chore: remove unused prisma.types import from db_test.py

Co-authored-by: Nicholas Tindle <ntindle@users.noreply.github.com>

.branchlet.json

@@ -1,37 +0,0 @@
-{
-  "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

@@ -1,9 +1,6 @@
 # Ignore everything by default, selectively add things to context
 *
 
-# Documentation (for embeddings/search)
-!docs/
-
 # Platform - Libs
 !autogpt_platform/autogpt_libs/autogpt_libs/
 !autogpt_platform/autogpt_libs/pyproject.toml
@@ -19,7 +16,6 @@
 !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 && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # 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 && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # 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 && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
@@ -108,16 +108,6 @@ 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 && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       - id: supabase
         name: Start Supabase
@@ -176,7 +176,7 @@ jobs:
           }
 
       - name: Run Database Migrations
-        run: poetry run prisma migrate deploy
+        run: poetry run prisma migrate dev --name updates
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

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

@@ -11,7 +11,6 @@ on:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
   merge_group:
-  workflow_dispatch:
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
@@ -152,14 +151,6 @@ jobs:
         run: |
           cp ../.env.default ../.env
 
-      - name: Copy backend .env and set OpenAI API key
-        run: |
-          cp ../backend/.env.default ../backend/.env
-          echo "OPENAI_INTERNAL_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> ../backend/.env
-        env:
-          # Used by E2E test data script to generate embeddings for approved store agents
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -235,25 +226,13 @@ jobs:
 
       - name: Run Playwright tests
         run: pnpm test:no-build
-        continue-on-error: false
 
-      - name: Upload Playwright report
-        if: always()
+      - name: Upload Playwright artifacts
+        if: failure()
         uses: actions/upload-artifact@v4
         with:
           name: playwright-report
           path: playwright-report
-          if-no-files-found: ignore
-          retention-days: 3
-
-      - name: Upload Playwright test results
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: playwright-test-results
-          path: test-results
-          if-no-files-found: ignore
-          retention-days: 3
 
       - name: Print Final Docker Compose logs
         if: always()

autogpt_platform/Makefile

@@ -6,14 +6,12 @@ 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:
@@ -35,7 +33,6 @@ 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
@@ -61,4 +58,4 @@ 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/.env.default

@@ -58,13 +58,6 @@ V0_API_KEY=
 OPEN_ROUTER_API_KEY=
 NVIDIA_API_KEY=
 
-# Langfuse Prompt Management
-# Used for managing the CoPilot system prompt externally
-# Get credentials from https://cloud.langfuse.com or your self-hosted instance
-LANGFUSE_PUBLIC_KEY=
-LANGFUSE_SECRET_KEY=
-LANGFUSE_HOST=https://cloud.langfuse.com
-
 # OAuth Credentials
 # For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
 # e.g. http://localhost:3000/auth/integrations/oauth_callback

autogpt_platform/backend/.gitignore

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

autogpt_platform/backend/Dockerfile

@@ -48,8 +48,7 @@ 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
-COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
-RUN poetry run prisma generate && poetry run gen-prisma-stub
+RUN poetry run prisma generate
 
 FROM debian:13-slim AS server_dependencies
 
@@ -100,7 +99,6 @@ COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migration
 FROM server_dependencies AS server
 
 COPY autogpt_platform/backend /app/autogpt_platform/backend
-COPY docs /app/docs
 RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000

autogpt_platform/backend/backend/api/external/v1/tools.py

@@ -70,7 +70,7 @@ class RunAgentRequest(BaseModel):
     )
 
 
-def _create_ephemeral_session(user_id: str) -> ChatSession:
+def _create_ephemeral_session(user_id: str | None) -> ChatSession:
     """Create an ephemeral session for stateless API requests."""
     return ChatSession.new(user_id)
 

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

@@ -1,6 +1,7 @@
 """Configuration management for chat system."""
 
 import os
+from pathlib import Path
 
 from pydantic import Field, field_validator
 from pydantic_settings import BaseSettings
@@ -11,11 +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(
@@ -26,6 +23,12 @@ class ChatConfig(BaseSettings):
     # Session TTL Configuration - 12 hours
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
+    # System Prompt Configuration
+    system_prompt_path: str = Field(
+        default="prompts/chat_system.md",
+        description="Path to system prompt file relative to chat module",
+    )
+
     # Streaming Configuration
     max_context_messages: int = Field(
         default=50, ge=1, le=200, description="Maximum context messages"
@@ -38,13 +41,6 @@ class ChatConfig(BaseSettings):
         default=3, description="Maximum number of agent schedules"
     )
 
