fix(backend/migrations): remove explicit schema from pgvector extension

- Change from 'CREATE EXTENSION ... WITH SCHEMA public' to 'CREATE EXTENSION ...'
- Remove public. prefix from vector type and vector_cosine_ops
- Aligns with Supabase extension creation behavior where extensions are installed without explicit schema
- Fixes migration failure when user lacks SUPERUSER privileges for cross-schema operations

Context: Supabase requires extensions to be enabled via Dashboard first, then migrations verify existence.

.github/workflows/platform-backend-ci.yml

@@ -176,7 +176,7 @@ jobs:
           }
 
       - name: Run Database Migrations
-        run: poetry run prisma migrate dev --name updates
+        run: poetry run prisma migrate deploy
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

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

@@ -1,250 +0,0 @@
-import logging
-
-import autogpt_libs.auth
-import fastapi
-import fastapi.responses
-
-import backend.api.features.store.db as store_db
-import backend.api.features.store.model as store_model
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter(
-    prefix="/admin/waitlist",
-    tags=["store", "admin", "waitlist"],
-    dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
-)
-
-
-@router.post(
-    "",
-    summary="Create Waitlist",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def create_waitlist(
-    request: store_model.WaitlistCreateRequest,
-    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
-):
-    """
-    Create a new waitlist (admin only).
-
-    Args:
-        request: Waitlist creation details
-        user_id: Authenticated admin user creating the waitlist
-
-    Returns:
-        WaitlistAdminResponse with the created waitlist details
-    """
-    try:
-        waitlist = await store_db.create_waitlist_admin(
-            admin_user_id=user_id,
-            data=request,
-        )
-        return waitlist
-    except Exception as e:
-        logger.exception("Error creating waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while creating the waitlist"},
-        )
-
-
-@router.get(
-    "",
-    summary="List All Waitlists",
-    response_model=store_model.WaitlistAdminListResponse,
-)
-async def list_waitlists():
-    """
-    Get all waitlists with admin details (admin only).
-
-    Returns:
-        WaitlistAdminListResponse with all waitlists
-    """
-    try:
-        return await store_db.get_waitlists_admin()
-    except Exception as e:
-        logger.exception("Error listing waitlists: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching waitlists"},
-        )
-
-
-@router.get(
-    "/{waitlist_id}",
-    summary="Get Waitlist Details",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def get_waitlist(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Get a single waitlist with admin details (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to retrieve
-
-    Returns:
-        WaitlistAdminResponse with waitlist details
-    """
-    try:
-        return await store_db.get_waitlist_admin(waitlist_id)
-    except ValueError:
-        logger.warning("Waitlist not found: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error fetching waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching the waitlist"},
-        )
-
-
-@router.put(
-    "/{waitlist_id}",
-    summary="Update Waitlist",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def update_waitlist(
-    request: store_model.WaitlistUpdateRequest,
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Update a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to update
-        request: Fields to update
-
-    Returns:
-        WaitlistAdminResponse with updated waitlist details
-    """
-    try:
-        return await store_db.update_waitlist_admin(waitlist_id, request)
-    except ValueError:
-        logger.warning("Waitlist not found for update: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error updating waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while updating the waitlist"},
-        )
-
-
-@router.delete(
-    "/{waitlist_id}",
-    summary="Delete Waitlist",
-)
-async def delete_waitlist(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Soft delete a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist to delete
-
-    Returns:
-        Success message
-    """
-    try:
-        deleted = await store_db.delete_waitlist_admin(waitlist_id)
-        if deleted:
-            return {"message": "Waitlist deleted successfully"}
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error deleting waitlist: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while deleting the waitlist"},
-        )
-
-
-@router.get(
-    "/{waitlist_id}/signups",
-    summary="Get Waitlist Signups",
-    response_model=store_model.WaitlistSignupListResponse,
-)
-async def get_waitlist_signups(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-):
-    """
-    Get all signups for a waitlist (admin only).
-
-    Args:
-        waitlist_id: ID of the waitlist
-
-    Returns:
-        WaitlistSignupListResponse with all signups
-    """
-    try:
-        return await store_db.get_waitlist_signups_admin(waitlist_id)
-    except ValueError:
-        logger.warning("Waitlist not found for signups: %s", waitlist_id)
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist not found"},
-        )
-    except Exception as e:
-        logger.exception("Error fetching waitlist signups: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while fetching waitlist signups"},
-        )
-
-
-@router.post(
-    "/{waitlist_id}/link",
-    summary="Link Waitlist to Store Listing",
-    response_model=store_model.WaitlistAdminResponse,
-)
-async def link_waitlist_to_listing(
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist"),
-    store_listing_id: str = fastapi.Body(
-        ..., embed=True, description="The ID of the store listing"
-    ),
-):
-    """
-    Link a waitlist to a store listing (admin only).
-
-    When the linked store listing is approved/published, waitlist users
-    will be automatically notified.
-
-    Args:
-        waitlist_id: ID of the waitlist
-        store_listing_id: ID of the store listing to link
-
-    Returns:
-        WaitlistAdminResponse with updated waitlist details
-    """
-    try:
-        return await store_db.link_waitlist_to_listing_admin(
-            waitlist_id, store_listing_id
-        )
-    except ValueError:
-        logger.warning(
-            "Link failed - waitlist or listing not found: %s, %s",
-            waitlist_id,
-            store_listing_id,
-        )
-        return fastapi.responses.JSONResponse(
-            status_code=404,
-            content={"detail": "Waitlist or store listing not found"},
-        )
-    except Exception as e:
-        logger.exception("Error linking waitlist to listing: %s", e)
-        return fastapi.responses.JSONResponse(
-            status_code=500,
-            content={"detail": "An error occurred while linking the waitlist"},
-        )

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

@@ -1,4 +1,5 @@
 import uuid
+from unittest.mock import AsyncMock, patch
 
 import orjson
 import pytest
@@ -17,6 +18,17 @@ setup_test_data = setup_test_data
 setup_firecrawl_test_data = setup_firecrawl_test_data
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(scope="session")
 async def test_run_agent(setup_test_data):
     """Test that the run_agent tool successfully executes an approved agent"""

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

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""
+CLI script to backfill embeddings for store agents.
+
+Usage:
+    poetry run python -m backend.api.features.store.backfill_embeddings [--batch-size N]
+"""
+
+import argparse
+import asyncio
+import logging
+import sys
+
+import prisma
+
+from backend.api.features.store.embeddings import (
+    backfill_missing_embeddings,
+    get_embedding_stats,
+)
+
+logger = logging.getLogger(__name__)
+
+
+async def main(batch_size: int = 100) -> int:
+    """Run the backfill process - processes ALL missing embeddings in batches."""
+    client = prisma.Prisma()
+    await client.connect()
+    prisma.register(client)
+
+    try:
+        stats = await get_embedding_stats()
+
+        # Check for error from get_embedding_stats() first
+        if "error" in stats:
+            logger.error(f"Failed to get embedding stats: {stats['error']}")
+            return 1
+
+        logger.info(
+            f"Current coverage: {stats['with_embeddings']}/{stats['total_approved']} "
+            f"({stats['coverage_percent']}%)"
+        )
+
+        if stats["without_embeddings"] == 0:
+            logger.info("All agents have embeddings - nothing to backfill")
+            return 0
+
+        logger.info(
+            f"Backfilling {stats['without_embeddings']} missing embeddings "
+            f"(batch size: {batch_size})"
+        )
+
+        total_processed = 0
+        total_success = 0
+        total_failed = 0
+
+        while True:
+            result = await backfill_missing_embeddings(batch_size=batch_size)
+            if result["processed"] == 0:
+                break
+
+            total_processed += result["processed"]
+            total_success += result["success"]
+            total_failed += result["failed"]
+
+            logger.info(
+                f"Batch complete: {result['success']}/{result['processed']} succeeded"
+            )
+
+            await asyncio.sleep(1)
+
+        # Final stats
+        stats = await get_embedding_stats()
+        logger.info(
+            f"Backfill complete: {total_success}/{total_processed} succeeded, "
+            f"{total_failed} failed"
+        )
+        if "error" not in stats:
+            logger.info(f"Final coverage: {stats['coverage_percent']}%")
+        else:
+            logger.warning("Could not retrieve final coverage stats")
+
+        return 0 if total_failed == 0 else 1
+
+    finally:
+        await client.disconnect()
+
+
+if __name__ == "__main__":
+    # Configure logging for CLI usage
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(levelname)s: %(message)s",
+    )
+
+    parser = argparse.ArgumentParser(description="Backfill embeddings for store agents")
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=100,
+        help="Number of embeddings to generate per batch (default: 100)",
+    )
+    args = parser.parse_args()
+
+    sys.exit(asyncio.run(main(batch_size=args.batch_size)))

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

@@ -1,8 +1,7 @@
 import asyncio
 import logging
-import typing
 from datetime import datetime, timezone
-from typing import Literal
+from typing import Any, Literal
 
 import fastapi
 import prisma.enums
@@ -10,7 +9,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import query_raw_with_schema, transaction
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -23,7 +22,6 @@ from backend.data.notifications import (
     AgentApprovalData,
     AgentRejectionData,
     NotificationEventModel,
-    WaitlistLaunchData,
 )
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
@@ -31,6 +29,8 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
+from .embeddings import ensure_embedding
+from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -51,128 +51,77 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view
+    Get PUBLIC store agents from the StoreAgent view.
+
+    Search behavior:
+    - With search_query: Uses hybrid search (semantic + lexical)
+    - Fallback: If embeddings unavailable, gracefully degrades to lexical-only
+    - Rationale: User-facing endpoint prioritizes availability over accuracy
+
+    Note: Admin operations (approval) use fail-fast to prevent inconsistent state.
     """
     logger.debug(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
+    search_used_hybrid = False
+    store_agents: list[store_model.StoreAgent] = []
+    agents: list[dict[str, Any]] = []
+    total = 0
+    total_pages = 0
+
     try:
-        # If search_query is provided, use full-text search
+        # If search_query is provided, use hybrid search (embeddings + tsvector)
         if search_query:
-            offset = (page - 1) * page_size
+            # Try hybrid search combining semantic and lexical signals
+            # Falls back to lexical-only if OpenAI unavailable (user-facing, high SLA)
+            try:
+                agents, total = await hybrid_search(
+                    query=search_query,
+                    featured=featured,
+                    creators=creators,
+                    category=category,
+                    sorted_by="relevance",  # Use hybrid scoring for relevance
+                    page=page,
+                    page_size=page_size,
+                )
+                search_used_hybrid = True
+            except Exception as e:
+                # Log error but fall back to lexical search for better UX
+                logger.error(
+                    f"Hybrid search failed (likely OpenAI unavailable), "
+                    f"falling back to lexical search: {e}"
+                )
+                # search_used_hybrid remains False, will use fallback path below
 
-            # Whitelist allowed order_by columns
-            ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
-            }
+            # Convert hybrid search results (dict format) if hybrid succeeded
+            if search_used_hybrid:
+                total_pages = (total + page_size - 1) // page_size
+                store_agents: list[store_model.StoreAgent] = []
+                for agent in agents:
+                    try:
+                        store_agent = store_model.StoreAgent(
+                            slug=agent["slug"],
+                            agent_name=agent["agent_name"],
+                            agent_image=(
+                                agent["agent_image"][0] if agent["agent_image"] else ""
+                            ),
+                            creator=agent["creator_username"] or "Needs Profile",
+                            creator_avatar=agent["creator_avatar"] or "",
+                            sub_heading=agent["sub_heading"],
+                            description=agent["description"],
+                            runs=agent["runs"],
+                            rating=agent["rating"],
+                        )
+                        store_agents.append(store_agent)
+                    except Exception as e:
+                        logger.error(
+                            f"Error parsing Store agent from hybrid search results: {e}"
+                        )
+                        continue
 
-            # Validate and get order clause
-            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
-                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
-            else:
-                order_by_clause = "updated_at DESC, rank DESC"
-
-            # Build WHERE conditions and parameters list
-            where_parts: list[str] = []
-            params: list[typing.Any] = [search_query]  # $1 - search term
-            param_index = 2  # Start at $2 for next parameter
-
-            # Always filter for available agents
-            where_parts.append("is_available = true")
-
-            if featured:
-                where_parts.append("featured = true")
-
-            if creators and creators:
-                # Use ANY with array parameter
-                where_parts.append(f"creator_username = ANY(${param_index})")
-                params.append(creators)
-                param_index += 1
-
-            if category and category:
-                where_parts.append(f"${param_index} = ANY(categories)")
-                params.append(category)
-                param_index += 1
-
-            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
-
-            # Add pagination params
-            params.extend([page_size, offset])
-            limit_param = f"${param_index}"
-            offset_param = f"${param_index + 1}"
-
-            # Execute full-text search query with parameterized values
-            sql_query = f"""
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    ts_rank_cd(search, query) AS rank
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-                ORDER BY {order_by_clause}
-                LIMIT {limit_param} OFFSET {offset_param}
-            """
-
-            # Count query for pagination - only uses search term parameter
-            count_query = f"""
-                SELECT COUNT(*) as count
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-            """
-
-            # Execute both queries with parameters
-            agents = await query_raw_with_schema(sql_query, *params)
-
-            # For count, use params without pagination (last 2 params)
-            count_params = params[:-2]
-            count_result = await query_raw_with_schema(count_query, *count_params)
-
-            total = count_result[0]["count"] if count_result else 0
-            total_pages = (total + page_size - 1) // page_size
-
-            # Convert raw results to StoreAgent models
-            store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
-                try:
-                    store_agent = store_model.StoreAgent(
-                        slug=agent["slug"],
-                        agent_name=agent["agent_name"],
-                        agent_image=(
-                            agent["agent_image"][0] if agent["agent_image"] else ""
-                        ),
-                        creator=agent["creator_username"] or "Needs Profile",
-                        creator_avatar=agent["creator_avatar"] or "",
-                        sub_heading=agent["sub_heading"],
-                        description=agent["description"],
-                        runs=agent["runs"],
-                        rating=agent["rating"],
-                    )
-                    store_agents.append(store_agent)
-                except Exception as e:
-                    logger.error(f"Error parsing Store agent from search results: {e}")
-                    continue
-
-        else:
-            # Non-search query path (original logic)
+        if not search_used_hybrid:
+            # Fallback path - use basic search or no search
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -181,6 +130,14 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
+            # Add basic text search if search_query provided but hybrid failed
+            if search_query:
+                where_clause["OR"] = [
+                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
+                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
+                    {"description": {"contains": search_query, "mode": "insensitive"}},
+                ]
+
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -189,7 +146,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            agents = await prisma.models.StoreAgent.prisma().find_many(
+            db_agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -200,7 +157,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
+            for agent in db_agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -1578,7 +1535,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma().update(
+                await prisma.models.AgentGraph.prisma(tx).update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1593,6 +1550,23 @@ async def review_store_submission(
                     },
                 )
 
+                # Generate embedding for approved listing (blocking - admin operation)
+                # Inside transaction: if embedding fails, entire transaction rolls back
+                embedding_success = await ensure_embedding(
+                    version_id=store_listing_version_id,
+                    name=store_listing_version.name,
+                    description=store_listing_version.description,
+                    sub_heading=store_listing_version.subHeading,
+                    categories=store_listing_version.categories or [],
+                    tx=tx,
+                )
+                if not embedding_success:
+                    raise ValueError(
+                        f"Failed to generate embedding for listing {store_listing_version_id}. "
+                        "This is likely due to OpenAI API being unavailable. "
+                        "Please try again later or contact support if the issue persists."
+                    )
+
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},
                     data={
@@ -1743,29 +1717,6 @@ async def review_store_submission(
                 # Don't fail the review process if email sending fails
                 pass
 
-        # Notify waitlist users if this is an approval and has a linked waitlist
-        if is_approved and submission.StoreListing:
-            try:
-                frontend_base_url = (
-                    settings.config.frontend_base_url
-                    or settings.config.platform_base_url
-                )
-                store_agent = (
-                    await prisma.models.StoreAgent.prisma().find_first_or_raise(
-                        where={"storeListingVersionId": submission.id}
-                    )
-                )
-                creator_username = store_agent.creator_username or "unknown"
-                store_url = f"{frontend_base_url}/marketplace/agent/{creator_username}/{store_agent.slug}"
-                await notify_waitlist_users_on_launch(
-                    store_listing_id=submission.StoreListing.id,
-                    agent_name=submission.name,
-                    store_url=store_url,
-                )
-            except Exception as e:
-                logger.error(f"Failed to notify waitlist users on agent approval: {e}")
-                # Don't fail the approval process
-
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
             listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
@@ -2013,507 +1964,3 @@ async def get_agent_as_admin(
         )
 
