = a.started_at?.getTime() ?? 0;
-                      const bTime = b.started_at?.getTime() ?? 0;
-                      return bTime - aTime;
-                    })
+                    .toSorted(
+                      (a, b) => b.started_at.getTime() - a.started_at.getTime(),
+                    )
                     .map((run) => (
                       <AgentRunSummaryCard
                         className={listItemClasses}
@@ -201,7 +199,7 @@ export function AgentRunsSelectorList({
                                 ?.name
                             : null) ?? agent.name
                         }
-                        timestamp={run.started_at ?? undefined}
+                        timestamp={run.started_at}
                         selected={selectedView.id === run.id}
                         onClick={() => onSelectRun(run.id)}
                         onDelete={() => doDeleteRun(run as GraphExecutionMeta)}

autogpt_platform/frontend/src/app/(platform)/monitoring/components/AgentFlowList.tsx

@@ -120,11 +120,9 @@ export const AgentFlowList = ({
                   lastRun =
                     runCount == 0
                       ? null
-                      : _flowRuns.reduce((a, c) => {
-                          const aTime = a.started_at?.getTime() ?? 0;
-                          const cTime = c.started_at?.getTime() ?? 0;
-                          return aTime > cTime ? a : c;
-                        });
+                      : _flowRuns.reduce((a, c) =>
+                          a.started_at > c.started_at ? a : c,
+                        );
                 }
                 return { flow, runCount, lastRun };
               })
@@ -132,9 +130,10 @@ export const AgentFlowList = ({
                 if (!a.lastRun && !b.lastRun) return 0;
                 if (!a.lastRun) return 1;
                 if (!b.lastRun) return -1;
-                const bTime = b.lastRun.started_at?.getTime() ?? 0;
-                const aTime = a.lastRun.started_at?.getTime() ?? 0;
-                return bTime - aTime;
+                return (
+                  b.lastRun.started_at.getTime() -
+                  a.lastRun.started_at.getTime()
+                );
               })
               .map(({ flow, runCount, lastRun }) => (
                 <TableRow

autogpt_platform/frontend/src/app/(platform)/monitoring/components/FlowRunsStatus.tsx

@@ -29,10 +29,7 @@ export const FlowRunsStatus: React.FC<{
         : statsSince;
   const filteredFlowRuns =
     statsSinceTimestamp != null
-      ? executions.filter(
-          (fr) =>
-            fr.started_at && fr.started_at.getTime() > statsSinceTimestamp,
-        )
+      ? executions.filter((fr) => fr.started_at.getTime() > statsSinceTimestamp)
       : executions;
 
   return (

autogpt_platform/frontend/src/app/(platform)/monitoring/components/FlowRunsTimeline.tsx

@@ -98,43 +98,40 @@ export const FlowRunsTimeline = ({
         <Scatter
           key={flow.id}
           data={executions
-            .filter((e) => e.graph_id == flow.graph_id && e.started_at)
+            .filter((e) => e.graph_id == flow.graph_id)
             .map((e) => ({
               ...e,
               time:
-                (e.started_at?.getTime() ?? 0) +
-                (e.stats?.node_exec_time ?? 0) * 1000,
+                e.started_at.getTime() + (e.stats?.node_exec_time ?? 0) * 1000,
               _duration: e.stats?.node_exec_time ?? 0,
             }))}
           name={flow.name}
           fill={`hsl(${(hashString(flow.id) * 137.5) % 360}, 70%, 50%)`}
         />
       ))}
-      {executions
-        .filter((e) => e.started_at && e.ended_at)
-        .map((execution) => (
-          <Line
-            key={execution.id}
-            type="linear"
-            dataKey="_duration"
-            data={[
-              {
-                ...execution,
-                time: execution.started_at!.getTime(),
-                _duration: 0,
-              },
-              {
-                ...execution,
-                time: execution.ended_at!.getTime(),
-                _duration: execution.stats?.node_exec_time ?? 0,
-              },
-            ]}
-            stroke={`hsl(${(hashString(execution.graph_id) * 137.5) % 360}, 70%, 50%)`}
-            strokeWidth={2}
-            dot={false}
-            legendType="none"
-          />
-        ))}
+      {executions.map((execution) => (
+        <Line
+          key={execution.id}
+          type="linear"
+          dataKey="_duration"
+          data={[
+            {
+              ...execution,
+              time: execution.started_at.getTime(),
+              _duration: 0,
+            },
+            {
+              ...execution,
+              time: execution.ended_at.getTime(),
+              _duration: execution.stats?.node_exec_time ?? 0,
+            },
+          ]}
+          stroke={`hsl(${(hashString(execution.graph_id) * 137.5) % 360}, 70%, 50%)`}
+          strokeWidth={2}
+          dot={false}
+          legendType="none"
+        />
+      ))}
       <Legend
         content={<ScrollableLegend />}
         wrapperStyle={{

