Merge branch 'hackathon-copilot-search' of github.com:Significant-Gravitas/AutoGPT into feat/backfill_block_and_docs

- Changed from patching prisma.get_client() to patching query_raw_with_schema
- Follows the pattern used in hybrid_search_test.py
- Tests now properly exercise the schema-prefixing wrapper logic
- Fixes issue where SET search_path call was unmocked
- Removed unused mocker parameters
- All 18 tests passing

.branchlet.json

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

.dockerignore

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

.github/workflows/claude-dependabot.yml

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

.github/workflows/claude.yml

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

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

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

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

@@ -134,7 +134,7 @@ jobs:
         run: poetry install
 
       - name: Generate Prisma Client
-        run: poetry run prisma generate
+        run: poetry run prisma generate && poetry run gen-prisma-stub
 
       - id: supabase
         name: Start Supabase
@@ -176,7 +176,7 @@ jobs:
           }
 
       - name: Run Database Migrations
-        run: poetry run prisma migrate dev --name updates
+        run: poetry run prisma migrate deploy
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

autogpt_platform/Makefile

@@ -6,11 +6,13 @@ start-core:
 
 # Stop core services
 stop-core:
-	docker compose stop 
+	docker compose stop deps
 
 reset-db:
-	docker compose stop db
 	rm -rf db/docker/volumes/db/data
+	cd backend && poetry run prisma migrate deploy
+	cd backend && poetry run prisma generate
+	cd backend && poetry run gen-prisma-stub
 	
 # View logs for core services
 logs-core:
@@ -32,6 +34,7 @@ init-env:
 migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
+	cd backend && poetry run gen-prisma-stub
 
 run-backend:
 	cd backend && poetry run app
@@ -57,4 +60,4 @@ help:
 	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
 	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  load-store-agents - Load store agents from agents/ folder into test database"

autogpt_platform/backend/.gitignore

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

autogpt_platform/backend/Dockerfile

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

autogpt_platform/backend/backend/api/features/chat/config.py

@@ -12,11 +12,7 @@ class ChatConfig(BaseSettings):
 
     # OpenAI API Configuration
     model: str = Field(
-        default="anthropic/claude-opus-4.5", description="Default model to use"
-    )
-    title_model: str = Field(
-        default="openai/gpt-4o-mini",
-        description="Model to use for generating session titles (should be fast/cheap)",
+        default="qwen/qwen3-235b-a22b-2507", description="Default model to use"
     )
     api_key: str | None = Field(default=None, description="OpenAI API key")
     base_url: str | None = Field(
@@ -76,31 +72,8 @@ class ChatConfig(BaseSettings):
                 v = "https://openrouter.ai/api/v1"
         return v
 
-    # Prompt paths for different contexts
-    PROMPT_PATHS: dict[str, str] = {
-        "default": "prompts/chat_system.md",
-        "onboarding": "prompts/onboarding_system.md",
-    }
-
-    def get_system_prompt_for_type(
-        self, prompt_type: str = "default", **template_vars
-    ) -> str:
-        """Load and render a system prompt by type.
-
-        Args:
-            prompt_type: The type of prompt to load ("default" or "onboarding")
-            **template_vars: Variables to substitute in the template
-
-        Returns:
-            Rendered system prompt string
-        """
-        prompt_path_str = self.PROMPT_PATHS.get(
-            prompt_type, self.PROMPT_PATHS["default"]
-        )
-        return self._load_prompt_from_path(prompt_path_str, **template_vars)
-
     def get_system_prompt(self, **template_vars) -> str:
-        """Load and render the default system prompt from file.
+        """Load and render the system prompt from file.
 
         Args:
             **template_vars: Variables to substitute in the template
@@ -109,21 +82,9 @@ class ChatConfig(BaseSettings):
             Rendered system prompt string
 
         """
-        return self._load_prompt_from_path(self.system_prompt_path, **template_vars)
-
-    def _load_prompt_from_path(self, prompt_path_str: str, **template_vars) -> str:
-        """Load and render a system prompt from a given path.
-
-        Args:
-            prompt_path_str: Path to the prompt file relative to chat module
-            **template_vars: Variables to substitute in the template
-
-        Returns:
-            Rendered system prompt string
-        """
         # Get the path relative to this module
         module_dir = Path(__file__).parent
-        prompt_path = module_dir / prompt_path_str
+        prompt_path = module_dir / self.system_prompt_path
 
         # Check for .j2 extension first (Jinja2 template)
         j2_path = Path(str(prompt_path) + ".j2")

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

@@ -1,215 +0,0 @@
-"""Database operations for chat sessions."""
-
-import logging
-from datetime import UTC, datetime
-from typing import Any, cast
-
-from prisma.models import ChatMessage as PrismaChatMessage
-from prisma.models import ChatSession as PrismaChatSession
-from prisma.types import (
-    ChatMessageCreateInput,
-    ChatSessionCreateInput,
-    ChatSessionUpdateInput,
-)
-
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-
-async def get_chat_session(session_id: str) -> PrismaChatSession | None:
-    """Get a chat session by ID from the database."""
-    session = await PrismaChatSession.prisma().find_unique(
-        where={"id": session_id},
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        # Sort messages by sequence in Python since Prisma doesn't support order_by in include
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def create_chat_session(
-    session_id: str,
-    user_id: str | None,
-) -> PrismaChatSession:
-    """Create a new chat session in the database."""
-    data = ChatSessionCreateInput(
-        id=session_id,
-        userId=user_id,
-        credentials=SafeJson({}),
-        successfulAgentRuns=SafeJson({}),
-        successfulAgentSchedules=SafeJson({}),
-    )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
-
-
-async def update_chat_session(
-    session_id: str,
-    credentials: dict[str, Any] | None = None,
-    successful_agent_runs: dict[str, Any] | None = None,
-    successful_agent_schedules: dict[str, Any] | None = None,
-    total_prompt_tokens: int | None = None,
-    total_completion_tokens: int | None = None,
-    title: str | None = None,
-) -> PrismaChatSession | None:
-    """Update a chat session's metadata."""
-    data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
-
-    if credentials is not None:
-        data["credentials"] = SafeJson(credentials)
-    if successful_agent_runs is not None:
-        data["successfulAgentRuns"] = SafeJson(successful_agent_runs)
-    if successful_agent_schedules is not None:
-        data["successfulAgentSchedules"] = SafeJson(successful_agent_schedules)
-    if total_prompt_tokens is not None:
-        data["totalPromptTokens"] = total_prompt_tokens
-    if total_completion_tokens is not None:
-        data["totalCompletionTokens"] = total_completion_tokens
-    if title is not None:
-        data["title"] = title
-
-    session = await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data=data,
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def add_chat_message(
-    session_id: str,
-    role: str,
-    sequence: int,
-    content: str | None = None,
-    name: str | None = None,
-    tool_call_id: str | None = None,
-    refusal: str | None = None,
-    tool_calls: list[dict[str, Any]] | None = None,
-    function_call: dict[str, Any] | None = None,
-) -> PrismaChatMessage:
-    """Add a message to a chat session."""
-    # Build the input dict dynamically - only include optional fields when they
-    # have values, as Prisma TypedDict validation fails when optional fields
-    # are explicitly set to None
-    data: dict[str, Any] = {
-        "Session": {"connect": {"id": session_id}},
-        "role": role,
-        "sequence": sequence,
-    }
-
-    # Add optional string fields
-    if content is not None:
-        data["content"] = content
-    if name is not None:
-        data["name"] = name
-    if tool_call_id is not None:
-        data["toolCallId"] = tool_call_id
-    if refusal is not None:
-        data["refusal"] = refusal
-
-    # Add optional JSON fields only when they have values
-    if tool_calls is not None:
-        data["toolCalls"] = SafeJson(tool_calls)
-    if function_call is not None:
-        data["functionCall"] = SafeJson(function_call)
-
-    # Update session's updatedAt timestamp
-    await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data={"updatedAt": datetime.now(UTC)},
-    )
-
-    return await PrismaChatMessage.prisma().create(
-        data=cast(ChatMessageCreateInput, data)
-    )
-
-
-async def add_chat_messages_batch(
-    session_id: str,
-    messages: list[dict[str, Any]],
-    start_sequence: int,
-) -> list[PrismaChatMessage]:
-    """Add multiple messages to a chat session in a batch."""
-    if not messages:
-        return []
-
-    created_messages = []
-    for i, msg in enumerate(messages):
-        # Build the input dict dynamically - only include optional JSON fields
-        # when they have values, as Prisma TypedDict validation fails when
-        # optional fields are explicitly set to None
-        data: dict[str, Any] = {
-            "Session": {"connect": {"id": session_id}},
-            "role": msg["role"],
-            "sequence": start_sequence + i,
-        }
-
-        # Add optional string fields
-        if msg.get("content") is not None:
-            data["content"] = msg["content"]
-        if msg.get("name") is not None:
-            data["name"] = msg["name"]
-        if msg.get("tool_call_id") is not None:
-            data["toolCallId"] = msg["tool_call_id"]
-        if msg.get("refusal") is not None:
-            data["refusal"] = msg["refusal"]
-
-        # Add optional JSON fields only when they have values
-        if msg.get("tool_calls") is not None:
-            data["toolCalls"] = SafeJson(msg["tool_calls"])
-        if msg.get("function_call") is not None:
-            data["functionCall"] = SafeJson(msg["function_call"])
-
-        created = await PrismaChatMessage.prisma().create(
-            data=cast(ChatMessageCreateInput, data)
-        )
-        created_messages.append(created)
-
-    # Update session's updatedAt timestamp
-    await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data={"updatedAt": datetime.now(UTC)},
-    )
-
-    return created_messages
-
-
-async def get_user_chat_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[PrismaChatSession]:
-    """Get chat sessions for a user, ordered by most recent."""
-    return await PrismaChatSession.prisma().find_many(
-        where={"userId": user_id},
-        order={"updatedAt": "desc"},
-        take=limit,
-        skip=offset,
-    )
-
-
-async def get_user_session_count(user_id: str) -> int:
-    """Get the total number of chat sessions for a user."""
-    return await PrismaChatSession.prisma().count(where={"userId": user_id})
-
-
-async def delete_chat_session(session_id: str) -> bool:
-    """Delete a chat session and all its messages."""
-    try:
-        await PrismaChatSession.prisma().delete(where={"id": session_id})
-        return True
-    except Exception as e:
-        logger.error(f"Failed to delete chat session {session_id}: {e}")
-        return False
-
-
-async def get_chat_session_message_count(session_id: str) -> int:
-    """Get the number of messages in a chat session."""
-    count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
-    return count

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

@@ -16,15 +16,11 @@ from openai.types.chat.chat_completion_message_tool_call_param import (
     ChatCompletionMessageToolCallParam,
     Function,
 )
-from prisma.models import ChatMessage as PrismaChatMessage
-from prisma.models import ChatSession as PrismaChatSession
 from pydantic import BaseModel
 
 from backend.data.redis_client import get_redis_async
-from backend.util import json
 from backend.util.exceptions import RedisError
 
-from . import db as chat_db
 from .config import ChatConfig
 
 logger = logging.getLogger(__name__)
@@ -50,7 +46,6 @@ class Usage(BaseModel):
 class ChatSession(BaseModel):
     session_id: str
     user_id: str | None
-    title: str | None = None
     messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
@@ -64,7 +59,6 @@ class ChatSession(BaseModel):
         return ChatSession(
             session_id=str(uuid.uuid4()),
             user_id=user_id,
-            title=None,
             messages=[],
             usage=[],
             credentials={},
@@ -72,85 +66,6 @@ class ChatSession(BaseModel):
             updated_at=datetime.now(UTC),
         )
 
-    @staticmethod
-    def from_prisma(
-        prisma_session: PrismaChatSession,
-        prisma_messages: list[PrismaChatMessage] | None = None,
-    ) -> "ChatSession":
-        """Convert Prisma models to Pydantic ChatSession."""
-        messages = []
-        if prisma_messages:
-            for msg in prisma_messages:
-                tool_calls = None
-                if msg.toolCalls:
-                    tool_calls = (
-                        json.loads(msg.toolCalls)
-                        if isinstance(msg.toolCalls, str)
-                        else msg.toolCalls
-                    )
-
-                function_call = None
-                if msg.functionCall:
-                    function_call = (
-                        json.loads(msg.functionCall)
-                        if isinstance(msg.functionCall, str)
-                        else msg.functionCall
-                    )
-
-                messages.append(
-                    ChatMessage(
-                        role=msg.role,
-                        content=msg.content,
-                        name=msg.name,
-                        tool_call_id=msg.toolCallId,
-                        refusal=msg.refusal,
-                        tool_calls=tool_calls,
-                        function_call=function_call,
-                    )
-                )
-
-        # Parse JSON fields from Prisma
-        credentials = (
-            json.loads(prisma_session.credentials)
-            if isinstance(prisma_session.credentials, str)
-            else prisma_session.credentials or {}
-        )
-        successful_agent_runs = (
-            json.loads(prisma_session.successfulAgentRuns)
-            if isinstance(prisma_session.successfulAgentRuns, str)
-            else prisma_session.successfulAgentRuns or {}
-        )
-        successful_agent_schedules = (
-            json.loads(prisma_session.successfulAgentSchedules)
-            if isinstance(prisma_session.successfulAgentSchedules, str)
-            else prisma_session.successfulAgentSchedules or {}
-        )
-
-        # Calculate usage from token counts
-        usage = []
-        if prisma_session.totalPromptTokens or prisma_session.totalCompletionTokens:
-            usage.append(
-                Usage(
-                    prompt_tokens=prisma_session.totalPromptTokens or 0,
-                    completion_tokens=prisma_session.totalCompletionTokens or 0,
-                    total_tokens=(prisma_session.totalPromptTokens or 0)
-                    + (prisma_session.totalCompletionTokens or 0),
-                )
-            )
-
-        return ChatSession(
-            session_id=prisma_session.id,
-            user_id=prisma_session.userId,
-            title=prisma_session.title,
-            messages=messages,
-            usage=usage,
-            credentials=credentials,
-            started_at=prisma_session.createdAt,
-            updated_at=prisma_session.updatedAt,
-            successful_agent_runs=successful_agent_runs,
-            successful_agent_schedules=successful_agent_schedules,
-        )
-
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -240,234 +155,50 @@ class ChatSession(BaseModel):
         return messages
 
 
-async def _get_session_from_cache(session_id: str) -> ChatSession | None:
-    """Get a chat session from Redis cache."""
-    redis_key = f"chat:session:{session_id}"
-    async_redis = await get_redis_async()
-    raw_session: bytes | None = await async_redis.get(redis_key)
-
-    if raw_session is None:
-        return None
-
-    try:
-        session = ChatSession.model_validate_json(raw_session)
-        logger.info(
-            f"Loading session {session_id} from cache: "
-            f"message_count={len(session.messages)}, "
-            f"roles={[m.role for m in session.messages]}"
-        )
-        return session
-    except Exception as e:
-        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
-        raise RedisError(f"Corrupted session data for {session_id}") from e
-
-
-async def _cache_session(session: ChatSession) -> None:
-    """Cache a chat session in Redis."""
-    redis_key = f"chat:session:{session.session_id}"
-    async_redis = await get_redis_async()
-    await async_redis.setex(redis_key, config.session_ttl, session.model_dump_json())
-
-
-async def _get_session_from_db(session_id: str) -> ChatSession | None:
-    """Get a chat session from the database."""
-    prisma_session = await chat_db.get_chat_session(session_id)
-    if not prisma_session:
-        return None
-
-    messages = prisma_session.Messages
-    logger.info(
-        f"Loading session {session_id} from DB: "
-        f"has_messages={messages is not None}, "
-        f"message_count={len(messages) if messages else 0}, "
-        f"roles={[m.role for m in messages] if messages else []}"
-    )
-
-    return ChatSession.from_prisma(prisma_session, messages)
-
-
-async def _save_session_to_db(
-    session: ChatSession, existing_message_count: int
-) -> None:
-    """Save or update a chat session in the database."""
-    # Check if session exists in DB
-    existing = await chat_db.get_chat_session(session.session_id)
-
-    if not existing:
-        # Create new session
-        await chat_db.create_chat_session(
-            session_id=session.session_id,
-            user_id=session.user_id,
-        )
-        existing_message_count = 0
-
-    # Calculate total tokens from usage
-    total_prompt = sum(u.prompt_tokens for u in session.usage)
-    total_completion = sum(u.completion_tokens for u in session.usage)
-
-    # Update session metadata
-    await chat_db.update_chat_session(
-        session_id=session.session_id,
-        credentials=session.credentials,
-        successful_agent_runs=session.successful_agent_runs,
-        successful_agent_schedules=session.successful_agent_schedules,
-        total_prompt_tokens=total_prompt,
-        total_completion_tokens=total_completion,
-    )
-
-    # Add new messages (only those after existing count)
-    new_messages = session.messages[existing_message_count:]
-    if new_messages:
-        messages_data = []
-        for msg in new_messages:
-            messages_data.append(
-                {
-                    "role": msg.role,
-                    "content": msg.content,
-                    "name": msg.name,
-                    "tool_call_id": msg.tool_call_id,
-                    "refusal": msg.refusal,
-                    "tool_calls": msg.tool_calls,
-                    "function_call": msg.function_call,
-                }
-            )
-        logger.info(
-            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}, "
-            f"start_sequence={existing_message_count}"
-        )
-        await chat_db.add_chat_messages_batch(
-            session_id=session.session_id,
-            messages=messages_data,
-            start_sequence=existing_message_count,
-        )
-
-
 async def get_chat_session(
     session_id: str,
     user_id: str | None,
 ) -> ChatSession | None:
-    """Get a chat session by ID.
+    """Get a chat session by ID."""
+    redis_key = f"chat:session:{session_id}"
+    async_redis = await get_redis_async()
 
