fix(backend/store): wrap semantic SELECT in subquery to fix UNION ORDER BY

- ORDER BY uce.embedding was applying to UNION result, not just semantic SELECT
- uce table only exists in semantic SELECT, causing 'missing FROM-clause' error
- Wrapped semantic SELECT in subquery so ORDER BY applies within correct scope
- UNION can now properly combine lexical and semantic candidates

Fixes marketplace search completely failing and falling back to lexical-only

.branchlet.json

@@ -0,0 +1,37 @@
+{
+  "worktreeCopyPatterns": [
+    ".env*",
+    ".vscode/**",
+    ".auth/**",
+    ".claude/**",
+    "autogpt_platform/.env*",
+    "autogpt_platform/backend/.env*",
+    "autogpt_platform/frontend/.env*",
+    "autogpt_platform/frontend/.auth/**",
+    "autogpt_platform/db/docker/.env*"
+  ],
+  "worktreeCopyIgnores": [
+    "**/node_modules/**",
+    "**/dist/**",
+    "**/.git/**",
+    "**/Thumbs.db",
+    "**/.DS_Store",
+    "**/.next/**",
+    "**/__pycache__/**",
+    "**/.ruff_cache/**",
+    "**/.pytest_cache/**",
+    "**/*.pyc",
+    "**/playwright-report/**",
+    "**/logs/**",
+    "**/site/**"
+  ],
+  "worktreePathTemplate": "$BASE_PATH.worktree",
+  "postCreateCmd": [
+    "cd autogpt_platform/autogpt_libs && poetry install",
+    "cd autogpt_platform/backend && poetry install && poetry run prisma generate",
+    "cd autogpt_platform/frontend && pnpm install",
+    "cd docs && pip install -r requirements.txt"
+  ],
+  "terminalCommand": "code .",
+  "deleteBranchWithWorktree": false
+}

.dockerignore

@@ -16,6 +16,7 @@
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
 !autogpt_platform/backend/.env
+!autogpt_platform/backend/gen_prisma_types_stub.py
 
 # Platform - Market
 !autogpt_platform/market/market/

.github/workflows/claude-dependabot.yml

@@ -74,7 +74,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
+        run: poetry run prisma generate && poetry run gen-prisma-stub
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

.github/workflows/claude.yml

@@ -90,7 +90,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
+        run: poetry run prisma generate && poetry run gen-prisma-stub
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

.github/workflows/copilot-setup-steps.yml

@@ -72,7 +72,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
+        run: poetry run prisma generate && poetry run gen-prisma-stub
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
@@ -108,6 +108,16 @@ jobs:
       #   run: pnpm playwright install --with-deps chromium
 
       # Docker setup for development environment
+      - name: Free up disk space
+        run: |
+          # Remove large unused tools to free disk space for Docker builds
+          sudo rm -rf /usr/share/dotnet
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /opt/ghc
+          sudo rm -rf /opt/hostedtoolcache/CodeQL
+          sudo docker system prune -af
+          df -h
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 

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

@@ -134,7 +134,7 @@ jobs:
         run: poetry install
 
       - name: Generate Prisma Client
-        run: poetry run prisma generate
+        run: poetry run prisma generate && poetry run gen-prisma-stub
 
       - id: supabase
         name: Start Supabase
@@ -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/Makefile

@@ -12,6 +12,7 @@ reset-db:
 	rm -rf db/docker/volumes/db/data
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
+	cd backend && poetry run gen-prisma-stub
 	
 # View logs for core services
 logs-core:
@@ -33,6 +34,7 @@ init-env:
 migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
+	cd backend && poetry run gen-prisma-stub
 
 run-backend:
 	cd backend && poetry run app

autogpt_platform/backend/.gitignore

@@ -18,3 +18,4 @@ load-tests/results/
 load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
+migrations/*/rollback*.sql

autogpt_platform/backend/Dockerfile

@@ -48,7 +48,8 @@ RUN poetry install --no-ansi --no-root
 # Generate Prisma client
 COPY autogpt_platform/backend/schema.prisma ./
 COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
-RUN poetry run prisma generate
+COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
+RUN poetry run prisma generate && poetry run gen-prisma-stub
 
 FROM debian:13-slim AS server_dependencies
 

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/library/db.py

@@ -489,7 +489,7 @@ async def update_agent_version_in_library(
     agent_graph_version: int,
 ) -> library_model.LibraryAgent:
     """
-    Updates the agent version in the library if useGraphIsActiveVersion is True.
+    Updates the agent version in the library for any agent owned by the user.
 
     Args:
         user_id: Owner of the LibraryAgent.
@@ -498,20 +498,31 @@ async def update_agent_version_in_library(
 
     Raises:
         DatabaseError: If there's an error with the update.
+        NotFoundError: If no library agent is found for this user and agent.
     """
     logger.debug(
         f"Updating agent version in library for user #{user_id}, "
         f"agent #{agent_graph_id} v{agent_graph_version}"
     )
-    try:
-        library_agent = await prisma.models.LibraryAgent.prisma().find_first_or_raise(
+    async with transaction() as tx:
+        library_agent = await prisma.models.LibraryAgent.prisma(tx).find_first_or_raise(
             where={
                 "userId": user_id,
                 "agentGraphId": agent_graph_id,
-                "useGraphIsActiveVersion": True,
             },
         )
-        lib = await prisma.models.LibraryAgent.prisma().update(
+
+        # Delete any conflicting LibraryAgent for the target version
+        await prisma.models.LibraryAgent.prisma(tx).delete_many(
+            where={
+                "userId": user_id,
+                "agentGraphId": agent_graph_id,
+                "agentGraphVersion": agent_graph_version,
+                "id": {"not": library_agent.id},
+            }
+        )
+
+        lib = await prisma.models.LibraryAgent.prisma(tx).update(
             where={"id": library_agent.id},
             data={
                 "AgentGraph": {
@@ -525,13 +536,13 @@ async def update_agent_version_in_library(
             },
             include={"AgentGraph": True},
         )
-        if lib is None:
-            raise NotFoundError(f"Library agent {library_agent.id} not found")
 
-        return library_model.LibraryAgent.from_db(lib)
-    except prisma.errors.PrismaError as e:
-        logger.error(f"Database error updating agent version in library: {e}")
-        raise DatabaseError("Failed to update agent version in library") from e
+    if lib is None:
+        raise NotFoundError(
+            f"Failed to update library agent for {agent_graph_id} v{agent_graph_version}"
+        )
+
+    return library_model.LibraryAgent.from_db(lib)
 
 
 async def update_library_agent(
@@ -825,6 +836,7 @@ async def add_store_agent_to_library(
                     }
                 },
                 "isCreatedByUser": False,
+                "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
                     _initialize_graph_settings(graph_model).model_dump()
                 ),

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

@@ -48,6 +48,7 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
+    owner_user_id: str  # ID of user who owns/created this agent graph
 
     image_url: str | None
 
@@ -163,6 +164,7 @@ class LibraryAgent(pydantic.BaseModel):
             id=agent.id,
             graph_id=agent.agentGraphId,
             graph_version=agent.agentGraphVersion,
+            owner_user_id=agent.userId,
             image_url=agent.imageUrl,
             creator_name=creator_name,
             creator_image_url=creator_image_url,

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

@@ -42,6 +42,7 @@ async def test_get_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
+                owner_user_id=test_user_id,
                 name="Test Agent 1",
                 description="Test Description 1",
                 image_url=None,
@@ -64,6 +65,7 @@ async def test_get_library_agents_success(
                 id="test-agent-2",
                 graph_id="test-agent-2",
                 graph_version=1,
+                owner_user_id=test_user_id,
                 name="Test Agent 2",
                 description="Test Description 2",
                 image_url=None,
@@ -138,6 +140,7 @@ async def test_get_favorite_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
+                owner_user_id=test_user_id,
                 name="Favorite Agent 1",
                 description="Test Favorite Description 1",
                 image_url=None,
@@ -205,6 +208,7 @@ def test_add_agent_to_library_success(
         id="test-library-agent-id",
         graph_id="test-agent-1",
         graph_version=1,
+        owner_user_id=test_user_id,
         name="Test Agent 1",
         description="Test Description 1",
         image_url=None,

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,
@@ -30,6 +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()
@@ -50,128 +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
@@ -180,6 +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"})
@@ -188,7 +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,
@@ -199,7 +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(
@@ -614,6 +572,7 @@ async def get_store_submissions(
         submission_models = []
         for sub in submissions:
             submission_model = store_model.StoreSubmission(
+                listing_id=sub.listing_id,
                 agent_id=sub.agent_id,
                 agent_version=sub.agent_version,
                 name=sub.name,
@@ -667,35 +626,48 @@ async def delete_store_submission(
     submission_id: str,
 ) -> bool:
     """
-    Delete a store listing submission as the submitting user.
+    Delete a store submission version as the submitting user.
 
     Args:
         user_id: ID of the authenticated user
-        submission_id: ID of the submission to be deleted
+        submission_id: StoreListingVersion ID to delete
 
     Returns:
-        bool: True if the submission was successfully deleted, False otherwise
+        bool: True if successfully deleted
     """
-    logger.debug(f"Deleting store submission {submission_id} for user {user_id}")
-
     try:
-        # Verify the submission belongs to this user
-        submission = await prisma.models.StoreListing.prisma().find_first(
-            where={"agentGraphId": submission_id, "owningUserId": user_id}
+        # Find the submission version with ownership check
+        version = await prisma.models.StoreListingVersion.prisma().find_first(
+            where={"id": submission_id}, include={"StoreListing": True}
         )
 
-        if not submission:
-            logger.warning(f"Submission not found for user {user_id}: {submission_id}")
-            raise store_exceptions.SubmissionNotFoundError(
-                f"Submission not found for this user. User ID: {user_id}, Submission ID: {submission_id}"
+        if (
+            not version
+            or not version.StoreListing
+            or version.StoreListing.owningUserId != user_id
+        ):
+            raise store_exceptions.SubmissionNotFoundError("Submission not found")
+
+        # Prevent deletion of approved submissions
+        if version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
+            raise store_exceptions.InvalidOperationError(
+                "Cannot delete approved submissions"
             )
 
-        # Delete the submission
-        await prisma.models.StoreListing.prisma().delete(where={"id": submission.id})
-
-        logger.debug(
-            f"Successfully deleted submission {submission_id} for user {user_id}"
+        # Delete the version
+        await prisma.models.StoreListingVersion.prisma().delete(
+            where={"id": version.id}
         )
+
+        # Clean up empty listing if this was the last version
+        remaining = await prisma.models.StoreListingVersion.prisma().count(
+            where={"storeListingId": version.storeListingId}
+        )
+        if remaining == 0:
+            await prisma.models.StoreListing.prisma().delete(
+                where={"id": version.storeListingId}
+            )
+
         return True
 
     except Exception as e:
@@ -759,9 +731,15 @@ async def create_store_submission(
             logger.warning(
                 f"Agent not found for user {user_id}: {agent_id} v{agent_version}"
             )
-            raise store_exceptions.AgentNotFoundError(
-                f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
-            )
+            # Provide more user-friendly error message when agent_id is empty
+            if not agent_id or agent_id.strip() == "":
+                raise store_exceptions.AgentNotFoundError(
+                    "No agent selected. Please select an agent before submitting to the store."
+                )
+            else:
+                raise store_exceptions.AgentNotFoundError(
+                    f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
+                )
 
         # Check if listing already exists for this agent
         existing_listing = await prisma.models.StoreListing.prisma().find_first(
@@ -833,6 +811,7 @@ async def create_store_submission(
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return store_model.StoreSubmission(
+            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -944,81 +923,56 @@ async def edit_store_submission(
         # Currently we are not allowing user to update the agent associated with a submission
         # If we allow it in future, then we need a check here to verify the agent belongs to this user.
 
-        # Check if we can edit this submission
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
+        # Only allow editing of PENDING submissions
+        if current_version.submissionStatus != prisma.enums.SubmissionStatus.PENDING:
             raise store_exceptions.InvalidOperationError(
-                "Cannot edit a rejected submission"
-            )
-
-        # For APPROVED submissions, we need to create a new version
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
-            # Create a new version for the existing listing
-            return await create_store_version(
-                user_id=user_id,
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
-                store_listing_id=current_version.storeListingId,
-                name=name,
-                video_url=video_url,
-                agent_output_demo_url=agent_output_demo_url,
-                image_urls=image_urls,
-                description=description,
-                sub_heading=sub_heading,
-                categories=categories,
-                changes_summary=changes_summary,
-                recommended_schedule_cron=recommended_schedule_cron,
-                instructions=instructions,
+                f"Cannot edit a {current_version.submissionStatus.value.lower()} submission. Only pending submissions can be edited."
             )
 
         # For PENDING submissions, we can update the existing version
-        elif current_version.submissionStatus == prisma.enums.SubmissionStatus.PENDING:
-            # Update the existing version
-            updated_version = await prisma.models.StoreListingVersion.prisma().update(
-                where={"id": store_listing_version_id},
-                data=prisma.types.StoreListingVersionUpdateInput(
-                    name=name,
-                    videoUrl=video_url,
-                    agentOutputDemoUrl=agent_output_demo_url,
-                    imageUrls=image_urls,
-                    description=description,
-                    categories=categories,
-                    subHeading=sub_heading,
-                    changesSummary=changes_summary,
-                    recommendedScheduleCron=recommended_schedule_cron,
-                    instructions=instructions,
-                ),
-            )
-
-            logger.debug(
-                f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
-            )
-
-            if not updated_version:
-                raise DatabaseError("Failed to update store listing version")
-            return store_model.StoreSubmission(
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
+        # Update the existing version
+        updated_version = await prisma.models.StoreListingVersion.prisma().update(
+            where={"id": store_listing_version_id},
+            data=prisma.types.StoreListingVersionUpdateInput(
                 name=name,
-                sub_heading=sub_heading,
-                slug=current_version.StoreListing.slug,
+                videoUrl=video_url,
+                agentOutputDemoUrl=agent_output_demo_url,
+                imageUrls=image_urls,
                 description=description,
-                instructions=instructions,
-                image_urls=image_urls,
-                date_submitted=updated_version.submittedAt or updated_version.createdAt,
-                status=updated_version.submissionStatus,
-                runs=0,
-                rating=0.0,
-                store_listing_version_id=updated_version.id,
-                changes_summary=changes_summary,
-                video_url=video_url,
                 categories=categories,
-                version=updated_version.version,
-            )
+                subHeading=sub_heading,
+                changesSummary=changes_summary,
+                recommendedScheduleCron=recommended_schedule_cron,
+                instructions=instructions,
+            ),
+        )
 
-        else:
-            raise store_exceptions.InvalidOperationError(
-                f"Cannot edit submission with status: {current_version.submissionStatus}"
-            )
+        logger.debug(
+            f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
+        )
+
+        if not updated_version:
+            raise DatabaseError("Failed to update store listing version")
+        return store_model.StoreSubmission(
+            listing_id=current_version.StoreListing.id,
+            agent_id=current_version.agentGraphId,
+            agent_version=current_version.agentGraphVersion,
+            name=name,
+            sub_heading=sub_heading,
+            slug=current_version.StoreListing.slug,
+            description=description,
+            instructions=instructions,
+            image_urls=image_urls,
+            date_submitted=updated_version.submittedAt or updated_version.createdAt,
+            status=updated_version.submissionStatus,
+            runs=0,
+            rating=0.0,
+            store_listing_version_id=updated_version.id,
+            changes_summary=changes_summary,
+            video_url=video_url,
+            categories=categories,
+            version=updated_version.version,
+        )
 
     except (
         store_exceptions.SubmissionNotFoundError,
@@ -1097,38 +1051,78 @@ async def create_store_version(
                 f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
             )
 
-        # Get the latest version number
-        latest_version = listing.Versions[0] if listing.Versions else None
-
-        next_version = (latest_version.version + 1) if latest_version else 1
-
-        # Create a new version for the existing listing
-        new_version = await prisma.models.StoreListingVersion.prisma().create(
-            data=prisma.types.StoreListingVersionCreateInput(
-                version=next_version,
-                agentGraphId=agent_id,
-                agentGraphVersion=agent_version,
-                name=name,
-                videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
-                imageUrls=image_urls,
-                description=description,
-                instructions=instructions,
-                categories=categories,
-                subHeading=sub_heading,
-                submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                submittedAt=datetime.now(),
-                changesSummary=changes_summary,
-                recommendedScheduleCron=recommended_schedule_cron,
-                storeListingId=store_listing_id,
+        # Check if there's already a PENDING submission for this agent (any version)
+        existing_pending_submission = (
+            await prisma.models.StoreListingVersion.prisma().find_first(
+                where=prisma.types.StoreListingVersionWhereInput(
+                    storeListingId=store_listing_id,
+                    agentGraphId=agent_id,
+                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                    isDeleted=False,
+                )
             )
         )
 
+        # Handle existing pending submission and create new one atomically
+        async with transaction() as tx:
+            # Get the latest version number first
+            latest_listing = await prisma.models.StoreListing.prisma(tx).find_first(
+                where=prisma.types.StoreListingWhereInput(
+                    id=store_listing_id, owningUserId=user_id
+                ),
+                include={"Versions": {"order_by": {"version": "desc"}, "take": 1}},
+            )
+
+            if not latest_listing:
+                raise store_exceptions.ListingNotFoundError(
+                    f"Store listing not found. User ID: {user_id}, Listing ID: {store_listing_id}"
+                )
+
+            latest_version = (
+                latest_listing.Versions[0] if latest_listing.Versions else None
+            )
+            next_version = (latest_version.version + 1) if latest_version else 1
+
+            # If there's an existing pending submission, delete it atomically before creating new one
+            if existing_pending_submission:
+                logger.info(
+                    f"Found existing PENDING submission for agent {agent_id} (was v{existing_pending_submission.agentGraphVersion}, now v{agent_version}), replacing existing submission instead of creating duplicate"
+                )
+                await prisma.models.StoreListingVersion.prisma(tx).delete(
+                    where={"id": existing_pending_submission.id}
+                )
+                logger.debug(
+                    f"Deleted existing pending submission {existing_pending_submission.id}"
+                )
+
+            # Create a new version for the existing listing
+            new_version = await prisma.models.StoreListingVersion.prisma(tx).create(
+                data=prisma.types.StoreListingVersionCreateInput(
+                    version=next_version,
+                    agentGraphId=agent_id,
+                    agentGraphVersion=agent_version,
+                    name=name,
+                    videoUrl=video_url,
+                    agentOutputDemoUrl=agent_output_demo_url,
+                    imageUrls=image_urls,
+                    description=description,
+                    instructions=instructions,
+                    categories=categories,
+                    subHeading=sub_heading,
+                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                    submittedAt=datetime.now(),
+                    changesSummary=changes_summary,
+                    recommendedScheduleCron=recommended_schedule_cron,
+                    storeListingId=store_listing_id,
+                )
+            )
+
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
         # Return submission details
         return store_model.StoreSubmission(
+            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -1541,7 +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,
@@ -1556,6 +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={
@@ -1708,15 +1719,12 @@ async def review_store_submission(
 
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
+            listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
             agent_id=submission.agentGraphId,
             agent_version=submission.agentGraphVersion,
             name=submission.name,
             sub_heading=submission.subHeading,
-            slug=(
-                submission.StoreListing.slug
-                if hasattr(submission, "storeListing") and submission.StoreListing
-                else ""
-            ),
+            slug=(submission.StoreListing.slug if submission.StoreListing else ""),
             description=submission.description,
             instructions=submission.instructions,
             image_urls=submission.imageUrls or [],
@@ -1818,9 +1826,7 @@ async def get_admin_listings_with_versions(
         where = prisma.types.StoreListingWhereInput(**where_dict)
         include = prisma.types.StoreListingInclude(
             Versions=prisma.types.FindManyStoreListingVersionArgsFromStoreListing(
-                order_by=prisma.types._StoreListingVersion_version_OrderByInput(
-                    version="desc"
-                )
+                order_by={"version": "desc"}
             ),
             OwningUser=True,
         )
@@ -1845,6 +1851,7 @@ async def get_admin_listings_with_versions(
             # If we have versions, turn them into StoreSubmission models
             for version in listing.Versions or []:
                 version_model = store_model.StoreSubmission(
+                    listing_id=listing.id,
                     agent_id=version.agentGraphId,
                     agent_version=version.agentGraphVersion,
                     name=version.name,