     return graph
-
-
-def _waitlist_to_store_entry(
-    waitlist: prisma.models.WaitlistEntry,
-) -> store_model.StoreWaitlistEntry:
-    """Convert a WaitlistEntry to StoreWaitlistEntry for public display."""
-    return store_model.StoreWaitlistEntry(
-        waitlistId=waitlist.id,
-        slug=waitlist.slug,
-        name=waitlist.name,
-        subHeading=waitlist.subHeading,
-        videoUrl=waitlist.videoUrl,
-        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
-        imageUrls=waitlist.imageUrls or [],
-        description=waitlist.description,
-        categories=waitlist.categories,
-    )
-
-
-async def get_waitlist() -> list[store_model.StoreWaitlistEntry]:
-    """Get all active waitlists for public display."""
-    try:
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
-        )
-
-        # Filter out closed/done waitlists and sort by votes (descending)
-        excluded_statuses = {
-            prisma.enums.WaitlistExternalStatus.CANCELED,
-            prisma.enums.WaitlistExternalStatus.DONE,
-        }
-        active_waitlists = [w for w in waitlists if w.status not in excluded_statuses]
-        sorted_list = sorted(active_waitlists, key=lambda x: x.votes, reverse=True)
-
-        return [_waitlist_to_store_entry(w) for w in sorted_list]
-    except Exception as e:
-        logger.error(f"Error fetching waitlists: {e}")
-        raise DatabaseError("Failed to fetch waitlists") from e
-
-
-async def get_user_waitlist_memberships(user_id: str) -> list[str]:
-    """Get all waitlist IDs that a user has joined."""
-    try:
-        user = await prisma.models.User.prisma().find_unique(
-            where={"id": user_id},
-            include={"joinedWaitlists": True},
-        )
-        if not user or not user.joinedWaitlists:
-            return []
-        return [w.id for w in user.joinedWaitlists]
-    except Exception as e:
-        logger.error(f"Error fetching user waitlist memberships: {e}")
-        raise DatabaseError("Failed to fetch waitlist memberships") from e
-
-
-async def add_user_to_waitlist(
-    waitlist_id: str, user_id: str | None, email: str | None
-) -> store_model.StoreWaitlistEntry:
-    """
-    Add a user to a waitlist.
-
-    For logged-in users: connects via joinedUsers relation
-    For anonymous users: adds email to unaffiliatedEmailUsers array
-    """
-    logger.debug(f"Adding user {user_id or email} to waitlist {waitlist_id}")
-
-    if not user_id and not email:
-        raise ValueError("Either user_id or email must be provided")
-
-    try:
-        # Find the waitlist
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} is no longer available")
-
-        if waitlist.status in [
-            prisma.enums.WaitlistExternalStatus.CANCELED,
-            prisma.enums.WaitlistExternalStatus.DONE,
-        ]:
-            raise ValueError(f"Waitlist {waitlist_id} is closed")
-
-        if user_id:
-            # Check if user already joined
-            joined_user_ids = [u.id for u in (waitlist.joinedUsers or [])]
-            if user_id in joined_user_ids:
-                # Already joined - return waitlist info
-                logger.debug(f"User {user_id} already joined waitlist {waitlist_id}")
-            else:
-                # Connect user to waitlist
-                await prisma.models.WaitlistEntry.prisma().update(
-                    where={"id": waitlist_id},
-                    data={"joinedUsers": {"connect": [{"id": user_id}]}},
-                )
-                logger.info(f"User {user_id} joined waitlist {waitlist_id}")
-
-            # If user was previously in email list, remove them
-            # Use transaction to prevent race conditions
-            if email:
-                async with transaction() as tx:
-                    current_waitlist = await tx.waitlistentry.find_unique(
-                        where={"id": waitlist_id}
-                    )
-                    if current_waitlist and email in (
-                        current_waitlist.unaffiliatedEmailUsers or []
-                    ):
-                        updated_emails: list[str] = [
-                            e
-                            for e in (current_waitlist.unaffiliatedEmailUsers or [])
-                            if e != email
-                        ]
-                        await tx.waitlistentry.update(
-                            where={"id": waitlist_id},
-                            data={"unaffiliatedEmailUsers": updated_emails},
-                        )
-        elif email:
-            # Add email to unaffiliated list if not already present
-            # Use transaction to prevent race conditions with concurrent signups
-            async with transaction() as tx:
-                # Re-fetch within transaction to get latest state
-                current_waitlist = await tx.waitlistentry.find_unique(
-                    where={"id": waitlist_id}
-                )
-                if current_waitlist:
-                    current_emails: list[str] = list(
-                        current_waitlist.unaffiliatedEmailUsers or []
-                    )
-                    if email not in current_emails:
-                        current_emails.append(email)
-                        await tx.waitlistentry.update(
-                            where={"id": waitlist_id},
-                            data={"unaffiliatedEmailUsers": current_emails},
-                        )
-                        logger.info(f"Email {email} added to waitlist {waitlist_id}")
-                    else:
-                        logger.debug(f"Email {email} already on waitlist {waitlist_id}")
-
-        # Re-fetch to return updated data
-        updated_waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id}
-        )
-        return _waitlist_to_store_entry(updated_waitlist or waitlist)
-
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error adding user to waitlist: {e}")
-        raise DatabaseError("Failed to add user to waitlist") from e
-
-
-# ============== Admin Waitlist Functions ==============
-
-
-def _waitlist_to_admin_response(
-    waitlist: prisma.models.WaitlistEntry,
-) -> store_model.WaitlistAdminResponse:
-    """Convert a WaitlistEntry to WaitlistAdminResponse."""
-    joined_count = len(waitlist.joinedUsers) if waitlist.joinedUsers else 0
-    email_count = (
-        len(waitlist.unaffiliatedEmailUsers) if waitlist.unaffiliatedEmailUsers else 0
-    )
-
-    return store_model.WaitlistAdminResponse(
-        id=waitlist.id,
-        createdAt=waitlist.createdAt.isoformat() if waitlist.createdAt else "",
-        updatedAt=waitlist.updatedAt.isoformat() if waitlist.updatedAt else "",
-        slug=waitlist.slug,
-        name=waitlist.name,
-        subHeading=waitlist.subHeading,
-        description=waitlist.description,
-        categories=waitlist.categories,
-        imageUrls=waitlist.imageUrls or [],
-        videoUrl=waitlist.videoUrl,
-        agentOutputDemoUrl=waitlist.agentOutputDemoUrl,
-        status=waitlist.status or prisma.enums.WaitlistExternalStatus.NOT_STARTED,
-        votes=waitlist.votes,
-        signupCount=joined_count + email_count,
-        storeListingId=waitlist.storeListingId,
-        owningUserId=waitlist.owningUserId,
-    )
-
-
-async def create_waitlist_admin(
-    admin_user_id: str,
-    data: store_model.WaitlistCreateRequest,
-) -> store_model.WaitlistAdminResponse:
-    """Create a new waitlist (admin only)."""
-    logger.info(f"Admin {admin_user_id} creating waitlist: {data.name}")
-
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().create(
-            data=prisma.types.WaitlistEntryCreateInput(
-                name=data.name,
-                slug=data.slug,
-                subHeading=data.subHeading,
-                description=data.description,
-                categories=data.categories,
-                imageUrls=data.imageUrls,
-                videoUrl=data.videoUrl,
-                agentOutputDemoUrl=data.agentOutputDemoUrl,
-                owningUserId=admin_user_id,
-                status=prisma.enums.WaitlistExternalStatus.NOT_STARTED,
-            ),
-            include={"joinedUsers": True},
-        )
-
-        return _waitlist_to_admin_response(waitlist)
-    except Exception as e:
-        logger.error(f"Error creating waitlist: {e}")
-        raise DatabaseError("Failed to create waitlist") from e
-
-
-async def get_waitlists_admin() -> store_model.WaitlistAdminListResponse:
-    """Get all waitlists with admin details."""
-    try:
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where=prisma.types.WaitlistEntryWhereInput(isDeleted=False),
-            include={"joinedUsers": True},
-            order={"createdAt": "desc"},
-        )
-
-        return store_model.WaitlistAdminListResponse(
-            waitlists=[_waitlist_to_admin_response(w) for w in waitlists],
-            totalCount=len(waitlists),
-        )
-    except Exception as e:
-        logger.error(f"Error fetching waitlists for admin: {e}")
-        raise DatabaseError("Failed to fetch waitlists") from e
-
-
-async def get_waitlist_admin(
-    waitlist_id: str,
-) -> store_model.WaitlistAdminResponse:
-    """Get a single waitlist with admin details."""
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        if waitlist.isDeleted:
-            raise ValueError(f"Waitlist {waitlist_id} has been deleted")
-
-        return _waitlist_to_admin_response(waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error fetching waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to fetch waitlist") from e
-
-
-async def update_waitlist_admin(
-    waitlist_id: str,
-    data: store_model.WaitlistUpdateRequest,
-) -> store_model.WaitlistAdminResponse:
-    """Update a waitlist (admin only)."""
-    logger.info(f"Updating waitlist {waitlist_id}")
-
-    try:
-        # Build update data from explicitly provided fields
-        # Use model_fields_set to allow clearing fields by setting them to None
-        field_mappings = {
-            "name": data.name,
-            "slug": data.slug,
-            "subHeading": data.subHeading,
-            "description": data.description,
-            "categories": data.categories,
-            "imageUrls": data.imageUrls,
-            "videoUrl": data.videoUrl,
-            "agentOutputDemoUrl": data.agentOutputDemoUrl,
-            "storeListingId": data.storeListingId,
-        }
-        update_data: dict[str, typing.Any] = {
-            k: v for k, v in field_mappings.items() if k in data.model_fields_set
-        }
-
-        # Handle status separately due to enum conversion
-        if "status" in data.model_fields_set and data.status is not None:
-            update_data["status"] = prisma.enums.WaitlistExternalStatus(data.status)
-
-        if not update_data:
-            # No updates, just return current data
-            return await get_waitlist_admin(waitlist_id)
-
-        waitlist = await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data=prisma.types.WaitlistEntryUpdateInput(**update_data),
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        return _waitlist_to_admin_response(waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error updating waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to update waitlist") from e
-
-
-async def delete_waitlist_admin(waitlist_id: str) -> bool:
-    """Soft delete a waitlist (admin only)."""
-    logger.info(f"Soft deleting waitlist {waitlist_id}")
-
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data={"isDeleted": True},
-        )
-
-        return waitlist is not None
-    except Exception as e:
-        logger.error(f"Error deleting waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to delete waitlist") from e
-
-
-async def get_waitlist_signups_admin(
-    waitlist_id: str,
-) -> store_model.WaitlistSignupListResponse:
-    """Get all signups for a waitlist (admin only)."""
-    try:
-        waitlist = await prisma.models.WaitlistEntry.prisma().find_unique(
-            where={"id": waitlist_id},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        signups: list[store_model.WaitlistSignup] = []
-
-        # Add user signups
-        for user in waitlist.joinedUsers or []:
-            signups.append(
-                store_model.WaitlistSignup(
-                    type="user",
-                    userId=user.id,
-                    email=user.email,
-                    username=user.name,
-                )
-            )
-
-        # Add email signups
-        for email in waitlist.unaffiliatedEmailUsers or []:
-            signups.append(
-                store_model.WaitlistSignup(
-                    type="email",
-                    email=email,
-                )
-            )
-
-        return store_model.WaitlistSignupListResponse(
-            waitlistId=waitlist_id,
-            signups=signups,
-            totalCount=len(signups),
-        )
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error fetching signups for waitlist {waitlist_id}: {e}")
-        raise DatabaseError("Failed to fetch waitlist signups") from e
-
-
-async def link_waitlist_to_listing_admin(
-    waitlist_id: str,
-    store_listing_id: str,
-) -> store_model.WaitlistAdminResponse:
-    """Link a waitlist to a store listing (admin only)."""
-    logger.info(f"Linking waitlist {waitlist_id} to listing {store_listing_id}")
-
-    try:
-        # Verify the store listing exists
-        listing = await prisma.models.StoreListing.prisma().find_unique(
-            where={"id": store_listing_id}
-        )
-
-        if not listing:
-            raise ValueError(f"Store listing {store_listing_id} not found")
-
-        waitlist = await prisma.models.WaitlistEntry.prisma().update(
-            where={"id": waitlist_id},
-            data={"StoreListing": {"connect": {"id": store_listing_id}}},
-            include={"joinedUsers": True},
-        )
-
-        if not waitlist:
-            raise ValueError(f"Waitlist {waitlist_id} not found")
-
-        return _waitlist_to_admin_response(waitlist)
-    except ValueError:
-        raise
-    except Exception as e:
-        logger.error(f"Error linking waitlist to listing: {e}")
-        raise DatabaseError("Failed to link waitlist to listing") from e
-
-
-async def notify_waitlist_users_on_launch(
-    store_listing_id: str,
-    agent_name: str,
-    store_url: str,
-) -> int:
-    """
-    Notify all users on waitlists linked to a store listing when the agent is launched.
-
-    Args:
-        store_listing_id: The ID of the store listing that was approved
-        agent_name: The name of the approved agent
-        store_url: The URL to the agent's store page
-
-    Returns:
-        The number of notifications sent
-    """
-    logger.info(f"Notifying waitlist users for store listing {store_listing_id}")
-
-    try:
-        # Find all waitlists linked to this store listing
-        waitlists = await prisma.models.WaitlistEntry.prisma().find_many(
-            where={
-                "storeListingId": store_listing_id,
-                "isDeleted": False,
-            },
-            include={"joinedUsers": True},
-        )
-
-        if not waitlists:
-            logger.info(f"No waitlists found for store listing {store_listing_id}")
-            return 0
-
-        notification_count = 0
-        launched_at = datetime.now(tz=timezone.utc)
-
-        for waitlist in waitlists:
-            # Track notification results for this waitlist
-            users_to_notify = waitlist.joinedUsers or []
-            failed_user_ids: list[str] = []
-
-            # Notify registered users
-            for user in users_to_notify:
-                try:
-                    notification_data = WaitlistLaunchData(
-                        agent_name=agent_name,
-                        waitlist_name=waitlist.name,
-                        store_url=store_url,
-                        launched_at=launched_at,
-                    )
-
-                    notification_event = NotificationEventModel[WaitlistLaunchData](
-                        user_id=user.id,
-                        type=prisma.enums.NotificationType.WAITLIST_LAUNCH,
-                        data=notification_data,
-                    )
-
-                    await queue_notification_async(notification_event)
-                    notification_count += 1
-                except Exception as e:
-                    logger.error(
-                        f"Failed to send waitlist launch notification to user {user.id}: {e}"
-                    )
-                    failed_user_ids.append(user.id)
-
-            # Note: For unaffiliated email users, you would need to send emails directly
-            # since they don't have user IDs for the notification system.
-            # This could be done via a separate email service.
-            # For now, we log these for potential manual follow-up or future implementation.
-            if waitlist.unaffiliatedEmailUsers:
-                logger.info(
-                    f"Waitlist {waitlist.id} has {len(waitlist.unaffiliatedEmailUsers)} "
-                    f"unaffiliated email users that need email notifications"
-                )
-
-            # Only mark waitlist as DONE if all registered user notifications succeeded
-            if not failed_user_ids:
-                await prisma.models.WaitlistEntry.prisma().update(
-                    where={"id": waitlist.id},
-                    data={"status": prisma.enums.WaitlistExternalStatus.DONE},
-                )
-                logger.info(f"Updated waitlist {waitlist.id} status to DONE")
-            else:
-                logger.warning(
-                    f"Waitlist {waitlist.id} not marked as DONE due to "
-                    f"{len(failed_user_ids)} failed notifications"
-                )
-
-        logger.info(
-            f"Sent {notification_count} waitlist launch notifications for store listing {store_listing_id}"
-        )
-        return notification_count
-
-    except Exception as e:
-        logger.error(
-            f"Error notifying waitlist users for store listing {store_listing_id}: {e}"
-        )
-        # Don't raise - we don't want to fail the approval process
-        return 0

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

@@ -0,0 +1,533 @@
+"""
+Unified Content Embeddings Service
+
+Handles generation and storage of OpenAI embeddings for all content types
+(store listings, blocks, documentation, library agents) to enable semantic/hybrid search.
+"""
+
+import asyncio
+import logging
+import time
+from typing import Any
+
+import prisma
+from prisma.enums import ContentType
+
+from backend.data.db import execute_raw_with_schema, query_raw_with_schema
+from backend.util.clients import get_openai_client
+from backend.util.json import dumps
+
+logger = logging.getLogger(__name__)
+
+
+# OpenAI embedding model configuration
+EMBEDDING_MODEL = "text-embedding-3-small"
+
+
+def build_searchable_text(
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+) -> str:
+    """
+    Build searchable text from listing version fields.
+
+    Combines relevant fields into a single string for embedding.
+    """
+    parts = []
+
+    # Name is important - include it
+    if name:
+        parts.append(name)
+
+    # Sub-heading provides context
+    if sub_heading:
+        parts.append(sub_heading)
+
+    # Description is the main content
+    if description:
+        parts.append(description)
+
+    # Categories help with semantic matching
+    if categories:
+        parts.append(" ".join(categories))
+
+    return " ".join(parts)
+
+
+async def generate_embedding(text: str) -> list[float] | None:
+    """
+    Generate embedding for text using OpenAI API.
+
+    Returns None if embedding generation fails.
+    Fail-fast: no retries to maintain consistency with approval flow.
+    """
+    try:
+        client = get_openai_client()
+        if not client:
+            logger.error("openai_internal_api_key not set, cannot generate embedding")
+            return None
+
+        # Truncate text to avoid token limits (~32k chars for safety)
+        truncated_text = text[:32000]
+
+        start_time = time.time()
+        response = await client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=truncated_text,
+        )
+        latency_ms = (time.time() - start_time) * 1000
+
+        embedding = response.data[0].embedding
+        logger.info(
+            f"Generated embedding: {len(embedding)} dims, "
+            f"{len(truncated_text)} chars, {latency_ms:.0f}ms"
+        )
+        return embedding
+
+    except Exception as e:
+        logger.error(f"Failed to generate embedding: {e}")
+        return None
+
+
+async def store_embedding(
+    version_id: str,
+    embedding: list[float],
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the database.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    DEPRECATED: Use ensure_embedding() instead (includes searchable_text).
+    """
+    return await store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=version_id,
+        embedding=embedding,
+        searchable_text="",  # Empty for backward compat; ensure_embedding() populates this
+        metadata=None,
+        user_id=None,  # Store agents are public
+        tx=tx,
+    )
+
+
+async def store_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    embedding: list[float],
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the unified content embeddings table.
+
+    New function for unified content embedding storage.
+    Uses raw SQL since Prisma doesn't natively support pgvector.
+    """
+    try:
+        client = tx if tx else prisma.get_client()
+
+        # Convert embedding to PostgreSQL vector format
+        embedding_str = embedding_to_vector_string(embedding)
+        metadata_json = dumps(metadata or {})
+
+        # Upsert the embedding
+        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())
+            ON CONFLICT ("contentType", "contentId", "userId")
+            DO UPDATE SET
+                "embedding" = $4::vector,
+                "searchableText" = $5,
+                "metadata" = $6::jsonb,
+                "updatedAt" = NOW()
+            """,
+            content_type,
+            content_id,
+            user_id,
+            embedding_str,
+            searchable_text,
+            metadata_json,
+            client=client,
+        )
+
+        logger.info(f"Stored embedding for {content_type}:{content_id}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding(version_id: str) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Returns dict with storeListingVersionId, embedding, timestamps or None if not found.
+    """
+    result = await get_content_embedding(
+        ContentType.STORE_AGENT, version_id, user_id=None
+    )
+    if result:
+        # Transform to old format for backward compatibility
+        return {
+            "storeListingVersionId": result["contentId"],
+            "embedding": result["embedding"],
+            "createdAt": result["createdAt"],
+            "updatedAt": result["updatedAt"],
+        }
+    return None
+
+
+async def get_content_embedding(
+    content_type: ContentType, content_id: str, user_id: str | None = None
+) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for any content type.
+
+    New function for unified content embedding retrieval.
+    Returns dict with contentType, contentId, embedding, timestamps or None if not found.
+    """
+    try:
+        result = await query_raw_with_schema(
+            """
+            SELECT
+                "contentType",
+                "contentId",
+                "userId",
+                "embedding"::text as "embedding",
+                "searchableText",
+                "metadata",
+                "createdAt",
+                "updatedAt"
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+        )
+
+        if result and len(result) > 0:
+            return result[0]
+        return None
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
+        return None
+
+
+async def ensure_embedding(
+    version_id: str,
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for the listing version.
+
+    Creates embedding if missing. Use force=True to regenerate.
+    Backward-compatible wrapper for store listings.
+
+    Args:
+        version_id: The StoreListingVersion ID
+        name: Agent name
+        description: Agent description
+        sub_heading: Agent sub-heading
+        categories: Agent categories
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_embedding(version_id)
+            if existing and existing.get("embedding"):
+                logger.debug(f"Embedding for version {version_id} already exists")
+                return True
+
+        # Build searchable text for embedding
+        searchable_text = build_searchable_text(
+            name, description, sub_heading, categories
+        )
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(f"Could not generate embedding for version {version_id}")
+            return False
+
+        # Store the embedding with metadata using new function
+        metadata = {
+            "name": name,
+            "subHeading": sub_heading,
+            "categories": categories,
+        }
+        return await store_content_embedding(
+            content_type=ContentType.STORE_AGENT,
+            content_id=version_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata,
+            user_id=None,  # Store agents are public
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
+        return False
+
+
+async def delete_embedding(version_id: str) -> bool:
+    """
+    Delete embedding for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+    """
+    return await delete_content_embedding(ContentType.STORE_AGENT, version_id)
+
+
+async def delete_content_embedding(content_type: ContentType, content_id: str) -> bool:
+    """
+    Delete embedding for any content type.
+
+    New function for unified content embedding deletion.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+    """
+    try:
+        client = prisma.get_client()
+
+        await execute_raw_with_schema(
+            """
+            DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2
+            """,
+            content_type,
+            content_id,
+            client=client,
+        )
+
+        logger.info(f"Deleted embedding for {content_type}:{content_id}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to delete embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding_stats() -> dict[str, Any]:
+    """
+    Get statistics about embedding coverage.
+
+    Returns counts of:
+    - Total approved listing versions
+    - Versions with embeddings
+    - Versions without embeddings
+    """
+    try:
+        # Count approved versions
+        approved_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion"
+            WHERE "submissionStatus" = 'APPROVED'
+            AND "isDeleted" = false
+            """
+        )
+        total_approved = approved_result[0]["count"] if approved_result else 0
+
+        # Count versions with embeddings
+        embedded_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion" slv
+            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            """
+        )
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total_approved": total_approved,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_approved - with_embeddings,
+            "coverage_percent": (
+                round(with_embeddings / total_approved * 100, 1)
+                if total_approved > 0
+                else 0
+            ),
+        }
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding stats: {e}")
+        return {
+            "total_approved": 0,
+            "with_embeddings": 0,
+            "without_embeddings": 0,
+            "coverage_percent": 0,
+            "error": str(e),
+        }
+
+
+async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
+    """
+    Generate embeddings for approved listings that don't have them.
+
+    Args:
+        batch_size: Number of embeddings to generate in one call
+
+    Returns:
+        Dict with success/failure counts
+    """
+    try:
+        # Find approved versions without embeddings
+        missing = await query_raw_with_schema(
+            """
+            SELECT
+                slv.id,
+                slv.name,
+                slv.description,
+                slv."subHeading",
+                slv.categories
+            FROM {schema_prefix}"StoreListingVersion" slv
+            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
+                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            AND uce."contentId" IS NULL
+            LIMIT $1
+            """,
+            batch_size,
+        )
+
+        if not missing:
+            return {
+                "processed": 0,
+                "success": 0,
+                "failed": 0,
+                "message": "No missing embeddings",
+            }
+
+        # Process embeddings concurrently for better performance
+        embedding_tasks = [
+            ensure_embedding(
+                version_id=row["id"],
+                name=row["name"],
+                description=row["description"],
+                sub_heading=row["subHeading"],
+                categories=row["categories"] or [],
+            )
+            for row in missing
+        ]
+
+        results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
+
+        success = sum(1 for result in results if result is True)
+        failed = len(results) - success
+
+        return {
+            "processed": len(missing),
+            "success": success,
+            "failed": failed,
+            "message": f"Backfilled {success} embeddings, {failed} failed",
+        }
+
+    except Exception as e:
+        logger.error(f"Failed to backfill embeddings: {e}")
+        return {
+            "processed": 0,
+            "success": 0,
+            "failed": 0,
+            "error": str(e),
+        }
+
+
+async def embed_query(query: str) -> list[float] | None:
+    """
+    Generate embedding for a search query.
+
+    Same as generate_embedding but with clearer intent.
+    """
+    return await generate_embedding(query)
+
+
+def embedding_to_vector_string(embedding: list[float]) -> str:
+    """Convert embedding list to PostgreSQL vector string format."""
+    return "[" + ",".join(str(x) for x in embedding) + "]"
+
+
+async def ensure_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for any content type.
+
+    Generic function for creating embeddings for store agents, blocks, docs, etc.
+
+    Args:
+        content_type: ContentType enum value (STORE_AGENT, BLOCK, etc.)
+        content_id: Unique identifier for the content
+        searchable_text: Combined text for embedding generation
+        metadata: Optional metadata to store with embedding
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_content_embedding(content_type, content_id, user_id)
+            if existing and existing.get("embedding"):
+                logger.debug(
+                    f"Embedding for {content_type}:{content_id} already exists"
+                )
+                return True
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(
+                f"Could not generate embedding for {content_type}:{content_id}"
+            )
+            return False
+
+        # Store the embedding
+        return await store_content_embedding(
+            content_type=content_type,
+            content_id=content_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata or {},
+            user_id=user_id,
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
+        return False