-    Checks Redis cache first, falls back to database if not found.
-    Caches database results back to Redis.
-    """
-    # Try cache first
-    try:
-        session = await _get_session_from_cache(session_id)
-        if session:
-            # Verify user ownership
-            if session.user_id is not None and session.user_id != user_id:
-                logger.warning(
-                    f"Session {session_id} user id mismatch: {session.user_id} != {user_id}"
-                )
-                return None
-            return session
-    except RedisError:
-        logger.warning(f"Cache error for session {session_id}, trying database")
-    except Exception as e:
-        logger.warning(f"Unexpected cache error for session {session_id}: {e}")
+    raw_session: bytes | None = await async_redis.get(redis_key)
 
-    # Fall back to database
-    logger.info(f"Session {session_id} not in cache, checking database")
-    session = await _get_session_from_db(session_id)
-
-    if session is None:
-        logger.warning(f"Session {session_id} not found in cache or database")
+    if raw_session is None:
+        logger.warning(f"Session {session_id} not found in Redis")
         return None
 
-    # Verify user ownership
+    try:
+        session = ChatSession.model_validate_json(raw_session)
+    except Exception as e:
+        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
+        raise RedisError(f"Corrupted session data for {session_id}") from e
+
     if session.user_id is not None and session.user_id != user_id:
         logger.warning(
             f"Session {session_id} user id mismatch: {session.user_id} != {user_id}"
         )
         return None
 
-    # Cache the session from DB
-    try:
-        await _cache_session(session)
-        logger.info(f"Cached session {session_id} from database")
-    except Exception as e:
-        logger.warning(f"Failed to cache session {session_id}: {e}")
-
     return session
 
 
 async def upsert_chat_session(
     session: ChatSession,
 ) -> ChatSession:
-    """Update a chat session in both cache and database."""
-    # Get existing message count from DB for incremental saves
-    existing_message_count = await chat_db.get_chat_session_message_count(
-        session.session_id
+    """Update a chat session with the given messages."""
+
+    redis_key = f"chat:session:{session.session_id}"
+
+    async_redis = await get_redis_async()
+    resp = await async_redis.setex(
+        redis_key, config.session_ttl, session.model_dump_json()
     )
 
-    # Save to database
-    try:
-        await _save_session_to_db(session, existing_message_count)
-    except Exception as e:
-        logger.error(f"Failed to save session {session.session_id} to database: {e}")
-        # Continue to cache even if DB fails
-
-    # Save to cache
-    try:
-        await _cache_session(session)
-    except Exception as e:
+    if not resp:
         raise RedisError(
-            f"Failed to persist chat session {session.session_id} to Redis: {e}"
-        ) from e
-
-    return session
-
-
-async def create_chat_session(user_id: str | None) -> ChatSession:
-    """Create a new chat session and persist it."""
-    session = ChatSession.new(user_id)
-
-    # Create in database first
-    try:
-        await chat_db.create_chat_session(
-            session_id=session.session_id,
-            user_id=user_id,
+            f"Failed to persist chat session {session.session_id} to Redis: {resp}"
         )
-    except Exception as e:
-        logger.error(f"Failed to create session in database: {e}")
-        # Continue even if DB fails - cache will still work
-
-    # Cache the session
-    try:
-        await _cache_session(session)
-    except Exception as e:
-        logger.warning(f"Failed to cache new session: {e}")
 
     return session
-
-
-async def get_user_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[ChatSession]:
-    """Get all chat sessions for a user from the database."""
-    prisma_sessions = await chat_db.get_user_chat_sessions(user_id, limit, offset)
-
-    sessions = []
-    for prisma_session in prisma_sessions:
-        # Convert without messages for listing (lighter weight)
-        sessions.append(ChatSession.from_prisma(prisma_session, None))
-
-    return sessions
-
-
-async def delete_chat_session(session_id: str) -> bool:
-    """Delete a chat session from both cache and database."""
-    # Delete from cache
-    try:
-        redis_key = f"chat:session:{session_id}"
-        async_redis = await get_redis_async()
-        await async_redis.delete(redis_key)
-    except Exception as e:
-        logger.warning(f"Failed to delete session {session_id} from cache: {e}")
-
-    # Delete from database
-    return await chat_db.delete_chat_session(session_id)

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

@@ -68,50 +68,3 @@ async def test_chatsession_redis_storage_user_id_mismatch():
     s2 = await get_chat_session(s.session_id, None)
 
     assert s2 is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_chatsession_db_storage():
-    """Test that messages are correctly saved to and loaded from DB (not cache)."""
-    from backend.data.redis_client import get_redis_async
-
-    # Create session with messages including assistant message
-    s = ChatSession.new(user_id=None)
-    s.messages = messages  # Contains user, assistant, and tool messages
-    assert s.session_id is not None, "Session id is not set"
-    # Upsert to save to both cache and DB
-    s = await upsert_chat_session(s)
-
-    # Clear the Redis cache to force DB load
-    redis_key = f"chat:session:{s.session_id}"
-    async_redis = await get_redis_async()
-    await async_redis.delete(redis_key)
-
-    # Load from DB (cache was cleared)
-    s2 = await get_chat_session(
-        session_id=s.session_id,
-        user_id=s.user_id,
-    )
-
-    assert s2 is not None, "Session not found after loading from DB"
-    assert len(s2.messages) == len(
-        s.messages
-    ), f"Message count mismatch: expected {len(s.messages)}, got {len(s2.messages)}"
-
-    # Verify all roles are present
-    roles = [m.role for m in s2.messages]
-    assert "user" in roles, f"User message missing. Roles found: {roles}"
-    assert "assistant" in roles, f"Assistant message missing. Roles found: {roles}"
-    assert "tool" in roles, f"Tool message missing. Roles found: {roles}"
-
-    # Verify message content
-    for orig, loaded in zip(s.messages, s2.messages):
-        assert orig.role == loaded.role, f"Role mismatch: {orig.role} != {loaded.role}"
-        assert (
-            orig.content == loaded.content
-        ), f"Content mismatch for {orig.role}: {orig.content} != {loaded.content}"
-        if orig.tool_calls:
-            assert (
-                loaded.tool_calls is not None
-            ), f"Tool calls missing for {orig.role} message"
-            assert len(orig.tool_calls) == len(loaded.tool_calls)

autogpt_platform/backend/backend/api/features/chat/prompts/chat_system.md

@@ -1,80 +1,12 @@
-You are Otto, an AI Co-Pilot and Forward Deployed Engineer for AutoGPT, an AI Business Automation tool. Your mission is to help users quickly find, create, and set up AutoGPT agents to solve their business problems.
+You are Otto, an AI Co-Pilot and Forward Deployed Engineer for AutoGPT, an AI Business Automation tool. Your mission is to help users quickly find and set up AutoGPT agents to solve their business problems.
 
 Here are the functions available to you:
 
 <functions>
-**Understanding & Discovery:**
-1. **add_understanding** - Save information about the user's business context (use this as you learn about them)
-2. **find_agent** - Search the marketplace for pre-built agents that solve the user's problem
-3. **find_library_agent** - Search the user's personal library of saved agents
-4. **find_block** - Search for individual blocks (building components for agents)
-5. **search_platform_docs** - Search AutoGPT documentation for help
-
-**Agent Creation & Editing:**
-6. **create_agent** - Create a new custom agent from scratch based on user requirements
-7. **edit_agent** - Modify an existing agent (add/remove blocks, change configuration)
-
-**Execution & Output:**
-8. **run_agent** - Run or schedule an agent (automatically handles setup)
-9. **run_block** - Run a single block directly without creating an agent
-10. **agent_output** - Get the output/results from a running or completed agent execution
+1. **find_agent** - Search for agents that solve the user's problem
+2. **run_agent** - Run or schedule an agent (automatically handles setup)
 </functions>
 
-## ALWAYS GET THE USER'S NAME
-
-**This is critical:** If you don't know the user's name, ask for it in your first response. Use a friendly, natural approach:
-- "Hi! I'm Otto. What's your name?"
-- "Hey there! Before we dive in, what should I call you?"
-
-Once you have their name, immediately save it with `add_understanding(user_name="...")` and use it throughout the conversation.
-
-## BUILDING USER UNDERSTANDING
-
-**If no User Business Context is provided below**, gather information naturally during conversation - don't interrogate them.
-
-**Key information to gather (in priority order):**
-1. Their name (ALWAYS first if unknown)
-2. Their job title and role
-3. Their business/company and industry
-4. Pain points and what they want to automate
-5. Tools they currently use
-
-**How to gather this information:**
-- Ask naturally as part of helping them (e.g., "What's your role?" or "What industry are you in?")
-- When they share information, immediately save it using `add_understanding`
-- Don't ask all questions at once - spread them across the conversation
-- Prioritize understanding their immediate problem first
-
-**Example:**
-```
-User: "I need help automating my social media"
-Otto: I can help with that! I'm Otto - what's your name?
-User: "I'm Sarah"
-Otto: [calls add_understanding with user_name="Sarah"]
-Nice to meet you, Sarah! What's your role - are you a social media manager or business owner?
-User: "I'm the marketing director at a fintech startup"
-Otto: [calls add_understanding with job_title="Marketing Director", industry="fintech", business_size="startup"]
-Great! Let me find social media automation agents for you.
-[calls find_agent with query="social media automation marketing"]
-```
-
-## WHEN TO USE WHICH TOOL
-
-**Finding existing agents:**
-- `find_agent` - Search the marketplace for pre-built agents others have created
-- `find_library_agent` - Search agents the user has already saved to their library
-
-**Creating/editing agents:**
-- `create_agent` - When user wants a custom agent that doesn't exist, or has specific requirements
-- `edit_agent` - When user wants to modify an existing agent (change inputs, add blocks, etc.)
-
-**Running agents:**
-- `run_agent` - To execute an agent (handles credentials and inputs automatically)
-- `agent_output` - To check the results of a running or completed agent execution
-
-**Direct execution:**
-- `run_block` - Run a single block directly without needing a full agent
-
 ## HOW run_agent WORKS
 
 The `run_agent` tool automatically handles the entire setup flow:
@@ -89,61 +21,49 @@ Parameters:
 - `use_defaults`: Set to `true` to run with default values (only after user confirms)
 - `schedule_name` + `cron`: For scheduled execution
 
-## HOW create_agent WORKS
-
-Use `create_agent` when the user wants to build a custom automation:
-- Describe what the agent should do
-- The tool will create the agent structure with appropriate blocks
-- Returns the agent ID for further editing or running
-
-## HOW agent_output WORKS
-
-Use `agent_output` to get results from agent executions:
-- Pass the execution_id from a run_agent response
-- Returns the current status and any outputs produced
-- Useful for checking if an agent has completed and what it produced
-
 ## WORKFLOW
 
-1. **Get their name** - If unknown, ask for it first
-2. **Understand context** - Ask 1-2 questions about their problem while helping
-3. **Find or create** - Use find_agent for existing solutions, create_agent for custom needs
-4. **Set up and run** - Use run_agent to execute, agent_output to get results
+1. **find_agent** - Search for agents that solve the user's problem
+2. **run_agent** (first call, no inputs) - Get available inputs for the agent
+3. **Ask user** what values they want to use OR if they want to use defaults
+4. **run_agent** (second call) - Either with `inputs={...}` or `use_defaults=true`
 
 ## YOUR APPROACH
 
-**Step 1: Greet and Identify**
-- If you don't know their name, ask for it
-- Be friendly and conversational
-
-**Step 2: Understand the Problem**
+**Step 1: Understand the Problem**
 - Ask maximum 1-2 targeted questions
 - Focus on: What business problem are they solving?
-- If they want to create/edit an agent, understand what it should do
+- Move quickly to searching for solutions
 
-**Step 3: Find or Create**
-- For existing solutions: Use `find_agent` with relevant keywords
-- For custom needs: Use `create_agent` with their requirements
-- For modifications: Use `edit_agent` on an existing agent
+**Step 2: Find Agents**
+- Use `find_agent` immediately with relevant keywords
+- Suggest the best option from search results
+- Explain briefly how it solves their problem
 
-**Step 4: Execute**
-- Call `run_agent` without inputs first to see what's available
-- Ask user what values they want or if defaults are okay
-- Call `run_agent` again with inputs or `use_defaults=true`
-- Use `agent_output` to check results when needed
+**Step 3: Get Agent Inputs**
+- Call `run_agent(username_agent_slug="creator/agent-name")` without inputs
+- This returns the available inputs (required and optional)
+- Present these to the user and ask what values they want
 
-## USING add_understanding
+**Step 4: Run with User's Choice**
+- If user provides values: `run_agent(username_agent_slug="...", inputs={...})`
+- If user says "use defaults": `run_agent(username_agent_slug="...", use_defaults=true)`
+- On success, share the agent link with the user
 
-Call `add_understanding` whenever you learn something about the user:
+**For Scheduled Execution:**
+- Add `schedule_name` and `cron` parameters
+- Example: `run_agent(username_agent_slug="...", inputs={...}, schedule_name="Daily Report", cron="0 9 * * *")`
 
-**User info:** `user_name`, `job_title`
-**Business:** `business_name`, `industry`, `business_size` (1-10, 11-50, 51-200, 201-1000, 1000+), `user_role` (decision maker, implementer, end user)
-**Processes:** `key_workflows` (array), `daily_activities` (array)
-**Pain points:** `pain_points` (array), `bottlenecks` (array), `manual_tasks` (array), `automation_goals` (array)
-**Tools:** `current_software` (array), `existing_automation` (array)
-**Other:** `additional_notes`
+## FUNCTION CALL FORMAT
 
-Example: `add_understanding(user_name="Sarah", job_title="Marketing Director", industry="fintech")`
+To call a function, use this exact format:
+`<function_call>function_name(parameter="value")</function_call>`
+
+Examples:
+- `<function_call>find_agent(query="social media automation")</function_call>`
+- `<function_call>run_agent(username_agent_slug="creator/agent-name")</function_call>` (get inputs)
+- `<function_call>run_agent(username_agent_slug="creator/agent-name", inputs={"topic": "AI news"})</function_call>`
+- `<function_call>run_agent(username_agent_slug="creator/agent-name", use_defaults=true)</function_call>`
 
 ## KEY RULES
 
@@ -153,12 +73,8 @@ Example: `add_understanding(user_name="Sarah", job_title="Marketing Director", i
 - Don't run agents without first showing available inputs to the user
 - Don't use `use_defaults=true` without user explicitly confirming
 - Don't write responses longer than 3 sentences
-- Don't interrogate users with many questions - gather info naturally
 
 **What You DO:**
-- ALWAYS ask for user's name if you don't have it
-- Save user information with `add_understanding` as you learn it
-- Use their name when addressing them
 - Always call run_agent first without inputs to see what's available
 - Ask user what values they want OR if they want to use defaults
 - Keep all responses to maximum 3 sentences
@@ -171,22 +87,18 @@ Example: `add_understanding(user_name="Sarah", job_title="Marketing Director", i
 ## RESPONSE STRUCTURE
 
 Before responding, wrap your analysis in <thinking> tags to systematically plan your approach:
-- Check if you know the user's name - if not, ask for it
-- Check if you have user context - if not, plan to gather some naturally
 - Extract the key business problem or request from the user's message
 - Determine what function call (if any) you need to make next
 - Plan your response to stay under the 3-sentence maximum
 
 Example interaction:
 ```
-User: "Hi, I want to build an agent that monitors my competitors"
-Otto: <thinking>I don't know this user's name. I should ask for it while acknowledging their request.</thinking>
-Hi! I'm Otto and I'd love to help you build a competitor monitoring agent. What's your name?
-User: "I'm Mike"
-Otto: [calls add_understanding with user_name="Mike"]
-<thinking>Now I know Mike wants competitor monitoring. I should search for existing agents first.</thinking>
-Great to meet you, Mike! Let me search for competitor monitoring agents.
-[calls find_agent with query="competitor monitoring analysis"]
+User: "Run the AI news agent for me"
+Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news")</function_call>
+[Tool returns: Agent accepts inputs - Required: topic. Optional: num_articles (default: 5)]
+Otto: The AI News agent needs a topic. What topic would you like news about, or should I use the defaults?
+User: "Use defaults"
+Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news", use_defaults=true)</function_call>
 ```
 