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

@@ -0,0 +1,566 @@
+"""
+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 tiktoken import encoding_for_model
+
+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"
+# OpenAI embedding token limit (8,191 with 1 token buffer for safety)
+EMBEDDING_MAX_TOKENS = 8191
+
+
+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 token limit using tiktoken
+        # Character-based truncation is insufficient because token ratios vary by content type
+        enc = encoding_for_model(EMBEDDING_MODEL)
+        tokens = enc.encode(text)
+        if len(tokens) > EMBEDDING_MAX_TOKENS:
+            tokens = tokens[:EMBEDDING_MAX_TOKENS]
+            truncated_text = enc.decode(tokens)
+            logger.info(
+                f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
+            )
+        else:
+            truncated_text = text
+
+        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(tokens)} tokens, {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
+        # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        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()
+            WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
+                AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
+                AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
+            """,
+            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, user_id: str | None = None
+) -> 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.
+
+    Args:
+        content_type: The type of content (STORE_AGENT, LIBRARY_AGENT, etc.)
+        content_id: The unique identifier for the content
+        user_id: Optional user ID. For public content (STORE_AGENT, BLOCK), pass None.
+                 For user-scoped content (LIBRARY_AGENT), pass the user's ID to avoid
+                 deleting embeddings belonging to other users.
+
+    Returns:
+        True if deletion succeeded, False otherwise
+    """
+    try:
+        client = prisma.get_client()
+
+        await execute_raw_with_schema(
+            """
+            DELETE 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,
+            client=client,
+        )
+
+        user_str = f" (user: {user_id})" if user_id else ""
+        logger.info(f"Deleted embedding for {content_type}:{content_id}{user_str}")
+        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'::{schema_prefix}"ContentType"
+            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'::{schema_prefix}"ContentType"
+            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 +1,380 @@
+from unittest.mock import AsyncMock, 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")
+async def test_generate_embedding_success():
+    """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
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        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")
+async def test_generate_embedding_no_api_key():
+    """Test embedding generation without API key."""
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = None
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_api_error():
+    """Test embedding generation with API error."""
+    mock_client = MagicMock()
+    mock_client.embeddings.create = AsyncMock(side_effect=Exception("API Error"))
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_text_truncation():
+    """Test that long text is properly truncated using tiktoken."""
+    from tiktoken import encoding_for_model
+
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1] * 1536
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        # Create text that will exceed 8191 tokens
+        # Use varied characters to ensure token-heavy text: each word is ~1 token
+        words = [f"word{i}" for i in range(10000)]
+        long_text = " ".join(words)  # ~10000 tokens
+
+        await embeddings.generate_embedding(long_text)
+
+        # Verify text was truncated to 8191 tokens
+        call_args = mock_client.embeddings.create.call_args
+        truncated_text = call_args.kwargs["input"]
+
+        # Count actual tokens in truncated text
+        enc = encoding_for_model("text-embedding-3-small")
+        actual_tokens = len(enc.encode(truncated_text))
+
+        # Should be at or just under 8191 tokens
+        assert actual_tokens <= 8191
+        # Should be close to the limit (not over-truncated)
+        assert actual_tokens >= 8100
+
+
+@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 +1,391 @@
+"""
+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 $N parameter placeholders
+    # No user input is concatenated directly into the SQL string
+    where_clause = " AND ".join(where_parts)
+
+    # Embedding is required for hybrid search - fail fast if unavailable
+    if query_embedding is None or not query_embedding:
+        # Log detailed error server-side
+        logger.error(
+            "Failed to generate query embedding. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+        )
+        # Raise generic error to client
+        raise ValueError("Search service temporarily unavailable")
+
+    # Add embedding parameter
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${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 "storeListingVersionId"
+                FROM (
+                    SELECT sa."storeListingVersionId", uce.embedding
+                    FROM {{schema_prefix}}"StoreAgent" sa
+                    INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                        ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+                    WHERE {where_clause}
+                    ORDER BY uce.embedding <=> {embedding_param}::vector
+                    LIMIT 200
+                ) semantic_results
+            ),
+            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'::{{schema_prefix}}"ContentType"
+            ),
+            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

@@ -110,6 +110,7 @@ class Profile(pydantic.BaseModel):
 
 
 class StoreSubmission(pydantic.BaseModel):
+    listing_id: str
     agent_id: str
     agent_version: int
     name: str
@@ -164,8 +165,12 @@ class StoreListingsWithVersionsResponse(pydantic.BaseModel):
 
 
 class StoreSubmissionRequest(pydantic.BaseModel):
-    agent_id: str
-    agent_version: int
+    agent_id: str = pydantic.Field(
+        ..., min_length=1, description="Agent ID cannot be empty"
+    )
+    agent_version: int = pydantic.Field(
+        ..., gt=0, description="Agent version must be greater than 0"
+    )
     slug: str
     name: str
     sub_heading: str

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

@@ -138,6 +138,7 @@ def test_creator_details():
 
 def test_store_submission():
     submission = store_model.StoreSubmission(
+        listing_id="listing123",
         agent_id="agent123",
         agent_version=1,
         sub_heading="Test subheading",
@@ -159,6 +160,7 @@ def test_store_submissions_response():
     response = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
+                listing_id="listing123",
                 agent_id="agent123",
                 agent_version=1,
                 sub_heading="Test subheading",

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

@@ -521,6 +521,7 @@ def test_get_submissions_success(
     mocked_value = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
+                listing_id="test-listing-id",
                 name="Test Agent",
                 description="Test agent description",
                 image_urls=["test.jpg"],

autogpt_platform/backend/backend/blocks/airtable/_webhook.py

@@ -6,6 +6,9 @@ import hashlib
 import hmac
 import logging
 from enum import Enum
+from typing import cast
+
+from prisma.types import Serializable
 
 from backend.sdk import (
     BaseWebhooksManager,
@@ -84,7 +87,9 @@ class AirtableWebhookManager(BaseWebhooksManager):
         # update webhook config
         await update_webhook(
             webhook.id,
-            config={"base_id": base_id, "cursor": response.cursor},
+            config=cast(
+                dict[str, Serializable], {"base_id": base_id, "cursor": response.cursor}
+            ),
         )
 
         event_type = "notification"

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

@@ -0,0 +1,184 @@
+"""
+Shared helpers for Human-In-The-Loop (HITL) review functionality.
+Used by both the dedicated HumanInTheLoopBlock and blocks that require human review.
+"""
+
+import logging
+from typing import Any, Optional
+
+from prisma.enums import ReviewStatus
+from pydantic import BaseModel
+
+from backend.data.execution import ExecutionContext, ExecutionStatus
+from backend.data.human_review import ReviewResult
+from backend.executor.manager import async_update_node_execution_status
+from backend.util.clients import get_database_manager_async_client
+
+logger = logging.getLogger(__name__)
+
+
+class ReviewDecision(BaseModel):
+    """Result of a review decision."""
+
+    should_proceed: bool
+    message: str
+    review_result: ReviewResult
+
+
+class HITLReviewHelper:
+    """Helper class for Human-In-The-Loop review operations."""
+
+    @staticmethod
+    async def get_or_create_human_review(**kwargs) -> Optional[ReviewResult]:
+        """Create or retrieve a human review from the database."""
+        return await get_database_manager_async_client().get_or_create_human_review(
+            **kwargs
+        )
+
+    @staticmethod
+    async def update_node_execution_status(**kwargs) -> None:
+        """Update the execution status of a node."""
+        await async_update_node_execution_status(
+            db_client=get_database_manager_async_client(), **kwargs
+        )
+
+    @staticmethod
+    async def update_review_processed_status(
+        node_exec_id: str, processed: bool
+    ) -> None:
+        """Update the processed status of a review."""
+        return await get_database_manager_async_client().update_review_processed_status(
+            node_exec_id, processed
+        )
+
+    @staticmethod
+    async def _handle_review_request(
+        input_data: Any,
+        user_id: str,
+        node_exec_id: str,
+        graph_exec_id: str,
+        graph_id: str,
+        graph_version: int,
+        execution_context: ExecutionContext,
+        block_name: str = "Block",
+        editable: bool = False,
+    ) -> Optional[ReviewResult]:
+        """
+        Handle a review request for a block that requires human review.
+
+        Args:
+            input_data: The input data to be reviewed
+            user_id: ID of the user requesting the review
+            node_exec_id: ID of the node execution
+            graph_exec_id: ID of the graph execution
+            graph_id: ID of the graph
+            graph_version: Version of the graph
+            execution_context: Current execution context
+            block_name: Name of the block requesting review
+            editable: Whether the reviewer can edit the data
+
+        Returns:
+            ReviewResult if review is complete, None if waiting for human input
+
+        Raises:
+            Exception: If review creation or status update fails
+        """
+        # Skip review if safe mode is disabled - return auto-approved result
+        if not execution_context.safe_mode:
+            logger.info(
+                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
+            )
+            return ReviewResult(
+                data=input_data,
+                status=ReviewStatus.APPROVED,
+                message="Auto-approved (safe mode disabled)",
+                processed=True,
+                node_exec_id=node_exec_id,
+            )
+
+        result = await HITLReviewHelper.get_or_create_human_review(
+            user_id=user_id,
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            input_data=input_data,
+            message=f"Review required for {block_name} execution",
+            editable=editable,
+        )
+
+        if result is None:
+            logger.info(
+                f"Block {block_name} pausing execution for node {node_exec_id} - awaiting human review"
+            )
+            await HITLReviewHelper.update_node_execution_status(
+                exec_id=node_exec_id,
+                status=ExecutionStatus.REVIEW,
+            )
+            return None  # Signal that execution should pause
+
+        # Mark review as processed if not already done
+        if not result.processed:
+            await HITLReviewHelper.update_review_processed_status(
+                node_exec_id=node_exec_id, processed=True
+            )
+
+        return result
+
+    @staticmethod
+    async def handle_review_decision(
+        input_data: Any,
+        user_id: str,
+        node_exec_id: str,
+        graph_exec_id: str,
+        graph_id: str,
+        graph_version: int,
+        execution_context: ExecutionContext,
+        block_name: str = "Block",
+        editable: bool = False,
+    ) -> Optional[ReviewDecision]:
+        """
+        Handle a review request and return the decision in a single call.
+
+        Args:
+            input_data: The input data to be reviewed
+            user_id: ID of the user requesting the review
+            node_exec_id: ID of the node execution
+            graph_exec_id: ID of the graph execution
+            graph_id: ID of the graph
+            graph_version: Version of the graph
+            execution_context: Current execution context
+            block_name: Name of the block requesting review
+            editable: Whether the reviewer can edit the data
+
+        Returns:
+            ReviewDecision if review is complete (approved/rejected),
+            None if execution should pause (awaiting review)
+        """
+        review_result = await HITLReviewHelper._handle_review_request(
+            input_data=input_data,
+            user_id=user_id,
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            execution_context=execution_context,
+            block_name=block_name,
+            editable=editable,
+        )
+
+        if review_result is None:
+            # Still awaiting review - return None to pause execution
+            return None
+
+        # Review is complete, determine outcome
+        should_proceed = review_result.status == ReviewStatus.APPROVED
+        message = review_result.message or (
+            "Execution approved by reviewer"
+            if should_proceed
+            else "Execution rejected by reviewer"
+        )
+
+        return ReviewDecision(
+            should_proceed=should_proceed, message=message, review_result=review_result
+        )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -3,6 +3,7 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
+from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -11,11 +12,9 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus
+from backend.data.execution import ExecutionContext
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField
-from backend.executor.manager import async_update_node_execution_status
-from backend.util.clients import get_database_manager_async_client
 
 logger = logging.getLogger(__name__)
 
@@ -72,32 +71,26 @@ class HumanInTheLoopBlock(Block):
                 ("approved_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
-                "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
-                    data={"name": "John Doe", "age": 30},
-                    status=ReviewStatus.APPROVED,
-                    message="",
-                    processed=False,
-                    node_exec_id="test-node-exec-id",
-                ),
-                "update_node_execution_status": lambda *_args, **_kwargs: None,
-                "update_review_processed_status": lambda *_args, **_kwargs: None,
+                "handle_review_decision": lambda **kwargs: type(
+                    "ReviewDecision",
+                    (),
+                    {
+                        "should_proceed": True,
+                        "message": "Test approval message",
+                        "review_result": ReviewResult(
+                            data={"name": "John Doe", "age": 30},
+                            status=ReviewStatus.APPROVED,
+                            message="",
+                            processed=False,
+                            node_exec_id="test-node-exec-id",
+                        ),
+                    },
+                )(),
             },
         )
 
-    async def get_or_create_human_review(self, **kwargs):
-        return await get_database_manager_async_client().get_or_create_human_review(
-            **kwargs
-        )
-
-    async def update_node_execution_status(self, **kwargs):
-        return await async_update_node_execution_status(
-            db_client=get_database_manager_async_client(), **kwargs
-        )
-
-    async def update_review_processed_status(self, node_exec_id: str, processed: bool):
-        return await get_database_manager_async_client().update_review_processed_status(
-            node_exec_id, processed
-        )
+    async def handle_review_decision(self, **kwargs):
+        return await HITLReviewHelper.handle_review_decision(**kwargs)
 
     async def run(
         self,
@@ -109,7 +102,7 @@ class HumanInTheLoopBlock(Block):
         graph_id: str,
         graph_version: int,
         execution_context: ExecutionContext,
-        **kwargs,
+        **_kwargs,
     ) -> BlockOutput:
         if not execution_context.safe_mode:
             logger.info(
@@ -119,48 +112,28 @@ class HumanInTheLoopBlock(Block):
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
-        try:
-            result = await self.get_or_create_human_review(
-                user_id=user_id,
-                node_exec_id=node_exec_id,
-                graph_exec_id=graph_exec_id,
-                graph_id=graph_id,
-                graph_version=graph_version,
-                input_data=input_data.data,
-                message=input_data.name,
-                editable=input_data.editable,
-            )
-        except Exception as e:
-            logger.error(f"Error in HITL block for node {node_exec_id}: {str(e)}")
-            raise
+        decision = await self.handle_review_decision(
+            input_data=input_data.data,
+            user_id=user_id,
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            execution_context=execution_context,
+            block_name=self.name,
+            editable=input_data.editable,
+        )
 
-        if result is None:
-            logger.info(
-                f"HITL block pausing execution for node {node_exec_id} - awaiting human review"
-            )
-            try:
-                await self.update_node_execution_status(
-                    exec_id=node_exec_id,
-                    status=ExecutionStatus.REVIEW,
-                )
-                return
-            except Exception as e:
-                logger.error(
-                    f"Failed to update node status for HITL block {node_exec_id}: {str(e)}"
-                )
-                raise
+        if decision is None:
+            return
 
-        if not result.processed:
-            await self.update_review_processed_status(
-                node_exec_id=node_exec_id, processed=True
-            )
+        status = decision.review_result.status
+        if status == ReviewStatus.APPROVED:
+            yield "approved_data", decision.review_result.data
+        elif status == ReviewStatus.REJECTED:
+            yield "rejected_data", decision.review_result.data
+        else:
+            raise RuntimeError(f"Unexpected review status: {status}")
 
-            if result.status == ReviewStatus.APPROVED:
-                yield "approved_data", result.data
-                if result.message:
-                    yield "review_message", result.message
-
-            elif result.status == ReviewStatus.REJECTED:
-                yield "rejected_data", result.data
-                if result.message:
-                    yield "review_message", result.message
+        if decision.message:
+            yield "review_message", decision.message

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/blocks/smart_decision_maker.py

@@ -391,8 +391,12 @@ class SmartDecisionMakerBlock(Block):
         """
         block = sink_node.block
 
+        # Use custom name from node metadata if set, otherwise fall back to block.name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else block.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(block.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -489,8 +493,12 @@ class SmartDecisionMakerBlock(Block):
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
             )
 
+        # Use custom name from node metadata if set, otherwise fall back to graph name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else sink_graph_meta.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": sink_graph_meta.description,
         }
 
@@ -981,10 +989,28 @@ class SmartDecisionMakerBlock(Block):
         graph_version: int,
         execution_context: ExecutionContext,
         execution_processor: "ExecutionProcessor",
+        nodes_to_skip: set[str] | None = None,
         **kwargs,
     ) -> BlockOutput:
 
         tool_functions = await self._create_tool_node_signatures(node_id)
+        original_tool_count = len(tool_functions)
+
+        # Filter out tools for nodes that should be skipped (e.g., missing optional credentials)
+        if nodes_to_skip:
+            tool_functions = [
+                tf
+                for tf in tool_functions
+                if tf.get("function", {}).get("_sink_node_id") not in nodes_to_skip
+            ]
+
+            # Only raise error if we had tools but they were all filtered out
+            if original_tool_count > 0 and not tool_functions:
+                raise ValueError(
+                    "No available tools to execute - all downstream nodes are unavailable "
+                    "(possibly due to missing optional credentials)"
+                )
+
         yield "tool_functions", json.dumps(tool_functions)
 
         conversation_history = input_data.conversation_history or []

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

@@ -1057,3 +1057,153 @@ async def test_smart_decision_maker_traditional_mode_default():
         )  # Should yield individual tool parameters
         assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
         assert "conversations" in outputs
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_uses_customized_name_for_blocks():
+    """Test that SmartDecisionMakerBlock uses customized_name from node metadata for tool names."""
+    from unittest.mock import MagicMock
+
+    from backend.blocks.basic import StoreValueBlock
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node with customized_name in metadata
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-node-id"
+    mock_node.block_id = StoreValueBlock().id
+    mock_node.metadata = {"customized_name": "My Custom Tool Name"}
+    mock_node.block = StoreValueBlock()
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "input"
+
+    # Call the function directly
+    result = await SmartDecisionMakerBlock._create_block_function_signature(
+        mock_node, [mock_link]
+    )
+
+    # Verify the tool name uses the customized name (cleaned up)
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "my_custom_tool_name"  # Cleaned version
+    assert result["function"]["_sink_node_id"] == "test-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_falls_back_to_block_name():
+    """Test that SmartDecisionMakerBlock falls back to block.name when no customized_name."""
+    from unittest.mock import MagicMock
+
+    from backend.blocks.basic import StoreValueBlock
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node without customized_name
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-node-id"
+    mock_node.block_id = StoreValueBlock().id
+    mock_node.metadata = {}  # No customized_name
+    mock_node.block = StoreValueBlock()
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "input"
+
+    # Call the function directly
+    result = await SmartDecisionMakerBlock._create_block_function_signature(
+        mock_node, [mock_link]
+    )
+
+    # Verify the tool name uses the block's default name
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "storevalueblock"  # Default block name cleaned
+    assert result["function"]["_sink_node_id"] == "test-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_uses_customized_name_for_agents():
+    """Test that SmartDecisionMakerBlock uses customized_name from metadata for agent nodes."""
+    from unittest.mock import AsyncMock, MagicMock, patch
+
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node with customized_name in metadata
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-agent-node-id"
+    mock_node.metadata = {"customized_name": "My Custom Agent"}
+    mock_node.input_default = {
+        "graph_id": "test-graph-id",
+        "graph_version": 1,
+        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
+    }
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "test_input"
+
+    # Mock the database client
+    mock_graph_meta = MagicMock()
+    mock_graph_meta.name = "Original Agent Name"
+    mock_graph_meta.description = "Agent description"
+
+    mock_db_client = AsyncMock()
+    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
+
+    with patch(
+        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
+        return_value=mock_db_client,
+    ):
+        result = await SmartDecisionMakerBlock._create_agent_function_signature(
+            mock_node, [mock_link]
+        )
+
+    # Verify the tool name uses the customized name (cleaned up)
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "my_custom_agent"  # Cleaned version
+    assert result["function"]["_sink_node_id"] == "test-agent-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_agent_falls_back_to_graph_name():
+    """Test that agent node falls back to graph name when no customized_name."""
+    from unittest.mock import AsyncMock, MagicMock, patch
+
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node without customized_name
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-agent-node-id"
+    mock_node.metadata = {}  # No customized_name
+    mock_node.input_default = {
+        "graph_id": "test-graph-id",
+        "graph_version": 1,
+        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
+    }
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "test_input"
+
+    # Mock the database client
+    mock_graph_meta = MagicMock()
+    mock_graph_meta.name = "Original Agent Name"
+    mock_graph_meta.description = "Agent description"
+
+    mock_db_client = AsyncMock()
+    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
+
+    with patch(
+        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
+        return_value=mock_db_client,
+    ):
+        result = await SmartDecisionMakerBlock._create_agent_function_signature(
+            mock_node, [mock_link]
+        )
+
+    # Verify the tool name uses the graph's default name
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "original_agent_name"  # Graph name cleaned
+    assert result["function"]["_sink_node_id"] == "test-agent-node-id"

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dict.py

@@ -15,6 +15,7 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields
     mock_links = [
@@ -77,6 +78,7 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [

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

@@ -44,6 +44,7 @@ async def test_create_block_function_signature_with_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields (source sanitized, sink original)
     mock_links = [
@@ -106,6 +107,7 @@ async def test_create_block_function_signature_with_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [
@@ -159,6 +161,7 @@ async def test_create_block_function_signature_with_object_fields():
     mock_node.block = MatchTextPatternBlock()
     mock_node.block_id = MatchTextPatternBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic object fields
     mock_links = [
@@ -208,11 +211,13 @@ async def test_create_tool_node_signatures():
         mock_dict_node.block = CreateDictionaryBlock()
         mock_dict_node.block_id = CreateDictionaryBlock().id
         mock_dict_node.input_default = {}
+        mock_dict_node.metadata = {}
 
         mock_list_node = Mock()
         mock_list_node.block = AddToListBlock()
         mock_list_node.block_id = AddToListBlock().id
         mock_list_node.input_default = {}
+        mock_list_node.metadata = {}
 
         # Mock links with dynamic fields
         dict_link1 = Mock(
@@ -423,6 +428,7 @@ async def test_mixed_regular_and_dynamic_fields():
     mock_node.block.name = "TestBlock"
     mock_node.block.description = "A test block"
     mock_node.block.input_schema = Mock()
+    mock_node.metadata = {}
 
     # Mock the get_field_schema to return a proper schema for regular fields
     def get_field_schema(field_name):

autogpt_platform/backend/backend/blocks/wordpress/__init__.py

@@ -1,3 +1,3 @@
-from .blog import WordPressCreatePostBlock
+from .blog import WordPressCreatePostBlock, WordPressGetAllPostsBlock
 
-__all__ = ["WordPressCreatePostBlock"]
+__all__ = ["WordPressCreatePostBlock", "WordPressGetAllPostsBlock"]

autogpt_platform/backend/backend/blocks/wordpress/_api.py

@@ -161,7 +161,7 @@ async def oauth_exchange_code_for_tokens(
         grant_type="authorization_code",
     ).model_dump(exclude_none=True)
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -205,7 +205,7 @@ async def oauth_refresh_tokens(
         grant_type="refresh_token",
     ).model_dump(exclude_none=True)
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests().get(
+    response = await Requests(raise_for_status=False).get(
         f"{WORDPRESS_BASE_URL}oauth2/token-info",
         params=params,
     )
@@ -296,7 +296,7 @@ async def make_api_request(
 
     url = f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}"
 
-    request_method = getattr(Requests(), method.lower())
+    request_method = getattr(Requests(raise_for_status=False), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,6 +476,7 @@ async def create_post(
         data["tags"] = ",".join(str(t) for t in data["tags"])
 
