fix(backend): sort imports alphabetically in run_agent.py

Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>

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/run_agent.py

@@ -32,7 +32,7 @@ from .models import (
     UserReadiness,
 )
 from .utils import (
-    check_user_has_required_credentials,
+    build_missing_credentials_from_graph,
     extract_credentials_from_schema,
     fetch_graph_from_store_slug,
     get_or_create_library_agent,
@@ -235,15 +235,13 @@ class RunAgentTool(BaseTool):
                 # Return credentials needed response with input data info
                 # The UI handles credential setup automatically, so the message
                 # focuses on asking about input data
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
+                requirements_creds_dict = build_missing_credentials_from_graph(
+                    graph, None
                 )
-                missing_creds_check = await check_user_has_required_credentials(
-                    user_id, credentials
+                missing_credentials_dict = build_missing_credentials_from_graph(
+                    graph, graph_credentials
                 )
-                missing_credentials_dict = {
-                    c.id: c.model_dump() for c in missing_creds_check
-                }
+                requirements_creds_list = list(requirements_creds_dict.values())
 
                 return SetupRequirementsResponse(
                     message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
@@ -257,7 +255,7 @@ class RunAgentTool(BaseTool):
                             ready_to_run=False,
                         ),
                         requirements={
-                            "credentials": [c.model_dump() for c in credentials],
+                            "credentials": requirements_creds_list,
                             "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },

autogpt_platform/backend/backend/api/features/chat/tools/run_block.py

@@ -20,6 +20,7 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
+from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -186,7 +187,11 @@ class RunBlockTool(BaseTool):
 
         if missing_credentials:
             # Return setup requirements response with missing credentials
-            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
+            credentials_fields_info = block.input_schema.get_credentials_fields_info()
+            missing_creds_dict = build_missing_credentials_from_field_info(
+                credentials_fields_info, set(matched_credentials.keys())
+            )
+            missing_creds_list = list(missing_creds_dict.values())
 
             return SetupRequirementsResponse(
                 message=(
@@ -203,7 +208,7 @@ class RunBlockTool(BaseTool):
                         ready_to_run=False,
                     ),
                     requirements={
-                        "credentials": [c.model_dump() for c in missing_credentials],
+                        "credentials": missing_creds_list,
                         "inputs": self._get_inputs_list(block),
                         "execution_modes": ["immediate"],
                     },

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsMetaInput
+from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -89,6 +89,59 @@ def extract_credentials_from_schema(
     return credentials
 
 
+def _serialize_missing_credential(
+    field_key: str, field_info: CredentialsFieldInfo
+) -> dict[str, Any]:
+    """
+    Convert credential field info into a serializable dict that preserves all supported
+    credential types (e.g., api_key + oauth2) so the UI can offer multiple options.
+    """
+    supported_types = sorted(field_info.supported_types)
+    provider = next(iter(field_info.provider), "unknown")
+    scopes = sorted(field_info.required_scopes or [])
+
+    return {
+        "id": field_key,
+        "title": field_key.replace("_", " ").title(),
+        "provider": provider,
+        "provider_name": provider.replace("_", " ").title(),
+        "type": supported_types[0] if supported_types else "api_key",
+        "types": supported_types,
+        "scopes": scopes,
+    }
+
+
+def build_missing_credentials_from_graph(
+    graph: GraphModel, matched_credentials: dict[str, CredentialsMetaInput] | None
+) -> dict[str, Any]:
+    """
+    Build a missing_credentials mapping from a graph's aggregated credentials inputs,
+    preserving all supported credential types for each field.
+    """
+    matched_keys = set(matched_credentials.keys()) if matched_credentials else set()
+    aggregated_fields = graph.aggregate_credentials_inputs()
+
+    return {
+        field_key: _serialize_missing_credential(field_key, field_info)
+        for field_key, (field_info, _node_fields) in aggregated_fields.items()
+        if field_key not in matched_keys
+    }
+
+
+def build_missing_credentials_from_field_info(
+    credential_fields: dict[str, CredentialsFieldInfo],
+    matched_keys: set[str],
+) -> dict[str, Any]:
+    """
+    Build missing_credentials mapping from a simple credentials field info dictionary.
+    """
+    return {
+        field_key: _serialize_missing_credential(field_key, field_info)
+        for field_key, field_info in credential_fields.items()
+        if field_key not in matched_keys
+    }
+
+
 def extract_credentials_as_dict(
     credentials_input_schema: dict[str, Any] | None,
 ) -> dict[str, CredentialsMetaInput]:

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/execution.py

@@ -153,14 +153,8 @@ 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 +229,10 @@ 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 +902,6 @@ 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/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/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)/chat/components/Chat/components/ChatContainer/helpers.ts

@@ -267,23 +267,34 @@ export function extractCredentialsNeeded(
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
       const agentName = (setupInfo?.agent_name as string) || "this block";
-      const credentials = Object.values(missingCreds).map((credInfo) => ({
-        provider: (credInfo.provider as string) || "unknown",
-        providerName:
-          (credInfo.provider_name as string) ||
-          (credInfo.provider as string) ||
-          "Unknown Provider",
-        credentialType:
+      const credentials = Object.values(missingCreds).map((credInfo) => {
+        // Normalize to array at boundary - prefer 'types' array, fall back to single 'type'
+        const typesArray = credInfo.types as
+          | Array<"api_key" | "oauth2" | "user_password" | "host_scoped">
+          | undefined;
+        const singleType =
           (credInfo.type as
             | "api_key"
             | "oauth2"
             | "user_password"
-            | "host_scoped") || "api_key",
-        title:
-          (credInfo.title as string) ||
-          `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
-        scopes: credInfo.scopes as string[] | undefined,
-      }));
+            | "host_scoped"
+            | undefined) || "api_key";
+        const credentialTypes =
+          typesArray && typesArray.length > 0 ? typesArray : [singleType];
+
+        return {
+          provider: (credInfo.provider as string) || "unknown",
+          providerName:
+            (credInfo.provider_name as string) ||
+            (credInfo.provider as string) ||
+            "Unknown Provider",
+          credentialTypes,
+          title:
+            (credInfo.title as string) ||
+            `${(credInfo.provider_name as string) || (credInfo.provider as string)} credentials`,
+          scopes: credInfo.scopes as string[] | undefined,
+        };
+      });
       return {
         type: "credentials_needed",
         toolName,
@@ -358,11 +369,14 @@ export function extractInputsNeeded(
       credentials.forEach((cred) => {
         const id = cred.id as string;
         if (id) {
+          const credentialTypes = Array.isArray(cred.types)
+            ? cred.types
+            : [(cred.type as string) || "api_key"];
           credentialsSchema[id] = {
             type: "object",
             properties: {},
             credentials_provider: [cred.provider as string],
-            credentials_types: [(cred.type as string) || "api_key"],
+            credentials_types: credentialTypes,
             credentials_scopes: cred.scopes as string[] | undefined,
           };
         }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatCredentialsSetup/ChatCredentialsSetup.tsx

@@ -9,7 +9,9 @@ import { useChatCredentialsSetup } from "./useChatCredentialsSetup";
 export interface CredentialInfo {
   provider: string;
   providerName: string;
-  credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
+  credentialTypes: Array<
+    "api_key" | "oauth2" | "user_password" | "host_scoped"
+  >;
   title: string;
   scopes?: string[];
 }
@@ -30,7 +32,7 @@ function createSchemaFromCredentialInfo(
     type: "object",
     properties: {},
     credentials_provider: [credential.provider],
-    credentials_types: [credential.credentialType],
+    credentials_types: credential.credentialTypes,
     credentials_scopes: credential.scopes,
     discriminator: undefined,
     discriminator_mapping: undefined,

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatMessage/useChatMessage.ts

@@ -41,7 +41,9 @@ export type ChatMessageData =
       credentials: Array<{
         provider: string;
         providerName: string;
-        credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
+        credentialTypes: Array<
+          "api_key" | "oauth2" | "user_password" | "host_scoped"
+        >;
         title: string;
         scopes?: string[];
       }>;

