fix(backend): Use explicit {schema}.vector for pgvector types

The unqualified ::vector type fails in ORDER BY context with PgBouncer.
Use explicit schema qualification ({schema}.vector) which resolves to
platform.vector where the pgvector extension is installed.

Changes:
- Add {schema} placeholder to db.py for raw schema name
- Use {schema}.vector instead of unqualified ::vector in embeddings.py
- Use {{schema}}.vector instead of unqualified ::vector in hybrid_search.py

Tested on dev: explicit platform.vector works in all contexts.

Fixes: AUTOGPT-SERVER-76B

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -154,16 +154,15 @@ async def store_content_embedding(
 
         # Upsert the embedding
         # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-        # Use unqualified ::vector - pgvector is in search_path on all environments
         await execute_raw_with_schema(
             """
             INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
                 "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
             )
-            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::{schema}.vector, $5, $6::jsonb, NOW(), NOW())
             ON CONFLICT ("contentType", "contentId", "userId")
             DO UPDATE SET
-                "embedding" = $4::vector,
+                "embedding" = $4::{schema}.vector,
                 "searchableText" = $5,
                 "metadata" = $6::jsonb,
                 "updatedAt" = NOW()
@@ -879,7 +878,6 @@ async def semantic_search(
         min_similarity_idx = len(params) + 1
         params.append(min_similarity)
 
-        # Use unqualified ::vector and <=> operator - pgvector is in search_path on all environments
         sql = (
             """
             SELECT
@@ -889,7 +887,7 @@ async def semantic_search(
                 metadata,
                 1 - (embedding <=> '"""
             + embedding_str
-            + """'::vector) as similarity
+            + """'::{schema}.vector) as similarity
             FROM {schema_prefix}"UnifiedContentEmbedding"
             WHERE "contentType" IN ("""
             + content_type_placeholders
@@ -899,7 +897,7 @@ async def semantic_search(
             + """
             AND 1 - (embedding <=> '"""
             + embedding_str
-            + """'::vector) >= $"""
+            + """'::{schema}.vector) >= $"""
             + str(min_similarity_idx)
             + """
             ORDER BY similarity DESC

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -295,7 +295,7 @@ async def unified_hybrid_search(
                 FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
                 {user_filter}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding <=> {embedding_param}::{{schema}}.vector
                 LIMIT 200
             )
         ),
@@ -307,7 +307,7 @@ async def unified_hybrid_search(
                 uce.metadata,
                 uce."updatedAt" as updated_at,
                 -- Semantic score: cosine similarity (1 - distance)
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::{{schema}}.vector), 0) as semantic_score,
                 -- Lexical score: ts_rank_cd
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match from metadata
@@ -583,7 +583,7 @@ async def hybrid_search(
                 WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
                 AND uce."userId" IS NULL
                 AND {where_clause}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
+                ORDER BY uce.embedding <=> {embedding_param}::{{schema}}.vector
                 LIMIT 200
             ) uce
         ),
@@ -605,7 +605,7 @@ async def hybrid_search(
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::{{schema}}.vector), 0) as semantic_score,
                 -- Lexical score (raw, will normalize)
                 COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
                 -- Category match

autogpt_platform/backend/backend/data/db.py

@@ -120,12 +120,7 @@ async def _raw_with_schema(
 
     Supports placeholders:
         - {schema_prefix}: Table/type prefix (e.g., "platform".)
-        - {schema}: Raw schema name for application tables (e.g., platform)
-
-    Note on pgvector types:
-        Use unqualified ::vector and <=> operator in queries. PostgreSQL resolves
-        these via search_path, which includes the schema where pgvector is installed
-        on all environments (local, CI, dev).
+        - {schema}: Raw schema name (e.g., platform) for pgvector types
 
     Args:
         query_template: SQL query with {schema_prefix} and/or {schema} placeholders
@@ -137,19 +132,16 @@ async def _raw_with_schema(
         - list[dict] if execute=False (query results)
         - int if execute=True (number of affected rows)
 
-    Example with vector type:
+    Example:
         await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::vector)',
+            'INSERT INTO {schema_prefix}"Embedding" (vec) VALUES ($1::{schema}.vector)',
             embedding_data
         )
     """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
 