     # Make the API request
+    site = normalize_site(site)
     endpoint = f"/rest/v1.1/sites/{site}/posts/new"
 
     headers = {
@@ -483,7 +484,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -499,3 +500,132 @@ async def create_post(
     )
     error_message = error_data.get("message", response.text)
     raise ValueError(f"Failed to create post: {response.status} - {error_message}")
+
+
+class Post(BaseModel):
+    """Response model for individual posts in a posts list response.
+
+    This is a simplified version compared to PostResponse, as the list endpoint
+    returns less detailed information than the create/get single post endpoints.
+    """
+
+    ID: int
+    site_ID: int
+    author: PostAuthor
+    date: datetime
+    modified: datetime
+    title: str
+    URL: str
+    short_URL: str
+    content: str | None = None
+    excerpt: str | None = None
+    slug: str
+    guid: str
+    status: str
+    sticky: bool
+    password: str | None = ""
+    parent: Union[Dict[str, Any], bool, None] = None
+    type: str
+    discussion: Dict[str, Union[str, bool, int]] | None = None
+    likes_enabled: bool | None = None
+    sharing_enabled: bool | None = None
+    like_count: int | None = None
+    i_like: bool | None = None
+    is_reblogged: bool | None = None
+    is_following: bool | None = None
+    global_ID: str | None = None
+    featured_image: str | None = None
+    post_thumbnail: Dict[str, Any] | None = None
+    format: str | None = None
+    geo: Union[Dict[str, Any], bool, None] = None
+    menu_order: int | None = None
+    page_template: str | None = None
+    publicize_URLs: List[str] | None = None
+    terms: Dict[str, Dict[str, Any]] | None = None
+    tags: Dict[str, Dict[str, Any]] | None = None
+    categories: Dict[str, Dict[str, Any]] | None = None
+    attachments: Dict[str, Dict[str, Any]] | None = None
+    attachment_count: int | None = None
+    metadata: List[Dict[str, Any]] | None = None
+    meta: Dict[str, Any] | None = None
+    capabilities: Dict[str, bool] | None = None
+    revisions: List[int] | None = None
+    other_URLs: Dict[str, Any] | None = None
+
+
+class PostsResponse(BaseModel):
+    """Response model for WordPress posts list."""
+
+    found: int
+    posts: List[Post]
+    meta: Dict[str, Any]
+
+
+def normalize_site(site: str) -> str:
+    """
+    Normalize a site identifier by stripping protocol and trailing slashes.
+
+    Args:
+        site: Site URL, domain, or ID (e.g., "https://myblog.wordpress.com/", "myblog.wordpress.com", "123456789")
+
+    Returns:
+        Normalized site identifier (domain or ID only)
+    """
+    site = site.strip()
+    if site.startswith("https://"):
+        site = site[8:]
+    elif site.startswith("http://"):
+        site = site[7:]
+    return site.rstrip("/")
+
+
+async def get_posts(
+    credentials: Credentials,
+    site: str,
+    status: PostStatus | None = None,
+    number: int = 100,
+    offset: int = 0,
+) -> PostsResponse:
+    """
+    Get posts from a WordPress site.
+
+    Args:
+        credentials: OAuth credentials
+        site: Site ID or domain (e.g., "myblog.wordpress.com" or "123456789")
+        status: Filter by post status using PostStatus enum, or None for all
+        number: Number of posts to retrieve (max 100)
+        offset: Number of posts to skip (for pagination)
+
+    Returns:
+        PostsResponse with the list of posts
+    """
+    site = normalize_site(site)
+    endpoint = f"/rest/v1.1/sites/{site}/posts"
+
+    headers = {
+        "Authorization": credentials.auth_header(),
+    }
+
+    params: Dict[str, Any] = {
+        "number": max(1, min(number, 100)),  # 1–100 posts per request
+        "offset": offset,
+    }
+
+    if status:
+        params["status"] = status.value
+    response = await Requests(raise_for_status=False).get(
+        f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
+        headers=headers,
+        params=params,
+    )
+
+    if response.ok:
+        return PostsResponse.model_validate(response.json())
+
+    error_data = (
+        response.json()
+        if response.headers.get("content-type", "").startswith("application/json")
+        else {}
+    )
+    error_message = error_data.get("message", response.text)
+    raise ValueError(f"Failed to get posts: {response.status} - {error_message}")

autogpt_platform/backend/backend/blocks/wordpress/blog.py

@@ -9,7 +9,15 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
+from ._api import (
+    CreatePostRequest,
+    Post,
+    PostResponse,
+    PostsResponse,
+    PostStatus,
+    create_post,
+    get_posts,
+)
 from ._config import wordpress
 
 
@@ -49,8 +57,15 @@ class WordPressCreatePostBlock(Block):
         media_urls: list[str] = SchemaField(
             description="URLs of images to sideload and attach to the post", default=[]
         )
+        publish_as_draft: bool = SchemaField(
+            description="If True, publishes the post as a draft. If False, publishes it publicly.",
+            default=False,
+        )
 
     class Output(BlockSchemaOutput):
+        site: str = SchemaField(
+            description="The site ID or domain (pass-through for chaining with other blocks)"
+        )
         post_id: int = SchemaField(description="The ID of the created post")
         post_url: str = SchemaField(description="The full URL of the created post")
         short_url: str = SchemaField(description="The shortened wp.me URL")
@@ -78,7 +93,9 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=PostStatus.PUBLISH,
+            status=(
+                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
+            ),
         )
 
         post_response: PostResponse = await create_post(
@@ -87,7 +104,69 @@ class WordPressCreatePostBlock(Block):
             post_data=post_request,
         )
 
+        yield "site", input_data.site
         yield "post_id", post_response.ID
         yield "post_url", post_response.URL
         yield "short_url", post_response.short_URL
         yield "post_data", post_response.model_dump()
+
+
+class WordPressGetAllPostsBlock(Block):
+    """
+    Fetches all posts from a WordPress.com site or Jetpack-enabled site.
+    Supports filtering by status and pagination.
+    """
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = wordpress.credentials_field()
+        site: str = SchemaField(
+            description="Site ID or domain (e.g., 'myblog.wordpress.com' or '123456789')"
+        )
+        status: PostStatus | None = SchemaField(
+            description="Filter by post status, or None for all",
+            default=None,
+        )
+        number: int = SchemaField(
+            description="Number of posts to retrieve (max 100 per request)", default=20
+        )
+        offset: int = SchemaField(
+            description="Number of posts to skip (for pagination)", default=0
+        )
+
+    class Output(BlockSchemaOutput):
+        site: str = SchemaField(
+            description="The site ID or domain (pass-through for chaining with other blocks)"
+        )
+        found: int = SchemaField(description="Total number of posts found")
+        posts: list[Post] = SchemaField(
+            description="List of post objects with their details"
+        )
+        post: Post = SchemaField(
+            description="Individual post object (yielded for each post)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="97728fa7-7f6f-4789-ba0c-f2c114119536",
+            description="Fetch all posts from WordPress.com or Jetpack sites",
+            categories={BlockCategory.SOCIAL},
+            input_schema=self.Input,
+            output_schema=self.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: Credentials, **kwargs
+    ) -> BlockOutput:
+        posts_response: PostsResponse = await get_posts(
+            credentials=credentials,
+            site=input_data.site,
+            status=input_data.status,
+            number=input_data.number,
+            offset=input_data.offset,
+        )
+
+        yield "site", input_data.site
+        yield "found", posts_response.found
+        yield "posts", posts_response.posts
+        for post in posts_response.posts:
+            yield "post", post

autogpt_platform/backend/backend/data/block.py

@@ -50,6 +50,8 @@ from .model import (
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
+    from backend.data.execution import ExecutionContext
+
     from .graph import Link
 
 app_config = Config()
@@ -472,6 +474,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
+        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -614,7 +617,77 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                     block_id=self.id,
                 ) from ex
 
+    async def is_block_exec_need_review(
+        self,
+        input_data: BlockInput,
+        *,
+        user_id: str,
+        node_exec_id: str,
+        graph_exec_id: str,
+        graph_id: str,
+        graph_version: int,
+        execution_context: "ExecutionContext",
+        **kwargs,
+    ) -> tuple[bool, BlockInput]:
+        """
+        Check if this block execution needs human review and handle the review process.
+
+        Returns:
+            Tuple of (should_pause, input_data_to_use)
+            - should_pause: True if execution should be paused for review
+            - input_data_to_use: The input data to use (may be modified by reviewer)
+        """
+        # Skip review if not required or safe mode is disabled
+        if not self.requires_human_review or not execution_context.safe_mode:
+            return False, input_data
+
+        from backend.blocks.helpers.review import HITLReviewHelper
+
+        # Handle the review request and get decision
+        decision = await HITLReviewHelper.handle_review_decision(
+            input_data=input_data,
+            user_id=user_id,
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            execution_context=execution_context,
+            block_name=self.name,
+            editable=True,
+        )
+
+        if decision is None:
+            # We're awaiting review - pause execution
+            return True, input_data
+
+        if not decision.should_proceed:
+            # Review was rejected, raise an error to stop execution
+            raise BlockExecutionError(
+                message=f"Block execution rejected by reviewer: {decision.message}",
+                block_name=self.name,
+                block_id=self.id,
+            )
+
+        # Review was approved - use the potentially modified data
+        # ReviewResult.data must be a dict for block inputs
+        reviewed_data = decision.review_result.data
+        if not isinstance(reviewed_data, dict):
+            raise BlockExecutionError(
+                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
+                block_name=self.name,
+                block_id=self.id,
+            )
+        return False, reviewed_data
+
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
+        # Check for review requirement and get potentially modified input data
+        should_pause, input_data = await self.is_block_exec_need_review(
+            input_data, **kwargs
+        )
+        if should_pause:
+            return
+
+        # Validate the input data (original or reviewer-modified) once
         if error := self.input_schema.validate_data(input_data):
             raise BlockInputError(
                 message=f"Unable to execute block with invalid input data: {error}",
@@ -622,6 +695,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                 block_id=self.id,
             )
 
+        # Use the validated input data
         async for output_name, output_data in self.run(
             self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
             **kwargs,

autogpt_platform/backend/backend/data/db.py

@@ -38,6 +38,20 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
+# Add public schema to search_path for pgvector type access
+# The vector extension is in public schema, but search_path is determined by schema parameter
+# Extract the schema from DATABASE_URL or default to 'platform'
+parsed_url = urlparse(DATABASE_URL)
+url_params = dict(parse_qsl(parsed_url.query))
+db_schema = url_params.get("schema", "platform")
+# Build search_path, avoiding duplicates if db_schema is already 'public'
+search_path_schemas = list(
+    dict.fromkeys([db_schema, "public"])
+)  # Preserves order, removes duplicates
+search_path = ",".join(search_path_schemas)
+# This allows using ::vector without schema qualification
+DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
+
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -108,21 +122,84 @@ 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/execution.py

@@ -383,6 +383,7 @@ class GraphExecutionWithNodes(GraphExecution):
         self,
         execution_context: ExecutionContext,
         compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
+        nodes_to_skip: Optional[set[str]] = None,
     ):
         return GraphExecutionEntry(
             user_id=self.user_id,
@@ -390,6 +391,7 @@ class GraphExecutionWithNodes(GraphExecution):
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
             nodes_input_masks=compiled_nodes_input_masks,
+            nodes_to_skip=nodes_to_skip or set(),
             execution_context=execution_context,
         )
 
@@ -1145,6 +1147,8 @@ class GraphExecutionEntry(BaseModel):
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[NodesInputMasks] = None
+    nodes_to_skip: set[str] = Field(default_factory=set)
+    """Node IDs that should be skipped due to optional credentials not being configured."""
     execution_context: ExecutionContext = Field(default_factory=ExecutionContext)
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -94,6 +94,15 @@ class Node(BaseDbModel):
     input_links: list[Link] = []
     output_links: list[Link] = []
 
+    @property
+    def credentials_optional(self) -> bool:
+        """
+        Whether credentials are optional for this node.
+        When True and credentials are not configured, the node will be skipped
+        during execution rather than causing a validation error.
+        """
+        return self.metadata.get("credentials_optional", False)
+
     @property
     def block(self) -> AnyBlockSchema | "_UnknownBlockBase":
         """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
@@ -235,7 +244,10 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+            if (
+                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+                or node.block.requires_human_review
+            )
         )
 
     @property
@@ -326,7 +338,35 @@ class Graph(BaseGraph):
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        return self._credentials_input_schema.jsonschema()
+        schema = self._credentials_input_schema.jsonschema()
+
+        # Determine which credential fields are required based on credentials_optional metadata
+        graph_credentials_inputs = self.aggregate_credentials_inputs()
+        required_fields = []
+
+        # Build a map of node_id -> node for quick lookup
+        all_nodes = {node.id: node for node in self.nodes}
+        for sub_graph in self.sub_graphs:
+            for node in sub_graph.nodes:
+                all_nodes[node.id] = node
+
+        for field_key, (
+            _field_info,
+            node_field_pairs,
+        ) in graph_credentials_inputs.items():
+            # A field is required if ANY node using it has credentials_optional=False
+            is_required = False
+            for node_id, _field_name in node_field_pairs:
+                node = all_nodes.get(node_id)
+                if node and not node.credentials_optional:
+                    is_required = True
+                    break
+
+            if is_required:
+                required_fields.append(field_key)
+
+        schema["required"] = required_fields
+        return schema
 
     @property
     def _credentials_input_schema(self) -> type[BlockSchema]:

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):
     """
@@ -396,3 +408,58 @@ async def test_access_store_listing_graph(server: SpinTestServer):
         created_graph.id, created_graph.version, "3e53486c-cf57-477e-ba2a-cb02dc828e1b"
     )
     assert got_graph is not None
+
+
+# ============================================================================
+# Tests for Optional Credentials Feature
+# ============================================================================
+
+
+def test_node_credentials_optional_default():
+    """Test that credentials_optional defaults to False when not set in metadata."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={},
+    )
+    assert node.credentials_optional is False
+
+
+def test_node_credentials_optional_true():
+    """Test that credentials_optional returns True when explicitly set."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={"credentials_optional": True},
+    )
+    assert node.credentials_optional is True
+
+
+def test_node_credentials_optional_false():
+    """Test that credentials_optional returns False when explicitly set to False."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={"credentials_optional": False},
+    )
+    assert node.credentials_optional is False
+
+
+def test_node_credentials_optional_with_other_metadata():
+    """Test that credentials_optional works correctly with other metadata present."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={
+            "position": {"x": 100, "y": 200},
+            "customized_name": "My Custom Node",
+            "credentials_optional": True,
+        },
+    )
+    assert node.credentials_optional is True
+    assert node.metadata["position"] == {"x": 100, "y": 200}
+    assert node.metadata["customized_name"] == "My Custom Node"

autogpt_platform/backend/backend/executor/database.py

@@ -7,6 +7,10 @@ from backend.api.features.library.db import (
     list_library_agents,
 )
 from backend.api.features.store.db import get_store_agent_details, get_store_agents
+from backend.api.features.store.embeddings import (
+    backfill_missing_embeddings,
+    get_embedding_stats,
+)
 from backend.data import db
 from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
@@ -208,6 +212,10 @@ class DatabaseManager(AppService):
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
 
+    # Store Embeddings
+    get_embedding_stats = _(get_embedding_stats)
+    backfill_missing_embeddings = _(backfill_missing_embeddings)
+
     # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
 
@@ -259,6 +267,10 @@ class DatabaseManagerClient(AppServiceClient):
     get_store_agents = _(d.get_store_agents)
     get_store_agent_details = _(d.get_store_agent_details)
 
+    # Store Embeddings
+    get_embedding_stats = _(d.get_embedding_stats)
+    backfill_missing_embeddings = _(d.backfill_missing_embeddings)
+
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager

autogpt_platform/backend/backend/executor/manager.py

@@ -178,6 +178,7 @@ async def execute_node(
     execution_processor: "ExecutionProcessor",
     execution_stats: NodeExecutionStats | None = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
+    nodes_to_skip: Optional[set[str]] = None,
 ) -> BlockOutput:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -245,6 +246,7 @@ async def execute_node(
         "user_id": user_id,
         "execution_context": execution_context,
         "execution_processor": execution_processor,
+        "nodes_to_skip": nodes_to_skip or set(),
     }
 
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
@@ -542,6 +544,7 @@ class ExecutionProcessor:
         node_exec_progress: NodeExecutionProgress,
         nodes_input_masks: Optional[NodesInputMasks],
         graph_stats_pair: tuple[GraphExecutionStats, threading.Lock],
+        nodes_to_skip: Optional[set[str]] = None,
     ) -> NodeExecutionStats:
         log_metadata = LogMetadata(
             logger=_logger,
@@ -564,6 +567,7 @@ class ExecutionProcessor:
             db_client=db_client,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
+            nodes_to_skip=nodes_to_skip,
         )
         if isinstance(status, BaseException):
             raise status
@@ -609,6 +613,7 @@ class ExecutionProcessor:
         db_client: "DatabaseManagerAsyncClient",
         log_metadata: LogMetadata,
         nodes_input_masks: Optional[NodesInputMasks] = None,
+        nodes_to_skip: Optional[set[str]] = None,
     ) -> ExecutionStatus:
         status = ExecutionStatus.RUNNING
 
@@ -645,6 +650,7 @@ class ExecutionProcessor:
                 execution_processor=self,
                 execution_stats=stats,
                 nodes_input_masks=nodes_input_masks,
+                nodes_to_skip=nodes_to_skip,
             ):
                 await persist_output(output_name, output_data)
 
@@ -956,6 +962,21 @@ class ExecutionProcessor:
 
                 queued_node_exec = execution_queue.get()
 
+                # Check if this node should be skipped due to optional credentials
+                if queued_node_exec.node_id in graph_exec.nodes_to_skip:
+                    log_metadata.info(
+                        f"Skipping node execution {queued_node_exec.node_exec_id} "
+                        f"for node {queued_node_exec.node_id} - optional credentials not configured"
+                    )
+                    # Mark the node as completed without executing
+                    # No outputs will be produced, so downstream nodes won't trigger
+                    update_node_execution_status(
+                        db_client=db_client,
+                        exec_id=queued_node_exec.node_exec_id,
+                        status=ExecutionStatus.COMPLETED,
+                    )
+                    continue
+
                 log_metadata.debug(
                     f"Dispatching node execution {queued_node_exec.node_exec_id} "
                     f"for node {queued_node_exec.node_id}",
@@ -1016,6 +1037,7 @@ class ExecutionProcessor:
                             execution_stats,
                             execution_stats_lock,
                         ),
+                        nodes_to_skip=graph_exec.nodes_to_skip,
                     ),
                     self.node_execution_loop,
                 )

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

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import os
 import threading
+import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -37,7 +38,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_scheduler_client
+from backend.util.clients import get_database_manager_client, get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -254,6 +255,74 @@ 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
+    """
+    db_client = get_database_manager_client()
+    stats = db_client.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 = db_client.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
+
+        if result["success"] == 0 and result["processed"] > 0:
+            # All attempts in this batch failed - stop to avoid infinite loop
+            logger.error(
+                f"All {result['processed']} embedding attempts failed - stopping backfill"
+            )
+            break
+
+        # Small delay between batches to avoid rate limits
+        time.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,
+    }
+
+
 # Monitoring functions are now imported from monitoring module
 
 
@@ -475,6 +544,19 @@ 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 +714,11 @@ 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/executor/utils.py

@@ -239,14 +239,19 @@ async def _validate_node_input_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> dict[str, dict[str, str]]:
+) -> tuple[dict[str, dict[str, str]], set[str]]:
     """
-    Checks all credentials for all nodes of the graph and returns structured errors.
+    Checks all credentials for all nodes of the graph and returns structured errors
+    and a set of nodes that should be skipped due to optional missing credentials.
 
     Returns:
-        dict[node_id, dict[field_name, error_message]]: Credential validation errors per node
+        tuple[
+            dict[node_id, dict[field_name, error_message]]: Credential validation errors per node,
+            set[node_id]: Nodes that should be skipped (optional credentials not configured)
+        ]
     """
     credential_errors: dict[str, dict[str, str]] = defaultdict(dict)
+    nodes_to_skip: set[str] = set()
 
     for node in graph.nodes:
         block = node.block
@@ -256,27 +261,46 @@ async def _validate_node_input_credentials(
         if not credentials_fields:
             continue
 
+        # Track if any credential field is missing for this node
+        has_missing_credentials = False
+
         for field_name, credentials_meta_type in credentials_fields.items():
             try:
+                # Check nodes_input_masks first, then input_default
+                field_value = None
                 if (
                     nodes_input_masks
                     and (node_input_mask := nodes_input_masks.get(node.id))
                     and field_name in node_input_mask
                 ):
-                    credentials_meta = credentials_meta_type.model_validate(
-                        node_input_mask[field_name]
-                    )
+                    field_value = node_input_mask[field_name]
                 elif field_name in node.input_default:
-                    credentials_meta = credentials_meta_type.model_validate(
-                        node.input_default[field_name]
-                    )
-                else:
-                    # Missing credentials
-                    credential_errors[node.id][
-                        field_name
-                    ] = "These credentials are required"
-                    continue
+                    # For optional credentials, don't use input_default - treat as missing
+                    # This prevents stale credential IDs from failing validation
+                    if node.credentials_optional:
+                        field_value = None
+                    else:
+                        field_value = node.input_default[field_name]
+
+                # Check if credentials are missing (None, empty, or not present)
+                if field_value is None or (
+                    isinstance(field_value, dict) and not field_value.get("id")
+                ):
+                    has_missing_credentials = True
+                    # If node has credentials_optional flag, mark for skipping instead of error
+                    if node.credentials_optional:
+                        continue  # Don't add error, will be marked for skip after loop
+                    else:
+                        credential_errors[node.id][
+                            field_name
+                        ] = "These credentials are required"
+                        continue
+
+                credentials_meta = credentials_meta_type.model_validate(field_value)
+
             except ValidationError as e:
+                # Validation error means credentials were provided but invalid
+                # This should always be an error, even if optional
                 credential_errors[node.id][field_name] = f"Invalid credentials: {e}"
                 continue
 
@@ -287,6 +311,7 @@ async def _validate_node_input_credentials(
                 )
             except Exception as e:
                 # Handle any errors fetching credentials
+                # If credentials were explicitly configured but unavailable, it's an error
                 credential_errors[node.id][
                     field_name
                 ] = f"Credentials not available: {e}"
@@ -313,7 +338,19 @@ async def _validate_node_input_credentials(
                 ] = "Invalid credentials: type/provider mismatch"
                 continue
 
-    return credential_errors
+        # If node has optional credentials and any are missing, mark for skipping
+        # But only if there are no other errors for this node
+        if (
+            has_missing_credentials
+            and node.credentials_optional
+            and node.id not in credential_errors
+        ):
+            nodes_to_skip.add(node.id)
+            logger.info(
+                f"Node #{node.id} will be skipped: optional credentials not configured"
+            )
+
+    return credential_errors, nodes_to_skip
 
 
 def make_node_credentials_input_map(
@@ -355,21 +392,25 @@ async def validate_graph_with_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> Mapping[str, Mapping[str, str]]:
+) -> tuple[Mapping[str, Mapping[str, str]], set[str]]:
     """
-    Validate graph including credentials and return structured errors per node.
+    Validate graph including credentials and return structured errors per node,
+    along with a set of nodes that should be skipped due to optional missing credentials.
 