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

@@ -7148,20 +7148,6 @@
           "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 +7254,14 @@
           },
           "status": { "$ref": "#/components/schemas/AgentExecutionStatus" },
           "started_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Started At",
-            "description": "When execution started running. Null if not yet started (QUEUED)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Started At"
           },
           "ended_at": {
-            "anyOf": [
-              { "type": "string", "format": "date-time" },
-              { "type": "null" }
-            ],
-            "title": "Ended At",
-            "description": "When execution finished. Null if not yet completed (QUEUED, RUNNING, INCOMPLETE, REVIEW)."
+            "type": "string",
+            "format": "date-time",
+            "title": "Ended At"
           },
           "is_shared": {
             "type": "boolean",
@@ -7315,6 +7295,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats",
           "outputs"
         ],
@@ -7413,20 +7395,14 @@
           },
           "status": { "$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 +7431,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats"
         ],
         "title": "GraphExecutionMeta"
@@ -7501,20 +7479,14 @@
           },
           "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 +7525,8 @@
           "nodes_input_masks",
           "preset_id",
           "status",
+          "started_at",
+          "ended_at",
           "stats",
           "outputs",
           "node_executions"

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/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;

autogpt_platform/frontend/src/tests/agent-activity.spec.ts

@@ -39,9 +39,10 @@ test.beforeEach(async ({ page }) => {
   await page.waitForTimeout(1000);
 
   await page.goto("/library");
-  // Navigate to the specific agent we just created, not just the first one
-  await LibraryPage.navigateToAgentByName(page, "Test Agent");
+  await LibraryPage.clickFirstAgent(page);
   await LibraryPage.waitForAgentPageLoad(page);
+  const { getRole } = getSelectors(page);
+  await isVisible(getRole("heading", "Test Agent"), 8000);
 });
 
 test("shows badge with count when agent is running", async ({ page }) => {

autogpt_platform/frontend/src/tests/pages/library.page.ts

@@ -450,72 +450,45 @@ export async function navigateToAgentByName(
   agentName: string,
 ): Promise<void> {
   const agentCard = getAgentCards(page).filter({ hasText: agentName }).first();
-  // Wait for the agent card to be visible before clicking
-  // This handles async loading of agents after page navigation
-  await agentCard.waitFor({ state: "visible", timeout: 15000 });
   await agentCard.click();
 }
 
 export async function clickRunButton(page: Page): Promise<void> {
   const { getId } = getSelectors(page);
 
-  // Wait for page to stabilize and buttons to render
-  // The NewAgentLibraryView shows either "Setup your task" (empty state)
-  // or "New task" (with items) button
   const setupTaskButton = page.getByRole("button", {
     name: /Setup your task/i,
   });
-  const newTaskButton = page.getByRole("button", { name: /New task/i });
-  const runButton = getId("agent-run-button");
-  const runAgainButton = getId("run-again-button");
 
-  // Use Promise.race with waitFor to wait for any of the buttons to appear
-  // This handles the async rendering in CI environments
-  try {
-    await Promise.race([
-      setupTaskButton.waitFor({ state: "visible", timeout: 15000 }),
-      newTaskButton.waitFor({ state: "visible", timeout: 15000 }),
-      runButton.waitFor({ state: "visible", timeout: 15000 }),
-      runAgainButton.waitFor({ state: "visible", timeout: 15000 }),
-    ]);
-  } catch {
-    throw new Error(
-      "Could not find run/start task button - none of the expected buttons appeared",
-    );
-  }
-
-  // Now check which button is visible and click it
   if (await setupTaskButton.isVisible()) {
     await setupTaskButton.click();
     const startTaskButton = page
       .getByRole("button", { name: /Start Task/i })
       .first();
-    await startTaskButton.waitFor({ state: "visible", timeout: 10000 });
     await startTaskButton.click();
     return;
   }
 
+  const newTaskButton = page.getByRole("button", { name: /New task/i });
   if (await newTaskButton.isVisible()) {
     await newTaskButton.click();
     const startTaskButton = page
       .getByRole("button", { name: /Start Task/i })
       .first();
-    await startTaskButton.waitFor({ state: "visible", timeout: 10000 });
     await startTaskButton.click();
     return;
   }
 
+  const runButton = getId("agent-run-button");
+  const runAgainButton = getId("run-again-button");
+
   if (await runButton.isVisible()) {
     await runButton.click();
-    return;
-  }
-
-  if (await runAgainButton.isVisible()) {
+  } else if (await runAgainButton.isVisible()) {
     await runAgainButton.click();
-    return;
+  } else {
+    throw new Error("Could not find run/start task button");
   }
-
-  throw new Error("Could not find run/start task button");
 }
 
 export async function clickNewRunButton(page: Page): Promise<void> {

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()