 KEEP ANSWERS TO 3 SENTENCES

autogpt_platform/backend/backend/api/features/chat/prompts/onboarding_system.md

@@ -1,155 +0,0 @@
-You are Otto, an AI Co-Pilot helping new users get started with AutoGPT, an AI Business Automation platform. Your mission is to welcome them, learn about their needs, and help them run their first successful agent.
-
-Here are the functions available to you:
-
-<functions>
-**Understanding & Discovery:**
-1. **add_understanding** - Save information about the user's business context (use this as you learn about them)
-2. **find_agent** - Search the marketplace for pre-built agents that solve the user's problem
-3. **find_library_agent** - Search the user's personal library of saved agents
-4. **find_block** - Search for individual blocks (building components for agents)
-5. **search_platform_docs** - Search AutoGPT documentation for help
-
-**Agent Creation & Editing:**
-6. **create_agent** - Create a new custom agent from scratch based on user requirements
-7. **edit_agent** - Modify an existing agent (add/remove blocks, change configuration)
-
-**Execution & Output:**
-8. **run_agent** - Run or schedule an agent (automatically handles setup)
-9. **run_block** - Run a single block directly without creating an agent
-10. **agent_output** - Get the output/results from a running or completed agent execution
-</functions>
-
-## YOUR ONBOARDING MISSION
-
-You are guiding a new user through their first experience with AutoGPT. Your goal is to:
-1. Welcome them warmly and get their name
-2. Learn about them and their business
-3. Find or create an agent that solves a real problem for them
-4. Get that agent running successfully
-5. Celebrate their success and point them to next steps
-
-## PHASE 1: WELCOME & INTRODUCTION
-
-**Start every conversation by:**
-- Giving a warm, friendly greeting
-- Introducing yourself as Otto, their AI assistant
-- Asking for their name immediately
-
-**Example opening:**
-```
-Hi! I'm Otto, your AI assistant. Welcome to AutoGPT! I'm here to help you set up your first automation. What's your name?
-```
-
-Once you have their name, save it immediately with `add_understanding(user_name="...")` and use it throughout.
-
-## PHASE 2: DISCOVERY
-
-**After getting their name, learn about them:**
-- What's their role/job title?
-- What industry/business are they in?
-- What's one thing they'd love to automate?
-
-**Keep it conversational - don't interrogate. Example:**
-```
-Nice to meet you, Sarah! What do you do for work, and what's one task you wish you could automate?
-```
-
-Save everything you learn with `add_understanding`.
-
-## PHASE 3: FIND OR CREATE AN AGENT
-
-**Once you understand their need:**
-- Search for existing agents with `find_agent`
-- Present the best match and explain how it helps them
-- If nothing fits, offer to create a custom agent with `create_agent`
-
-**Be enthusiastic about the solution:**
-```
-I found a great agent for you! The "Social Media Scheduler" can automatically post to your accounts on a schedule. Want to try it?
-```
-
-## PHASE 4: SETUP & RUN
-
-**Guide them through running the agent:**
-1. Call `run_agent` without inputs first to see what's needed
-2. Explain each input in simple terms
-3. Ask what values they want to use
-4. Run the agent with their inputs or defaults
-
-**Don't mention credentials** - the UI handles that automatically.
-
-## PHASE 5: CELEBRATE & HANDOFF
-
-**After successful execution:**
-- Congratulate them on their first automation!
-- Tell them where to find this agent (their Library)
-- Mention they can explore more agents in the Marketplace
-- Offer to help with anything else
-
-**Example:**
-```
-You did it! Your first agent is running. You can find it anytime in your Library. Ready to explore more automations?
-```
-
-## KEY RULES
-
-**What You DON'T Do:**
-- Don't help with login (frontend handles this)
-- Don't mention credentials (UI handles automatically)
-- Don't run agents without showing inputs first
-- Don't use `use_defaults=true` without explicit confirmation
-- Don't write responses longer than 3 sentences
-- Don't overwhelm with too many questions at once
-
-**What You DO:**
-- ALWAYS get the user's name first
-- Be warm, encouraging, and celebratory
-- Save info with `add_understanding` as you learn it
-- Use their name when addressing them
-- Keep responses to maximum 3 sentences
-- Make them feel successful at each step
-
-## USING add_understanding
-
-Save information as you learn it:
-
-**User info:** `user_name`, `job_title`
-**Business:** `business_name`, `industry`, `business_size`, `user_role`
-**Pain points:** `pain_points`, `manual_tasks`, `automation_goals`
-**Tools:** `current_software`
-
-Example: `add_understanding(user_name="Sarah", job_title="Marketing Manager", automation_goals=["social media scheduling"])`
-
-## HOW run_agent WORKS
-
-1. **First call** (no inputs) → Shows available inputs
-2. **Credentials** → UI handles automatically (don't mention)
-3. **Execution** → Run with `inputs={...}` or `use_defaults=true`
-
-## RESPONSE STRUCTURE
-
-Before responding, plan your approach in <thinking> tags:
-- What phase am I in? (Welcome/Discovery/Find/Setup/Celebrate)
-- Do I know their name? If not, ask for it
-- What's the next step to move them forward?
-- Keep response under 3 sentences
-
-**Example flow:**
-```
-User: "Hi"
-Otto: <thinking>Phase 1 - I need to welcome them and get their name.</thinking>
-Hi! I'm Otto, welcome to AutoGPT! I'm here to help you set up your first automation - what's your name?
-
-User: "I'm Alex"
-Otto: [calls add_understanding with user_name="Alex"]
-<thinking>Got their name. Phase 2 - learn about them.</thinking>
-Great to meet you, Alex! What do you do for work, and what's one task you'd love to automate?
-
-User: "I run an e-commerce store and spend hours on customer support emails"
-Otto: [calls add_understanding with industry="e-commerce", pain_points=["customer support emails"]]
-<thinking>Phase 3 - search for agents.</thinking>
-[calls find_agent with query="customer support email automation"]
-```
-
-KEEP ANSWERS TO 3 SENTENCES - Be warm, helpful, and focused on their success!

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

@@ -26,14 +26,6 @@ router = APIRouter(
 # ========== Request/Response Models ==========
 
 
-class StreamChatRequest(BaseModel):
-    """Request model for streaming chat with optional context."""
-
-    message: str
-    is_user_message: bool = True
-    context: dict[str, str] | None = None  # {url: str, content: str}
-
-
 class CreateSessionResponse(BaseModel):
     """Response model containing information on a newly created chat session."""
 
@@ -52,64 +44,9 @@ class SessionDetailResponse(BaseModel):
     messages: list[dict]
 
 
-class SessionSummaryResponse(BaseModel):
-    """Response model for a session summary (without messages)."""
-
-    id: str
-    created_at: str
-    updated_at: str
-    title: str | None = None
-
-
-class ListSessionsResponse(BaseModel):
-    """Response model for listing chat sessions."""
-
-    sessions: list[SessionSummaryResponse]
-    total: int
-
-
 # ========== Routes ==========
 
 
-@router.get(
-    "/sessions",
-    dependencies=[Security(auth.requires_user)],
-)
-async def list_sessions(
-    user_id: Annotated[str, Security(auth.get_user_id)],
-    limit: int = Query(default=50, ge=1, le=100),
-    offset: int = Query(default=0, ge=0),
-) -> ListSessionsResponse:
-    """
-    List chat sessions for the authenticated user.
-
-    Returns a paginated list of chat sessions belonging to the current user,
-    ordered by most recently updated.
-
-    Args:
-        user_id: The authenticated user's ID.
-        limit: Maximum number of sessions to return (1-100).
-        offset: Number of sessions to skip for pagination.
-
-    Returns:
-        ListSessionsResponse: List of session summaries and total count.
-    """
-    sessions = await chat_service.get_user_sessions(user_id, limit, offset)
-
-    return ListSessionsResponse(
-        sessions=[
-            SessionSummaryResponse(
-                id=session.session_id,
-                created_at=session.started_at.isoformat(),
-                updated_at=session.updated_at.isoformat(),
-                title=None,  # TODO: Add title support
-            )
-            for session in sessions
-        ],
-        total=len(sessions),
-    )
-
-
 @router.post(
     "/sessions",
 )
@@ -165,89 +102,26 @@ async def get_session(
     session = await chat_service.get_session(session_id, user_id)
     if not session:
         raise NotFoundError(f"Session {session_id} not found")
-
-    messages = [message.model_dump() for message in session.messages]
-    logger.info(
-        f"Returning session {session_id}: "
-        f"message_count={len(messages)}, "
-        f"roles={[m.get('role') for m in messages]}"
-    )
-
     return SessionDetailResponse(
         id=session.session_id,
         created_at=session.started_at.isoformat(),
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
-        messages=messages,
-    )
-
-
-@router.post(
-    "/sessions/{session_id}/stream",
-)
-async def stream_chat_post(
-    session_id: str,
-    request: StreamChatRequest,
-    user_id: str | None = Depends(auth.get_user_id),
-):
-    """
-    Stream chat responses for a session (POST with context support).
-
-    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
-      - Text fragments as they are generated
-      - Tool call UI elements (if invoked)
-      - Tool execution results
-
-    Args:
-        session_id: The chat session identifier to associate with the streamed messages.
-        request: Request body containing message, is_user_message, and optional context.
-        user_id: Optional authenticated user ID.
-    Returns:
-        StreamingResponse: SSE-formatted response chunks.
-
-    """
-    # Validate session exists before starting the stream
-    # This prevents errors after the response has already started
-    session = await chat_service.get_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(f"Session {session_id} not found. ")
-    if session.user_id is None and user_id is not None:
-        session = await chat_service.assign_user_to_session(session_id, user_id)
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            request.message,
-            is_user_message=request.is_user_message,
-            user_id=user_id,
-            session=session,  # Pass pre-fetched session to avoid double-fetch
-            context=request.context,
-        ):
-            yield chunk.to_sse()
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
-        },
+        messages=[message.model_dump() for message in session.messages],
     )
 
 
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def stream_chat_get(
+async def stream_chat(
     session_id: str,
     message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
     is_user_message: bool = Query(default=True),
 ):
     """
-    Stream chat responses for a session (GET - legacy endpoint).
+    Stream chat responses for a session.
 
     Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
       - Text fragments as they are generated
@@ -319,133 +193,6 @@ async def session_assign_user(
     return {"status": "ok"}
 
 
-# ========== Onboarding Routes ==========
-# These routes use a specialized onboarding system prompt
-
-
-@router.post(
-    "/onboarding/sessions",
-)
-async def create_onboarding_session(
-    user_id: Annotated[str | None, Depends(auth.get_user_id)],
-) -> CreateSessionResponse:
-    """
-    Create a new onboarding chat session.
-
-    Initiates a new chat session specifically for user onboarding,
-    using a specialized prompt that guides users through their first
-    experience with AutoGPT.
-
-    Args:
-        user_id: The optional authenticated user ID parsed from the JWT.
-
-    Returns:
-        CreateSessionResponse: Details of the created onboarding session.
-    """
-    logger.info(
-        f"Creating onboarding session with user_id: "
-        f"...{user_id[-8:] if user_id and len(user_id) > 8 else '<redacted>'}"
-    )
-
-    session = await chat_service.create_chat_session(user_id)
-
-    return CreateSessionResponse(
-        id=session.session_id,
-        created_at=session.started_at.isoformat(),
-        user_id=session.user_id or None,
-    )
-
-
-@router.get(
-    "/onboarding/sessions/{session_id}",
-)
-async def get_onboarding_session(
-    session_id: str,
-    user_id: Annotated[str | None, Depends(auth.get_user_id)],
-) -> SessionDetailResponse:
-    """
-    Retrieve the details of an onboarding chat session.
-
-    Args:
-        session_id: The unique identifier for the onboarding session.
-        user_id: The optional authenticated user ID.
-
-    Returns:
-        SessionDetailResponse: Details for the requested session.
-    """
-    session = await chat_service.get_session(session_id, user_id)
-    if not session:
-        raise NotFoundError(f"Session {session_id} not found")
-
-    messages = [message.model_dump() for message in session.messages]
-    logger.info(
-        f"Returning onboarding session {session_id}: "
-        f"message_count={len(messages)}, "
-        f"roles={[m.get('role') for m in messages]}"
-    )
-
-    return SessionDetailResponse(
-        id=session.session_id,
-        created_at=session.started_at.isoformat(),
-        updated_at=session.updated_at.isoformat(),
-        user_id=session.user_id or None,
-        messages=messages,
-    )
-
-
-@router.post(
-    "/onboarding/sessions/{session_id}/stream",
-)
-async def stream_onboarding_chat(
-    session_id: str,
-    request: StreamChatRequest,
-    user_id: str | None = Depends(auth.get_user_id),
-):
-    """
-    Stream onboarding chat responses for a session.
-
-    Uses the specialized onboarding system prompt to guide new users
-    through their first experience with AutoGPT. Streams AI responses
-    in real time over Server-Sent Events (SSE).
-
-    Args:
-        session_id: The onboarding session identifier.
-        request: Request body containing message and optional context.
-        user_id: Optional authenticated user ID.
-
-    Returns:
-        StreamingResponse: SSE-formatted response chunks.
-    """
-    session = await chat_service.get_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(f"Session {session_id} not found.")
-    if session.user_id is None and user_id is not None:
-        session = await chat_service.assign_user_to_session(session_id, user_id)
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            request.message,
-            is_user_message=request.is_user_message,
-            user_id=user_id,
-            session=session,
-            context=request.context,
-            prompt_type="onboarding",  # Use onboarding system prompt
-        ):
-            yield chunk.to_sse()
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-        },
-    )
-
-
 # ========== Health Check ==========
 
 

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -7,17 +7,16 @@ import orjson
 from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
-from backend.data.understanding import (
-    format_understanding_for_prompt,
-    get_business_understanding,
-)
 from backend.util.exceptions import NotFoundError
 
-from . import db as chat_db
 from .config import ChatConfig
-from .model import ChatMessage, ChatSession, Usage
-from .model import create_chat_session as model_create_chat_session
-from .model import get_chat_session, upsert_chat_session
+from .model import (
+    ChatMessage,
+    ChatSession,
+    Usage,
+    get_chat_session,
+    upsert_chat_session,
+)
 from .response_model import (
     StreamBaseResponse,
     StreamEnd,
@@ -37,109 +36,15 @@ config = ChatConfig()
 client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
 
-async def _is_first_session(user_id: str) -> bool:
-    """Check if this is the user's first chat session.
-
-    Returns True if the user has 1 or fewer sessions (meaning this is their first).
-    """
-    try:
-        session_count = await chat_db.get_user_session_count(user_id)
-        return session_count <= 1
-    except Exception as e:
-        logger.warning(f"Failed to check session count for user {user_id}: {e}")
-        return False  # Default to non-onboarding if we can't check
-
-
-async def _build_system_prompt(
-    user_id: str | None, prompt_type: str = "default"
-) -> str:
-    """Build the full system prompt including business understanding if available.
-
-    Args:
-        user_id: The user ID for fetching business understanding
-        prompt_type: The type of prompt to load ("default" or "onboarding")
-                     If "default" and this is the user's first session, will use "onboarding" instead.
-
-    Returns:
-        The full system prompt with business understanding context if available
-    """
-    # Auto-detect: if using default prompt and this is user's first session, use onboarding
-    effective_prompt_type = prompt_type
-    if prompt_type == "default" and user_id:
-        if await _is_first_session(user_id):
-            logger.info("First session detected for user, using onboarding prompt")
-            effective_prompt_type = "onboarding"
-
-    # Start with the base system prompt for the specified type
-    base_prompt = config.get_system_prompt_for_type(effective_prompt_type)
-
-    # If user is authenticated, try to fetch their business understanding
-    if user_id:
-        try:
-            understanding = await get_business_understanding(user_id)
-            if understanding:
-                context = format_understanding_for_prompt(understanding)
-                if context:
-                    return (
-                        f"{base_prompt}\n\n---\n\n"
-                        f"{context}\n\n"
-                        "Use this context to provide more personalized recommendations "
-                        "and to better understand the user's business needs when "
-                        "suggesting agents and automations."
-                    )
-        except Exception as e:
-            logger.warning(f"Failed to fetch business understanding: {e}")
-
-    return base_prompt
-
-
-async def _generate_session_title(message: str) -> str | None:
-    """Generate a concise title for a chat session based on the first message.
-
-    Args:
-        message: The first user message in the session
-
-    Returns:
-        A short title (3-6 words) or None if generation fails
-    """
-    try:
-        response = await client.chat.completions.create(
-            model=config.title_model,
-            messages=[
-                {
-                    "role": "system",
-                    "content": (
-                        "Generate a very short title (3-6 words) for a chat conversation "
-                        "based on the user's first message. The title should capture the "
-                        "main topic or intent. Return ONLY the title, no quotes or punctuation."
-                    ),
-                },
-                {"role": "user", "content": message[:500]},  # Limit input length
-            ],
-            max_tokens=20,
-            temperature=0.7,
-        )
-        title = response.choices[0].message.content
-        if title:
-            # Clean up the title
-            title = title.strip().strip("\"'")
-            # Limit length
-            if len(title) > 50:
-                title = title[:47] + "..."
-            return title
-        return None
-    except Exception as e:
-        logger.warning(f"Failed to generate session title: {e}")
-        return None
-
-
 async def create_chat_session(
     user_id: str | None = None,
 ) -> ChatSession:
     """
     Create a new chat session and persist it to the database.
     """
-    return await model_create_chat_session(user_id)
+    session = ChatSession.new(user_id)
+    # Persist the session immediately so it can be used for streaming
+    return await upsert_chat_session(session)
 
 
 async def get_session(
@@ -152,19 +57,6 @@ async def get_session(
     return await get_chat_session(session_id, user_id)
 
 
-async def get_user_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> list[ChatSession]:
-    """
-    Get all chat sessions for a user.
-    """
-    from .model import get_user_sessions as model_get_user_sessions
-
-    return await model_get_user_sessions(user_id, limit, offset)
-
-
 async def assign_user_to_session(
     session_id: str,
     user_id: str,
@@ -186,8 +78,6 @@ async def stream_chat_completion(
     user_id: str | None = None,
     retry_count: int = 0,
     session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # {url: str, content: str}
-    prompt_type: str = "default",
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """Main entry point for streaming chat completions with database handling.
 