     Returns:
-        dict[node_id, dict[field_name, error_message]]: Validation errors per node
+        tuple[
+            dict[node_id, dict[field_name, error_message]]: Validation errors per node,
+            set[node_id]: Nodes that should be skipped (optional credentials not configured)
+        ]
     """
     # Get input validation errors
     node_input_errors = GraphModel.validate_graph_get_errors(
         graph, for_run=True, nodes_input_masks=nodes_input_masks
     )
 
-    # Get credential input/availability/validation errors
-    node_credential_input_errors = await _validate_node_input_credentials(
-        graph, user_id, nodes_input_masks
+    # Get credential input/availability/validation errors and nodes to skip
+    node_credential_input_errors, nodes_to_skip = (
+        await _validate_node_input_credentials(graph, user_id, nodes_input_masks)
     )
 
     # Merge credential errors with structural errors
@@ -378,7 +419,7 @@ async def validate_graph_with_credentials(
             node_input_errors[node_id] = {}
         node_input_errors[node_id].update(field_errors)
 
-    return node_input_errors
+    return node_input_errors, nodes_to_skip
 
 
 async def _construct_starting_node_execution_input(
@@ -386,7 +427,7 @@ async def _construct_starting_node_execution_input(
     user_id: str,
     graph_inputs: BlockInput,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> list[tuple[str, BlockInput]]:
+) -> tuple[list[tuple[str, BlockInput]], set[str]]:
     """
     Validates and prepares the input data for executing a graph.
     This function checks the graph for starting nodes, validates the input data
@@ -400,11 +441,14 @@ async def _construct_starting_node_execution_input(
         node_credentials_map: `dict[node_id, dict[input_name, CredentialsMetaInput]]`
 
     Returns:
-        list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID and
-            the corresponding input data for that node.
+        tuple[
+            list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID
+                and the corresponding input data for that node.
+            set[str]: Node IDs that should be skipped (optional credentials not configured)
+        ]
     """
     # Use new validation function that includes credentials
-    validation_errors = await validate_graph_with_credentials(
+    validation_errors, nodes_to_skip = await validate_graph_with_credentials(
         graph, user_id, nodes_input_masks
     )
     n_error_nodes = len(validation_errors)
@@ -445,7 +489,7 @@ async def _construct_starting_node_execution_input(
             "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
         )
 
-    return nodes_input
+    return nodes_input, nodes_to_skip
 
 
 async def validate_and_construct_node_execution_input(
@@ -456,7 +500,7 @@ async def validate_and_construct_node_execution_input(
     graph_credentials_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
     is_sub_graph: bool = False,
-) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks]:
+) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks, set[str]]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -473,6 +517,7 @@ async def validate_and_construct_node_execution_input(
         GraphModel: Full graph object for the given `graph_id`.
         list[tuple[node_id, BlockInput]]: Starting node IDs with corresponding inputs.
         dict[str, BlockInput]: Node input masks including all passed-in credentials.
+        set[str]: Node IDs that should be skipped (optional credentials not configured).
 
     Raises:
         NotFoundError: If the graph is not found.
@@ -514,14 +559,16 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks or {},
     )
 
-    starting_nodes_input = await _construct_starting_node_execution_input(
-        graph=graph,
-        user_id=user_id,
-        graph_inputs=graph_inputs,
-        nodes_input_masks=nodes_input_masks,
+    starting_nodes_input, nodes_to_skip = (
+        await _construct_starting_node_execution_input(
+            graph=graph,
+            user_id=user_id,
+            graph_inputs=graph_inputs,
+            nodes_input_masks=nodes_input_masks,
+        )
     )
 
-    return graph, starting_nodes_input, nodes_input_masks
+    return graph, starting_nodes_input, nodes_input_masks, nodes_to_skip
 
 
 def _merge_nodes_input_masks(
@@ -779,6 +826,9 @@ async def add_graph_execution(
 
         # Use existing execution's compiled input masks
         compiled_nodes_input_masks = graph_exec.nodes_input_masks or {}
+        # For resumed executions, nodes_to_skip was already determined at creation time
+        # TODO: Consider storing nodes_to_skip in DB if we need to preserve it across resumes
+        nodes_to_skip: set[str] = set()
 
         logger.info(f"Resuming graph execution #{graph_exec.id} for graph #{graph_id}")
     else:
@@ -787,7 +837,7 @@ async def add_graph_execution(
         )
 
         # Create new execution
-        graph, starting_nodes_input, compiled_nodes_input_masks = (
+        graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip = (
             await validate_and_construct_node_execution_input(
                 graph_id=graph_id,
                 user_id=user_id,
@@ -836,6 +886,7 @@ async def add_graph_execution(
     try:
         graph_exec_entry = graph_exec.to_graph_execution_entry(
             compiled_nodes_input_masks=compiled_nodes_input_masks,
+            nodes_to_skip=nodes_to_skip,
             execution_context=execution_context,
         )
         logger.info(f"Publishing execution {graph_exec.id} to execution queue")

autogpt_platform/backend/backend/executor/utils_test.py

@@ -367,10 +367,13 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     )
 
     # Setup mock returns
+    # The function returns (graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip)
+    nodes_to_skip: set[str] = set()
     mock_validate.return_value = (
         mock_graph,
         starting_nodes_input,
         compiled_nodes_input_masks,
+        nodes_to_skip,
     )
     mock_prisma.is_connected.return_value = True
     mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
@@ -456,3 +459,212 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Both executions should succeed (though they create different objects)
     assert result1 == mock_graph_exec
     assert result2 == mock_graph_exec_2
+
+
+# ============================================================================
+# Tests for Optional Credentials Feature
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_returns_nodes_to_skip(
+    mocker: MockerFixture,
+):
+    """
+    Test that _validate_node_input_credentials returns nodes_to_skip set
+    for nodes with credentials_optional=True and missing credentials.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    # Create a mock node with credentials_optional=True
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-optional-creds"
+    mock_node.credentials_optional = True
+    mock_node.input_default = {}  # No credentials configured
+
+    # Create a mock block with credentials field
+    mock_block = mocker.MagicMock()
+    mock_credentials_field_type = mocker.MagicMock()
+    mock_block.input_schema.get_credentials_fields.return_value = {
+        "credentials": mock_credentials_field_type
+    }
+    mock_node.block = mock_block
+
+    # Create mock graph
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    # Call the function
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="test-user-id",
+        nodes_input_masks=None,
+    )
+
+    # Node should be in nodes_to_skip, not in errors
+    assert mock_node.id in nodes_to_skip
+    assert mock_node.id not in errors
+
+
+@pytest.mark.asyncio
+async def test_validate_node_input_credentials_required_missing_creds_error(
+    mocker: MockerFixture,
+):
+    """
+    Test that _validate_node_input_credentials returns errors
+    for nodes with credentials_optional=False and missing credentials.
+    """
+    from backend.executor.utils import _validate_node_input_credentials
+
+    # Create a mock node with credentials_optional=False (required)
+    mock_node = mocker.MagicMock()
+    mock_node.id = "node-with-required-creds"
+    mock_node.credentials_optional = False
+    mock_node.input_default = {}  # No credentials configured
+
+    # Create a mock block with credentials field
+    mock_block = mocker.MagicMock()
+    mock_credentials_field_type = mocker.MagicMock()
+    mock_block.input_schema.get_credentials_fields.return_value = {
+        "credentials": mock_credentials_field_type
+    }
+    mock_node.block = mock_block
+
+    # Create mock graph
+    mock_graph = mocker.MagicMock()
+    mock_graph.nodes = [mock_node]
+
+    # Call the function
+    errors, nodes_to_skip = await _validate_node_input_credentials(
+        graph=mock_graph,
+        user_id="test-user-id",
+        nodes_input_masks=None,
+    )
+
+    # Node should be in errors, not in nodes_to_skip
+    assert mock_node.id in errors
+    assert "credentials" in errors[mock_node.id]
+    assert "required" in errors[mock_node.id]["credentials"].lower()
+    assert mock_node.id not in nodes_to_skip
+
+
+@pytest.mark.asyncio
+async def test_validate_graph_with_credentials_returns_nodes_to_skip(
+    mocker: MockerFixture,
+):
+    """
+    Test that validate_graph_with_credentials returns nodes_to_skip set
+    from _validate_node_input_credentials.
+    """
+    from backend.executor.utils import validate_graph_with_credentials
+
+    # Mock _validate_node_input_credentials to return specific values
+    mock_validate = mocker.patch(
+        "backend.executor.utils._validate_node_input_credentials"
+    )
+    expected_errors = {"node1": {"field": "error"}}
+    expected_nodes_to_skip = {"node2", "node3"}
+    mock_validate.return_value = (expected_errors, expected_nodes_to_skip)
+
+    # Mock GraphModel with validate_graph_get_errors method
+    mock_graph = mocker.MagicMock()
+    mock_graph.validate_graph_get_errors.return_value = {}
+
+    # Call the function
+    errors, nodes_to_skip = await validate_graph_with_credentials(
+        graph=mock_graph,
+        user_id="test-user-id",
+        nodes_input_masks=None,
+    )
+
+    # Verify nodes_to_skip is passed through
+    assert nodes_to_skip == expected_nodes_to_skip
+    assert "node1" in errors
+
+
+@pytest.mark.asyncio
+async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
+    """
+    Test that add_graph_execution properly passes nodes_to_skip
+    to the graph execution entry.
+    """
+    from backend.data.execution import GraphExecutionWithNodes
+    from backend.executor.utils import add_graph_execution
+
+    # Mock data
+    graph_id = "test-graph-id"
+    user_id = "test-user-id"
+    inputs = {"test_input": "test_value"}
+    graph_version = 1
+
+    # Mock the graph object
+    mock_graph = mocker.MagicMock()
+    mock_graph.version = graph_version
+
+    # Starting nodes and masks
+    starting_nodes_input = [("node1", {"input1": "value1"})]
+    compiled_nodes_input_masks = {}
+    nodes_to_skip = {"skipped-node-1", "skipped-node-2"}
+
+    # Mock the graph execution object
+    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
+    mock_graph_exec.id = "execution-id-123"
+    mock_graph_exec.node_executions = []
+
+    # Track what's passed to to_graph_execution_entry
+    captured_kwargs = {}
+
+    def capture_to_entry(**kwargs):
+        captured_kwargs.update(kwargs)
+        return mocker.MagicMock()
+
+    mock_graph_exec.to_graph_execution_entry.side_effect = capture_to_entry
+
+    # Setup mocks
+    mock_validate = mocker.patch(
+        "backend.executor.utils.validate_and_construct_node_execution_input"
+    )
+    mock_edb = mocker.patch("backend.executor.utils.execution_db")
+    mock_prisma = mocker.patch("backend.executor.utils.prisma")
+    mock_udb = mocker.patch("backend.executor.utils.user_db")
+    mock_gdb = mocker.patch("backend.executor.utils.graph_db")
+    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
+    mock_get_event_bus = mocker.patch(
+        "backend.executor.utils.get_async_execution_event_bus"
+    )
+
+    # Setup returns - include nodes_to_skip in the tuple
+    mock_validate.return_value = (
+        mock_graph,
+        starting_nodes_input,
+        compiled_nodes_input_masks,
+        nodes_to_skip,  # This should be passed through
+    )
+    mock_prisma.is_connected.return_value = True
+    mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
+    mock_edb.update_graph_execution_stats = mocker.AsyncMock(
+        return_value=mock_graph_exec
+    )
+    mock_edb.update_node_execution_status_batch = mocker.AsyncMock()
+
+    mock_user = mocker.MagicMock()
+    mock_user.timezone = "UTC"
+    mock_settings = mocker.MagicMock()
+    mock_settings.human_in_the_loop_safe_mode = True
+
+    mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
+    mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
+    mock_get_queue.return_value = mocker.AsyncMock()
+    mock_get_event_bus.return_value = mocker.MagicMock(publish=mocker.AsyncMock())
+
+    # Call the function
+    await add_graph_execution(
+        graph_id=graph_id,
+        user_id=user_id,
+        inputs=inputs,
+        graph_version=graph_version,
+    )
+
+    # Verify nodes_to_skip was passed to to_graph_execution_entry
+    assert "nodes_to_skip" in captured_kwargs
+    assert captured_kwargs["nodes_to_skip"] == nodes_to_skip

autogpt_platform/backend/backend/integrations/oauth/__init__.py

@@ -8,6 +8,7 @@ from .discord import DiscordOAuthHandler
 from .github import GitHubOAuthHandler
 from .google import GoogleOAuthHandler
 from .notion import NotionOAuthHandler
+from .reddit import RedditOAuthHandler
 from .twitter import TwitterOAuthHandler
 
 if TYPE_CHECKING:
@@ -20,6 +21,7 @@ _ORIGINAL_HANDLERS = [
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,
+    RedditOAuthHandler,
     TwitterOAuthHandler,
     TodoistOAuthHandler,
 ]

autogpt_platform/backend/backend/integrations/oauth/reddit.py

@@ -0,0 +1,208 @@
+import time
+import urllib.parse
+from typing import ClassVar, Optional
+
+from pydantic import SecretStr
+
+from backend.data.model import OAuth2Credentials
+from backend.integrations.oauth.base import BaseOAuthHandler
+from backend.integrations.providers import ProviderName
+from backend.util.request import Requests
+from backend.util.settings import Settings
+
+settings = Settings()
+
+
+class RedditOAuthHandler(BaseOAuthHandler):
+    """
+    Reddit OAuth 2.0 handler.
+
+    Based on the documentation at:
+    - https://github.com/reddit-archive/reddit/wiki/OAuth2
+
+    Notes:
+    - Reddit requires `duration=permanent` to get refresh tokens
+    - Access tokens expire after 1 hour (3600 seconds)
+    - Reddit requires HTTP Basic Auth for token requests
+    - Reddit requires a unique User-Agent header
+    """
+
+    PROVIDER_NAME = ProviderName.REDDIT
+    DEFAULT_SCOPES: ClassVar[list[str]] = [
+        "identity",  # Get username, verify auth
+        "read",  # Access posts and comments
+        "submit",  # Submit new posts and comments
+        "edit",  # Edit own posts and comments
+        "history",  # Access user's post history
+        "privatemessages",  # Access inbox and send private messages
+        "flair",  # Access and set flair on posts/subreddits
+    ]
+
+    AUTHORIZE_URL = "https://www.reddit.com/api/v1/authorize"
+    TOKEN_URL = "https://www.reddit.com/api/v1/access_token"
+    USERNAME_URL = "https://oauth.reddit.com/api/v1/me"
+    REVOKE_URL = "https://www.reddit.com/api/v1/revoke_token"
+
+    def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
+        self.client_id = client_id
+        self.client_secret = client_secret
+        self.redirect_uri = redirect_uri
+
+    def get_login_url(
+        self, scopes: list[str], state: str, code_challenge: Optional[str]
+    ) -> str:
+        """Generate Reddit OAuth 2.0 authorization URL"""
+        scopes = self.handle_default_scopes(scopes)
+
+        params = {
+            "response_type": "code",
+            "client_id": self.client_id,
+            "redirect_uri": self.redirect_uri,
+            "scope": " ".join(scopes),
+            "state": state,
+            "duration": "permanent",  # Required for refresh tokens
+        }
+
+        return f"{self.AUTHORIZE_URL}?{urllib.parse.urlencode(params)}"
+
+    async def exchange_code_for_tokens(
+        self, code: str, scopes: list[str], code_verifier: Optional[str]
+    ) -> OAuth2Credentials:
+        """Exchange authorization code for access tokens"""
+        scopes = self.handle_default_scopes(scopes)
+
+        headers = {
+            "Content-Type": "application/x-www-form-urlencoded",
+            "User-Agent": settings.config.reddit_user_agent,
+        }
+
+        data = {
+            "grant_type": "authorization_code",
+            "code": code,
+            "redirect_uri": self.redirect_uri,
+        }
+
+        # Reddit requires HTTP Basic Auth for token requests
+        auth = (self.client_id, self.client_secret)
+
+        response = await Requests().post(
+            self.TOKEN_URL, headers=headers, data=data, auth=auth
+        )
+
+        if not response.ok:
+            error_text = response.text()
+            raise ValueError(
+                f"Reddit token exchange failed: {response.status} - {error_text}"
+            )
+
+        tokens = response.json()
+
+        if "error" in tokens:
+            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
+
+        username = await self._get_username(tokens["access_token"])
+
+        return OAuth2Credentials(
+            provider=self.PROVIDER_NAME,
+            title=None,
+            username=username,
+            access_token=tokens["access_token"],
+            refresh_token=tokens.get("refresh_token"),
+            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
+            refresh_token_expires_at=None,  # Reddit refresh tokens don't expire
+            scopes=scopes,
+        )
+
+    async def _get_username(self, access_token: str) -> str:
+        """Get the username from the access token"""
+        headers = {
+            "Authorization": f"Bearer {access_token}",
+            "User-Agent": settings.config.reddit_user_agent,
+        }
+
+        response = await Requests().get(self.USERNAME_URL, headers=headers)
+
+        if not response.ok:
+            raise ValueError(f"Failed to get Reddit username: {response.status}")
+
+        data = response.json()
+        return data.get("name", "unknown")
+
+    async def _refresh_tokens(
+        self, credentials: OAuth2Credentials
+    ) -> OAuth2Credentials:
+        """Refresh access tokens using refresh token"""
+        if not credentials.refresh_token:
+            raise ValueError("No refresh token available")
+
+        headers = {
+            "Content-Type": "application/x-www-form-urlencoded",
+            "User-Agent": settings.config.reddit_user_agent,
+        }
+
+        data = {
+            "grant_type": "refresh_token",
+            "refresh_token": credentials.refresh_token.get_secret_value(),
+        }
+
+        auth = (self.client_id, self.client_secret)
+
+        response = await Requests().post(
+            self.TOKEN_URL, headers=headers, data=data, auth=auth
+        )
+
+        if not response.ok:
+            error_text = response.text()
+            raise ValueError(
+                f"Reddit token refresh failed: {response.status} - {error_text}"
+            )
+
+        tokens = response.json()
+
+        if "error" in tokens:
+            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
+
+        username = await self._get_username(tokens["access_token"])
+
+        # Reddit may or may not return a new refresh token
+        new_refresh_token = tokens.get("refresh_token")
+        if new_refresh_token:
+            refresh_token: SecretStr | None = SecretStr(new_refresh_token)
+        elif credentials.refresh_token:
+            # Keep the existing refresh token
+            refresh_token = credentials.refresh_token
+        else:
+            refresh_token = None
+
+        return OAuth2Credentials(
+            id=credentials.id,
+            provider=self.PROVIDER_NAME,
+            title=credentials.title,
+            username=username,
+            access_token=tokens["access_token"],
+            refresh_token=refresh_token,
+            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
+            refresh_token_expires_at=None,
+            scopes=credentials.scopes,
+        )
+
+    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
+        """Revoke the access token"""
+        headers = {
+            "Content-Type": "application/x-www-form-urlencoded",
+            "User-Agent": settings.config.reddit_user_agent,
+        }
+
+        data = {
+            "token": credentials.access_token.get_secret_value(),
+            "token_type_hint": "access_token",
+        }
+
+        auth = (self.client_id, self.client_secret)
+
+        response = await Requests().post(
+            self.REVOKE_URL, headers=headers, data=data, auth=auth
+        )
+
+        # Reddit returns 204 No Content on successful revocation
+        return response.ok