-    formatted_query = query_template.format(
-        schema_prefix=schema_prefix,
-        schema=schema,
-    )
+    formatted_query = query_template.format(schema_prefix=schema_prefix, schema=schema)
 
     import prisma as prisma_module
 

autogpt_platform/backend/backend/data/event_bus.py

@@ -103,18 +103,8 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
         return redis.get_redis()
 
     def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            self.connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        self.connection.publish(full_channel_name, message)
 
     def listen_events(self, channel_key: str) -> Generator[M, None, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(
@@ -138,19 +128,9 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
         return await redis.get_redis_async()
 
     async def publish_event(self, event: M, channel_key: str):
-        """
-        Publish an event to Redis. Gracefully handles connection failures
-        by logging the error instead of raising exceptions.
-        """
-        try:
-            message, full_channel_name = self._serialize_message(event, channel_key)
-            connection = await self.connection
-            await connection.publish(full_channel_name, message)
-        except Exception:
-            logger.exception(
-                f"Failed to publish event to Redis channel {channel_key}. "
-                "Event bus operation will continue without Redis connectivity."
-            )
+        message, full_channel_name = self._serialize_message(event, channel_key)
+        connection = await self.connection
+        await connection.publish(full_channel_name, message)
 
     async def listen_events(self, channel_key: str) -> AsyncGenerator[M, None]:
         pubsub, full_channel_name = self._get_pubsub_channel(

autogpt_platform/backend/backend/data/event_bus_test.py

@@ -1,56 +0,0 @@
-"""
-Tests for event_bus graceful degradation when Redis is unavailable.
-"""
-
-from unittest.mock import AsyncMock, patch
-
-import pytest
-from pydantic import BaseModel
-
-from backend.data.event_bus import AsyncRedisEventBus
-
-
-class TestEvent(BaseModel):
-    """Test event model."""
-
-    message: str
-
-
-class TestNotificationBus(AsyncRedisEventBus[TestEvent]):
-    """Test implementation of AsyncRedisEventBus."""
-
-    Model = TestEvent
-
-    @property
-    def event_bus_name(self) -> str:
-        return "test_event_bus"
-
-
-@pytest.mark.asyncio
-async def test_publish_event_handles_connection_failure_gracefully():
-    """Test that publish_event logs exception instead of raising when Redis is unavailable."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock get_redis_async to raise connection error
-    with patch(
-        "backend.data.event_bus.redis.get_redis_async",
-        side_effect=ConnectionError("Authentication required."),
-    ):
-        # Should not raise exception
-        await bus.publish_event(event, "test_channel")
-
-
-@pytest.mark.asyncio
-async def test_publish_event_works_with_redis_available():
-    """Test that publish_event works normally when Redis is available."""
-    bus = TestNotificationBus()
-    event = TestEvent(message="test message")
-
-    # Mock successful Redis connection
-    mock_redis = AsyncMock()
-    mock_redis.publish = AsyncMock()
-
-    with patch("backend.data.event_bus.redis.get_redis_async", return_value=mock_redis):
-        await bus.publish_event(event, "test_channel")
-        mock_redis.publish.assert_called_once()

autogpt_platform/backend/backend/data/execution.py

@@ -81,8 +81,6 @@ class ExecutionContext(BaseModel):
     This includes information needed by blocks, sub-graphs, and execution management.
     """
 
-    model_config = {"extra": "ignore"}
-
     human_in_the_loop_safe_mode: bool = True
     sensitive_action_safe_mode: bool = False
     user_timezone: str = "UTC"

autogpt_platform/backend/backend/data/graph.py

@@ -64,8 +64,6 @@ logger = logging.getLogger(__name__)
 class GraphSettings(BaseModel):
     # Use Annotated with BeforeValidator to coerce None to default values.
     # This handles cases where the database has null values for these fields.
-    model_config = {"extra": "ignore"}
-
     human_in_the_loop_safe_mode: Annotated[
         bool, BeforeValidator(lambda v: v if v is not None else True)
     ] = True

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -1,10 +1,9 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
--- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
+-- Create in public schema so vector type is available across all schemas
 DO $$
 BEGIN
-    CREATE EXTENSION IF NOT EXISTS "vector";
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
 EXCEPTION WHEN OTHERS THEN
     RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
@@ -20,7 +19,7 @@ CREATE TABLE "UnifiedContentEmbedding" (
     "contentType" "ContentType" NOT NULL,
     "contentId" TEXT NOT NULL,
     "userId" TEXT,
-    "embedding" vector(1536) NOT NULL,
+    "embedding" public.vector(1536) NOT NULL,
     "searchableText" TEXT NOT NULL,
     "metadata" JSONB NOT NULL DEFAULT '{}',
 
@@ -46,4 +45,4 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
 -- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
 DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
-CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/scripts/generate_block_docs.py

@@ -366,12 +366,12 @@ def generate_block_markdown(
     lines.append("")
 