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

@@ -0,0 +1,329 @@
+"""
+Integration tests for embeddings with schema handling.
+
+These tests verify that embeddings operations work correctly across different database schemas.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+
+# Schema prefix tests removed - functionality moved to db.raw_with_schema() helper
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_store_content_embedding_with_schema():
+    """Test storing embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.store_content_embedding(
+                content_type=ContentType.STORE_AGENT,
+                content_id="test-id",
+                embedding=[0.1] * 1536,
+                searchable_text="test text",
+                metadata={"test": "data"},
+                user_id=None,
+            )
+
+            # Verify the query was called
+            assert mock_client.execute_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.execute_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_get_content_embedding_with_schema():
+    """Test retrieving embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_client.query_raw.return_value = [
+                {
+                    "contentType": "STORE_AGENT",
+                    "contentId": "test-id",
+                    "userId": None,
+                    "embedding": "[0.1, 0.2]",
+                    "searchableText": "test",
+                    "metadata": {},
+                    "createdAt": "2024-01-01",
+                    "updatedAt": "2024-01-01",
+                }
+            ]
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.get_content_embedding(
+                ContentType.STORE_AGENT,
+                "test-id",
+                user_id=None,
+            )
+
+            # Verify the query was called
+            assert mock_client.query_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.query_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is not None
+            assert result["contentId"] == "test-id"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_delete_content_embedding_with_schema():
+    """Test deleting embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.delete_content_embedding(
+                ContentType.STORE_AGENT,
+                "test-id",
+            )
+
+            # Verify the query was called
+            assert mock_client.execute_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.execute_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_get_embedding_stats_with_schema():
+    """Test embedding statistics with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            # Mock both query results
+            mock_client.query_raw.side_effect = [
+                [{"count": 100}],  # total_approved
+                [{"count": 80}],  # with_embeddings
+            ]
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.get_embedding_stats()
+
+            # Verify both queries were called
+            assert mock_client.query_raw.call_count == 2
+
+            # Get both SQL queries
+            first_call = mock_client.query_raw.call_args_list[0]
+            second_call = mock_client.query_raw.call_args_list[1]
+
+            first_sql = first_call[0][0]
+            second_sql = second_call[0][0]
+
+            # Verify schema prefix in both queries
+            assert '"platform"."StoreListingVersion"' in first_sql
+            assert '"platform"."StoreListingVersion"' in second_sql
+            assert '"platform"."UnifiedContentEmbedding"' in second_sql
+
+            # Verify results
+            assert result["total_approved"] == 100
+            assert result["with_embeddings"] == 80
+            assert result["without_embeddings"] == 20
+            assert result["coverage_percent"] == 80.0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_backfill_missing_embeddings_with_schema():
+    """Test backfilling embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            # Mock missing embeddings query
+            mock_client.query_raw.return_value = [
+                {
+                    "id": "version-1",
+                    "name": "Test Agent",
+                    "description": "Test description",
+                    "subHeading": "Test heading",
+                    "categories": ["test"],
+                }
+            ]
+            mock_get_client.return_value = mock_client
+
+            with patch(
+                "backend.api.features.store.embeddings.ensure_embedding"
+            ) as mock_ensure:
+                mock_ensure.return_value = True
+
+                result = await embeddings.backfill_missing_embeddings(batch_size=10)
+
+                # Verify the query was called
+                assert mock_client.query_raw.called
+
+                # Get the SQL query
+                call_args = mock_client.query_raw.call_args
+                sql_query = call_args[0][0]
+
+                # Verify schema prefix in query
+                assert '"platform"."StoreListingVersion"' in sql_query
+                assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+                # Verify ensure_embedding was called
+                assert mock_ensure.called
+
+                # Verify results
+                assert result["processed"] == 1
+                assert result["success"] == 1
+                assert result["failed"] == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_ensure_content_embedding_with_schema():
+    """Test ensuring embeddings exist with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch(
+            "backend.api.features.store.embeddings.get_content_embedding"
+        ) as mock_get:
+            # Simulate no existing embedding
+            mock_get.return_value = None
+
+            with patch(
+                "backend.api.features.store.embeddings.generate_embedding"
+            ) as mock_generate:
+                mock_generate.return_value = [0.1] * 1536
+
+                with patch(
+                    "backend.api.features.store.embeddings.store_content_embedding"
+                ) as mock_store:
+                    mock_store.return_value = True
+
+                    result = await embeddings.ensure_content_embedding(
+                        content_type=ContentType.STORE_AGENT,
+                        content_id="test-id",
+                        searchable_text="test text",
+                        metadata={"test": "data"},
+                        user_id=None,
+                        force=False,
+                    )
+
+                    # Verify the flow
+                    assert mock_get.called
+                    assert mock_generate.called
+                    assert mock_store.called
+                    assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_backward_compatibility_store_embedding():
+    """Test backward compatibility wrapper for store_embedding."""
+    with patch(
+        "backend.api.features.store.embeddings.store_content_embedding"
+    ) as mock_store:
+        mock_store.return_value = True
+
+        result = await embeddings.store_embedding(
+            version_id="test-version-id",
+            embedding=[0.1] * 1536,
+            tx=None,
+        )
+
+        # Verify it calls the new function with correct parameters
+        assert mock_store.called
+        call_args = mock_store.call_args
+
+        assert call_args[1]["content_type"] == ContentType.STORE_AGENT
+        assert call_args[1]["content_id"] == "test-version-id"
+        assert call_args[1]["user_id"] is None
+        assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_backward_compatibility_get_embedding():
+    """Test backward compatibility wrapper for get_embedding."""
+    with patch(
+        "backend.api.features.store.embeddings.get_content_embedding"
+    ) as mock_get:
+        mock_get.return_value = {
+            "contentType": "STORE_AGENT",
+            "contentId": "test-version-id",
+            "embedding": "[0.1, 0.2]",
+            "createdAt": "2024-01-01",
+            "updatedAt": "2024-01-01",
+        }
+
+        result = await embeddings.get_embedding("test-version-id")
+
+        # Verify it calls the new function
+        assert mock_get.called
+
+        # Verify it transforms to old format
+        assert result is not None
+        assert result["storeListingVersionId"] == "test-version-id"
+        assert "embedding" in result
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_schema_handling_error_cases():
+    """Test error handling in schema-aware operations."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_client.execute_raw.side_effect = Exception("Database error")
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.store_content_embedding(
+                content_type=ContentType.STORE_AGENT,
+                content_id="test-id",
+                embedding=[0.1] * 1536,
+                searchable_text="test",
+                metadata=None,
+                user_id=None,
+            )
+
+            # Should return False on error, not raise
+            assert result is False
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

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

@@ -0,0 +1,348 @@
+from unittest.mock import MagicMock, patch
+
+import prisma
+import pytest
+from prisma import Prisma
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+
+
+@pytest.fixture(autouse=True)
+async def setup_prisma():
+    """Setup Prisma client for tests."""
+    try:
+        Prisma()
+    except prisma.errors.ClientAlreadyRegisteredError:
+        pass
+    yield
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text():
+    """Test searchable text building from listing fields."""
+    result = embeddings.build_searchable_text(
+        name="AI Assistant",
+        description="A helpful AI assistant for productivity",
+        sub_heading="Boost your productivity",
+        categories=["AI", "Productivity"],
+    )
+
+    expected = "AI Assistant Boost your productivity A helpful AI assistant for productivity AI Productivity"
+    assert result == expected
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text_empty_fields():
+    """Test searchable text building with empty fields."""
+    result = embeddings.build_searchable_text(
+        name="", description="Test description", sub_heading="", categories=[]
+    )
+
+    assert result == "Test description"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.util.clients.get_openai_client")
+async def test_generate_embedding_success(mock_get_client):
+    """Test successful embedding generation."""
+    # Mock OpenAI response
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1, 0.2, 0.3] * 512  # 1536 dimensions
+    mock_client.embeddings.create.return_value = mock_response
+    mock_get_client.return_value = mock_client
+
+    result = await embeddings.generate_embedding("test text")
+
+    assert result is not None
+    assert len(result) == 1536
+    assert result[0] == 0.1
+
+    mock_client.embeddings.create.assert_called_once_with(
+        model="text-embedding-3-small", input="test text"
+    )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.util.clients.get_openai_client")
+async def test_generate_embedding_no_api_key(mock_get_client):
+    """Test embedding generation without API key."""
+    mock_get_client.return_value = None
+
+    result = await embeddings.generate_embedding("test text")
+
+    assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.util.clients.get_openai_client")
+async def test_generate_embedding_api_error(mock_get_client):
+    """Test embedding generation with API error."""
+    mock_client = MagicMock()
+    mock_client.embeddings.create.side_effect = Exception("API Error")
+    mock_get_client.return_value = mock_client
+
+    result = await embeddings.generate_embedding("test text")
+
+    assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.util.clients.get_openai_client")
+async def test_generate_embedding_text_truncation(mock_get_client):
+    """Test that long text is properly truncated."""
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1] * 1536
+    mock_client.embeddings.create.return_value = mock_response
+    mock_get_client.return_value = mock_client
+
+    # Create text longer than 32k chars
+    long_text = "a" * 35000
+
+    await embeddings.generate_embedding(long_text)
+
+    # Verify truncated text was sent to API
+    call_args = mock_client.embeddings.create.call_args
+    assert len(call_args.kwargs["input"]) == 32000
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_success(mocker):
+    """Test successful embedding storage."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw = mocker.AsyncMock()
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is True
+    mock_client.execute_raw.assert_called_once()
+    call_args = mock_client.execute_raw.call_args[0]
+    assert "test-version-id" in call_args
+    assert "[0.1,0.2,0.3]" in call_args
+    assert None in call_args  # userId should be None for store agents
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_database_error(mocker):
+    """Test embedding storage with database error."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw.side_effect = Exception("Database error")
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is False
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_success(mocker):
+    """Test successful embedding retrieval."""
+    mock_client = mocker.AsyncMock()
+    mock_result = [
+        {
+            "contentType": "STORE_AGENT",
+            "contentId": "test-version-id",
+            "embedding": "[0.1,0.2,0.3]",
+            "searchableText": "Test text",
+            "metadata": {},
+            "createdAt": "2024-01-01T00:00:00Z",
+            "updatedAt": "2024-01-01T00:00:00Z",
+        }
+    ]
+    mock_client.query_raw.return_value = mock_result
+
+    with patch("prisma.get_client", return_value=mock_client):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is not None
+        assert result["storeListingVersionId"] == "test-version-id"
+        assert result["embedding"] == "[0.1,0.2,0.3]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_not_found(mocker):
+    """Test embedding retrieval when not found."""
+    mock_client = mocker.AsyncMock()
+    mock_client.query_raw.return_value = []
+
+    with patch("prisma.get_client", return_value=mock_client):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_already_exists(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding when embedding already exists."""
+    mock_get.return_value = {"embedding": "[0.1,0.2,0.3]"}
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_not_called()
+    mock_store.assert_not_called()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_content_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding creating new embedding."""
+    mock_get.return_value = None
+    mock_generate.return_value = [0.1, 0.2, 0.3]
+    mock_store.return_value = True
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_called_once_with("Test Test heading Test description test")
+    mock_store.assert_called_once_with(
+        content_type=ContentType.STORE_AGENT,
+        content_id="test-id",
+        embedding=[0.1, 0.2, 0.3],
+        searchable_text="Test Test heading Test description test",
+        metadata={"name": "Test", "subHeading": "Test heading", "categories": ["test"]},
+        user_id=None,
+        tx=None,
+    )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
+    """Test ensure_embedding when generation fails."""
+    mock_get.return_value = None
+    mock_generate.return_value = None
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is False
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_stats(mocker):
+    """Test embedding statistics retrieval."""
+    mock_client = mocker.AsyncMock()
+
+    # Mock approved count query
+    mock_approved_result = [{"count": 100}]
+    # Mock embedded count query
+    mock_embedded_result = [{"count": 75}]
+
+    mock_client.query_raw.side_effect = [mock_approved_result, mock_embedded_result]
+
+    with patch("prisma.get_client", return_value=mock_client):
+        result = await embeddings.get_embedding_stats()
+
+        assert result["total_approved"] == 100
+        assert result["with_embeddings"] == 75
+        assert result["without_embeddings"] == 25
+        assert result["coverage_percent"] == 75.0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.ensure_embedding")
+async def test_backfill_missing_embeddings_success(mock_ensure, mocker):
+    """Test backfill with successful embedding generation."""
+    mock_client = mocker.AsyncMock()
+
+    # Mock missing embeddings query
+    mock_missing = [
+        {
+            "id": "version-1",
+            "name": "Agent 1",
+            "description": "Description 1",
+            "subHeading": "Heading 1",
+            "categories": ["AI"],
+        },
+        {
+            "id": "version-2",
+            "name": "Agent 2",
+            "description": "Description 2",
+            "subHeading": "Heading 2",
+            "categories": ["Productivity"],
+        },
+    ]
+    mock_client.query_raw.return_value = mock_missing
+
+    # Mock ensure_embedding to succeed for first, fail for second
+    mock_ensure.side_effect = [True, False]
+
+    with patch("prisma.get_client", return_value=mock_client):
+        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+
+        assert result["processed"] == 2
+        assert result["success"] == 1
+        assert result["failed"] == 1
+        assert mock_ensure.call_count == 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_backfill_missing_embeddings_no_missing(mocker):
+    """Test backfill when no embeddings are missing."""
+    mock_client = mocker.AsyncMock()
+    mock_client.query_raw.return_value = []
+
+    with patch("prisma.get_client", return_value=mock_client):
+        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+
+        assert result["processed"] == 0
+        assert result["success"] == 0
+        assert result["failed"] == 0
+        assert result["message"] == "No missing embeddings"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_embedding_to_vector_string():
+    """Test embedding to PostgreSQL vector string conversion."""
+    embedding = [0.1, 0.2, 0.3, -0.4]
+    result = embeddings.embedding_to_vector_string(embedding)
+    assert result == "[0.1,0.2,0.3,-0.4]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_embed_query():
+    """Test embed_query function (alias for generate_embedding)."""
+    with patch(
+        "backend.api.features.store.embeddings.generate_embedding"
+    ) as mock_generate:
+        mock_generate.return_value = [0.1, 0.2, 0.3]
+
+        result = await embeddings.embed_query("test query")
+
+        assert result == [0.1, 0.2, 0.3]
+        mock_generate.assert_called_once_with("test query")

