chore: review suggestions (2)

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

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

autogpt_platform/backend/.gitignore

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

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

@@ -1,5 +1,4 @@
 import uuid
-from unittest.mock import AsyncMock, patch
 
 import orjson
 import pytest
@@ -18,17 +17,6 @@ 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/integrations/router.py

@@ -175,6 +175,7 @@ async def callback(
         f"Successfully processed OAuth callback for user {user_id} "
         f"and provider {provider.value}"
     )
+
     return CredentialsMetaResponse(
         id=credentials.id,
         provider=credentials.provider,
@@ -193,6 +194,7 @@ async def list_credentials(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[CredentialsMetaResponse]:
     credentials = await creds_manager.store.get_all_creds(user_id)
+
     return [
         CredentialsMetaResponse(
             id=cred.id,
@@ -215,6 +217,7 @@ async def list_credentials_by_provider(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[CredentialsMetaResponse]:
     credentials = await creds_manager.store.get_creds_by_provider(user_id, provider)
+
     return [
         CredentialsMetaResponse(
             id=cred.id,
@@ -831,6 +834,18 @@ async def list_providers() -> List[str]:
     return all_providers
 
 
+@router.get("/providers/system", response_model=List[str])
+async def list_system_providers() -> List[str]:
+    """
+    Get a list of providers that have platform credits (system credentials) available.
+
+    These providers can be used without the user providing their own API keys.
+    """
+    from backend.integrations.credentials_store import SYSTEM_PROVIDERS
+
+    return list(SYSTEM_PROVIDERS)
+
+
 @router.get("/providers/names", response_model=ProviderNamesResponse)
 async def get_provider_names() -> ProviderNamesResponse:
     """

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

@@ -1,417 +0,0 @@
-"""
-Content Type Handlers for Unified Embeddings
-
-Pluggable system for different content sources (store agents, blocks, docs).
-Each handler knows how to fetch and process its content type for embedding.
-"""
-
-import logging
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-from prisma.enums import ContentType
-
-from backend.data.db import query_raw_with_schema
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ContentItem:
-    """Represents a piece of content to be embedded."""
-
-    content_id: str  # Unique identifier (DB ID or file path)
-    content_type: ContentType
-    searchable_text: str  # Combined text for embedding
-    metadata: dict[str, Any]  # Content-specific metadata
-    user_id: str | None = None  # For user-scoped content
-
-
-class ContentHandler(ABC):
-    """Base handler for fetching and processing content for embeddings."""
-
-    @property
-    @abstractmethod
-    def content_type(self) -> ContentType:
-        """The ContentType this handler manages."""
-        pass
-
-    @abstractmethod
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """
-        Fetch items that don't have embeddings yet.
-
-        Args:
-            batch_size: Maximum number of items to return
-
-        Returns:
-            List of ContentItem objects ready for embedding
-        """
-        pass
-
-    @abstractmethod
-    async def get_stats(self) -> dict[str, int]:
-        """
-        Get statistics about embedding coverage.
-
-        Returns:
-            Dict with keys: total, with_embeddings, without_embeddings
-        """
-        pass
-
-
-class StoreAgentHandler(ContentHandler):
-    """Handler for marketplace store agent listings."""
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.STORE_AGENT
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch approved store listings without embeddings."""
-        from backend.api.features.store.embeddings import build_searchable_text
-
-        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,
-        )
-
-        return [
-            ContentItem(
-                content_id=row["id"],
-                content_type=ContentType.STORE_AGENT,
-                searchable_text=build_searchable_text(
-                    name=row["name"],
-                    description=row["description"],
-                    sub_heading=row["subHeading"],
-                    categories=row["categories"] or [],
-                ),
-                metadata={
-                    "name": row["name"],
-                    "categories": row["categories"] or [],
-                },
-                user_id=None,  # Store agents are public
-            )
-            for row in missing
-        ]
-
-    async def get_stats(self) -> dict[str, int]:
-        """Get statistics about store agent embedding coverage."""
-        # 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": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-        }
-
-
-class BlockHandler(ContentHandler):
-    """Handler for block definitions (Python classes)."""
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.BLOCK
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch blocks without embeddings."""
-        from backend.data.block import get_blocks
-
-        # Get all available blocks
-        all_blocks = get_blocks()
-
-        # Check which ones have embeddings
-        if not all_blocks:
-            return []
-
-        block_ids = list(all_blocks.keys())
-
-        # Query for existing embeddings
-        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
-        existing_result = await query_raw_with_schema(
-            f"""
-            SELECT "contentId"
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *block_ids,
-        )
-
-        existing_ids = {row["contentId"] for row in existing_result}
-        missing_blocks = [
-            (block_id, block_cls)
-            for block_id, block_cls in all_blocks.items()
-            if block_id not in existing_ids
-        ]
-
-        # Convert to ContentItem
-        items = []
-        for block_id, block_cls in missing_blocks[:batch_size]:
-            try:
-                block_instance = block_cls()
-
-                # Build searchable text from block metadata
-                parts = []
-                if hasattr(block_instance, "name") and block_instance.name:
-                    parts.append(block_instance.name)
-                if (
-                    hasattr(block_instance, "description")
-                    and block_instance.description
-                ):
-                    parts.append(block_instance.description)
-                if hasattr(block_instance, "categories") and block_instance.categories:
-                    # Convert BlockCategory enum to strings
-                    parts.append(
-                        " ".join(str(cat.value) for cat in block_instance.categories)
-                    )
-
-                # Add input/output schema info
-                if hasattr(block_instance, "input_schema"):
-                    schema = block_instance.input_schema
-                    if hasattr(schema, "model_json_schema"):
-                        schema_dict = schema.model_json_schema()
-                        if "properties" in schema_dict:
-                            for prop_name, prop_info in schema_dict[
-                                "properties"
-                            ].items():
-                                if "description" in prop_info:
-                                    parts.append(
-                                        f"{prop_name}: {prop_info['description']}"
-                                    )
-
-                searchable_text = " ".join(parts)
-
-                items.append(
-                    ContentItem(
-                        content_id=block_id,
-                        content_type=ContentType.BLOCK,
-                        searchable_text=searchable_text,
-                        metadata={
-                            "name": getattr(block_instance, "name", ""),
-                            "categories": getattr(block_instance, "categories", []),
-                        },
-                        user_id=None,  # Blocks are public
-                    )
-                )
-            except Exception as e:
-                logger.warning(f"Failed to process block {block_id}: {e}")
-                continue
-
-        return items
-
-    async def get_stats(self) -> dict[str, int]:
-        """Get statistics about block embedding coverage."""
-        from backend.data.block import get_blocks
-
-        all_blocks = get_blocks()
-        total_blocks = len(all_blocks)
-
-        if total_blocks == 0:
-            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
-
-        block_ids = list(all_blocks.keys())
-        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
-
-        embedded_result = await query_raw_with_schema(
-            f"""
-            SELECT COUNT(*) as count
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *block_ids,
-        )
-
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total": total_blocks,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_blocks - with_embeddings,
-        }
-
-
-class DocumentationHandler(ContentHandler):
-    """Handler for documentation files (.md/.mdx)."""
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.DOCUMENTATION
-
-    def _get_docs_root(self) -> Path:
-        """Get the documentation root directory."""
-        # Assuming docs are in /docs relative to project root
-        backend_root = Path(__file__).parent.parent.parent.parent
-        docs_root = backend_root.parent.parent / "docs"
-        return docs_root
-
-    def _extract_title_and_content(self, file_path: Path) -> tuple[str, str]:
-        """Extract title and content from markdown file."""
-        try:
-            content = file_path.read_text(encoding="utf-8")
-
-            # Try to extract title from first # heading
-            lines = content.split("\n")
-            title = ""
-            body_lines = []
-
-            for line in lines:
-                if line.startswith("# ") and not title:
-                    title = line[2:].strip()
-                else:
-                    body_lines.append(line)
-
-            # If no title found, use filename
-            if not title:
-                title = file_path.stem.replace("-", " ").replace("_", " ").title()
-
-            body = "\n".join(body_lines)
-
-            return title, body
-        except Exception as e:
-            logger.warning(f"Failed to read {file_path}: {e}")
-            return file_path.stem, ""
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch documentation files without embeddings."""
-        docs_root = self._get_docs_root()
-
-        if not docs_root.exists():
-            logger.warning(f"Documentation root not found: {docs_root}")
-            return []
-
-        # Find all .md and .mdx files
-        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
-
-        # Get relative paths for content IDs
-        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
-
-        if not doc_paths:
-            return []
-
-        # Check which ones have embeddings
-        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
-        existing_result = await query_raw_with_schema(
-            f"""
-            SELECT "contentId"
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *doc_paths,
-        )
-
-        existing_ids = {row["contentId"] for row in existing_result}
-        missing_docs = [
-            (doc_path, doc_file)
-            for doc_path, doc_file in zip(doc_paths, all_docs)
-            if doc_path not in existing_ids
-        ]
-
-        # Convert to ContentItem
-        items = []
-        for doc_path, doc_file in missing_docs[:batch_size]:
-            try:
-                title, content = self._extract_title_and_content(doc_file)
-
-                # Build searchable text
-                searchable_text = f"{title} {content}"
-
-                items.append(
-                    ContentItem(
-                        content_id=doc_path,
-                        content_type=ContentType.DOCUMENTATION,
-                        searchable_text=searchable_text,
-                        metadata={
-                            "title": title,
-                            "path": doc_path,
-                        },
-                        user_id=None,  # Documentation is public
-                    )
-                )
-            except Exception as e:
-                logger.warning(f"Failed to process doc {doc_path}: {e}")
-                continue
-
-        return items
-
-    async def get_stats(self) -> dict[str, int]:
-        """Get statistics about documentation embedding coverage."""
-        docs_root = self._get_docs_root()
-
-        if not docs_root.exists():
-            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
-
-        # Count all .md and .mdx files
-        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
-        total_docs = len(all_docs)
-
-        if total_docs == 0:
-            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
-
-        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
-        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
-
-        embedded_result = await query_raw_with_schema(
-            f"""
-            SELECT COUNT(*) as count
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *doc_paths,
-        )
-
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total": total_docs,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_docs - with_embeddings,
-        }
-
-
-# Content handler registry
-CONTENT_HANDLERS: dict[ContentType, ContentHandler] = {
-    ContentType.STORE_AGENT: StoreAgentHandler(),
-    ContentType.BLOCK: BlockHandler(),
-    ContentType.DOCUMENTATION: DocumentationHandler(),
-}

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

@@ -1,214 +0,0 @@
-"""
-Integration tests for content handlers using real DB.
-
-Run with: poetry run pytest backend/api/features/store/content_handlers_integration_test.py -xvs
-
-These tests use the real database but mock OpenAI calls.
-"""
-
-from unittest.mock import patch
-
-import pytest
-
-from backend.api.features.store.content_handlers import (
-    CONTENT_HANDLERS,
-    BlockHandler,
-    DocumentationHandler,
-    StoreAgentHandler,
-)
-from backend.api.features.store.embeddings import (
-    backfill_all_content_types,
-    ensure_content_embedding,
-    get_embedding_stats,
-)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_agent_handler_real_db():
-    """Test StoreAgentHandler with real database queries."""
-    handler = StoreAgentHandler()
-
-    # Get stats from real DB
-    stats = await handler.get_stats()
-
-    # Stats should have correct structure
-    assert "total" in stats
-    assert "with_embeddings" in stats
-    assert "without_embeddings" in stats
-    assert stats["total"] >= 0
-    assert stats["with_embeddings"] >= 0
-    assert stats["without_embeddings"] >= 0
-
-    # Get missing items (max 1 to keep test fast)
-    items = await handler.get_missing_items(batch_size=1)
-
-    # Items should be list (may be empty if all have embeddings)
-    assert isinstance(items, list)
-
-    if items:
-        item = items[0]
-        assert item.content_id is not None
-        assert item.content_type.value == "STORE_AGENT"
-        assert item.searchable_text != ""
-        assert item.user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_real_db():
-    """Test BlockHandler with real database queries."""
-    handler = BlockHandler()
-
-    # Get stats from real DB
-    stats = await handler.get_stats()
-
-    # Stats should have correct structure
-    assert "total" in stats
-    assert "with_embeddings" in stats
-    assert "without_embeddings" in stats
-    assert stats["total"] >= 0  # Should have at least some blocks
-    assert stats["with_embeddings"] >= 0
-    assert stats["without_embeddings"] >= 0
-
-    # Get missing items (max 1 to keep test fast)
-    items = await handler.get_missing_items(batch_size=1)
-
-    # Items should be list
-    assert isinstance(items, list)
-
-    if items:
-        item = items[0]
-        assert item.content_id is not None  # Should be block UUID
-        assert item.content_type.value == "BLOCK"
-        assert item.searchable_text != ""
-        assert item.user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_real_fs():
-    """Test DocumentationHandler with real filesystem."""
-    handler = DocumentationHandler()
-
-    # Get stats from real filesystem
-    stats = await handler.get_stats()
-
-    # Stats should have correct structure
-    assert "total" in stats
-    assert "with_embeddings" in stats
-    assert "without_embeddings" in stats
-    assert stats["total"] >= 0
-    assert stats["with_embeddings"] >= 0
-    assert stats["without_embeddings"] >= 0
-
-    # Get missing items (max 1 to keep test fast)
-    items = await handler.get_missing_items(batch_size=1)
-
-    # Items should be list
-    assert isinstance(items, list)
-
-    if items:
-        item = items[0]
-        assert item.content_id is not None  # Should be relative path
-        assert item.content_type.value == "DOCUMENTATION"
-        assert item.searchable_text != ""
-        assert item.user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_embedding_stats_all_types():
-    """Test get_embedding_stats aggregates all content types."""
-    stats = await get_embedding_stats()
-
-    # Should have structure with by_type and totals
-    assert "by_type" in stats
-    assert "totals" in stats
-
-    # Check each content type is present
-    by_type = stats["by_type"]
-    assert "STORE_AGENT" in by_type
-    assert "BLOCK" in by_type
-    assert "DOCUMENTATION" in by_type
-
-    # Check totals are aggregated
-    totals = stats["totals"]
-    assert totals["total"] >= 0
-    assert totals["with_embeddings"] >= 0
-    assert totals["without_embeddings"] >= 0
-    assert "coverage_percent" in totals
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.generate_embedding")
-async def test_ensure_content_embedding_blocks(mock_generate):
-    """Test creating embeddings for blocks (mocked OpenAI)."""
-    # Mock OpenAI to return fake embedding
-    mock_generate.return_value = [0.1] * 1536
-
-    # Get one block without embedding
-    handler = BlockHandler()
-    items = await handler.get_missing_items(batch_size=1)
-
-    if not items:
-        pytest.skip("No blocks without embeddings")
-
-    item = items[0]
-
-    # Try to create embedding (OpenAI mocked)
-    result = await ensure_content_embedding(
-        content_type=item.content_type,
-        content_id=item.content_id,
-        searchable_text=item.searchable_text,
-        metadata=item.metadata,
-        user_id=item.user_id,
-    )
-
-    # Should succeed with mocked OpenAI
-    assert result is True
-    mock_generate.assert_called_once()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.generate_embedding")
-async def test_backfill_all_content_types_dry_run(mock_generate):
-    """Test backfill_all_content_types processes all handlers in order."""
-    # Mock OpenAI to return fake embedding
-    mock_generate.return_value = [0.1] * 1536
-
-    # Run backfill with batch_size=1 to process max 1 per type
-    result = await backfill_all_content_types(batch_size=1)
-
-    # Should have results for all content types
-    assert "by_type" in result
-    assert "totals" in result
-
-    by_type = result["by_type"]
-    assert "BLOCK" in by_type
-    assert "STORE_AGENT" in by_type
-    assert "DOCUMENTATION" in by_type
-
-    # Each type should have correct structure
-    for content_type, type_result in by_type.items():
-        assert "processed" in type_result
-        assert "success" in type_result
-        assert "failed" in type_result
-
-    # Totals should aggregate
-    totals = result["totals"]
-    assert totals["processed"] >= 0
-    assert totals["success"] >= 0
-    assert totals["failed"] >= 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_content_handler_registry():
-    """Test all handlers are registered in correct order."""
-    from prisma.enums import ContentType
-
-    # All three types should be registered
-    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
-    assert ContentType.BLOCK in CONTENT_HANDLERS
-    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
-
-    # Check handler types
-    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)