     # What it is (full description)
-    lines.append("### What it is")
+    lines.append(f"### What it is")
     lines.append(block.description or "No description available.")
     lines.append("")
 
     # How it works (manual section)
-    lines.append("### How it works")
+    lines.append(f"### How it works")
     how_it_works = manual_content.get(
         "how_it_works", "_Add technical explanation here._"
     )
@@ -383,7 +383,7 @@ def generate_block_markdown(
     # Inputs table (auto-generated)
     visible_inputs = [f for f in block.inputs if not f.hidden]
     if visible_inputs:
-        lines.append("### Inputs")
+        lines.append(f"### Inputs")
         lines.append("")
         lines.append("| Input | Description | Type | Required |")
         lines.append("|-------|-------------|------|----------|")
@@ -400,7 +400,7 @@ def generate_block_markdown(
     # Outputs table (auto-generated)
     visible_outputs = [f for f in block.outputs if not f.hidden]
     if visible_outputs:
-        lines.append("### Outputs")
+        lines.append(f"### Outputs")
         lines.append("")
         lines.append("| Output | Description | Type |")
         lines.append("|--------|-------------|------|")
@@ -414,7 +414,7 @@ def generate_block_markdown(
         lines.append("")
 
     # Possible use case (manual section)
-    lines.append("### Possible use case")
+    lines.append(f"### Possible use case")
     use_case = manual_content.get("use_case", "_Add practical use case examples here._")
     lines.append("<!-- MANUAL: use_case -->")
     lines.append(use_case)

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-run-draft-view.tsx

@@ -1,15 +1,8 @@
 "use client";
-import React, {
-  useCallback,
-  useContext,
-  useEffect,
-  useMemo,
-  useState,
-} from "react";
+import React, { useCallback, useEffect, useMemo, useState } from "react";
 
 import {
   CredentialsMetaInput,
-  CredentialsType,
   GraphExecutionID,
   GraphMeta,
   LibraryAgentPreset,
@@ -36,11 +29,7 @@ import {
 } from "@/components/__legacy__/ui/icons";
 import { Input } from "@/components/__legacy__/ui/input";
 import { Button } from "@/components/atoms/Button/Button";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
-import {
-  findSavedCredentialByProviderAndType,
-  findSavedUserCredentialByProviderAndType,
-} from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
   useToast,
@@ -48,7 +37,6 @@ import {
 } from "@/components/molecules/Toast/use-toast";
 import { humanizeCronExpression } from "@/lib/cron-expression-utils";
 import { cn, isEmpty } from "@/lib/utils";
-import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
 import { ClockIcon, CopyIcon, InfoIcon } from "@phosphor-icons/react";
 import { CalendarClockIcon, Trash2Icon } from "lucide-react";
 
@@ -102,7 +90,6 @@ export function AgentRunDraftView({
   const api = useBackendAPI();
   const { toast } = useToast();
   const toastOnFail = useToastOnFail();
-  const allProviders = useContext(CredentialsProvidersContext);
 
   const [inputValues, setInputValues] = useState<Record<string, any>>({});
   const [inputCredentials, setInputCredentials] = useState<
@@ -141,77 +128,6 @@ export function AgentRunDraftView({
     () => graph.credentials_input_schema.properties,
     [graph],
   );
-  const credentialFields = useMemo(
-    function getCredentialFields() {
-      return Object.entries(agentCredentialsInputFields);
-    },
-    [agentCredentialsInputFields],
-  );
-  const requiredCredentials = useMemo(
-    function getRequiredCredentials() {
-      return new Set(
-        (graph.credentials_input_schema?.required as string[]) || [],
-      );
-    },
-    [graph.credentials_input_schema?.required],
-  );
-
-  useEffect(
-    function initializeDefaultCredentials() {
-      if (!allProviders) return;
-      if (!graph.credentials_input_schema?.properties) return;
-      if (requiredCredentials.size === 0) return;
-
-      setInputCredentials(function updateCredentials(currentCreds) {
-        const next = { ...currentCreds };
-        let didAdd = false;
-
-        for (const key of requiredCredentials) {
-          if (next[key]) continue;
-          const schema = graph.credentials_input_schema.properties[key];
-          if (!schema) continue;
-
-          const providerNames = schema.credentials_provider || [];
-          const credentialTypes = schema.credentials_types || [];
-          const requiredScopes = schema.credentials_scopes;
-
-          const userCredential = findSavedUserCredentialByProviderAndType(
-            providerNames,
-            credentialTypes,
-            requiredScopes,
-            allProviders,
-          );
-
-          const savedCredential =
-            userCredential ||
-            findSavedCredentialByProviderAndType(
-              providerNames,
-              credentialTypes,
-              requiredScopes,
-              allProviders,
-            );
-
-          if (!savedCredential) continue;
-
-          next[key] = {
-            id: savedCredential.id,
-            provider: savedCredential.provider,
-            type: savedCredential.type as CredentialsType,
-            title: savedCredential.title,
-          };
-          didAdd = true;
-        }
-
-        if (!didAdd) return currentCreds;
-        return next;
-      });
-    },
-    [
-      allProviders,
-      graph.credentials_input_schema?.properties,
-      requiredCredentials,
-    ],
-  );
 