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

@@ -0,0 +1,388 @@
+"""
+Hybrid Search for Store Agents
+
+Combines semantic (embedding) search with lexical (tsvector) search
+for improved relevance in marketplace agent discovery.
+"""
+
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Literal
+
+from backend.api.features.store.embeddings import (
+    embed_query,
+    embedding_to_vector_string,
+)
+from backend.data.db import query_raw_with_schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HybridSearchWeights:
+    """Weights for combining search signals."""
+
+    semantic: float = 0.30  # Embedding cosine similarity
+    lexical: float = 0.30  # tsvector ts_rank_cd score
+    category: float = 0.20  # Category match boost
+    recency: float = 0.10  # Newer agents ranked higher
+    popularity: float = 0.10  # Agent usage/runs (PageRank-like)
+
+    def __post_init__(self):
+        """Validate weights are non-negative and sum to approximately 1.0."""
+        total = (
+            self.semantic
+            + self.lexical
+            + self.category
+            + self.recency
+            + self.popularity
+        )
+
+        if any(
+            w < 0
+            for w in [
+                self.semantic,
+                self.lexical,
+                self.category,
+                self.recency,
+                self.popularity,
+            ]
+        ):
+            raise ValueError("All weights must be non-negative")
+
+        if not (0.99 <= total <= 1.01):
+            raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
+
+
+DEFAULT_WEIGHTS = HybridSearchWeights()
+
+# Minimum relevance score threshold - agents below this are filtered out
+# With weights (0.30 semantic + 0.30 lexical + 0.20 category + 0.10 recency + 0.10 popularity):
+# - 0.20 means at least ~60% semantic match OR strong lexical match required
+# - Ensures only genuinely relevant results are returned
+# - Recency/popularity alone (0.10 each) won't pass the threshold
+DEFAULT_MIN_SCORE = 0.20
+
+
+@dataclass
+class HybridSearchResult:
+    """A single search result with score breakdown."""
+
+    slug: str
+    agent_name: str
+    agent_image: str
+    creator_username: str
+    creator_avatar: str
+    sub_heading: str
+    description: str
+    runs: int
+    rating: float
+    categories: list[str]
+    featured: bool
+    is_available: bool
+    updated_at: datetime
+
+    # Score breakdown (for debugging/tuning)
+    combined_score: float
+    semantic_score: float = 0.0
+    lexical_score: float = 0.0
+    category_score: float = 0.0
+    recency_score: float = 0.0
+    popularity_score: float = 0.0
+
+
+async def hybrid_search(
+    query: str,
+    featured: bool = False,
+    creators: list[str] | None = None,
+    category: str | None = None,
+    sorted_by: (
+        Literal["relevance", "rating", "runs", "name", "updated_at"] | None
+    ) = None,
+    page: int = 1,
+    page_size: int = 20,
+    weights: HybridSearchWeights | None = None,
+    min_score: float | None = None,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Perform hybrid search combining semantic and lexical signals.
+
+    Args:
+        query: Search query string
+        featured: Filter for featured agents only
+        creators: Filter by creator usernames
+        category: Filter by category
+        sorted_by: Sort order (relevance uses hybrid scoring)
+        page: Page number (1-indexed)
+        page_size: Results per page
+        weights: Custom weights for search signals
+        min_score: Minimum relevance score threshold (0-1). Results below
+                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
+
+    Returns:
+        Tuple of (results list, total count). Returns empty list if no
+        results meet the minimum relevance threshold.
+    """
+    # Validate inputs
+    query = query.strip()
+    if not query:
+        return [], 0  # Empty query returns no results
+
+    if page < 1:
+        page = 1
+    if page_size < 1:
+        page_size = 1
+    if page_size > 100:  # Cap at reasonable limit to prevent performance issues
+        page_size = 100
+
+    if weights is None:
+        weights = DEFAULT_WEIGHTS
+    if min_score is None:
+        min_score = DEFAULT_MIN_SCORE
+
+    offset = (page - 1) * page_size
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Build WHERE clause conditions
+    where_parts: list[str] = ["sa.is_available = true"]
+    params: list[Any] = []
+    param_index = 1
+
+    # Add search query for lexical matching
+    params.append(query)
+    query_param = f"${param_index}"
+    param_index += 1
+
+    # Add lowercased query for category matching
+    params.append(query.lower())
+    query_lower_param = f"${param_index}"
+    param_index += 1
+
+    if featured:
+        where_parts.append("sa.featured = true")
+
+    if creators:
+        where_parts.append(f"sa.creator_username = ANY(${param_index})")
+        params.append(creators)
+        param_index += 1
+
+    if category:
+        where_parts.append(f"${param_index} = ANY(sa.categories)")
+        params.append(category)
+        param_index += 1
+
+    # Safe: where_parts only contains hardcoded strings with $N parameter placeholders
+    # No user input is concatenated directly into the SQL string
+    where_clause = " AND ".join(where_parts)
+
+    # Embedding is required for hybrid search - fail fast if unavailable
+    if query_embedding is None:
+        # Log detailed error server-side
+        logger.error(
+            "Failed to generate query embedding. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+        )
+        # Raise generic error to client
+        raise ValueError("Search service temporarily unavailable")
+
+    # Add embedding parameter
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_index}"
+    param_index += 1
+
+    # Add weight parameters for SQL calculation
+    params.append(weights.semantic)
+    weight_semantic_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.lexical)
+    weight_lexical_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.category)
+    weight_category_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.recency)
+    weight_recency_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.popularity)
+    weight_popularity_param = f"${param_index}"
+    param_index += 1
+
+    # Add min_score parameter
+    params.append(min_score)
+    min_score_param = f"${param_index}"
+    param_index += 1
+
+    # Optimized hybrid search query:
+    # 1. Direct join to UnifiedContentEmbedding via contentId=storeListingVersionId (no redundant JOINs)
+    # 2. UNION approach (deduplicates agents matching both branches)
+    # 3. COUNT(*) OVER() to get total count in single query
+    # 4. Optimized category matching with EXISTS + unnest
+    # 5. Pre-calculated max values for lexical and popularity normalization
+    # 6. Simplified recency calculation with linear decay
+    # 7. Logarithmic popularity scaling to prevent viral agents from dominating
+    sql_query = f"""
+            WITH candidates AS (
+                -- Lexical matches (uses GIN index on search column)
+                SELECT sa."storeListingVersionId"
+                FROM {{schema_prefix}}"StoreAgent" sa
+                WHERE {where_clause}
+                AND sa.search @@ plainto_tsquery('english', {query_param})
+
+                UNION
+
+                -- Semantic matches (uses HNSW index on embedding with KNN)
+                SELECT sa."storeListingVersionId"
+                FROM {{schema_prefix}}"StoreAgent" sa
+                INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
+                WHERE {where_clause}
+                ORDER BY uce.embedding <=> {embedding_param}::vector
+                LIMIT 200
+            ),
+            search_scores AS (
+                SELECT
+                    sa.slug,
+                    sa.agent_name,
+                    sa.agent_image,
+                    sa.creator_username,
+                    sa.creator_avatar,
+                    sa.sub_heading,
+                    sa.description,
+                    sa.runs,
+                    sa.rating,
+                    sa.categories,
+                    sa.featured,
+                    sa.is_available,
+                    sa.updated_at,
+                    -- Semantic score: cosine similarity (1 - distance)
+                    COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                    -- Lexical score: ts_rank_cd (will be normalized later)
+                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                    -- Category match: optimized with unnest for better performance
+                    CASE
+                        WHEN EXISTS (
+                            SELECT 1 FROM unnest(sa.categories) cat
+                            WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                        )
+                        THEN 1.0
+                        ELSE 0.0
+                    END as category_score,
+                    -- Recency score: linear decay over 90 days (simpler than exponential)
+                    GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
+                    -- Popularity raw: agent runs count (will be normalized with log scaling)
+                    sa.runs as popularity_raw
+                FROM candidates c
+                INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                    ON c."storeListingVersionId" = sa."storeListingVersionId"
+                LEFT JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
+            ),
+            max_lexical AS (
+                SELECT MAX(lexical_raw) as max_val FROM search_scores
+            ),
+            max_popularity AS (
+                SELECT MAX(popularity_raw) as max_val FROM search_scores
+            ),
+            normalized AS (
+                SELECT
+                    ss.*,
+                    -- Normalize lexical score by pre-calculated max
+                    CASE
+                        WHEN ml.max_val > 0
+                        THEN ss.lexical_raw / ml.max_val
+                        ELSE 0
+                    END as lexical_score,
+                    -- Normalize popularity with logarithmic scaling to prevent viral agents from dominating
+                    -- LOG(1 + runs) / LOG(1 + max_runs) ensures score is 0-1 range
+                    CASE
+                        WHEN mp.max_val > 0 AND ss.popularity_raw > 0
+                        THEN LN(1 + ss.popularity_raw) / LN(1 + mp.max_val)
+                        ELSE 0
+                    END as popularity_score
+                FROM search_scores ss
+                CROSS JOIN max_lexical ml
+                CROSS JOIN max_popularity mp
+            ),
+            scored AS (
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    semantic_score,
+                    lexical_score,
+                    category_score,
+                    recency_score,
+                    popularity_score,
+                    (
+                        {weight_semantic_param} * semantic_score +
+                        {weight_lexical_param} * lexical_score +
+                        {weight_category_param} * category_score +
+                        {weight_recency_param} * recency_score +
+                        {weight_popularity_param} * popularity_score
+                    ) as combined_score
+                FROM normalized
+            ),
+            filtered AS (
+                SELECT
+                    *,
+                    COUNT(*) OVER () as total_count
+                FROM scored
+                WHERE combined_score >= {min_score_param}
+            )
+            SELECT * FROM filtered
+            ORDER BY combined_score DESC
+            LIMIT ${param_index} OFFSET ${param_index + 1}
+    """
+
+    # Add pagination params
+    params.extend([page_size, offset])
+
+    # Execute search query - includes total_count via window function
+    results = await query_raw_with_schema(sql_query, *params)
+
+    # Extract total count from first result (all rows have same count)
+    total = results[0]["total_count"] if results else 0
+
+    # Remove total_count from results before returning
+    for result in results:
+        result.pop("total_count", None)
+
+    # Log without sensitive query content
+    logger.info(f"Hybrid search: {len(results)} results, {total} total")
+
+    return results, total
+
+
+async def hybrid_search_simple(
+    query: str,
+    page: int = 1,
+    page_size: int = 20,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Simplified hybrid search for common use cases.
+
+    Uses default weights and no filters.
+    """
+    return await hybrid_search(
+        query=query,
+        page=page,
+        page_size=page_size,
+    )

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

@@ -0,0 +1,334 @@
+"""
+Integration tests for hybrid search with schema handling.
+
+These tests verify that hybrid search works correctly across different database schemas.
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from backend.api.features.store.hybrid_search import HybridSearchWeights, hybrid_search
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_schema_handling():
+    """Test that hybrid search correctly handles database schema prefixes."""
+    # Test with a mock query to ensure schema handling works
+    query = "test agent"
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Mock the query result
+        mock_query.return_value = [
+            {
+                "slug": "test/agent",
+                "agent_name": "Test Agent",
+                "agent_image": "test.png",
+                "creator_username": "test",
+                "creator_avatar": "avatar.png",
+                "sub_heading": "Test sub-heading",
+                "description": "Test description",
+                "runs": 10,
+                "rating": 4.5,
+                "categories": ["test"],
+                "featured": False,
+                "is_available": True,
+                "updated_at": "2024-01-01T00:00:00Z",
+                "combined_score": 0.8,
+                "semantic_score": 0.7,
+                "lexical_score": 0.6,
+                "category_score": 0.5,
+                "recency_score": 0.4,
+                "total_count": 1,
+            }
+        ]
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536  # Mock embedding
+
+            results, total = await hybrid_search(
+                query=query,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify the query was called
+            assert mock_query.called
+            # Verify the SQL template uses schema_prefix placeholder
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            assert "{schema_prefix}" in sql_template
+
+            # Verify results
+            assert len(results) == 1
+            assert total == 1
+            assert results[0]["slug"] == "test/agent"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_public_schema():
+    """Test hybrid search when using public schema (no prefix needed)."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "public"
+
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            mock_query.return_value = []
+
+            with patch(
+                "backend.api.features.store.hybrid_search.embed_query"
+            ) as mock_embed:
+                mock_embed.return_value = [0.1] * 1536
+
+                results, total = await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+                # Verify the mock was set up correctly
+                assert mock_schema.return_value == "public"
+
+                # Results should work even with empty results
+                assert results == []
+                assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_custom_schema():
+    """Test hybrid search when using custom schema (e.g., 'platform')."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            mock_query.return_value = []
+
+            with patch(
+                "backend.api.features.store.hybrid_search.embed_query"
+            ) as mock_embed:
+                mock_embed.return_value = [0.1] * 1536
+
+                results, total = await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+                # Verify the mock was set up correctly
+                assert mock_schema.return_value == "platform"
+
+                assert results == []
+                assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_without_embeddings():
+    """Test hybrid search fails fast when embeddings are unavailable."""
+    # Patch where the function is used, not where it's defined
+    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
+        # Simulate embedding failure
+        mock_embed.return_value = None
+
+        # Should raise ValueError with helpful message
+        with pytest.raises(ValueError) as exc_info:
+            await hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+        # Verify error message is generic (doesn't leak implementation details)
+        assert "Search service temporarily unavailable" in str(exc_info.value)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_filters():
+    """Test hybrid search with various filters."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test with featured filter
+            results, total = await hybrid_search(
+                query="test",
+                featured=True,
+                creators=["user1", "user2"],
+                category="productivity",
+                page=1,
+                page_size=10,
+            )
+
+            # Verify filters were applied in the query
+            call_args = mock_query.call_args
+            params = call_args[0][1:]  # Skip SQL template
+
+            # Should have query, query_lower, creators array, category
+            assert len(params) >= 4
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_weights():
+    """Test hybrid search with custom weights."""
+    custom_weights = HybridSearchWeights(
+        semantic=0.5,
+        lexical=0.3,
+        category=0.1,
+        recency=0.1,
+        popularity=0.0,
+    )
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            results, total = await hybrid_search(
+                query="test",
+                weights=custom_weights,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify custom weights were used in the query
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]  # Get all parameters passed
+
+            # Check that SQL uses parameterized weights (not f-string interpolation)
+            assert "$" in sql_template  # Verify parameterization is used
+
+            # Check that custom weights are in the params
+            assert 0.5 in params  # semantic weight
+            assert 0.3 in params  # lexical weight
+            assert 0.1 in params  # category and recency weights
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_min_score_filtering():
+    """Test hybrid search minimum score threshold."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Return results with varying scores
+        mock_query.return_value = [
+            {
+                "slug": "high-score/agent",
+                "agent_name": "High Score Agent",
+                "combined_score": 0.8,
+                "total_count": 1,
+                # ... other fields
+            }
+        ]
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test with custom min_score
+            results, total = await hybrid_search(
+                query="test",
+                min_score=0.5,  # High threshold
+                page=1,
+                page_size=20,
+            )
+
+            # Verify min_score was applied in query
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]  # Get all parameters
+
+            # Check that SQL uses parameterized min_score
+            assert "combined_score >=" in sql_template
+            assert "$" in sql_template  # Verify parameterization
+
+            # Check that custom min_score is in the params
+            assert 0.5 in params
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_pagination():
+    """Test hybrid search pagination."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test page 2 with page_size 10
+            results, total = await hybrid_search(
+                query="test",
+                page=2,
+                page_size=10,
+            )
+
+            # Verify pagination parameters
+            call_args = mock_query.call_args
+            params = call_args[0]
+
+            # Last two params should be LIMIT and OFFSET
+            limit = params[-2]
+            offset = params[-1]
+
+            assert limit == 10  # page_size
+            assert offset == 10  # (page - 1) * page_size = (2 - 1) * 10
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_error_handling():
+    """Test hybrid search error handling."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Simulate database error
+        mock_query.side_effect = Exception("Database connection error")
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Should raise exception
+            with pytest.raises(Exception) as exc_info:
+                await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+            assert "Database connection error" in str(exc_info.value)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

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

@@ -221,99 +221,3 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     is_approved: bool
     comments: str  # External comments visible to creator
     internal_comments: str | None = None  # Private admin notes
-
-
-class StoreWaitlistEntry(pydantic.BaseModel):
-    """Public waitlist entry - no PII fields exposed."""
-
-    waitlistId: str
-    slug: str
-
-    # Content fields
-    name: str
-    subHeading: str
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    imageUrls: list[str]
-    description: str
-    categories: list[str]
-
-
-class StoreWaitlistsAllResponse(pydantic.BaseModel):
-    listings: list[StoreWaitlistEntry]
-
-
-# Admin Waitlist Models
-
-
-class WaitlistCreateRequest(pydantic.BaseModel):
-    """Request model for creating a new waitlist."""
-
-    name: str
-    slug: str
-    subHeading: str
-    description: str
-    categories: list[str] = []
-    imageUrls: list[str] = []
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-
-
-class WaitlistUpdateRequest(pydantic.BaseModel):
-    """Request model for updating a waitlist."""
-
-    name: str | None = None
-    slug: str | None = None
-    subHeading: str | None = None
-    description: str | None = None
-    categories: list[str] | None = None
-    imageUrls: list[str] | None = None
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    status: str | None = None  # WaitlistExternalStatus enum value
-    storeListingId: str | None = None  # Link to a store listing
-
-
-class WaitlistAdminResponse(pydantic.BaseModel):
-    """Admin response model with full waitlist details including internal data."""
-
-    id: str
-    createdAt: str
-    updatedAt: str
-    slug: str
-    name: str
-    subHeading: str
-    description: str
-    categories: list[str]
-    imageUrls: list[str]
-    videoUrl: str | None = None
-    agentOutputDemoUrl: str | None = None
-    status: prisma.enums.WaitlistExternalStatus
-    votes: int
-    signupCount: int  # Total count of joinedUsers + unaffiliatedEmailUsers
-    storeListingId: str | None = None
-    owningUserId: str
-
-
-class WaitlistSignup(pydantic.BaseModel):
-    """Individual signup entry for a waitlist."""
-
-    type: str  # "user" or "email"
-    userId: str | None = None
-    email: str | None = None
-    username: str | None = None  # For user signups
-
-
-class WaitlistSignupListResponse(pydantic.BaseModel):
-    """Response model for listing waitlist signups."""
-
-    waitlistId: str
-    signups: list[WaitlistSignup]
-    totalCount: int
-
-
-class WaitlistAdminListResponse(pydantic.BaseModel):
-    """Response model for listing all waitlists (admin view)."""
-
-    waitlists: list[WaitlistAdminResponse]
-    totalCount: int

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

@@ -7,7 +7,6 @@ from typing import Literal
 import autogpt_libs.auth
 import fastapi
 import fastapi.responses
-from autogpt_libs.auth.dependencies import get_optional_user_id
 
 import backend.data.graph
 import backend.util.json
@@ -79,63 +78,6 @@ async def update_or_create_profile(
     return updated_profile
 
 
-##############################################
-############## Waitlist Endpoints ############
-##############################################
-@router.get(
-    "/waitlist",
-    summary="Get the agent waitlist",
-    tags=["store", "public"],
-    response_model=store_model.StoreWaitlistsAllResponse,
-)
-async def get_waitlist():
-    """
-    Get all active waitlists for public display.
-    """
-    waitlists = await store_db.get_waitlist()
-    return store_model.StoreWaitlistsAllResponse(listings=waitlists)
-
-
-@router.get(
-    "/waitlist/my-memberships",
-    summary="Get waitlist IDs the current user has joined",
-    tags=["store", "private"],
-)
-async def get_my_waitlist_memberships(
-    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
-) -> list[str]:
-    """Returns list of waitlist IDs the authenticated user has joined."""
-    return await store_db.get_user_waitlist_memberships(user_id)
-
-
-@router.post(
-    path="/waitlist/{waitlist_id}/join",
-    summary="Add self to the agent waitlist",
-    tags=["store", "public"],
-    response_model=store_model.StoreWaitlistEntry,
-)
-async def add_self_to_waitlist(
-    user_id: str | None = fastapi.Security(get_optional_user_id),
-    waitlist_id: str = fastapi.Path(..., description="The ID of the waitlist to join"),
-    email: str | None = fastapi.Body(
-        default=None, embed=True, description="Email address for unauthenticated users"
-    ),
-):
-    """
-    Add the current user to the agent waitlist.
-    """
-    if not user_id and not email:
-        raise fastapi.HTTPException(
-            status_code=400,
-            detail="Either user authentication or email address is required",
-        )
-
-    waitlist_entry = await store_db.add_user_to_waitlist(
-        waitlist_id=waitlist_id, user_id=user_id, email=email
-    )
-    return waitlist_entry
-
-
 ##############################################
 ############### Agent Endpoints ##############
 ##############################################

autogpt_platform/backend/backend/api/rest_api.py

@@ -19,7 +19,6 @@ from prisma.errors import PrismaError
 import backend.api.features.admin.credit_admin_routes
 import backend.api.features.admin.execution_analytics_routes
 import backend.api.features.admin.store_admin_routes
-import backend.api.features.admin.waitlist_admin_routes
 import backend.api.features.builder
 import backend.api.features.builder.routes
 import backend.api.features.chat.routes as chat_routes
@@ -284,11 +283,6 @@ app.include_router(
     tags=["v2", "admin"],
     prefix="/api/store",
 )
-app.include_router(
-    backend.api.features.admin.waitlist_admin_routes.router,
-    tags=["v2", "admin"],
-    prefix="/api/store",
-)
 app.include_router(
     backend.api.features.admin.credit_admin_routes.router,
     tags=["v2", "admin"],

autogpt_platform/backend/backend/blocks/search.py

@@ -18,6 +18,7 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
+from backend.util.request import DEFAULT_USER_AGENT
 
 
 class GetWikipediaSummaryBlock(Block, GetRequest):
@@ -39,17 +40,27 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
             output_schema=GetWikipediaSummaryBlock.Output,
             test_input={"topic": "Artificial Intelligence"},
             test_output=("summary", "summary content"),
-            test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
+            test_mock={
+                "get_request": lambda url, headers, json: {"extract": "summary content"}
+            },
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.topic
-        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
+        # URL-encode the topic to handle spaces and special characters
+        encoded_topic = quote(topic, safe="")
+        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{encoded_topic}"
+
+        # Set headers per Wikimedia robot policy (https://w.wiki/4wJS)
+        # - User-Agent: Required, must identify the bot
+        # - Accept-Encoding: gzip recommended to reduce bandwidth
+        headers = {
+            "User-Agent": DEFAULT_USER_AGENT,
+            "Accept-Encoding": "gzip, deflate",
+        }
 