@@ -199,7 +89,6 @@ async def stream_chat_completion(
         user_message: User's input message
         user_id: User ID for authentication (None for anonymous)
         session: Optional pre-loaded session object (for recursive calls to avoid Redis refetch)
-        prompt_type: The type of prompt to use ("default" or "onboarding")
 
     Yields:
         StreamBaseResponse objects formatted as SSE
@@ -232,18 +121,9 @@ async def stream_chat_completion(
         )
 
     if message:
-        # Build message content with context if provided
-        message_content = message
-        if context and context.get("url") and context.get("content"):
-            context_text = f"Page URL: {context['url']}\n\nPage Content:\n{context['content']}\n\n---\n\nUser Message: {message}"
-            message_content = context_text
-            logger.info(
-                f"Including page context: URL={context['url']}, content_length={len(context['content'])}"
-            )
-
         session.messages.append(
             ChatMessage(
-                role="user" if is_user_message else "assistant", content=message_content
+                role="user" if is_user_message else "assistant", content=message
             )
         )
         logger.info(
@@ -261,32 +141,6 @@ async def stream_chat_completion(
     session = await upsert_chat_session(session)
     assert session, "Session not found"
 
-    # Generate title for new sessions on first user message (non-blocking)
-    # Check: is_user_message, no title yet, and this is the first user message
-    if is_user_message and message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            # First user message - generate title in background
-            import asyncio
-
-            async def _update_title():
-                try:
-                    title = await _generate_session_title(message)
-                    if title:
-                        session.title = title
-                        await upsert_chat_session(session)
-                        logger.info(
-                            f"Generated title for session {session_id}: {title}"
-                        )
-                except Exception as e:
-                    logger.warning(f"Failed to update session title: {e}")
-
-            # Fire and forget - don't block the chat response
-            asyncio.create_task(_update_title())
-
-    # Build system prompt with business understanding
-    system_prompt = await _build_system_prompt(user_id, prompt_type)
-
     assistant_response = ChatMessage(
         role="assistant",
         content="",
@@ -305,7 +159,6 @@ async def stream_chat_completion(
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
-            system_prompt=system_prompt,
         ):
 
             if isinstance(chunk, StreamTextChunk):
@@ -426,7 +279,6 @@ async def stream_chat_completion(
             user_id=user_id,
             retry_count=retry_count + 1,
             session=session,
-            prompt_type=prompt_type,
         ):
             yield chunk
         return  # Exit after retry to avoid double-saving in finally block
@@ -472,7 +324,6 @@ async def stream_chat_completion(
             session_id=session.session_id,
             user_id=user_id,
             session=session,  # Pass session object to avoid Redis refetch
-            prompt_type=prompt_type,
         ):
             yield chunk
 
@@ -480,7 +331,6 @@ async def stream_chat_completion(
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
-    system_prompt: str | None = None,
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """
     Pure streaming function for OpenAI chat completions with tool calling.
@@ -488,9 +338,9 @@ async def _stream_chat_chunks(
     This function is database-agnostic and focuses only on streaming logic.
 
     Args:
-        session: Chat session with conversation history
-        tools: Available tools for the model
-        system_prompt: System prompt to prepend to messages
+        messages: Conversation context as ChatCompletionMessageParam list
+        session_id: Session ID
+        user_id: User ID for tool execution
 
     Yields:
         SSE formatted JSON response objects
@@ -500,17 +350,6 @@ async def _stream_chat_chunks(
 
     logger.info("Starting pure chat stream")
 
-    # Build messages with system prompt prepended
-    messages = session.to_openai_messages()
-    if system_prompt:
-        from openai.types.chat import ChatCompletionSystemMessageParam
-
-        system_message = ChatCompletionSystemMessageParam(
-            role="system",
-            content=system_prompt,
-        )
-        messages = [system_message] + messages
-
     # Loop to handle tool calls and continue conversation
     while True:
         try:
@@ -519,7 +358,7 @@ async def _stream_chat_chunks(
             # Create the stream with proper types
             stream = await client.chat.completions.create(
                 model=model,
-                messages=messages,
+                messages=session.to_openai_messages(),
                 tools=tools,
                 tool_choice="auto",
                 stream=True,
@@ -663,12 +502,8 @@ async def _yield_tool_call(
     """
     logger.info(f"Yielding tool call: {tool_calls[yield_idx]}")
 
-    # Parse tool call arguments - handle empty arguments gracefully
-    raw_arguments = tool_calls[yield_idx]["function"]["arguments"]
-    if raw_arguments:
-        arguments = orjson.loads(raw_arguments)
-    else:
-        arguments = {}
+    # Parse tool call arguments - exceptions will propagate to caller
+    arguments = orjson.loads(tool_calls[yield_idx]["function"]["arguments"])
 
     yield StreamToolCall(
         tool_id=tool_calls[yield_idx]["id"],

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

@@ -4,30 +4,21 @@ from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
 
-from .add_understanding import AddUnderstandingTool
-from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .find_agent import FindAgentTool
-from .find_library_agent import FindLibraryAgentTool
 from .run_agent import RunAgentTool
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolExecutionResult
 
 # Initialize tool instances
-add_understanding_tool = AddUnderstandingTool()
 find_agent_tool = FindAgentTool()
-find_library_agent_tool = FindLibraryAgentTool()
 run_agent_tool = RunAgentTool()
-agent_output_tool = AgentOutputTool()
 
 # Export tools as OpenAI format
 tools: list[ChatCompletionToolParam] = [
-    add_understanding_tool.as_openai_tool(),
     find_agent_tool.as_openai_tool(),
-    find_library_agent_tool.as_openai_tool(),
     run_agent_tool.as_openai_tool(),
-    agent_output_tool.as_openai_tool(),
 ]
 
 
@@ -40,11 +31,8 @@ async def execute_tool(
 ) -> "StreamToolExecutionResult":
 
     tool_map: dict[str, BaseTool] = {
-        "add_understanding": add_understanding_tool,
         "find_agent": find_agent_tool,
-        "find_library_agent": find_library_agent_tool,
         "run_agent": run_agent_tool,
-        "agent_output": agent_output_tool,
     }
     if tool_name not in tool_map:
         raise ValueError(f"Tool {tool_name} not found")

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

@@ -3,7 +3,6 @@ from datetime import UTC, datetime
 from os import getenv
 
 import pytest
-from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
 from backend.api.features.chat.model import ChatSession
@@ -50,13 +49,13 @@ async def setup_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # 2. Create a test graph with agent input -> agent output
@@ -173,13 +172,13 @@ async def setup_llm_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile for LLM tests",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile for LLM tests",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # 2. Create test OpenAI credentials for the user
@@ -333,13 +332,13 @@ async def setup_firecrawl_test_data():
     # 1b. Create a profile with username for the user (required for store agent lookup)
     username = user.email.split("@")[0]
     await prisma.profile.create(
-        data=ProfileCreateInput(
-            userId=user.id,
-            username=username,
-            name=f"Test User {username}",
-            description="Test user profile for Firecrawl tests",
-            links=[],  # Required field - empty array for test profiles
-        )
+        data={
+            "userId": user.id,
+            "username": username,
+            "name": f"Test User {username}",
+            "description": "Test user profile for Firecrawl tests",
+            "links": [],  # Required field - empty array for test profiles
+        }
     )
 
     # NOTE: We deliberately do NOT create Firecrawl credentials for this user

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

@@ -1,202 +0,0 @@
-"""Tool for capturing user business understanding incrementally."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.data.understanding import (
-    BusinessUnderstandingInput,
-    upsert_business_understanding,
-)
-
-from .base import BaseTool
-from .models import ErrorResponse, ToolResponseBase, UnderstandingUpdatedResponse
-
-logger = logging.getLogger(__name__)
-
-
-class AddUnderstandingTool(BaseTool):
-    """Tool for capturing user's business understanding incrementally."""
-
-    @property
-    def name(self) -> str:
-        return "add_understanding"
-
-    @property
-    def description(self) -> str:
-        return """Capture and store information about the user's business context,
-workflows, pain points, and automation goals. Call this tool whenever the user
-shares information about their business. Each call incrementally adds to the
-existing understanding - you don't need to provide all fields at once.
-
-Use this to build a comprehensive profile that helps recommend better agents
-and automations for the user's specific needs."""
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "user_name": {
-                    "type": "string",
-                    "description": "The user's name",
-                },
-                "job_title": {
-                    "type": "string",
-                    "description": "The user's job title (e.g., 'Marketing Manager', 'CEO', 'Software Engineer')",
-                },
-                "business_name": {
-                    "type": "string",
-                    "description": "Name of the user's business or organization",
-                },
-                "industry": {
-                    "type": "string",
-                    "description": "Industry or sector (e.g., 'e-commerce', 'healthcare', 'finance')",
-                },
-                "business_size": {
-                    "type": "string",
-                    "description": "Company size: '1-10', '11-50', '51-200', '201-1000', or '1000+'",
-                },
-                "user_role": {
-                    "type": "string",
-                    "description": "User's role in organization context (e.g., 'decision maker', 'implementer', 'end user')",
-                },
-                "key_workflows": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Key business workflows (e.g., 'lead qualification', 'content publishing')",
-                },
-                "daily_activities": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Regular daily activities the user performs",
-                },
-                "pain_points": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Current pain points or challenges",
-                },
-                "bottlenecks": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Process bottlenecks slowing things down",
-                },
-                "manual_tasks": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Manual or repetitive tasks that could be automated",
-                },
-                "automation_goals": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Desired automation outcomes or goals",
-                },
-                "current_software": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Software and tools currently in use",
-                },
-                "existing_automation": {
-                    "type": "array",
-                    "items": {"type": "string"},
-                    "description": "Any existing automations or integrations",
-                },
-                "additional_notes": {
-                    "type": "string",
-                    "description": "Any other relevant context or notes",
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        """Requires authentication to store user-specific data."""
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """
-        Capture and store business understanding incrementally.
-
-        Each call merges new data with existing understanding:
-        - String fields are overwritten if provided
-        - List fields are appended (with deduplication)
-        """
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required to save business understanding.",
-                session_id=session_id,
-            )
-
-        # Check if any data was provided
-        if not any(v is not None for v in kwargs.values()):
-            return ErrorResponse(
-                message="Please provide at least one field to update.",
-                session_id=session_id,
-            )
-
-        # Build input model
-        input_data = BusinessUnderstandingInput(
-            user_name=kwargs.get("user_name"),
-            job_title=kwargs.get("job_title"),
-            business_name=kwargs.get("business_name"),
-            industry=kwargs.get("industry"),
-            business_size=kwargs.get("business_size"),
-            user_role=kwargs.get("user_role"),
-            key_workflows=kwargs.get("key_workflows"),
-            daily_activities=kwargs.get("daily_activities"),
-            pain_points=kwargs.get("pain_points"),
-            bottlenecks=kwargs.get("bottlenecks"),
-            manual_tasks=kwargs.get("manual_tasks"),
-            automation_goals=kwargs.get("automation_goals"),
-            current_software=kwargs.get("current_software"),
-            existing_automation=kwargs.get("existing_automation"),
-            additional_notes=kwargs.get("additional_notes"),
-        )
-
-        # Track which fields were updated
-        updated_fields = [k for k, v in kwargs.items() if v is not None]
-
-        # Upsert with merge
-        understanding = await upsert_business_understanding(user_id, input_data)
-
-        # Build current understanding summary for the response
-        current_understanding = {
-            "user_name": understanding.user_name,
-            "job_title": understanding.job_title,
-            "business_name": understanding.business_name,
-            "industry": understanding.industry,
-            "business_size": understanding.business_size,
-            "user_role": understanding.user_role,
-            "key_workflows": understanding.key_workflows,
-            "daily_activities": understanding.daily_activities,
-            "pain_points": understanding.pain_points,
-            "bottlenecks": understanding.bottlenecks,
-            "manual_tasks": understanding.manual_tasks,
-            "automation_goals": understanding.automation_goals,
-            "current_software": understanding.current_software,
-            "existing_automation": understanding.existing_automation,
-            "additional_notes": understanding.additional_notes,
-        }
-
-        # Filter out empty values for cleaner response
-        current_understanding = {
-            k: v
-            for k, v in current_understanding.items()
-            if v is not None and v != [] and v != ""
-        }
-
-        return UnderstandingUpdatedResponse(
-            message=f"Updated understanding with: {', '.join(updated_fields)}. "
-            "I now have a better picture of your business context.",
-            session_id=session_id,
-            updated_fields=updated_fields,
-            current_understanding=current_understanding,
-        )