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

@@ -1,324 +0,0 @@
-"""
-E2E tests for content handlers (blocks, store agents, documentation).
-
-Tests the full flow: discovering content → generating embeddings → storing.
-"""
-
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store.content_handlers import (
-    CONTENT_HANDLERS,
-    BlockHandler,
-    DocumentationHandler,
-    StoreAgentHandler,
-)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_agent_handler_get_missing_items(mocker):
-    """Test StoreAgentHandler fetches approved agents without embeddings."""
-    handler = StoreAgentHandler()
-
-    # Mock database query
-    mock_missing = [
-        {
-            "id": "agent-1",
-            "name": "Test Agent",
-            "description": "A test agent",
-            "subHeading": "Test heading",
-            "categories": ["AI", "Testing"],
-        }
-    ]
-
-    with patch(
-        "backend.api.features.store.content_handlers.query_raw_with_schema",
-        return_value=mock_missing,
-    ):
-        items = await handler.get_missing_items(batch_size=10)
-
-        assert len(items) == 1
-        assert items[0].content_id == "agent-1"
-        assert items[0].content_type == ContentType.STORE_AGENT
-        assert "Test Agent" in items[0].searchable_text
-        assert "A test agent" in items[0].searchable_text
-        assert items[0].metadata["name"] == "Test Agent"
-        assert items[0].user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_agent_handler_get_stats(mocker):
-    """Test StoreAgentHandler returns correct stats."""
-    handler = StoreAgentHandler()
-
-    # Mock approved count query
-    mock_approved = [{"count": 50}]
-    # Mock embedded count query
-    mock_embedded = [{"count": 30}]
-
-    with patch(
-        "backend.api.features.store.content_handlers.query_raw_with_schema",
-        side_effect=[mock_approved, mock_embedded],
-    ):
-        stats = await handler.get_stats()
-
-        assert stats["total"] == 50
-        assert stats["with_embeddings"] == 30
-        assert stats["without_embeddings"] == 20
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_get_missing_items(mocker):
-    """Test BlockHandler discovers blocks without embeddings."""
-    handler = BlockHandler()
-
-    # Mock get_blocks to return test blocks
-    mock_block_class = MagicMock()
-    mock_block_instance = MagicMock()
-    mock_block_instance.name = "Calculator Block"
-    mock_block_instance.description = "Performs calculations"
-    mock_block_instance.categories = [MagicMock(value="MATH")]
-    mock_block_instance.input_schema.model_json_schema.return_value = {
-        "properties": {"expression": {"description": "Math expression to evaluate"}}
-    }
-    mock_block_class.return_value = mock_block_instance
-
-    mock_blocks = {"block-uuid-1": mock_block_class}
-
-    # Mock existing embeddings query (no embeddings exist)
-    mock_existing = []
-
-    with patch(
-        "backend.api.features.store.content_handlers.get_blocks",
-        return_value=mock_blocks,
-    ):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=mock_existing,
-        ):
-            items = await handler.get_missing_items(batch_size=10)
-
-            assert len(items) == 1
-            assert items[0].content_id == "block-uuid-1"
-            assert items[0].content_type == ContentType.BLOCK
-            assert "Calculator Block" in items[0].searchable_text
-            assert "Performs calculations" in items[0].searchable_text
-            assert "MATH" in items[0].searchable_text
-            assert "expression: Math expression" in items[0].searchable_text
-            assert items[0].user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_get_stats(mocker):
-    """Test BlockHandler returns correct stats."""
-    handler = BlockHandler()
-
-    # Mock get_blocks
-    mock_blocks = {
-        "block-1": MagicMock(),
-        "block-2": MagicMock(),
-        "block-3": MagicMock(),
-    }
-
-    # Mock embedded count query (2 blocks have embeddings)
-    mock_embedded = [{"count": 2}]
-
-    with patch(
-        "backend.api.features.store.content_handlers.get_blocks",
-        return_value=mock_blocks,
-    ):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=mock_embedded,
-        ):
-            stats = await handler.get_stats()
-
-            assert stats["total"] == 3
-            assert stats["with_embeddings"] == 2
-            assert stats["without_embeddings"] == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_get_missing_items(tmp_path, mocker):
-    """Test DocumentationHandler discovers docs without embeddings."""
-    handler = DocumentationHandler()
-
-    # Create temporary docs directory with test files
-    docs_root = tmp_path / "docs"
-    docs_root.mkdir()
-
-    (docs_root / "guide.md").write_text("# Getting Started\n\nThis is a guide.")
-    (docs_root / "api.mdx").write_text("# API Reference\n\nAPI documentation.")
-
-    # Mock _get_docs_root to return temp dir
-    with patch.object(handler, "_get_docs_root", return_value=docs_root):
-        # Mock existing embeddings query (no embeddings exist)
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=[],
-        ):
-            items = await handler.get_missing_items(batch_size=10)
-
-            assert len(items) == 2
-
-            # Check guide.md
-            guide_item = next(
-                (item for item in items if item.content_id == "guide.md"), None
-            )
-            assert guide_item is not None
-            assert guide_item.content_type == ContentType.DOCUMENTATION
-            assert "Getting Started" in guide_item.searchable_text
-            assert "This is a guide" in guide_item.searchable_text
-            assert guide_item.metadata["title"] == "Getting Started"
-            assert guide_item.user_id is None
-
-            # Check api.mdx
-            api_item = next(
-                (item for item in items if item.content_id == "api.mdx"), None
-            )
-            assert api_item is not None
-            assert "API Reference" in api_item.searchable_text
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_get_stats(tmp_path, mocker):
-    """Test DocumentationHandler returns correct stats."""
-    handler = DocumentationHandler()
-
-    # Create temporary docs directory
-    docs_root = tmp_path / "docs"
-    docs_root.mkdir()
-    (docs_root / "doc1.md").write_text("# Doc 1")
-    (docs_root / "doc2.md").write_text("# Doc 2")
-    (docs_root / "doc3.mdx").write_text("# Doc 3")
-
-    # Mock embedded count query (1 doc has embedding)
-    mock_embedded = [{"count": 1}]
-
-    with patch.object(handler, "_get_docs_root", return_value=docs_root):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=mock_embedded,
-        ):
-            stats = await handler.get_stats()
-
-            assert stats["total"] == 3
-            assert stats["with_embeddings"] == 1
-            assert stats["without_embeddings"] == 2
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_title_extraction(tmp_path):
-    """Test DocumentationHandler extracts title from markdown heading."""
-    handler = DocumentationHandler()
-
-    # Test with heading
-    doc_with_heading = tmp_path / "with_heading.md"
-    doc_with_heading.write_text("# My Title\n\nContent here")
-    title, content = handler._extract_title_and_content(doc_with_heading)
-    assert title == "My Title"
-    assert "# My Title" not in content
-    assert "Content here" in content
-
-    # Test without heading
-    doc_without_heading = tmp_path / "no-heading.md"
-    doc_without_heading.write_text("Just content, no heading")
-    title, content = handler._extract_title_and_content(doc_without_heading)
-    assert title == "No Heading"  # Uses filename
-    assert "Just content" in content
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_content_handlers_registry():
-    """Test all content types are registered."""
-    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
-    assert ContentType.BLOCK in CONTENT_HANDLERS
-    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
-
-    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_handles_missing_attributes():
-    """Test BlockHandler gracefully handles blocks with missing attributes."""
-    handler = BlockHandler()
-
-    # Mock block with minimal attributes
-    mock_block_class = MagicMock()
-    mock_block_instance = MagicMock()
-    mock_block_instance.name = "Minimal Block"
-    # No description, categories, or schema
-    del mock_block_instance.description
-    del mock_block_instance.categories
-    del mock_block_instance.input_schema
-    mock_block_class.return_value = mock_block_instance
-
-    mock_blocks = {"block-minimal": mock_block_class}
-
-    with patch(
-        "backend.api.features.store.content_handlers.get_blocks",
-        return_value=mock_blocks,
-    ):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=[],
-        ):
-            items = await handler.get_missing_items(batch_size=10)
-
-            assert len(items) == 1
-            assert items[0].searchable_text == "Minimal Block"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_skips_failed_blocks():
-    """Test BlockHandler skips blocks that fail to instantiate."""
-    handler = BlockHandler()
-
-    # Mock one good block and one bad block
-    good_block = MagicMock()
-    good_instance = MagicMock()
-    good_instance.name = "Good Block"
-    good_instance.description = "Works fine"
-    good_instance.categories = []
-    good_block.return_value = good_instance
-
-    bad_block = MagicMock()
-    bad_block.side_effect = Exception("Instantiation failed")
-
-    mock_blocks = {"good-block": good_block, "bad-block": bad_block}
-
-    with patch(
-        "backend.api.features.store.content_handlers.get_blocks",
-        return_value=mock_blocks,
-    ):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=[],
-        ):
-            items = await handler.get_missing_items(batch_size=10)
-
-            # Should only get the good block
-            assert len(items) == 1
-            assert items[0].content_id == "good-block"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_missing_docs_directory():
-    """Test DocumentationHandler handles missing docs directory gracefully."""
-    handler = DocumentationHandler()
-
-    # Mock _get_docs_root to return non-existent path
-    fake_path = Path("/nonexistent/docs")
-    with patch.object(handler, "_get_docs_root", return_value=fake_path):
-        items = await handler.get_missing_items(batch_size=10)
-        assert items == []
-
-        stats = await handler.get_stats()
-        assert stats["total"] == 0
-        assert stats["with_embeddings"] == 0
-        assert stats["without_embeddings"] == 0

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

@@ -1,7 +1,8 @@
 import asyncio
 import logging
+import typing
 from datetime import datetime, timezone
-from typing import Any, Literal
+from typing import Literal
 
 import fastapi
 import prisma.enums
@@ -9,7 +10,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import transaction
+from backend.data.db import query_raw_with_schema, transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -29,8 +30,6 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
-from .embeddings import ensure_embedding
-from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -51,77 +50,128 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    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.
+    Get PUBLIC store agents from the StoreAgent view
     """
     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 hybrid search (embeddings + tsvector)
+        # If search_query is provided, use full-text search
         if search_query:
-            # 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
+            offset = (page - 1) * page_size
 
-            # 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
+            # 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",
+            }
 
-        if not search_used_hybrid:
-            # Fallback path - use basic search or no search
+            # 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)
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -130,14 +180,6 @@ 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"})
@@ -146,7 +188,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            db_agents = await prisma.models.StoreAgent.prisma().find_many(
+            agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -157,7 +199,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in db_agents:
+            for agent in agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -1535,7 +1577,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma(tx).update(
+                await prisma.models.AgentGraph.prisma().update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1550,23 +1592,6 @@ 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={

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

@@ -1,628 +0,0 @@
-"""
-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.api.features.store.content_handlers import CONTENT_HANDLERS
-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,
-            set_public_search_path=True,
-        )
-
-        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,
-            set_public_search_path=True,
-        )
-
-        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 for all content types.
-
-    Returns stats per content type and overall totals.
-    """
-    try:
-        stats_by_type = {}
-        total_items = 0
-        total_with_embeddings = 0
-        total_without_embeddings = 0
-
-        # Aggregate stats from all handlers
-        for content_type, handler in CONTENT_HANDLERS.items():
-            try:
-                stats = await handler.get_stats()
-                stats_by_type[content_type.value] = {
-                    "total": stats["total"],
-                    "with_embeddings": stats["with_embeddings"],
-                    "without_embeddings": stats["without_embeddings"],
-                    "coverage_percent": (
-                        round(stats["with_embeddings"] / stats["total"] * 100, 1)
-                        if stats["total"] > 0
-                        else 0
-                    ),
-                }
-
-                total_items += stats["total"]
-                total_with_embeddings += stats["with_embeddings"]
-                total_without_embeddings += stats["without_embeddings"]
-
-            except Exception as e:
-                logger.error(f"Failed to get stats for {content_type.value}: {e}")
-                stats_by_type[content_type.value] = {
-                    "total": 0,
-                    "with_embeddings": 0,
-                    "without_embeddings": 0,
-                    "coverage_percent": 0,
-                    "error": str(e),
-                }
-
-        return {
-            "by_type": stats_by_type,
-            "totals": {
-                "total": total_items,
-                "with_embeddings": total_with_embeddings,
-                "without_embeddings": total_without_embeddings,
-                "coverage_percent": (
-                    round(total_with_embeddings / total_items * 100, 1)
-                    if total_items > 0
-                    else 0
-                ),
-            },
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding stats: {e}")
-        return {
-            "by_type": {},
-            "totals": {
-                "total": 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.
-
-    BACKWARD COMPATIBILITY: Maintained for existing usage.
-    This now delegates to backfill_all_content_types() to process all content types.
-
-    Args:
-        batch_size: Number of embeddings to generate per content type
-
-    Returns:
-        Dict with success/failure counts aggregated across all content types
-    """
-    # Delegate to the new generic backfill system
-    result = await backfill_all_content_types(batch_size)
-
-    # Return in the old format for backward compatibility
-    return result["totals"]
-
-
-async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
-    """
-    Generate embeddings for all content types using registered handlers.
-
-    Processes content types in order: BLOCK → STORE_AGENT → DOCUMENTATION.
-    This ensures foundational content (blocks) are searchable first.
-
-    Args:
-        batch_size: Number of embeddings to generate per content type
-
-    Returns:
-        Dict with stats per content type and overall totals
-    """
-    results_by_type = {}
-    total_processed = 0
-    total_success = 0
-    total_failed = 0
-
-    # Process content types in explicit order
-    processing_order = [
-        ContentType.BLOCK,
-        ContentType.STORE_AGENT,
-        ContentType.DOCUMENTATION,
-    ]
-
-    for content_type in processing_order:
-        handler = CONTENT_HANDLERS.get(content_type)
-        if not handler:
-            logger.warning(f"No handler registered for {content_type.value}")
-            continue
-        try:
-            logger.info(f"Processing {content_type.value} content type...")
-
-            # Get missing items from handler
-            missing_items = await handler.get_missing_items(batch_size)
-
-            if not missing_items:
-                results_by_type[content_type.value] = {
-                    "processed": 0,
-                    "success": 0,
-                    "failed": 0,
-                    "message": "No missing embeddings",
-                }
-                continue
-
-            # Process embeddings concurrently for better performance
-            embedding_tasks = [
-                ensure_content_embedding(
-                    content_type=item.content_type,
-                    content_id=item.content_id,
-                    searchable_text=item.searchable_text,
-                    metadata=item.metadata,
-                    user_id=item.user_id,
-                )
-                for item in missing_items
-            ]
-
-            results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
-
-            success = sum(1 for result in results if result is True)
-            failed = len(results) - success
-
-            results_by_type[content_type.value] = {
-                "processed": len(missing_items),
-                "success": success,
-                "failed": failed,
-                "message": f"Backfilled {success} embeddings, {failed} failed",
-            }
-
-            total_processed += len(missing_items)
-            total_success += success
-            total_failed += failed
-
-            logger.info(
-                f"{content_type.value}: processed {len(missing_items)}, "
-                f"success {success}, failed {failed}"
-            )
-
-        except Exception as e:
-            logger.error(f"Failed to process {content_type.value}: {e}")
-            results_by_type[content_type.value] = {
-                "processed": 0,
-                "success": 0,
-                "failed": 0,
-                "error": str(e),
-            }
-
-    return {
-        "by_type": results_by_type,
-        "totals": {
-            "processed": total_processed,
-            "success": total_success,
-            "failed": total_failed,
-            "message": f"Overall: {total_success} succeeded, {total_failed} failed",
-        },
-    }
-
-
-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

@@ -1,329 +0,0 @@
-"""
-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

@@ -1,387 +0,0 @@
-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
-    # execute_raw is called twice: once for SET search_path, once for INSERT
-    assert mock_client.execute_raw.call_count == 2
-
-    # First call: SET search_path
-    first_call_args = mock_client.execute_raw.call_args_list[0][0]
-    assert "SET search_path" in first_call_args[0]
-
-    # Second call: INSERT query with the actual data
-    second_call_args = mock_client.execute_raw.call_args_list[1][0]
-    assert "test-version-id" in second_call_args
-    assert "[0.1,0.2,0.3]" in second_call_args
-    assert None in second_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():
-    """Test successful embedding retrieval."""
-    mock_result = [
-        {
-            "contentType": "STORE_AGENT",
-            "contentId": "test-version-id",
-            "userId": None,
-            "embedding": "[0.1,0.2,0.3]",
-            "searchableText": "Test text",
-            "metadata": {},
-            "createdAt": "2024-01-01T00:00:00Z",
-            "updatedAt": "2024-01-01T00:00:00Z",
-        }
-    ]
-
-    with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_result,
-    ):
-        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():
-    """Test embedding retrieval when not found."""
-    with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=[],
-    ):
-        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():
-    """Test embedding statistics retrieval."""
-    # Mock approved count query and embedded count query
-    mock_approved_result = [{"count": 100}]
-    mock_embedded_result = [{"count": 75}]
-
-    with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        side_effect=[mock_approved_result, mock_embedded_result],
-    ):
-        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):
-    """Test backfill with successful embedding generation."""
-    # 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 ensure_embedding to succeed for first, fail for second
-    mock_ensure.side_effect = [True, False]
-
-    with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_missing,
-    ):
-        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():
-    """Test backfill when no embeddings are missing."""
-    with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=[],
-    ):
-        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

@@ -1,393 +0,0 @@
-"""
-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, set_public_search_path=True
-    )
-
-    # 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

@@ -1,334 +0,0 @@
-"""
-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/data/db.py

@@ -38,20 +38,6 @@ 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(
@@ -122,102 +108,21 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
-async def _raw_with_schema(
-    query_template: str,
-    *args,
-    execute: bool = False,
-    client: Prisma | None = None,
-    set_public_search_path: bool = False,
-) -> 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).
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    Returns:
-        - list[dict] if execute=False (query results)
-        - int if execute=True (number of affected rows)
-    """
+async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
+    """Execute raw SQL query with proper schema handling."""
     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
 
-    db_client = client if client else prisma_module.get_client()
-
-    # Set search_path to include public schema if requested
-    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
-    # This is idempotent and safe to call multiple times
-    if set_public_search_path:
-        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
-
-    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
+    result = await prisma_module.get_client().query_raw(
+        formatted_query, *args  # type: ignore
+    )
 
     return result
 
 
-async def query_raw_with_schema(
-    query_template: str, *args, set_public_search_path: bool = False
-) -> list[dict]:
-    """Execute raw SQL SELECT query with proper schema handling.
-
-    Args:
-        query_template: SQL query with {schema_prefix} placeholder
-        *args: Query parameters
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    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, set_public_search_path=set_public_search_path)  # type: ignore
-
-
-async def execute_raw_with_schema(
-    query_template: str,
-    *args,
-    client: Prisma | None = None,
-    set_public_search_path: bool = False,
-) -> 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
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    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, set_public_search_path=set_public_search_path)  # type: ignore
-
-
 class BaseDbModel(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid4()))
 

autogpt_platform/backend/backend/data/graph_test.py

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

autogpt_platform/backend/backend/executor/database.py

@@ -7,10 +7,6 @@ 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,
@@ -212,10 +208,6 @@ 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)
 
@@ -267,10 +259,6 @@ 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_test.py

@@ -1,5 +1,4 @@
 import logging
-from unittest.mock import AsyncMock, patch
 
 import fastapi.responses
 import pytest
@@ -20,17 +19,6 @@ 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

@@ -2,7 +2,6 @@ import asyncio
 import logging
 import os
 import threading
-import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -38,7 +37,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_database_manager_client, get_scheduler_client
+from backend.util.clients import get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -255,88 +254,6 @@ def execution_accuracy_alerts():
     return report_execution_accuracy_alerts()
 
 
-def ensure_embeddings_coverage():
-    """
-    Ensure all content types (store agents, blocks, docs) have embeddings for search.
-
-    Processes ALL missing embeddings in batches of 10 per content type until 100% coverage.
-    Missing embeddings = content invisible in hybrid search.
-
-    Schedule: Runs every 6 hours (balanced between coverage and API costs).
-    - Catches new content added between scheduled runs
-    - Batch size 10 per content type: 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"]}
-
-    # Extract totals from new stats structure
-    totals = stats.get("totals", {})
-    without_embeddings = totals.get("without_embeddings", 0)
-    coverage_percent = totals.get("coverage_percent", 0)
-
-    if without_embeddings == 0:
-        logger.info("All content has embeddings, skipping backfill")
-        return {"processed": 0, "success": 0, "failed": 0}
-
-    # Log per-content-type stats for visibility
-    by_type = stats.get("by_type", {})
-    for content_type, type_stats in by_type.items():
-        if type_stats.get("without_embeddings", 0) > 0:
-            logger.info(
-                f"{content_type}: {type_stats['without_embeddings']} items without embeddings "
-                f"({type_stats['coverage_percent']}% coverage)"
-            )
-
-    logger.info(
-        f"Total: {without_embeddings} items without embeddings "
-        f"({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
 