autogpt_platform/frontend/src/app/(platform)/monitoring/page.tsx

@@ -98,11 +98,7 @@ const Monitor = () => {
           ...(selectedFlow
             ? executions.filter((v) => v.graph_id == selectedFlow.graph_id)
             : executions),
-        ].sort((a, b) => {
-          const aTime = a.started_at?.getTime() ?? 0;
-          const bTime = b.started_at?.getTime() ?? 0;
-          return bTime - aTime;
-        })}
+        ].sort((a, b) => b.started_at.getTime() - a.started_at.getTime())}
         selectedRun={selectedRun}
         onSelectRun={(r) => setSelectedRun(r.id == selectedRun?.id ? null : r)}
       />

autogpt_platform/frontend/src/app/(platform)/profile/(user)/integrations/page.tsx

@@ -116,9 +116,6 @@ export default function UserIntegrationsPage() {
       "63a6e279-2dc2-448e-bf57-85776f7176dc", // ZeroBounce
       "9aa1bde0-4947-4a70-a20c-84daa3850d52", // Google Maps
       "d44045af-1c33-4833-9e19-752313214de2", // Llama API
-      "c4e6d1a0-3b5f-4789-a8e2-9b123456789f", // V0 by Vercel
-      "a5b3c7d9-2e4f-4a6b-8c1d-9e0f1a2b3c4d", // Webshare Proxy
-      "8b3d4e5f-6a7b-8c9d-0e1f-2a3b4c5d6e7f", // OpenWeatherMap
     ],
     [],
   );

autogpt_platform/frontend/src/app/api/openapi.json

@@ -6383,6 +6383,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -6399,6 +6404,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info"
         ],
         "title": "BaseGraph"
@@ -7148,20 +7154,6 @@
           "error_message": {
             "anyOf": [{ "type": "string" }, { "type": "null" }],
             "title": "Error Message"
-          },
-          "started_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Started At"
-          },
-          "ended_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Ended At"
           }
         },
         "type": "object",
@@ -7268,20 +7260,14 @@
           },
           "status": { "$ref": "#/components/schemas/AgentExecutionStatus" },
           "started_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Started At",
-            "description": "When execution started running. Null if not yet started (QUEUED)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Started At"
           },
           "ended_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Ended At",
-            "description": "When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Ended At"
           },
           "is_shared": {
             "type": "boolean",
@@ -7315,6 +7301,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats",
           "outputs"
         ],
@@ -7413,20 +7401,14 @@
           },
           "status": { "$ref": "#/components/schemas/AgentExecutionStatus" },
           "started_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Started At",
-            "description": "When execution started running. Null if not yet started (QUEUED)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Started At"
           },
           "ended_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Ended At",
-            "description": "When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Ended At"
           },
           "is_shared": {
             "type": "boolean",
@@ -7455,6 +7437,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats"
         ],
         "title": "GraphExecutionMeta"
@@ -7501,20 +7485,14 @@
           },
           "status": { "$ref": "#/components/schemas/AgentExecutionStatus" },
           "started_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Started At",
-            "description": "When execution started running. Null if not yet started (QUEUED)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Started At"
           },
           "ended_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Ended At",
-            "description": "When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Ended At"
           },
           "is_shared": {
             "type": "boolean",
@@ -7553,6 +7531,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats",
           "outputs",
           "node_executions"
@@ -7629,6 +7609,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7652,6 +7637,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7730,6 +7716,11 @@
             "title": "Has Human In The Loop",
             "readOnly": true
           },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "readOnly": true
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7754,6 +7745,7 @@
           "output_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
+          "has_sensitive_action",
           "trigger_setup_info",
           "credentials_input_schema"
         ],
@@ -7762,8 +7754,14 @@
       "GraphSettings": {
         "properties": {
           "human_in_the_loop_safe_mode": {
-            "anyOf": [{ "type": "boolean" }, { "type": "null" }],
-            "title": "Human In The Loop Safe Mode"
+            "type": "boolean",
+            "title": "Human In The Loop Safe Mode",
+            "default": true
+          },
+          "sensitive_action_safe_mode": {
+            "type": "boolean",
+            "title": "Sensitive Action Safe Mode",
+            "default": false
           }
         },
         "type": "object",
@@ -7921,6 +7919,16 @@
             "title": "Has External Trigger",
             "description": "Whether the agent has an external trigger (e.g. webhook) node"
           },
+          "has_human_in_the_loop": {
+            "type": "boolean",
+            "title": "Has Human In The Loop",
+            "description": "Whether the agent has human-in-the-loop blocks"
+          },
+          "has_sensitive_action": {
+            "type": "boolean",
+            "title": "Has Sensitive Action",
+            "description": "Whether the agent has sensitive action blocks"
+          },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
@@ -7967,6 +7975,8 @@
           "output_schema",
           "credentials_input_schema",
           "has_external_trigger",
+          "has_human_in_the_loop",
+          "has_sensitive_action",
           "new_output",
           "can_access_graph",
           "is_latest_version",

autogpt_platform/frontend/src/components/layout/Navbar/components/AgentActivityDropdown/components/ActivityItem.tsx