-        # Note: User-Agent is now automatically set by the request library
-        # to comply with Wikimedia's robot policy (https://w.wiki/4wJS)
         try:
-            response = await self.get_request(url, json=True)
+            response = await self.get_request(url, headers=headers, json=True)
             if "extract" not in response:
                 raise ValueError(f"Unable to parse Wikipedia response: {response}")
             yield "summary", response["extract"]

autogpt_platform/backend/backend/data/db.py

@@ -108,21 +108,84 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
-async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
-    """Execute raw SQL query with proper schema handling."""
+async def _raw_with_schema(
+    query_template: str,
+    *args,
+    execute: bool = False,
+    client: Prisma | None = None,
+) -> list[dict] | int:
+    """Internal: Execute raw SQL with proper schema handling.
+
+    Use query_raw_with_schema() or execute_raw_with_schema() instead.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+        execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
+        client: Optional Prisma client for transactions (only used when execute=True).
+
+    Returns:
+        - list[dict] if execute=False (query results)
+        - int if execute=True (number of affected rows)
+    """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
     formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
-    result = await prisma_module.get_client().query_raw(
-        formatted_query, *args  # type: ignore
-    )
+    db_client = client if client else prisma_module.get_client()
+
+    if execute:
+        result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
+    else:
+        result = await db_client.query_raw(formatted_query, *args)  # type: ignore
 
     return result
 
 
+async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
+    """Execute raw SQL SELECT query with proper schema handling.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+
+    Returns:
+        List of result rows as dictionaries
+
+    Example:
+        results = await query_raw_with_schema(
+            'SELECT * FROM {schema_prefix}"User" WHERE id = $1',
+            user_id
+        )
+    """
+    return await _raw_with_schema(query_template, *args, execute=False)  # type: ignore
+
+
+async def execute_raw_with_schema(
+    query_template: str, *args, client: Prisma | None = None
+) -> int:
+    """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+        client: Optional Prisma client for transactions
+
+    Returns:
+        Number of affected rows
+
+    Example:
+        await execute_raw_with_schema(
+            'INSERT INTO {schema_prefix}"User" (id, name) VALUES ($1, $2)',
+            user_id, name,
+            client=tx  # Optional transaction client
+        )
+    """
+    return await _raw_with_schema(query_template, *args, execute=True, client=client)  # type: ignore
+
+
 class BaseDbModel(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid4()))
 

autogpt_platform/backend/backend/data/graph_test.py

@@ -1,5 +1,6 @@
 import json
 from typing import Any
+from unittest.mock import AsyncMock, patch
 from uuid import UUID
 
 import fastapi.exceptions
@@ -18,6 +19,17 @@ from backend.usecases.sample import create_test_user
 from backend.util.test import SpinTestServer
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_graph_creation(server: SpinTestServer, snapshot: Snapshot):
     """

autogpt_platform/backend/backend/data/notifications.py

@@ -211,22 +211,6 @@ class AgentRejectionData(BaseNotificationData):
         return value
 
 
-class WaitlistLaunchData(BaseNotificationData):
-    """Notification data for when an agent from a waitlist is launched."""
-
-    agent_name: str
-    waitlist_name: str
-    store_url: str
-    launched_at: datetime
-
-    @field_validator("launched_at")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
 NotificationData = Annotated[
     Union[
         AgentRunData,
@@ -239,7 +223,6 @@ NotificationData = Annotated[
         DailySummaryData,
         RefundRequestData,
         BaseSummaryData,
-        WaitlistLaunchData,
     ],
     Field(discriminator="type"),
 ]
@@ -290,7 +273,6 @@ def get_notif_data_type(
         NotificationType.REFUND_PROCESSED: RefundRequestData,
         NotificationType.AGENT_APPROVED: AgentApprovalData,
         NotificationType.AGENT_REJECTED: AgentRejectionData,
-        NotificationType.WAITLIST_LAUNCH: WaitlistLaunchData,
     }[notification_type]
 
 
@@ -336,7 +318,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
             NotificationType.AGENT_APPROVED: QueueType.IMMEDIATE,
             NotificationType.AGENT_REJECTED: QueueType.IMMEDIATE,
-            NotificationType.WAITLIST_LAUNCH: QueueType.IMMEDIATE,
         }
         return BATCHING_RULES.get(self.notification_type, QueueType.IMMEDIATE)
 
@@ -356,7 +337,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "refund_processed.html",
             NotificationType.AGENT_APPROVED: "agent_approved.html",
             NotificationType.AGENT_REJECTED: "agent_rejected.html",
-            NotificationType.WAITLIST_LAUNCH: "waitlist_launch.html",
         }[self.notification_type]
 
     @property
@@ -374,7 +354,6 @@ class NotificationTypeOverride:
             NotificationType.REFUND_PROCESSED: "Refund for ${{data.amount / 100}} to {{data.user_name}} has been processed",
             NotificationType.AGENT_APPROVED: "🎉 Your agent '{{data.agent_name}}' has been approved!",
             NotificationType.AGENT_REJECTED: "Your agent '{{data.agent_name}}' needs some updates",
-            NotificationType.WAITLIST_LAUNCH: "🚀 {{data.agent_name}} is now available!",
         }[self.notification_type]
 
 

autogpt_platform/backend/backend/executor/manager_test.py

@@ -1,4 +1,5 @@
 import logging
+from unittest.mock import AsyncMock, patch
 
 import fastapi.responses
 import pytest
@@ -19,6 +20,17 @@ from backend.util.test import SpinTestServer, wait_execution
 logger = logging.getLogger(__name__)
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 async def create_graph(s: SpinTestServer, g: graph.Graph, u: User) -> graph.Graph:
     logger.info(f"Creating graph for user {u.id}")
     return await s.agent_server.test_create_graph(CreateGraph(graph=g), u.id)

autogpt_platform/backend/backend/executor/scheduler.py

@@ -23,6 +23,10 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy import MetaData, create_engine
 
+from backend.api.features.store.embeddings import (
+    backfill_missing_embeddings,
+    get_embedding_stats,
+)
 from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
@@ -254,6 +258,72 @@ def execution_accuracy_alerts():
     return report_execution_accuracy_alerts()
 
 
+def ensure_embeddings_coverage():
+    """
+    Ensure approved store agents have embeddings for hybrid search.
+
+    Processes ALL missing embeddings in batches of 10 until 100% coverage.
+    Missing embeddings = agents invisible in hybrid search.
+
+    Schedule: Runs every 6 hours (balanced between coverage and API costs).
+    - Catches agents approved between scheduled runs
+    - Batch size 10: gradual processing to avoid rate limits
+    - Manual trigger available via execute_ensure_embeddings_coverage endpoint
+    """
+
+    async def _ensure():
+        import asyncio
+
+        stats = await get_embedding_stats()
+
+        # Check for error from get_embedding_stats() first
+        if "error" in stats:
+            logger.error(
+                f"Failed to get embedding stats: {stats['error']} - skipping backfill"
+            )
+            return {"processed": 0, "success": 0, "failed": 0, "error": stats["error"]}
+
+        if stats["without_embeddings"] == 0:
+            logger.info("All approved agents have embeddings, skipping backfill")
+            return {"processed": 0, "success": 0, "failed": 0}
+
+        logger.info(
+            f"Found {stats['without_embeddings']} agents without embeddings "
+            f"({stats['coverage_percent']}% coverage) - processing all"
+        )
+
+        total_processed = 0
+        total_success = 0
+        total_failed = 0
+
+        # Process in batches until no more missing embeddings
+        while True:
+            result = await backfill_missing_embeddings(batch_size=10)
+
+            total_processed += result["processed"]
+            total_success += result["success"]
+            total_failed += result["failed"]
+
+            if result["processed"] == 0:
+                # No more missing embeddings
+                break
+
+            # Small delay between batches to avoid rate limits
+            await asyncio.sleep(1)
+
+        logger.info(
+            f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
+            f"{total_failed} failed"
+        )
+        return {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+        }
+
+    return run_async(_ensure())
+
+
 # Monitoring functions are now imported from monitoring module
 
 
@@ -475,6 +545,19 @@ class Scheduler(AppService):
                 jobstore=Jobstores.EXECUTION.value,
             )
 
+            # Embedding Coverage - Every 6 hours
+            # Ensures all approved agents have embeddings for hybrid search
+            # Critical: missing embeddings = agents invisible in search
+            self.scheduler.add_job(
+                ensure_embeddings_coverage,
+                id="ensure_embeddings_coverage",
+                trigger="interval",
+                hours=6,
+                replace_existing=True,
+                max_instances=1,  # Prevent overlapping runs
+                jobstore=Jobstores.EXECUTION.value,
+            )
+
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.add_listener(job_missed_listener, EVENT_JOB_MISSED)
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
@@ -632,6 +715,11 @@ class Scheduler(AppService):
         """Manually trigger execution accuracy alert checking."""
         return execution_accuracy_alerts()
 
+    @expose
+    def execute_ensure_embeddings_coverage(self):
+        """Manually trigger embedding backfill for approved store agents."""
+        return ensure_embeddings_coverage()
+
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/notifications/templates/waitlist_launch.html.jinja2

@@ -1,59 +0,0 @@
-{# Waitlist Launch Notification Email Template #}
-{#
-  Template variables:
-  data.agent_name: the name of the launched agent
-  data.waitlist_name: the name of the waitlist the user joined
-  data.store_url: URL to view the agent in the store
-  data.launched_at: when the agent was launched
-
-  Subject: {{ data.agent_name }} is now available!
-#}
-
-{% block content %}
-<h1 style="color: #7c3aed; font-size: 32px; font-weight: 700; margin: 0 0 24px 0; text-align: center;">
-  The wait is over!
-</h1>
-
-<p style="color: #586069; font-size: 18px; text-align: center; margin: 0 0 24px 0;">
-  <strong>'{{ data.agent_name }}'</strong> is now live in the AutoGPT Store!
-</p>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #f3e8ff; border: 1px solid #d8b4fe; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #6b21a8; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    You're one of the first to know!
-  </h3>
-  <p style="color: #6b21a8; margin: 0; font-size: 16px; line-height: 1.5;">
-    You signed up for the <strong>{{ data.waitlist_name }}</strong> waitlist, and we're excited to let you know that this agent is now ready for you to use.
-  </p>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="text-align: center; margin: 24px 0;">
-  <a href="{{ data.store_url }}" style="display: inline-block; background: linear-gradient(135deg, #7c3aed 0%, #5b21b6 100%); color: white; text-decoration: none; padding: 14px 28px; border-radius: 6px; font-weight: 600; font-size: 16px;">
-    Get {{ data.agent_name }} Now
-  </a>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #d1ecf1; border: 1px solid #bee5eb; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #0c5460; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    What can you do now?
-  </h3>
-  <ul style="color: #0c5460; margin: 0; padding-left: 18px; font-size: 16px; line-height: 1.6;">
-    <li>Visit the store to learn more about what this agent can do</li>
-    <li>Install and start using the agent right away</li>
-    <li>Share it with others who might find it useful</li>
-  </ul>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<p style="color: #6a737d; font-size: 14px; text-align: center; margin: 24px 0;">
-  Thank you for helping us prioritize what to build! Your interest made this happen.
-</p>
-
-{% endblock %}

autogpt_platform/backend/backend/util/clients.py

@@ -10,6 +10,7 @@ from backend.util.settings import Settings
 settings = Settings()
 
 if TYPE_CHECKING:
+    from openai import AsyncOpenAI
     from supabase import AClient, Client
 
     from backend.data.execution import (
@@ -139,6 +140,24 @@ async def get_async_supabase() -> "AClient":
     )
 
 
+# ============ OpenAI Client ============ #
+
+
+@cached(ttl_seconds=3600)
+def get_openai_client() -> "AsyncOpenAI | None":
+    """
+    Get a process-cached async OpenAI client for embeddings.
+
+    Returns None if API key is not configured.
+    """
+    from openai import AsyncOpenAI
+
+    api_key = settings.secrets.openai_internal_api_key
+    if not api_key:
+        return None
+    return AsyncOpenAI(api_key=api_key)
+
+
 # ============ Notification Queue Helpers ============ #
 
 

autogpt_platform/backend/migrations/20260108032037_add_waitlist_launch_notification/migration.sql

@@ -1,53 +0,0 @@
--- CreateEnum
-CREATE TYPE "WaitlistExternalStatus" AS ENUM ('DONE', 'NOT_STARTED', 'CANCELED', 'WORK_IN_PROGRESS');
-
--- AlterEnum
-ALTER TYPE "NotificationType" ADD VALUE 'WAITLIST_LAUNCH';
-
--- CreateTable
-CREATE TABLE "WaitlistEntry" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "storeListingId" TEXT,
-    "owningUserId" TEXT NOT NULL,
-    "slug" TEXT NOT NULL,
-    "search" tsvector DEFAULT ''::tsvector,
-    "name" TEXT NOT NULL,
-    "subHeading" TEXT NOT NULL,
-    "videoUrl" TEXT,
-    "agentOutputDemoUrl" TEXT,
-    "imageUrls" TEXT[],
-    "description" TEXT NOT NULL,
-    "categories" TEXT[],
-    "status" "WaitlistExternalStatus" NOT NULL DEFAULT 'NOT_STARTED',
-    "votes" INTEGER NOT NULL DEFAULT 0,
-    "unaffiliatedEmailUsers" TEXT[] DEFAULT ARRAY[]::TEXT[],
-    "isDeleted" BOOLEAN NOT NULL DEFAULT false,
-
-    CONSTRAINT "WaitlistEntry_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "_joinedWaitlists" (
-    "A" TEXT NOT NULL,
-    "B" TEXT NOT NULL
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "_joinedWaitlists_AB_unique" ON "_joinedWaitlists"("A", "B");
-
--- CreateIndex
-CREATE INDEX "_joinedWaitlists_B_index" ON "_joinedWaitlists"("B");
-
--- AddForeignKey
-ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_storeListingId_fkey" FOREIGN KEY ("storeListingId") REFERENCES "StoreListing"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "WaitlistEntry" ADD CONSTRAINT "WaitlistEntry_owningUserId_fkey" FOREIGN KEY ("owningUserId") REFERENCES "User"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_A_fkey" FOREIGN KEY ("A") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "_joinedWaitlists" ADD CONSTRAINT "_joinedWaitlists_B_fkey" FOREIGN KEY ("B") REFERENCES "WaitlistEntry"("id") ON DELETE CASCADE ON UPDATE CASCADE;

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

@@ -0,0 +1,42 @@
+-- CreateExtension
+-- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
+-- This migration only verifies the extension exists and is accessible
+-- The vector type is available across all schemas once enabled
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- CreateEnum
+CREATE TYPE "ContentType" AS ENUM ('STORE_AGENT', 'BLOCK', 'INTEGRATION', 'DOCUMENTATION', 'LIBRARY_AGENT');
+
+-- CreateTable
+CREATE TABLE "UnifiedContentEmbedding" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "contentType" "ContentType" NOT NULL,
+    "contentId" TEXT NOT NULL,
+    "userId" TEXT,
+    "embedding" vector(1536) NOT NULL,
+    "searchableText" TEXT NOT NULL,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "UnifiedContentEmbedding_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_idx" ON "UnifiedContentEmbedding"("contentType");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_userId_idx" ON "UnifiedContentEmbedding"("userId");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_userId_idx" ON "UnifiedContentEmbedding"("contentType", "userId");
+
+-- CreateIndex
+-- NULLS NOT DISTINCT ensures only one public (NULL userId) embedding per contentType+contentId
+-- Requires PostgreSQL 15+. Supabase uses PostgreSQL 15+.
+CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" ON "UnifiedContentEmbedding"("contentType", "contentId", "userId") NULLS NOT DISTINCT;
+
+-- CreateIndex
+-- HNSW index for fast vector similarity search on embeddings
+-- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" vector_cosine_ops);

autogpt_platform/backend/migrations/20260112173500_add_supabase_extensions_to_platform_schema/migration.sql

@@ -0,0 +1,71 @@
+-- Acknowledge Supabase-managed extensions to prevent drift warnings
+-- These extensions are pre-installed by Supabase in specific schemas
+-- This migration ensures they exist where available (Supabase) or skips gracefully (CI)
+
+-- Create schemas (safe in both CI and Supabase)
+CREATE SCHEMA IF NOT EXISTS "extensions";
+
+-- Extensions that exist in both CI and Supabase
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgcrypto" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgcrypto extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "uuid-ossp" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'uuid-ossp extension not available, skipping';
+END $$;
+
+-- Supabase-specific extensions (skip gracefully in CI)
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_stat_statements" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_stat_statements extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_net" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_net extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgjwt" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgjwt extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "graphql";
+    CREATE EXTENSION IF NOT EXISTS "pg_graphql" WITH SCHEMA "graphql";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_graphql extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "pgsodium";
+    CREATE EXTENSION IF NOT EXISTS "pgsodium" WITH SCHEMA "pgsodium";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgsodium extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "vault";
+    CREATE EXTENSION IF NOT EXISTS "supabase_vault" WITH SCHEMA "vault";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'supabase_vault extension not available, skipping';
+END $$;
+
+
+-- Return to platform
+CREATE SCHEMA IF NOT EXISTS "platform";

autogpt_platform/backend/schema.prisma

@@ -1,14 +1,15 @@
 datasource db {
-  provider  = "postgresql"
-  url       = env("DATABASE_URL")
-  directUrl = env("DIRECT_URL")
+  provider   = "postgresql"
+  url        = env("DATABASE_URL")
+  directUrl  = env("DIRECT_URL")
+  extensions = [pgvector(map: "vector")]
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch"]
+  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -67,10 +68,6 @@ model User {
   OAuthAuthorizationCodes OAuthAuthorizationCode[]
   OAuthAccessTokens       OAuthAccessToken[]
   OAuthRefreshTokens      OAuthRefreshToken[]
-
-  // Waitlist relations
-  waitlistEntries       WaitlistEntry[]
-  joinedWaitlists       WaitlistEntry[] @relation("joinedWaitlists")
 }
 
 enum OnboardingStep {
@@ -131,8 +128,8 @@ model BuilderSearchHistory {
   updatedAt DateTime @default(now()) @updatedAt
 
   searchQuery String
-  filter      String[]  @default([])
-  byCreator   String[]  @default([])
+  filter      String[] @default([])
+  byCreator   String[] @default([])
 
   userId String
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
@@ -232,7 +229,6 @@ enum NotificationType {
   REFUND_PROCESSED
   AGENT_APPROVED
   AGENT_REJECTED
-  WAITLIST_LAUNCH
 }
 
 model NotificationEvent {
@@ -726,26 +722,25 @@ view StoreAgent {
   storeListingVersionId String
   updated_at            DateTime
 
-  slug                String
-  agent_name          String
-  agent_video         String?
-  agent_output_demo   String?
-  agent_image         String[]
+  slug              String
+  agent_name        String
+  agent_video       String?
+  agent_output_demo String?
+  agent_image       String[]
 
-  featured         Boolean                  @default(false)
-  creator_username String?
-  creator_avatar   String?
-  sub_heading      String
-  description      String
-  categories       String[]
-  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
-  runs                Int
-  rating              Float
-  versions            String[]
-  agentGraphVersions  String[]
-  agentGraphId        String
-  is_available        Boolean                  @default(true)
-  useForOnboarding    Boolean                  @default(false)
+  featured           Boolean  @default(false)
+  creator_username   String?
+  creator_avatar     String?
+  sub_heading        String
+  description        String
+  categories         String[]
+  runs               Int
+  rating             Float
+  versions           String[]
+  agentGraphVersions String[]
+  agentGraphId       String
+  is_available       Boolean  @default(true)
+  useForOnboarding   Boolean  @default(false)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId
@@ -839,8 +834,7 @@ model StoreListing {
   OwningUser   User   @relation(fields: [owningUserId], references: [id])
 
   // Relations
-  Versions        StoreListingVersion[] @relation("ListingVersions")
-  waitlistEntries WaitlistEntry[]
+  Versions StoreListingVersion[] @relation("ListingVersions")
 
   // Unique index on agentId to ensure only one listing per agent, regardless of number of versions the agent has.
   @@unique([agentGraphId])
@@ -862,14 +856,14 @@ model StoreListingVersion {
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
 
   // Content fields
-  name                String
-  subHeading          String
-  videoUrl            String?
-  agentOutputDemoUrl  String?
-  imageUrls           String[]
-  description         String
-  instructions        String?
-  categories          String[]
+  name               String
+  subHeading         String
+  videoUrl           String?
+  agentOutputDemoUrl String?
+  imageUrls          String[]
+  description        String
+  instructions       String?
+  categories         String[]
 
   isFeatured Boolean @default(false)
 