 
@@ -558,19 +475,6 @@ 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)
@@ -728,11 +632,6 @@ 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/integrations/credentials_store.py

@@ -245,6 +245,21 @@ DEFAULT_CREDENTIALS = [
     webshare_proxy_credentials,
 ]
 
+SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
+
+# Set of providers that have system credentials available
+SYSTEM_PROVIDERS = {cred.provider for cred in DEFAULT_CREDENTIALS}
+
+
+def is_system_credential(credential_id: str) -> bool:
+    """Check if a credential ID belongs to a system-managed credential."""
+    return credential_id in SYSTEM_CREDENTIAL_IDS
+
+
+def is_system_provider(provider: str) -> bool:
+    """Check if a provider has system-managed credentials available."""
+    return provider in SYSTEM_PROVIDERS
+
 
 class IntegrationCredentialsStore:
     def __init__(self):

autogpt_platform/backend/backend/util/clients.py

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

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

@@ -1,46 +0,0 @@
--- 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

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

autogpt_platform/backend/schema.prisma

@@ -1,15 +1,14 @@
 datasource db {
-  provider   = "postgresql"
-  url        = env("DATABASE_URL")
-  directUrl  = env("DIRECT_URL")
-  extensions = [pgvector(map: "vector")]
+  provider  = "postgresql"
+  url       = env("DATABASE_URL")
+  directUrl = env("DIRECT_URL")
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
+  previewFeatures        = ["views", "fullTextSearch"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -128,8 +127,8 @@ 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)
@@ -722,25 +721,26 @@ 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[]
-  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[]
+  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)
 
   // 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)
 
@@ -899,9 +899,6 @@ 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])
@@ -909,42 +906,6 @@ 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())
@@ -1037,16 +998,16 @@ model OAuthApplication {
   updatedAt DateTime @updatedAt
 
   // Application metadata
-  name             String
-  description      String?
-  logoUrl          String? // URL to app logo stored in GCS
-  clientId         String  @unique
-  clientSecret     String // Hashed with Scrypt (same as API keys)
-  clientSecretSalt String // Salt for Scrypt hashing
+  name               String
+  description        String?
+  logoUrl            String? // URL to app logo stored in GCS
+  clientId           String @unique
+  clientSecret       String // Hashed with Scrypt (same as API keys)
+  clientSecretSalt   String // Salt for Scrypt hashing
 
   // OAuth configuration
   redirectUris String[] // Allowed callback URLs
-  grantTypes   String[]           @default(["authorization_code", "refresh_token"])
+  grantTypes   String[] @default(["authorization_code", "refresh_token"])
   scopes       APIKeyPermission[] // Which permissions the app can request
 
   // Application management

autogpt_platform/frontend/next.config.mjs

@@ -3,6 +3,13 @@ import { withSentryConfig } from "@sentry/nextjs";
 /** @type {import('next').NextConfig} */
 const nextConfig = {
   productionBrowserSourceMaps: true,
+  // Externalize OpenTelemetry packages to fix Turbopack HMR issues
+  serverExternalPackages: [
+    "@opentelemetry/instrumentation",
+    "@opentelemetry/sdk-node",
+    "import-in-the-middle",
+    "require-in-the-middle",
+  ],
   experimental: {
     serverActions: {
       bodySizeLimit: "256mb",

autogpt_platform/frontend/package.json

@@ -32,6 +32,7 @@
     "@hookform/resolvers": "5.2.2",
     "@next/third-parties": "15.4.6",
     "@phosphor-icons/react": "2.1.10",
+    "@radix-ui/react-accordion": "1.2.12",
     "@radix-ui/react-alert-dialog": "1.1.15",
     "@radix-ui/react-avatar": "1.1.10",
     "@radix-ui/react-checkbox": "1.3.3",
@@ -117,6 +118,7 @@
   },
   "devDependencies": {
     "@chromatic-com/storybook": "4.1.2",
+    "@opentelemetry/instrumentation": "0.209.0",
     "@playwright/test": "1.56.1",
     "@storybook/addon-a11y": "9.1.5",
     "@storybook/addon-docs": "9.1.5",
@@ -140,6 +142,7 @@
     "eslint": "8.57.1",
     "eslint-config-next": "15.5.7",
     "eslint-plugin-storybook": "9.1.5",
+    "import-in-the-middle": "2.0.2",
     "msw": "2.11.6",
     "msw-storybook-addon": "2.0.6",
     "orval": "7.13.0",
@@ -147,7 +150,7 @@
     "postcss": "8.5.6",
     "prettier": "3.6.2",
     "prettier-plugin-tailwindcss": "0.7.1",
-    "require-in-the-middle": "7.5.2",
+    "require-in-the-middle": "8.0.1",
     "storybook": "9.1.5",
     "tailwindcss": "3.4.17",
     "typescript": "5.9.3"
@@ -157,5 +160,10 @@
       "public"
     ]
   },
+  "pnpm": {
+    "overrides": {
+      "@opentelemetry/instrumentation": "0.209.0"
+    }
+  },
   "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"
 }

autogpt_platform/frontend/pnpm-lock.yaml

@@ -4,6 +4,9 @@ settings:
   autoInstallPeers: true
   excludeLinksFromLockfile: false
 
+overrides:
+  '@opentelemetry/instrumentation': 0.209.0
+
 importers:
 
   .:
@@ -20,6 +23,9 @@ importers:
       '@phosphor-icons/react':
         specifier: 2.1.10
         version: 2.1.10(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      '@radix-ui/react-accordion':
+        specifier: 1.2.12
+        version: 1.2.12(@types/react-dom@18.3.5(@types/react@18.3.17))(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       '@radix-ui/react-alert-dialog':
         specifier: 1.1.15
         version: 1.1.15(@types/react-dom@18.3.5(@types/react@18.3.17))(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -270,6 +276,9 @@ importers:
       '@chromatic-com/storybook':
         specifier: 4.1.2
         version: 4.1.2(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))
+      '@opentelemetry/instrumentation':
+        specifier: 0.209.0
+        version: 0.209.0(@opentelemetry/api@1.9.0)
       '@playwright/test':
         specifier: 1.56.1
         version: 1.56.1
@@ -339,6 +348,9 @@ importers:
       eslint-plugin-storybook:
         specifier: 9.1.5
         version: 9.1.5(eslint@8.57.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(typescript@5.9.3)
+      import-in-the-middle:
+        specifier: 2.0.2
+        version: 2.0.2
       msw:
         specifier: 2.11.6
         version: 2.11.6(@types/node@24.10.0)(typescript@5.9.3)
@@ -361,8 +373,8 @@ importers:
         specifier: 0.7.1
         version: 0.7.1(prettier@3.6.2)
       require-in-the-middle:
-        specifier: 7.5.2
-        version: 7.5.2
+        specifier: 8.0.1
+        version: 8.0.1
       storybook:
         specifier: 9.1.5
         version: 9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2)
@@ -1543,8 +1555,8 @@ packages:
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+      react-dom: ^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc
+    peerDependenciesMeta:
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+      '@types/react-dom':
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+
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-      '@opentelemetry/instrumentation': 0.208.0(@opentelemetry/api@1.9.0)
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@@ -9108,6 +9129,23 @@ snapshots:
 
   '@radix-ui/primitive@1.1.3': {}
 
+  '@radix-ui/react-accordion@1.2.12(@types/react-dom@18.3.5(@types/react@18.3.17))(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)':
+    dependencies:
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+      '@radix-ui/react-collapsible': 1.1.12(@types/react-dom@18.3.5(@types/react@18.3.17))(@types/react@18.3.17)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
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+      '@radix-ui/react-compose-refs': 1.1.2(@types/react@18.3.17)(react@18.3.1)
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+      '@radix-ui/react-id': 1.1.1(@types/react@18.3.17)(react@18.3.1)
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+      '@radix-ui/react-use-controllable-state': 1.2.2(@types/react@18.3.17)(react@18.3.1)
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+    optionalDependencies:
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+      '@types/react-dom': 18.3.5(@types/react@18.3.17)
+
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-      '@opentelemetry/instrumentation': 0.208.0(@opentelemetry/api@1.9.0)
+      '@opentelemetry/instrumentation': 0.209.0(@opentelemetry/api@1.9.0)
       '@opentelemetry/instrumentation-amqplib': 0.55.0(@opentelemetry/api@1.9.0)
       '@opentelemetry/instrumentation-connect': 0.52.0(@opentelemetry/api@1.9.0)
       '@opentelemetry/instrumentation-dataloader': 0.26.0(@opentelemetry/api@1.9.0)
@@ -9981,9 +10019,9 @@ snapshots:
       '@opentelemetry/semantic-conventions': 1.38.0
       '@prisma/instrumentation': 6.19.0(@opentelemetry/api@1.9.0)
       '@sentry/core': 10.27.0
-      '@sentry/node-core': 10.27.0(@opentelemetry/api@1.9.0)(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/instrumentation@0.208.0(@opentelemetry/api@1.9.0))(@opentelemetry/resources@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/semantic-conventions@1.38.0)
+      '@sentry/node-core': 10.27.0(@opentelemetry/api@1.9.0)(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/instrumentation@0.209.0(@opentelemetry/api@1.9.0))(@opentelemetry/resources@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/semantic-conventions@1.38.0)
       '@sentry/opentelemetry': 10.27.0(@opentelemetry/api@1.9.0)(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/semantic-conventions@1.38.0)
-      import-in-the-middle: 2.0.1
+      import-in-the-middle: 2.0.2
       minimatch: 9.0.5
     transitivePeerDependencies:
       - supports-color
@@ -12792,7 +12830,7 @@ snapshots:
       parent-module: 1.0.1
       resolve-from: 4.0.0
 
-  import-in-the-middle@2.0.1:
+  import-in-the-middle@2.0.2:
     dependencies:
       acorn: 8.15.0
       acorn-import-attributes: 1.9.5(acorn@8.15.0)
@@ -14631,14 +14669,6 @@ snapshots:
 
   require-from-string@2.0.2: {}
 
-  require-in-the-middle@7.5.2:
-    dependencies:
-      debug: 4.4.3
-      module-details-from-path: 1.0.4
-      resolve: 1.22.11
-    transitivePeerDependencies:
-      - supports-color
-
   require-in-the-middle@8.0.1:
     dependencies:
       debug: 4.4.3

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/components/AgentOnboardingCredentials/AgentOnboardingCredentials.tsx

@@ -1,4 +1,4 @@
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
 import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
 import { useState } from "react";

autogpt_platform/frontend/src/app/(platform)/auth/integrations/setup-wizard/page.tsx

@@ -1,22 +1,22 @@
 "use client";
 
-import Image from "next/image";
-import Link from "next/link";
-import { useSearchParams } from "next/navigation";
-import { useState, useMemo, useRef } from "react";
-import { AuthCard } from "@/components/auth/AuthCard";
-import { Text } from "@/components/atoms/Text/Text";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
+import { useGetOauthGetOauthAppInfo } from "@/app/api/__generated__/endpoints/oauth/oauth";
+import { okData } from "@/app/api/helpers";
 import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { AuthCard } from "@/components/auth/AuthCard";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import type {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
   CredentialsType,
 } from "@/lib/autogpt-server-api";
 import { CheckIcon, CircleIcon } from "@phosphor-icons/react";
-import { useGetOauthGetOauthAppInfo } from "@/app/api/__generated__/endpoints/oauth/oauth";
-import { okData } from "@/app/api/helpers";
+import Image from "next/image";
+import Link from "next/link";
+import { useSearchParams } from "next/navigation";
+import { useMemo, useRef, useState } from "react";
 
 // All credential types - we accept any type of credential
 const ALL_CREDENTIAL_TYPES: CredentialsType[] = [

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeInputs.tsx

@@ -3,7 +3,7 @@ import {
   CustomNodeData,
 } from "@/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode";
 import { NodeTableInput } from "@/app/(platform)/build/components/legacy-builder/NodeTableInput";
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { Button } from "@/components/__legacy__/ui/button";
 import { Calendar } from "@/components/__legacy__/ui/calendar";
 import { LocalValuedInput } from "@/components/__legacy__/ui/input";

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

@@ -1,4 +1,4 @@
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { Card } from "@/components/atoms/Card/Card";
 import { Text } from "@/components/atoms/Text/Text";
 import type { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api";

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

@@ -1,32 +1,31 @@
 "use client";
 
 import { Button } from "@/components/atoms/Button/Button";
+import { PublishAgentModal } from "@/components/contextual/PublishAgentModal/PublishAgentModal";
 import { Breadcrumbs } from "@/components/molecules/Breadcrumbs/Breadcrumbs";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { cn } from "@/lib/utils";
 import { PlusIcon } from "@phosphor-icons/react";
 import { useEffect, useState } from "react";
-import { RunAgentModal } from "./components/modals/RunAgentModal/RunAgentModal";
-import { useMarketplaceUpdate } from "./hooks/useMarketplaceUpdate";
 import { AgentVersionChangelog } from "./components/AgentVersionChangelog";
-import { MarketplaceBanners } from "@/components/contextual/MarketplaceBanners/MarketplaceBanners";
-import { PublishAgentModal } from "@/components/contextual/PublishAgentModal/PublishAgentModal";
-import { AgentSettingsButton } from "./components/other/AgentSettingsButton";
+import { AgentSettingsModal } from "./components/modals/AgentSettingsModal/AgentSettingsModal";
+import { RunAgentModal } from "./components/modals/RunAgentModal/RunAgentModal";
 import { AgentRunsLoading } from "./components/other/AgentRunsLoading";
 import { EmptySchedules } from "./components/other/EmptySchedules";
 import { EmptyTasks } from "./components/other/EmptyTasks";
 import { EmptyTemplates } from "./components/other/EmptyTemplates";
 import { EmptyTriggers } from "./components/other/EmptyTriggers";
+import { MarketplaceBanners } from "./components/other/MarketplaceBanners";
 import { SectionWrap } from "./components/other/SectionWrap";
 import { LoadingSelectedContent } from "./components/selected-views/LoadingSelectedContent";
 import { SelectedRunView } from "./components/selected-views/SelectedRunView/SelectedRunView";
 import { SelectedScheduleView } from "./components/selected-views/SelectedScheduleView/SelectedScheduleView";
-import { SelectedSettingsView } from "./components/selected-views/SelectedSettingsView/SelectedSettingsView";
 import { SelectedTemplateView } from "./components/selected-views/SelectedTemplateView/SelectedTemplateView";
 import { SelectedTriggerView } from "./components/selected-views/SelectedTriggerView/SelectedTriggerView";
 import { SelectedViewLayout } from "./components/selected-views/SelectedViewLayout";
 import { SidebarRunsList } from "./components/sidebar/SidebarRunsList/SidebarRunsList";
 import { AGENT_LIBRARY_SECTION_PADDING_X } from "./helpers";
+import { useMarketplaceUpdate } from "./hooks/useMarketplaceUpdate";
 import { useNewAgentLibraryView } from "./useNewAgentLibraryView";
 
 export function NewAgentLibraryView() {
@@ -45,7 +44,6 @@ export function NewAgentLibraryView() {
     handleSelectRun,
     handleCountsChange,
     handleClearSelectedRun,
-    handleSelectSettings,
     onRunInitiated,
     onTriggerSetup,
     onScheduleCreated,
@@ -137,13 +135,16 @@ export function NewAgentLibraryView() {
     return (
       <>
         <div className="flex h-full flex-col">
-          <div className="mx-6 pt-4">
-            <Breadcrumbs
-              items={[
-                { name: "My Library", link: "/library" },
-                { name: agent.name, link: `/library/agents/${agentId}` },
-              ]}
-            />
+          <div className="mx-6 flex flex-col gap-4 pt-4">
+            <div className="flex items-center justify-between">
+              <Breadcrumbs
+                items={[
+                  { name: "My Library", link: "/library" },
+                  { name: agent.name, link: `/library/agents/${agentId}` },
+                ]}
+              />
+              <AgentSettingsModal agent={agent} />
+            </div>
           </div>
           <div className="flex min-h-0 flex-1">
             <EmptyTasks
@@ -170,31 +171,24 @@ export function NewAgentLibraryView() {
               AGENT_LIBRARY_SECTION_PADDING_X,
             )}
           >
-            <div className="flex items-center gap-2">
-              <RunAgentModal
-                triggerSlot={
-                  <Button
-                    variant="primary"
-                    size="large"
-                    className="flex-1"
-                    disabled={isTemplateLoading && activeTab === "templates"}
-                  >
-                    <PlusIcon size={20} /> New task
-                  </Button>
-                }
-                agent={agent}
-                onRunCreated={onRunInitiated}
-                onScheduleCreated={onScheduleCreated}
-                onTriggerSetup={onTriggerSetup}
-                initialInputValues={activeTemplate?.inputs}
-                initialInputCredentials={activeTemplate?.credentials}
-              />
-              <AgentSettingsButton
-                agent={agent}
-                onSelectSettings={handleSelectSettings}
-                selected={activeItem === "settings"}
-              />
-            </div>
+            <RunAgentModal
+              triggerSlot={
+                <Button
+                  variant="primary"
+                  size="large"
+                  className="w-full"
+                  disabled={isTemplateLoading && activeTab === "templates"}
+                >
+                  <PlusIcon size={20} /> New task
+                </Button>
+              }
+              agent={agent}
+              onRunCreated={onRunInitiated}
+              onScheduleCreated={onScheduleCreated}
+              onTriggerSetup={onTriggerSetup}
+              initialInputValues={activeTemplate?.inputs}
+              initialInputCredentials={activeTemplate?.credentials}
+            />
           </div>
 
           <SidebarRunsList
@@ -208,12 +202,7 @@ export function NewAgentLibraryView() {
         </SectionWrap>
 
         {activeItem ? (
-          activeItem === "settings" ? (
-            <SelectedSettingsView
-              agent={agent}
-              onClearSelectedRun={handleClearSelectedRun}
-            />
-          ) : activeTab === "scheduled" ? (
+          activeTab === "scheduled" ? (
             <SelectedScheduleView
               agent={agent}
               scheduleId={activeItem}
@@ -246,8 +235,6 @@ export function NewAgentLibraryView() {
               onSelectRun={handleSelectRun}
               onClearSelectedRun={handleClearSelectedRun}
               banner={renderMarketplaceUpdateBanner()}
-              onSelectSettings={handleSelectSettings}
-              selectedSettings={activeItem === "settings"}
             />
           )
         ) : sidebarLoading ? (