-    # Langfuse Prompt Management Configuration
-    # Note: Langfuse credentials are in Settings().secrets (settings.py)
-    langfuse_prompt_name: str = Field(
-        default="CoPilot Prompt",
-        description="Name of the prompt in Langfuse to fetch",
-    )
-
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):
@@ -76,11 +72,43 @@ 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(self, **template_vars) -> str:
+        """Load and render the system prompt from file.
+
+        Args:
+            **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 / self.system_prompt_path
+
+        # Check for .j2 extension first (Jinja2 template)
+        j2_path = Path(str(prompt_path) + ".j2")
+        if j2_path.exists():
+            try:
+                from jinja2 import Template
+
+                template = Template(j2_path.read_text())
+                return template.render(**template_vars)
+            except ImportError:
+                # Jinja2 not installed, fall back to reading as plain text
+                return j2_path.read_text()
+
+        # Check for markdown file
+        if prompt_path.exists():
+            content = prompt_path.read_text()
+
+            # Simple variable substitution if Jinja2 is not available
+            for key, value in template_vars.items():
+                placeholder = f"{{{key}}}"
+                content = content.replace(placeholder, str(value))
+
+            return content
+        raise FileNotFoundError(f"System prompt file not found: {prompt_path}")
 
     class Config:
         """Pydantic config."""

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

@@ -1,249 +0,0 @@
-"""Database operations for chat sessions."""
-
-import asyncio
-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,
-    ChatSessionWhereInput,
-)
-
-from backend.data.db import transaction
-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 - Prisma Python client doesn't support
-        # order_by in include clauses (unlike Prisma JS), so we sort after fetching
-        session.Messages.sort(key=lambda m: m.sequence)
-    return session
-
-
-async def create_chat_session(
-    session_id: str,
-    user_id: str,
-) -> 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:
-        # Sort in Python - Prisma Python doesn't support order_by in include clauses
-        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 input dict dynamically rather than using ChatMessageCreateInput directly
-    # because Prisma's TypedDict validation rejects optional fields set to None.
-    # We only include fields that have values, then cast at the end.
-    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)
-
-    # Run message create and session timestamp update in parallel for lower latency
-    _, message = await asyncio.gather(
-        PrismaChatSession.prisma().update(
-            where={"id": session_id},
-            data={"updatedAt": datetime.now(UTC)},
-        ),
-        PrismaChatMessage.prisma().create(data=cast(ChatMessageCreateInput, data)),
-    )
-    return message
-
-
-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.
-
-    Uses a transaction for atomicity - if any message creation fails,
-    the entire batch is rolled back.
-    """
-    if not messages:
-        return []
-
-    created_messages = []
-
-    async with transaction() as tx:
-        for i, msg in enumerate(messages):
-            # Build input dict dynamically rather than using ChatMessageCreateInput
-            # directly because Prisma's TypedDict validation rejects optional fields
-            # set to None. We only include fields that have values, then cast.
-            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(tx).create(
-                data=cast(ChatMessageCreateInput, data)
-            )
-            created_messages.append(created)
-
-        # Update session's updatedAt timestamp within the same transaction.
-        # Note: Token usage (total_prompt_tokens, total_completion_tokens) is updated
-        # separately via update_chat_session() after streaming completes.
-        await PrismaChatSession.prisma(tx).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, user_id: str | None = None) -> bool:
-    """Delete a chat session and all its messages.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: If provided, validates that the session belongs to this user
-            before deletion. This prevents unauthorized deletion of other
-            users' sessions.
-
-    Returns:
-        True if deleted successfully, False otherwise.
-    """
-    try:
-        # Build typed where clause with optional user_id validation
-        where_clause: ChatSessionWhereInput = {"id": session_id}
-        if user_id is not None:
-            where_clause["userId"] = user_id
-
-        result = await PrismaChatSession.prisma().delete_many(where=where_clause)
-        if result == 0:
-            logger.warning(
-                f"No session deleted for {session_id} "
-                f"(user_id validation: {user_id is not None})"
-            )
-            return False
-        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