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

@@ -1,455 +0,0 @@
-"""Tool for retrieving agent execution outputs from user's library."""
-
-import logging
-import re
-from datetime import datetime, timedelta, timezone
-from typing import Any
-
-from pydantic import BaseModel, field_validator
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.library import db as library_db
-from backend.api.features.library.model import LibraryAgent
-from backend.data import execution as execution_db
-from backend.data.execution import ExecutionStatus, GraphExecution, GraphExecutionMeta
-
-from .base import BaseTool
-from .models import (
-    AgentOutputResponse,
-    ErrorResponse,
-    ExecutionOutputInfo,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-from .utils import fetch_graph_from_store_slug
-
-logger = logging.getLogger(__name__)
-
-
-class AgentOutputInput(BaseModel):
-    """Input parameters for the agent_output tool."""
-
-    agent_name: str = ""
-    library_agent_id: str = ""
-    store_slug: str = ""
-    execution_id: str = ""
-    run_time: str = "latest"
-
-    @field_validator(
-        "agent_name",
-        "library_agent_id",
-        "store_slug",
-        "execution_id",
-        "run_time",
-        mode="before",
-    )
-    @classmethod
-    def strip_strings(cls, v: Any) -> Any:
-        """Strip whitespace from string fields."""
-        return v.strip() if isinstance(v, str) else v
-
-
-def parse_time_expression(
-    time_expr: str | None,
-) -> tuple[datetime | None, datetime | None]:
-    """
-    Parse time expression into datetime range (start, end).
-
-    Supports:
-    - "latest" or None -> returns (None, None) to get most recent
-    - "yesterday" -> 24h window for yesterday
-    - "today" -> Today from midnight
-    - "last week" / "last 7 days" -> 7 day window
-    - "last month" / "last 30 days" -> 30 day window
-    - ISO date "YYYY-MM-DD" -> 24h window for that date
-    """
-    if not time_expr or time_expr.lower() == "latest":
-        return None, None
-
-    now = datetime.now(timezone.utc)
-    expr = time_expr.lower().strip()
-
-    # Relative expressions
-    if expr == "yesterday":
-        end = now.replace(hour=0, minute=0, second=0, microsecond=0)
-        start = end - timedelta(days=1)
-        return start, end
-
-    if expr in ("last week", "last 7 days"):
-        return now - timedelta(days=7), now
-
-    if expr in ("last month", "last 30 days"):
-        return now - timedelta(days=30), now
-
-    if expr == "today":
-        start = now.replace(hour=0, minute=0, second=0, microsecond=0)
-        return start, now
-
-    # Try ISO date format (YYYY-MM-DD)
-    date_match = re.match(r"^(\d{4})-(\d{2})-(\d{2})$", expr)
-    if date_match:
-        year, month, day = map(int, date_match.groups())
-        start = datetime(year, month, day, 0, 0, 0, tzinfo=timezone.utc)
-        end = start + timedelta(days=1)
-        return start, end
-
-    # Try ISO datetime
-    try:
-        parsed = datetime.fromisoformat(expr.replace("Z", "+00:00"))
-        if parsed.tzinfo is None:
-            parsed = parsed.replace(tzinfo=timezone.utc)
-        # Return +/- 1 hour window around the specified time
-        return parsed - timedelta(hours=1), parsed + timedelta(hours=1)
-    except ValueError:
-        pass
-
-    # Fallback: treat as "latest"
-    return None, None
-
-
-class AgentOutputTool(BaseTool):
-    """Tool for retrieving execution outputs from user's library agents."""
-
-    @property
-    def name(self) -> str:
-        return "agent_output"
-
-    @property
-    def description(self) -> str:
-        return """Retrieve execution outputs from agents in the user's library.
-
-        Identify the agent using one of:
-        - agent_name: Fuzzy search in user's library
-        - library_agent_id: Exact library agent ID
-        - store_slug: Marketplace format 'username/agent-name'
-
-        Select which run to retrieve using:
-        - execution_id: Specific execution ID
-        - run_time: 'latest' (default), 'yesterday', 'last week', or ISO date 'YYYY-MM-DD'
-        """
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "agent_name": {
-                    "type": "string",
-                    "description": "Agent name to search for in user's library (fuzzy match)",
-                },
-                "library_agent_id": {
-                    "type": "string",
-                    "description": "Exact library agent ID",
-                },
-                "store_slug": {
-                    "type": "string",
-                    "description": "Marketplace identifier: 'username/agent-slug'",
-                },
-                "execution_id": {
-                    "type": "string",
-                    "description": "Specific execution ID to retrieve",
-                },
-                "run_time": {
-                    "type": "string",
-                    "description": (
-                        "Time filter: 'latest', 'yesterday', 'last week', or 'YYYY-MM-DD'"
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _resolve_agent(
-        self,
-        user_id: str,
-        agent_name: str | None,
-        library_agent_id: str | None,
-        store_slug: str | None,
-    ) -> tuple[LibraryAgent | None, str | None]:
-        """
-        Resolve agent from provided identifiers.
-        Returns (library_agent, error_message).
-        """
-        # Priority 1: Exact library agent ID
-        if library_agent_id:
-            try:
-                agent = await library_db.get_library_agent(library_agent_id, user_id)
-                return agent, None
-            except Exception as e:
-                logger.warning(f"Failed to get library agent by ID: {e}")
-                return None, f"Library agent '{library_agent_id}' not found"
-
-        # Priority 2: Store slug (username/agent-name)
-        if store_slug and "/" in store_slug:
-            username, agent_slug = store_slug.split("/", 1)
-            graph, _ = await fetch_graph_from_store_slug(username, agent_slug)
-            if not graph:
-                return None, f"Agent '{store_slug}' not found in marketplace"
-
-            # Find in user's library by graph_id
-            agent = await library_db.get_library_agent_by_graph_id(user_id, graph.id)
-            if not agent:
-                return (
-                    None,
-                    f"Agent '{store_slug}' is not in your library. "
-                    "Add it first to see outputs.",
-                )
-            return agent, None
-
-        # Priority 3: Fuzzy name search in library
-        if agent_name:
-            try:
-                response = await library_db.list_library_agents(
-                    user_id=user_id,
-                    search_term=agent_name,
-                    page_size=5,
-                )
-                if not response.agents:
-                    return (
-                        None,
-                        f"No agents matching '{agent_name}' found in your library",
-                    )
-
-                # Return best match (first result from search)
-                return response.agents[0], None
-            except Exception as e:
-                logger.error(f"Error searching library agents: {e}")
-                return None, f"Error searching for agent: {e}"
-
-        return (
-            None,
-            "Please specify an agent name, library_agent_id, or store_slug",
-        )
-
-    async def _get_execution(
-        self,
-        user_id: str,
-        graph_id: str,
-        execution_id: str | None,
-        time_start: datetime | None,
-        time_end: datetime | None,
-    ) -> tuple[GraphExecution | None, list[GraphExecutionMeta], str | None]:
-        """
-        Fetch execution(s) based on filters.
-        Returns (single_execution, available_executions_meta, error_message).
-        """
-        # If specific execution_id provided, fetch it directly
-        if execution_id:
-            execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=execution_id,
-                include_node_executions=False,
-            )
-            if not execution:
-                return None, [], f"Execution '{execution_id}' not found"
-            return execution, [], None
-
-        # Get completed executions with time filters
-        executions = await execution_db.get_graph_executions(
-            graph_id=graph_id,
-            user_id=user_id,
-            statuses=[ExecutionStatus.COMPLETED],
-            created_time_gte=time_start,
-            created_time_lte=time_end,
-            limit=10,
-        )
-
-        if not executions:
-            return None, [], None  # No error, just no executions
-
-        # If only one execution, fetch full details
-        if len(executions) == 1:
-            full_execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=executions[0].id,
-                include_node_executions=False,
-            )
-            return full_execution, [], None
-
-        # Multiple executions - return latest with full details, plus list of available
-        full_execution = await execution_db.get_graph_execution(
-            user_id=user_id,
-            execution_id=executions[0].id,
-            include_node_executions=False,
-        )
-        return full_execution, executions, None
-
-    def _build_response(
-        self,
-        agent: LibraryAgent,
-        execution: GraphExecution | None,
-        available_executions: list[GraphExecutionMeta],
-        session_id: str | None,
-    ) -> AgentOutputResponse:
-        """Build the response based on execution data."""
-        library_agent_link = f"/library/agents/{agent.id}"
-
-        if not execution:
-            return AgentOutputResponse(
-                message=f"No completed executions found for agent '{agent.name}'",
-                session_id=session_id,
-                agent_name=agent.name,
-                agent_id=agent.graph_id,
-                library_agent_id=agent.id,
-                library_agent_link=library_agent_link,
-                total_executions=0,
-            )
-
-        execution_info = ExecutionOutputInfo(
-            execution_id=execution.id,
-            status=execution.status.value,
-            started_at=execution.started_at,
-            ended_at=execution.ended_at,
-            outputs=dict(execution.outputs),
-            inputs_summary=execution.inputs if execution.inputs else None,
-        )
-
-        available_list = None
-        if len(available_executions) > 1:
-            available_list = [
-                {
-                    "id": e.id,
-                    "status": e.status.value,
-                    "started_at": e.started_at.isoformat() if e.started_at else None,
-                }
-                for e in available_executions[:5]
-            ]
-
-        message = f"Found execution outputs for agent '{agent.name}'"
-        if len(available_executions) > 1:
-            message += (
-                f". Showing latest of {len(available_executions)} matching executions."
-            )
-
-        return AgentOutputResponse(
-            message=message,
-            session_id=session_id,
-            agent_name=agent.name,
-            agent_id=agent.graph_id,
-            library_agent_id=agent.id,
-            library_agent_link=library_agent_link,
-            execution=execution_info,
-            available_executions=available_list,
-            total_executions=len(available_executions) if available_executions else 1,
-        )
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the agent_output tool."""
-        session_id = session.session_id
-
-        # Parse and validate input
-        try:
-            input_data = AgentOutputInput(**kwargs)
-        except Exception as e:
-            logger.error(f"Invalid input: {e}")
-            return ErrorResponse(
-                message="Invalid input parameters",
-                error=str(e),
-                session_id=session_id,
-            )
-
-        # Ensure user_id is present (should be guaranteed by requires_auth)
-        if not user_id:
-            return ErrorResponse(
-                message="User authentication required",
-                session_id=session_id,
-            )
-
-        # Check if at least one identifier is provided
-        if not any(
-            [
-                input_data.agent_name,
-                input_data.library_agent_id,
-                input_data.store_slug,
-                input_data.execution_id,
-            ]
-        ):
-            return ErrorResponse(
-                message=(
-                    "Please specify at least one of: agent_name, "
-                    "library_agent_id, store_slug, or execution_id"
-                ),
-                session_id=session_id,
-            )
-
-        # If only execution_id provided, we need to find the agent differently
-        if (
-            input_data.execution_id
-            and not input_data.agent_name
-            and not input_data.library_agent_id
-            and not input_data.store_slug
-        ):
-            # Fetch execution directly to get graph_id
-            execution = await execution_db.get_graph_execution(
-                user_id=user_id,
-                execution_id=input_data.execution_id,
-                include_node_executions=False,
-            )
-            if not execution:
-                return ErrorResponse(
-                    message=f"Execution '{input_data.execution_id}' not found",
-                    session_id=session_id,
-                )
-
-            # Find library agent by graph_id
-            agent = await library_db.get_library_agent_by_graph_id(
-                user_id, execution.graph_id
-            )
-            if not agent:
-                return NoResultsResponse(
-                    message=(
-                        f"Execution found but agent not in your library. "
-                        f"Graph ID: {execution.graph_id}"
-                    ),
-                    session_id=session_id,
-                    suggestions=["Add the agent to your library to see more details"],
-                )
-
-            return self._build_response(agent, execution, [], session_id)
-
-        # Resolve agent from identifiers
-        agent, error = await self._resolve_agent(
-            user_id=user_id,
-            agent_name=input_data.agent_name or None,
-            library_agent_id=input_data.library_agent_id or None,
-            store_slug=input_data.store_slug or None,
-        )
-
-        if error or not agent:
-            return NoResultsResponse(
-                message=error or "Agent not found",
-                session_id=session_id,
-                suggestions=[
-                    "Check the agent name or ID",
-                    "Make sure the agent is in your library",
-                ],
-            )
-
-        # Parse time expression
-        time_start, time_end = parse_time_expression(input_data.run_time)
-
-        # Fetch execution(s)
-        execution, available_executions, exec_error = await self._get_execution(
-            user_id=user_id,
-            graph_id=agent.graph_id,
-            execution_id=input_data.execution_id or None,
-            time_start=time_start,
-            time_end=time_end,
-        )
-
-        if exec_error:
-            return ErrorResponse(
-                message=exec_error,
-                session_id=session_id,
-            )
-
-        return self._build_response(agent, execution, available_executions, session_id)

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

@@ -1,157 +0,0 @@
-"""Tool for searching agents in the user's library."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.library import db as library_db
-from backend.util.exceptions import DatabaseError
-
-from .base import BaseTool
-from .models import (
-    AgentCarouselResponse,
-    AgentInfo,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class FindLibraryAgentTool(BaseTool):
-    """Tool for searching agents in the user's library."""
-
-    @property
-    def name(self) -> str:
-        return "find_library_agent"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search for agents in the user's library. Use this to find agents "
-            "the user has already added to their library, including agents they "
-            "created or added from the marketplace."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query to find agents by name or description. "
-                        "Use keywords for best results."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search for agents in the user's library.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            AgentCarouselResponse: List of agents found in the library
-            NoResultsResponse: No agents found
-            ErrorResponse: Error message
-        """
-        query = kwargs.get("query", "").strip()
-        session_id = session.session_id
-
-        if not query:
-            return ErrorResponse(
-                message="Please provide a search query",
-                session_id=session_id,
-            )
-
-        if not user_id:
-            return ErrorResponse(
-                message="User authentication required to search library",
-                session_id=session_id,
-            )
-
-        agents = []
-        try:
-            logger.info(f"Searching user library for: {query}")
-            library_results = await library_db.list_library_agents(
-                user_id=user_id,
-                search_term=query,
-                page_size=10,
-            )
-
-            logger.info(
-                f"Find library agents tool found {len(library_results.agents)} agents"
-            )
-
-            for agent in library_results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=agent.id,
-                        name=agent.name,
-                        description=agent.description or "",
-                        source="library",
-                        in_library=True,
-                        creator=agent.creator_name,
-                        status=agent.status.value,
-                        can_access_graph=agent.can_access_graph,
-                        has_external_trigger=agent.has_external_trigger,
-                        new_output=agent.new_output,
-                        graph_id=agent.graph_id,
-                    ),
-                )
-
-        except DatabaseError as e:
-            logger.error(f"Error searching library agents: {e}", exc_info=True)
-            return ErrorResponse(
-                message="Failed to search library. Please try again.",
-                error=str(e),
-                session_id=session_id,
-            )
-
-        if not agents:
-            return NoResultsResponse(
-                message=(
-                    f"No agents found matching '{query}' in your library. "
-                    "Try different keywords or use find_agent to search the marketplace."
-                ),
-                session_id=session_id,
-                suggestions=[
-                    "Try more general terms",
-                    "Use find_agent to search the marketplace",
-                    "Check your library at /library",
-                ],
-            )
-
-        title = (
-            f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} "
-            f"in your library for '{query}'"
-        )
-
-        return AgentCarouselResponse(
-            message=(
-                "Found agents in the user's library. You can provide a link to "
-                "view an agent at: /library/agents/{agent_id}. "
-                "Use agent_output to get execution results, or run_agent to execute."
-            ),
-            title=title,
-            agents=agents,
-            count=len(agents),
-            session_id=session_id,
-        )

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

@@ -1,6 +1,5 @@
 """Pydantic models for tool responses."""
 
-from datetime import datetime
 from enum import Enum
 from typing import Any
 
@@ -20,15 +19,6 @@ class ResponseType(str, Enum):
     ERROR = "error"
     NO_RESULTS = "no_results"
     SUCCESS = "success"
-    DOC_SEARCH_RESULTS = "doc_search_results"
-    AGENT_OUTPUT = "agent_output"
-    BLOCK_LIST = "block_list"
-    BLOCK_OUTPUT = "block_output"
-    UNDERSTANDING_UPDATED = "understanding_updated"
-    # Agent generation responses
-    AGENT_PREVIEW = "agent_preview"
-    AGENT_SAVED = "agent_saved"
-    CLARIFICATION_NEEDED = "clarification_needed"
 
 
 # Base response model
@@ -183,128 +173,3 @@ class ErrorResponse(ToolResponseBase):
     type: ResponseType = ResponseType.ERROR
     error: str | None = None
     details: dict[str, Any] | None = None
-
-
-# Documentation search models
-class DocSearchResult(BaseModel):
-    """A single documentation search result."""
-
-    title: str
-    path: str
-    section: str
-    snippet: str  # Short excerpt for UI display
-    content: str  # Full text content for LLM to read and understand
-    score: float
-    doc_url: str | None = None
-
-
-class DocSearchResultsResponse(ToolResponseBase):
-    """Response for search_docs tool."""
-
-    type: ResponseType = ResponseType.DOC_SEARCH_RESULTS
-    results: list[DocSearchResult]
-    count: int
-    query: str
-
-
-# Agent output models
-class ExecutionOutputInfo(BaseModel):
-    """Summary of a single execution's outputs."""
-
-    execution_id: str
-    status: str
-    started_at: datetime | None = None
-    ended_at: datetime | None = None
-    outputs: dict[str, list[Any]]
-    inputs_summary: dict[str, Any] | None = None
-
-
-class AgentOutputResponse(ToolResponseBase):
-    """Response for agent_output tool."""
-
-    type: ResponseType = ResponseType.AGENT_OUTPUT
-    agent_name: str
-    agent_id: str
-    library_agent_id: str | None = None
-    library_agent_link: str | None = None
-    execution: ExecutionOutputInfo | None = None
-    available_executions: list[dict[str, Any]] | None = None
-    total_executions: int = 0
-
-
-# Block models
-class BlockInfoSummary(BaseModel):
-    """Summary of a block for search results."""
-
-    id: str
-    name: str
-    description: str
-    categories: list[str]
-    input_schema: dict[str, Any]
-    output_schema: dict[str, Any]
-
-
-class BlockListResponse(ToolResponseBase):
-    """Response for find_block tool."""
-
-    type: ResponseType = ResponseType.BLOCK_LIST
-    blocks: list[BlockInfoSummary]
-    count: int
-    query: str
-
-
-class BlockOutputResponse(ToolResponseBase):
-    """Response for run_block tool."""
-
-    type: ResponseType = ResponseType.BLOCK_OUTPUT
-    block_id: str
-    block_name: str
-    outputs: dict[str, list[Any]]
-    success: bool = True
-
-
-# Business understanding models
-class UnderstandingUpdatedResponse(ToolResponseBase):
-    """Response for add_understanding tool."""
-
-    type: ResponseType = ResponseType.UNDERSTANDING_UPDATED
-    updated_fields: list[str] = Field(default_factory=list)
-    current_understanding: dict[str, Any] = Field(default_factory=dict)
-
-
-# Agent generation models
-class ClarifyingQuestion(BaseModel):
-    """A question that needs user clarification."""
-
-    question: str
-    keyword: str
-    example: str | None = None
-
-
-class AgentPreviewResponse(ToolResponseBase):
-    """Response for previewing a generated agent before saving."""
-
-    type: ResponseType = ResponseType.AGENT_PREVIEW
-    agent_json: dict[str, Any]
-    agent_name: str
-    description: str
-    node_count: int
-    link_count: int = 0
-
-
-class AgentSavedResponse(ToolResponseBase):
-    """Response when an agent is saved to the library."""
-
-    type: ResponseType = ResponseType.AGENT_SAVED
-    agent_id: str
-    agent_name: str
-    library_agent_id: str
-    library_agent_link: str
-    agent_page_link: str  # Link to the agent builder/editor page
-
-
-class ClarificationNeededResponse(ToolResponseBase):
-    """Response when the LLM needs more information from the user."""
-
-    type: ResponseType = ResponseType.CLARIFICATION_NEEDED
-    questions: list[ClarifyingQuestion] = Field(default_factory=list)

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

@@ -7,7 +7,6 @@ from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.library import db as library_db
 from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
 from backend.data.user import get_user_by_id
@@ -58,7 +57,6 @@ class RunAgentInput(BaseModel):
     """Input parameters for the run_agent tool."""
 
     username_agent_slug: str = ""
-    library_agent_id: str = ""
     inputs: dict[str, Any] = Field(default_factory=dict)
     use_defaults: bool = False
     schedule_name: str = ""
@@ -66,12 +64,7 @@ class RunAgentInput(BaseModel):
     timezone: str = "UTC"
 
     @field_validator(
-        "username_agent_slug",
-        "library_agent_id",
-        "schedule_name",
-        "cron",
-        "timezone",
-        mode="before",
+        "username_agent_slug", "schedule_name", "cron", "timezone", mode="before"
     )
     @classmethod
     def strip_strings(cls, v: Any) -> Any:
@@ -97,7 +90,7 @@ class RunAgentTool(BaseTool):
 
     @property
     def description(self) -> str:
-        return """Run or schedule an agent from the marketplace or user's library.
+        return """Run or schedule an agent from the marketplace.
 
         The tool automatically handles the setup flow:
         - Returns missing inputs if required fields are not provided
@@ -105,10 +98,6 @@ class RunAgentTool(BaseTool):
         - Executes immediately if all requirements are met
         - Schedules execution if cron expression is provided
 
-        Identify the agent using either:
-        - username_agent_slug: Marketplace format 'username/agent-name'
-        - library_agent_id: ID of an agent in the user's library
-
         For scheduled execution, provide: schedule_name, cron, and optionally timezone."""
 