@@ -50,9 +50,7 @@ export function ActivityItem({ execution }: Props) {
       execution.status === AgentExecutionStatus.QUEUED;
 
     if (isActiveStatus) {
-      const timeAgo = execution.started_at
-        ? formatTimeAgo(execution.started_at.toString())
-        : "recently";
+      const timeAgo = formatTimeAgo(execution.started_at.toString());
       const statusText =
         execution.status === AgentExecutionStatus.QUEUED ? "queued" : "running";
       return [
@@ -63,9 +61,7 @@ export function ActivityItem({ execution }: Props) {
     // Handle all other statuses with time display
     const timeAgo = execution.ended_at
       ? formatTimeAgo(execution.ended_at.toString())
-      : execution.started_at
-        ? formatTimeAgo(execution.started_at.toString())
-        : "recently";
+      : formatTimeAgo(execution.started_at.toString());
 
     let statusText = "ended";
     switch (execution.status) {

autogpt_platform/frontend/src/hooks/useAgentSafeMode.ts

@@ -20,11 +20,15 @@ function hasHITLBlocks(graph: GraphModel | LibraryAgent | Graph): boolean {
   if ("has_human_in_the_loop" in graph) {
     return !!graph.has_human_in_the_loop;
   }
+  return false;
+}
 
-  if (isLibraryAgent(graph)) {
-    return graph.settings?.human_in_the_loop_safe_mode !== null;
+function hasSensitiveActionBlocks(
+  graph: GraphModel | LibraryAgent | Graph,
+): boolean {
+  if ("has_sensitive_action" in graph) {
+    return !!graph.has_sensitive_action;
   }
-
   return false;
 }
 
@@ -40,7 +44,9 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
 
   const graphId = getGraphId(graph);
   const isAgent = isLibraryAgent(graph);
-  const shouldShowToggle = hasHITLBlocks(graph);
+  const showHITLToggle = hasHITLBlocks(graph);
+  const showSensitiveActionToggle = hasSensitiveActionBlocks(graph);
+  const shouldShowToggle = showHITLToggle || showSensitiveActionToggle;
 
   const { mutateAsync: updateGraphSettings, isPending } =
     usePatchV1UpdateGraphSettings();
@@ -56,27 +62,37 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
     },
   );
 
-  const [localSafeMode, setLocalSafeMode] = useState<boolean | null>(null);
+  const [localHITLSafeMode, setLocalHITLSafeMode] = useState<boolean>(true);
+  const [localSensitiveActionSafeMode, setLocalSensitiveActionSafeMode] =
+    useState<boolean>(false);
+  const [isLocalStateLoaded, setIsLocalStateLoaded] = useState<boolean>(false);
 
   useEffect(() => {
     if (!isAgent && libraryAgent) {
-      const backendValue = libraryAgent.settings?.human_in_the_loop_safe_mode;
-      if (backendValue !== undefined) {
-        setLocalSafeMode(backendValue);
-      }
+      setLocalHITLSafeMode(
+        libraryAgent.settings?.human_in_the_loop_safe_mode ?? true,
+      );
+      setLocalSensitiveActionSafeMode(
+        libraryAgent.settings?.sensitive_action_safe_mode ?? false,
+      );
+      setIsLocalStateLoaded(true);
     }
   }, [isAgent, libraryAgent]);
 
-  const currentSafeMode = isAgent
-    ? graph.settings?.human_in_the_loop_safe_mode
-    : localSafeMode;
+  const currentHITLSafeMode = isAgent
+    ? (graph.settings?.human_in_the_loop_safe_mode ?? true)
+    : localHITLSafeMode;
 
-  const isStateUndetermined = isAgent
-    ? graph.settings?.human_in_the_loop_safe_mode == null
-    : isLoading || localSafeMode === null;
+  const currentSensitiveActionSafeMode = isAgent
+    ? (graph.settings?.sensitive_action_safe_mode ?? false)
+    : localSensitiveActionSafeMode;
 