@@ -1,9 +1,6 @@
-import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any
-from weakref import WeakValueDictionary
 
 from openai.types.chat import (
     ChatCompletionAssistantMessageParam,
@@ -19,63 +16,17 @@ 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 DatabaseError, RedisError
+from backend.util.exceptions import RedisError
 
-from . import db as chat_db
 from .config import ChatConfig
 
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 
 
-def _parse_json_field(value: str | dict | list | None, default: Any = None) -> Any:
-    """Parse a JSON field that may be stored as string or already parsed."""
-    if value is None:
-        return default
-    if isinstance(value, str):
-        return json.loads(value)
-    return value
-
-
-# Redis cache key prefix for chat sessions
-CHAT_SESSION_CACHE_PREFIX = "chat:session:"
-
-
-def _get_session_cache_key(session_id: str) -> str:
-    """Get the Redis cache key for a chat session."""
-    return f"{CHAT_SESSION_CACHE_PREFIX}{session_id}"
-
-
-# Session-level locks to prevent race conditions during concurrent upserts.
-# Uses WeakValueDictionary to automatically garbage collect locks when no longer referenced,
-# preventing unbounded memory growth while maintaining lock semantics for active sessions.
-# Invalidation: Locks are auto-removed by GC when no coroutine holds a reference (after
-# async with lock: completes). Explicit cleanup also occurs in delete_chat_session().
-_session_locks: WeakValueDictionary[str, asyncio.Lock] = WeakValueDictionary()
-_session_locks_mutex = asyncio.Lock()
-
-
-async def _get_session_lock(session_id: str) -> asyncio.Lock:
-    """Get or create a lock for a specific session to prevent concurrent upserts.
-
-    Uses WeakValueDictionary for automatic cleanup: locks are garbage collected
-    when no coroutine holds a reference to them, preventing memory leaks from
-    unbounded growth of session locks.
-    """
-    async with _session_locks_mutex:
-        lock = _session_locks.get(session_id)
-        if lock is None:
-            lock = asyncio.Lock()
-            _session_locks[session_id] = lock
-        return lock
-
-
 class ChatMessage(BaseModel):
     role: str
     content: str | None = None
@@ -94,8 +45,7 @@ class Usage(BaseModel):
 
 class ChatSession(BaseModel):
     session_id: str
-    user_id: str
-    title: str | None = None
+    user_id: str | None
     messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
@@ -105,11 +55,10 @@ class ChatSession(BaseModel):
     successful_agent_schedules: dict[str, int] = {}
 
     @staticmethod
-    def new(user_id: str) -> "ChatSession":
+    def new(user_id: str | None) -> "ChatSession":
         return ChatSession(
             session_id=str(uuid.uuid4()),
             user_id=user_id,
-            title=None,
             messages=[],
             usage=[],
             credentials={},
@@ -117,61 +66,6 @@ class ChatSession(BaseModel):
             updated_at=datetime.now(UTC),
         )
 
-    @staticmethod
-    def from_db(
-        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:
-                messages.append(
-                    ChatMessage(
-                        role=msg.role,
-                        content=msg.content,
-                        name=msg.name,
-                        tool_call_id=msg.toolCallId,
-                        refusal=msg.refusal,
-                        tool_calls=_parse_json_field(msg.toolCalls),
-                        function_call=_parse_json_field(msg.functionCall),
-                    )
-                )
-
-        # Parse JSON fields from Prisma
-        credentials = _parse_json_field(prisma_session.credentials, default={})
-        successful_agent_runs = _parse_json_field(
-            prisma_session.successfulAgentRuns, default={}
-        )
-        successful_agent_schedules = _parse_json_field(
-            prisma_session.successfulAgentSchedules, default={}
-        )
-
-        # 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:
@@ -261,337 +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 = _get_session_cache_key(session_id)
+async def get_chat_session(
+    session_id: str,
+    user_id: str | None,
+) -> ChatSession | None:
+    """Get a chat session by ID."""
+    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:
+        logger.warning(f"Session {session_id} not found in Redis")
         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 = _get_session_cache_key(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_db(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 = None,
-) -> ChatSession | None:
-    """Get a chat session by ID.
-
-    Checks Redis cache first, falls back to database if not found.
-    Caches database results back to Redis.
-
-    Args:
-        session_id: The session ID to fetch.
-        user_id: If provided, validates that the session belongs to this user.
-            If None, ownership is not validated (admin/system access).
-    """
-    # Try cache first
-    try:
-        session = await _get_session_from_cache(session_id)
-        if session:
-            # Verify user ownership if user_id was provided for validation
-            if 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}")
-
-    # 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")
-        return None
-
-    # Verify user ownership if user_id was provided for validation
-    if user_id is not None and session.user_id != user_id:
+    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.
+    """Update a chat session with the given messages."""
 