@@ -905,6 +899,9 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
+  // Note: Embeddings now stored in UnifiedContentEmbedding table
+  // Use contentType=STORE_AGENT and contentId=storeListingVersionId
+
   @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
@@ -912,6 +909,42 @@ model StoreListingVersion {
   @@index([agentGraphId, agentGraphVersion]) // Non-unique index for efficient lookups
 }
 
+// Content type enum for unified search across store agents, blocks, docs
+// Note: BLOCK/INTEGRATION are file-based (Python classes), not DB records
+// DOCUMENTATION are file-based (.md files), not DB records  
+// Only STORE_AGENT and LIBRARY_AGENT are stored in database
+enum ContentType {
+  STORE_AGENT // Database: StoreListingVersion
+  BLOCK // File-based: Python classes in /backend/blocks/
+  INTEGRATION // File-based: Python classes (blocks with credentials)
+  DOCUMENTATION // File-based: .md/.mdx files
+  LIBRARY_AGENT // Database: User's personal agents
+}
+
+// Unified embeddings table for all searchable content types
+// Supports both public content (userId=null) and user-specific content (userId=userID)
+model UnifiedContentEmbedding {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  // Content identification
+  contentType ContentType
+  contentId   String // DB ID (storeListingVersionId) or file identifier (block.id, file_path)
+  userId      String? // NULL for public content (store, blocks, docs), userId for private content (library agents)
+
+  // Search data
+  embedding      Unsupported("vector(1536)") // pgvector embedding (extension in platform schema)
+  searchableText String // Combined text for search and fallback
+  metadata       Json                        @default("{}") // Content-specific metadata
+
+  @@unique([contentType, contentId, userId], map: "UnifiedContentEmbedding_contentType_contentId_userId_key")
+  @@index([contentType])
+  @@index([userId])
+  @@index([contentType, userId])
+  @@index([embedding], map: "UnifiedContentEmbedding_embedding_idx")
+}
+
 model StoreListingReview {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
@@ -930,47 +963,6 @@ model StoreListingReview {
   @@index([reviewByUserId])
 }
 
-enum WaitlistExternalStatus {
-  DONE
-  NOT_STARTED
-  CANCELED
-  WORK_IN_PROGRESS
-}
-
-model WaitlistEntry {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  storeListingId String?
-  StoreListing   StoreListing? @relation(fields: [storeListingId], references: [id], onDelete: SetNull)
-
-  owningUserId String
-  OwningUser   User   @relation(fields: [owningUserId], references: [id])
-
-  slug   String
-  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
-
-  // Content fields
-  name               String
-  subHeading         String
-  videoUrl           String?
-  agentOutputDemoUrl String?
-  imageUrls          String[]
-  description        String
-  categories         String[]
-
-  //Waitlist specific fields
-  status                 WaitlistExternalStatus @default(NOT_STARTED)
-  votes                  Int                    @default(0) // Hide from frontend api
-  joinedUsers            User[]                 @relation("joinedWaitlists")
-  // NOTE: DO NOT DOUBLE SEND TO THESE USERS, IF THEY HAVE SIGNED UP SINCE THEY MAY HAVE ALREADY RECEIVED AN EMAIL
-  // DOUBLE CHECK WHEN SENDING THAT THEY ARE NOT IN THE JOINED USERS LIST ALSO
-  unaffiliatedEmailUsers String[]               @default([])
-
-  isDeleted Boolean @default(false)
-}
-
 enum SubmissionStatus {
   DRAFT // Being prepared, not yet submitted
   PENDING // Submitted, awaiting review
@@ -1045,16 +1037,16 @@ model OAuthApplication {
   updatedAt DateTime @updatedAt
 
   // Application metadata
-  name               String
-  description        String?
-  logoUrl            String? // URL to app logo stored in GCS
-  clientId           String @unique
-  clientSecret       String // Hashed with Scrypt (same as API keys)
-  clientSecretSalt   String // Salt for Scrypt hashing
+  name             String
+  description      String?
+  logoUrl          String? // URL to app logo stored in GCS
+  clientId         String  @unique
+  clientSecret     String // Hashed with Scrypt (same as API keys)
+  clientSecretSalt String // Salt for Scrypt hashing
 
   // OAuth configuration
   redirectUris String[] // Allowed callback URLs
-  grantTypes   String[] @default(["authorization_code", "refresh_token"])
+  grantTypes   String[]           @default(["authorization_code", "refresh_token"])
   scopes       APIKeyPermission[] // Which permissions the app can request
 
   // Application management

autogpt_platform/frontend/src/app/(platform)/admin/layout.tsx

@@ -1,5 +1,5 @@
 import { Sidebar } from "@/components/__legacy__/Sidebar";
-import { Users, DollarSign, UserSearch, FileText, Clock } from "lucide-react";
+import { Users, DollarSign, UserSearch, FileText } from "lucide-react";
 
 import { IconSliders } from "@/components/__legacy__/ui/icons";
 
@@ -11,11 +11,6 @@ const sidebarLinkGroups = [
         href: "/admin/marketplace",
         icon: <Users className="h-6 w-6" />,
       },
-      {
-        text: "Waitlist Management",
-        href: "/admin/waitlist",
-        icon: <Clock className="h-6 w-6" />,
-      },
       {
         text: "User Spending",
         href: "/admin/spending",

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/CreateWaitlistButton.tsx

@@ -1,217 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useQueryClient } from "@tanstack/react-query";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import {
-  usePostV2CreateWaitlist,
-  getGetV2ListAllWaitlistsQueryKey,
-} from "@/app/api/__generated__/endpoints/admin/admin";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { Plus } from "@phosphor-icons/react";
-
-export function CreateWaitlistButton() {
-  const [open, setOpen] = useState(false);
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-
-  const createWaitlistMutation = usePostV2CreateWaitlist({
-    mutation: {
-      onSuccess: (response) => {
-        if (response.status === 200) {
-          toast({
-            title: "Success",
-            description: "Waitlist created successfully",
-          });
-          setOpen(false);
-          setFormData({
-            name: "",
-            slug: "",
-            subHeading: "",
-            description: "",
-            categories: "",
-            imageUrls: "",
-            videoUrl: "",
-            agentOutputDemoUrl: "",
-          });
-          queryClient.invalidateQueries({
-            queryKey: getGetV2ListAllWaitlistsQueryKey(),
-          });
-        } else {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to create waitlist",
-          });
-        }
-      },
-      onError: (error) => {
-        console.error("Error creating waitlist:", error);
-        toast({
-          variant: "destructive",
-          title: "Error",
-          description: "Failed to create waitlist",
-        });
-      },
-    },
-  });
-
-  const [formData, setFormData] = useState({
-    name: "",
-    slug: "",
-    subHeading: "",
-    description: "",
-    categories: "",
-    imageUrls: "",
-    videoUrl: "",
-    agentOutputDemoUrl: "",
-  });
-
-  function handleInputChange(id: string, value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      [id]: value,
-    }));
-  }
-
-  function generateSlug(name: string) {
-    return name
-      .toLowerCase()
-      .replace(/[^a-z0-9]+/g, "-")
-      .replace(/^-|-$/g, "");
-  }
-
-  function handleSubmit(e: React.FormEvent) {
-    e.preventDefault();
-
-    createWaitlistMutation.mutate({
-      data: {
-        name: formData.name,
-        slug: formData.slug || generateSlug(formData.name),
-        subHeading: formData.subHeading,
-        description: formData.description,
-        categories: formData.categories
-          ? formData.categories.split(",").map((c) => c.trim())
-          : [],
-        imageUrls: formData.imageUrls
-          ? formData.imageUrls.split(",").map((u) => u.trim())
-          : [],
-        videoUrl: formData.videoUrl || null,
-        agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
-      },
-    });
-  }
-
-  return (
-    <>
-      <Button onClick={() => setOpen(true)}>
-        <Plus size={16} className="mr-2" />
-        Create Waitlist
-      </Button>
-
-      <Dialog
-        title="Create New Waitlist"
-        controlled={{
-          isOpen: open,
-          set: async (isOpen) => setOpen(isOpen),
-        }}
-        onClose={() => setOpen(false)}
-        styling={{ maxWidth: "600px" }}
-      >
-        <Dialog.Content>
-          <p className="mb-4 text-sm text-zinc-500">
-            Create a new waitlist for an upcoming agent. Users can sign up to be
-            notified when it launches.
-          </p>
-          <form onSubmit={handleSubmit} className="flex flex-col gap-2">
-            <Input
-              id="name"
-              label="Name"
-              value={formData.name}
-              onChange={(e) => handleInputChange("name", e.target.value)}
-              placeholder="SEO Analysis Agent"
-              required
-            />
-
-            <Input
-              id="slug"
-              label="Slug"
-              value={formData.slug}
-              onChange={(e) => handleInputChange("slug", e.target.value)}
-              placeholder="seo-analysis-agent (auto-generated if empty)"
-            />
-
-            <Input
-              id="subHeading"
-              label="Subheading"
-              value={formData.subHeading}
-              onChange={(e) => handleInputChange("subHeading", e.target.value)}
-              placeholder="Analyze your website's SEO in minutes"
-              required
-            />
-
-            <Input
-              id="description"
-              label="Description"
-              type="textarea"
-              value={formData.description}
-              onChange={(e) => handleInputChange("description", e.target.value)}
-              placeholder="Detailed description of what this agent does..."
-              rows={4}
-              required
-            />
-
-            <Input
-              id="categories"
-              label="Categories (comma-separated)"
-              value={formData.categories}
-              onChange={(e) => handleInputChange("categories", e.target.value)}
-              placeholder="SEO, Marketing, Analysis"
-            />
-
-            <Input
-              id="imageUrls"
-              label="Image URLs (comma-separated)"
-              value={formData.imageUrls}
-              onChange={(e) => handleInputChange("imageUrls", e.target.value)}
-              placeholder="https://example.com/image1.jpg, https://example.com/image2.jpg"
-            />
-
-            <Input
-              id="videoUrl"
-              label="Video URL (optional)"
-              value={formData.videoUrl}
-              onChange={(e) => handleInputChange("videoUrl", e.target.value)}
-              placeholder="https://youtube.com/watch?v=..."
-            />
-
-            <Input
-              id="agentOutputDemoUrl"
-              label="Output Demo URL (optional)"
-              value={formData.agentOutputDemoUrl}
-              onChange={(e) =>
-                handleInputChange("agentOutputDemoUrl", e.target.value)
-              }
-              placeholder="https://example.com/demo-output.mp4"
-            />
-
-            <Dialog.Footer>
-              <Button
-                type="button"
-                variant="secondary"
-                onClick={() => setOpen(false)}
-              >
-                Cancel
-              </Button>
-              <Button type="submit" loading={createWaitlistMutation.isPending}>
-                Create Waitlist
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </Dialog.Content>
-      </Dialog>
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/EditWaitlistDialog.tsx

@@ -1,221 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Select } from "@/components/atoms/Select/Select";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { usePutV2UpdateWaitlist } from "@/app/api/__generated__/endpoints/admin/admin";
-import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
-import type { WaitlistUpdateRequest } from "@/app/api/__generated__/models/waitlistUpdateRequest";
-import { WaitlistExternalStatus } from "@/app/api/__generated__/models/waitlistExternalStatus";
-
-type EditWaitlistDialogProps = {
-  waitlist: WaitlistAdminResponse;
-  onClose: () => void;
-  onSave: () => void;
-};
-
-const STATUS_OPTIONS = [
-  { value: WaitlistExternalStatus.NOT_STARTED, label: "Not Started" },
-  { value: WaitlistExternalStatus.WORK_IN_PROGRESS, label: "Work In Progress" },
-  { value: WaitlistExternalStatus.DONE, label: "Done" },
-  { value: WaitlistExternalStatus.CANCELED, label: "Canceled" },
-];
-
-export function EditWaitlistDialog({
-  waitlist,
-  onClose,
-  onSave,
-}: EditWaitlistDialogProps) {
-  const { toast } = useToast();
-  const updateWaitlistMutation = usePutV2UpdateWaitlist();
-
-  const [formData, setFormData] = useState({
-    name: waitlist.name,
-    slug: waitlist.slug,
-    subHeading: waitlist.subHeading,
-    description: waitlist.description,
-    categories: waitlist.categories.join(", "),
-    imageUrls: waitlist.imageUrls.join(", "),
-    videoUrl: waitlist.videoUrl || "",
-    agentOutputDemoUrl: waitlist.agentOutputDemoUrl || "",
-    status: waitlist.status,
-    storeListingId: waitlist.storeListingId || "",
-  });
-
-  function handleInputChange(id: string, value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      [id]: value,
-    }));
-  }
-
-  function handleStatusChange(value: string) {
-    setFormData((prev) => ({
-      ...prev,
-      status: value as WaitlistExternalStatus,
-    }));
-  }
-
-  async function handleSubmit(e: React.FormEvent) {
-    e.preventDefault();
-
-    const updateData: WaitlistUpdateRequest = {
-      name: formData.name,
-      slug: formData.slug,
-      subHeading: formData.subHeading,
-      description: formData.description,
-      categories: formData.categories
-        ? formData.categories.split(",").map((c) => c.trim())
-        : [],
-      imageUrls: formData.imageUrls
-        ? formData.imageUrls.split(",").map((u) => u.trim())
-        : [],
-      videoUrl: formData.videoUrl || null,
-      agentOutputDemoUrl: formData.agentOutputDemoUrl || null,
-      status: formData.status,
-      storeListingId: formData.storeListingId || null,
-    };
-
-    updateWaitlistMutation.mutate(
-      { waitlistId: waitlist.id, data: updateData },
-      {
-        onSuccess: (response) => {
-          if (response.status === 200) {
-            toast({
-              title: "Success",
-              description: "Waitlist updated successfully",
-            });
-            onSave();
-          } else {
-            toast({
-              variant: "destructive",
-              title: "Error",
-              description: "Failed to update waitlist",
-            });
-          }
-        },
-        onError: () => {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to update waitlist",
-          });
-        },
-      },
-    );
-  }
-
-  return (
-    <Dialog
-      title="Edit Waitlist"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "600px" }}
-    >
-      <Dialog.Content>
-        <p className="mb-4 text-sm text-zinc-500">
-          Update the waitlist details. Changes will be reflected immediately.
-        </p>
-        <form onSubmit={handleSubmit} className="flex flex-col gap-2">
-          <Input
-            id="name"
-            label="Name"
-            value={formData.name}
-            onChange={(e) => handleInputChange("name", e.target.value)}
-            required
-          />
-
-          <Input
-            id="slug"
-            label="Slug"
-            value={formData.slug}
-            onChange={(e) => handleInputChange("slug", e.target.value)}
-          />
-
-          <Input
-            id="subHeading"
-            label="Subheading"
-            value={formData.subHeading}
-            onChange={(e) => handleInputChange("subHeading", e.target.value)}
-            required
-          />
-
-          <Input
-            id="description"
-            label="Description"
-            type="textarea"
-            value={formData.description}
-            onChange={(e) => handleInputChange("description", e.target.value)}
-            rows={4}
-            required
-          />
-
-          <Select
-            id="status"
-            label="Status"
-            value={formData.status}
-            onValueChange={handleStatusChange}
-            options={STATUS_OPTIONS}
-          />
-
-          <Input
-            id="categories"
-            label="Categories (comma-separated)"
-            value={formData.categories}
-            onChange={(e) => handleInputChange("categories", e.target.value)}
-          />
-
-          <Input
-            id="imageUrls"
-            label="Image URLs (comma-separated)"
-            value={formData.imageUrls}
-            onChange={(e) => handleInputChange("imageUrls", e.target.value)}
-          />
-
-          <Input
-            id="videoUrl"
-            label="Video URL"
-            value={formData.videoUrl}
-            onChange={(e) => handleInputChange("videoUrl", e.target.value)}
-          />
-
-          <Input
-            id="agentOutputDemoUrl"
-            label="Output Demo URL"
-            value={formData.agentOutputDemoUrl}
-            onChange={(e) =>
-              handleInputChange("agentOutputDemoUrl", e.target.value)
-            }
-          />
-
-          <Input
-            id="storeListingId"
-            label="Store Listing ID (for linking)"
-            value={formData.storeListingId}
-            onChange={(e) =>
-              handleInputChange("storeListingId", e.target.value)
-            }
-            placeholder="Leave empty if not linked"
-          />
-
-          <Dialog.Footer>
-            <Button type="button" variant="secondary" onClick={onClose}>
-              Cancel
-            </Button>
-            <Button type="submit" loading={updateWaitlistMutation.isPending}>
-              Save Changes
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistSignupsDialog.tsx

@@ -1,156 +0,0 @@
-"use client";
-
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { User, Envelope, DownloadSimple } from "@phosphor-icons/react";
-import { useGetV2GetWaitlistSignups } from "@/app/api/__generated__/endpoints/admin/admin";
-
-type WaitlistSignupsDialogProps = {
-  waitlistId: string;
-  onClose: () => void;
-};
-
-export function WaitlistSignupsDialog({
-  waitlistId,
-  onClose,
-}: WaitlistSignupsDialogProps) {
-  const {
-    data: signupsResponse,
-    isLoading,
-    isError,
-  } = useGetV2GetWaitlistSignups(waitlistId);
-
-  const signups = signupsResponse?.status === 200 ? signupsResponse.data : null;
-
-  function exportToCSV() {
-    if (!signups) return;
-
-    const headers = ["Type", "Email", "User ID", "Username"];
-    const rows = signups.signups.map((signup) => [
-      signup.type,
-      signup.email || "",
-      signup.userId || "",
-      signup.username || "",
-    ]);
-
-    const escapeCell = (cell: string) => `"${cell.replace(/"/g, '""')}"`;
-
-    const csvContent = [
-      headers.join(","),
-      ...rows.map((row) => row.map(escapeCell).join(",")),
-    ].join("\n");
-
-    const blob = new Blob([csvContent], { type: "text/csv" });
-    const url = window.URL.createObjectURL(blob);
-    const a = document.createElement("a");
-    a.href = url;
-    a.download = `waitlist-${waitlistId}-signups.csv`;
-    a.click();
-    window.URL.revokeObjectURL(url);
-  }
-
-  function renderContent() {
-    if (isLoading) {
-      return <div className="py-10 text-center">Loading signups...</div>;
-    }
-
-    if (isError) {
-      return (
-        <div className="py-10 text-center text-red-500">
-          Failed to load signups. Please try again.
-        </div>
-      );
-    }
-
-    if (!signups || signups.signups.length === 0) {
-      return (
-        <div className="py-10 text-center text-gray-500">
-          No signups yet for this waitlist.
-        </div>
-      );
-    }
-
-    return (
-      <>
-        <div className="flex justify-end">
-          <Button variant="secondary" size="small" onClick={exportToCSV}>
-            <DownloadSimple className="mr-2 h-4 w-4" size={16} />
-            Export CSV
-          </Button>
-        </div>
-        <div className="max-h-[400px] overflow-y-auto rounded-md border">
-          <table className="w-full">
-            <thead className="bg-gray-50 dark:bg-gray-800">
-              <tr>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  Type
-                </th>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  Email / Username
-                </th>
-                <th className="px-4 py-3 text-left text-sm font-medium">
-                  User ID
-                </th>
-              </tr>
-            </thead>
-            <tbody className="divide-y">
-              {signups.signups.map((signup, index) => (
-                <tr key={index}>
-                  <td className="px-4 py-3">
-                    {signup.type === "user" ? (
-                      <span className="flex items-center gap-1 text-blue-600">
-                        <User className="h-4 w-4" size={16} /> User
-                      </span>
-                    ) : (
-                      <span className="flex items-center gap-1 text-gray-600">
-                        <Envelope className="h-4 w-4" size={16} /> Email
-                      </span>
-                    )}
-                  </td>
-                  <td className="px-4 py-3">
-                    {signup.type === "user"
-                      ? signup.username || signup.email
-                      : signup.email}
-                  </td>
-                  <td className="px-4 py-3 font-mono text-sm">
-                    {signup.userId || "-"}
-                  </td>
-                </tr>
-              ))}
-            </tbody>
-          </table>
-        </div>
-      </>
-    );
-  }
-
-  return (
-    <Dialog
-      title="Waitlist Signups"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "700px" }}
-    >
-      <Dialog.Content>
-        <p className="mb-4 text-sm text-zinc-500">
-          {signups
-            ? `${signups.totalCount} total signups`
-            : "Loading signups..."}
-        </p>
-
-        {renderContent()}
-
-        <Dialog.Footer>
-          <Button variant="secondary" onClick={onClose}>
-            Close
-          </Button>
-        </Dialog.Footer>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/components/WaitlistTable.tsx