-  const handleToggle = useCallback(async () => {
-    const newSafeMode = !currentSafeMode;
+  const isHITLStateUndetermined = isAgent
+    ? false
+    : isLoading || !isLocalStateLoaded;
+
+  const handleHITLToggle = useCallback(async () => {
+    const newSafeMode = !currentHITLSafeMode;
 
     try {
       await updateGraphSettings({
@@ -85,7 +101,7 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
       });
 
       if (!isAgent) {
-        setLocalSafeMode(newSafeMode);
+        setLocalHITLSafeMode(newSafeMode);
       }
 
       if (isAgent) {
@@ -101,37 +117,62 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
       queryClient.invalidateQueries({ queryKey: ["v2", "executions"] });
 
       toast({
-        title: `Safe mode ${newSafeMode ? "enabled" : "disabled"}`,
+        title: `HITL safe mode ${newSafeMode ? "enabled" : "disabled"}`,
         description: newSafeMode
           ? "Human-in-the-loop blocks will require manual review"
           : "Human-in-the-loop blocks will proceed automatically",
         duration: 2000,
       });
     } catch (error) {
-      const isNotFoundError =
-        error instanceof Error &&
-        (error.message.includes("404") || error.message.includes("not found"));
-
-      if (!isAgent && isNotFoundError) {
-        toast({
-          title: "Safe mode not available",
-          description:
-            "To configure safe mode, please save this graph to your library first.",
-          variant: "destructive",
-        });
-      } else {
-        toast({
-          title: "Failed to update safe mode",
-          description:
-            error instanceof Error
-              ? error.message
-              : "An unexpected error occurred.",
-          variant: "destructive",
-        });
-      }
+      handleToggleError(error, isAgent, toast);
     }
   }, [
-    currentSafeMode,
+    currentHITLSafeMode,
+    graphId,
+    isAgent,
+    graph.id,
+    updateGraphSettings,
+    queryClient,
+    toast,
+  ]);
+
+  const handleSensitiveActionToggle = useCallback(async () => {
+    const newSafeMode = !currentSensitiveActionSafeMode;
+
+    try {
+      await updateGraphSettings({
+        graphId,
+        data: { sensitive_action_safe_mode: newSafeMode },
+      });
+
+      if (!isAgent) {
+        setLocalSensitiveActionSafeMode(newSafeMode);
+      }
+
+      if (isAgent) {
+        queryClient.invalidateQueries({
+          queryKey: getGetV2GetLibraryAgentQueryOptions(graph.id.toString())
+            .queryKey,
+        });
+      }
+
+      queryClient.invalidateQueries({
+        queryKey: ["v1", "graphs", graphId, "executions"],
+      });
+      queryClient.invalidateQueries({ queryKey: ["v2", "executions"] });
+
+      toast({
+        title: `Sensitive action safe mode ${newSafeMode ? "enabled" : "disabled"}`,
+        description: newSafeMode
+          ? "Sensitive action blocks will require manual review"
+          : "Sensitive action blocks will proceed automatically",
+        duration: 2000,
+      });
+    } catch (error) {
+      handleToggleError(error, isAgent, toast);
+    }
+  }, [
+    currentSensitiveActionSafeMode,
     graphId,
     isAgent,
     graph.id,
@@ -141,11 +182,53 @@ export function useAgentSafeMode(graph: GraphModel | LibraryAgent | Graph) {
   ]);
 
   return {
-    currentSafeMode,
+    // HITL safe mode
+    currentHITLSafeMode,
+    showHITLToggle,
+    isHITLStateUndetermined,
+    handleHITLToggle,
+
+    // Sensitive action safe mode
+    currentSensitiveActionSafeMode,
+    showSensitiveActionToggle,
+    handleSensitiveActionToggle,
+
+    // General
     isPending,
     shouldShowToggle,
-    isStateUndetermined,
-    handleToggle,
-    hasHITLBlocks: shouldShowToggle,
+
+    // Backwards compatibility
+    currentSafeMode: currentHITLSafeMode,
+    isStateUndetermined: isHITLStateUndetermined,
+    handleToggle: handleHITLToggle,
+    hasHITLBlocks: showHITLToggle,
   };
 }
+
+function handleToggleError(
+  error: unknown,
+  isAgent: boolean,
+  toast: ReturnType<typeof useToast>["toast"],
+) {
+  const isNotFoundError =
+    error instanceof Error &&
+    (error.message.includes("404") || error.message.includes("not found"));
+
+  if (!isAgent && isNotFoundError) {
+    toast({
+      title: "Safe mode not available",
+      description:
+        "To configure safe mode, please save this graph to your library first.",
+      variant: "destructive",
+    });
+  } else {
+    toast({
+      title: "Failed to update safe mode",
+      description:
+        error instanceof Error
+          ? error.message
+          : "An unexpected error occurred.",
+      variant: "destructive",
+    });
+  }
+}

autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts

@@ -327,8 +327,8 @@ export type GraphExecutionMeta = {
     | "FAILED"
     | "INCOMPLETE"
     | "REVIEW";
-  started_at: Date | null;
-  ended_at: Date | null;
+  started_at: Date;
+  ended_at: Date;
   stats: {
     error: string | null;
     cost: number;