     @property
@@ -120,10 +109,6 @@ class RunAgentTool(BaseTool):
                     "type": "string",
                     "description": "Agent identifier in format 'username/agent-name'",
                 },
-                "library_agent_id": {
-                    "type": "string",
-                    "description": "Library agent ID from user's library",
-                },
                 "inputs": {
                     "type": "object",
                     "description": "Input values for the agent",
@@ -146,7 +131,7 @@ class RunAgentTool(BaseTool):
                     "description": "IANA timezone for schedule (default: UTC)",
                 },
             },
-            "required": [],
+            "required": ["username_agent_slug"],
         }
 
     @property
@@ -164,16 +149,10 @@ class RunAgentTool(BaseTool):
         params = RunAgentInput(**kwargs)
         session_id = session.session_id
 
-        # Validate at least one identifier is provided
-        has_slug = params.username_agent_slug and "/" in params.username_agent_slug
-        has_library_id = bool(params.library_agent_id)
-
-        if not has_slug and not has_library_id:
+        # Validate agent slug format
+        if not params.username_agent_slug or "/" not in params.username_agent_slug:
             return ErrorResponse(
-                message=(
-                    "Please provide either a username_agent_slug "
-                    "(format 'username/agent-name') or a library_agent_id"
-                ),
+                message="Please provide an agent slug in format 'username/agent-name'",
                 session_id=session_id,
             )
 
@@ -188,41 +167,13 @@ class RunAgentTool(BaseTool):
         is_schedule = bool(params.schedule_name or params.cron)
 
         try:
-            # Step 1: Fetch agent details
-            graph: GraphModel | None = None
-            library_agent = None
-
-            # Priority: library_agent_id if provided
-            if has_library_id:
-                library_agent = await library_db.get_library_agent(
-                    params.library_agent_id, user_id
-                )
-                if not library_agent:
-                    return ErrorResponse(
-                        message=f"Library agent '{params.library_agent_id}' not found",
-                        session_id=session_id,
-                    )
-                # Get the graph from the library agent
-                from backend.data.graph import get_graph
-
-                graph = await get_graph(
-                    library_agent.graph_id,
-                    library_agent.graph_version,
-                    user_id=user_id,
-                )
-            else:
-                # Fetch from marketplace slug
-                username, agent_name = params.username_agent_slug.split("/", 1)
-                graph, _ = await fetch_graph_from_store_slug(username, agent_name)
+            # Step 1: Fetch agent details (always happens first)
+            username, agent_name = params.username_agent_slug.split("/", 1)
+            graph, store_agent = await fetch_graph_from_store_slug(username, agent_name)
 
             if not graph:
-                identifier = (
-                    params.library_agent_id
-                    if has_library_id
-                    else params.username_agent_slug
-                )
                 return ErrorResponse(
-                    message=f"Agent '{identifier}' not found",
+                    message=f"Agent '{params.username_agent_slug}' not found in marketplace",
                     session_id=session_id,
                 )
 

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,417 @@
+"""
+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

@@ -0,0 +1,214 @@
+"""
+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

@@ -0,0 +1,324 @@
+"""
+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

@@ -1,8 +1,7 @@
 import asyncio
 import logging
-import typing
 from datetime import datetime, timezone
-from typing import Literal
+from typing import Any, Literal
 
 import fastapi
 import prisma.enums
@@ -10,7 +9,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import query_raw_with_schema, transaction
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -30,6 +29,8 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
+from .embeddings import ensure_embedding
+from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -50,128 +51,77 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view
+    Get PUBLIC store agents from the StoreAgent view.
+
+    Search behavior:
+    - With search_query: Uses hybrid search (semantic + lexical)
+    - Fallback: If embeddings unavailable, gracefully degrades to lexical-only
+    - Rationale: User-facing endpoint prioritizes availability over accuracy
+
+    Note: Admin operations (approval) use fail-fast to prevent inconsistent state.
     """
     logger.debug(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
+    search_used_hybrid = False
+    store_agents: list[store_model.StoreAgent] = []
+    agents: list[dict[str, Any]] = []
+    total = 0
+    total_pages = 0
+
     try:
-        # If search_query is provided, use full-text search
+        # If search_query is provided, use hybrid search (embeddings + tsvector)
         if search_query:
-            offset = (page - 1) * page_size
+            # Try hybrid search combining semantic and lexical signals
+            # Falls back to lexical-only if OpenAI unavailable (user-facing, high SLA)
+            try:
+                agents, total = await hybrid_search(
+                    query=search_query,
+                    featured=featured,
+                    creators=creators,
+                    category=category,
+                    sorted_by="relevance",  # Use hybrid scoring for relevance
+                    page=page,
+                    page_size=page_size,
+                )
+                search_used_hybrid = True
+            except Exception as e:
+                # Log error but fall back to lexical search for better UX
+                logger.error(
+                    f"Hybrid search failed (likely OpenAI unavailable), "
+                    f"falling back to lexical search: {e}"
+                )
+                # search_used_hybrid remains False, will use fallback path below
 
-            # Whitelist allowed order_by columns
-            ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
-            }
+            # Convert hybrid search results (dict format) if hybrid succeeded
+            if search_used_hybrid:
+                total_pages = (total + page_size - 1) // page_size
+                store_agents: list[store_model.StoreAgent] = []
+                for agent in agents:
+                    try:
+                        store_agent = store_model.StoreAgent(
+                            slug=agent["slug"],
+                            agent_name=agent["agent_name"],
+                            agent_image=(
+                                agent["agent_image"][0] if agent["agent_image"] else ""
+                            ),
+                            creator=agent["creator_username"] or "Needs Profile",
+                            creator_avatar=agent["creator_avatar"] or "",
+                            sub_heading=agent["sub_heading"],
+                            description=agent["description"],
+                            runs=agent["runs"],
+                            rating=agent["rating"],
+                        )
+                        store_agents.append(store_agent)
+                    except Exception as e:
+                        logger.error(
+                            f"Error parsing Store agent from hybrid search results: {e}"
+                        )
+                        continue
 
-            # Validate and get order clause
-            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
-                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
-            else:
-                order_by_clause = "updated_at DESC, rank DESC"
-
-            # Build WHERE conditions and parameters list
-            where_parts: list[str] = []
-            params: list[typing.Any] = [search_query]  # $1 - search term
-            param_index = 2  # Start at $2 for next parameter
-
-            # Always filter for available agents
-            where_parts.append("is_available = true")
-
-            if featured:
-                where_parts.append("featured = true")
-
-            if creators and creators:
-                # Use ANY with array parameter
-                where_parts.append(f"creator_username = ANY(${param_index})")
-                params.append(creators)
-                param_index += 1
-
-            if category and category:
-                where_parts.append(f"${param_index} = ANY(categories)")
-                params.append(category)
-                param_index += 1
-
-            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
-
-            # Add pagination params
-            params.extend([page_size, offset])
-            limit_param = f"${param_index}"
-            offset_param = f"${param_index + 1}"
-
-            # Execute full-text search query with parameterized values
-            sql_query = f"""
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    ts_rank_cd(search, query) AS rank
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-                ORDER BY {order_by_clause}
-                LIMIT {limit_param} OFFSET {offset_param}
-            """
-
-            # Count query for pagination - only uses search term parameter
-            count_query = f"""
-                SELECT COUNT(*) as count
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-            """
-
-            # Execute both queries with parameters
-            agents = await query_raw_with_schema(sql_query, *params)
-
-            # For count, use params without pagination (last 2 params)
-            count_params = params[:-2]
-            count_result = await query_raw_with_schema(count_query, *count_params)
-
-            total = count_result[0]["count"] if count_result else 0
-            total_pages = (total + page_size - 1) // page_size
-
-            # Convert raw results to StoreAgent models
-            store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
-                try:
-                    store_agent = store_model.StoreAgent(
-                        slug=agent["slug"],
-                        agent_name=agent["agent_name"],
-                        agent_image=(
-                            agent["agent_image"][0] if agent["agent_image"] else ""
-                        ),
-                        creator=agent["creator_username"] or "Needs Profile",
-                        creator_avatar=agent["creator_avatar"] or "",
-                        sub_heading=agent["sub_heading"],
-                        description=agent["description"],
-                        runs=agent["runs"],
-                        rating=agent["rating"],
-                    )
-                    store_agents.append(store_agent)
-                except Exception as e:
-                    logger.error(f"Error parsing Store agent from search results: {e}")
-                    continue
-
-        else:
-            # Non-search query path (original logic)
+        if not search_used_hybrid:
+            # Fallback path - use basic search or no search
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -180,6 +130,14 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
+            # Add basic text search if search_query provided but hybrid failed
+            if search_query:
+                where_clause["OR"] = [
+                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
+                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
+                    {"description": {"contains": search_query, "mode": "insensitive"}},
+                ]
+
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -188,7 +146,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            agents = await prisma.models.StoreAgent.prisma().find_many(
+            db_agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -199,7 +157,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
+            for agent in db_agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -614,6 +572,7 @@ async def get_store_submissions(
         submission_models = []
         for sub in submissions:
             submission_model = store_model.StoreSubmission(
+                listing_id=sub.listing_id,
                 agent_id=sub.agent_id,
                 agent_version=sub.agent_version,
                 name=sub.name,
@@ -667,35 +626,48 @@ async def delete_store_submission(
     submission_id: str,
 ) -> bool:
     """
-    Delete a store listing submission as the submitting user.
+    Delete a store submission version as the submitting user.
 
     Args:
         user_id: ID of the authenticated user
-        submission_id: ID of the submission to be deleted
+        submission_id: StoreListingVersion ID to delete
 
     Returns:
-        bool: True if the submission was successfully deleted, False otherwise
+        bool: True if successfully deleted
     """
-    logger.debug(f"Deleting store submission {submission_id} for user {user_id}")
-
     try:
-        # Verify the submission belongs to this user
-        submission = await prisma.models.StoreListing.prisma().find_first(
-            where={"agentGraphId": submission_id, "owningUserId": user_id}
+        # Find the submission version with ownership check
+        version = await prisma.models.StoreListingVersion.prisma().find_first(
+            where={"id": submission_id}, include={"StoreListing": True}
         )
 
-        if not submission:
-            logger.warning(f"Submission not found for user {user_id}: {submission_id}")
-            raise store_exceptions.SubmissionNotFoundError(
-                f"Submission not found for this user. User ID: {user_id}, Submission ID: {submission_id}"
+        if (
+            not version
+            or not version.StoreListing
+            or version.StoreListing.owningUserId != user_id
+        ):
+            raise store_exceptions.SubmissionNotFoundError("Submission not found")
+
+        # Prevent deletion of approved submissions
+        if version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
+            raise store_exceptions.InvalidOperationError(
+                "Cannot delete approved submissions"
             )
 
-        # Delete the submission
-        await prisma.models.StoreListing.prisma().delete(where={"id": submission.id})
-
-        logger.debug(
-            f"Successfully deleted submission {submission_id} for user {user_id}"
+        # Delete the version
+        await prisma.models.StoreListingVersion.prisma().delete(
+            where={"id": version.id}
         )
+
+        # Clean up empty listing if this was the last version
+        remaining = await prisma.models.StoreListingVersion.prisma().count(
+            where={"storeListingId": version.storeListingId}
+        )
+        if remaining == 0:
+            await prisma.models.StoreListing.prisma().delete(
+                where={"id": version.storeListingId}
+            )
+
         return True
 
     except Exception as e:
@@ -759,9 +731,15 @@ async def create_store_submission(
             logger.warning(
                 f"Agent not found for user {user_id}: {agent_id} v{agent_version}"
             )
-            raise store_exceptions.AgentNotFoundError(
-                f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
-            )
+            # Provide more user-friendly error message when agent_id is empty
+            if not agent_id or agent_id.strip() == "":
+                raise store_exceptions.AgentNotFoundError(
+                    "No agent selected. Please select an agent before submitting to the store."
+                )
+            else:
+                raise store_exceptions.AgentNotFoundError(
+                    f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
+                )
 
         # Check if listing already exists for this agent
         existing_listing = await prisma.models.StoreListing.prisma().find_first(
@@ -833,6 +811,7 @@ async def create_store_submission(
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return store_model.StoreSubmission(
+            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -944,81 +923,56 @@ async def edit_store_submission(
         # Currently we are not allowing user to update the agent associated with a submission
         # If we allow it in future, then we need a check here to verify the agent belongs to this user.
 
-        # Check if we can edit this submission
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
+        # Only allow editing of PENDING submissions
+        if current_version.submissionStatus != prisma.enums.SubmissionStatus.PENDING:
             raise store_exceptions.InvalidOperationError(
-                "Cannot edit a rejected submission"
-            )
-
-        # For APPROVED submissions, we need to create a new version
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
-            # Create a new version for the existing listing
-            return await create_store_version(
-                user_id=user_id,
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
-                store_listing_id=current_version.storeListingId,
-                name=name,
-                video_url=video_url,
-                agent_output_demo_url=agent_output_demo_url,
-                image_urls=image_urls,
-                description=description,
-                sub_heading=sub_heading,
-                categories=categories,
-                changes_summary=changes_summary,
-                recommended_schedule_cron=recommended_schedule_cron,
-                instructions=instructions,
+                f"Cannot edit a {current_version.submissionStatus.value.lower()} submission. Only pending submissions can be edited."
             )
 
         # For PENDING submissions, we can update the existing version
-        elif current_version.submissionStatus == prisma.enums.SubmissionStatus.PENDING:
-            # Update the existing version
-            updated_version = await prisma.models.StoreListingVersion.prisma().update(
-                where={"id": store_listing_version_id},
-                data=prisma.types.StoreListingVersionUpdateInput(
-                    name=name,
-                    videoUrl=video_url,
-                    agentOutputDemoUrl=agent_output_demo_url,
-                    imageUrls=image_urls,
-                    description=description,
-                    categories=categories,
-                    subHeading=sub_heading,
-                    changesSummary=changes_summary,
-                    recommendedScheduleCron=recommended_schedule_cron,
-                    instructions=instructions,
-                ),
-            )
-
-            logger.debug(
-                f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
-            )
-
-            if not updated_version:
-                raise DatabaseError("Failed to update store listing version")
-            return store_model.StoreSubmission(
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
+        # Update the existing version
+        updated_version = await prisma.models.StoreListingVersion.prisma().update(
+            where={"id": store_listing_version_id},
+            data=prisma.types.StoreListingVersionUpdateInput(
                 name=name,
-                sub_heading=sub_heading,
-                slug=current_version.StoreListing.slug,
+                videoUrl=video_url,
+                agentOutputDemoUrl=agent_output_demo_url,
+                imageUrls=image_urls,
                 description=description,
-                instructions=instructions,
-                image_urls=image_urls,
-                date_submitted=updated_version.submittedAt or updated_version.createdAt,
-                status=updated_version.submissionStatus,
-                runs=0,
-                rating=0.0,
-                store_listing_version_id=updated_version.id,
-                changes_summary=changes_summary,
-                video_url=video_url,
                 categories=categories,
-                version=updated_version.version,
-            )
+                subHeading=sub_heading,
+                changesSummary=changes_summary,
+                recommendedScheduleCron=recommended_schedule_cron,
+                instructions=instructions,
+            ),
+        )
 
-        else:
-            raise store_exceptions.InvalidOperationError(
-                f"Cannot edit submission with status: {current_version.submissionStatus}"
-            )
+        logger.debug(
+            f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
+        )
+
+        if not updated_version:
+            raise DatabaseError("Failed to update store listing version")
+        return store_model.StoreSubmission(
+            listing_id=current_version.StoreListing.id,
+            agent_id=current_version.agentGraphId,
+            agent_version=current_version.agentGraphVersion,
+            name=name,
+            sub_heading=sub_heading,
+            slug=current_version.StoreListing.slug,
+            description=description,
+            instructions=instructions,
+            image_urls=image_urls,
+            date_submitted=updated_version.submittedAt or updated_version.createdAt,
+            status=updated_version.submissionStatus,
+            runs=0,
+            rating=0.0,
+            store_listing_version_id=updated_version.id,
+            changes_summary=changes_summary,
+            video_url=video_url,
+            categories=categories,
+            version=updated_version.version,
+        )
 
     except (
         store_exceptions.SubmissionNotFoundError,
@@ -1097,38 +1051,78 @@ async def create_store_version(
                 f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
             )
 
-        # Get the latest version number
-        latest_version = listing.Versions[0] if listing.Versions else None
-
-        next_version = (latest_version.version + 1) if latest_version else 1
-
-        # Create a new version for the existing listing
-        new_version = await prisma.models.StoreListingVersion.prisma().create(
-            data=prisma.types.StoreListingVersionCreateInput(
-                version=next_version,
-                agentGraphId=agent_id,
-                agentGraphVersion=agent_version,
-                name=name,
-                videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
-                imageUrls=image_urls,
-                description=description,
-                instructions=instructions,
-                categories=categories,
-                subHeading=sub_heading,
-                submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                submittedAt=datetime.now(),
-                changesSummary=changes_summary,
-                recommendedScheduleCron=recommended_schedule_cron,
-                storeListingId=store_listing_id,
+        # Check if there's already a PENDING submission for this agent (any version)
+        existing_pending_submission = (
+            await prisma.models.StoreListingVersion.prisma().find_first(
+                where=prisma.types.StoreListingVersionWhereInput(
+                    storeListingId=store_listing_id,
+                    agentGraphId=agent_id,
+                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                    isDeleted=False,
+                )
             )
         )
 