@@ -1,206 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useQueryClient } from "@tanstack/react-query";
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/__legacy__/ui/table";
-import { Button } from "@/components/atoms/Button/Button";
-import {
-  useGetV2ListAllWaitlists,
-  useDeleteV2DeleteWaitlist,
-  getGetV2ListAllWaitlistsQueryKey,
-} from "@/app/api/__generated__/endpoints/admin/admin";
-import type { WaitlistAdminResponse } from "@/app/api/__generated__/models/waitlistAdminResponse";
-import { EditWaitlistDialog } from "./EditWaitlistDialog";
-import { WaitlistSignupsDialog } from "./WaitlistSignupsDialog";
-import { Trash, PencilSimple, Users, Link } from "@phosphor-icons/react";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-
-export function WaitlistTable() {
-  const [editingWaitlist, setEditingWaitlist] =
-    useState<WaitlistAdminResponse | null>(null);
-  const [viewingSignups, setViewingSignups] = useState<string | null>(null);
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-
-  const { data: response, isLoading, error } = useGetV2ListAllWaitlists();
-
-  const deleteWaitlistMutation = useDeleteV2DeleteWaitlist({
-    mutation: {
-      onSuccess: () => {
-        toast({
-          title: "Success",
-          description: "Waitlist deleted successfully",
-        });
-        queryClient.invalidateQueries({
-          queryKey: getGetV2ListAllWaitlistsQueryKey(),
-        });
-      },
-      onError: (error) => {
-        console.error("Error deleting waitlist:", error);
-        toast({
-          variant: "destructive",
-          title: "Error",
-          description: "Failed to delete waitlist",
-        });
-      },
-    },
-  });
-
-  function handleDelete(waitlistId: string) {
-    if (!confirm("Are you sure you want to delete this waitlist?")) return;
-    deleteWaitlistMutation.mutate({ waitlistId });
-  }
-
-  function handleWaitlistSaved() {
-    setEditingWaitlist(null);
-    queryClient.invalidateQueries({
-      queryKey: getGetV2ListAllWaitlistsQueryKey(),
-    });
-  }
-
-  function formatStatus(status: string) {
-    const statusColors: Record<string, string> = {
-      NOT_STARTED: "bg-gray-100 text-gray-800",
-      WORK_IN_PROGRESS: "bg-blue-100 text-blue-800",
-      DONE: "bg-green-100 text-green-800",
-      CANCELED: "bg-red-100 text-red-800",
-    };
-
-    return (
-      <span
-        className={`rounded-full px-2 py-1 text-xs font-medium ${statusColors[status] || "bg-gray-100 text-gray-700"}`}
-      >
-        {status.replace(/_/g, " ")}
-      </span>
-    );
-  }
-
-  function formatDate(dateStr: string) {
-    if (!dateStr) return "-";
-    return new Intl.DateTimeFormat("en-US", {
-      month: "short",
-      day: "numeric",
-      year: "numeric",
-    }).format(new Date(dateStr));
-  }
-
-  if (isLoading) {
-    return <div className="py-10 text-center">Loading waitlists...</div>;
-  }
-
-  if (error) {
-    return (
-      <div className="py-10 text-center text-red-500">
-        Error loading waitlists. Please try again.
-      </div>
-    );
-  }
-
-  const waitlists = response?.status === 200 ? response.data.waitlists : [];
-
-  if (waitlists.length === 0) {
-    return (
-      <div className="py-10 text-center text-gray-500">
-        No waitlists found. Create one to get started!
-      </div>
-    );
-  }
-
-  return (
-    <>
-      <div className="rounded-md border bg-white">
-        <Table>
-          <TableHeader className="bg-gray-50">
-            <TableRow>
-              <TableHead className="font-medium">Name</TableHead>
-              <TableHead className="font-medium">Status</TableHead>
-              <TableHead className="font-medium">Signups</TableHead>
-              <TableHead className="font-medium">Votes</TableHead>
-              <TableHead className="font-medium">Created</TableHead>
-              <TableHead className="font-medium">Linked Agent</TableHead>
-              <TableHead className="font-medium">Actions</TableHead>
-            </TableRow>
-          </TableHeader>
-          <TableBody>
-            {waitlists.map((waitlist) => (
-              <TableRow key={waitlist.id}>
-                <TableCell>
-                  <div>
-                    <div className="font-medium">{waitlist.name}</div>
-                    <div className="text-sm text-gray-500">
-                      {waitlist.subHeading}
-                    </div>
-                  </div>
-                </TableCell>
-                <TableCell>{formatStatus(waitlist.status)}</TableCell>
-                <TableCell>{waitlist.signupCount}</TableCell>
-                <TableCell>{waitlist.votes}</TableCell>
-                <TableCell>{formatDate(waitlist.createdAt)}</TableCell>
-                <TableCell>
-                  {waitlist.storeListingId ? (
-                    <span className="text-green-600">
-                      <Link size={16} className="inline" /> Linked
-                    </span>
-                  ) : (
-                    <span className="text-gray-400">Not linked</span>
-                  )}
-                </TableCell>
-                <TableCell>
-                  <div className="flex gap-2">
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => setViewingSignups(waitlist.id)}
-                      title="View signups"
-                    >
-                      <Users size={16} />
-                    </Button>
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => setEditingWaitlist(waitlist)}
-                      title="Edit"
-                    >
-                      <PencilSimple size={16} />
-                    </Button>
-                    <Button
-                      variant="ghost"
-                      size="small"
-                      onClick={() => handleDelete(waitlist.id)}
-                      title="Delete"
-                      disabled={deleteWaitlistMutation.isPending}
-                    >
-                      <Trash size={16} className="text-red-500" />
-                    </Button>
-                  </div>
-                </TableCell>
-              </TableRow>
-            ))}
-          </TableBody>
-        </Table>
-      </div>
-
-      {editingWaitlist && (
-        <EditWaitlistDialog
-          waitlist={editingWaitlist}
-          onClose={() => setEditingWaitlist(null)}
-          onSave={handleWaitlistSaved}
-        />
-      )}
-
-      {viewingSignups && (
-        <WaitlistSignupsDialog
-          waitlistId={viewingSignups}
-          onClose={() => setViewingSignups(null)}
-        />
-      )}
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/waitlist/page.tsx

@@ -1,36 +0,0 @@
-import { withRoleAccess } from "@/lib/withRoleAccess";
-import { Suspense } from "react";
-import { WaitlistTable } from "./components/WaitlistTable";
-import { CreateWaitlistButton } from "./components/CreateWaitlistButton";
-
-function WaitlistDashboard() {
-  return (
-    <div className="mx-auto p-6">
-      <div className="flex flex-col gap-4">
-        <div className="flex items-center justify-between">
-          <div>
-            <h1 className="text-3xl font-bold">Waitlist Management</h1>
-            <p className="text-gray-500">
-              Manage upcoming agent waitlists and track signups
-            </p>
-          </div>
-          <CreateWaitlistButton />
-        </div>
-
-        <Suspense
-          fallback={
-            <div className="py-10 text-center">Loading waitlists...</div>
-          }
-        >
-          <WaitlistTable />
-        </Suspense>
-      </div>
-    </div>
-  );
-}
-
-export default async function WaitlistDashboardPage() {
-  const withAdminAccess = await withRoleAccess(["admin"]);
-  const ProtectedWaitlistDashboard = await withAdminAccess(WaitlistDashboard);
-  return <ProtectedWaitlistDashboard />;
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/MainMarketplacePage/MainMarketplacePage.tsx

@@ -8,7 +8,6 @@ import { useMainMarketplacePage } from "./useMainMarketplacePage";
 import { FeaturedCreators } from "../FeaturedCreators/FeaturedCreators";
 import { MainMarketplacePageLoading } from "../MainMarketplacePageLoading";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { WaitlistSection } from "../WaitlistSection/WaitlistSection";
 
 export const MainMarkeplacePage = () => {
   const { featuredAgents, topAgents, featuredCreators, isLoading, hasError } =
@@ -47,10 +46,6 @@ export const MainMarkeplacePage = () => {
         {/* 100px margin because our featured sections button are placed 40px below the container */}
         <Separator className="mb-6 mt-24" />
 
-        {/* Waitlist Section - "Help Shape What's Next" */}
-        <WaitlistSection />
-        <Separator className="mb-6 mt-12" />
-
         {topAgents && (
           <AgentsSection sectionTitle="Top Agents" agents={topAgents.agents} />
         )}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistCard/WaitlistCard.tsx

@@ -1,105 +0,0 @@
-"use client";
-
-import Image from "next/image";
-import { Button } from "@/components/atoms/Button/Button";
-import { Check } from "@phosphor-icons/react";
-
-interface WaitlistCardProps {
-  name: string;
-  subHeading: string;
-  description: string;
-  imageUrl: string | null;
-  isMember?: boolean;
-  onCardClick: () => void;
-  onJoinClick: (e: React.MouseEvent) => void;
-}
-
-export function WaitlistCard({
-  name,
-  subHeading,
-  description,
-  imageUrl,
-  isMember = false,
-  onCardClick,
-  onJoinClick,
-}: WaitlistCardProps) {
-  function handleJoinClick(e: React.MouseEvent) {
-    e.stopPropagation();
-    onJoinClick(e);
-  }
-
-  return (
-    <div
-      className="flex h-[24rem] w-full max-w-md cursor-pointer flex-col items-start rounded-3xl bg-white transition-all duration-300 hover:shadow-lg dark:bg-zinc-900 dark:hover:shadow-gray-700"
-      onClick={onCardClick}
-      data-testid="waitlist-card"
-      role="button"
-      tabIndex={0}
-      aria-label={`${name} waitlist card`}
-      onKeyDown={(e) => {
-        if (e.key === "Enter" || e.key === " ") {
-          onCardClick();
-        }
-      }}
-    >
-      {/* Image Section */}
-      <div className="relative aspect-[2/1.2] w-full overflow-hidden rounded-large md:aspect-[2.17/1]">
-        {imageUrl ? (
-          <Image
-            src={imageUrl}
-            alt={`${name} preview image`}
-            fill
-            className="object-cover"
-          />
-        ) : (
-          <div className="flex h-full w-full items-center justify-center bg-gradient-to-br from-neutral-200 to-neutral-300 dark:from-neutral-700 dark:to-neutral-800">
-            <span className="text-4xl font-bold text-neutral-400 dark:text-neutral-500">
-              {name.charAt(0)}
-            </span>
-          </div>
-        )}
-      </div>
-
-      <div className="mt-3 flex w-full flex-1 flex-col px-4">
-        {/* Name and Subheading */}
-        <div className="flex w-full flex-col">
-          <h3 className="line-clamp-1 font-poppins text-xl font-semibold text-[#272727] dark:text-neutral-100">
-            {name}
-          </h3>
-          <p className="mt-1 line-clamp-1 text-sm text-neutral-500 dark:text-neutral-400">
-            {subHeading}
-          </p>
-        </div>
-
-        {/* Description */}
-        <div className="mt-2 flex w-full flex-col">
-          <p className="line-clamp-5 text-sm font-normal leading-relaxed text-neutral-600 dark:text-neutral-400">
-            {description}
-          </p>
-        </div>
-
-        <div className="flex-grow" />
-
-        {/* Join Waitlist Button */}
-        <div className="mt-4 w-full pb-4">
-          {isMember ? (
-            <Button
-              disabled
-              className="w-full rounded-full bg-green-600 text-white hover:bg-green-600 dark:bg-green-700 dark:hover:bg-green-700"
-            >
-              <Check className="mr-2" size={16} weight="bold" />
-              On the waitlist
-            </Button>
-          ) : (
-            <Button
-              onClick={handleJoinClick}
-              className="w-full rounded-full bg-zinc-800 text-white hover:bg-zinc-700 dark:bg-zinc-700 dark:hover:bg-zinc-600"
-            >
-              Join waitlist
-            </Button>
-          )}
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistDetailModal/WaitlistDetailModal.tsx

@@ -1,300 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import Image from "next/image";
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Input } from "@/components/atoms/Input/Input";
-import {
-  Carousel,
-  CarouselContent,
-  CarouselItem,
-  CarouselNext,
-  CarouselPrevious,
-} from "@/components/__legacy__/ui/carousel";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { Check, Play } from "@phosphor-icons/react";
-import { useSupabaseStore } from "@/lib/supabase/hooks/useSupabaseStore";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { usePostV2AddSelfToTheAgentWaitlist } from "@/app/api/__generated__/endpoints/store/store";
-
-interface MediaItem {
-  type: "image" | "video";
-  url: string;
-  label?: string;
-}
-
-function MediaCarousel({ waitlist }: { waitlist: StoreWaitlistEntry }) {
-  const [activeVideo, setActiveVideo] = useState<string | null>(null);
-
-  // Build media items array: videos first, then images
-  const mediaItems: MediaItem[] = [
-    ...(waitlist.videoUrl
-      ? [{ type: "video" as const, url: waitlist.videoUrl, label: "Video" }]
-      : []),
-    ...(waitlist.agentOutputDemoUrl
-      ? [
-          {
-            type: "video" as const,
-            url: waitlist.agentOutputDemoUrl,
-            label: "Demo",
-          },
-        ]
-      : []),
-    ...waitlist.imageUrls.map((url) => ({ type: "image" as const, url })),
-  ];
-
-  if (mediaItems.length === 0) return null;
-
-  // Single item - no carousel needed
-  if (mediaItems.length === 1) {
-    const item = mediaItems[0];
-    return (
-      <div className="relative aspect-[350/196] w-full overflow-hidden rounded-large">
-        {item.type === "image" ? (
-          <Image
-            src={item.url}
-            alt={`${waitlist.name} preview`}
-            fill
-            className="object-cover"
-          />
-        ) : (
-          <video
-            src={item.url}
-            controls
-            className="h-full w-full object-cover"
-          />
-        )}
-      </div>
-    );
-  }
-
-  // Multiple items - use carousel
-  return (
-    <Carousel className="w-full">
-      <CarouselContent>
-        {mediaItems.map((item, index) => (
-          <CarouselItem key={index}>
-            <div className="relative aspect-[350/196] w-full overflow-hidden rounded-large">
-              {item.type === "image" ? (
-                <Image
-                  src={item.url}
-                  alt={`${waitlist.name} preview ${index + 1}`}
-                  fill
-                  className="object-cover"
-                />
-              ) : activeVideo === item.url ? (
-                <video
-                  src={item.url}
-                  controls
-                  autoPlay
-                  className="h-full w-full object-cover"
-                />
-              ) : (
-                <button
-                  onClick={() => setActiveVideo(item.url)}
-                  className="group relative h-full w-full bg-zinc-900"
-                >
-                  <div className="absolute inset-0 flex items-center justify-center">
-                    <div className="flex h-16 w-16 items-center justify-center rounded-full bg-white/90 transition-transform group-hover:scale-110">
-                      <Play size={32} weight="fill" className="text-zinc-800" />
-                    </div>
-                  </div>
-                  <span className="absolute bottom-3 left-3 text-sm text-white">
-                    {item.label}
-                  </span>
-                </button>
-              )}
-            </div>
-          </CarouselItem>
-        ))}
-      </CarouselContent>
-      <CarouselPrevious className="left-2 top-1/2 -translate-y-1/2" />
-      <CarouselNext className="right-2 top-1/2 -translate-y-1/2" />
-    </Carousel>
-  );
-}
-
-interface WaitlistDetailModalProps {
-  waitlist: StoreWaitlistEntry;
-  isMember?: boolean;
-  onClose: () => void;
-  onJoinSuccess?: (waitlistId: string) => void;
-}
-
-export function WaitlistDetailModal({
-  waitlist,
-  isMember = false,
-  onClose,
-  onJoinSuccess,
-}: WaitlistDetailModalProps) {
-  const { user } = useSupabaseStore();
-  const [email, setEmail] = useState("");
-  const [success, setSuccess] = useState(false);
-  const { toast } = useToast();
-  const joinWaitlistMutation = usePostV2AddSelfToTheAgentWaitlist();
-
-  function handleJoin() {
-    joinWaitlistMutation.mutate(
-      {
-        waitlistId: waitlist.waitlistId,
-        data: { email: user ? undefined : email },
-      },
-      {
-        onSuccess: (response) => {
-          if (response.status === 200) {
-            setSuccess(true);
-            toast({
-              title: "You're on the waitlist!",
-              description: `We'll notify you when ${waitlist.name} goes live.`,
-            });
-            onJoinSuccess?.(waitlist.waitlistId);
-          } else {
-            toast({
-              variant: "destructive",
-              title: "Error",
-              description: "Failed to join waitlist. Please try again.",
-            });
-          }
-        },
-        onError: () => {
-          toast({
-            variant: "destructive",
-            title: "Error",
-            description: "Failed to join waitlist. Please try again.",
-          });
-        },
-      },
-    );
-  }
-
-  // Success state
-  if (success) {
-    return (
-      <Dialog
-        title=""
-        controlled={{
-          isOpen: true,
-          set: async (open) => {
-            if (!open) onClose();
-          },
-        }}
-        onClose={onClose}
-        styling={{ maxWidth: "500px" }}
-      >
-        <Dialog.Content>
-          <div className="flex flex-col items-center justify-center py-4 text-center">
-            {/* Party emoji */}
-            <span className="mb-2 text-5xl">🎉</span>
-
-            {/* Title */}
-            <h2 className="mb-2 font-poppins text-[22px] font-medium leading-7 text-zinc-900 dark:text-zinc-100">
-              You&apos;re on the waitlist
-            </h2>
-
-            {/* Subtitle */}
-            <p className="text-base leading-[26px] text-zinc-600 dark:text-zinc-400">
-              Thanks for helping us prioritize which agents to build next.
-              We&apos;ll notify you when this agent goes live in the
-              marketplace.
-            </p>
-          </div>
-
-          {/* Close button */}
-          <Dialog.Footer className="flex justify-center pb-2 pt-4">
-            <Button
-              variant="secondary"
-              onClick={onClose}
-              className="rounded-full border border-zinc-700 bg-white px-4 py-3 text-zinc-900 hover:bg-zinc-100 dark:border-zinc-500 dark:bg-zinc-800 dark:text-zinc-100 dark:hover:bg-zinc-700"
-            >
-              Close
-            </Button>
-          </Dialog.Footer>
-        </Dialog.Content>
-      </Dialog>
-    );
-  }
-
-  // Main modal - handles both member and non-member states
-  return (
-    <Dialog
-      title="Join the waitlist"
-      controlled={{
-        isOpen: true,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "500px" }}
-    >
-      <Dialog.Content>
-        {/* Subtitle */}
-        <p className="mb-6 text-center text-base text-zinc-600 dark:text-zinc-400">
-          Help us decide what to build next — and get notified when this agent
-          is ready
-        </p>
-
-        {/* Media Carousel */}
-        <MediaCarousel waitlist={waitlist} />
-
-        {/* Agent Name */}
-        <h3 className="mt-4 font-poppins text-[22px] font-medium leading-7 text-zinc-800 dark:text-zinc-100">
-          {waitlist.name}
-        </h3>
-
-        {/* Agent Description */}
-        <p className="mt-2 line-clamp-5 text-sm leading-[22px] text-zinc-500 dark:text-zinc-400">
-          {waitlist.description}
-        </p>
-
-        {/* Email input for non-logged-in users who haven't joined */}
-        {!isMember && !user && (
-          <div className="mt-4 pr-1">
-            <Input
-              id="email"
-              label="Email address"
-              type="email"
-              placeholder="you@example.com"
-              value={email}
-              onChange={(e) => setEmail(e.target.value)}
-              required
-            />
-          </div>
-        )}
-
-        {/* Footer buttons */}
-        <Dialog.Footer className="sticky bottom-0 mt-6 flex justify-center gap-3 bg-white pb-2 pt-4 dark:bg-zinc-900">
-          {isMember ? (
-            <Button
-              disabled
-              className="rounded-full bg-green-600 px-4 py-3 text-white hover:bg-green-600 dark:bg-green-700 dark:hover:bg-green-700"
-            >
-              <Check size={16} className="mr-2" />
-              You&apos;re on the waitlist
-            </Button>
-          ) : (
-            <>
-              <Button
-                onClick={handleJoin}
-                loading={joinWaitlistMutation.isPending}
-                disabled={!user && !email}
-                className="rounded-full bg-zinc-800 px-4 py-3 text-white hover:bg-zinc-700 dark:bg-zinc-700 dark:hover:bg-zinc-600"
-              >
-                Join waitlist
-              </Button>
-              <Button
-                type="button"
-                variant="secondary"
-                onClick={onClose}
-                className="rounded-full bg-zinc-200 px-4 py-3 text-zinc-900 hover:bg-zinc-300 dark:bg-zinc-700 dark:text-zinc-100 dark:hover:bg-zinc-600"
-              >
-                Not now
-              </Button>
-            </>
-          )}
-        </Dialog.Footer>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistSection/WaitlistSection.tsx