backend/blocks/video/__init__.py

@@ -1,28 +0,0 @@
-"""Video editing blocks for AutoGPT Platform.
-
-This module provides blocks for:
-- Downloading videos from URLs (YouTube, Vimeo, news sites, direct links)
-- Clipping/trimming video segments
-- Concatenating multiple videos
-- Adding text overlays
-- Adding AI-generated narration
-
-Dependencies:
-- yt-dlp: For video downloading
-- moviepy: For video editing operations
-- requests: For API calls (narration block)
-"""
-
-from .download import VideoDownloadBlock
-from .clip import VideoClipBlock
-from .concat import VideoConcatBlock
-from .text_overlay import VideoTextOverlayBlock
-from .narration import VideoNarrationBlock
-
-__all__ = [
-    "VideoClipBlock",
-    "VideoConcatBlock",
-    "VideoDownloadBlock",
-    "VideoNarrationBlock",
-    "VideoTextOverlayBlock",
-]

backend/blocks/video/clip.py

@@ -1,93 +0,0 @@
-"""
-VideoClipBlock - Extract a segment from a video file
-"""
-import uuid
-
-from backend.data.block import Block, BlockCategory, BlockOutput
-from backend.data.block import BlockSchemaInput, BlockSchemaOutput
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-
-
-class VideoClipBlock(Block):
-    """Extract a time segment from a video."""
-
-    class Input(BlockSchemaInput):
-        video_in: str = SchemaField(
-            description="Input video (URL, data URI, or file path)",
-            json_schema_extra={"format": "file"}
-        )
-        start_time: float = SchemaField(
-            description="Start time in seconds",
-            ge=0.0
-        )
-        end_time: float = SchemaField(
-            description="End time in seconds",
-            ge=0.0
-        )
-        output_format: str = SchemaField(
-            description="Output format",
-            default="mp4",
-            advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Clipped video file",
-            json_schema_extra={"format": "file"}
-        )
-        duration: float = SchemaField(description="Clip duration in seconds")
-
-    def __init__(self):
-        super().__init__(
-            id="b2c3d4e5-f6a7-8901-bcde-f23456789012",
-            description="Extract a time segment from a video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"video_in": "/tmp/test.mp4", "start_time": 0.0, "end_time": 10.0},
-            test_output=[("video_out", str), ("duration", float)],
-            test_mock={"_clip_video": lambda *args: ("/tmp/clip.mp4", 10.0)}
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Validate time range
-        if input_data.end_time <= input_data.start_time:
-            raise BlockExecutionError(
-                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
-                block_name=self.name,
-                block_id=str(self.id)
-            )
-
-        try:
-            from moviepy.video.io.VideoFileClip import VideoFileClip
-        except ImportError as e:
-            raise BlockExecutionError(
-                message="moviepy is not installed. Please install it with: pip install moviepy",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-
-        clip = None
-        subclip = None
-        try:
-            clip = VideoFileClip(input_data.video_in)
-            subclip = clip.subclip(input_data.start_time, input_data.end_time)
-
-            output_path = f"/tmp/clip_{uuid.uuid4()}.{input_data.output_format}"
-            subclip.write_videofile(output_path, logger=None)
-
-            yield "video_out", output_path
-            yield "duration", subclip.duration
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to clip video: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-        finally:
-            if subclip:
-                subclip.close()
-            if clip:
-                clip.close()