-    Uses session-level locking to prevent race conditions when concurrent
-    operations (e.g., background title update and main stream handler)
-    attempt to upsert the same session simultaneously.
+    redis_key = f"chat:session:{session.session_id}"
 
-    Raises:
-        DatabaseError: If the database write fails. The cache is still updated
-            as a best-effort optimization, but the error is propagated to ensure
-            callers are aware of the persistence failure.
-        RedisError: If the cache write fails (after successful DB write).
-    """
-    # Acquire session-specific lock to prevent concurrent upserts
-    lock = await _get_session_lock(session.session_id)
+    async_redis = await get_redis_async()
+    resp = await async_redis.setex(
+        redis_key, config.session_ttl, session.model_dump_json()
+    )
 
-    async with lock:
-        # Get existing message count from DB for incremental saves
-        existing_message_count = await chat_db.get_chat_session_message_count(
-            session.session_id
+    if not resp:
+        raise RedisError(
+            f"Failed to persist chat session {session.session_id} to Redis: {resp}"
         )
 
-        db_error: Exception | None = None
-
-        # Save to database (primary storage)
-        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}"
-            )
-            db_error = e
-
-        # Save to cache (best-effort, even if DB failed)
-        try:
-            await _cache_session(session)
-        except Exception as e:
-            # If DB succeeded but cache failed, raise cache error
-            if db_error is None:
-                raise RedisError(
-                    f"Failed to persist chat session {session.session_id} to Redis: {e}"
-                ) from e
-            # If both failed, log cache error but raise DB error (more critical)
-            logger.warning(
-                f"Cache write also failed for session {session.session_id}: {e}"
-            )
-
-        # Propagate DB error after attempting cache (prevents data loss)
-        if db_error is not None:
-            raise DatabaseError(
-                f"Failed to persist chat session {session.session_id} to database"
-            ) from db_error
-
-        return session
-
-
-async def create_chat_session(user_id: str) -> ChatSession:
-    """Create a new chat session and persist it.
-
-    Raises:
-        DatabaseError: If the database write fails. We fail fast to ensure
-            callers never receive a non-persisted session that only exists
-            in cache (which would be lost when the cache expires).
-    """
-    session = ChatSession.new(user_id)
-
-    # Create in database first - fail fast if this fails
-    try:
-        await chat_db.create_chat_session(
-            session_id=session.session_id,
-            user_id=user_id,
-        )
-    except Exception as e:
-        logger.error(f"Failed to create session {session.session_id} in database: {e}")
-        raise DatabaseError(
-            f"Failed to create chat session {session.session_id} in database"
-        ) from e
-
-    # Cache the session (best-effort optimization, DB is source of truth)
-    try:
-        await _cache_session(session)
-    except Exception as e:
-        logger.warning(f"Failed to cache new session {session.session_id}: {e}")
-
     return session
-
-
-async def get_user_sessions(
-    user_id: str,
-    limit: int = 50,
-    offset: int = 0,
-) -> tuple[list[ChatSession], int]:
-    """Get chat sessions for a user from the database with total count.
-
-    Returns:
-        A tuple of (sessions, total_count) where total_count is the overall
-        number of sessions for the user (not just the current page).
-    """
-    prisma_sessions = await chat_db.get_user_chat_sessions(user_id, limit, offset)
-    total_count = await chat_db.get_user_session_count(user_id)
-
-    sessions = []
-    for prisma_session in prisma_sessions:
-        # Convert without messages for listing (lighter weight)
-        sessions.append(ChatSession.from_db(prisma_session, None))
-
-    return sessions, total_count
-
-
-async def delete_chat_session(session_id: str, user_id: str | None = None) -> bool:
-    """Delete a chat session from both cache and database.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: If provided, validates that the session belongs to this user
-            before deletion. This prevents unauthorized deletion.
-
-    Returns:
-        True if deleted successfully, False otherwise.
-    """
-    # Delete from database first (with optional user_id validation)
-    # This confirms ownership before invalidating cache
-    deleted = await chat_db.delete_chat_session(session_id, user_id)
-
-    if not deleted:
-        return False
-
-    # Only invalidate cache and clean up lock after DB confirms deletion
-    try:
-        redis_key = _get_session_cache_key(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}")
-
-    # Clean up session lock (belt-and-suspenders with WeakValueDictionary)
-    async with _session_locks_mutex:
-        _session_locks.pop(session_id, None)
-
-    return True
-
-
-async def update_session_title(session_id: str, title: str) -> bool:
-    """Update only the title of a chat session.
-
-    This is a lightweight operation that doesn't touch messages, avoiding
-    race conditions with concurrent message updates. Use this for background
-    title generation instead of upsert_chat_session.
-
-    Args:
-        session_id: The session ID to update.
-        title: The new title to set.
-
-    Returns:
-        True if updated successfully, False otherwise.
-    """
-    try:
-        result = await chat_db.update_chat_session(session_id=session_id, title=title)
-        if result is None:
-            logger.warning(f"Session {session_id} not found for title update")
-            return False
-
-        # Invalidate cache so next fetch gets updated title
-        try:
-            redis_key = _get_session_cache_key(session_id)
-            async_redis = await get_redis_async()
-            await async_redis.delete(redis_key)
-        except Exception as e:
-            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-
-        return True
-    except Exception as e:
-        logger.error(f"Failed to update title for session {session_id}: {e}")
-        return False