+        # Handle existing pending submission and create new one atomically
+        async with transaction() as tx:
+            # Get the latest version number first
+            latest_listing = await prisma.models.StoreListing.prisma(tx).find_first(
+                where=prisma.types.StoreListingWhereInput(
+                    id=store_listing_id, owningUserId=user_id
+                ),
+                include={"Versions": {"order_by": {"version": "desc"}, "take": 1}},
+            )
+
+            if not latest_listing:
+                raise store_exceptions.ListingNotFoundError(
+                    f"Store listing not found. User ID: {user_id}, Listing ID: {store_listing_id}"
+                )
+
+            latest_version = (
+                latest_listing.Versions[0] if latest_listing.Versions else None
+            )
+            next_version = (latest_version.version + 1) if latest_version else 1
+
+            # If there's an existing pending submission, delete it atomically before creating new one
+            if existing_pending_submission:
+                logger.info(
+                    f"Found existing PENDING submission for agent {agent_id} (was v{existing_pending_submission.agentGraphVersion}, now v{agent_version}), replacing existing submission instead of creating duplicate"
+                )
+                await prisma.models.StoreListingVersion.prisma(tx).delete(
+                    where={"id": existing_pending_submission.id}
+                )
+                logger.debug(
+                    f"Deleted existing pending submission {existing_pending_submission.id}"
+                )
+
+            # Create a new version for the existing listing
+            new_version = await prisma.models.StoreListingVersion.prisma(tx).create(
+                data=prisma.types.StoreListingVersionCreateInput(
+                    version=next_version,
+                    agentGraphId=agent_id,
+                    agentGraphVersion=agent_version,
+                    name=name,
+                    videoUrl=video_url,
+                    agentOutputDemoUrl=agent_output_demo_url,
+                    imageUrls=image_urls,
+                    description=description,
+                    instructions=instructions,
+                    categories=categories,
+                    subHeading=sub_heading,
+                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                    submittedAt=datetime.now(),
+                    changesSummary=changes_summary,
+                    recommendedScheduleCron=recommended_schedule_cron,
+                    storeListingId=store_listing_id,
+                )
+            )
+
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
         # Return submission details
         return store_model.StoreSubmission(
+            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -1541,7 +1535,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma().update(
+                await prisma.models.AgentGraph.prisma(tx).update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1556,6 +1550,23 @@ async def review_store_submission(
                     },
                 )
 
+                # Generate embedding for approved listing (blocking - admin operation)
+                # Inside transaction: if embedding fails, entire transaction rolls back
+                embedding_success = await ensure_embedding(
+                    version_id=store_listing_version_id,
+                    name=store_listing_version.name,
+                    description=store_listing_version.description,
+                    sub_heading=store_listing_version.subHeading,
+                    categories=store_listing_version.categories or [],
+                    tx=tx,
+                )
+                if not embedding_success:
+                    raise ValueError(
+                        f"Failed to generate embedding for listing {store_listing_version_id}. "
+                        "This is likely due to OpenAI API being unavailable. "
+                        "Please try again later or contact support if the issue persists."
+                    )
+
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},
                     data={
@@ -1708,15 +1719,12 @@ async def review_store_submission(
 
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
+            listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
             agent_id=submission.agentGraphId,
             agent_version=submission.agentGraphVersion,
             name=submission.name,
             sub_heading=submission.subHeading,
-            slug=(
-                submission.StoreListing.slug
-                if hasattr(submission, "storeListing") and submission.StoreListing
-                else ""
-            ),
+            slug=(submission.StoreListing.slug if submission.StoreListing else ""),
             description=submission.description,
             instructions=submission.instructions,
             image_urls=submission.imageUrls or [],
@@ -1818,9 +1826,7 @@ async def get_admin_listings_with_versions(
         where = prisma.types.StoreListingWhereInput(**where_dict)
         include = prisma.types.StoreListingInclude(
             Versions=prisma.types.FindManyStoreListingVersionArgsFromStoreListing(
-                order_by=prisma.types._StoreListingVersion_version_OrderByInput(
-                    version="desc"
-                )
+                order_by={"version": "desc"}
             ),
             OwningUser=True,
         )
@@ -1845,6 +1851,7 @@ async def get_admin_listings_with_versions(
             # If we have versions, turn them into StoreSubmission models
             for version in listing.Versions or []:
                 version_model = store_model.StoreSubmission(
+                    listing_id=listing.id,
                     agent_id=version.agentGraphId,
                     agent_version=version.agentGraphVersion,
                     name=version.name,

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

@@ -0,0 +1,628 @@
+"""
+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

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

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

@@ -0,0 +1,387 @@
+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

@@ -0,0 +1,393 @@
+"""
+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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

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

autogpt_platform/backend/backend/blocks/search.py

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

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -391,8 +391,12 @@ class SmartDecisionMakerBlock(Block):
         """
         block = sink_node.block
 
+        # Use custom name from node metadata if set, otherwise fall back to block.name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else block.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(block.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -489,14 +493,24 @@ class SmartDecisionMakerBlock(Block):
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
             )
 
+        # Use custom name from node metadata if set, otherwise fall back to graph name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else sink_graph_meta.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": sink_graph_meta.description,
         }
 
         properties = {}
+        field_mapping = {}
 
         for link in links:
+            field_name = link.sink_name
+
+            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
+            field_mapping[clean_field_name] = field_name
+
             sink_block_input_schema = sink_node.input_default["input_schema"]
             sink_block_properties = sink_block_input_schema.get("properties", {}).get(
                 link.sink_name, {}
@@ -506,7 +520,7 @@ class SmartDecisionMakerBlock(Block):
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[link.sink_name] = {
+            properties[clean_field_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -519,7 +533,7 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        # Store node info for later use in output processing
+        tool_function["_field_mapping"] = field_mapping
         tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
@@ -975,10 +989,28 @@ class SmartDecisionMakerBlock(Block):
         graph_version: int,
         execution_context: ExecutionContext,
         execution_processor: "ExecutionProcessor",
+        nodes_to_skip: set[str] | None = None,
         **kwargs,
     ) -> BlockOutput:
 
         tool_functions = await self._create_tool_node_signatures(node_id)
+        original_tool_count = len(tool_functions)
+
+        # Filter out tools for nodes that should be skipped (e.g., missing optional credentials)
+        if nodes_to_skip:
+            tool_functions = [
+                tf
+                for tf in tool_functions
+                if tf.get("function", {}).get("_sink_node_id") not in nodes_to_skip
+            ]
+
+            # Only raise error if we had tools but they were all filtered out
+            if original_tool_count > 0 and not tool_functions:
+                raise ValueError(
+                    "No available tools to execute - all downstream nodes are unavailable "
+                    "(possibly due to missing optional credentials)"
+                )
+
         yield "tool_functions", json.dumps(tool_functions)
 
         conversation_history = input_data.conversation_history or []
@@ -1129,8 +1161,9 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_arg_name)
 
-                sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
+                # Use original_field_name directly (not sanitized) to match link sink_name
+                # The field_mapping already translates from LLM's cleaned names to original names
+                emit_key = f"tools_^_{sink_node_id}_~_{original_field_name}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/data/block.py

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

autogpt_platform/backend/backend/data/db.py

@@ -38,6 +38,20 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
+# Add public schema to search_path for pgvector type access
+# The vector extension is in public schema, but search_path is determined by schema parameter
+# Extract the schema from DATABASE_URL or default to 'platform'
+parsed_url = urlparse(DATABASE_URL)
+url_params = dict(parse_qsl(parsed_url.query))
+db_schema = url_params.get("schema", "platform")
+# Build search_path, avoiding duplicates if db_schema is already 'public'
+search_path_schemas = list(
+    dict.fromkeys([db_schema, "public"])
+)  # Preserves order, removes duplicates
+search_path = ",".join(search_path_schemas)
+# This allows using ::vector without schema qualification
+DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
+
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -108,21 +122,102 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
-async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
-    """Execute raw SQL query with proper schema handling."""
+async def _raw_with_schema(
+    query_template: str,
+    *args,
+    execute: bool = False,
+    client: Prisma | None = None,
+    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)
+    """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
     formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
-    result = await prisma_module.get_client().query_raw(
-        formatted_query, *args  # type: ignore
-    )
+    db_client = client if client else prisma_module.get_client()
+
+    # 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
 
     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/execution.py

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

autogpt_platform/backend/backend/data/graph.py

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

autogpt_platform/backend/backend/data/graph_test.py

@@ -1,5 +1,6 @@
 import json
 from typing import Any
+from unittest.mock import AsyncMock, patch
 from uuid import UUID
 
 import fastapi.exceptions
@@ -18,6 +19,17 @@ from backend.usecases.sample import create_test_user
 from backend.util.test import SpinTestServer
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_graph_creation(server: SpinTestServer, snapshot: Snapshot):
     """
@@ -396,3 +408,58 @@ async def test_access_store_listing_graph(server: SpinTestServer):
         created_graph.id, created_graph.version, "3e53486c-cf57-477e-ba2a-cb02dc828e1b"
     )
     assert got_graph is not None
+
+
+# ============================================================================
+# Tests for Optional Credentials Feature
+# ============================================================================
+
+
+def test_node_credentials_optional_default():
+    """Test that credentials_optional defaults to False when not set in metadata."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={},
+    )
+    assert node.credentials_optional is False
+
+
+def test_node_credentials_optional_true():
+    """Test that credentials_optional returns True when explicitly set."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={"credentials_optional": True},
+    )
+    assert node.credentials_optional is True
+
+
+def test_node_credentials_optional_false():
+    """Test that credentials_optional returns False when explicitly set to False."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={"credentials_optional": False},
+    )
+    assert node.credentials_optional is False
+
+
+def test_node_credentials_optional_with_other_metadata():
+    """Test that credentials_optional works correctly with other metadata present."""
+    node = Node(
+        id="test_node",
+        block_id=StoreValueBlock().id,
+        input_default={},
+        metadata={
+            "position": {"x": 100, "y": 200},
+            "customized_name": "My Custom Node",
+            "credentials_optional": True,
+        },
+    )
+    assert node.credentials_optional is True
+    assert node.metadata["position"] == {"x": 100, "y": 200}
+    assert node.metadata["customized_name"] == "My Custom Node"