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

@@ -3,7 +3,8 @@
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Text } from "@/components/atoms/Text/Text";
 import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
-import { CredentialsInput } from "../CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "../CredentialsInputs/CredentialsInput";
+import { isSystemCredential } from "../CredentialsInputs/helpers";
 import { RunAgentInputs } from "../RunAgentInputs/RunAgentInputs";
 import { getAgentCredentialsFields, getAgentInputFields } from "./helpers";
 
@@ -71,6 +72,7 @@ export function AgentInputsReadOnly({
           {credentialFieldEntries.map(([key, inputSubSchema]) => {
             const credential = credentialInputs![key];
             if (!credential) return null;
+            if (isSystemCredential(credential)) return null;
 
             return (
               <CredentialsInput

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

@@ -0,0 +1,81 @@
+"use client";
+
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Button } from "@/components/atoms/Button/Button";
+import { Switch } from "@/components/atoms/Switch/Switch";
+import { Text } from "@/components/atoms/Text/Text";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useAgentSafeMode } from "@/hooks/useAgentSafeMode";
+import { GearIcon } from "@phosphor-icons/react";
+import { useState } from "react";
+
+interface Props {
+  agent: LibraryAgent;
+  controlledOpen?: boolean;
+  onOpenChange?: (open: boolean) => void;
+}
+
+export function AgentSettingsModal({
+  agent,
+  controlledOpen,
+  onOpenChange,
+}: Props) {
+  const [internalIsOpen, setInternalIsOpen] = useState(false);
+  const isOpen = controlledOpen !== undefined ? controlledOpen : internalIsOpen;
+
+  function setIsOpen(open: boolean) {
+    if (onOpenChange) {
+      onOpenChange(open);
+    } else {
+      setInternalIsOpen(open);
+    }
+  }
+
+  const { currentSafeMode, isPending, hasHITLBlocks, handleToggle } =
+    useAgentSafeMode(agent);
+
+  if (!hasHITLBlocks) return null;
+
+  return (
+    <Dialog
+      controlled={{ isOpen, set: setIsOpen }}
+      styling={{ maxWidth: "600px", maxHeight: "90vh" }}
+      title="Agent Settings"
+    >
+      {controlledOpen === undefined && (
+        <Dialog.Trigger>
+          <Button
+            variant="ghost"
+            size="small"
+            className="m-0 min-w-0 rounded-full p-0 px-1"
+            aria-label="Agent Settings"
+          >
+            <GearIcon size={18} className="text-zinc-600" />
+            <Text variant="small">Agent Settings</Text>
+          </Button>
+        </Dialog.Trigger>
+      )}
+      <Dialog.Content>
+        <div className="space-y-6">
+          <div className="flex w-full flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
+            <div className="flex w-full items-start justify-between gap-4">
+              <div className="flex-1">
+                <Text variant="large-semibold">Require human approval</Text>
+                <Text variant="large" className="mt-1 text-zinc-900">
+                  The agent will pause and wait for your review before
+                  continuing
+                </Text>
+              </div>
+              <Switch
+                checked={currentSafeMode || false}
+                onCheckedChange={handleToggle}
+                disabled={isPending}
+                className="mt-1"
+              />
+            </div>
+          </div>
+        </div>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

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

@@ -1,6 +1,4 @@
-import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
-import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
@@ -8,13 +6,11 @@ import {
 import { cn } from "@/lib/utils";
 import { toDisplayName } from "@/providers/agent-credentials/helper";
 import { APIKeyCredentialsModal } from "./components/APIKeyCredentialsModal/APIKeyCredentialsModal";
-import { CredentialRow } from "./components/CredentialRow/CredentialRow";
-import { CredentialsSelect } from "./components/CredentialsSelect/CredentialsSelect";
-import { DeleteConfirmationModal } from "./components/DeleteConfirmationModal/DeleteConfirmationModal";
+import { CredentialsFlatView } from "./components/CredentialsFlatView/CredentialsFlatView";
 import { HostScopedCredentialsModal } from "./components/HotScopedCredentialsModal/HotScopedCredentialsModal";
 import { OAuthFlowWaitingModal } from "./components/OAuthWaitingModal/OAuthWaitingModal";
 import { PasswordCredentialsModal } from "./components/PasswordCredentialsModal/PasswordCredentialsModal";
-import { getCredentialDisplayName } from "./helpers";
+import { isSystemCredential } from "./helpers";
 import {
   CredentialsInputState,
   useCredentialsInput,
@@ -72,115 +68,53 @@ export function CredentialsInput({
     supportsOAuth2,
     supportsUserPassword,
     supportsHostScoped,
-    credentialsToShow,
+    userCredentials,
+    systemCredentials,
     oAuthError,
     isAPICredentialsModalOpen,
     isUserPasswordCredentialsModalOpen,
     isHostScopedCredentialsModalOpen,
     isOAuth2FlowInProgress,
     oAuthPopupController,
-    credentialToDelete,
-    deleteCredentialsMutation,
     actionButtonText,
     setAPICredentialsModalOpen,
     setUserPasswordCredentialsModalOpen,
     setHostScopedCredentialsModalOpen,
-    setCredentialToDelete,
     handleActionButtonClick,
     handleCredentialSelect,
-    handleDeleteCredential,
-    handleDeleteConfirm,
   } = hookData;
 
   const displayName = toDisplayName(provider);
-  const hasCredentialsToShow = credentialsToShow.length > 0;
+  const selectedCredentialIsSystem =
+    selectedCredential && isSystemCredential(selectedCredential);
+
+  const allCredentials = [...userCredentials, ...systemCredentials];
+
+  if (readOnly && selectedCredentialIsSystem) {
+    return null;
+  }
 
   return (
     <div className={cn("mb-6", className)}>
-      {showTitle && (
-        <div className="mb-2 flex items-center gap-2">
-          <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} />
-          )}
-        </div>
-      )}
-
-      {hasCredentialsToShow ? (
-        <>
-          {(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">
-              {credentialsToShow.map((credential) => {
-                return (
-                  <CredentialRow
-                    key={credential.id}
-                    credential={credential}
-                    provider={provider}
-                    displayName={displayName}
-                    onSelect={() => handleCredentialSelect(credential.id)}
-                    onDelete={() =>
-                      handleDeleteCredential({
-                        id: credential.id,
-                        title: getCredentialDisplayName(
-                          credential,
-                          displayName,
-                        ),
-                      })
-                    }
-                    readOnly={readOnly}
-                  />
-                );
-              })}
-            </div>
-          )}
-          {!readOnly && (
-            <Button
-              variant="secondary"
-              size="small"
-              onClick={handleActionButtonClick}
-              className="w-fit"
-              type="button"
-            >
-              {actionButtonText}
-            </Button>
-          )}
-        </>
-      ) : (
-        !readOnly && (
-          <Button
-            variant="secondary"
-            size="small"
-            onClick={handleActionButtonClick}
-            className="w-fit"
-            type="button"
-          >
-            {actionButtonText}
-          </Button>
-        )
-      )}
+      <CredentialsFlatView
+        schema={schema}
+        provider={provider}
+        displayName={displayName}
+        credentials={allCredentials}
+        selectedCredential={selectedCredential}
+        onSelectCredential={handleCredentialSelect}
+        onClearCredential={() => onSelectCredential(undefined)}
+        onAddCredential={handleActionButtonClick}
+        actionButtonText={actionButtonText}
+        isOptional={isOptional}
+        showTitle={showTitle}
+        readOnly={readOnly}
+        variant={variant}
+      />
 
       {!readOnly && (
         <>
-          {supportsApiKey ? (
+          {supportsApiKey && (
             <APIKeyCredentialsModal
               schema={schema}
               open={isAPICredentialsModalOpen}
@@ -191,15 +125,15 @@ export function CredentialsInput({
               }}
               siblingInputs={siblingInputs}
             />
-          ) : null}
-          {supportsOAuth2 ? (
+          )}
+          {supportsOAuth2 && (
             <OAuthFlowWaitingModal
               open={isOAuth2FlowInProgress}
               onClose={() => oAuthPopupController?.abort("canceled")}
               providerName={providerName}
             />
-          ) : null}
-          {supportsUserPassword ? (
+          )}
+          {supportsUserPassword && (
             <PasswordCredentialsModal
               schema={schema}
               open={isUserPasswordCredentialsModalOpen}
@@ -210,8 +144,8 @@ export function CredentialsInput({
               }}
               siblingInputs={siblingInputs}
             />
-          ) : null}
-          {supportsHostScoped ? (
+          )}
+          {supportsHostScoped && (
             <HostScopedCredentialsModal
               schema={schema}
               open={isHostScopedCredentialsModalOpen}
@@ -222,20 +156,13 @@ export function CredentialsInput({
               }}
               siblingInputs={siblingInputs}
             />
-          ) : null}
+          )}
 
-          {oAuthError ? (
+          {oAuthError && (
             <Text variant="body" className="mt-2 text-red-500">
               Error: {oAuthError}
             </Text>
-          ) : null}
-
-          <DeleteConfirmationModal
-            credentialToDelete={credentialToDelete}
-            isDeleting={deleteCredentialsMutation.isPending}
-            onClose={() => setCredentialToDelete(null)}
-            onConfirm={handleDeleteConfirm}
-          />
+          )}
         </>
       )}
     </div>

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

@@ -1,11 +1,11 @@
-import { Input } from "@/components/atoms/Input/Input";
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import {
   Form,
   FormDescription,
   FormField,
 } from "@/components/__legacy__/ui/form";
+import { Button } from "@/components/atoms/Button/Button";
+import { Input } from "@/components/atoms/Input/Input";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
@@ -60,7 +60,23 @@ export function APIKeyCredentialsModal({
         )}
 
         <Form {...form}>
-          <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-2">
+          <form
+            onSubmit={form.handleSubmit(onSubmit)}
+            className="space-y-2 px-2"
+          >
+            <FormField
+              control={form.control}
+              name="title"
+              render={({ field }) => (
+                <Input
+                  id="title"
+                  label="Name"
+                  type="text"
+                  placeholder="Enter a name for this API Key..."
+                  {...field}
+                />
+              )}
+            />
             <FormField
               control={form.control}
               name="apiKey"
@@ -70,8 +86,7 @@ export function APIKeyCredentialsModal({
                     id="apiKey"
                     label="API Key"
                     type="password"
-                    placeholder="Enter API key..."
-                    size="small"
+                    placeholder="Enter API Key..."
                     hint={
                       schema.credentials_scopes ? (
                         <FormDescription>
@@ -90,20 +105,7 @@ export function APIKeyCredentialsModal({
                 </>
               )}
             />
-            <FormField
-              control={form.control}
-              name="title"
-              render={({ field }) => (
-                <Input
-                  id="title"
-                  label="Name"
-                  type="text"
-                  placeholder="Enter a name for this API key..."
-                  size="small"
-                  {...field}
-                />
-              )}
-            />
+
             <FormField
               control={form.control}
               name="expiresAt"
@@ -113,13 +115,31 @@ export function APIKeyCredentialsModal({
                   label="Expiration Date"
                   type="datetime-local"
                   placeholder="Select expiration date..."
-                  size="small"
-                  {...field}
+                  value={field.value}
+                  onChange={(e) => {
+                    const value = e.target.value;
+                    if (value) {
+                      const dateTime = new Date(value);
+                      dateTime.setHours(0, 0, 0, 0);
+                      const year = dateTime.getFullYear();
+                      const month = String(dateTime.getMonth() + 1).padStart(
+                        2,
+                        "0",
+                      );
+                      const day = String(dateTime.getDate()).padStart(2, "0");
+                      const normalizedValue = `${year}-${month}-${day}T00:00`;
+                      field.onChange(normalizedValue);
+                    } else {
+                      field.onChange(value);
+                    }
+                  }}
+                  onBlur={field.onBlur}
+                  name={field.name}
                 />
               )}
             />
-            <Button type="submit" size="small" className="min-w-68">
-              Save & use this API key
+            <Button type="submit" className="min-w-68">
+              Add API Key
             </Button>
           </form>
         </Form>

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

@@ -1,11 +1,11 @@
-import { z } from "zod";
-import { useForm, type UseFormReturn } from "react-hook-form";
-import { zodResolver } from "@hookform/resolvers/zod";
 import useCredentials from "@/hooks/useCredentials";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
+import { zodResolver } from "@hookform/resolvers/zod";
+import { useForm, type UseFormReturn } from "react-hook-form";
+import { z } from "zod";
 
 export type APIKeyFormValues = {
   apiKey: string;
@@ -40,12 +40,24 @@ export function useAPIKeyCredentialsModal({
     expiresAt: z.string().optional(),
   });
 
+  function getDefaultExpirationDate(): string {
+    const tomorrow = new Date();
+    tomorrow.setDate(tomorrow.getDate() + 1);
+    tomorrow.setHours(0, 0, 0, 0);
+    const year = tomorrow.getFullYear();
+    const month = String(tomorrow.getMonth() + 1).padStart(2, "0");
+    const day = String(tomorrow.getDate()).padStart(2, "0");
+    const hours = String(tomorrow.getHours()).padStart(2, "0");
+    const minutes = String(tomorrow.getMinutes()).padStart(2, "0");
+    return `${year}-${month}-${day}T${hours}:${minutes}`;
+  }
+
   const form = useForm<APIKeyFormValues>({
     resolver: zodResolver(formSchema),
     defaultValues: {
       apiKey: "",
       title: "",
-      expiresAt: "",
+      expiresAt: getDefaultExpirationDate(),
     },
   });
 

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

@@ -7,7 +7,8 @@ import {
   DropdownMenuTrigger,
 } from "@/components/molecules/DropdownMenu/DropdownMenu";
 import { cn } from "@/lib/utils";
-import { CaretDown, DotsThreeVertical } from "@phosphor-icons/react";
+import { CaretDownIcon, DotsThreeVertical } from "@phosphor-icons/react";
+import { useEffect, useRef, useState } from "react";
 import {
   fallbackIcon,
   getCredentialDisplayName,
@@ -26,7 +27,7 @@ type CredentialRowProps = {
   provider: string;
   displayName: string;
   onSelect: () => void;
-  onDelete: () => void;
+  onDelete?: () => void;
   readOnly?: boolean;
   showCaret?: boolean;
   asSelectTrigger?: boolean;
@@ -47,11 +48,32 @@ export function CredentialRow({
 }: CredentialRowProps) {
   const ProviderIcon = providerIcons[provider] || fallbackIcon;
   const isNodeVariant = variant === "node";
+  const containerRef = useRef<HTMLDivElement>(null);
+  const [showMaskedKey, setShowMaskedKey] = useState(true);
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container) return;
+
+    const resizeObserver = new ResizeObserver((entries) => {
+      for (const entry of entries) {
+        const width = entry.contentRect.width;
+        setShowMaskedKey(width >= 360);
+      }
+    });
+
+    resizeObserver.observe(container);
+
+    return () => {
+      resizeObserver.disconnect();
+    };
+  }, []);
 
   return (
     <div
+      ref={containerRef}
       className={cn(
-        "flex items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
+        "flex min-w-[20rem] items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
         asSelectTrigger && isNodeVariant
           ? "min-w-0 flex-1 overflow-hidden border-0 bg-transparent"
           : asSelectTrigger
@@ -73,34 +95,35 @@ export function CredentialRow({
       <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
       <div
         className={cn(
-          "flex min-w-0 flex-1 flex-nowrap items-center gap-4",
+          "relative flex min-w-0 flex-1 flex-nowrap items-center gap-4",
           isNodeVariant && "overflow-hidden",
         )}
       >
         <Text
           variant="body"
           className={cn(
-            "tracking-tight",
-            isNodeVariant
-              ? "truncate"
-              : "line-clamp-1 flex-[0_0_50%] text-ellipsis",
+            "min-w-0 flex-1 tracking-tight",
+            isNodeVariant ? "truncate" : "line-clamp-1 text-ellipsis",
           )}
         >
           {getCredentialDisplayName(credential, displayName)}
         </Text>
-        {!(asSelectTrigger && isNodeVariant) && (
+        {!(asSelectTrigger && isNodeVariant) && showMaskedKey && (
           <Text
             variant="large"
-            className="relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
+            className={cn(
+              "absolute top-[65%] -translate-y-1/2 overflow-hidden whitespace-nowrap font-mono tracking-tight",
+              asSelectTrigger ? "right-0" : "right-6",
+            )}
           >
             {"*".repeat(MASKED_KEY_LENGTH)}
           </Text>
         )}
       </div>
-      {showCaret && !asSelectTrigger && (
-        <CaretDown className="h-4 w-4 shrink-0 text-gray-400" />
+      {(showCaret || (asSelectTrigger && !readOnly)) && (
+        <CaretDownIcon className="h-4 w-4 shrink-0 text-gray-400" />
       )}
-      {!readOnly && !showCaret && !asSelectTrigger && (
+      {!readOnly && !showCaret && !asSelectTrigger && onDelete && (
         <DropdownMenu>
           <DropdownMenuTrigger asChild>
             <button