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/backend/util/settings.py

@@ -264,7 +264,7 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
     )
 
     reddit_user_agent: str = Field(
-        default="AutoGPT:1.0 (by /u/autogpt)",
+        default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
     )
 

autogpt_platform/backend/gen_prisma_types_stub.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""
+Generate a lightweight stub for prisma/types.py that collapses all exported
+symbols to Any. This prevents Pyright from spending time/budget on Prisma's
+query DSL types while keeping runtime behavior unchanged.
+
+Usage:
+    poetry run gen-prisma-stub
+
+This script automatically finds the prisma package location and generates
+the types.pyi stub file in the same directory as types.py.
+"""
+
+from __future__ import annotations
+
+import ast
+import importlib.util
+import sys
+from pathlib import Path
+from typing import Iterable, Set
+
+
+def _iter_assigned_names(target: ast.expr) -> Iterable[str]:
+    """Extract names from assignment targets (handles tuple unpacking)."""
+    if isinstance(target, ast.Name):
+        yield target.id
+    elif isinstance(target, (ast.Tuple, ast.List)):
+        for elt in target.elts:
+            yield from _iter_assigned_names(elt)
+
+
+def _is_private(name: str) -> bool:
+    """Check if a name is private (starts with _ but not __)."""
+    return name.startswith("_") and not name.startswith("__")
+
+
+def _is_safe_type_alias(node: ast.Assign) -> bool:
+    """Check if an assignment is a safe type alias that shouldn't be stubbed.
+
+    Safe types are:
+    - Literal types (don't cause type budget issues)
+    - Simple type references (SortMode, SortOrder, etc.)
+    - TypeVar definitions
+    """
+    if not node.value:
+        return False
+
+    # Check if it's a Subscript (like Literal[...], Union[...], TypeVar[...])
+    if isinstance(node.value, ast.Subscript):
+        # Get the base type name
+        if isinstance(node.value.value, ast.Name):
+            base_name = node.value.value.id
+            # Literal types are safe
+            if base_name == "Literal":
+                return True
+            # TypeVar is safe
+            if base_name == "TypeVar":
+                return True
+        elif isinstance(node.value.value, ast.Attribute):
+            # Handle typing_extensions.Literal etc.
+            if node.value.value.attr == "Literal":
+                return True
+
+    # Check if it's a simple Name reference (like SortMode = _types.SortMode)
+    if isinstance(node.value, ast.Attribute):
+        return True
+
+    # Check if it's a Call (like TypeVar(...))
+    if isinstance(node.value, ast.Call):
+        if isinstance(node.value.func, ast.Name):
+            if node.value.func.id == "TypeVar":
+                return True
+
+    return False
+
+
+def collect_top_level_symbols(
+    tree: ast.Module, source_lines: list[str]
+) -> tuple[Set[str], Set[str], list[str], Set[str]]:
+    """Collect all top-level symbols from an AST module.
+
+    Returns:
+        Tuple of (class_names, function_names, safe_variable_sources, unsafe_variable_names)
+        safe_variable_sources contains the actual source code lines for safe variables
+    """
+    classes: Set[str] = set()
+    functions: Set[str] = set()
+    safe_variable_sources: list[str] = []
+    unsafe_variables: Set[str] = set()
+
+    for node in tree.body:
+        if isinstance(node, ast.ClassDef):
+            if not _is_private(node.name):
+                classes.add(node.name)
+        elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+            if not _is_private(node.name):
+                functions.add(node.name)
+        elif isinstance(node, ast.Assign):
+            is_safe = _is_safe_type_alias(node)
+            names = []
+            for t in node.targets:
+                for n in _iter_assigned_names(t):
+                    if not _is_private(n):
+                        names.append(n)
+            if names:
+                if is_safe:
+                    # Extract the source code for this assignment
+                    start_line = node.lineno - 1  # 0-indexed
+                    end_line = node.end_lineno if node.end_lineno else node.lineno
+                    source = "\n".join(source_lines[start_line:end_line])
+                    safe_variable_sources.append(source)
+                else:
+                    unsafe_variables.update(names)
+        elif isinstance(node, ast.AnnAssign) and node.target:
+            # Annotated assignments are always stubbed
+            for n in _iter_assigned_names(node.target):
+                if not _is_private(n):
+                    unsafe_variables.add(n)
+
+    return classes, functions, safe_variable_sources, unsafe_variables
+
+
+def find_prisma_types_path() -> Path:
+    """Find the prisma types.py file in the installed package."""
+    spec = importlib.util.find_spec("prisma")
+    if spec is None or spec.origin is None:
+        raise RuntimeError("Could not find prisma package. Is it installed?")
+
+    prisma_dir = Path(spec.origin).parent
+    types_path = prisma_dir / "types.py"
+
+    if not types_path.exists():
+        raise RuntimeError(f"prisma/types.py not found at {types_path}")
+
+    return types_path
+
+
+def generate_stub(src_path: Path, stub_path: Path) -> int:
+    """Generate the .pyi stub file from the source types.py."""
+    code = src_path.read_text(encoding="utf-8", errors="ignore")
+    source_lines = code.splitlines()
+    tree = ast.parse(code, filename=str(src_path))
+    classes, functions, safe_variable_sources, unsafe_variables = (
+        collect_top_level_symbols(tree, source_lines)
+    )
+
+    header = """\
+# -*- coding: utf-8 -*-
+# Auto-generated stub file - DO NOT EDIT
+# Generated by gen_prisma_types_stub.py
+#
+# This stub intentionally collapses complex Prisma query DSL types to Any.
+# Prisma's generated types can explode Pyright's type inference budgets
+# on large schemas. We collapse them to Any so the rest of the codebase
+# can remain strongly typed while keeping runtime behavior unchanged.
+#
+# Safe types (Literal, TypeVar, simple references) are preserved from the
+# original types.py to maintain proper type checking where possible.
+
+from __future__ import annotations
+from typing import Any
+from typing_extensions import Literal
+
+# Re-export commonly used typing constructs that may be imported from this module
+from typing import TYPE_CHECKING, TypeVar, Generic, Union, Optional, List, Dict
+
+# Base type alias for stubbed Prisma types - allows any dict structure
+_PrismaDict = dict[str, Any]
+
+"""
+
+    lines = [header]
+
+    # Include safe variable definitions (Literal types, TypeVars, etc.)
+    lines.append("# Safe type definitions preserved from original types.py")
+    for source in safe_variable_sources:
+        lines.append(source)
+    lines.append("")
+
+    # Stub all classes and unsafe variables uniformly as dict[str, Any] aliases
+    # This allows:
+    # 1. Use in type annotations: x: SomeType
+    # 2. Constructor calls: SomeType(...)
+    # 3. Dict literal assignments: x: SomeType = {...}
+    lines.append(
+        "# Stubbed types (collapsed to dict[str, Any] to prevent type budget exhaustion)"
+    )
+    all_stubbed = sorted(classes | unsafe_variables)
+    for name in all_stubbed:
+        lines.append(f"{name} = _PrismaDict")
+
+    lines.append("")
+
+    # Stub functions
+    for name in sorted(functions):
+        lines.append(f"def {name}(*args: Any, **kwargs: Any) -> Any: ...")
+
+    lines.append("")
+
+    stub_path.write_text("\n".join(lines), encoding="utf-8")
+    return (
+        len(classes)
+        + len(functions)
+        + len(safe_variable_sources)
+        + len(unsafe_variables)
+    )
+
+
+def main() -> None:
+    """Main entry point."""
+    try:
+        types_path = find_prisma_types_path()
+        stub_path = types_path.with_suffix(".pyi")
+
+        print(f"Found prisma types.py at: {types_path}")
+        print(f"Generating stub at: {stub_path}")
+
+        num_symbols = generate_stub(types_path, stub_path)
+        print(f"Generated {stub_path.name} with {num_symbols} Any-typed symbols")
+
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

autogpt_platform/backend/linter.py

@@ -25,6 +25,9 @@ def run(*command: str) -> None:
 
 
 def lint():
+    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
+    run("gen-prisma-stub")
+
     lint_step_args: list[list[str]] = [
         ["ruff", "check", *TARGET_DIRS, "--exit-zero"],
         ["ruff", "format", "--diff", "--check", LIBS_DIR],
@@ -49,4 +52,6 @@ def format():
     run("ruff", "format", LIBS_DIR)
     run("isort", "--profile", "black", BACKEND_DIR)
     run("black", BACKEND_DIR)
+    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
+    run("gen-prisma-stub")
     run("pyright", *TARGET_DIRS)

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

@@ -0,0 +1,46 @@
+-- CreateExtension
+-- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
+-- Create in public schema so vector type is available across all schemas
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'vector extension not available or already exists, skipping';
+END $$;
+
+-- 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" public.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" public.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/pyproject.toml

@@ -117,6 +117,7 @@ lint = "linter:lint"
 test = "run_tests:test"
 load-store-agents = "test.load_store_agents:run"
 export-api-schema = "backend.cli.generate_openapi_json:main"
+gen-prisma-stub = "gen_prisma_types_stub:main"
 oauth-tool = "backend.cli.oauth_tool:cli"
 
 [tool.isort]
@@ -134,6 +135,9 @@ ignore_patterns = []
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "session"
+# Disable syrupy plugin to avoid conflict with pytest-snapshot
+# Both provide --snapshot-update argument causing ArgumentError
+addopts = "-p no:syrupy"
 filterwarnings = [
     "ignore:'audioop' is deprecated:DeprecationWarning:discord.player",
     "ignore:invalid escape sequence:DeprecationWarning:tweepy.api",

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"
 }
 
@@ -127,8 +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)
@@ -721,26 +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
@@ -856,14 +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)
 
@@ -899,6 +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])
@@ -906,6 +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())
@@ -998,16 +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/backend/snapshots/grph_single

@@ -2,6 +2,7 @@
   "created_at": "2025-09-04T13:37:00",
   "credentials_input_schema": {
     "properties": {},
+    "required": [],
     "title": "TestGraphCredentialsInputSchema",
     "type": "object"
   },

autogpt_platform/backend/snapshots/grphs_all

@@ -2,6 +2,7 @@
   {
     "credentials_input_schema": {
       "properties": {},
+      "required": [],
       "title": "TestGraphCredentialsInputSchema",
       "type": "object"
     },

autogpt_platform/backend/snapshots/lib_agts_search

@@ -4,6 +4,7 @@
       "id": "test-agent-1",
       "graph_id": "test-agent-1",
       "graph_version": 1,
+      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",
@@ -41,6 +42,7 @@
       "id": "test-agent-2",
       "graph_id": "test-agent-2",
       "graph_version": 1,
+      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",

autogpt_platform/backend/snapshots/sub_success

@@ -1,6 +1,7 @@
 {
   "submissions": [
     {
+      "listing_id": "test-listing-id",
       "agent_id": "test-agent-id",
       "agent_version": 1,
       "name": "Test Agent",

autogpt_platform/docker-compose.platform.yml

@@ -37,7 +37,7 @@ services:
       context: ../
       dockerfile: autogpt_platform/backend/Dockerfile
       target: migrate
-    command: ["sh", "-c", "poetry run prisma generate && poetry run prisma migrate deploy"]
+    command: ["sh", "-c", "poetry run prisma generate && poetry run gen-prisma-stub && poetry run prisma migrate deploy"]
     develop:
       watch:
         - path: ./

autogpt_platform/frontend/package.json

@@ -92,7 +92,6 @@
     "react-currency-input-field": "4.0.3",
     "react-day-picker": "9.11.1",
     "react-dom": "18.3.1",
-    "react-drag-drop-files": "2.4.0",
     "react-hook-form": "7.66.0",
     "react-icons": "5.5.0",
     "react-markdown": "9.0.3",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -200,9 +200,6 @@ importers:
       react-dom:
         specifier: 18.3.1
         version: 18.3.1(react@18.3.1)
-      react-drag-drop-files:
-        specifier: 2.4.0
-        version: 2.4.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       react-hook-form:
         specifier: 7.66.0
         version: 7.66.0(react@18.3.1)
@@ -1004,9 +1001,6 @@ packages:
   '@emotion/memoize@0.8.1':
     resolution: {integrity: sha512-W2P2c/VRW1/1tLox0mVUalvnWXxavmv/Oum2aPsRcoDJuob75FC3Y8FbpfLwUegRcxINtGUMPq0tFCvYNTBXNA==}
 
-  '@emotion/unitless@0.8.1':
-    resolution: {integrity: sha512-KOEGMu6dmJZtpadb476IsZBclKvILjopjUii3V+7MnXIQCYh8W3NgNcgwo21n9LXZX6EDIKvqfjYxXebDwxKmQ==}
-
   '@epic-web/invariant@1.0.0':
     resolution: {integrity: sha512-lrTPqgvfFQtR/eY/qkIzp98OGdNJu0m5ji3q/nJI8v3SXkRKEnWiOxMmbvcSoAIzv/cGiuvRy57k4suKQSAdwA==}
 
@@ -3122,9 +3116,6 @@ packages:
   '@types/statuses@2.0.6':
     resolution: {integrity: sha512-xMAgYwceFhRA2zY+XbEA7mxYbA093wdiW8Vu6gZPGWy9cmOyU9XesH1tNcEWsKFd5Vzrqx5T3D38PWx1FIIXkA==}
 
-  '@types/stylis@4.2.7':
-    resolution: {integrity: sha512-VgDNokpBoKF+wrdvhAAfS55OMQpL6QRglwTwNC3kIgBrzZxA4WsFj+2eLfEA/uMUDzBcEhYmjSbwQakn/i3ajA==}
-
   '@types/tedious@4.0.14':
     resolution: {integrity: sha512-KHPsfX/FoVbUGbyYvk1q9MMQHLPeRZhRJZdO45Q4YjvFkv4hMNghCWTvy7rdKessBsmtz4euWCWAB6/tVpI1Iw==}
 
@@ -3781,9 +3772,6 @@ packages:
     resolution: {integrity: sha512-QOSvevhslijgYwRx6Rv7zKdMF8lbRmx+uQGx2+vDc+KI/eBnsy9kit5aj23AgGu3pa4t9AgwbnXWqS+iOY+2aA==}
     engines: {node: '>= 6'}
 
-  camelize@1.0.1:
-    resolution: {integrity: sha512-dU+Tx2fsypxTgtLoE36npi3UqcjSSMNYfkqgmoEhtZrraP5VWq0K7FkWVTYa8eMPtnU/G2txVsfdCJTn9uzpuQ==}
-
   caniuse-lite@1.0.30001762:
     resolution: {integrity: sha512-PxZwGNvH7Ak8WX5iXzoK1KPZttBXNPuaOvI2ZYU7NrlM+d9Ov+TUvlLOBNGzVXAntMSMMlJPd+jY6ovrVjSmUw==}
 
@@ -3997,10 +3985,6 @@ packages:
     resolution: {integrity: sha512-r4ESw/IlusD17lgQi1O20Fa3qNnsckR126TdUuBgAu7GBYSIPvdNyONd3Zrxh0xCwA4+6w/TDArBPsMvhur+KQ==}
     engines: {node: '>= 0.10'}
 
-  css-color-keywords@1.0.0:
-    resolution: {integrity: sha512-FyyrDHZKEjXDpNJYvVsV960FiqQyXc/LlYmsxl2BcdMb2WPx0OGRVgTg55rPSyLSNMqP52R9r8geSp7apN3Ofg==}
-    engines: {node: '>=4'}
-
   css-loader@6.11.0:
     resolution: {integrity: sha512-CTJ+AEQJjq5NzLga5pE39qdiSV56F8ywCIsqNIRF0r7BDgWsN25aazToqAFg7ZrtA/U016xudB3ffgweORxX7g==}
     engines: {node: '>= 12.13.0'}
@@ -4016,9 +4000,6 @@ packages:
   css-select@4.3.0:
     resolution: {integrity: sha512-wPpOYtnsVontu2mODhA19JrqWxNsfdatRKd64kmpRbQgh1KtItko5sTnEpPdpSaJszTOhEMlF/RPz28qj4HqhQ==}
 
-  css-to-react-native@3.2.0:
-    resolution: {integrity: sha512-e8RKaLXMOFii+02mOlqwjbD00KSEKqblnpO9e++1aXS1fPQOpS1YoqdVHBqPjHNoxeF2mimzVqawm2KCbEdtHQ==}
-
   css-what@6.2.2:
     resolution: {integrity: sha512-u/O3vwbptzhMs3L1fQE82ZSLHQQfto5gyZzwteVIEyeaY5Fc7R4dapF/BvRoSYFeqfBk4m0V1Vafq5Pjv25wvA==}
     engines: {node: '>= 6'}
@@ -6131,10 +6112,6 @@ packages:
     resolution: {integrity: sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==}
     engines: {node: ^10 || ^12 || >=14}
 
-  postcss@8.4.49:
-    resolution: {integrity: sha512-OCVPnIObs4N29kxTjzLfUryOkvZEq+pf8jTF0lg8E7uETuWHA+v7j3c/xJmiqpX450191LlmZfUKkXxkTry7nA==}
-    engines: {node: ^10 || ^12 || >=14}
-
   postcss@8.5.6:
     resolution: {integrity: sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg==}
     engines: {node: ^10 || ^12 || >=14}
@@ -6306,12 +6283,6 @@ packages:
     peerDependencies:
       react: ^18.3.1
 
-  react-drag-drop-files@2.4.0:
-    resolution: {integrity: sha512-MGPV3HVVnwXEXq3gQfLtSU3jz5j5jrabvGedokpiSEMoONrDHgYl/NpIOlfsqGQ4zBv1bzzv7qbKURZNOX32PA==}
-    peerDependencies:
-      react: ^18.0.0
-      react-dom: ^18.0.0
-
   react-hook-form@7.66.0:
     resolution: {integrity: sha512-xXBqsWGKrY46ZqaHDo+ZUYiMUgi8suYu5kdrS20EG8KiL7VRQitEbNjm+UcrDYrNi1YLyfpmAeGjCZYXLT9YBw==}
     engines: {node: '>=18.0.0'}
@@ -6678,9 +6649,6 @@ packages:
     engines: {node: '>= 0.10'}
     hasBin: true
 
-  shallowequal@1.1.0:
-    resolution: {integrity: sha512-y0m1JoUZSlPAjXVtPPW70aZWfIL/dSP7AFkRnniLCrK/8MDKog3TySTBmckD+RObVxH0v4Tox67+F14PdED2oQ==}
-
   sharp@0.34.5:
     resolution: {integrity: sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==}
     engines: {node: ^18.17.0 || ^20.3.0 || >=21.0.0}
@@ -6894,13 +6862,6 @@ packages:
   style-to-object@1.0.14:
     resolution: {integrity: sha512-LIN7rULI0jBscWQYaSswptyderlarFkjQ+t79nzty8tcIAceVomEVlLzH5VP4Cmsv6MtKhs7qaAiwlcp+Mgaxw==}
 
-  styled-components@6.2.0:
-    resolution: {integrity: sha512-ryFCkETE++8jlrBmC+BoGPUN96ld1/Yp0s7t5bcXDobrs4XoXroY1tN+JbFi09hV6a5h3MzbcVi8/BGDP0eCgQ==}
-    engines: {node: '>= 16'}
-    peerDependencies:
-      react: '>= 16.8.0'
-      react-dom: '>= 16.8.0'
-
   styled-jsx@5.1.6:
     resolution: {integrity: sha512-qSVyDTeMotdvQYoHWLNGwRFJHC+i+ZvdBRYosOFgC+Wg1vx4frN2/RG/NA7SYqqvKNLf39P2LSRA2pu6n0XYZA==}
     engines: {node: '>= 12.0.0'}
@@ -6927,9 +6888,6 @@ packages:
       babel-plugin-macros:
         optional: true
 
-  stylis@4.3.6:
-    resolution: {integrity: sha512-yQ3rwFWRfwNUY7H5vpU0wfdkNSnvnJinhF9830Swlaxl03zsOjCfmX0ugac+3LtK0lYSgwL/KXc8oYL3mG4YFQ==}
-
   sucrase@3.35.1:
     resolution: {integrity: sha512-DhuTmvZWux4H1UOnWMB3sk0sbaCVOoQZjv8u1rDoTV0HTdGem9hkAZtl4JZy8P2z4Bg0nT+YMeOFyVr4zcG5Tw==}
     engines: {node: '>=16 || 14 >=14.17'}
@@ -7096,9 +7054,6 @@ packages:
   tslib@1.14.1:
     resolution: {integrity: sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==}
 
-  tslib@2.6.2:
-    resolution: {integrity: sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==}
-
   tslib@2.8.1:
     resolution: {integrity: sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==}
 
@@ -8335,10 +8290,10 @@ snapshots:
   '@emotion/is-prop-valid@1.2.2':
     dependencies:
       '@emotion/memoize': 0.8.1
+    optional: true
 
-  '@emotion/memoize@0.8.1': {}
-
-  '@emotion/unitless@0.8.1': {}
+  '@emotion/memoize@0.8.1':
+    optional: true
 
   '@epic-web/invariant@1.0.0': {}
 
@@ -10734,8 +10689,6 @@ snapshots:
 
   '@types/statuses@2.0.6': {}
 
-  '@types/stylis@4.2.7': {}
-
   '@types/tedious@4.0.14':
     dependencies:
       '@types/node': 24.10.0
@@ -11432,8 +11385,6 @@ snapshots:
 
   camelcase-css@2.0.1: {}
 
-  camelize@1.0.1: {}
-
   caniuse-lite@1.0.30001762: {}
 
   case-sensitive-paths-webpack-plugin@2.4.0: {}
@@ -11645,8 +11596,6 @@ snapshots:
       randombytes: 2.1.0
       randomfill: 1.0.4
 
-  css-color-keywords@1.0.0: {}
-
   css-loader@6.11.0(webpack@5.104.1(esbuild@0.25.12)):
     dependencies:
       icss-utils: 5.1.0(postcss@8.5.6)
@@ -11668,12 +11617,6 @@ snapshots:
       domutils: 2.8.0
       nth-check: 2.1.1
 
-  css-to-react-native@3.2.0:
-    dependencies:
-      camelize: 1.0.1
-      css-color-keywords: 1.0.0
-      postcss-value-parser: 4.2.0
-
   css-what@6.2.2: {}
 
   css.escape@1.5.1: {}
@@ -12127,8 +12070,8 @@ snapshots:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
       eslint-plugin-jsx-a11y: 6.10.2(eslint@8.57.1)
       eslint-plugin-react: 7.37.5(eslint@8.57.1)
       eslint-plugin-react-hooks: 5.2.0(eslint@8.57.1)
@@ -12147,7 +12090,7 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1):
+  eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1):
     dependencies:
       '@nolyfill/is-core-module': 1.0.39
       debug: 4.4.3
@@ -12158,22 +12101,22 @@ snapshots:
       tinyglobby: 0.2.15
       unrs-resolver: 1.11.1
     optionalDependencies:
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
+  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
     dependencies:
       debug: 3.2.7
     optionalDependencies:
       '@typescript-eslint/parser': 8.52.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1):
+  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
     dependencies:
       '@rtsao/scc': 1.1.0
       array-includes: 3.1.9
@@ -12184,7 +12127,7 @@ snapshots:
       doctrine: 2.1.0
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1(eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1))(eslint@8.57.1))(eslint@8.57.1)
+      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.52.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
       hasown: 2.0.2
       is-core-module: 2.16.1
       is-glob: 4.0.3
@@ -14259,12 +14202,6 @@ snapshots:
       picocolors: 1.1.1
       source-map-js: 1.2.1
 
-  postcss@8.4.49:
-    dependencies:
-      nanoid: 3.3.11
-      picocolors: 1.1.1
-      source-map-js: 1.2.1
-
   postcss@8.5.6:
     dependencies:
       nanoid: 3.3.11
@@ -14386,13 +14323,6 @@ snapshots:
       react: 18.3.1
       scheduler: 0.23.2
 