backend/blocks/video/concat.py

@@ -1,123 +0,0 @@
-"""
-VideoConcatBlock - Concatenate multiple video clips into one
-"""
-import uuid
-
-from backend.data.block import Block, BlockCategory, BlockOutput
-from backend.data.block import BlockSchemaInput, BlockSchemaOutput
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-
-
-class VideoConcatBlock(Block):
-    """Merge multiple video clips into one continuous video."""
-
-    class Input(BlockSchemaInput):
-        videos: list[str] = SchemaField(
-            description="List of video files to concatenate (in order)"
-        )
-        transition: str = SchemaField(
-            description="Transition between clips",
-            default="none",
-            enum=["none", "crossfade", "fade_black"]
-        )
-        transition_duration: float = SchemaField(
-            description="Transition duration in seconds",
-            default=0.5,
-            advanced=True
-        )
-        output_format: str = SchemaField(
-            description="Output format",
-            default="mp4",
-            advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Concatenated video file",
-            json_schema_extra={"format": "file"}
-        )
-        total_duration: float = SchemaField(description="Total duration in seconds")
-
-    def __init__(self):
-        super().__init__(
-            id="c3d4e5f6-a7b8-9012-cdef-345678901234",
-            description="Merge multiple video clips into one continuous video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"videos": ["/tmp/a.mp4", "/tmp/b.mp4"]},
-            test_output=[("video_out", str), ("total_duration", float)],
-            test_mock={"_concat_videos": lambda *args: ("/tmp/concat.mp4", 20.0)}
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            from moviepy.editor import VideoFileClip, concatenate_videoclips
-        except ImportError as e:
-            raise BlockExecutionError(
-                message="moviepy is not installed. Please install it with: pip install moviepy",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-
-        # Validate minimum clips
-        if len(input_data.videos) < 2:
-            raise BlockExecutionError(
-                message="At least 2 videos are required for concatenation",
-                block_name=self.name,
-                block_id=str(self.id)
-            )
-
-        clips = []
-        faded_clips = []
-        final = None
-        try:
-            # Load clips one by one to handle partial failures
-            for v in input_data.videos:
-                clips.append(VideoFileClip(v))
-
-            if input_data.transition == "crossfade":
-                # Apply crossfade between clips using crossfadein/crossfadeout
-                transition_dur = input_data.transition_duration
-                for i, clip in enumerate(clips):
-                    if i > 0:
-                        clip = clip.crossfadein(transition_dur)
-                    if i < len(clips) - 1:
-                        clip = clip.crossfadeout(transition_dur)
-                    faded_clips.append(clip)
-                final = concatenate_videoclips(
-                    faded_clips,
-                    method="compose",
-                    padding=-transition_dur
-                )
-            elif input_data.transition == "fade_black":
-                # Fade to black between clips
-                for clip in clips:
-                    faded = clip.fadein(input_data.transition_duration).fadeout(
-                        input_data.transition_duration
-                    )
-                    faded_clips.append(faded)
-                final = concatenate_videoclips(faded_clips)
-            else:
-                final = concatenate_videoclips(clips)
-
-            output_path = f"/tmp/concat_{uuid.uuid4()}.{input_data.output_format}"
-            final.write_videofile(output_path, logger=None)
-
-            yield "video_out", output_path
-            yield "total_duration", final.duration
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to concatenate videos: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-        finally:
-            if final:
-                final.close()
-            for clip in faded_clips:
-                clip.close()
-            for clip in clips:
-                clip.close()

backend/blocks/video/download.py

@@ -1,102 +0,0 @@
-"""
-VideoDownloadBlock - Download video from URL (YouTube, Vimeo, news sites, direct links)
-"""
-import uuid
-from typing import Literal
-
-from backend.data.block import Block, BlockCategory, BlockOutput
-from backend.data.block import BlockSchemaInput, BlockSchemaOutput
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-
-
-class VideoDownloadBlock(Block):
-    """Download video from URL using yt-dlp."""
-
-    class Input(BlockSchemaInput):
-        url: str = SchemaField(
-            description="URL of the video to download (YouTube, Vimeo, direct link, etc.)",
-            placeholder="https://www.youtube.com/watch?v=..."
-        )
-        quality: Literal["best", "1080p", "720p", "480p", "audio_only"] = SchemaField(
-            description="Video quality preference",
-            default="720p"
-        )
-        output_format: Literal["mp4", "webm", "mkv"] = SchemaField(
-            description="Output video format",
-            default="mp4",
-            advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_file: str = SchemaField(
-            description="Path or data URI of downloaded video",
-            json_schema_extra={"format": "file"}
-        )
-        duration: float = SchemaField(description="Video duration in seconds")
-        title: str = SchemaField(description="Video title from source")
-        source_url: str = SchemaField(description="Original source URL")
-
-    def __init__(self):
-        super().__init__(
-            id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
-            description="Download video from URL (YouTube, Vimeo, news sites, direct links)",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "quality": "480p"},
-            test_output=[("video_file", str), ("duration", float), ("title", str), ("source_url", str)],
-            test_mock={"_download_video": lambda *args: ("/tmp/video.mp4", 212.0, "Test Video")}
-        )
-
-    def _get_format_string(self, quality: str) -> str:
-        formats = {
-            "best": "bestvideo+bestaudio/best",
-            "1080p": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
-            "720p": "bestvideo[height<=720]+bestaudio/best[height<=720]",
-            "480p": "bestvideo[height<=480]+bestaudio/best[height<=480]",
-            "audio_only": "bestaudio/best"
-        }
-        return formats.get(quality, formats["720p"])
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            import yt_dlp
-        except ImportError as e:
-            raise BlockExecutionError(
-                message="yt-dlp is not installed. Please install it with: pip install yt-dlp",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-
-        video_id = str(uuid.uuid4())[:8]
-        output_template = f"/tmp/{video_id}.%(ext)s"
-
-        ydl_opts = {
-            "format": self._get_format_string(input_data.quality),
-            "outtmpl": output_template,
-            "merge_output_format": input_data.output_format,
-            "quiet": True,
-            "no_warnings": True,
-        }
-
-        try:
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                info = ydl.extract_info(input_data.url, download=True)
-                video_path = ydl.prepare_filename(info)
-                
-                # Handle format conversion in filename
-                if not video_path.endswith(f".{input_data.output_format}"):
-                    video_path = video_path.rsplit(".", 1)[0] + f".{input_data.output_format}"
-
-                yield "video_file", video_path
-                yield "duration", info.get("duration") or 0.0
-                yield "title", info.get("title") or "Unknown"
-                yield "source_url", input_data.url
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to download video: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e