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

@@ -0,0 +1,203 @@
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import {
+  Accordion,
+  AccordionContent,
+  AccordionItem,
+  AccordionTrigger,
+} from "@/components/molecules/Accordion/Accordion";
+import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
+import {
+  BlockIOCredentialsSubSchema,
+  CredentialsMetaInput,
+} from "@/lib/autogpt-server-api/types";
+import { SlidersHorizontalIcon } from "lucide-react";
+import { CredentialRow } from "../CredentialRow/CredentialRow";
+import { CredentialsSelect } from "../CredentialsSelect/CredentialsSelect";
+
+type Credential = {
+  id: string;
+  title?: string;
+  username?: string;
+  type: string;
+  provider: string;
+};
+
+type Props = {
+  schema: BlockIOCredentialsSubSchema;
+  provider: string;
+  displayName: string;
+  userCredentials: Credential[];
+  systemCredentials: Credential[];
+  isSystemProvider: boolean;
+  selectedCredential?: CredentialsMetaInput;
+  onSelectCredential: (credentialId: string) => void;
+  onClearCredential: () => void;
+  onAddCredential: () => void;
+  actionButtonText: string;
+  isOptional: boolean;
+  showTitle: boolean;
+  variant: "default" | "node";
+};
+
+export function CredentialsAccordionView({
+  schema,
+  provider,
+  displayName,
+  userCredentials,
+  systemCredentials,
+  isSystemProvider,
+  selectedCredential,
+  onSelectCredential,
+  onClearCredential,
+  onAddCredential,
+  actionButtonText,
+  isOptional,
+  showTitle,
+  variant,
+}: Props) {
+  const allCredentials = [...userCredentials, ...systemCredentials];
+  const hasSystemCredentials = systemCredentials.length > 0;
+  const hasUserCredentials = userCredentials.length > 0;
+
+  const credentialsInAccordion = isSystemProvider
+    ? allCredentials
+    : systemCredentials;
+
+  const shouldOpenAccordionByDefault =
+    hasSystemCredentials && !isOptional && !selectedCredential;
+
+  const showUserCredentialsOutsideAccordion =
+    !isSystemProvider && hasUserCredentials;
+
+  return (
+    <>
+      {showTitle && showUserCredentialsOutsideAccordion && (
+        <div className="mb-2 flex items-center gap-2">
+          <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} />
+          )}
+        </div>
+      )}
+
+      {showUserCredentialsOutsideAccordion && (
+        <>
+          {userCredentials.length > 1 || isOptional ? (
+            <CredentialsSelect
+              credentials={userCredentials}
+              provider={provider}
+              displayName={displayName}
+              selectedCredentials={selectedCredential}
+              onSelectCredential={onSelectCredential}
+              onClearCredential={onClearCredential}
+              allowNone={isOptional}
+              variant={variant}
+            />
+          ) : (
+            <div className="mb-4 space-y-2">
+              {userCredentials.map((credential) => (
+                <CredentialRow
+                  key={credential.id}
+                  credential={credential}
+                  provider={provider}
+                  displayName={displayName}
+                  onSelect={() => onSelectCredential(credential.id)}
+                />
+              ))}
+            </div>
+          )}
+          <Button
+            variant="secondary"
+            size="small"
+            onClick={onAddCredential}
+            className="w-fit"
+            type="button"
+          >
+            {actionButtonText}
+          </Button>
+        </>
+      )}
+
+      {hasSystemCredentials && (
+        <Accordion
+          type="single"
+          collapsible
+          defaultValue={
+            shouldOpenAccordionByDefault ? "system-credentials" : undefined
+          }
+          className={showUserCredentialsOutsideAccordion ? "mt-4" : ""}
+        >
+          <AccordionItem value="system-credentials" className="border-none">
+            <AccordionTrigger className="py-2 text-sm text-muted-foreground hover:no-underline">
+              <div className="flex items-center gap-1">
+                <SlidersHorizontalIcon className="size-4" /> System credentials
+              </div>
+            </AccordionTrigger>
+            <AccordionContent>
+              <div className="space-y-4 px-1 pt-2">
+                {showTitle && (
+                  <div className="flex items-center gap-2">
+                    <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} />
+                    )}
+                  </div>
+                )}
+                {credentialsInAccordion.length > 0 && (
+                  <CredentialsSelect
+                    credentials={credentialsInAccordion}
+                    provider={provider}
+                    displayName={displayName}
+                    selectedCredentials={selectedCredential}
+                    onSelectCredential={onSelectCredential}
+                    onClearCredential={onClearCredential}
+                    allowNone={isOptional}
+                    variant={variant}
+                  />
+                )}
+                {isSystemProvider && (
+                  <Button
+                    variant="secondary"
+                    size="small"
+                    onClick={onAddCredential}
+                    className="w-fit"
+                    type="button"
+                  >
+                    {actionButtonText}
+                  </Button>
+                )}
+              </div>
+            </AccordionContent>
+          </AccordionItem>
+        </Accordion>
+      )}
+
+      {!showUserCredentialsOutsideAccordion && !isSystemProvider && (
+        <Button
+          variant="secondary"
+          size="small"
+          onClick={onAddCredential}
+          className="w-fit"
+          type="button"
+        >
+          {actionButtonText}
+        </Button>
+      )}
+    </>
+  );
+}

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

@@ -0,0 +1,134 @@
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
+import {
+  BlockIOCredentialsSubSchema,
+  CredentialsMetaInput,
+} from "@/lib/autogpt-server-api/types";
+import { ExclamationTriangleIcon } from "@radix-ui/react-icons";
+import { CredentialRow } from "../CredentialRow/CredentialRow";
+import { CredentialsSelect } from "../CredentialsSelect/CredentialsSelect";
+
+type Credential = {
+  id: string;
+  title?: string;
+  username?: string;
+  type: string;
+  provider: string;
+};
+
+type Props = {
+  schema: BlockIOCredentialsSubSchema;
+  provider: string;
+  displayName: string;
+  credentials: Credential[];
+  selectedCredential?: CredentialsMetaInput;
+  actionButtonText: string;
+  isOptional: boolean;
+  showTitle: boolean;
+  readOnly: boolean;
+  variant: "default" | "node";
+  onSelectCredential: (credentialId: string) => void;
+  onClearCredential: () => void;
+  onAddCredential: () => void;
+};
+
+export function CredentialsFlatView({
+  schema,
+  provider,
+  displayName,
+  credentials,
+  selectedCredential,
+  actionButtonText,
+  isOptional,
+  showTitle,
+  readOnly,
+  variant,
+  onSelectCredential,
+  onClearCredential,
+  onAddCredential,
+}: Props) {
+  const hasCredentials = credentials.length > 0;
+
+  return (
+    <>
+      {showTitle && (
+        <div className="mb-2 flex items-center gap-2">
+          <Text variant="large-medium" className="flex items-center gap-2">
+            <span className="inline-flex items-center gap-1">
+              {displayName} credentials
+              {isOptional && (
+                <span className="text-sm font-normal text-gray-500">
+                  (optional)
+                </span>
+              )}
+              {!isOptional && !selectedCredential && (
+                <span className="inline-flex items-center gap-1 text-red-600">
+                  <ExclamationTriangleIcon className="size-4" />
+                  <span className="text-sm font-normal">required</span>
+                </span>
+              )}
+            </span>
+          </Text>
+          {schema.description && (
+            <InformationTooltip description={schema.description} />
+          )}
+        </div>
+      )}
+
+      {hasCredentials ? (
+        <>
+          {(credentials.length > 1 || isOptional) && !readOnly ? (
+            <CredentialsSelect
+              credentials={credentials}
+              provider={provider}
+              displayName={displayName}
+              selectedCredentials={selectedCredential}
+              onSelectCredential={onSelectCredential}
+              onClearCredential={onClearCredential}
+              readOnly={readOnly}
+              allowNone={isOptional}
+              variant={variant}
+            />
+          ) : (
+            <div className="mb-4 space-y-2">
+              {credentials.map((credential) => (
+                <CredentialRow
+                  key={credential.id}
+                  credential={credential}
+                  provider={provider}
+                  displayName={displayName}
+                  onSelect={() => onSelectCredential(credential.id)}
+                  readOnly={readOnly}
+                />
+              ))}
+            </div>
+          )}
+          {!readOnly && (
+            <Button
+              variant="secondary"
+              size="small"
+              onClick={onAddCredential}
+              className="w-fit"
+              type="button"
+            >
+              {actionButtonText}
+            </Button>
+          )}
+        </>
+      ) : (
+        !readOnly && (
+          <Button
+            variant="secondary"
+            size="small"
+            onClick={onAddCredential}
+            className="w-fit"
+            type="button"
+          >
+            {actionButtonText}
+          </Button>
+        )
+      )}
+    </>
+  );
+}

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

@@ -1,14 +1,4 @@
-import {
-  Select,
-  SelectContent,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} 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 { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
 import { getCredentialDisplayName } from "../../helpers";
 import { CredentialRow } from "../CredentialRow/CredentialRow";
 
@@ -42,76 +32,77 @@ export function CredentialsSelect({
   allowNone = true,
   variant = "default",
 }: Props) {
-  // Auto-select first credential if none is selected (only if allowNone is false)
-  useEffect(() => {
-    if (!allowNone && !selectedCredentials && credentials.length > 0) {
-      onSelectCredential(credentials[0].id);
-    }
-  }, [allowNone, selectedCredentials, credentials, onSelectCredential]);
-
-  const handleValueChange = (value: string) => {
+  function handleValueChange(e: React.ChangeEvent<HTMLSelectElement>) {
+    const value = e.target.value;
     if (value === "__none__") {
       onClearCredential?.();
     } else {
       onSelectCredential(value);
     }
-  };
+  }
+
+  const selectedCredential = selectedCredentials
+    ? credentials.find((c) => c.id === selectedCredentials.id)
+    : null;
+
+  const displayCredential = selectedCredential
+    ? {
+        id: selectedCredential.id,
+        title: selectedCredential.title,
+        username: selectedCredential.username,
+        type: selectedCredential.type,
+        provider: selectedCredential.provider,
+      }
+    : allowNone
+      ? {
+          id: "__none__",
+          title: "None (skip this credential)",
+          type: "none",
+          provider: provider,
+        }
+      : {
+          id: "__placeholder__",
+          title: "Select credential",
+          type: "placeholder",
+          provider: provider,
+        };
 
   return (
     <div className="mb-4 w-full">
-      <Select
-        value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
-        onValueChange={handleValueChange}
-      >
-        <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",
-          )}
+      <div className="relative">
+        <select
+          value={selectedCredentials?.id ?? "__none__"}
+          onChange={handleValueChange}
+          disabled={readOnly}
+          className="absolute inset-0 z-10 cursor-pointer opacity-0"
+          aria-label={`Select ${displayName} credential`}
         >
-          {selectedCredentials ? (
-            <SelectValue key={selectedCredentials.id} asChild>
-              <CredentialRow
-                credential={{
-                  id: selectedCredentials.id,
-                  title: selectedCredentials.title || undefined,
-                  type: selectedCredentials.type,
-                  provider: selectedCredentials.provider,
-                }}
-                provider={provider}
-                displayName={displayName}
-                onSelect={() => {}}
-                onDelete={() => {}}
-                readOnly={readOnly}
-                asSelectTrigger={true}
-                variant={variant}
-              />
-            </SelectValue>
+          {allowNone ? (
+            <option value="__none__">None (skip this credential)</option>
           ) : (
-            <SelectValue key="placeholder" placeholder="Select credential" />
-          )}
-        </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>
+            <option value="__none__" disabled hidden>
+              Select a credential
+            </option>
           )}
           {credentials.map((credential) => (
-            <SelectItem key={credential.id} value={credential.id}>
-              <div className="flex items-center gap-2">
-                <Text variant="body" className="tracking-tight">
-                  {getCredentialDisplayName(credential, displayName)}
-                </Text>
-              </div>
-            </SelectItem>
+            <option key={credential.id} value={credential.id}>
+              {getCredentialDisplayName(credential, displayName)}
+            </option>
           ))}
-        </SelectContent>
-      </Select>
+        </select>
+        <div className="rounded-medium border border-zinc-200 bg-white">
+          <CredentialRow
+            credential={displayCredential}
+            provider={provider}
+            displayName={displayName}
+            onSelect={() => {}}
+            onDelete={() => {}}
+            readOnly={readOnly}
+            asSelectTrigger={true}
+            variant={variant}
+          />
+        </div>
+      </div>
     </div>
   );
 }

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

@@ -99,4 +99,30 @@ export function getCredentialDisplayName(
 }
 
 export const OAUTH_TIMEOUT_MS = 5 * 60 * 1000;
-export const MASKED_KEY_LENGTH = 30;
+export const MASKED_KEY_LENGTH = 15;
+
+export function isSystemCredential(credential: {
+  title?: string | null;
+  is_system?: boolean;
+}): boolean {
+  if (credential.is_system === true) return true;
+  if (!credential.title) return false;
+  const titleLower = credential.title.toLowerCase();
+  return (
+    titleLower.includes("system") ||
+    titleLower.startsWith("use credits for") ||
+    titleLower.includes("use credits")
+  );
+}
+
+export function filterSystemCredentials<
+  T extends { title?: string; is_system?: boolean },
+>(credentials: T[]): T[] {
+  return credentials.filter((cred) => !isSystemCredential(cred));
+}
+
+export function getSystemCredentials<
+  T extends { title?: string; is_system?: boolean },
+>(credentials: T[]): T[] {
+  return credentials.filter((cred) => isSystemCredential(cred));
+}

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

@@ -6,9 +6,11 @@ import {
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 import { useQueryClient } from "@tanstack/react-query";
-import { useEffect, useMemo, useState } from "react";
+import { useEffect, useRef, useState } from "react";
 import {
+  filterSystemCredentials,
   getActionButtonText,
+  getSystemCredentials,
   OAUTH_TIMEOUT_MS,
   OAuthPopupResultMessage,
 } from "./helpers";
@@ -54,6 +56,7 @@ export function useCredentialsInput({
   const api = useBackendAPI();
   const queryClient = useQueryClient();
   const credentials = useCredentials(schema, siblingInputs);
+  const hasAttemptedAutoSelect = useRef(false);
 
   const deleteCredentialsMutation = useDeleteV1DeleteCredentials({
     mutation: {
@@ -82,38 +85,51 @@ export function useCredentialsInput({
   useEffect(() => {
     if (readOnly) return;
     if (!credentials || !("savedCredentials" in credentials)) return;
+    const availableCreds = credentials.savedCredentials;
     if (
       selectedCredential &&
-      !credentials.savedCredentials.some((c) => c.id === selectedCredential.id)
+      !availableCreds.some((c) => c.id === selectedCredential.id)
     ) {
       onSelectCredential(undefined);
+      // Reset auto-selection flag so it can run again after unsetting invalid credential
+      hasAttemptedAutoSelect.current = false;
     }
   }, [credentials, selectedCredential, onSelectCredential, readOnly]);
 
-  // The available credential, if there is only one
-  const singleCredential = useMemo(() => {
-    if (!credentials || !("savedCredentials" in credentials)) {
-      return null;
-    }
-
-    return credentials.savedCredentials.length === 1
-      ? credentials.savedCredentials[0]
-      : null;
-  }, [credentials]);
-
-  // Auto-select the one available credential (only if not optional)
+  // Auto-select the first available credential on initial mount
+  // Once a user has made a selection, we don't override it
   useEffect(() => {
     if (readOnly) return;
-    if (isOptional) return; // Don't auto-select when credential is optional
-    if (singleCredential && !selectedCredential) {
-      onSelectCredential(singleCredential);
+    if (!credentials || !("savedCredentials" in credentials)) return;
+
+    // If already selected, don't auto-select
+    if (selectedCredential?.id) return;
+
+    // Only attempt auto-selection once
+    if (hasAttemptedAutoSelect.current) return;
+    hasAttemptedAutoSelect.current = true;
+
+    // If optional, don't auto-select (user can choose "None")
+    if (isOptional) return;
+
+    const savedCreds = credentials.savedCredentials;
+
+    // Auto-select the first credential if any are available
+    if (savedCreds.length > 0) {
+      const cred = savedCreds[0];
+      onSelectCredential({
+        id: cred.id,
+        type: cred.type,
+        provider: credentials.provider,
+        title: (cred as any).title,
+      });
     }
   }, [
-    singleCredential,
-    selectedCredential,
-    onSelectCredential,
+    credentials,
+    selectedCredential?.id,
     readOnly,
     isOptional,
+    onSelectCredential,
   ]);
 
   if (
@@ -135,8 +151,13 @@ export function useCredentialsInput({
     supportsHostScoped,
     savedCredentials,
     oAuthCallback,
+    isSystemProvider,
   } = credentials;
 
+  // Split credentials into user and system
+  const userCredentials = filterSystemCredentials(savedCredentials);
+  const systemCredentials = getSystemCredentials(savedCredentials);
+
   async function handleOAuthLogin() {
     setOAuthError(null);
     const { login_url, state_token } = await api.oAuthLogin(
@@ -291,7 +312,10 @@ export function useCredentialsInput({
     supportsOAuth2,
     supportsUserPassword,
     supportsHostScoped,
-    credentialsToShow: savedCredentials,
+    isSystemProvider,
+    userCredentials,
+    systemCredentials,
+    allCredentials: savedCredentials,
     selectedCredential,
     oAuthError,
     isAPICredentialsModalOpen,
@@ -306,7 +330,7 @@ export function useCredentialsInput({
       supportsApiKey,
       supportsUserPassword,
       supportsHostScoped,
-      savedCredentials.length > 0,
+      userCredentials.length > 0,
     ),
     setAPICredentialsModalOpen,
     setUserPasswordCredentialsModalOpen,

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

@@ -12,7 +12,7 @@ import {
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { useState } from "react";
+import { useEffect, useRef, useState } from "react";
 import { ScheduleAgentModal } from "../ScheduleAgentModal/ScheduleAgentModal";
 import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { ModalRunSection } from "./components/ModalRunSection/ModalRunSection";
@@ -82,6 +82,8 @@ export function RunAgentModal({
   });
 
   const [isScheduleModalOpen, setIsScheduleModalOpen] = useState(false);
+  const [hasOverflow, setHasOverflow] = useState(false);
+  const contentRef = useRef<HTMLDivElement>(null);
 
   const hasAnySetupFields =
     Object.keys(agentInputFields || {}).length > 0 ||
@@ -89,6 +91,43 @@ export function RunAgentModal({
 
   const isTriggerRunType = defaultRunType.includes("trigger");
 
+  useEffect(() => {
+    if (!isOpen) return;
+
+    function checkOverflow() {
+      if (!contentRef.current) return;
+      const scrollableParent = contentRef.current
+        .closest("[data-dialog-content]")
+        ?.querySelector('[class*="overflow-y-auto"]');
+      if (scrollableParent) {
+        setHasOverflow(
+          scrollableParent.scrollHeight > scrollableParent.clientHeight,
+        );
+      }
+    }
+
+    const timeoutId = setTimeout(checkOverflow, 100);
+    const resizeObserver = new ResizeObserver(checkOverflow);
+    if (contentRef.current) {
+      const scrollableParent = contentRef.current
+        .closest("[data-dialog-content]")
+        ?.querySelector('[class*="overflow-y-auto"]');
+      if (scrollableParent) {
+        resizeObserver.observe(scrollableParent);
+      }
+    }
+
+    return () => {
+      clearTimeout(timeoutId);
+      resizeObserver.disconnect();
+    };
+  }, [
+    isOpen,
+    hasAnySetupFields,
+    agentInputFields,
+    agentCredentialsInputFields,
+  ]);
+
   function handleInputChange(key: string, value: string) {
     setInputValues((prev) => ({
       ...prev,
@@ -134,91 +173,97 @@ export function RunAgentModal({
       >
         <Dialog.Trigger>{triggerSlot}</Dialog.Trigger>
         <Dialog.Content>
-          {/* Header */}
-          <ModalHeader agent={agent} />
+          <div ref={contentRef} className="flex min-h-full flex-col">
+            <div className="flex-1">
+              {/* Header */}
+              <ModalHeader agent={agent} />
 