-  react-drag-drop-files@2.4.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
-    dependencies:
-      prop-types: 15.8.1
-      react: 18.3.1
-      react-dom: 18.3.1(react@18.3.1)
-      styled-components: 6.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
-
   react-hook-form@7.66.0(react@18.3.1):
     dependencies:
       react: 18.3.1
@@ -14886,8 +14816,6 @@ snapshots:
       safe-buffer: 5.2.1
       to-buffer: 1.2.2
 
-  shallowequal@1.1.0: {}
-
   sharp@0.34.5:
     dependencies:
       '@img/colour': 1.0.0
@@ -15178,20 +15106,6 @@ snapshots:
     dependencies:
       inline-style-parser: 0.2.7
 
-  styled-components@6.2.0(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
-    dependencies:
-      '@emotion/is-prop-valid': 1.2.2
-      '@emotion/unitless': 0.8.1
-      '@types/stylis': 4.2.7
-      css-to-react-native: 3.2.0
-      csstype: 3.2.3
-      postcss: 8.4.49
-      react: 18.3.1
-      react-dom: 18.3.1(react@18.3.1)
-      shallowequal: 1.1.0
-      stylis: 4.3.6
-      tslib: 2.6.2
-
   styled-jsx@5.1.6(@babel/core@7.28.5)(react@18.3.1):
     dependencies:
       client-only: 0.0.1
@@ -15206,8 +15120,6 @@ snapshots:
     optionalDependencies:
       '@babel/core': 7.28.5
 
-  stylis@4.3.6: {}
-
   sucrase@3.35.1:
     dependencies:
       '@jridgewell/gen-mapping': 0.3.13
@@ -15390,8 +15302,6 @@ snapshots:
 
   tslib@1.14.1: {}
 
-  tslib@2.6.2: {}
-
   tslib@2.8.1: {}
 
   tty-browserify@0.0.1: {}

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/RunInputDialog.tsx

@@ -66,6 +66,7 @@ export const RunInputDialog = ({
                     formContext={{
                       showHandles: false,
                       size: "large",
+                      showOptionalToggle: false,
                     }}
                   />
                 </div>

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -66,7 +66,7 @@ export const useRunInputDialog = ({
         if (isCredentialFieldSchema(fieldSchema)) {
           dynamicUiSchema[fieldName] = {
             ...dynamicUiSchema[fieldName],
-            "ui:field": "credentials",
+            "ui:field": "custom/credential_field",
           };
         }
       });
@@ -76,12 +76,18 @@ export const useRunInputDialog = ({
   }, [credentialsSchema]);
 
   const handleManualRun = async () => {
+    // Filter out incomplete credentials (those without a valid id)
+    // RJSF auto-populates const values (provider, type) but not id field
+    const validCredentials = Object.fromEntries(
+      Object.entries(credentialValues).filter(([_, cred]) => cred && cred.id),
+    );
+
     await executeGraph({
       graphId: flowID ?? "",
       graphVersion: flowVersion || null,
       data: {
         inputs: inputValues,
-        credentials_inputs: credentialValues,
+        credentials_inputs: validCredentials,
         source: "builder",
       },
     });

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/Flow.tsx

@@ -97,6 +97,9 @@ export const Flow = () => {
           onConnect={onConnect}
           onEdgesChange={onEdgesChange}
           onNodeDragStop={onNodeDragStop}
+          onNodeContextMenu={(event) => {
+            event.preventDefault();
+          }}
           maxZoom={2}
           minZoom={0.1}
           onDragOver={onDragOver}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/CustomNode.tsx

@@ -1,24 +1,25 @@
-import React from "react";
-import { Node as XYNode, NodeProps } from "@xyflow/react";
-import { RJSFSchema } from "@rjsf/utils";
-import { BlockUIType } from "../../../types";
-import { StickyNoteBlock } from "./components/StickyNoteBlock";
-import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
-import { BlockCost } from "@/app/api/__generated__/models/blockCost";
 import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
+import { BlockCost } from "@/app/api/__generated__/models/blockCost";
+import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
 import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
-import { NodeContainer } from "./components/NodeContainer";
-import { NodeHeader } from "./components/NodeHeader";
-import { FormCreator } from "../FormCreator";
-import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
-import { OutputHandler } from "../OutputHandler";
-import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
-import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
-import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
-import { cn } from "@/lib/utils";
-import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
-import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
 import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
+import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
+import { cn } from "@/lib/utils";
+import { RJSFSchema } from "@rjsf/utils";
+import { NodeProps, Node as XYNode } from "@xyflow/react";
+import React from "react";
+import { BlockUIType } from "../../../types";
+import { FormCreator } from "../FormCreator";
+import { OutputHandler } from "../OutputHandler";
+import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
+import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
+import { NodeContainer } from "./components/NodeContainer";
+import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
+import { NodeHeader } from "./components/NodeHeader";
+import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
+import { NodeRightClickMenu } from "./components/NodeRightClickMenu";
+import { StickyNoteBlock } from "./components/StickyNoteBlock";
+import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
 
 export type CustomNodeData = {
   hardcodedValues: {
@@ -88,7 +89,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     // Currently all blockTypes design are similar - that's why i am using the same component for all of them
     // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
-    return (
+    const node = (
       <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
@@ -117,6 +118,15 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
         <NodeExecutionBadge nodeId={nodeId} />
       </NodeContainer>
     );
+
+    return (
+      <NodeRightClickMenu
+        nodeId={nodeId}
+        subGraphID={data.hardcodedValues?.graph_id}
+      >
+        {node}
+      </NodeRightClickMenu>
+    );
   },
 );
 

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeContextMenu.tsx

@@ -1,26 +1,31 @@
-import { Separator } from "@/components/__legacy__/ui/separator";
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import {
   DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
   DropdownMenuTrigger,
 } from "@/components/molecules/DropdownMenu/DropdownMenu";
-import { DotsThreeOutlineVerticalIcon } from "@phosphor-icons/react";
-import { Copy, Trash2, ExternalLink } from "lucide-react";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import {
+  SecondaryDropdownMenuContent,
+  SecondaryDropdownMenuItem,
+  SecondaryDropdownMenuSeparator,
+} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
+import {
+  ArrowSquareOutIcon,
+  CopyIcon,
+  DotsThreeOutlineVerticalIcon,
+  TrashIcon,
+} from "@phosphor-icons/react";
 import { useReactFlow } from "@xyflow/react";
 
-export const NodeContextMenu = ({
-  nodeId,
-  subGraphID,
-}: {
+type Props = {
   nodeId: string;
   subGraphID?: string;
-}) => {
+};
+
+export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
   const { deleteElements } = useReactFlow();
 
-  const handleCopy = () => {
+  function handleCopy() {
     useNodeStore.setState((state) => ({
       nodes: state.nodes.map((node) => ({
         ...node,
@@ -30,47 +35,47 @@ export const NodeContextMenu = ({
 
     useCopyPasteStore.getState().copySelectedNodes();
     useCopyPasteStore.getState().pasteNodes();
-  };
+  }
 
-  const handleDelete = () => {
+  function handleDelete() {
     deleteElements({ nodes: [{ id: nodeId }] });
-  };
+  }
 
   return (
     <DropdownMenu>
       <DropdownMenuTrigger className="py-2">
         <DotsThreeOutlineVerticalIcon size={16} weight="fill" />
       </DropdownMenuTrigger>
-      <DropdownMenuContent
-        side="right"
-        align="start"
-        className="rounded-xlarge"
-      >
-        <DropdownMenuItem onClick={handleCopy} className="hover:rounded-xlarge">
-          <Copy className="mr-2 h-4 w-4" />
-          Copy Node
-        </DropdownMenuItem>
+      <SecondaryDropdownMenuContent side="right" align="start">
+        <SecondaryDropdownMenuItem onClick={handleCopy}>
+          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
+          <span className="dark:text-gray-100">Copy</span>
+        </SecondaryDropdownMenuItem>
+        <SecondaryDropdownMenuSeparator />
 
         {subGraphID && (
-          <DropdownMenuItem
-            onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            className="hover:rounded-xlarge"
-          >
-            <ExternalLink className="mr-2 h-4 w-4" />
-            Open Agent
-          </DropdownMenuItem>
+          <>
+            <SecondaryDropdownMenuItem
+              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            >
+              <ArrowSquareOutIcon
+                size={20}
+                className="mr-2 dark:text-gray-100"
+              />
+              <span className="dark:text-gray-100">Open agent</span>
+            </SecondaryDropdownMenuItem>
+            <SecondaryDropdownMenuSeparator />
+          </>
         )}
 
-        <Separator className="my-2" />
-
-        <DropdownMenuItem
-          onClick={handleDelete}
-          className="text-red-600 hover:rounded-xlarge"
-        >
-          <Trash2 className="mr-2 h-4 w-4" />
-          Delete
-        </DropdownMenuItem>
-      </DropdownMenuContent>
+        <SecondaryDropdownMenuItem variant="destructive" onClick={handleDelete}>
+          <TrashIcon
+            size={20}
+            className="mr-2 text-red-500 dark:text-red-400"
+          />
+          <span className="dark:text-red-400">Delete</span>
+        </SecondaryDropdownMenuItem>
+      </SecondaryDropdownMenuContent>
     </DropdownMenu>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeHeader.tsx

@@ -1,25 +1,24 @@
-import { Text } from "@/components/atoms/Text/Text";
-import { beautifyString, cn } from "@/lib/utils";
-import { NodeCost } from "./NodeCost";
-import { NodeBadges } from "./NodeBadges";
-import { NodeContextMenu } from "./NodeContextMenu";
-import { CustomNodeData } from "../CustomNode";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { useState } from "react";
+import { Text } from "@/components/atoms/Text/Text";
 import {
   Tooltip,
   TooltipContent,
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
+import { beautifyString, cn } from "@/lib/utils";
+import { useState } from "react";
+import { CustomNodeData } from "../CustomNode";
+import { NodeBadges } from "./NodeBadges";
+import { NodeContextMenu } from "./NodeContextMenu";
+import { NodeCost } from "./NodeCost";
 
-export const NodeHeader = ({
-  data,
-  nodeId,
-}: {
+type Props = {
   data: CustomNodeData;
   nodeId: string;
-}) => {
+};
+
+export const NodeHeader = ({ data, nodeId }: Props) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
   const title = (data.metadata?.customized_name as string) || data.title;
   const [isEditingTitle, setIsEditingTitle] = useState(false);
@@ -69,7 +68,10 @@ export const NodeHeader = ({
                 <Tooltip>
                   <TooltipTrigger asChild>
                     <div>
-                      <Text variant="large-semibold" className="line-clamp-1">
+                      <Text
+                        variant="large-semibold"
+                        className="line-clamp-1 hover:cursor-text"
+                      >
                         {beautifyString(title).replace("Block", "").trim()}
                       </Text>
                     </div>

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeOutput/components/NodeDataViewer/NodeDataViewer.tsx

@@ -151,7 +151,7 @@ export const NodeDataViewer: FC<NodeDataViewerProps> = ({
         </div>
 
         <div className="flex justify-end pt-4">
-          {outputItems.length > 0 && (
+          {outputItems.length > 1 && (
             <OutputActions
               items={outputItems.map((item) => ({
                 value: item.value,

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeRightClickMenu.tsx

@@ -0,0 +1,104 @@
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import {
+  SecondaryMenuContent,
+  SecondaryMenuItem,
+  SecondaryMenuSeparator,
+} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
+import { ArrowSquareOutIcon, CopyIcon, TrashIcon } from "@phosphor-icons/react";
+import * as ContextMenu from "@radix-ui/react-context-menu";
+import { useReactFlow } from "@xyflow/react";
+import { useEffect, useRef } from "react";
+import { CustomNode } from "../CustomNode";
+
+type Props = {
+  nodeId: string;
+  subGraphID?: string;
+  children: React.ReactNode;
+};
+
+const DOUBLE_CLICK_TIMEOUT = 300;
+
+export function NodeRightClickMenu({ nodeId, subGraphID, children }: Props) {
+  const { deleteElements } = useReactFlow<CustomNode>();
+  const lastRightClickTime = useRef<number>(0);
+  const containerRef = useRef<HTMLDivElement>(null);
+
+  function copyNode() {
+    useNodeStore.setState((state) => ({
+      nodes: state.nodes.map((node) => ({
+        ...node,
+        selected: node.id === nodeId,
+      })),
+    }));
+
+    useCopyPasteStore.getState().copySelectedNodes();
+    useCopyPasteStore.getState().pasteNodes();
+  }
+
+  function deleteNode() {
+    deleteElements({ nodes: [{ id: nodeId }] });
+  }
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+
+    function handleContextMenu(e: MouseEvent) {
+      const now = Date.now();
+      const timeSinceLastClick = now - lastRightClickTime.current;
+
+      if (timeSinceLastClick < DOUBLE_CLICK_TIMEOUT) {
+        e.stopImmediatePropagation();
+        lastRightClickTime.current = 0;
+        return;
+      }
+
+      lastRightClickTime.current = now;
+    }
+
+    container.addEventListener("contextmenu", handleContextMenu, true);
+
+    return () => {
+      container.removeEventListener("contextmenu", handleContextMenu, true);
+    };
+  }, []);
+
+  return (
+    <ContextMenu.Root>
+      <ContextMenu.Trigger asChild>
+        <div ref={containerRef}>{children}</div>
+      </ContextMenu.Trigger>
+      <SecondaryMenuContent>
+        <SecondaryMenuItem onSelect={copyNode}>
+          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
+          <span className="dark:text-gray-100">Copy</span>
+        </SecondaryMenuItem>
+        <SecondaryMenuSeparator />
+
+        {subGraphID && (
+          <>
+            <SecondaryMenuItem
+              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            >
+              <ArrowSquareOutIcon
+                size={20}
+                className="mr-2 dark:text-gray-100"
+              />
+              <span className="dark:text-gray-100">Open agent</span>
+            </SecondaryMenuItem>
+            <SecondaryMenuSeparator />
+          </>
+        )}
+
+        <SecondaryMenuItem variant="destructive" onSelect={deleteNode}>
+          <TrashIcon
+            size={20}
+            className="mr-2 text-red-500 dark:text-red-400"
+          />
+          <span className="dark:text-red-400">Delete</span>
+        </SecondaryMenuItem>
+      </SecondaryMenuContent>
+    </ContextMenu.Root>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -89,6 +89,18 @@ export function extractOptions(
 
 // get display type and color for schema types [need for type display next to field name]
 export const getTypeDisplayInfo = (schema: any) => {
+  if (
+    schema?.type === "array" &&
+    "format" in schema &&
+    schema.format === "table"
+  ) {
+    return {
+      displayType: "table",
+      colorClass: "!text-indigo-500",
+      hexColor: "#6366f1",
+    };
+  }
+
   if (schema?.type === "string" && schema?.format) {
     const formatMap: Record<
       string,

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/uiSchema.ts

@@ -1,6 +1,6 @@
 export const uiSchema = {
   credentials: {
-    "ui:field": "credentials",
+    "ui:field": "custom/credential_field",
     provider: { "ui:widget": "hidden" },
     type: { "ui:widget": "hidden" },
     id: { "ui:autofocus": true },

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/BlockMenuFilters.tsx

@@ -0,0 +1,57 @@
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { FilterChip } from "../FilterChip";
+import { categories } from "./constants";
+import { FilterSheet } from "../FilterSheet/FilterSheet";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const BlockMenuFilters = () => {
+  const {
+    filters,
+    addFilter,
+    removeFilter,
+    categoryCounts,
+    creators,
+    addCreator,
+    removeCreator,
+  } = useBlockMenuStore();
+
+  const handleFilterClick = (filter: GetV2BuilderSearchFilterAnyOfItem) => {
+    if (filters.includes(filter)) {
+      removeFilter(filter);
+    } else {
+      addFilter(filter);
+    }
+  };
+
+  const handleCreatorClick = (creator: string) => {
+    if (creators.includes(creator)) {
+      removeCreator(creator);
+    } else {
+      addCreator(creator);
+    }
+  };
+
+  return (
+    <div className="flex flex-wrap gap-2">
+      <FilterSheet categories={categories} />
+      {creators.length > 0 &&
+        creators.map((creator) => (
+          <FilterChip
+            key={creator}
+            name={"Created by " + creator.slice(0, 10) + "..."}
+            selected={creators.includes(creator)}
+            onClick={() => handleCreatorClick(creator)}
+          />
+        ))}
+      {categories.map((category) => (
+        <FilterChip
+          key={category.key}
+          name={category.name}
+          selected={filters.includes(category.key)}
+          onClick={() => handleFilterClick(category.key)}
+          number={categoryCounts[category.key] ?? 0}
+        />
+      ))}
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/constants.ts

@@ -0,0 +1,15 @@
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+import { CategoryKey } from "./types";
+
+export const categories: Array<{ key: CategoryKey; name: string }> = [
+  { key: GetV2BuilderSearchFilterAnyOfItem.blocks, name: "Blocks" },
+  {
+    key: GetV2BuilderSearchFilterAnyOfItem.integrations,
+    name: "Integrations",
+  },
+  {
+    key: GetV2BuilderSearchFilterAnyOfItem.marketplace_agents,
+    name: "Marketplace agents",
+  },
+  { key: GetV2BuilderSearchFilterAnyOfItem.my_agents, name: "My agents" },
+];

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/types.ts

@@ -0,0 +1,26 @@
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export type DefaultStateType =
+  | "suggestion"
+  | "all_blocks"
+  | "input_blocks"
+  | "action_blocks"
+  | "output_blocks"
+  | "integrations"
+  | "marketplace_agents"
+  | "my_agents";
+
+export type CategoryKey = GetV2BuilderSearchFilterAnyOfItem;
+
+export interface Filters {
+  categories: {
+    blocks: boolean;
+    integrations: boolean;
+    marketplace_agents: boolean;
+    my_agents: boolean;
+    providers: boolean;
+  };
+  createdBy: string[];
+}
+
+export type CategoryCounts = Record<CategoryKey, number>;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/BlockMenuSearch.tsx

@@ -1,111 +1,14 @@
 import { Text } from "@/components/atoms/Text/Text";
-import { useBlockMenuSearch } from "./useBlockMenuSearch";
-import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { LoadingSpinner } from "@/components/__legacy__/ui/loading";
-import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
-import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
-import { Block } from "../Block";
-import { UGCAgentBlock } from "../UGCAgentBlock";
-import { getSearchItemType } from "./helper";
-import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { blockMenuContainerStyle } from "../style";
-import { cn } from "@/lib/utils";
-import { NoSearchResult } from "../NoSearchResult";
+import { BlockMenuFilters } from "../BlockMenuFilters/BlockMenuFilters";
+import { BlockMenuSearchContent } from "../BlockMenuSearchContent/BlockMenuSearchContent";
 
 export const BlockMenuSearch = () => {
-  const {
-    searchResults,
-    isFetchingNextPage,
-    fetchNextPage,
-    hasNextPage,
-    searchLoading,
-    handleAddLibraryAgent,
-    handleAddMarketplaceAgent,
-    addingLibraryAgentId,
-    addingMarketplaceAgentSlug,
-  } = useBlockMenuSearch();
-  const { searchQuery } = useBlockMenuStore();
-
-  if (searchLoading) {
-    return (
-      <div
-        className={cn(
-          blockMenuContainerStyle,
-          "flex items-center justify-center",
-        )}
-      >
-        <LoadingSpinner className="size-13" />
-      </div>
-    );
-  }
-
-  if (searchResults.length === 0) {
-    return <NoSearchResult />;
-  }
-
   return (
     <div className={blockMenuContainerStyle}>
+      <BlockMenuFilters />
       <Text variant="body-medium">Search results</Text>
-      <InfiniteScroll
-        isFetchingNextPage={isFetchingNextPage}
-        fetchNextPage={fetchNextPage}
-        hasNextPage={hasNextPage}
-        loader={<LoadingSpinner className="size-13" />}
-        className="space-y-2.5"
-      >
-        {searchResults.map((item: SearchResponseItemsItem, index: number) => {
-          const { type, data } = getSearchItemType(item);
-          // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
-          switch (type) {
-            case "store_agent":
-              return (
-                <MarketplaceAgentBlock
-                  key={index}
-                  slug={data.slug}
-                  highlightedText={searchQuery}
-                  title={data.agent_name}
-                  image_url={data.agent_image}
-                  creator_name={data.creator}
-                  number_of_runs={data.runs}
-                  loading={addingMarketplaceAgentSlug === data.slug}
-                  onClick={() =>
-                    handleAddMarketplaceAgent({
-                      creator_name: data.creator,
-                      slug: data.slug,
-                    })
-                  }
-                />
-              );
-            case "block":
-              return (
-                <Block
-                  key={index}
-                  title={data.name}
-                  highlightedText={searchQuery}
-                  description={data.description}
-                  blockData={data}
-                />
-              );
-
-            case "library_agent":
-              return (
-                <UGCAgentBlock
-                  key={index}
-                  title={data.name}
-                  highlightedText={searchQuery}
-                  image_url={data.image_url}
-                  version={data.graph_version}
-                  edited_time={data.updated_at}
-                  isLoading={addingLibraryAgentId === data.id}
-                  onClick={() => handleAddLibraryAgent(data)}
-                />
-              );
-
-            default:
-              return null;
-          }
-        })}
-      </InfiniteScroll>
+      <BlockMenuSearchContent />
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/BlockMenuSearchContent.tsx