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

@@ -43,9 +43,9 @@ async def test_chatsession_serialization_deserialization():
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_chatsession_redis_storage(setup_test_user, test_user_id):
+async def test_chatsession_redis_storage():
 
-    s = ChatSession.new(user_id=test_user_id)
+    s = ChatSession.new(user_id=None)
     s.messages = messages
 
     s = await upsert_chat_session(s)
@@ -59,61 +59,12 @@ async def test_chatsession_redis_storage(setup_test_user, test_user_id):
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_chatsession_redis_storage_user_id_mismatch(
-    setup_test_user, test_user_id
-):
+async def test_chatsession_redis_storage_user_id_mismatch():
 
-    s = ChatSession.new(user_id=test_user_id)
+    s = ChatSession.new(user_id="abc123")
     s.messages = messages
     s = await upsert_chat_session(s)
 
-    s2 = await get_chat_session(s.session_id, "different_user_id")
+    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(setup_test_user, test_user_id):
-    """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=test_user_id)
-    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

@@ -0,0 +1,104 @@
+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>
+1. **find_agent** - Search for agents that solve the user's problem
+2. **run_agent** - Run or schedule an agent (automatically handles setup)
+</functions>
+
+## HOW run_agent WORKS
+
+The `run_agent` tool automatically handles the entire setup flow:
+
+1. **First call** (no inputs) → Returns available inputs so user can decide what values to use
+2. **Credentials check** → If missing, UI automatically prompts user to add them (you don't need to mention this)
+3. **Execution** → Runs when you provide `inputs` OR set `use_defaults=true`
+
+Parameters:
+- `username_agent_slug` (required): Agent identifier like "creator/agent-name"
+- `inputs`: Object with input values for the agent
+- `use_defaults`: Set to `true` to run with default values (only after user confirms)
+- `schedule_name` + `cron`: For scheduled execution
+
+## WORKFLOW
+
+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: Understand the Problem**
+- Ask maximum 1-2 targeted questions
+- Focus on: What business problem are they solving?
+- Move quickly to searching for solutions
+
+**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 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
+
+**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
+
+**For Scheduled Execution:**
+- Add `schedule_name` and `cron` parameters
+- Example: `run_agent(username_agent_slug="...", inputs={...}, schedule_name="Daily Report", cron="0 9 * * *")`
+
+## FUNCTION CALL FORMAT
+
+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
+
+**What You DON'T Do:**
+- Don't help with login (frontend handles this)
+- Don't mention or explain credentials to the user (frontend handles this automatically)
+- 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
+
+**What You DO:**
+- 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
+- Include the agent link in your response after successful execution
+
+**Error Handling:**
+- Authentication needed → "Please sign in via the interface"
+- Credentials missing → The UI handles this automatically. Focus on asking the user about input values instead.
+
+## RESPONSE STRUCTURE
+
+Before responding, wrap your analysis in <thinking> tags to systematically plan your approach:
+- 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: "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/response_model.py