@@ -1,102 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import {
-  Carousel,
-  CarouselContent,
-  CarouselItem,
-} from "@/components/__legacy__/ui/carousel";
-import { WaitlistCard } from "../WaitlistCard/WaitlistCard";
-import { WaitlistDetailModal } from "../WaitlistDetailModal/WaitlistDetailModal";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { useWaitlistSection } from "./useWaitlistSection";
-
-export function WaitlistSection() {
-  const { waitlists, joinedWaitlistIds, isLoading, hasError, markAsJoined } =
-    useWaitlistSection();
-  const [selectedWaitlist, setSelectedWaitlist] =
-    useState<StoreWaitlistEntry | null>(null);
-
-  function handleOpenModal(waitlist: StoreWaitlistEntry) {
-    setSelectedWaitlist(waitlist);
-  }
-
-  function handleJoinSuccess(waitlistId: string) {
-    markAsJoined(waitlistId);
-  }
-
-  // Don't render if loading, error, or no waitlists
-  if (isLoading || hasError || !waitlists || waitlists.length === 0) {
-    return null;
-  }
-
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <div className="w-full max-w-[1360px]">
-        {/* Section Header */}
-        <div className="mb-6">
-          <h2 className="font-poppins text-2xl font-semibold text-[#282828] dark:text-neutral-200">
-            Help Shape What&apos;s Next
-          </h2>
-          <p className="mt-2 text-base text-neutral-600 dark:text-neutral-400">
-            These agents are in development. Your interest helps us prioritize
-            what gets built — and we&apos;ll notify you when they&apos;re ready.
-          </p>
-        </div>
-
-        {/* Mobile Carousel View */}
-        <Carousel
-          className="md:hidden"
-          opts={{
-            loop: true,
-          }}
-        >
-          <CarouselContent>
-            {waitlists.map((waitlist) => (
-              <CarouselItem
-                key={waitlist.waitlistId}
-                className="min-w-64 max-w-71"
-              >
-                <WaitlistCard
-                  name={waitlist.name}
-                  subHeading={waitlist.subHeading}
-                  description={waitlist.description}
-                  imageUrl={waitlist.imageUrls[0] || null}
-                  isMember={joinedWaitlistIds.has(waitlist.waitlistId)}
-                  onCardClick={() => handleOpenModal(waitlist)}
-                  onJoinClick={() => handleOpenModal(waitlist)}
-                />
-              </CarouselItem>
-            ))}
-          </CarouselContent>
-        </Carousel>
-
-        {/* Desktop Grid View */}
-        <div className="hidden grid-cols-1 place-items-center gap-6 md:grid md:grid-cols-2 lg:grid-cols-3">
-          {waitlists.map((waitlist) => (
-            <WaitlistCard
-              key={waitlist.waitlistId}
-              name={waitlist.name}
-              subHeading={waitlist.subHeading}
-              description={waitlist.description}
-              imageUrl={waitlist.imageUrls[0] || null}
-              isMember={joinedWaitlistIds.has(waitlist.waitlistId)}
-              onCardClick={() => handleOpenModal(waitlist)}
-              onJoinClick={() => handleOpenModal(waitlist)}
-            />
-          ))}
-        </div>
-      </div>
-
-      {/* Single Modal for both viewing and joining */}
-      {selectedWaitlist && (
-        <WaitlistDetailModal
-          waitlist={selectedWaitlist}
-          isMember={joinedWaitlistIds.has(selectedWaitlist.waitlistId)}
-          onClose={() => setSelectedWaitlist(null)}
-          onJoinSuccess={handleJoinSuccess}
-        />
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/marketplace/components/WaitlistSection/useWaitlistSection.ts

@@ -1,57 +0,0 @@
-"use client";
-
-import { useMemo } from "react";
-import { useSupabaseStore } from "@/lib/supabase/hooks/useSupabaseStore";
-import {
-  useGetV2GetTheAgentWaitlist,
-  useGetV2GetWaitlistIdsTheCurrentUserHasJoined,
-} from "@/app/api/__generated__/endpoints/store/store";
-import type { StoreWaitlistEntry } from "@/app/api/__generated__/models/storeWaitlistEntry";
-import { useQueryClient } from "@tanstack/react-query";
-
-export function useWaitlistSection() {
-  const { user } = useSupabaseStore();
-  const queryClient = useQueryClient();
-
-  // Fetch waitlists
-  const {
-    data: waitlistsResponse,
-    isLoading: waitlistsLoading,
-    isError: waitlistsError,
-  } = useGetV2GetTheAgentWaitlist();
-
-  // Fetch memberships if logged in
-  const { data: membershipsResponse, isLoading: membershipsLoading } =
-    useGetV2GetWaitlistIdsTheCurrentUserHasJoined({
-      query: {
-        enabled: !!user,
-      },
-    });
-
-  const waitlists: StoreWaitlistEntry[] = useMemo(() => {
-    if (waitlistsResponse?.status === 200) {
-      return waitlistsResponse.data.listings;
-    }
-    return [];
-  }, [waitlistsResponse]);
-
-  const joinedWaitlistIds: Set<string> = useMemo(() => {
-    if (membershipsResponse?.status === 200) {
-      return new Set(membershipsResponse.data);
-    }
-    return new Set();
-  }, [membershipsResponse]);
-
-  const isLoading = waitlistsLoading || (!!user && membershipsLoading);
-  const hasError = waitlistsError;
-
-  // Function to add a waitlist ID to joined set (called after successful join)
-  function markAsJoined(_waitlistId: string) {
-    // Invalidate the memberships query to refetch
-    queryClient.invalidateQueries({
-      queryKey: ["getV2GetWaitlistIdsTheCurrentUserHasJoined"],
-    });
-  }
-
-  return { waitlists, joinedWaitlistIds, isLoading, hasError, markAsJoined };
-}

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

@@ -4965,301 +4965,6 @@
         }
       }
     },
-    "/api/store/admin/waitlist": {
-      "get": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "List All Waitlists",
-        "description": "Get all waitlists with admin details (admin only).\n\nReturns:\n    WaitlistAdminListResponse with all waitlists",
-        "operationId": "getV2List all waitlists",
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistAdminListResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          }
-        },
-        "security": [{ "HTTPBearerJWT": [] }]
-      },
-      "post": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Create Waitlist",
-        "description": "Create a new waitlist (admin only).\n\nArgs:\n    request: Waitlist creation details\n    user_id: Authenticated admin user creating the waitlist\n\nReturns:\n    WaitlistAdminResponse with the created waitlist details",
-        "operationId": "postV2Create waitlist",
-        "requestBody": {
-          "content": {
-            "application/json": {
-              "schema": { "$ref": "#/components/schemas/WaitlistCreateRequest" }
-            }
-          },
-          "required": true
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistAdminResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        },
-        "security": [{ "HTTPBearerJWT": [] }]
-      }
-    },
-    "/api/store/admin/waitlist/{waitlist_id}": {
-      "delete": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Delete Waitlist",
-        "description": "Soft delete a waitlist (admin only).\n\nArgs:\n    waitlist_id: ID of the waitlist to delete\n\nReturns:\n    Success message",
-        "operationId": "deleteV2Delete waitlist",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist"
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": { "application/json": { "schema": {} } }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      },
-      "get": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Get Waitlist Details",
-        "description": "Get a single waitlist with admin details (admin only).\n\nArgs:\n    waitlist_id: ID of the waitlist to retrieve\n\nReturns:\n    WaitlistAdminResponse with waitlist details",
-        "operationId": "getV2Get waitlist details",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist"
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistAdminResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      },
-      "put": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Update Waitlist",
-        "description": "Update a waitlist (admin only).\n\nArgs:\n    waitlist_id: ID of the waitlist to update\n    request: Fields to update\n\nReturns:\n    WaitlistAdminResponse with updated waitlist details",
-        "operationId": "putV2Update waitlist",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist"
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": { "$ref": "#/components/schemas/WaitlistUpdateRequest" }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistAdminResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
-    "/api/store/admin/waitlist/{waitlist_id}/link": {
-      "post": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Link Waitlist to Store Listing",
-        "description": "Link a waitlist to a store listing (admin only).\n\nWhen the linked store listing is approved/published, waitlist users\nwill be automatically notified.\n\nArgs:\n    waitlist_id: ID of the waitlist\n    store_listing_id: ID of the store listing to link\n\nReturns:\n    WaitlistAdminResponse with updated waitlist details",
-        "operationId": "postV2Link waitlist to store listing",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist"
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/Body_postV2Link_waitlist_to_store_listing"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistAdminResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
-    "/api/store/admin/waitlist/{waitlist_id}/signups": {
-      "get": {
-        "tags": ["v2", "admin", "store", "admin", "waitlist"],
-        "summary": "Get Waitlist Signups",
-        "description": "Get all signups for a waitlist (admin only).\n\nArgs:\n    waitlist_id: ID of the waitlist\n\nReturns:\n    WaitlistSignupListResponse with all signups",
-        "operationId": "getV2Get waitlist signups",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist"
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/WaitlistSignupListResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
     "/api/store/agents": {
       "get": {
         "tags": ["v2", "store", "public"],
@@ -6042,101 +5747,6 @@
         }
       }
     },
-    "/api/store/waitlist": {
-      "get": {
-        "tags": ["v2", "store", "public"],
-        "summary": "Get the agent waitlist",
-        "description": "Get all active waitlists for public display.",
-        "operationId": "getV2Get the agent waitlist",
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/StoreWaitlistsAllResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
-    "/api/store/waitlist/my-memberships": {
-      "get": {
-        "tags": ["v2", "store", "private"],
-        "summary": "Get waitlist IDs the current user has joined",
-        "description": "Returns list of waitlist IDs the authenticated user has joined.",
-        "operationId": "getV2Get waitlist ids the current user has joined",
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "items": { "type": "string" },
-                  "type": "array",
-                  "title": "Response Getv2Get Waitlist Ids The Current User Has Joined"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          }
-        },
-        "security": [{ "HTTPBearerJWT": [] }]
-      }
-    },
-    "/api/store/waitlist/{waitlist_id}/join": {
-      "post": {
-        "tags": ["v2", "store", "public"],
-        "summary": "Add self to the agent waitlist",
-        "description": "Add the current user to the agent waitlist.",
-        "operationId": "postV2Add self to the agent waitlist",
-        "security": [{ "HTTPBearer": [] }],
-        "parameters": [
-          {
-            "name": "waitlist_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string",
-              "description": "The ID of the waitlist to join",
-              "title": "Waitlist Id"
-            },
-            "description": "The ID of the waitlist to join"
-          }
-        ],
-        "requestBody": {
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/Body_postV2Add_self_to_the_agent_waitlist"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/StoreWaitlistEntry" }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      }
-    },
     "/health": {
       "get": {
         "tags": ["health"],
@@ -6884,17 +6494,6 @@
         "required": ["store_listing_version_id"],
         "title": "Body_postV2Add marketplace agent"
       },
-      "Body_postV2Add_self_to_the_agent_waitlist": {
-        "properties": {
-          "email": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Email",
-            "description": "Email address for unauthenticated users"
-          }
-        },
-        "type": "object",
-        "title": "Body_postV2Add self to the agent waitlist"
-      },
       "Body_postV2Execute_a_preset": {
         "properties": {
           "inputs": {
@@ -6913,18 +6512,6 @@
         "type": "object",
         "title": "Body_postV2Execute a preset"
       },
-      "Body_postV2Link_waitlist_to_store_listing": {
-        "properties": {
-          "store_listing_id": {
-            "type": "string",
-            "title": "Store Listing Id",
-            "description": "The ID of the store listing"
-          }
-        },
-        "type": "object",
-        "required": ["store_listing_id"],
-        "title": "Body_postV2Link waitlist to store listing"
-      },
       "Body_postV2Upload_submission_media": {
         "properties": {
           "file": { "type": "string", "format": "binary", "title": "File" }
@@ -8764,8 +8351,7 @@
           "REFUND_REQUEST",
           "REFUND_PROCESSED",
           "AGENT_APPROVED",
-          "AGENT_REJECTED",
-          "WAITLIST_LAUNCH"
+          "AGENT_REJECTED"
         ],
         "title": "NotificationType"
       },
@@ -10313,57 +9899,6 @@
         "required": ["submissions", "pagination"],
         "title": "StoreSubmissionsResponse"
       },
-      "StoreWaitlistEntry": {
-        "properties": {
-          "waitlistId": { "type": "string", "title": "Waitlistid" },
-          "slug": { "type": "string", "title": "Slug" },
-          "name": { "type": "string", "title": "Name" },
-          "subHeading": { "type": "string", "title": "Subheading" },
-          "videoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Videourl"
-          },
-          "agentOutputDemoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Agentoutputdemourl"
-          },
-          "imageUrls": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Imageurls"
-          },
-          "description": { "type": "string", "title": "Description" },
-          "categories": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Categories"
-          }
-        },
-        "type": "object",
-        "required": [
-          "waitlistId",
-          "slug",
-          "name",
-          "subHeading",
-          "imageUrls",
-          "description",
-          "categories"
-        ],
-        "title": "StoreWaitlistEntry",
-        "description": "Public waitlist entry - no PII fields exposed."
-      },
-      "StoreWaitlistsAllResponse": {
-        "properties": {
-          "listings": {
-            "items": { "$ref": "#/components/schemas/StoreWaitlistEntry" },
-            "type": "array",
-            "title": "Listings"
-          }
-        },
-        "type": "object",
-        "required": ["listings"],
-        "title": "StoreWaitlistsAllResponse"
-      },
       "SubmissionStatus": {
         "type": "string",
         "enum": ["DRAFT", "PENDING", "APPROVED", "REJECTED"],
@@ -12108,201 +11643,6 @@
         "required": ["loc", "msg", "type"],
         "title": "ValidationError"
       },
-      "WaitlistAdminListResponse": {
-        "properties": {
-          "waitlists": {
-            "items": { "$ref": "#/components/schemas/WaitlistAdminResponse" },
-            "type": "array",
-            "title": "Waitlists"
-          },
-          "totalCount": { "type": "integer", "title": "Totalcount" }
-        },
-        "type": "object",
-        "required": ["waitlists", "totalCount"],
-        "title": "WaitlistAdminListResponse",
-        "description": "Response model for listing all waitlists (admin view)."
-      },
-      "WaitlistAdminResponse": {
-        "properties": {
-          "id": { "type": "string", "title": "Id" },
-          "createdAt": { "type": "string", "title": "Createdat" },
-          "updatedAt": { "type": "string", "title": "Updatedat" },
-          "slug": { "type": "string", "title": "Slug" },
-          "name": { "type": "string", "title": "Name" },
-          "subHeading": { "type": "string", "title": "Subheading" },
-          "description": { "type": "string", "title": "Description" },
-          "categories": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Categories"
-          },
-          "imageUrls": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Imageurls"
-          },
-          "videoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Videourl"
-          },
-          "agentOutputDemoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Agentoutputdemourl"
-          },
-          "status": { "$ref": "#/components/schemas/WaitlistExternalStatus" },
-          "votes": { "type": "integer", "title": "Votes" },
-          "signupCount": { "type": "integer", "title": "Signupcount" },
-          "storeListingId": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Storelistingid"
-          },
-          "owningUserId": { "type": "string", "title": "Owninguserid" }
-        },
-        "type": "object",
-        "required": [
-          "id",
-          "createdAt",
-          "updatedAt",
-          "slug",
-          "name",
-          "subHeading",
-          "description",
-          "categories",
-          "imageUrls",
-          "status",
-          "votes",
-          "signupCount",
-          "owningUserId"
-        ],
-        "title": "WaitlistAdminResponse",
-        "description": "Admin response model with full waitlist details including internal data."
-      },
-      "WaitlistCreateRequest": {
-        "properties": {
-          "name": { "type": "string", "title": "Name" },
-          "slug": { "type": "string", "title": "Slug" },
-          "subHeading": { "type": "string", "title": "Subheading" },
-          "description": { "type": "string", "title": "Description" },
-          "categories": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Categories",
-            "default": []
-          },
-          "imageUrls": {
-            "items": { "type": "string" },
-            "type": "array",
-            "title": "Imageurls",
-            "default": []
-          },
-          "videoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Videourl"
-          },
-          "agentOutputDemoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Agentoutputdemourl"
-          }
-        },
-        "type": "object",
-        "required": ["name", "slug", "subHeading", "description"],
-        "title": "WaitlistCreateRequest",
-        "description": "Request model for creating a new waitlist."
-      },
-      "WaitlistExternalStatus": {
-        "type": "string",
-        "enum": ["DONE", "NOT_STARTED", "CANCELED", "WORK_IN_PROGRESS"],
-        "title": "WaitlistExternalStatus"
-      },
-      "WaitlistSignup": {
-        "properties": {
-          "type": { "type": "string", "title": "Type" },
-          "userId": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Userid"
-          },
-          "email": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Email"
-          },
-          "username": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Username"
-          }
-        },
-        "type": "object",
-        "required": ["type"],
-        "title": "WaitlistSignup",
-        "description": "Individual signup entry for a waitlist."
-      },
-      "WaitlistSignupListResponse": {
-        "properties": {
-          "waitlistId": { "type": "string", "title": "Waitlistid" },
-          "signups": {
-            "items": { "$ref": "#/components/schemas/WaitlistSignup" },
-            "type": "array",
-            "title": "Signups"
-          },
-          "totalCount": { "type": "integer", "title": "Totalcount" }
-        },
-        "type": "object",
-        "required": ["waitlistId", "signups", "totalCount"],
-        "title": "WaitlistSignupListResponse",
-        "description": "Response model for listing waitlist signups."
-      },
-      "WaitlistUpdateRequest": {
-        "properties": {
-          "name": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Name"
-          },
-          "slug": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Slug"
-          },
-          "subHeading": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Subheading"
-          },
-          "description": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Description"
-          },
-          "categories": {
-            "anyOf": [
-              { "items": { "type": "string" }, "type": "array" },
-              { "type": "null" }
-            ],
-            "title": "Categories"
-          },
-          "imageUrls": {
-            "anyOf": [
-              { "items": { "type": "string" }, "type": "array" },
-              { "type": "null" }
-            ],
-            "title": "Imageurls"
-          },
-          "videoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Videourl"
-          },
-          "agentOutputDemoUrl": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Agentoutputdemourl"
-          },
-          "status": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Status"
-          },
-          "storeListingId": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Storelistingid"
-          }
-        },
-        "type": "object",
-        "title": "WaitlistUpdateRequest",
-        "description": "Request model for updating a waitlist."
-      },
       "Webhook": {
         "properties": {
           "id": { "type": "string", "title": "Id" },
@@ -12353,7 +11693,6 @@
         "in": "header",
         "name": "X-Postmark-Webhook-Token"
       },
-      "HTTPBearer": { "type": "http", "scheme": "bearer" },
       "HTTPBearerJWT": {
         "type": "http",
         "scheme": "bearer",