@@ -0,0 +1,108 @@
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
+import { getSearchItemType } from "./helper";
+import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
+import { Block } from "../Block";
+import { UGCAgentBlock } from "../UGCAgentBlock";
+import { useBlockMenuSearchContent } from "./useBlockMenuSearchContent";
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { cn } from "@/lib/utils";
+import { blockMenuContainerStyle } from "../style";
+import { NoSearchResult } from "../NoSearchResult";
+
+export const BlockMenuSearchContent = () => {
+  const {
+    searchResults,
+    isFetchingNextPage,
+    fetchNextPage,
+    hasNextPage,
+    searchLoading,
+    handleAddLibraryAgent,
+    handleAddMarketplaceAgent,
+    addingLibraryAgentId,
+    addingMarketplaceAgentSlug,
+  } = useBlockMenuSearchContent();
+
+  const { searchQuery } = useBlockMenuStore();
+
+  if (searchLoading) {
+    return (
+      <div
+        className={cn(
+          blockMenuContainerStyle,
+          "flex items-center justify-center",
+        )}
+      >
+        <LoadingSpinner className="size-13" />
+      </div>
+    );
+  }
+
+  if (searchResults.length === 0) {
+    return <NoSearchResult />;
+  }
+
+  return (
+    <InfiniteScroll
+      isFetchingNextPage={isFetchingNextPage}
+      fetchNextPage={fetchNextPage}
+      hasNextPage={hasNextPage}
+      loader={<LoadingSpinner className="size-13" />}
+      className="space-y-2.5"
+    >
+      {searchResults.map((item: SearchResponseItemsItem, index: number) => {
+        const { type, data } = getSearchItemType(item);
+        // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
+        switch (type) {
+          case "store_agent":
+            return (
+              <MarketplaceAgentBlock
+                key={index}
+                slug={data.slug}
+                highlightedText={searchQuery}
+                title={data.agent_name}
+                image_url={data.agent_image}
+                creator_name={data.creator}
+                number_of_runs={data.runs}
+                loading={addingMarketplaceAgentSlug === data.slug}
+                onClick={() =>
+                  handleAddMarketplaceAgent({
+                    creator_name: data.creator,
+                    slug: data.slug,
+                  })
+                }
+              />
+            );
+          case "block":
+            return (
+              <Block
+                key={index}
+                title={data.name}
+                highlightedText={searchQuery}
+                description={data.description}
+                blockData={data}
+              />
+            );
+
+          case "library_agent":
+            return (
+              <UGCAgentBlock
+                key={index}
+                title={data.name}
+                highlightedText={searchQuery}
+                image_url={data.image_url}
+                version={data.graph_version}
+                edited_time={data.updated_at}
+                isLoading={addingLibraryAgentId === data.id}
+                onClick={() => handleAddLibraryAgent(data)}
+              />
+            );
+
+          default:
+            return null;
+        }
+      })}
+    </InfiniteScroll>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/useBlockMenuSearchContent.tsx

@@ -23,9 +23,19 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import * as Sentry from "@sentry/nextjs";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const useBlockMenuSearchContent = () => {
+  const {
+    searchQuery,
+    searchId,
+    setSearchId,
+    filters,
+    setCreatorsList,
+    creators,
+    setCategoryCounts,
+  } = useBlockMenuStore();
 
-export const useBlockMenuSearch = () => {
-  const { searchQuery, searchId, setSearchId } = useBlockMenuStore();
   const { toast } = useToast();
   const { addAgentToBuilder, addLibraryAgentToBuilder } =
     useAddAgentToBuilder();
@@ -57,6 +67,8 @@ export const useBlockMenuSearch = () => {
       page_size: 8,
       search_query: searchQuery,
       search_id: searchId,
+      filter: filters.length > 0 ? filters : undefined,
+      by_creator: creators.length > 0 ? creators : undefined,
     },
     {
       query: { getNextPageParam: getPaginationNextPageNumber },
@@ -98,6 +110,26 @@ export const useBlockMenuSearch = () => {
     }
   }, [searchQueryData, searchId, setSearchId]);
 
+  // from all the results, we need to get all the unique creators
+  useEffect(() => {
+    if (!searchQueryData?.pages?.length) {
+      return;
+    }
+    const latestData = okData(searchQueryData.pages.at(-1));
+    setCategoryCounts(
+      (latestData?.total_items as Record<
+        GetV2BuilderSearchFilterAnyOfItem,
+        number
+      >) || {
+        blocks: 0,
+        integrations: 0,
+        marketplace_agents: 0,
+        my_agents: 0,
+      },
+    );
+    setCreatorsList(latestData?.items || []);
+  }, [searchQueryData]);
+
   useEffect(() => {
     if (searchId && !searchQuery) {
       resetSearchSession();

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterChip.tsx

@@ -1,7 +1,9 @@
 import { Button } from "@/components/__legacy__/ui/button";
 import { cn } from "@/lib/utils";
-import { X } from "lucide-react";
-import React, { ButtonHTMLAttributes } from "react";
+import { XIcon } from "@phosphor-icons/react";
+import { AnimatePresence, motion } from "framer-motion";
+
+import React, { ButtonHTMLAttributes, useState } from "react";
 
 interface Props extends ButtonHTMLAttributes<HTMLButtonElement> {
   selected?: boolean;
@@ -16,39 +18,51 @@ export const FilterChip: React.FC<Props> = ({
   className,
   ...rest
 }) => {
+  const [isHovered, setIsHovered] = useState(false);
   return (
-    <Button
-      className={cn(
-        "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none transition-transform duration-300 ease-in-out",
-        "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
-        selected && "border-0 bg-violet-700 hover:border",
-        className,
-      )}
-      {...rest}
-    >
-      <span
+    <AnimatePresence mode="wait">
+      <Button
+        onMouseEnter={() => setIsHovered(true)}
+        onMouseLeave={() => setIsHovered(false)}
         className={cn(
-          "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
-          selected && "text-zinc-50",
+          "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none",
+          "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
+          selected && "border-0 bg-violet-700 hover:border",
+          className,
         )}
+        {...rest}
       >
-        {name}
-      </span>
-      {selected && (
-        <>
-          <span className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50 transition-all duration-300 ease-in-out group-hover:hidden">
-            <X
-              className="h-3 w-3 rounded-full text-violet-700"
-              strokeWidth={2}
-            />
-          </span>
-          {number !== undefined && (
-            <span className="hidden h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50 transition-all duration-300 ease-in-out animate-in fade-in zoom-in group-hover:flex">
-              {number > 100 ? "100+" : number}
-            </span>
+        <span
+          className={cn(
+            "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
+            selected && "text-zinc-50",
           )}
-        </>
-      )}
-    </Button>
+        >
+          {name}
+        </span>
+        {selected && !isHovered && (
+          <motion.span
+            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
+            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
+            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
+            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
+            className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50"
+          >
+            <XIcon size={12} weight="bold" className="text-violet-700" />
+          </motion.span>
+        )}
+        {number !== undefined && isHovered && (
+          <motion.span
+            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
+            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
+            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
+            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
+            className="flex h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50"
+          >
+            {number > 100 ? "100+" : number}
+          </motion.span>
+        )}
+      </Button>
+    </AnimatePresence>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/FilterSheet.tsx

@@ -0,0 +1,156 @@
+import { FilterChip } from "../FilterChip";
+import { cn } from "@/lib/utils";
+import { CategoryKey } from "../BlockMenuFilters/types";
+import { AnimatePresence, motion } from "framer-motion";
+import { XIcon } from "@phosphor-icons/react";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { Separator } from "@/components/__legacy__/ui/separator";
+import { Checkbox } from "@/components/__legacy__/ui/checkbox";
+import { useFilterSheet } from "./useFilterSheet";
+import { INITIAL_CREATORS_TO_SHOW } from "./constant";
+
+export function FilterSheet({
+  categories,
+}: {
+  categories: Array<{ key: CategoryKey; name: string }>;
+}) {
+  const {
+    isOpen,
+    localCategories,
+    localCreators,
+    displayedCreatorsCount,
+    handleLocalCategoryChange,
+    handleToggleShowMoreCreators,
+    handleLocalCreatorChange,
+    handleClearFilters,
+    handleCloseButton,
+    handleApplyFilters,
+    hasLocalActiveFilters,
+    visibleCreators,
+    creators,
+    handleOpenFilters,
+    hasActiveFilters,
+  } = useFilterSheet();
+
+  return (
+    <div className="m-0 inline w-fit p-0">
+      <FilterChip
+        name={hasActiveFilters() ? "Edit filters" : "All filters"}
+        onClick={handleOpenFilters}
+      />
+
+      <AnimatePresence>
+        {isOpen && (
+          <motion.div
+            className={cn(
+              "absolute bottom-2 left-2 top-2 z-20 w-3/4 max-w-[22.5rem] space-y-4 overflow-hidden rounded-[0.75rem] bg-white pb-4 shadow-[0_4px_12px_2px_rgba(0,0,0,0.1)]",
+            )}
+            initial={{ x: "-100%", filter: "blur(10px)" }}
+            animate={{ x: 0, filter: "blur(0px)" }}
+            exit={{ x: "-110%", filter: "blur(10px)" }}
+            transition={{ duration: 0.4, type: "spring", bounce: 0.2 }}
+          >
+            {/* Top section */}
+            <div className="flex items-center justify-between px-5 pt-4">
+              <Text variant="body">Filters</Text>
+              <Button
+                className="p-0"
+                variant="ghost"
+                size="icon"
+                onClick={handleCloseButton}
+              >
+                <XIcon size={20} />
+              </Button>
+            </div>
+
+            <Separator className="h-[1px] w-full text-zinc-300" />
+
+            {/* Category section */}
+            <div className="space-y-4 px-5">
+              <Text variant="large">Categories</Text>
+              <div className="space-y-2">
+                {categories.map((category) => (
+                  <div
+                    key={category.key}
+                    className="flex items-center space-x-2"
+                  >
+                    <Checkbox
+                      id={category.key}
+                      checked={localCategories.includes(category.key)}
+                      onCheckedChange={() =>
+                        handleLocalCategoryChange(category.key)
+                      }
+                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
+                    />
+                    <label
+                      htmlFor={category.key}
+                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
+                    >
+                      {category.name}
+                    </label>
+                  </div>
+                ))}
+              </div>
+            </div>
+
+            {/* Created by section */}
+            <div className="space-y-4 px-5">
+              <p className="font-sans text-base font-medium text-zinc-800">
+                Created by
+              </p>
+              <div className="space-y-2">
+                {visibleCreators.map((creator, i) => (
+                  <div key={i} className="flex items-center space-x-2">
+                    <Checkbox
+                      id={`creator-${creator}`}
+                      checked={localCreators.includes(creator)}
+                      onCheckedChange={() => handleLocalCreatorChange(creator)}
+                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
+                    />
+                    <label
+                      htmlFor={`creator-${creator}`}
+                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
+                    >
+                      {creator}
+                    </label>
+                  </div>
+                ))}
+              </div>
+              {creators.length > INITIAL_CREATORS_TO_SHOW && (
+                <Button
+                  variant={"link"}
+                  className="m-0 p-0 font-sans text-sm font-medium leading-[1.375rem] text-zinc-800 underline hover:text-zinc-600"
+                  onClick={handleToggleShowMoreCreators}
+                >
+                  {displayedCreatorsCount < creators.length ? "More" : "Less"}
+                </Button>
+              )}
+            </div>
+
+            {/* Footer section */}
+            <div className="fixed bottom-0 flex w-full justify-between gap-3 border-t border-zinc-200 bg-white px-5 py-3">
+              <Button
+                size="small"
+                variant={"outline"}
+                onClick={handleClearFilters}
+                className="rounded-[8px] px-2 py-1.5"
+              >
+                Clear
+              </Button>
+
+              <Button
+                size="small"
+                onClick={handleApplyFilters}
+                disabled={!hasLocalActiveFilters()}
+                className="rounded-[8px] px-2 py-1.5"
+              >
+                Apply filters
+              </Button>
+            </div>
+          </motion.div>
+        )}
+      </AnimatePresence>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/constant.ts

@@ -0,0 +1 @@
+export const INITIAL_CREATORS_TO_SHOW = 5;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/useFilterSheet.ts

@@ -0,0 +1,100 @@
+import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
+import { useState } from "react";
+import { INITIAL_CREATORS_TO_SHOW } from "./constant";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
+
+export const useFilterSheet = () => {
+  const { filters, creators_list, creators, setFilters, setCreators } =
+    useBlockMenuStore();
+
+  const [isOpen, setIsOpen] = useState(false);
+  const [localCategories, setLocalCategories] =
+    useState<GetV2BuilderSearchFilterAnyOfItem[]>(filters);
+  const [localCreators, setLocalCreators] = useState<string[]>(creators);
+  const [displayedCreatorsCount, setDisplayedCreatorsCount] = useState(
+    INITIAL_CREATORS_TO_SHOW,
+  );
+
+  const handleLocalCategoryChange = (
+    category: GetV2BuilderSearchFilterAnyOfItem,
+  ) => {
+    setLocalCategories((prev) => {
+      if (prev.includes(category)) {
+        return prev.filter((c) => c !== category);
+      }
+      return [...prev, category];
+    });
+  };
+
+  const hasActiveFilters = () => {
+    return filters.length > 0 || creators.length > 0;
+  };
+
+  const handleToggleShowMoreCreators = () => {
+    if (displayedCreatorsCount < creators.length) {
+      setDisplayedCreatorsCount(creators.length);
+    } else {
+      setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+    }
+  };
+
+  const handleLocalCreatorChange = (creator: string) => {
+    setLocalCreators((prev) => {
+      if (prev.includes(creator)) {
+        return prev.filter((c) => c !== creator);
+      }
+      return [...prev, creator];
+    });
+  };
+
+  const handleClearFilters = () => {
+    setLocalCategories([]);
+    setLocalCreators([]);
+    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+  };
+
+  const handleCloseButton = () => {
+    setIsOpen(false);
+    setLocalCategories(filters);
+    setLocalCreators(creators);
+    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
+  };
+
+  const handleApplyFilters = () => {
+    setFilters(localCategories);
+    setCreators(localCreators);
+    setIsOpen(false);
+  };
+
+  const handleOpenFilters = () => {
+    setIsOpen(true);
+    setLocalCategories(filters);
+    setLocalCreators(creators);
+  };
+
+  const hasLocalActiveFilters = () => {
+    return localCategories.length > 0 || localCreators.length > 0;
+  };
+
+  const visibleCreators = creators_list.slice(0, displayedCreatorsCount);
+
+  return {
+    creators,
+    isOpen,
+    setIsOpen,
+    localCategories,
+    localCreators,
+    displayedCreatorsCount,
+    setDisplayedCreatorsCount,
+    handleLocalCategoryChange,
+    handleToggleShowMoreCreators,
+    handleLocalCreatorChange,
+    handleClearFilters,
+    handleCloseButton,
+    handleOpenFilters,
+    handleApplyFilters,
+    hasLocalActiveFilters,
+    visibleCreators,
+    hasActiveFilters,
+  };
+};

autogpt_platform/frontend/src/app/(platform)/build/stores/blockMenuStore.ts

@@ -1,12 +1,30 @@
 import { create } from "zustand";
 import { DefaultStateType } from "../components/NewControlPanel/NewBlockMenu/types";
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { getSearchItemType } from "../components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/helper";
+import { StoreAgent } from "@/app/api/__generated__/models/storeAgent";
+import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
 
 type BlockMenuStore = {
   searchQuery: string;
   searchId: string | undefined;
   defaultState: DefaultStateType;
   integration: string | undefined;
+  filters: GetV2BuilderSearchFilterAnyOfItem[];
+  creators: string[];
+  creators_list: string[];
+  categoryCounts: Record<GetV2BuilderSearchFilterAnyOfItem, number>;
 
+  setCategoryCounts: (
+    counts: Record<GetV2BuilderSearchFilterAnyOfItem, number>,
+  ) => void;
+  setCreatorsList: (searchData: SearchResponseItemsItem[]) => void;
+  addCreator: (creator: string) => void;
+  setCreators: (creators: string[]) => void;
+  removeCreator: (creator: string) => void;
+  addFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
+  setFilters: (filters: GetV2BuilderSearchFilterAnyOfItem[]) => void;
+  removeFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
   setSearchQuery: (query: string) => void;
   setSearchId: (id: string | undefined) => void;
   setDefaultState: (state: DefaultStateType) => void;
@@ -19,11 +37,44 @@ export const useBlockMenuStore = create<BlockMenuStore>((set) => ({
   searchId: undefined,
   defaultState: DefaultStateType.SUGGESTION,
   integration: undefined,
+  filters: [],
+  creators: [], // creator filters that are applied to the search results
+  creators_list: [], // all creators that are available to filter by
+  categoryCounts: {
+    blocks: 0,
+    integrations: 0,
+    marketplace_agents: 0,
+    my_agents: 0,
+  },
 
+  setCategoryCounts: (counts) => set({ categoryCounts: counts }),
+  setCreatorsList: (searchData) => {
+    const marketplaceAgents = searchData.filter((item) => {
+      return getSearchItemType(item).type === "store_agent";
+    }) as StoreAgent[];
+
+    const newCreators = marketplaceAgents.map((agent) => agent.creator);
+
+    set((state) => ({
+      creators_list: Array.from(
+        new Set([...state.creators_list, ...newCreators]),
+      ),
+    }));
+  },
+  setCreators: (creators) => set({ creators }),
+  setFilters: (filters) => set({ filters }),
   setSearchQuery: (query) => set({ searchQuery: query }),
   setSearchId: (id) => set({ searchId: id }),
   setDefaultState: (state) => set({ defaultState: state }),
   setIntegration: (integration) => set({ integration }),
+  addFilter: (filter) =>
+    set((state) => ({ filters: [...state.filters, filter] })),
+  removeFilter: (filter) =>
+    set((state) => ({ filters: state.filters.filter((f) => f !== filter) })),
+  addCreator: (creator) =>
+    set((state) => ({ creators: [...state.creators, creator] })),
+  removeCreator: (creator) =>
+    set((state) => ({ creators: state.creators.filter((c) => c !== creator) })),
   reset: () =>
     set({
       searchQuery: "",

autogpt_platform/frontend/src/app/(platform)/build/stores/nodeStore.ts

@@ -68,6 +68,9 @@ type NodeStore = {
   clearAllNodeErrors: () => void; // Add this
 
   syncHardcodedValuesWithHandleIds: (nodeId: string) => void;
+
+  // Credentials optional helpers
+  setCredentialsOptional: (nodeId: string, optional: boolean) => void;
 };
 
 export const useNodeStore = create<NodeStore>((set, get) => ({
@@ -226,6 +229,9 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
         ...(node.data.metadata?.customized_name !== undefined && {
           customized_name: node.data.metadata.customized_name,
         }),
+        ...(node.data.metadata?.credentials_optional !== undefined && {
+          credentials_optional: node.data.metadata.credentials_optional,
+        }),
       },
     };
   },
@@ -342,4 +348,30 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       }));
     }
   },
+
+  setCredentialsOptional: (nodeId: string, optional: boolean) => {
+    set((state) => ({
+      nodes: state.nodes.map((n) =>
+        n.id === nodeId
+          ? {
+              ...n,
+              data: {
+                ...n.data,
+                metadata: {
+                  ...n.data.metadata,
+                  credentials_optional: optional,
+                },
+              },
+            }
+          : n,
+      ),
+    }));
+
+    const newState = {
+      nodes: get().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
+    useHistoryStore.getState().pushState(newState);
+  },
 }));

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

@@ -34,7 +34,9 @@ type Props = {
   onSelectCredentials: (newValue?: CredentialsMetaInput) => void;
   onLoaded?: (loaded: boolean) => void;
   readOnly?: boolean;
+  isOptional?: boolean;
   showTitle?: boolean;
+  variant?: "default" | "node";
 };
 
 export function CredentialsInput({
@@ -45,7 +47,9 @@ export function CredentialsInput({
   siblingInputs,
   onLoaded,
   readOnly = false,
+  isOptional = false,
   showTitle = true,
+  variant = "default",
 }: Props) {
   const hookData = useCredentialsInput({
     schema,
@@ -54,6 +58,7 @@ export function CredentialsInput({
     siblingInputs,
     onLoaded,
     readOnly,
+    isOptional,
   });
 
   if (!isLoaded(hookData)) {
@@ -94,7 +99,14 @@ export function CredentialsInput({
     <div className={cn("mb-6", className)}>
       {showTitle && (
         <div className="mb-2 flex items-center gap-2">
-          <Text variant="large-medium">{displayName} credentials</Text>
+          <Text variant="large-medium">
+            {displayName} credentials
+            {isOptional && (
+              <span className="ml-1 text-sm font-normal text-gray-500">
+                (optional)
+              </span>
+            )}
+          </Text>
           {schema.description && (
             <InformationTooltip description={schema.description} />
           )}
@@ -103,14 +115,17 @@ export function CredentialsInput({
 
       {hasCredentialsToShow ? (
         <>
-          {credentialsToShow.length > 1 && !readOnly ? (
+          {(credentialsToShow.length > 1 || isOptional) && !readOnly ? (
             <CredentialsSelect
               credentials={credentialsToShow}
               provider={provider}
               displayName={displayName}
               selectedCredentials={selectedCredential}
               onSelectCredential={handleCredentialSelect}
+              onClearCredential={() => onSelectCredential(undefined)}
               readOnly={readOnly}
+              allowNone={isOptional}
+              variant={variant}
             />
           ) : (
             <div className="mb-4 space-y-2">

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

@@ -30,6 +30,8 @@ type CredentialRowProps = {
   readOnly?: boolean;
   showCaret?: boolean;
   asSelectTrigger?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 };
 
 export function CredentialRow({
@@ -41,14 +43,22 @@ export function CredentialRow({
   readOnly = false,
   showCaret = false,
   asSelectTrigger = false,
+  variant = "default",
 }: CredentialRowProps) {
   const ProviderIcon = providerIcons[provider] || fallbackIcon;
+  const isNodeVariant = variant === "node";
 
   return (
     <div
       className={cn(
         "flex items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
-        asSelectTrigger ? "border-0 bg-transparent" : readOnly ? "w-fit" : "",
+        asSelectTrigger && isNodeVariant
+          ? "min-w-0 flex-1 overflow-hidden border-0 bg-transparent"
+          : asSelectTrigger
+            ? "border-0 bg-transparent"
+            : readOnly
+              ? "w-fit"
+              : "",
       )}
       onClick={readOnly || showCaret || asSelectTrigger ? undefined : onSelect}
       style={
@@ -61,19 +71,31 @@ export function CredentialRow({
         <ProviderIcon className="h-3 w-3 text-white" />
       </div>
       <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
-      <div className="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
+      <div
+        className={cn(
+          "flex min-w-0 flex-1 flex-nowrap items-center gap-4",
+          isNodeVariant && "overflow-hidden",
+        )}
+      >
         <Text
           variant="body"
-          className="line-clamp-1 flex-[0_0_50%] text-ellipsis tracking-tight"
+          className={cn(
+            "tracking-tight",
+            isNodeVariant
+              ? "truncate"
+              : "line-clamp-1 flex-[0_0_50%] text-ellipsis",
+          )}
         >
           {getCredentialDisplayName(credential, displayName)}
         </Text>
-        <Text
-          variant="large"
-          className="lex-[0_0_40%] relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
-        >
-          {"*".repeat(MASKED_KEY_LENGTH)}
-        </Text>
+        {!(asSelectTrigger && isNodeVariant) && (
+          <Text
+            variant="large"
+            className="relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
+          >
+            {"*".repeat(MASKED_KEY_LENGTH)}
+          </Text>
+        )}
       </div>
       {showCaret && !asSelectTrigger && (
         <CaretDown className="h-4 w-4 shrink-0 text-gray-400" />