-          {/* Content */}
-          {hasAnySetupFields ? (
-            <div className="mt-10">
-              <RunAgentModalContextProvider
-                value={{
-                  agent,
-                  defaultRunType,
-                  presetName,
-                  setPresetName,
-                  presetDescription,
-                  setPresetDescription,
-                  inputValues,
-                  setInputValue: handleInputChange,
-                  agentInputFields,
-                  inputCredentials,
-                  setInputCredentialsValue: handleCredentialsChange,
-                  agentCredentialsInputFields,
-                }}
-              >
-                <ModalRunSection />
-              </RunAgentModalContextProvider>
+              {/* Content */}
+              {hasAnySetupFields ? (
+                <div className="mt-10 pb-32">
+                  <RunAgentModalContextProvider
+                    value={{
+                      agent,
+                      defaultRunType,
+                      presetName,
+                      setPresetName,
+                      presetDescription,
+                      setPresetDescription,
+                      inputValues,
+                      setInputValue: handleInputChange,
+                      agentInputFields,
+                      inputCredentials,
+                      setInputCredentialsValue: handleCredentialsChange,
+                      agentCredentialsInputFields,
+                    }}
+                  >
+                    <ModalRunSection />
+                  </RunAgentModalContextProvider>
+                </div>
+              ) : null}
             </div>
-          ) : null}
 
-          <Dialog.Footer className="mt-6 bg-white pt-4">
-            <div className="flex items-center justify-end gap-3">
-              {isTriggerRunType ? null : !allRequiredInputsAreSet ? (
-                <TooltipProvider>
-                  <Tooltip>
-                    <TooltipTrigger asChild>
-                      <span>
-                        <Button
-                          variant="secondary"
-                          onClick={handleOpenScheduleModal}
-                          disabled={
-                            isExecuting ||
-                            isSettingUpTrigger ||
-                            !allRequiredInputsAreSet
-                          }
-                        >
-                          Schedule Task
-                        </Button>
-                      </span>
-                    </TooltipTrigger>
-                    <TooltipContent>
-                      <p>
-                        Please set up all required inputs and credentials before
-                        scheduling
-                      </p>
-                    </TooltipContent>
-                  </Tooltip>
-                </TooltipProvider>
-              ) : (
-                <Button
-                  variant="secondary"
-                  onClick={handleOpenScheduleModal}
-                  disabled={
-                    isExecuting ||
-                    isSettingUpTrigger ||
-                    !allRequiredInputsAreSet
-                  }
-                >
-                  Schedule Task
-                </Button>
-              )}
-              <RunActions
-                defaultRunType={defaultRunType}
-                onRun={handleRun}
-                isExecuting={isExecuting}
-                isSettingUpTrigger={isSettingUpTrigger}
-                isRunReady={allRequiredInputsAreSet}
+            <Dialog.Footer
+              className={`sticky bottom-0 z-10 bg-white pt-4 ${
+                hasOverflow
+                  ? "border-t border-neutral-100 shadow-[0_-2px_8px_rgba(0,0,0,0.04)]"
+                  : ""
+              }`}
+            >
+              <div className="flex items-center justify-end gap-3">
+                {isTriggerRunType ? null : !allRequiredInputsAreSet ? (
+                  <TooltipProvider>
+                    <Tooltip>
+                      <TooltipTrigger asChild>
+                        <span>
+                          <Button
+                            variant="secondary"
+                            onClick={handleOpenScheduleModal}
+                            disabled={
+                              isExecuting ||
+                              isSettingUpTrigger ||
+                              !allRequiredInputsAreSet
+                            }
+                          >
+                            Schedule Task
+                          </Button>
+                        </span>
+                      </TooltipTrigger>
+                      <TooltipContent>
+                        <p>
+                          Please set up all required inputs and credentials
+                          before scheduling
+                        </p>
+                      </TooltipContent>
+                    </Tooltip>
+                  </TooltipProvider>
+                ) : (
+                  <Button
+                    variant="secondary"
+                    onClick={handleOpenScheduleModal}
+                    disabled={isExecuting || isSettingUpTrigger}
+                  >
+                    Schedule Task
+                  </Button>
+                )}
+                <RunActions
+                  defaultRunType={defaultRunType}
+                  onRun={handleRun}
+                  isExecuting={isExecuting}
+                  isSettingUpTrigger={isSettingUpTrigger}
+                  isRunReady={allRequiredInputsAreSet}
+                />
+              </div>
+              <ScheduleAgentModal
+                isOpen={isScheduleModalOpen}
+                onClose={handleCloseScheduleModal}
+                agent={agent}
+                inputValues={inputValues}
+                inputCredentials={inputCredentials}
+                onScheduleCreated={handleScheduleCreated}
               />
-            </div>
-            <ScheduleAgentModal
-              isOpen={isScheduleModalOpen}
-              onClose={handleCloseScheduleModal}
-              agent={agent}
-              inputValues={inputValues}
-              inputCredentials={inputCredentials}
-              onScheduleCreated={handleScheduleCreated}
-            />
-          </Dialog.Footer>
+            </Dialog.Footer>
+          </div>
         </Dialog.Content>
       </Dialog>
     </>

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

@@ -0,0 +1,169 @@
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
+import {
+  Accordion,
+  AccordionContent,
+  AccordionItem,
+  AccordionTrigger,
+} from "@/components/molecules/Accordion/Accordion";
+import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
+import { SlidersHorizontal } from "@phosphor-icons/react";
+import { useContext, useEffect, useMemo, useRef } from "react";
+import { useRunAgentModalContext } from "../../context";
+import {
+  CredentialField,
+  findSavedCredentialByProviderAndType,
+  hasMissingRequiredSystemCredentials,
+  splitCredentialFieldsBySystem,
+} from "../helpers";
+
+type Props = {
+  credentialFields: CredentialField[];
+  requiredCredentials: Set<string>;
+};
+
+export function CredentialsGroupedView({
+  credentialFields,
+  requiredCredentials,
+}: Props) {
+  const allProviders = useContext(CredentialsProvidersContext);
+  const { inputCredentials, setInputCredentialsValue, inputValues } =
+    useRunAgentModalContext();
+
+  const { userCredentialFields, systemCredentialFields } = useMemo(
+    () =>
+      splitCredentialFieldsBySystem(
+        credentialFields,
+        allProviders,
+        inputCredentials,
+      ),
+    [credentialFields, allProviders, inputCredentials],
+  );
+
+  const hasSystemCredentials = systemCredentialFields.length > 0;
+  const hasUserCredentials = userCredentialFields.length > 0;
+  const hasAttemptedAutoSelect = useRef(false);
+
+  const hasMissingSystemCredentials = useMemo(
+    () =>
+      hasMissingRequiredSystemCredentials(
+        systemCredentialFields,
+        requiredCredentials,
+        inputCredentials,
+      ),
+    [systemCredentialFields, requiredCredentials, inputCredentials],
+  );
+
+  useEffect(() => {
+    if (hasAttemptedAutoSelect.current) return;
+    if (!hasSystemCredentials) return;
+
+    let appliedSelection = false;
+
+    for (const [key, schema] of systemCredentialFields) {
+      const alreadySelected = inputCredentials?.[key];
+      const isRequired = requiredCredentials.has(key);
+      if (alreadySelected || !isRequired) continue;
+
+      const providerNames = schema.credentials_provider || [];
+      const credentialTypes = schema.credentials_types || [];
+      const savedCredential = findSavedCredentialByProviderAndType(
+        providerNames,
+        credentialTypes,
+        allProviders,
+      );
+
+      if (savedCredential) {
+        appliedSelection = true;
+        setInputCredentialsValue(key, {
+          id: savedCredential.id,
+          provider: savedCredential.provider,
+          type: savedCredential.type,
+          title: (savedCredential as { title?: string }).title,
+        });
+      }
+    }
+
+    if (appliedSelection) {
+      hasAttemptedAutoSelect.current = true;
+    }
+  }, [
+    allProviders,
+    hasSystemCredentials,
+    systemCredentialFields,
+    requiredCredentials,
+    inputCredentials,
+    setInputCredentialsValue,
+  ]);
+
+  return (
+    <div className="space-y-6">
+      {hasUserCredentials && (
+        <>
+          {userCredentialFields.map(
+            ([key, inputSubSchema]: CredentialField) => {
+              const selectedCred = inputCredentials?.[key];
+
+              return (
+                <CredentialsInput
+                  key={key}
+                  schema={
+                    { ...inputSubSchema, discriminator: undefined } as any
+                  }
+                  selectedCredentials={selectedCred}
+                  onSelectCredentials={(value) => {
+                    setInputCredentialsValue(key, value);
+                  }}
+                  siblingInputs={inputValues}
+                  isOptional={!requiredCredentials.has(key)}
+                />
+              );
+            },
+          )}
+        </>
+      )}
+
+      {hasSystemCredentials && (
+        <Accordion
+          type="single"
+          collapsible
+          className={hasUserCredentials ? "mt-4" : ""}
+        >
+          <AccordionItem value="system-credentials" className="border-none">
+            <AccordionTrigger className="py-2 text-sm text-muted-foreground hover:no-underline">
+              <div className="flex items-center gap-1">
+                <SlidersHorizontal size={16} weight="bold" /> System credentials
+                {hasMissingSystemCredentials && (
+                  <span className="text-destructive">(missing)</span>
+                )}
+              </div>
+            </AccordionTrigger>
+            <AccordionContent>
+              <div className="space-y-6 px-1 pt-2">
+                {systemCredentialFields.map(
+                  ([key, inputSubSchema]: CredentialField) => {
+                    const selectedCred = inputCredentials?.[key];
+
+                    return (
+                      <CredentialsInput
+                        key={key}
+                        schema={
+                          { ...inputSubSchema, discriminator: undefined } as any
+                        }
+                        selectedCredentials={selectedCred}
+                        onSelectCredentials={(value) => {
+                          setInputCredentialsValue(key, value);
+                        }}
+                        siblingInputs={inputValues}
+                        isOptional={!requiredCredentials.has(key)}
+                      />
+                    );
+                  },
+                )}
+              </div>
+            </AccordionContent>
+          </AccordionItem>
+        </Accordion>
+      )}
+    </div>
+  );
+}

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

@@ -1,8 +1,9 @@
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import { Input } from "@/components/atoms/Input/Input";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
+import { useMemo } from "react";
 import { RunAgentInputs } from "../../../RunAgentInputs/RunAgentInputs";
 import { useRunAgentModalContext } from "../../context";
+import { CredentialsGroupedView } from "../CredentialsGroupedView/CredentialsGroupedView";
 import { ModalSection } from "../ModalSection/ModalSection";
 import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBanner";
 
@@ -17,15 +18,16 @@ export function ModalRunSection() {
     inputValues,
     setInputValue,
     agentInputFields,
-    inputCredentials,
-    setInputCredentialsValue,
     agentCredentialsInputFields,
   } = useRunAgentModalContext();
 
   const inputFields = Object.entries(agentInputFields || {});
-  const credentialFields = Object.entries(agentCredentialsInputFields || {});
 
-  // Get the list of required credentials from the schema
+  const credentialFields = useMemo(() => {
+    if (!agentCredentialsInputFields) return [];
+    return Object.entries(agentCredentialsInputFields);
+  }, [agentCredentialsInputFields]);
+
   const requiredCredentials = new Set(
     (agent.credentials_input_schema?.required as string[]) || [],
   );
@@ -97,24 +99,10 @@ export function ModalRunSection() {
           title="Task Credentials"
           subtitle="These are the credentials the agent will use to perform this task"
         >
-          <div className="space-y-6">
-            {Object.entries(agentCredentialsInputFields || {}).map(
-              ([key, inputSubSchema]) => (
-                <CredentialsInput
-                  key={key}
-                  schema={
-                    { ...inputSubSchema, discriminator: undefined } as any
-                  }
-                  selectedCredentials={inputCredentials?.[key]}
-                  onSelectCredentials={(value) =>
-                    setInputCredentialsValue(key, value)
-                  }
-                  siblingInputs={inputValues}
-                  isOptional={!requiredCredentials.has(key)}
-                />
-              ),
-            )}
-          </div>
+          <CredentialsGroupedView
+            credentialFields={credentialFields}
+            requiredCredentials={requiredCredentials}
+          />
         </ModalSection>
       ) : null}
     </div>

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

@@ -0,0 +1,93 @@
+import { CredentialsProvidersContextType } from "@/providers/agent-credentials/credentials-provider";
+
+export type CredentialField = [string, any];
+
+type SavedCredential = {
+  id: string;
+  provider: string;
+  type: string;
+  title?: string | null;
+};
+
+export function splitCredentialFieldsBySystem(
+  credentialFields: CredentialField[],
+  allProviders: CredentialsProvidersContextType | null,
+  inputCredentials?: Record<string, unknown>,
+) {
+  if (!allProviders || credentialFields.length === 0) {
+    return {
+      userCredentialFields: [] as CredentialField[],
+      systemCredentialFields: [] as CredentialField[],
+    };
+  }
+
+  const userFields: CredentialField[] = [];
+  const systemFields: CredentialField[] = [];
+
+  for (const [key, schema] of credentialFields) {
+    const providerNames = schema.credentials_provider || [];
+    const isSystemField = providerNames.some((providerName: string) => {
+      const providerData = allProviders[providerName];
+      return providerData?.isSystemProvider === true;
+    });
+
+    if (isSystemField) {
+      systemFields.push([key, schema]);
+    } else {
+      userFields.push([key, schema]);
+    }
+  }
+
+  const sortByUnsetFirst = (a: CredentialField, b: CredentialField) => {
+    const aIsSet = Boolean(inputCredentials?.[a[0]]);
+    const bIsSet = Boolean(inputCredentials?.[b[0]]);
+
+    if (aIsSet === bIsSet) return 0;
+    return aIsSet ? 1 : -1;
+  };
+
+  return {
+    userCredentialFields: userFields.sort(sortByUnsetFirst),
+    systemCredentialFields: systemFields.sort(sortByUnsetFirst),
+  };
+}
+
+export function hasMissingRequiredSystemCredentials(
+  systemCredentialFields: CredentialField[],
+  requiredCredentials: Set<string>,
+  inputCredentials?: Record<string, unknown>,
+) {
+  if (systemCredentialFields.length === 0) return false;
+
+  return systemCredentialFields.some(([key]) => {
+    const isRequired = requiredCredentials.has(key);
+    const selectedCred = inputCredentials?.[key];
+    return isRequired && !selectedCred;
+  });
+}
+
+export function findSavedCredentialByProviderAndType(
+  providerNames: string[],
+  credentialTypes: string[],
+  allProviders: CredentialsProvidersContextType | null,
+): SavedCredential | undefined {
+  for (const providerName of providerNames) {
+    const providerData = allProviders?.[providerName];
+    if (!providerData) continue;
+
+    const matchingCredential = providerData.savedCredentials.find(
+      (credential) => {
+        if (credentialTypes.length > 0) {
+          return credentialTypes.includes(credential.type);
+        }
+        return true;
+      },
+    );
+
+    if (matchingCredential) {
+      return matchingCredential as SavedCredential;
+    }
+  }
+
+  return undefined;
+}

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

@@ -11,9 +11,18 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { isEmpty } from "@/lib/utils";
+import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
 import { analytics } from "@/services/analytics";
 import { useQueryClient } from "@tanstack/react-query";
-import { useCallback, useEffect, useMemo, useState } from "react";
+import {
+  useCallback,
+  useContext,
+  useEffect,
+  useMemo,
+  useRef,
+  useState,
+} from "react";
+import { getSystemCredentials } from "../CredentialsInputs/helpers";
 import { showExecutionErrorToast } from "./errorHelpers";
 
 export type RunVariant =
@@ -42,8 +51,10 @@ export function useAgentRunModal(
   const [inputCredentials, setInputCredentials] = useState<Record<string, any>>(
     callbacks?.initialInputCredentials || {},
   );
+
   const [presetName, setPresetName] = useState<string>("");
   const [presetDescription, setPresetDescription] = useState<string>("");
+  const hasInitializedSystemCreds = useRef(false);
 
   // Determine the default run type based on agent capabilities
   const defaultRunType: RunVariant = agent.trigger_setup_info
@@ -58,6 +69,91 @@ export function useAgentRunModal(
     setInputCredentials(callbacks?.initialInputCredentials || {});
   }, [callbacks?.initialInputValues, callbacks?.initialInputCredentials]);
 
+  const allProviders = useContext(CredentialsProvidersContext);
+
+  // Initialize credentials with default system credentials
+  useEffect(() => {
+    if (!allProviders || !agent.credentials_input_schema?.properties) return;
+    if (callbacks?.initialInputCredentials) {
+      hasInitializedSystemCreds.current = true;
+      return;
+    }
+    if (hasInitializedSystemCreds.current) return;
+
+    const properties = agent.credentials_input_schema.properties as Record<
+      string,
+      any
+    >;
+
+    setInputCredentials((currentCreds) => {
+      const credsToAdd: Record<string, any> = {};
+
+      for (const [key, schema] of Object.entries(properties)) {
+        if (currentCreds[key]) continue;
+
+        const providerNames = schema.credentials_provider || [];
+        const supportedTypes = schema.credentials_types || [];
+        const requiredScopes = schema.credentials_scopes;
+
+        for (const providerName of providerNames) {
+          const providerData = allProviders[providerName];
+          if (!providerData) continue;
+
+          const systemCreds = getSystemCredentials(
+            providerData.savedCredentials ?? [],
+          );
+          const matchingSystemCreds = systemCreds.filter((cred) => {
+            if (!supportedTypes.includes(cred.type)) return false;
+
+            if (
+              cred.type === "oauth2" &&
+              requiredScopes &&
+              requiredScopes.length > 0
+            ) {
+              const grantedScopes = new Set(cred.scopes || []);
+              const hasAllRequiredScopes = requiredScopes.every(
+                (scope: string) => grantedScopes.has(scope),
+              );
+              if (!hasAllRequiredScopes) return false;
+            }
+
+            return true;
+          });
+
+          if (matchingSystemCreds.length === 1) {
+            const systemCred = matchingSystemCreds[0];
+            credsToAdd[key] = {
+              id: systemCred.id,
+              type: systemCred.type,
+              provider: providerName,
+              title: systemCred.title,
+            };
+            break;
+          }
+        }
+      }
+
+      if (Object.keys(credsToAdd).length > 0) {
+        hasInitializedSystemCreds.current = true;
+        return {
+          ...currentCreds,
+          ...credsToAdd,
+        };
+      }
+
+      return currentCreds;
+    });
+  }, [
+    allProviders,
+    agent.credentials_input_schema,
+    callbacks?.initialInputCredentials,
+  ]);
+
+  // Reset initialization flag when modal closes/opens or agent changes
+  useEffect(() => {
+    hasInitializedSystemCreds.current = false;
+  }, [isOpen, agent.graph_id]);
+
   // API mutations
   const executeGraphMutation = usePostV1ExecuteGraphAgent({
     mutation: {
@@ -66,7 +162,6 @@ export function useAgentRunModal(
           toast({
             title: "Agent execution started",
           });
-          // Invalidate runs list for this graph
           queryClient.invalidateQueries({
             queryKey: getGetV1ListGraphExecutionsQueryKey(agent.graph_id),
           });
@@ -163,14 +258,10 @@ export function useAgentRunModal(
   }, [agentInputSchema.required, inputValues]);
 
   const [allCredentialsAreSet, missingCredentials] = useMemo(() => {
-    // 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;
@@ -184,7 +275,6 @@ export function useAgentRunModal(
     [agentCredentialsInputFields],
   );
 