backend/blocks/video/narration.py

@@ -1,167 +0,0 @@
-"""
-VideoNarrationBlock - Generate AI voice narration and add to video
-"""
-import uuid
-from typing import Literal
-
-from backend.data.block import Block, BlockCategory, BlockOutput
-from backend.data.block import BlockSchemaInput, BlockSchemaOutput
-from backend.data.model import SchemaField, CredentialsMetaInput, APIKeyCredentials
-from backend.integrations.providers import ProviderName
-from backend.util.exceptions import BlockExecutionError
-
-
-class VideoNarrationBlock(Block):
-    """Generate AI narration and add to video."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput[
-            Literal[ProviderName.ELEVENLABS], Literal["api_key"]
-        ] = SchemaField(
-            description="ElevenLabs API key for voice synthesis"
-        )
-        video_in: str = SchemaField(
-            description="Input video file",
-            json_schema_extra={"format": "file"}
-        )
-        script: str = SchemaField(
-            description="Narration script text"
-        )
-        voice_id: str = SchemaField(
-            description="ElevenLabs voice ID",
-            default="21m00Tcm4TlvDq8ikWAM"  # Rachel
-        )
-        mix_mode: Literal["replace", "mix", "ducking"] = SchemaField(
-            description="How to combine with original audio",
-            default="ducking"
-        )
-        narration_volume: float = SchemaField(
-            description="Narration volume (0.0 to 2.0)",
-            default=1.0,
-            ge=0.0,
-            le=2.0,
-            advanced=True
-        )
-        original_volume: float = SchemaField(
-            description="Original audio volume when mixing (0.0 to 1.0)",
-            default=0.3,
-            ge=0.0,
-            le=1.0,
-            advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Video with narration",
-            json_schema_extra={"format": "file"}
-        )
-        audio_file: str = SchemaField(
-            description="Generated audio file",
-            json_schema_extra={"format": "file"}
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="e5f6a7b8-c9d0-1234-ef56-789012345678",
-            description="Generate AI narration and add to video",
-            categories={BlockCategory.MULTIMEDIA, BlockCategory.AI},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "video_in": "/tmp/test.mp4",
-                "script": "Hello world",
-                "credentials": {"provider": "elevenlabs", "id": "test", "type": "api_key"}
-            },
-            test_output=[("video_out", str), ("audio_file", str)],
-            test_mock={"_generate_narration": lambda *args: ("/tmp/narrated.mp4", "/tmp/audio.mp3")}
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        **kwargs
-    ) -> BlockOutput:
-        try:
-            import requests
-            from moviepy.editor import VideoFileClip, AudioFileClip, CompositeAudioClip
-        except ImportError as e:
-            raise BlockExecutionError(
-                message=f"Missing dependency: {e}. Install moviepy and requests.",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-
-        video = None
-        final = None
-        narration = None
-        try:
-            # Generate narration via ElevenLabs
-            response = requests.post(
-                f"https://api.elevenlabs.io/v1/text-to-speech/{input_data.voice_id}",
-                headers={
-                    "xi-api-key": credentials.api_key.get_secret_value(),
-                    "Content-Type": "application/json"
-                },
-                json={
-                    "text": input_data.script,
-                    "model_id": "eleven_monolingual_v1"
-                },
-                timeout=120
-            )
-            response.raise_for_status()
-
-            audio_path = f"/tmp/narration_{uuid.uuid4()}.mp3"
-            with open(audio_path, "wb") as f:
-                f.write(response.content)
-
-            # Combine with video
-            video = VideoFileClip(input_data.video_in)
-            narration = AudioFileClip(audio_path)
-            narration = narration.volumex(input_data.narration_volume)
-
-            if input_data.mix_mode == "replace":
-                final_audio = narration
-            elif input_data.mix_mode == "mix":
-                if video.audio:
-                    original = video.audio.volumex(input_data.original_volume)
-                    final_audio = CompositeAudioClip([original, narration])
-                else:
-                    final_audio = narration
-            else:  # ducking - lower original volume more when narration plays
-                if video.audio:
-                    # Apply stronger attenuation for ducking effect
-                    ducking_volume = input_data.original_volume * 0.3
-                    original = video.audio.volumex(ducking_volume)
-                    final_audio = CompositeAudioClip([original, narration])
-                else:
-                    final_audio = narration
-
-            final = video.set_audio(final_audio)
-
-            output_path = f"/tmp/narrated_{uuid.uuid4()}.mp4"
-            final.write_videofile(output_path, logger=None)
-
-            yield "video_out", output_path
-            yield "audio_file", audio_path
-
-        except requests.exceptions.RequestException as e:
-            raise BlockExecutionError(
-                message=f"ElevenLabs API error: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to add narration: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-        finally:
-            if narration:
-                narration.close()
-            if final:
-                final.close()
-            if video:
-                video.close()