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

@@ -7,6 +7,7 @@ import {
 } from "@/components/__legacy__/ui/select";
 import { Text } from "@/components/atoms/Text/Text";
 import { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
+import { cn } from "@/lib/utils";
 import { useEffect } from "react";
 import { getCredentialDisplayName } from "../../helpers";
 import { CredentialRow } from "../CredentialRow/CredentialRow";
@@ -23,7 +24,11 @@ interface Props {
   displayName: string;
   selectedCredentials?: CredentialsMetaInput;
   onSelectCredential: (credentialId: string) => void;
+  onClearCredential?: () => void;
   readOnly?: boolean;
+  allowNone?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 }
 
 export function CredentialsSelect({
@@ -32,22 +37,38 @@ export function CredentialsSelect({
   displayName,
   selectedCredentials,
   onSelectCredential,
+  onClearCredential,
   readOnly = false,
+  allowNone = true,
+  variant = "default",
 }: Props) {
-  // Auto-select first credential if none is selected
+  // Auto-select first credential if none is selected (only if allowNone is false)
   useEffect(() => {
-    if (!selectedCredentials && credentials.length > 0) {
+    if (!allowNone && !selectedCredentials && credentials.length > 0) {
       onSelectCredential(credentials[0].id);
     }
-  }, [selectedCredentials, credentials, onSelectCredential]);
+  }, [allowNone, selectedCredentials, credentials, onSelectCredential]);
+
+  const handleValueChange = (value: string) => {
+    if (value === "__none__") {
+      onClearCredential?.();
+    } else {
+      onSelectCredential(value);
+    }
+  };
 
   return (
     <div className="mb-4 w-full">
       <Select
-        value={selectedCredentials?.id || ""}
-        onValueChange={(value) => onSelectCredential(value)}
+        value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
+        onValueChange={handleValueChange}
       >
-        <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
+        <SelectTrigger
+          className={cn(
+            "h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none",
+            variant === "node" && "overflow-hidden",
+          )}
+        >
           {selectedCredentials ? (
             <SelectValue key={selectedCredentials.id} asChild>
               <CredentialRow
@@ -63,6 +84,7 @@ export function CredentialsSelect({
                 onDelete={() => {}}
                 readOnly={readOnly}
                 asSelectTrigger={true}
+                variant={variant}
               />
             </SelectValue>
           ) : (
@@ -70,6 +92,15 @@ export function CredentialsSelect({
           )}
         </SelectTrigger>
         <SelectContent>
+          {allowNone && (
+            <SelectItem key="__none__" value="__none__">
+              <div className="flex items-center gap-2">
+                <Text variant="body" className="tracking-tight text-gray-500">
+                  None (skip this credential)
+                </Text>
+              </div>
+            </SelectItem>
+          )}
           {credentials.map((credential) => (
             <SelectItem key={credential.id} value={credential.id}>
               <div className="flex items-center gap-2">

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/useCredentialsInput.ts

@@ -22,6 +22,7 @@ type Params = {
   siblingInputs?: Record<string, any>;
   onLoaded?: (loaded: boolean) => void;
   readOnly?: boolean;
+  isOptional?: boolean;
 };
 
 export function useCredentialsInput({
@@ -31,6 +32,7 @@ export function useCredentialsInput({
   siblingInputs,
   onLoaded,
   readOnly = false,
+  isOptional = false,
 }: Params) {
   const [isAPICredentialsModalOpen, setAPICredentialsModalOpen] =
     useState(false);
@@ -99,13 +101,20 @@ export function useCredentialsInput({
       : null;
   }, [credentials]);
 
-  // Auto-select the one available credential
+  // Auto-select the one available credential (only if not optional)
   useEffect(() => {
     if (readOnly) return;
+    if (isOptional) return; // Don't auto-select when credential is optional
     if (singleCredential && !selectedCredential) {
       onSelectCredential(singleCredential);
     }
-  }, [singleCredential, selectedCredential, onSelectCredential, readOnly]);
+  }, [
+    singleCredential,
+    selectedCredential,
+    onSelectCredential,
+    readOnly,
+    isOptional,
+  ]);
 
   if (
     !credentials ||

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

@@ -8,6 +8,7 @@ import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBann
 
 export function ModalRunSection() {
   const {
+    agent,
     defaultRunType,
     presetName,
     setPresetName,
@@ -24,6 +25,11 @@ export function ModalRunSection() {
   const inputFields = Object.entries(agentInputFields || {});
   const credentialFields = Object.entries(agentCredentialsInputFields || {});
 
+  // Get the list of required credentials from the schema
+  const requiredCredentials = new Set(
+    (agent.credentials_input_schema?.required as string[]) || [],
+  );
+
   return (
     <div className="flex flex-col gap-4">
       {defaultRunType === "automatic-trigger" ||
@@ -99,14 +105,12 @@ export function ModalRunSection() {
                   schema={
                     { ...inputSubSchema, discriminator: undefined } as any
                   }
-                  selectedCredentials={
-                    (inputCredentials && inputCredentials[key]) ??
-                    inputSubSchema.default
-                  }
+                  selectedCredentials={inputCredentials?.[key]}
                   onSelectCredentials={(value) =>
                     setInputCredentialsValue(key, value)
                   }
                   siblingInputs={inputValues}
+                  isOptional={!requiredCredentials.has(key)}
                 />
               ),
             )}

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

@@ -163,15 +163,21 @@ export function useAgentRunModal(
   }, [agentInputSchema.required, inputValues]);
 
   const [allCredentialsAreSet, missingCredentials] = useMemo(() => {
-    const availableCredentials = new Set(Object.keys(inputCredentials));
-    const allCredentials = new Set(
-      Object.keys(agentCredentialsInputFields || {}) ?? [],
-    );
-    const missing = [...allCredentials].filter(
-      (key) => !availableCredentials.has(key),
+    // Only check required credentials from schema, not all properties
+    // Credentials marked as optional in node metadata won't be in the required array
+    const requiredCredentials = new Set(
+      (agent.credentials_input_schema?.required as string[]) || [],
     );
+
+    // Check if required credentials have valid id (not just key existence)
+    // A credential is valid only if it has an id field set
+    const missing = [...requiredCredentials].filter((key) => {
+      const cred = inputCredentials[key];
+      return !cred || !cred.id;
+    });
+
     return [missing.length === 0, missing];
-  }, [agentCredentialsInputFields, inputCredentials]);
+  }, [agent.credentials_input_schema, inputCredentials]);
 
   const credentialsRequired = useMemo(
     () => Object.keys(agentCredentialsInputFields || {}).length > 0,
@@ -239,12 +245,18 @@ export function useAgentRunModal(
       });
     } else {
       // Manual execution
+      // Filter out incomplete credentials (optional ones not selected)
+      // Only send credentials that have a valid id field
+      const validCredentials = Object.fromEntries(
+        Object.entries(inputCredentials).filter(([_, cred]) => cred && cred.id),
+      );
+
       executeGraphMutation.mutate({
         graphId: agent.graph_id,
         graphVersion: agent.graph_version,
         data: {
           inputs: inputValues,
-          credentials_inputs: inputCredentials,
+          credentials_inputs: validCredentials,
           source: "library",
         },
       });

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

@@ -1,17 +1,25 @@
 "use client";
 
 import { getV1GetGraphVersion } from "@/app/api/__generated__/endpoints/graphs/graphs";
+import {
+  getGetV2ListLibraryAgentsQueryKey,
+  useDeleteV2DeleteLibraryAgent,
+} from "@/app/api/__generated__/endpoints/library/library";
 import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
 import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { ShowMoreText } from "@/components/molecules/ShowMoreText/ShowMoreText";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { exportAsJSONFile } from "@/lib/utils";
 import { formatDate } from "@/lib/utils/time";
+import { useQueryClient } from "@tanstack/react-query";
 import Link from "next/link";
+import { useRouter } from "next/navigation";
+import { useState } from "react";
 import { RunAgentModal } from "../modals/RunAgentModal/RunAgentModal";
 import { RunDetailCard } from "../selected-views/RunDetailCard/RunDetailCard";
 import { EmptyTasksIllustration } from "./EmptyTasksIllustration";
@@ -30,6 +38,41 @@ export function EmptyTasks({
   onScheduleCreated,
 }: Props) {
   const { toast } = useToast();
+  const queryClient = useQueryClient();
+  const router = useRouter();
+  const [showDeleteDialog, setShowDeleteDialog] = useState(false);
+  const [isDeletingAgent, setIsDeletingAgent] = useState(false);
+
+  const { mutateAsync: deleteAgent } = useDeleteV2DeleteLibraryAgent();
+
+  async function handleDeleteAgent() {
+    if (!agent.id) return;
+
+    setIsDeletingAgent(true);
+
+    try {
+      await deleteAgent({ libraryAgentId: agent.id });
+
+      await queryClient.refetchQueries({
+        queryKey: getGetV2ListLibraryAgentsQueryKey(),
+      });
+
+      toast({ title: "Agent deleted" });
+      setShowDeleteDialog(false);
+      router.push("/library");
+    } catch (error: unknown) {
+      toast({
+        title: "Failed to delete agent",
+        description:
+          error instanceof Error
+            ? error.message
+            : "An unexpected error occurred.",
+        variant: "destructive",
+      });
+    } finally {
+      setIsDeletingAgent(false);
+    }
+  }
 
   async function handleExport() {
     try {
@@ -147,9 +190,50 @@ export function EmptyTasks({
             <Button variant="secondary" size="small" onClick={handleExport}>
               Export agent to file
             </Button>
+            <Button
+              variant="secondary"
+              size="small"
+              onClick={() => setShowDeleteDialog(true)}
+            >
+              Delete agent
+            </Button>
           </div>
         </div>
       </div>
+
+      <Dialog
+        controlled={{
+          isOpen: showDeleteDialog,
+          set: setShowDeleteDialog,
+        }}
+        styling={{ maxWidth: "32rem" }}
+        title="Delete agent"
+      >
+        <Dialog.Content>
+          <div>
+            <Text variant="large">
+              Are you sure you want to delete this agent? This action cannot be
+              undone.
+            </Text>
+            <Dialog.Footer>
+              <Button
+                variant="secondary"
+                disabled={isDeletingAgent}
+                onClick={() => setShowDeleteDialog(false)}
+              >
+                Cancel
+              </Button>
+              <Button
+                variant="destructive"
+                onClick={handleDeleteAgent}
+                loading={isDeletingAgent}
+              >
+                Delete Agent
+              </Button>
+            </Dialog.Footer>
+          </div>
+        </Dialog.Content>
+      </Dialog>
     </div>
   );
 }

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

@@ -83,7 +83,9 @@ function renderCode(
         </div>
       )}
       <pre className="overflow-x-auto rounded-md bg-muted p-3">
-        <code className="font-mono text-sm">{codeValue}</code>
+        <code className="whitespace-pre-wrap break-words font-mono text-sm">
+          {codeValue}
+        </code>
       </pre>
     </div>
   );

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

@@ -13,7 +13,7 @@ import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";
 import { RunDetailHeader } from "../RunDetailHeader/RunDetailHeader";
 import { SelectedViewLayout } from "../SelectedViewLayout";
-import { SelectedScheduleActions } from "./components/SelectedScheduleActions";
+import { SelectedScheduleActions } from "./components/SelectedScheduleActions/SelectedScheduleActions";
 import { useSelectedScheduleView } from "./useSelectedScheduleView";
 
 interface Props {

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

@@ -1,40 +0,0 @@
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Button } from "@/components/atoms/Button/Button";
-import { EyeIcon } from "@phosphor-icons/react";
-import { AgentActionsDropdown } from "../../AgentActionsDropdown";
-import { useScheduleDetailHeader } from "../../RunDetailHeader/useScheduleDetailHeader";
-import { SelectedActionsWrap } from "../../SelectedActionsWrap";
-
-type Props = {
-  agent: LibraryAgent;
-  scheduleId: string;
-  onDeleted?: () => void;
-};
-
-export function SelectedScheduleActions({ agent, scheduleId }: Props) {
-  const { openInBuilderHref } = useScheduleDetailHeader(
-    agent.graph_id,
-    scheduleId,
-    agent.graph_version,
-  );
-
-  return (
-    <>
-      <SelectedActionsWrap>
-        {openInBuilderHref && (
-          <Button
-            variant="icon"
-            size="icon"
-            as="NextLink"
-            href={openInBuilderHref}
-            target="_blank"
-            aria-label="View scheduled task details"
-          >
-            <EyeIcon weight="bold" size={18} className="text-zinc-700" />
-          </Button>
-        )}
-        <AgentActionsDropdown agent={agent} scheduleId={scheduleId} />
-      </SelectedActionsWrap>
-    </>
-  );
-}

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

@@ -0,0 +1,96 @@
+"use client";
+
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Button } from "@/components/atoms/Button/Button";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { Text } from "@/components/atoms/Text/Text";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { EyeIcon, TrashIcon } from "@phosphor-icons/react";
+import { AgentActionsDropdown } from "../../../AgentActionsDropdown";
+import { SelectedActionsWrap } from "../../../SelectedActionsWrap";
+import { useSelectedScheduleActions } from "./useSelectedScheduleActions";
+
+type Props = {
+  agent: LibraryAgent;
+  scheduleId: string;
+  onDeleted?: () => void;
+};
+
+export function SelectedScheduleActions({
+  agent,
+  scheduleId,
+  onDeleted,
+}: Props) {
+  const {
+    openInBuilderHref,
+    showDeleteDialog,
+    setShowDeleteDialog,
+    handleDelete,
+    isDeleting,
+  } = useSelectedScheduleActions({ agent, scheduleId, onDeleted });
+
+  return (
+    <>
+      <SelectedActionsWrap>
+        {openInBuilderHref && (
+          <Button
+            variant="icon"
+            size="icon"
+            as="NextLink"
+            href={openInBuilderHref}
+            target="_blank"
+            aria-label="View scheduled task details"
+          >
+            <EyeIcon weight="bold" size={18} className="text-zinc-700" />
+          </Button>
+        )}
+        <Button
+          variant="icon"
+          size="icon"
+          aria-label="Delete schedule"
+          onClick={() => setShowDeleteDialog(true)}
+          disabled={isDeleting}
+        >
+          {isDeleting ? (
+            <LoadingSpinner size="small" />
+          ) : (
+            <TrashIcon weight="bold" size={18} />
+          )}
+        </Button>
+        <AgentActionsDropdown agent={agent} scheduleId={scheduleId} />
+      </SelectedActionsWrap>
+
+      <Dialog
+        controlled={{
+          isOpen: showDeleteDialog,
+          set: setShowDeleteDialog,
+        }}
+        styling={{ maxWidth: "32rem" }}
+        title="Delete schedule"
+      >
+        <Dialog.Content>
+          <Text variant="large">
+            Are you sure you want to delete this schedule? This action cannot be
+            undone.
+          </Text>
+          <Dialog.Footer>
+            <Button
+              variant="secondary"
+              onClick={() => setShowDeleteDialog(false)}
+              disabled={isDeleting}
+            >
+              Cancel
+            </Button>
+            <Button
+              variant="destructive"
+              onClick={handleDelete}
+              loading={isDeleting}
+            >
+              Delete Schedule
+            </Button>
+          </Dialog.Footer>
+        </Dialog.Content>
+      </Dialog>
+    </>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedScheduleView/components/SelectedScheduleActions/useSelectedScheduleActions.ts

@@ -0,0 +1,65 @@
+"use client";
+
+import {
+  getGetV1ListExecutionSchedulesForAGraphQueryOptions,
+  useDeleteV1DeleteExecutionSchedule,
+} from "@/app/api/__generated__/endpoints/schedules/schedules";
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { useQueryClient } from "@tanstack/react-query";
+import { useState } from "react";
+
+interface UseSelectedScheduleActionsProps {
+  agent: LibraryAgent;
+  scheduleId: string;
+  onDeleted?: () => void;
+}
+
+export function useSelectedScheduleActions({
+  agent,
+  scheduleId,
+  onDeleted,
+}: UseSelectedScheduleActionsProps) {
+  const { toast } = useToast();
+  const queryClient = useQueryClient();
+  const [showDeleteDialog, setShowDeleteDialog] = useState(false);
+
+  const deleteMutation = useDeleteV1DeleteExecutionSchedule({
+    mutation: {
+      onSuccess: () => {
+        toast({ title: "Schedule deleted" });
+        queryClient.invalidateQueries({
+          queryKey: getGetV1ListExecutionSchedulesForAGraphQueryOptions(
+            agent.graph_id,
+          ).queryKey,
+        });
+        setShowDeleteDialog(false);
+        onDeleted?.();
+      },
+      onError: (error: unknown) =>
+        toast({
+          title: "Failed to delete schedule",
+          description:
+            error instanceof Error
+              ? error.message
+              : "An unexpected error occurred.",
+          variant: "destructive",
+        }),
+    },
+  });
+
+  function handleDelete() {
+    if (!scheduleId) return;
+    deleteMutation.mutate({ scheduleId });
+  }
+
+  const openInBuilderHref = `/build?flowID=${agent.graph_id}&flowVersion=${agent.graph_version}`;
+
+  return {
+    openInBuilderHref,
+    showDeleteDialog,
+    setShowDeleteDialog,
+    handleDelete,
+    isDeleting: deleteMutation.isPending,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/hooks/useMarketplaceUpdate.ts

@@ -40,15 +40,17 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
     },
   );
 
-  // Get user's submissions to check for pending submissions
-  const { data: submissionsData } = useGetV2ListMySubmissions(
-    { page: 1, page_size: 50 }, // Get enough to cover recent submissions
-    {
-      query: {
-        enabled: !!user?.id, // Only fetch if user is authenticated
+  // Get user's submissions - only fetch if user is the creator
+  const { data: submissionsData, isLoading: isSubmissionsLoading } =
+    useGetV2ListMySubmissions(
+      { page: 1, page_size: 50 },
+      {
+        query: {
+          // Only fetch if user is the creator
+          enabled: !!(user?.id && agent?.owner_user_id === user.id),
+        },
       },
-    },
-  );
+    );
 
   const updateToLatestMutation = usePatchV2UpdateLibraryAgent({
     mutation: {
@@ -78,16 +80,45 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
   // Check if marketplace has a newer version than user's current version
   const marketplaceUpdateInfo = React.useMemo(() => {
     const storeAgent = okData(storeAgentData) as any;
-    if (!agent || !storeAgent) {
+
+    if (!agent || isSubmissionsLoading) {
       return {
         hasUpdate: false,
         latestVersion: undefined,
         isUserCreator: false,
+        hasPublishUpdate: false,
+      };
+    }
+
+    const isUserCreator = agent?.owner_user_id === user?.id;
+
+    const submissionsResponse = okData(submissionsData) as any;
+    const agentSubmissions =
+      submissionsResponse?.submissions?.filter(
+        (submission: StoreSubmission) => submission.agent_id === agent.graph_id,
+      ) || [];
+
+    const highestSubmittedVersion =
+      agentSubmissions.length > 0
+        ? Math.max(
+            ...agentSubmissions.map(
+              (submission: StoreSubmission) => submission.agent_version,
+            ),
+          )
+        : 0;
+
+    const hasUnpublishedChanges =
+      isUserCreator && agent.graph_version > highestSubmittedVersion;
+
+    if (!storeAgent) {
+      return {
+        hasUpdate: false,
+        latestVersion: undefined,
+        isUserCreator,
+        hasPublishUpdate: agentSubmissions.length > 0 && hasUnpublishedChanges,
       };
     }
 
-    // Get the latest version from the marketplace
-    // agentGraphVersions array contains graph version numbers as strings, get the highest one
     const latestMarketplaceVersion =
       storeAgent.agentGraphVersions?.length > 0
         ? Math.max(
@@ -97,32 +128,11 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
           )
         : undefined;
 
-    // Determine if the user is the creator of this agent
-    // Compare current user ID with the marketplace listing creator ID
-    const isUserCreator =
-      user?.id && agent.marketplace_listing?.creator.id === user.id;
-
-    // Check if there's a pending submission for this specific agent version
-    const submissionsResponse = okData(submissionsData) as any;
-    const hasPendingSubmissionForCurrentVersion =
-      isUserCreator &&
-      submissionsResponse?.submissions?.some(
-        (submission: StoreSubmission) =>
-          submission.agent_id === agent.graph_id &&
-          submission.agent_version === agent.graph_version &&
-          submission.status === "PENDING",
-      );
-
-    // If user is creator and their version is newer than marketplace, show publish update banner
-    // BUT only if there's no pending submission for this version
     const hasPublishUpdate =
       isUserCreator &&
-      !hasPendingSubmissionForCurrentVersion &&
-      latestMarketplaceVersion !== undefined &&
-      agent.graph_version > latestMarketplaceVersion;
+      agent.graph_version >
+        Math.max(latestMarketplaceVersion || 0, highestSubmittedVersion);
 
-    // If marketplace version is newer than user's version, show update banner
-    // This applies to both creators and non-creators
     const hasMarketplaceUpdate =
       latestMarketplaceVersion !== undefined &&
       latestMarketplaceVersion > agent.graph_version;
@@ -133,7 +143,7 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
       isUserCreator,
       hasPublishUpdate,
     };
-  }, [agent, storeAgentData, user, submissionsData]);
+  }, [agent, storeAgentData, user, submissionsData, isSubmissionsLoading]);
 
   const handlePublishUpdate = () => {
     setModalOpen(true);