   const [allRequiredInputsAreSet, missingInputs] = useMemo(() => {
     const nonEmptyInputs = new Set(
@@ -229,35 +145,18 @@ export function AgentRunDraftView({
     );
     return [isSuperset, difference];
   }, [agentInputSchema.required, inputValues]);
-  const [allCredentialsAreSet, missingCredentials] = useMemo(
-    function getCredentialStatus() {
-      const missing = Array.from(requiredCredentials).filter((key) => {
-        const cred = inputCredentials[key];
-        return !cred || !cred.id;
-      });
-      return [missing.length === 0, missing];
-    },
-    [requiredCredentials, inputCredentials],
-  );
-  function addChangedCredentials(prev: Set<keyof LibraryAgentPresetUpdatable>) {
-    const next = new Set(prev);
-    next.add("credentials");
-    return next;
-  }
-
-  function handleCredentialChange(key: string, value?: CredentialsMetaInput) {
-    setInputCredentials(function updateInputCredentials(currentCreds) {
-      const next = { ...currentCreds };
-      if (value === undefined) {
-        delete next[key];
-        return next;
-      }
-      next[key] = value;
-      return next;
-    });
-    setChangedPresetAttributes(addChangedCredentials);
-  }
-
+  const [allCredentialsAreSet, missingCredentials] = useMemo(() => {
+    const availableCredentials = new Set(Object.keys(inputCredentials));
+    const allCredentials = new Set(Object.keys(agentCredentialsInputFields));
+    // Backwards-compatible implementation of isSupersetOf and difference
+    const isSuperset = Array.from(allCredentials).every((item) =>
+      availableCredentials.has(item),
+    );
+    const difference = Array.from(allCredentials).filter(
+      (item) => !availableCredentials.has(item),
+    );
+    return [isSuperset, difference];
+  }, [agentCredentialsInputFields, inputCredentials]);
   const notifyMissingInputs = useCallback(
     (needPresetName: boolean = true) => {
       const allMissingFields = (
@@ -750,6 +649,35 @@ export function AgentRunDraftView({
               </>
             )}
 
+            {/* Credentials inputs */}
+            {Object.entries(agentCredentialsInputFields).map(
+              ([key, inputSubSchema]) => (
+                <CredentialsInput
+                  key={key}
+                  schema={{ ...inputSubSchema, discriminator: undefined }}
+                  selectedCredentials={
+                    inputCredentials[key] ?? inputSubSchema.default
+                  }
+                  onSelectCredentials={(value) => {
+                    setInputCredentials((obj) => {
+                      const newObj = { ...obj };
+                      if (value === undefined) {
+                        delete newObj[key];
+                        return newObj;
+                      }
+                      return {
+                        ...obj,
+                        [key]: value,
+                      };
+                    });
+                    setChangedPresetAttributes((prev) =>
+                      prev.add("credentials"),
+                    );
+                  }}
+                />
+              ),
+            )}
+
             {/* Regular inputs */}
             {Object.entries(agentInputFields).map(([key, inputSubSchema]) => (
               <RunAgentInputs
@@ -767,17 +695,6 @@ export function AgentRunDraftView({
                 data-testid={`agent-input-${key}`}
               />
             ))}
-
-            {/* Credentials inputs */}
-            {credentialFields.length > 0 && (
-              <CredentialsGroupedView
-                credentialFields={credentialFields}
-                requiredCredentials={requiredCredentials}
-                inputCredentials={inputCredentials}
-                inputValues={inputValues}
-                onCredentialChange={handleCredentialChange}
-              />
-            )}
           </CardContent>
         </Card>
       </div>

autogpt_platform/frontend/src/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers.ts

@@ -1,5 +1,5 @@
 import { CredentialsProvidersContextType } from "@/providers/agent-credentials/credentials-provider";
-import { filterSystemCredentials, getSystemCredentials } from "../../helpers";
+import { getSystemCredentials } from "../../helpers";
 
 export type CredentialField = [string, any];
 