-  // Final readiness flag combining inputs + credentials when credentials are shown
   const allRequiredInputsAreSet = useMemo(
     () =>
       allRequiredInputsAreSetRaw &&
@@ -223,7 +313,6 @@ export function useAgentRunModal(
       defaultRunType === "automatic-trigger" ||
       defaultRunType === "manual-trigger"
     ) {
-      // Setup trigger
       if (!presetName.trim()) {
         toast({
           title: "⚠️ Trigger name required",
@@ -244,9 +333,6 @@ 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),
       );
@@ -280,41 +366,24 @@ export function useAgentRunModal(
   }, [agentInputFields]);
 
   return {
-    // UI state
     isOpen,
     setIsOpen,
-
-    // Run mode
     defaultRunType: defaultRunType as RunVariant,
-
-    // Form: regular inputs
     inputValues,
     setInputValues,
-
-    // Form: credentials
     inputCredentials,
     setInputCredentials,
-
-    // Preset/trigger labels
     presetName,
     presetDescription,
     setPresetName,
     setPresetDescription,
-
-    // Validation/readiness
     allRequiredInputsAreSet,
     missingInputs,
-
-    // Schemas for rendering
     agentInputFields,
     agentCredentialsInputFields,
     hasInputFields,
-
-    // Async states
     isExecuting: executeGraphMutation.isPending,
     isSettingUpTrigger: setupTriggerMutation.isPending,
-
-    // Actions
     handleRun,
   };
 }

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

@@ -1,37 +1,17 @@
 import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
 import { GearIcon } from "@phosphor-icons/react";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { useAgentSafeMode } from "@/hooks/useAgentSafeMode";
-
-interface Props {
-  agent: LibraryAgent;
-  onSelectSettings: () => void;
-  selected?: boolean;
-}
-
-export function AgentSettingsButton({
-  agent,
-  onSelectSettings,
-  selected,
-}: Props) {
-  const { hasHITLBlocks } = useAgentSafeMode(agent);
-
-  if (!hasHITLBlocks) {
-    return null;
-  }
 
+export function AgentSettingsButton() {
   return (
     <Button
-      variant={selected ? "secondary" : "ghost"}
+      variant="ghost"
       size="small"
       className="m-0 min-w-0 rounded-full p-0 px-1"
-      onClick={onSelectSettings}
       aria-label="Agent Settings"
     >
-      <GearIcon
-        size={18}
-        className={selected ? "text-zinc-900" : "text-zinc-600"}
-      />
+      <GearIcon size={18} className="text-zinc-600" />
+      <Text variant="small">Agent Settings</Text>
     </Button>
   );
 }

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

@@ -1,3 +1,5 @@
+"use client";
+
 import { Text } from "@/components/atoms/Text/Text";
 
 export function EmptySchedules() {

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

@@ -1,3 +1,5 @@
+"use client";
+
 import { Text } from "@/components/atoms/Text/Text";
 
 export function EmptyTemplates() {

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

@@ -1,3 +1,5 @@
+"use client";
+
 import { Text } from "@/components/atoms/Text/Text";
 
 export function EmptyTriggers() {

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

@@ -3,7 +3,7 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 
-interface MarketplaceBannersProps {
+interface Props {
   hasUpdate?: boolean;
   latestVersion?: number;
   hasUnpublishedChanges?: boolean;
@@ -21,7 +21,7 @@ export function MarketplaceBanners({
   isUpdating,
   onUpdate,
   onPublish,
-}: MarketplaceBannersProps) {
+}: Props) {
   const renderUpdateBanner = () => {
     if (hasUpdate && latestVersion) {
       return (

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

@@ -1,3 +1,5 @@
+"use client";
+
 import { cn } from "@/lib/utils";
 
 type Props = {

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

@@ -1,22 +1,16 @@
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Skeleton } from "@/components/__legacy__/ui/skeleton";
 import { cn } from "@/lib/utils";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { AGENT_LIBRARY_SECTION_PADDING_X } from "../../helpers";
 import { SelectedViewLayout } from "./SelectedViewLayout";
 
 interface Props {
   agent: LibraryAgent;
-  onSelectSettings?: () => void;
-  selectedSettings?: boolean;
 }
 
 export function LoadingSelectedContent(props: Props) {
   return (
-    <SelectedViewLayout
-      agent={props.agent}
-      onSelectSettings={props.onSelectSettings}
-      selectedSettings={props.selectedSettings}
-    >
+    <SelectedViewLayout agent={props.agent}>
       <div
         className={cn("flex flex-col gap-4", AGENT_LIBRARY_SECTION_PADDING_X)}
       >

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

@@ -33,8 +33,6 @@ interface Props {
   onSelectRun?: (id: string) => void;
   onClearSelectedRun?: () => void;
   banner?: React.ReactNode;
-  onSelectSettings?: () => void;
-  selectedSettings?: boolean;
 }
 
 export function SelectedRunView({
@@ -43,8 +41,6 @@ export function SelectedRunView({
   onSelectRun,
   onClearSelectedRun,
   banner,
-  onSelectSettings,
-  selectedSettings,
 }: Props) {
   const { run, preset, isLoading, responseError, httpError } =
     useSelectedRunView(agent.graph_id, runId);
@@ -84,12 +80,7 @@ export function SelectedRunView({
   return (
     <div className="flex h-full w-full gap-4">
       <div className="flex min-h-0 min-w-0 flex-1 flex-col">
-        <SelectedViewLayout
-          agent={agent}
-          banner={banner}
-          onSelectSettings={onSelectSettings}
-          selectedSettings={selectedSettings}
-        >
+        <SelectedViewLayout agent={agent} banner={banner}>
           <div className="flex flex-col gap-4">
             <RunDetailHeader agent={agent} run={run} />
 

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

@@ -21,8 +21,6 @@ interface Props {
   scheduleId: string;
   onClearSelectedRun?: () => void;
   banner?: React.ReactNode;
-  onSelectSettings?: () => void;
-  selectedSettings?: boolean;
 }
 
 export function SelectedScheduleView({
@@ -30,8 +28,6 @@ export function SelectedScheduleView({
   scheduleId,
   onClearSelectedRun,
   banner,
-  onSelectSettings,
-  selectedSettings,
 }: Props) {
   const { schedule, isLoading, error } = useSelectedScheduleView(
     agent.graph_id,
@@ -76,12 +72,7 @@ export function SelectedScheduleView({
   return (
     <div className="flex h-full w-full gap-4">
       <div className="flex min-h-0 min-w-0 flex-1 flex-col">
-        <SelectedViewLayout
-          agent={agent}
-          banner={banner}
-          onSelectSettings={onSelectSettings}
-          selectedSettings={selectedSettings}
-        >
+        <SelectedViewLayout agent={agent} banner={banner}>
           <div className="flex flex-col gap-4">
             <div className="flex w-full flex-col gap-0">
               <RunDetailHeader

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

@@ -1,11 +1,11 @@
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Text } from "@/components/atoms/Text/Text";
-import { Switch } from "@/components/atoms/Switch/Switch";
 import { Button } from "@/components/atoms/Button/Button";
-import { ArrowLeftIcon } from "@phosphor-icons/react";
+import { Switch } from "@/components/atoms/Switch/Switch";
+import { Text } from "@/components/atoms/Text/Text";
 import { useAgentSafeMode } from "@/hooks/useAgentSafeMode";
-import { SelectedViewLayout } from "../SelectedViewLayout";
+import { ArrowLeftIcon } from "@phosphor-icons/react";
 import { AGENT_LIBRARY_SECTION_PADDING_X } from "../../../helpers";
+import { SelectedViewLayout } from "../SelectedViewLayout";
 
 interface Props {
   agent: LibraryAgent;
@@ -17,7 +17,7 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
     useAgentSafeMode(agent);
 
   return (
-    <SelectedViewLayout agent={agent} onSelectSettings={() => {}}>
+    <SelectedViewLayout agent={agent}>
       <div className="flex flex-col gap-4">
         <div
           className={`${AGENT_LIBRARY_SECTION_PADDING_X} mb-8 flex items-center gap-3`}
@@ -33,15 +33,8 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
           <Text variant="h2">Agent Settings</Text>
         </div>
 
-        <div className={AGENT_LIBRARY_SECTION_PADDING_X}>
-          {!hasHITLBlocks ? (
-            <div className="rounded-xl border border-zinc-100 bg-white p-6">
-              <Text variant="body" className="text-muted-foreground">
-                This agent doesn&apos;t have any human-in-the-loop blocks, so
-                there are no settings to configure.
-              </Text>
-            </div>
-          ) : (
+        <div className={`${AGENT_LIBRARY_SECTION_PADDING_X} space-y-6`}>
+          {hasHITLBlocks ? (
             <div className="flex w-full max-w-2xl flex-col items-start gap-4 rounded-xl border border-zinc-100 bg-white p-6">
               <div className="flex w-full items-start justify-between gap-4">
                 <div className="flex-1">
@@ -59,6 +52,12 @@ export function SelectedSettingsView({ agent, onClearSelectedRun }: Props) {
                 />
               </div>
             </div>
+          ) : (
+            <div className="rounded-xl border border-zinc-100 bg-white p-6">
+              <Text variant="body" className="text-muted-foreground">
+                This agent doesn&apos;t have any configurable settings.
+              </Text>
+            </div>
           )}
         </div>
       </div>

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

@@ -8,7 +8,7 @@ import {
   getAgentCredentialsFields,
   getAgentInputFields,
 } from "../../modals/AgentInputsReadOnly/helpers";
-import { CredentialsInput } from "../../modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "../../modals/CredentialsInputs/CredentialsInput";
 import { RunAgentInputs } from "../../modals/RunAgentInputs/RunAgentInputs";
 import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";

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

@@ -7,7 +7,7 @@ import {
   getAgentCredentialsFields,
   getAgentInputFields,
 } from "../../modals/AgentInputsReadOnly/helpers";
-import { CredentialsInput } from "../../modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "../../modals/CredentialsInputs/CredentialsInput";
 import { RunAgentInputs } from "../../modals/RunAgentInputs/RunAgentInputs";
 import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";

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

@@ -1,7 +1,7 @@
-import { Breadcrumbs } from "@/components/molecules/Breadcrumbs/Breadcrumbs";
-import { AgentSettingsButton } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/other/AgentSettingsButton";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Breadcrumbs } from "@/components/molecules/Breadcrumbs/Breadcrumbs";
 import { AGENT_LIBRARY_SECTION_PADDING_X } from "../../helpers";
+import { AgentSettingsModal } from "../modals/AgentSettingsModal/AgentSettingsModal";
 import { SectionWrap } from "../other/SectionWrap";
 
 interface Props {
@@ -9,8 +9,6 @@ interface Props {
   children: React.ReactNode;
   banner?: React.ReactNode;
   additionalBreadcrumb?: { name: string; link?: string };
-  onSelectSettings?: () => void;
-  selectedSettings?: boolean;
 }
 
 export function SelectedViewLayout(props: Props) {
@@ -19,8 +17,8 @@ export function SelectedViewLayout(props: Props) {
       <div
         className={`${AGENT_LIBRARY_SECTION_PADDING_X} flex-shrink-0 border-b border-zinc-100 pb-0 lg:pb-4`}
       >
-        {props.banner && <div className="mb-4">{props.banner}</div>}
-        <div className="relative flex w-fit items-center gap-2">
+        {props.banner}
+        <div className="relative flex w-full items-center justify-between">
           <Breadcrumbs
             items={[
               { name: "My Library", link: "/library" },
@@ -33,15 +31,9 @@ export function SelectedViewLayout(props: Props) {
                 : []),
             ]}
           />
-          {props.agent && props.onSelectSettings && (
-            <div className="absolute -right-8">
-              <AgentSettingsButton
-                agent={props.agent}
-                onSelectSettings={props.onSelectSettings}
-                selected={props.selectedSettings}
-              />
-            </div>
-          )}
+          <div className="absolute right-0">
+            <AgentSettingsModal agent={props.agent} />
+          </div>
         </div>
       </div>
       <div className="flex min-h-0 flex-1 flex-col overflow-y-auto overflow-x-visible">

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

@@ -12,7 +12,7 @@ import {
 } from "@/lib/autogpt-server-api";
 import { useBackendAPI } from "@/lib/autogpt-server-api/context";
 
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { RunAgentInputs } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentInputs/RunAgentInputs";
 import { ScheduleTaskDialog } from "@/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/cron-scheduler-dialog";
 import ActionButtonGroup from "@/components/__legacy__/action-button-group";

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

@@ -1,14 +1,7 @@
 "use client";
 
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { NewAgentLibraryView } from "./components/NewAgentLibraryView/NewAgentLibraryView";
-import { OldAgentLibraryView } from "./components/OldAgentLibraryView/OldAgentLibraryView";
 
 export default function AgentLibraryPage() {
-  const isNewLibraryPageEnabled = useGetFlag(Flag.NEW_AGENT_RUNS);
-  return isNewLibraryPageEnabled ? (
-    <NewAgentLibraryView />
-  ) : (
-    <OldAgentLibraryView />
-  );
+  return <NewAgentLibraryView />;
 }

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

@@ -2775,6 +2775,28 @@
         }
       }
     },
+    "/api/integrations/providers/system": {
+      "get": {
+        "tags": ["v1", "integrations"],
+        "summary": "List System Providers",
+        "description": "Get a list of providers that have platform credits (system credentials) available.\n\nThese providers can be used without the user providing their own API keys.",
+        "operationId": "getV1ListSystemProviders",
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "items": { "type": "string" },
+                  "type": "array",
+                  "title": "Response Getv1Listsystemproviders"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/api/integrations/webhooks/{webhook_id}/ping": {
       "post": {
         "tags": ["v1", "integrations"],

autogpt_platform/frontend/src/components/atoms/Button/Button.tsx

@@ -20,6 +20,7 @@ export function Button(props: ButtonProps) {
     rightIcon,
     children,
     as = "button",
+    asChild: _asChild, // Destructure to prevent passing to DOM
     ...restProps
   } = props;
 

autogpt_platform/frontend/src/components/contextual/GoogleDrivePicker/GoogleDrivePicker.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { Button } from "@/components/atoms/Button/Button";
 import { CircleNotchIcon, FolderOpenIcon } from "@phosphor-icons/react";
 import {

autogpt_platform/frontend/src/components/molecules/Accordion/Accordion.stories.tsx

@@ -0,0 +1,203 @@
+import type { Meta } from "@storybook/nextjs";
+import {
+  Accordion,
+  AccordionContent,
+  AccordionItem,
+  AccordionTrigger,
+} from "./Accordion";
+
+const meta: Meta<typeof Accordion> = {
+  title: "Molecules/Accordion",
+  component: Accordion,
+  parameters: {
+    layout: "centered",
+    docs: {
+      description: {
+        component: `
+## Accordion Component
+
+A vertically stacked set of interactive headings that each reveal an associated section of content.
+
+### ✨ Features
+
+- **Built on Radix UI** - Uses @radix-ui/react-accordion for accessibility and functionality
+- **Single or multiple** - Supports single or multiple items open at once
+- **Smooth animations** - Built-in expand/collapse animations
+- **Accessible** - Full keyboard navigation and screen reader support
+- **Customizable** - Style with Tailwind CSS classes
+
+### 🎯 Usage
+
+\`\`\`tsx
+<Accordion type="single" collapsible>
+  <AccordionItem value="item-1">
+    <AccordionTrigger>Is it accessible?</AccordionTrigger>
+    <AccordionContent>
+      Yes. It adheres to the WAI-ARIA design pattern.
+    </AccordionContent>
+  </AccordionItem>
+</Accordion>
+\`\`\`
+
+### Props
+
+**Accordion**:
+- **type**: "single" | "multiple" - Whether one or multiple items can be open
+- **collapsible**: boolean - When type is "single", allows closing all items
+- **defaultValue**: string | string[] - Default open item(s)
+- **value**: string | string[] - Controlled open item(s)
+- **onValueChange**: (value) => void - Callback when value changes
+
+**AccordionItem**:
+- **value**: string - Unique identifier for the item
+- **disabled**: boolean - Whether the item is disabled
+
+**AccordionTrigger**:
+- Standard button props
+
+**AccordionContent**:
+- Standard div props
+        `,
+      },
+    },
+  },
+  tags: ["autodocs"],
+  argTypes: {
+    type: {
+      control: "radio",
+      options: ["single", "multiple"],
+      description: "Whether one or multiple items can be open at the same time",
+      table: {
+        defaultValue: { summary: "single" },
+      },
+    },
+    collapsible: {
+      control: "boolean",
+      description:
+        'When type is "single", allows closing content when clicking on open trigger',
+      table: {
+        defaultValue: { summary: "false" },
+      },
+    },
+  },
+};
+
+export default meta;
+
+export function Default() {
+  return (
+    <Accordion type="single" collapsible className="w-96">
+      <AccordionItem value="item-1">
+        <AccordionTrigger>Is it accessible?</AccordionTrigger>
+        <AccordionContent>
+          Yes. It adheres to the WAI-ARIA design pattern.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-2">
+        <AccordionTrigger>Is it styled?</AccordionTrigger>
+        <AccordionContent>
+          Yes. It comes with default styles that match your design system.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-3">
+        <AccordionTrigger>Is it animated?</AccordionTrigger>
+        <AccordionContent>
+          Yes. It&apos;s animated by default with smooth expand/collapse
+          transitions.
+        </AccordionContent>
+      </AccordionItem>
+    </Accordion>
+  );
+}
+
+export function Multiple() {
+  return (
+    <Accordion type="multiple" className="w-96">
+      <AccordionItem value="item-1">
+        <AccordionTrigger>First section</AccordionTrigger>
+        <AccordionContent>
+          Multiple items can be open at the same time when type is set to
+          &quot;multiple&quot;.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-2">
+        <AccordionTrigger>Second section</AccordionTrigger>
+        <AccordionContent>
+          Try opening this one while the first is still open.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-3">
+        <AccordionTrigger>Third section</AccordionTrigger>
+        <AccordionContent>
+          All three can be open simultaneously.
+        </AccordionContent>
+      </AccordionItem>
+    </Accordion>
+  );
+}
+
+export function DefaultOpen() {
+  return (
+    <Accordion type="single" collapsible defaultValue="item-2" className="w-96">
+      <AccordionItem value="item-1">
+        <AccordionTrigger>Closed by default</AccordionTrigger>
+        <AccordionContent>This item starts closed.</AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-2">
+        <AccordionTrigger>Open by default</AccordionTrigger>
+        <AccordionContent>
+          This item starts open because defaultValue is set to
+          &quot;item-2&quot;.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-3">
+        <AccordionTrigger>Also closed</AccordionTrigger>
+        <AccordionContent>This item also starts closed.</AccordionContent>
+      </AccordionItem>
+    </Accordion>
+  );
+}
+
+export function WithDisabledItem() {
+  return (
+    <Accordion type="single" collapsible className="w-96">
+      <AccordionItem value="item-1">
+        <AccordionTrigger>Available item</AccordionTrigger>
+        <AccordionContent>This item can be toggled.</AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-2" disabled>
+        <AccordionTrigger>Disabled item</AccordionTrigger>
+        <AccordionContent>
+          This content cannot be accessed because the item is disabled.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-3">
+        <AccordionTrigger>Another available item</AccordionTrigger>
+        <AccordionContent>This item can also be toggled.</AccordionContent>
+      </AccordionItem>
+    </Accordion>
+  );
+}
+
+export function CustomStyled() {
+  return (
+    <Accordion type="single" collapsible className="w-96">
+      <AccordionItem value="item-1" className="border-none">
+        <AccordionTrigger className="rounded-lg bg-zinc-100 px-4 hover:bg-zinc-200 hover:no-underline">
+          Custom styled trigger
+        </AccordionTrigger>
+        <AccordionContent className="px-4 pt-2">
+          You can customize the styling using className props.
+        </AccordionContent>
+      </AccordionItem>
+      <AccordionItem value="item-2" className="mt-2 border-none">
+        <AccordionTrigger className="rounded-lg bg-blue-50 px-4 text-blue-700 hover:bg-blue-100 hover:no-underline">
+          Blue themed
+        </AccordionTrigger>
+        <AccordionContent className="px-4 pt-2 text-blue-600">
+          Each item can have different styles.
+        </AccordionContent>
+      </AccordionItem>
+    </Accordion>
+  );
+}