@@ -1,10 +1,3 @@
-"""
-Response models for Vercel AI SDK UI Stream Protocol.
-
-This module implements the AI SDK UI Stream Protocol (v1) for streaming chat responses.
-See: https://ai-sdk.dev/docs/ai-sdk-ui/stream-protocol
-"""
-
 from enum import Enum
 from typing import Any
 
@@ -12,133 +5,97 @@ from pydantic import BaseModel, Field
 
 
 class ResponseType(str, Enum):
-    """Types of streaming responses following AI SDK protocol."""
+    """Types of streaming responses."""
 
-    # Message lifecycle
-    START = "start"
-    FINISH = "finish"
-
-    # Text streaming
-    TEXT_START = "text-start"
-    TEXT_DELTA = "text-delta"
-    TEXT_END = "text-end"
-
-    # Tool interaction
-    TOOL_INPUT_START = "tool-input-start"
-    TOOL_INPUT_AVAILABLE = "tool-input-available"
-    TOOL_OUTPUT_AVAILABLE = "tool-output-available"
-
-    # Other
+    TEXT_CHUNK = "text_chunk"
+    TEXT_ENDED = "text_ended"
+    TOOL_CALL = "tool_call"
+    TOOL_CALL_START = "tool_call_start"
+    TOOL_RESPONSE = "tool_response"
     ERROR = "error"
     USAGE = "usage"
+    STREAM_END = "stream_end"
 
 
 class StreamBaseResponse(BaseModel):
     """Base response model for all streaming responses."""
 
     type: ResponseType
+    timestamp: str | None = None
 
     def to_sse(self) -> str:
         """Convert to SSE format."""
         return f"data: {self.model_dump_json()}\n\n"
 
 
-# ========== Message Lifecycle ==========
+class StreamTextChunk(StreamBaseResponse):
+    """Streaming text content from the assistant."""
+
+    type: ResponseType = ResponseType.TEXT_CHUNK
+    content: str = Field(..., description="Text content chunk")
 
 
-class StreamStart(StreamBaseResponse):
-    """Start of a new message."""
-
-    type: ResponseType = ResponseType.START
-    messageId: str = Field(..., description="Unique message ID")
-
-
-class StreamFinish(StreamBaseResponse):
-    """End of message/stream."""
-
-    type: ResponseType = ResponseType.FINISH
-
-
-# ========== Text Streaming ==========
-
-
-class StreamTextStart(StreamBaseResponse):
-    """Start of a text block."""
-
-    type: ResponseType = ResponseType.TEXT_START
-    id: str = Field(..., description="Text block ID")
-
-
-class StreamTextDelta(StreamBaseResponse):
-    """Streaming text content delta."""
-
-    type: ResponseType = ResponseType.TEXT_DELTA
-    id: str = Field(..., description="Text block ID")
-    delta: str = Field(..., description="Text content delta")
-
-
-class StreamTextEnd(StreamBaseResponse):
-    """End of a text block."""
-
-    type: ResponseType = ResponseType.TEXT_END
-    id: str = Field(..., description="Text block ID")
-
-
-# ========== Tool Interaction ==========
-
-
-class StreamToolInputStart(StreamBaseResponse):
+class StreamToolCallStart(StreamBaseResponse):
     """Tool call started notification."""
 
-    type: ResponseType = ResponseType.TOOL_INPUT_START
-    toolCallId: str = Field(..., description="Unique tool call ID")
-    toolName: str = Field(..., description="Name of the tool being called")
+    type: ResponseType = ResponseType.TOOL_CALL_START
+    tool_name: str = Field(..., description="Name of the tool that was executed")
+    tool_id: str = Field(..., description="Unique tool call ID")
 
 
-class StreamToolInputAvailable(StreamBaseResponse):
-    """Tool input is ready for execution."""
+class StreamToolCall(StreamBaseResponse):
+    """Tool invocation notification."""
 