@@ -208,42 +208,3 @@ export function findSavedCredentialByProviderAndType(
 
   return undefined;
 }
-
-export function findSavedUserCredentialByProviderAndType(
-  providerNames: string[],
-  credentialTypes: string[],
-  requiredScopes: string[] | undefined,
-  allProviders: CredentialsProvidersContextType | null,
-): SavedCredential | undefined {
-  for (const providerName of providerNames) {
-    const providerData = allProviders?.[providerName];
-    if (!providerData) continue;
-
-    const userCredentials = filterSystemCredentials(
-      providerData.savedCredentials ?? [],
-    );
-
-    const matchingCredentials: SavedCredential[] = [];
-
-    for (const credential of userCredentials) {
-      const typeMatches =
-        credentialTypes.length === 0 ||
-        credentialTypes.includes(credential.type);
-      const scopesMatch = hasRequiredScopes(credential, requiredScopes);
-
-      if (!typeMatches) continue;
-      if (!scopesMatch) continue;
-
-      matchingCredentials.push(credential as SavedCredential);
-    }
-
-    if (matchingCredentials.length === 1) {
-      return matchingCredentials[0];
-    }
-    if (matchingCredentials.length > 1) {
-      return undefined;
-    }
-  }
-
-  return undefined;
-}

autogpt_platform/frontend/src/components/contextual/CredentialsInput/useCredentialsInput.ts

@@ -98,20 +98,24 @@ export function useCredentialsInput({
 
   // Auto-select the first available credential on initial mount
   // Once a user has made a selection, we don't override it
-  useEffect(
-    function autoSelectCredential() {
-      if (readOnly) return;
-      if (!credentials || !("savedCredentials" in credentials)) return;
-      if (selectedCredential?.id) return;
+  useEffect(() => {
+    if (readOnly) return;
+    if (!credentials || !("savedCredentials" in credentials)) return;
 
-      const savedCreds = credentials.savedCredentials;
-      if (savedCreds.length === 0) return;
+    // If already selected, don't auto-select
+    if (selectedCredential?.id) return;
 
-      if (hasAttemptedAutoSelect.current) return;
-      hasAttemptedAutoSelect.current = true;
+    // Only attempt auto-selection once
+    if (hasAttemptedAutoSelect.current) return;
+    hasAttemptedAutoSelect.current = true;
 
-      if (isOptional) return;
+    // If optional, don't auto-select (user can choose "None")
+    if (isOptional) return;
 
+    const savedCreds = credentials.savedCredentials;
+
+    // Auto-select the first credential if any are available
+    if (savedCreds.length > 0) {
       const cred = savedCreds[0];
       onSelectCredential({
         id: cred.id,
@@ -119,15 +123,14 @@ export function useCredentialsInput({
         provider: credentials.provider,
         title: (cred as any).title,
       });
-    },
-    [
-      credentials,
-      selectedCredential?.id,
-      readOnly,
-      isOptional,
-      onSelectCredential,
-    ],
-  );
+    }
+  }, [
+    credentials,
+    selectedCredential?.id,
+    readOnly,
+    isOptional,
+    onSelectCredential,
+  ]);
 
   if (
     !credentials ||

autogpt_platform/frontend/src/lib/timezone-utils.ts

@@ -106,14 +106,9 @@ export function getTimezoneDisplayName(timezone: string): string {
     const parts = timezone.split("/");
     const city = parts[parts.length - 1].replace(/_/g, " ");
     const abbr = getTimezoneAbbreviation(timezone);
-    if (abbr && abbr !== timezone) {
-      return `${city} (${abbr})`;
-    }
-    // If abbreviation is same as timezone or not found, show timezone with underscores replaced
-    const timezoneDisplay = timezone.replace(/_/g, " ");
-    return `${city} (${timezoneDisplay})`;
+    return abbr ? `${city} (${abbr})` : city;
   } catch {
-    return timezone.replace(/_/g, " ");
+    return timezone;
   }
 }
 

autogpt_platform/frontend/src/tests/marketplace.spec.ts

@@ -4,7 +4,6 @@ import { LoginPage } from "./pages/login.page";
 import { MarketplacePage } from "./pages/marketplace.page";
 import { hasMinCount, hasUrl, isVisible, matchesUrl } from "./utils/assertion";
 
-// Marketplace tests for store agent search functionality
 test.describe("Marketplace – Basic Functionality", () => {
   test("User can access marketplace page when logged out", async ({ page }) => {
     const marketplacePage = new MarketplacePage(page);