autogpt_platform/frontend/src/components/molecules/Accordion/Accordion.tsx

@@ -0,0 +1,8 @@
+"use client";
+
+export {
+  Accordion,
+  AccordionContent,
+  AccordionItem,
+  AccordionTrigger,
+} from "@/components/ui/accordion";

autogpt_platform/frontend/src/components/molecules/Dialog/components/DrawerWrap.tsx

@@ -22,6 +22,9 @@ export function DrawerWrap({
   handleClose,
   isForceOpen,
 }: Props) {
+  const accessibleTitle = title ?? "Dialog";
+  const hasVisibleTitle = Boolean(title);
+
   const closeBtn = (
     <Button
       variant="link"
@@ -44,15 +47,19 @@ export function DrawerWrap({
       >
         <div
           className={`flex w-full shrink-0 items-center justify-between ${
-            title ? "pb-6" : "pb-0"
+            hasVisibleTitle ? "pb-6" : "pb-0"
           }`}
         >
-          {title ? (
-            <Drawer.Title className={drawerStyles.title}>{title}</Drawer.Title>
-          ) : null}
+          {hasVisibleTitle ? (
+            <Drawer.Title className={drawerStyles.title}>
+              {accessibleTitle}
+            </Drawer.Title>
+          ) : (
+            <Drawer.Title className="sr-only">{accessibleTitle}</Drawer.Title>
+          )}
 
           {!isForceOpen ? (
-            title ? (
+            hasVisibleTitle ? (
               closeBtn
             ) : (
               <div

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/CredentialField.tsx

@@ -1,14 +1,14 @@
-import React, { useMemo } from "react";
-import { FieldProps, getUiOptions } from "@rjsf/utils";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
+import { Switch } from "@/components/atoms/Switch/Switch";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api";
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { FieldProps, getUiOptions } from "@rjsf/utils";
+import { useMemo } from "react";
 import { useShallow } from "zustand/react/shallow";
 import { CredentialFieldTitle } from "./components/CredentialFieldTitle";
-import { Switch } from "@/components/atoms/Switch/Switch";
 
 export const CredentialsField = (props: FieldProps) => {
   const { formData, onChange, schema, registry, fieldPathId, required } = props;

autogpt_platform/frontend/src/components/ui/accordion.tsx

@@ -0,0 +1,57 @@
+"use client";
+
+import * as AccordionPrimitive from "@radix-ui/react-accordion";
+import { ChevronDown } from "lucide-react";
+import * as React from "react";
+
+import { cn } from "@/lib/utils";
+
+const Accordion = AccordionPrimitive.Root;
+
+const AccordionItem = React.forwardRef<
+  React.ElementRef<typeof AccordionPrimitive.Item>,
+  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Item>
+>(({ className, ...props }, ref) => (
+  <AccordionPrimitive.Item
+    ref={ref}
+    className={cn("border-b", className)}
+    {...props}
+  />
+));
+AccordionItem.displayName = "AccordionItem";
+
+const AccordionTrigger = React.forwardRef<
+  React.ElementRef<typeof AccordionPrimitive.Trigger>,
+  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Trigger>
+>(({ className, children, ...props }, ref) => (
+  <AccordionPrimitive.Header className="flex">
+    <AccordionPrimitive.Trigger
+      ref={ref}
+      className={cn(
+        "flex flex-1 items-center justify-between py-4 text-left text-sm font-medium transition-all hover:underline [&[data-state=open]>svg]:rotate-180",
+        className,
+      )}
+      {...props}
+    >
+      {children}
+      <ChevronDown className="h-4 w-4 shrink-0 text-neutral-500 transition-transform duration-200 dark:text-neutral-400" />
+    </AccordionPrimitive.Trigger>
+  </AccordionPrimitive.Header>
+));
+AccordionTrigger.displayName = AccordionPrimitive.Trigger.displayName;
+
+const AccordionContent = React.forwardRef<
+  React.ElementRef<typeof AccordionPrimitive.Content>,
+  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Content>
+>(({ className, children, ...props }, ref) => (
+  <AccordionPrimitive.Content
+    ref={ref}
+    className="overflow-hidden text-sm data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down"
+    {...props}
+  >
+    <div className={cn("pb-4 pt-0", className)}>{children}</div>
+  </AccordionPrimitive.Content>
+));
+AccordionContent.displayName = AccordionPrimitive.Content.displayName;
+
+export { Accordion, AccordionContent, AccordionItem, AccordionTrigger };

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

@@ -352,6 +352,10 @@ export default class BackendAPI {
     return this._get("/integrations/providers");
   }
 
+  listSystemProviders(): Promise<string[]> {
+    return this._get("/integrations/providers/system");
+  }
+
   listCredentials(provider?: string): Promise<CredentialsMetaResponse[]> {
     return this._get(
       provider

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

@@ -593,6 +593,7 @@ export type CredentialsMetaResponse = {
   scopes?: Array<string>;
   username?: string;
   host?: string;
+  is_system?: boolean;
 };
 
 /* Mirror of backend/server/integrations/router.py:CredentialsDeletionResponse */

autogpt_platform/frontend/src/providers/agent-credentials/credentials-provider.tsx

@@ -1,5 +1,4 @@
-import { createContext, useCallback, useEffect, useState } from "react";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useToastOnFail } from "@/components/molecules/Toast/use-toast";
 import {
   APIKeyCredentials,
   CredentialsDeleteNeedConfirmationResponse,
@@ -10,8 +9,9 @@ import {
   UserPasswordCredentials,
 } from "@/lib/autogpt-server-api";
 import { useBackendAPI } from "@/lib/autogpt-server-api/context";
-import { useToastOnFail } from "@/components/molecules/Toast/use-toast";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { toDisplayName } from "@/providers/agent-credentials/helper";
+import { createContext, useCallback, useEffect, useState } from "react";
 
 type APIKeyCredentialsCreatable = Omit<
   APIKeyCredentials,
@@ -32,6 +32,8 @@ export type CredentialsProviderData = {
   provider: CredentialsProviderName;
   providerName: string;
   savedCredentials: CredentialsMetaResponse[];
+  /** Whether this provider has platform credits available (system credentials) */
+  isSystemProvider: boolean;
   oAuthCallback: (
     code: string,
     state_token: string,
@@ -68,6 +70,9 @@ export default function CredentialsProvider({
   const [providers, setProviders] =
     useState<CredentialsProvidersContextType | null>(null);
   const [providerNames, setProviderNames] = useState<string[]>([]);
+  const [systemProviders, setSystemProviders] = useState<Set<string>>(
+    new Set(),
+  );
   const { isLoggedIn } = useSupabase();
   const api = useBackendAPI();
   const onFailToast = useToastOnFail();
@@ -218,17 +223,7 @@ export default function CredentialsProvider({
     [api, onFailToast],
   );
 
-  // Fetch provider names on mount
-  useEffect(() => {
-    api
-      .listProviders()
-      .then((names) => {
-        setProviderNames(names);
-      })
-      .catch(onFailToast("load provider names"));
-  }, [api, onFailToast]);
-
-  useEffect(() => {
+  const loadCredentials = useCallback(() => {
     if (!isLoggedIn || providerNames.length === 0) {
       if (isLoggedIn == false) setProviders({});
       return;
@@ -251,27 +246,32 @@ export default function CredentialsProvider({
         setProviders((prev) => ({
           ...prev,
           ...Object.fromEntries(
-            providerNames.map((provider) => [
-              provider,
-              {
+            providerNames.map((provider) => {
+              const providerCredentials = credentialsByProvider[provider] ?? [];
+
+              return [
                 provider,
-                providerName: toDisplayName(provider as string),
-                savedCredentials: credentialsByProvider[provider] ?? [],
-                oAuthCallback: (code: string, state_token: string) =>
-                  oAuthCallback(provider, code, state_token),
-                createAPIKeyCredentials: (
-                  credentials: APIKeyCredentialsCreatable,
-                ) => createAPIKeyCredentials(provider, credentials),
-                createUserPasswordCredentials: (
-                  credentials: UserPasswordCredentialsCreatable,
-                ) => createUserPasswordCredentials(provider, credentials),
-                createHostScopedCredentials: (
-                  credentials: HostScopedCredentialsCreatable,
-                ) => createHostScopedCredentials(provider, credentials),
-                deleteCredentials: (id: string, force: boolean = false) =>
-                  deleteCredentials(provider, id, force),
-              } satisfies CredentialsProviderData,
-            ]),
+                {
+                  provider,
+                  providerName: toDisplayName(provider as string),
+                  savedCredentials: providerCredentials,
+                  isSystemProvider: systemProviders.has(provider),
+                  oAuthCallback: (code: string, state_token: string) =>
+                    oAuthCallback(provider, code, state_token),
+                  createAPIKeyCredentials: (
+                    credentials: APIKeyCredentialsCreatable,
+                  ) => createAPIKeyCredentials(provider, credentials),
+                  createUserPasswordCredentials: (
+                    credentials: UserPasswordCredentialsCreatable,
+                  ) => createUserPasswordCredentials(provider, credentials),
+                  createHostScopedCredentials: (
+                    credentials: HostScopedCredentialsCreatable,
+                  ) => createHostScopedCredentials(provider, credentials),
+                  deleteCredentials: (id: string, force: boolean = false) =>
+                    deleteCredentials(provider, id, force),
+                } satisfies CredentialsProviderData,
+              ];
+            }),
           ),
         }));
       })
@@ -280,6 +280,7 @@ export default function CredentialsProvider({
     api,
     isLoggedIn,
     providerNames,
+    systemProviders,
     createAPIKeyCredentials,
     createUserPasswordCredentials,
     createHostScopedCredentials,
@@ -288,6 +289,20 @@ export default function CredentialsProvider({
     onFailToast,
   ]);
 
+  // Fetch provider names and system providers on mount
+  useEffect(() => {
+    Promise.all([api.listProviders(), api.listSystemProviders()])
+      .then(([names, systemList]) => {
+        setProviderNames(names);
+        setSystemProviders(new Set(systemList));
+      })
+      .catch(onFailToast("Load provider names"));
+  }, [api, onFailToast]);
+
+  useEffect(() => {
+    loadCredentials();
+  }, [loadCredentials]);
+
   return (
     <CredentialsProvidersContext.Provider value={providers}>
       {children}

autogpt_platform/frontend/tailwind.config.ts

@@ -22,13 +22,7 @@ const config = {
         poppins: ["var(--font-poppins)"],
       },
       colors: {
-        // *** APPROVED DESIGN SYSTEM COLORS ***
-        // These are the ONLY colors that should be used in our app
         ...colors,
-
-        // Legacy colors - DO NOT USE THESE IN NEW CODE
-        // These are kept only to prevent breaking existing styles
-        // Use the approved design system colors above instead
         border: "hsl(var(--border))",
         input: "hsl(var(--input))",
         ring: "hsl(var(--ring))",
@@ -63,70 +57,66 @@ const config = {
           foreground: "hsl(var(--card-foreground))",
         },
         customGray: {
-          100: "#d9d9d9",
-          200: "#a8a8a8",
-          300: "#878787",
-          400: "#646464",
-          500: "#474747",
-          600: "#282828",
-          700: "#272727",
+          "100": "#d9d9d9",
+          "200": "#a8a8a8",
+          "300": "#878787",
+          "400": "#646464",
+          "500": "#474747",
+          "600": "#282828",
+          "700": "#272727",
         },
       },
       spacing: {
-        // Tailwind spacing + custom sizes
-        0: "0rem", // 0px
-        0.5: "0.125rem", // 2px
-        1: "0.25rem", // 4px
-        1.5: "0.375rem", // 6px
-        2: "0.5rem", // 8px
-        2.5: "0.625rem", // 10px
-        3: "0.75rem", // 12px
-        3.5: "0.875rem", // 14px
-        4: "1rem", // 16px
-        5: "1.25rem", // 20px
-        6: "1.5rem", // 24px
-        7: "1.75rem", // 28px
-        7.5: "1.875rem", // 30px
-        8: "2rem", // 32px
-        8.5: "2.125rem", // 34px
-        9: "2.25rem", // 36px
-        10: "2.5rem", // 40px
-        11: "2.75rem", // 44px
-        12: "3rem", // 48px
-        14: "3.5rem", // 56px
-        16: "4rem", // 64px
-        18: "4.5rem", // 72px
-        20: "5rem", // 80px
-        24: "6rem", // 96px
-        28: "7rem", // 112px
-        32: "8rem", // 128px
-        36: "9rem", // 144px
-        40: "10rem", // 160px
-        44: "11rem", // 176px
-        48: "12rem", // 192px
-        52: "13rem", // 208px
-        56: "14rem", // 224px
-        60: "15rem", // 240px
-        64: "16rem", // 256px
-        68: "17rem", // 272px
-        70: "17.5rem", // 280px
-        71: "17.75rem", // 284px
-        72: "18rem", // 288px
-        76: "19rem", // 304px
-        80: "20rem", // 320px
-        96: "24rem", // 384px
+        "0": "0rem",
+        "1": "0.25rem",
+        "2": "0.5rem",
+        "3": "0.75rem",
+        "4": "1rem",
+        "5": "1.25rem",
+        "6": "1.5rem",
+        "7": "1.75rem",
+        "8": "2rem",
+        "9": "2.25rem",
+        "10": "2.5rem",
+        "11": "2.75rem",
+        "12": "3rem",
+        "14": "3.5rem",
+        "16": "4rem",
+        "18": "4.5rem",
+        "20": "5rem",
+        "24": "6rem",
+        "28": "7rem",
+        "32": "8rem",
+        "36": "9rem",
+        "40": "10rem",
+        "44": "11rem",
+        "48": "12rem",
+        "52": "13rem",
+        "56": "14rem",
+        "60": "15rem",
+        "64": "16rem",
+        "68": "17rem",
+        "70": "17.5rem",
+        "71": "17.75rem",
+        "72": "18rem",
+        "76": "19rem",
+        "80": "20rem",
+        "96": "24rem",
+        "0.5": "0.125rem",
+        "1.5": "0.375rem",
+        "2.5": "0.625rem",
+        "3.5": "0.875rem",
+        "7.5": "1.875rem",
+        "8.5": "2.125rem",
       },
       borderRadius: {
-        // Design system border radius tokens from Figma
-        xsmall: "0.25rem", // 4px
-        small: "0.5rem", // 8px
-        medium: "0.75rem", // 12px
-        large: "1rem", // 16px
-        xlarge: "1.25rem", // 20px
-        "2xlarge": "1.5rem", // 24px
-        full: "9999px", // For pill buttons
-
-        // Legacy values - kept for backward compatibility
+        xsmall: "0.25rem",
+        small: "0.5rem",
+        medium: "0.75rem",
+        large: "1rem",
+        xlarge: "1.25rem",
+        "2xlarge": "1.5rem",
+        full: "9999px",
         lg: "var(--radius)",
         md: "calc(var(--radius) - 2px)",
         sm: "calc(var(--radius) - 4px)",
@@ -136,16 +126,28 @@ const config = {
       },
       keyframes: {
         "accordion-down": {
-          from: { height: "0" },
-          to: { height: "var(--radix-accordion-content-height)" },
+          from: {
+            height: "0",
+          },
+          to: {
+            height: "var(--radix-accordion-content-height)",
+          },
         },
         "accordion-up": {
-          from: { height: "var(--radix-accordion-content-height)" },
-          to: { height: "0" },
+          from: {
+            height: "var(--radix-accordion-content-height)",
+          },
+          to: {
+            height: "0",
+          },
         },
         "fade-in": {
-          "0%": { opacity: "0" }, // Start with opacity 0
-          "100%": { opacity: "1" }, // End with opacity 1
+          "0%": {
+            opacity: "0",
+          },
+          "100%": {
+            opacity: "1",
+          },
         },
       },
       animation: {