backend/blocks/video/text_overlay.py

@@ -1,149 +0,0 @@
-"""
-VideoTextOverlayBlock - Add text overlay to video
-"""
-import uuid
-from typing import Literal
-
-from backend.data.block import Block, BlockCategory, BlockOutput
-from backend.data.block import BlockSchemaInput, BlockSchemaOutput
-from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
-
-
-class VideoTextOverlayBlock(Block):
-    """Add text overlay/caption to video."""
-
-    class Input(BlockSchemaInput):
-        video_in: str = SchemaField(
-            description="Input video file",
-            json_schema_extra={"format": "file"}
-        )
-        text: str = SchemaField(
-            description="Text to overlay on video"
-        )
-        position: Literal[
-            "top", "center", "bottom",
-            "top-left", "top-right",
-            "bottom-left", "bottom-right"
-        ] = SchemaField(
-            description="Position of text on screen",
-            default="bottom"
-        )
-        start_time: float | None = SchemaField(
-            description="When to show text (seconds). None = entire video",
-            default=None,
-            advanced=True
-        )
-        end_time: float | None = SchemaField(
-            description="When to hide text (seconds). None = until end",
-            default=None,
-            advanced=True
-        )
-        font_size: int = SchemaField(
-            description="Font size",
-            default=48,
-            ge=12,
-            le=200,
-            advanced=True
-        )
-        font_color: str = SchemaField(
-            description="Font color (hex or name)",
-            default="white",
-            advanced=True
-        )
-        bg_color: str | None = SchemaField(
-            description="Background color behind text (None for transparent)",
-            default=None,
-            advanced=True
-        )
-
-    class Output(BlockSchemaOutput):
-        video_out: str = SchemaField(
-            description="Video with text overlay",
-            json_schema_extra={"format": "file"}
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d4e5f6a7-b8c9-0123-def4-567890123456",
-            description="Add text overlay/caption to video",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={"video_in": "/tmp/test.mp4", "text": "Hello World"},
-            test_output=[("video_out", str)],
-            test_mock={"_add_text": lambda *args: "/tmp/overlay.mp4"}
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip
-        except ImportError as e:
-            raise BlockExecutionError(
-                message="moviepy is not installed. Please install it with: pip install moviepy",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-
-        # Validate time range if both are provided
-        if (input_data.start_time is not None and
-            input_data.end_time is not None and
-            input_data.end_time <= input_data.start_time):
-            raise BlockExecutionError(
-                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
-                block_name=self.name,
-                block_id=str(self.id)
-            )
-
-        video = None
-        final = None
-        txt_clip = None
-        try:
-            video = VideoFileClip(input_data.video_in)
-
-            txt_clip = TextClip(
-                input_data.text,
-                fontsize=input_data.font_size,
-                color=input_data.font_color,
-                bg_color=input_data.bg_color,
-            )
-
-            # Position mapping
-            pos_map = {
-                "top": ("center", "top"),
-                "center": ("center", "center"),
-                "bottom": ("center", "bottom"),
-                "top-left": ("left", "top"),
-                "top-right": ("right", "top"),
-                "bottom-left": ("left", "bottom"),
-                "bottom-right": ("right", "bottom"),
-            }
-
-            txt_clip = txt_clip.set_position(pos_map[input_data.position])
-
-            # Set timing
-            start = input_data.start_time or 0
-            end = input_data.end_time or video.duration
-            duration = max(0, end - start)
-            txt_clip = txt_clip.set_start(start).set_end(end).set_duration(duration)
-
-            final = CompositeVideoClip([video, txt_clip])
-
-            output_path = f"/tmp/overlay_{uuid.uuid4()}.mp4"
-            final.write_videofile(output_path, logger=None)
-
-            yield "video_out", output_path
-
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Failed to add text overlay: {e}",
-                block_name=self.name,
-                block_id=str(self.id)
-            ) from e
-        finally:
-            if txt_clip:
-                txt_clip.close()
-            if final:
-                final.close()
-            if video:
-                video.close()