autogpt_platform/backend/backend/data/understanding.py

@@ -1,429 +0,0 @@
-"""Data models and access layer for user business understanding."""
-
-import logging
-from datetime import datetime
-from typing import Any, Optional, cast
-
-import pydantic
-from prisma.models import UserBusinessUnderstanding
-from prisma.types import (
-    UserBusinessUnderstandingCreateInput,
-    UserBusinessUnderstandingUpdateInput,
-)
-
-from backend.data.redis_client import get_redis_async
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-# Cache configuration
-CACHE_KEY_PREFIX = "understanding"
-CACHE_TTL_SECONDS = 48 * 60 * 60  # 48 hours
-
-
-def _cache_key(user_id: str) -> str:
-    """Generate cache key for user business understanding."""
-    return f"{CACHE_KEY_PREFIX}:{user_id}"
-
-
-def _json_to_list(value: Any) -> list[str]:
-    """Convert Json field to list[str], handling None."""
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return cast(list[str], value)
-    return []
-
-
-class BusinessUnderstandingInput(pydantic.BaseModel):
-    """Input model for updating business understanding - all fields optional for incremental updates."""
-
-    # User info
-    user_name: Optional[str] = pydantic.Field(None, description="The user's name")
-    job_title: Optional[str] = pydantic.Field(None, description="The user's job title")
-
-    # Business basics
-    business_name: Optional[str] = pydantic.Field(
-        None, description="Name of the user's business"
-    )
-    industry: Optional[str] = pydantic.Field(None, description="Industry or sector")
-    business_size: Optional[str] = pydantic.Field(
-        None, description="Company size (e.g., '1-10', '11-50')"
-    )
-    user_role: Optional[str] = pydantic.Field(
-        None,
-        description="User's role in the organization (e.g., 'decision maker', 'implementer')",
-    )
-
-    # Processes & activities
-    key_workflows: Optional[list[str]] = pydantic.Field(
-        None, description="Key business workflows"
-    )
-    daily_activities: Optional[list[str]] = pydantic.Field(
-        None, description="Daily activities performed"
-    )
-
-    # Pain points & goals
-    pain_points: Optional[list[str]] = pydantic.Field(
-        None, description="Current pain points"
-    )
-    bottlenecks: Optional[list[str]] = pydantic.Field(
-        None, description="Process bottlenecks"
-    )
-    manual_tasks: Optional[list[str]] = pydantic.Field(
-        None, description="Manual/repetitive tasks"
-    )
-    automation_goals: Optional[list[str]] = pydantic.Field(
-        None, description="Desired automation goals"
-    )
-
-    # Current tools
-    current_software: Optional[list[str]] = pydantic.Field(
-        None, description="Software/tools currently used"
-    )
-    existing_automation: Optional[list[str]] = pydantic.Field(
-        None, description="Existing automations"
-    )
-
-    # Additional context
-    additional_notes: Optional[str] = pydantic.Field(
-        None, description="Any additional context"
-    )
-
-
-class BusinessUnderstanding(pydantic.BaseModel):
-    """Full business understanding model returned from database."""
-
-    id: str
-    user_id: str
-    created_at: datetime
-    updated_at: datetime
-
-    # User info
-    user_name: Optional[str] = None
-    job_title: Optional[str] = None
-
-    # Business basics
-    business_name: Optional[str] = None
-    industry: Optional[str] = None
-    business_size: Optional[str] = None
-    user_role: Optional[str] = None
-
-    # Processes & activities
-    key_workflows: list[str] = pydantic.Field(default_factory=list)
-    daily_activities: list[str] = pydantic.Field(default_factory=list)
-
-    # Pain points & goals
-    pain_points: list[str] = pydantic.Field(default_factory=list)
-    bottlenecks: list[str] = pydantic.Field(default_factory=list)
-    manual_tasks: list[str] = pydantic.Field(default_factory=list)
-    automation_goals: list[str] = pydantic.Field(default_factory=list)
-
-    # Current tools
-    current_software: list[str] = pydantic.Field(default_factory=list)
-    existing_automation: list[str] = pydantic.Field(default_factory=list)
-
-    # Additional context
-    additional_notes: Optional[str] = None
-
-    @classmethod
-    def from_db(cls, db_record: UserBusinessUnderstanding) -> "BusinessUnderstanding":
-        """Convert database record to Pydantic model."""
-        return cls(
-            id=db_record.id,
-            user_id=db_record.userId,
-            created_at=db_record.createdAt,
-            updated_at=db_record.updatedAt,
-            user_name=db_record.userName,
-            job_title=db_record.jobTitle,
-            business_name=db_record.businessName,
-            industry=db_record.industry,
-            business_size=db_record.businessSize,
-            user_role=db_record.userRole,
-            key_workflows=_json_to_list(db_record.keyWorkflows),
-            daily_activities=_json_to_list(db_record.dailyActivities),
-            pain_points=_json_to_list(db_record.painPoints),
-            bottlenecks=_json_to_list(db_record.bottlenecks),
-            manual_tasks=_json_to_list(db_record.manualTasks),
-            automation_goals=_json_to_list(db_record.automationGoals),
-            current_software=_json_to_list(db_record.currentSoftware),
-            existing_automation=_json_to_list(db_record.existingAutomation),
-            additional_notes=db_record.additionalNotes,
-        )
-
-
-def _merge_lists(existing: list | None, new: list | None) -> list | None:
-    """Merge two lists, removing duplicates while preserving order."""
-    if new is None:
-        return existing
-    if existing is None:
-        return new
-    # Preserve order, add new items that don't exist
-    merged = list(existing)
-    for item in new:
-        if item not in merged:
-            merged.append(item)
-    return merged
-
-
-async def _get_from_cache(user_id: str) -> Optional[BusinessUnderstanding]:
-    """Get business understanding from Redis cache."""
-    try:
-        redis = await get_redis_async()
-        cached_data = await redis.get(_cache_key(user_id))
-        if cached_data:
-            return BusinessUnderstanding.model_validate_json(cached_data)
-    except Exception as e:
-        logger.warning(f"Failed to get understanding from cache: {e}")
-    return None
-
-
-async def _set_cache(user_id: str, understanding: BusinessUnderstanding) -> None:
-    """Set business understanding in Redis cache with TTL."""
-    try:
-        redis = await get_redis_async()
-        await redis.setex(
-            _cache_key(user_id),
-            CACHE_TTL_SECONDS,
-            understanding.model_dump_json(),
-        )
-    except Exception as e:
-        logger.warning(f"Failed to set understanding in cache: {e}")
-
-
-async def _delete_cache(user_id: str) -> None:
-    """Delete business understanding from Redis cache."""
-    try:
-        redis = await get_redis_async()
-        await redis.delete(_cache_key(user_id))
-    except Exception as e:
-        logger.warning(f"Failed to delete understanding from cache: {e}")
-
-
-async def get_business_understanding(
-    user_id: str,
-) -> Optional[BusinessUnderstanding]:
-    """Get the business understanding for a user.
-
-    Checks cache first, falls back to database if not cached.
-    Results are cached for 48 hours.
-    """
-    # Try cache first
-    cached = await _get_from_cache(user_id)
-    if cached:
-        logger.debug(f"Business understanding cache hit for user {user_id}")
-        return cached
-
-    # Cache miss - load from database
-    logger.debug(f"Business understanding cache miss for user {user_id}")
-    record = await UserBusinessUnderstanding.prisma().find_unique(
-        where={"userId": user_id}
-    )
-    if record is None:
-        return None
-
-    understanding = BusinessUnderstanding.from_db(record)
-
-    # Store in cache for next time
-    await _set_cache(user_id, understanding)
-
-    return understanding
-
-
-async def upsert_business_understanding(
-    user_id: str,
-    data: BusinessUnderstandingInput,
-) -> BusinessUnderstanding:
-    """
-    Create or update business understanding with incremental merge strategy.
-
-    - String fields: new value overwrites if provided (not None)
-    - List fields: new items are appended to existing (deduplicated)
-    """
-    # Get existing record for merge
-    existing = await UserBusinessUnderstanding.prisma().find_unique(
-        where={"userId": user_id}
-    )
-
-    # Build update data with merge strategy
-    update_data: UserBusinessUnderstandingUpdateInput = {}
-    create_data: dict[str, Any] = {"userId": user_id}
-
-    # String fields - overwrite if provided
-    if data.user_name is not None:
-        update_data["userName"] = data.user_name
-        create_data["userName"] = data.user_name
-    if data.job_title is not None:
-        update_data["jobTitle"] = data.job_title
-        create_data["jobTitle"] = data.job_title
-    if data.business_name is not None:
-        update_data["businessName"] = data.business_name
-        create_data["businessName"] = data.business_name
-    if data.industry is not None:
-        update_data["industry"] = data.industry
-        create_data["industry"] = data.industry
-    if data.business_size is not None:
-        update_data["businessSize"] = data.business_size
-        create_data["businessSize"] = data.business_size
-    if data.user_role is not None:
-        update_data["userRole"] = data.user_role
-        create_data["userRole"] = data.user_role
-    if data.additional_notes is not None:
-        update_data["additionalNotes"] = data.additional_notes
-        create_data["additionalNotes"] = data.additional_notes
-
-    # List fields - merge with existing
-    if data.key_workflows is not None:
-        existing_list = _json_to_list(existing.keyWorkflows) if existing else None
-        merged = _merge_lists(existing_list, data.key_workflows)
-        update_data["keyWorkflows"] = SafeJson(merged)
-        create_data["keyWorkflows"] = SafeJson(merged)
-
-    if data.daily_activities is not None:
-        existing_list = _json_to_list(existing.dailyActivities) if existing else None
-        merged = _merge_lists(existing_list, data.daily_activities)
-        update_data["dailyActivities"] = SafeJson(merged)
-        create_data["dailyActivities"] = SafeJson(merged)
-
-    if data.pain_points is not None:
-        existing_list = _json_to_list(existing.painPoints) if existing else None
-        merged = _merge_lists(existing_list, data.pain_points)
-        update_data["painPoints"] = SafeJson(merged)
-        create_data["painPoints"] = SafeJson(merged)
-
-    if data.bottlenecks is not None:
-        existing_list = _json_to_list(existing.bottlenecks) if existing else None
-        merged = _merge_lists(existing_list, data.bottlenecks)
-        update_data["bottlenecks"] = SafeJson(merged)
-        create_data["bottlenecks"] = SafeJson(merged)
-
-    if data.manual_tasks is not None:
-        existing_list = _json_to_list(existing.manualTasks) if existing else None
-        merged = _merge_lists(existing_list, data.manual_tasks)
-        update_data["manualTasks"] = SafeJson(merged)
-        create_data["manualTasks"] = SafeJson(merged)
-
-    if data.automation_goals is not None:
-        existing_list = _json_to_list(existing.automationGoals) if existing else None
-        merged = _merge_lists(existing_list, data.automation_goals)
-        update_data["automationGoals"] = SafeJson(merged)
-        create_data["automationGoals"] = SafeJson(merged)
-
-    if data.current_software is not None:
-        existing_list = _json_to_list(existing.currentSoftware) if existing else None
-        merged = _merge_lists(existing_list, data.current_software)
-        update_data["currentSoftware"] = SafeJson(merged)
-        create_data["currentSoftware"] = SafeJson(merged)
-
-    if data.existing_automation is not None:
-        existing_list = _json_to_list(existing.existingAutomation) if existing else None
-        merged = _merge_lists(existing_list, data.existing_automation)
-        update_data["existingAutomation"] = SafeJson(merged)
-        create_data["existingAutomation"] = SafeJson(merged)
-
-    # Upsert
-    record = await UserBusinessUnderstanding.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": UserBusinessUnderstandingCreateInput(**create_data),
-            "update": update_data,
-        },
-    )
-
-    understanding = BusinessUnderstanding.from_db(record)
-
-    # Update cache with new understanding
-    await _set_cache(user_id, understanding)
-
-    return understanding
-
-
-async def clear_business_understanding(user_id: str) -> bool:
-    """Clear/delete business understanding for a user from both DB and cache."""
-    # Delete from cache first
-    await _delete_cache(user_id)
-
-    try:
-        await UserBusinessUnderstanding.prisma().delete(where={"userId": user_id})
-        return True
-    except Exception:
-        # Record might not exist
-        return False
-
-
-def format_understanding_for_prompt(understanding: BusinessUnderstanding) -> str:
-    """Format business understanding as text for system prompt injection."""
-    sections = []
-
-    # User info section
-    user_info = []
-    if understanding.user_name:
-        user_info.append(f"Name: {understanding.user_name}")
-    if understanding.job_title:
-        user_info.append(f"Job Title: {understanding.job_title}")
-    if user_info:
-        sections.append("## User\n" + "\n".join(user_info))
-
-    # Business section
-    business_info = []
-    if understanding.business_name:
-        business_info.append(f"Company: {understanding.business_name}")
-    if understanding.industry:
-        business_info.append(f"Industry: {understanding.industry}")
-    if understanding.business_size:
-        business_info.append(f"Size: {understanding.business_size}")
-    if understanding.user_role:
-        business_info.append(f"Role Context: {understanding.user_role}")
-    if business_info:
-        sections.append("## Business\n" + "\n".join(business_info))
-
-    # Processes section
-    processes = []
-    if understanding.key_workflows:
-        processes.append(f"Key Workflows: {', '.join(understanding.key_workflows)}")
-    if understanding.daily_activities:
-        processes.append(
-            f"Daily Activities: {', '.join(understanding.daily_activities)}"
-        )
-    if processes:
-        sections.append("## Processes\n" + "\n".join(processes))
-
-    # Pain points section
-    pain_points = []
-    if understanding.pain_points:
-        pain_points.append(f"Pain Points: {', '.join(understanding.pain_points)}")
-    if understanding.bottlenecks:
-        pain_points.append(f"Bottlenecks: {', '.join(understanding.bottlenecks)}")
-    if understanding.manual_tasks:
-        pain_points.append(f"Manual Tasks: {', '.join(understanding.manual_tasks)}")
-    if pain_points:
-        sections.append("## Pain Points\n" + "\n".join(pain_points))
-
-    # Goals section
-    if understanding.automation_goals:
-        sections.append(
-            "## Automation Goals\n"
-            + "\n".join(f"- {goal}" for goal in understanding.automation_goals)
-        )
-
-    # Current tools section
-    tools_info = []
-    if understanding.current_software:
-        tools_info.append(
-            f"Current Software: {', '.join(understanding.current_software)}"
-        )
-    if understanding.existing_automation:
-        tools_info.append(
-            f"Existing Automation: {', '.join(understanding.existing_automation)}"
-        )
-    if tools_info:
-        sections.append("## Current Tools\n" + "\n".join(tools_info))
-
-    # Additional notes
-    if understanding.additional_notes:
-        sections.append(f"## Additional Context\n{understanding.additional_notes}")
-
-    if not sections:
-        return ""
-
-    return "# User Business Context\n\n" + "\n\n".join(sections)

autogpt_platform/backend/backend/executor/database.py

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

autogpt_platform/backend/backend/executor/manager.py

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

autogpt_platform/backend/backend/executor/manager_test.py

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

autogpt_platform/backend/backend/executor/scheduler.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import os
 import threading
+import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -37,7 +38,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_scheduler_client
+from backend.util.clients import get_database_manager_client, get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -254,6 +255,88 @@ 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
 
 
@@ -475,6 +558,19 @@ class Scheduler(AppService):
                 jobstore=Jobstores.EXECUTION.value,
             )
 
+            # Embedding Coverage - Every 6 hours
+            # Ensures all approved agents have embeddings for hybrid search
+            # Critical: missing embeddings = agents invisible in search
+            self.scheduler.add_job(
+                ensure_embeddings_coverage,
+                id="ensure_embeddings_coverage",
+                trigger="interval",
+                hours=6,
+                replace_existing=True,
+                max_instances=1,  # Prevent overlapping runs
+                jobstore=Jobstores.EXECUTION.value,
+            )
+
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.add_listener(job_missed_listener, EVENT_JOB_MISSED)
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
@@ -632,6 +728,11 @@ class Scheduler(AppService):
         """Manually trigger execution accuracy alert checking."""
         return execution_accuracy_alerts()
 
+    @expose
+    def execute_ensure_embeddings_coverage(self):
+        """Manually trigger embedding backfill for approved store agents."""
+        return ensure_embeddings_coverage()
+
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/executor/utils_test.py

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

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

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

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

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

autogpt_platform/backend/backend/util/clients.py

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

autogpt_platform/backend/backend/util/settings.py

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

autogpt_platform/backend/gen_prisma_types_stub.py

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

autogpt_platform/backend/linter.py

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

autogpt_platform/backend/migrations/20260108110428_add_chat_tables/migration.sql

@@ -1,81 +0,0 @@
--- DropIndex
-DROP INDEX "StoreListingVersion_storeListingId_version_key";
-
--- CreateTable
-CREATE TABLE "UserBusinessUnderstanding" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT NOT NULL,
-    "userName" TEXT,
-    "jobTitle" TEXT,
-    "businessName" TEXT,
-    "industry" TEXT,
-    "businessSize" TEXT,
-    "userRole" TEXT,
-    "keyWorkflows" JSONB,
-    "dailyActivities" JSONB,
-    "painPoints" JSONB,
-    "bottlenecks" JSONB,
-    "manualTasks" JSONB,
-    "automationGoals" JSONB,
-    "currentSoftware" JSONB,
-    "existingAutomation" JSONB,
-    "additionalNotes" TEXT,
-
-    CONSTRAINT "UserBusinessUnderstanding_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatSession" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT,
-    "title" TEXT,
-    "credentials" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentRuns" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentSchedules" JSONB NOT NULL DEFAULT '{}',
-    "totalPromptTokens" INTEGER NOT NULL DEFAULT 0,
-    "totalCompletionTokens" INTEGER NOT NULL DEFAULT 0,
-
-    CONSTRAINT "ChatSession_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatMessage" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "sessionId" TEXT NOT NULL,
-    "role" TEXT NOT NULL,
-    "content" TEXT,
-    "name" TEXT,
-    "toolCallId" TEXT,
-    "refusal" TEXT,
-    "toolCalls" JSONB,
-    "functionCall" JSONB,
-    "sequence" INTEGER NOT NULL,
-
-    CONSTRAINT "ChatMessage_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "UserBusinessUnderstanding_userId_key" ON "UserBusinessUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "UserBusinessUnderstanding_userId_idx" ON "UserBusinessUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "ChatSession_userId_updatedAt_idx" ON "ChatSession"("userId", "updatedAt");
-
--- CreateIndex
-CREATE INDEX "ChatMessage_sessionId_sequence_idx" ON "ChatMessage"("sessionId", "sequence");
-
--- CreateIndex
-CREATE UNIQUE INDEX "ChatMessage_sessionId_sequence_key" ON "ChatMessage"("sessionId", "sequence");
-
--- AddForeignKey
-ALTER TABLE "UserBusinessUnderstanding" ADD CONSTRAINT "UserBusinessUnderstanding_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "ChatMessage" ADD CONSTRAINT "ChatMessage_sessionId_fkey" FOREIGN KEY ("sessionId") REFERENCES "ChatSession"("id") ON DELETE CASCADE ON UPDATE CASCADE;

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

@@ -0,0 +1,46 @@
+-- CreateExtension
+-- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
+-- Create in public schema so vector type is available across all schemas
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'vector extension not available or already exists, skipping';
+END $$;
+
+-- CreateEnum
+CREATE TYPE "ContentType" AS ENUM ('STORE_AGENT', 'BLOCK', 'INTEGRATION', 'DOCUMENTATION', 'LIBRARY_AGENT');
+
+-- CreateTable
+CREATE TABLE "UnifiedContentEmbedding" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "contentType" "ContentType" NOT NULL,
+    "contentId" TEXT NOT NULL,
+    "userId" TEXT,
+    "embedding" public.vector(1536) NOT NULL,
+    "searchableText" TEXT NOT NULL,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "UnifiedContentEmbedding_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_idx" ON "UnifiedContentEmbedding"("contentType");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_userId_idx" ON "UnifiedContentEmbedding"("userId");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_userId_idx" ON "UnifiedContentEmbedding"("contentType", "userId");
+
+-- CreateIndex
+-- NULLS NOT DISTINCT ensures only one public (NULL userId) embedding per contentType+contentId
+-- Requires PostgreSQL 15+. Supabase uses PostgreSQL 15+.
+CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" ON "UnifiedContentEmbedding"("contentType", "contentId", "userId") NULLS NOT DISTINCT;
+
+-- CreateIndex
+-- HNSW index for fast vector similarity search on embeddings
+-- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

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

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

autogpt_platform/backend/pyproject.toml

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

autogpt_platform/backend/schema.prisma

@@ -1,14 +1,15 @@
 datasource db {
-  provider  = "postgresql"
-  url       = env("DATABASE_URL")
-  directUrl = env("DIRECT_URL")
+  provider   = "postgresql"
+  url        = env("DATABASE_URL")
+  directUrl  = env("DIRECT_URL")
+  extensions = [pgvector(map: "vector")]
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch"]
+  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -53,7 +54,6 @@ model User {
 
   Profile               Profile[]
   UserOnboarding        UserOnboarding?
-  BusinessUnderstanding UserBusinessUnderstanding?
   BuilderSearchHistory  BuilderSearchHistory[]
   StoreListings         StoreListing[]
   StoreListingReviews   StoreListingReview[]
@@ -122,43 +122,6 @@ model UserOnboarding {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-model UserBusinessUnderstanding {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String @unique
-  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
-
-  // User info
-  userName String?
-  jobTitle String?
-
-  // Business basics (string columns)
-  businessName String?
-  industry     String?
-  businessSize String? // "1-10", "11-50", "51-200", "201-1000", "1000+"
-  userRole     String? // Role in organization context (e.g., "decision maker", "implementer")
-
-  // Processes & activities (JSON arrays)
-  keyWorkflows    Json?
-  dailyActivities Json?
-
-  // Pain points & goals (JSON arrays)
-  painPoints      Json?
-  bottlenecks     Json?
-  manualTasks     Json?
-  automationGoals Json?
-
-  // Current tools (JSON arrays)
-  currentSoftware    Json?
-  existingAutomation Json?
-
-  additionalNotes String?
-
-  @@index([userId])
-}
-
 model BuilderSearchHistory {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
@@ -172,59 +135,6 @@ model BuilderSearchHistory {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-////////////////   CHAT SESSION TABLES   ///////////////////
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-
-model ChatSession {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String?
-
-  // Session metadata
-  title       String?
-  credentials Json    @default("{}") // Map of provider -> credential metadata
-
-  // Rate limiting counters (stored as JSON maps)
-  successfulAgentRuns      Json @default("{}") // Map of graph_id -> count
-  successfulAgentSchedules Json @default("{}") // Map of graph_id -> count
-
-  // Usage tracking
-  totalPromptTokens     Int @default(0)
-  totalCompletionTokens Int @default(0)
-
-  Messages ChatMessage[]
-
-  @@index([userId, updatedAt])
-}
-
-model ChatMessage {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-
-  sessionId String
-  Session   ChatSession @relation(fields: [sessionId], references: [id], onDelete: Cascade)
-
-  // Message content
-  role         String // "user", "assistant", "system", "tool", "function"
-  content      String?
-  name         String?
-  toolCallId   String?
-  refusal      String?
-  toolCalls    Json? // List of tool calls for assistant messages
-  functionCall Json? // Deprecated but kept for compatibility
-
-  // Ordering within session
-  sequence Int
-
-  @@unique([sessionId, sequence])
-  @@index([sessionId, sequence])
-}
-
 // This model describes the Agent Graph/Flow (Multi Agent System).
 model AgentGraph {
   id        String    @default(uuid())
@@ -818,20 +728,19 @@ view StoreAgent {
   agent_output_demo String?
   agent_image       String[]
 
-  featured           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)
+  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
@@ -990,12 +899,52 @@ 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])
   @@index([reviewerId])
   @@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())

autogpt_platform/backend/snapshots/grph_single

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

autogpt_platform/backend/snapshots/grphs_all

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

autogpt_platform/backend/snapshots/lib_agts_search

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

autogpt_platform/backend/snapshots/sub_success

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

autogpt_platform/docker-compose.platform.yml

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

autogpt_platform/frontend/package.json

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

autogpt_platform/frontend/pnpm-lock.yaml

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

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

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

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

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

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

@@ -68,7 +68,10 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
                 <Tooltip>
                   <TooltipTrigger asChild>
                     <div>
-                      <Text variant="large-semibold" className="line-clamp-1">
+                      <Text
+                        variant="large-semibold"
+                        className="line-clamp-1 hover:cursor-text"
+                      >
                         {beautifyString(title).replace("Block", "").trim()}
                       </Text>
                     </div>

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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