-    type: ResponseType = ResponseType.TOOL_INPUT_AVAILABLE
-    toolCallId: str = Field(..., description="Unique tool call ID")
-    toolName: str = Field(..., description="Name of the tool being called")
-    input: dict[str, Any] = Field(
-        default_factory=dict, description="Tool input arguments"
+    type: ResponseType = ResponseType.TOOL_CALL
+    tool_id: str = Field(..., description="Unique tool call ID")
+    tool_name: str = Field(..., description="Name of the tool being called")
+    arguments: dict[str, Any] = Field(
+        default_factory=dict, description="Tool arguments"
     )
 
 
-class StreamToolOutputAvailable(StreamBaseResponse):
+class StreamToolExecutionResult(StreamBaseResponse):
     """Tool execution result."""
 
-    type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
-    toolCallId: str = Field(..., description="Tool call ID this responds to")
-    output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Additional fields for internal use (not part of AI SDK spec but useful)
-    toolName: str | None = Field(
-        default=None, description="Name of the tool that was executed"
-    )
+    type: ResponseType = ResponseType.TOOL_RESPONSE
+    tool_id: str = Field(..., description="Tool call ID this responds to")
+    tool_name: str = Field(..., description="Name of the tool that was executed")
+    result: str | dict[str, Any] = Field(..., description="Tool execution result")
     success: bool = Field(
         default=True, description="Whether the tool execution succeeded"
     )
 
 
-# ========== Other ==========
-
-
 class StreamUsage(StreamBaseResponse):
     """Token usage statistics."""
 
     type: ResponseType = ResponseType.USAGE
-    promptTokens: int = Field(..., description="Number of prompt tokens")
-    completionTokens: int = Field(..., description="Number of completion tokens")
-    totalTokens: int = Field(..., description="Total number of tokens")
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
 
 
 class StreamError(StreamBaseResponse):
     """Error response."""
 
     type: ResponseType = ResponseType.ERROR
-    errorText: str = Field(..., description="Error message text")
+    message: str = Field(..., description="Error message")
     code: str | None = Field(default=None, description="Error code")
     details: dict[str, Any] | None = Field(
         default=None, description="Additional error details"
     )
+
+
+class StreamTextEnded(StreamBaseResponse):
+    """Text streaming completed marker."""
+
+    type: ResponseType = ResponseType.TEXT_ENDED
+
+
+class StreamEnd(StreamBaseResponse):
+    """End of stream marker."""
+
+    type: ResponseType = ResponseType.STREAM_END
+    summary: dict[str, Any] | None = Field(
+        default=None, description="Stream summary statistics"
+    )

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

@@ -13,25 +13,12 @@ from backend.util.exceptions import NotFoundError
 
 from . import service as chat_service
 from .config import ChatConfig
-from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
 
 config = ChatConfig()
 
 
 logger = logging.getLogger(__name__)
 
-
-async def _validate_and_get_session(
-    session_id: str,
-    user_id: str | None,
-) -> ChatSession:
-    """Validate session exists and belongs to user."""
-    session = await get_chat_session(session_id, user_id)
-    if not session:
-        raise NotFoundError(f"Session {session_id} not found.")
-    return session
-
-
 router = APIRouter(
     tags=["chat"],
 )
@@ -39,14 +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."""
 
@@ -65,77 +44,22 @@ 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, total_count = await 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=session.title,
-            )
-            for session in sessions
-        ],
-        total=total_count,
-    )
-
-
 @router.post(
     "/sessions",
 )
 async def create_session(
-    user_id: Annotated[str, Depends(auth.get_user_id)],
+    user_id: Annotated[str | None, Depends(auth.get_user_id)],
 ) -> CreateSessionResponse:
     """
     Create a new chat session.
 
-    Initiates a new chat session for the authenticated user.
+    Initiates a new chat session for either an authenticated or anonymous user.
 
     Args:
-        user_id: The authenticated user ID parsed from the JWT (required).
+        user_id: The optional authenticated user ID parsed from the JWT. If missing, creates an anonymous session.
 
     Returns:
         CreateSessionResponse: Details of the created session.
@@ -143,15 +67,15 @@ async def create_session(
     """
     logger.info(
         f"Creating session with user_id: "
-        f"...{user_id[-8:] if len(user_id) > 8 else '<redacted>'}"
+        f"...{user_id[-8:] if user_id and len(user_id) > 8 else '<redacted>'}"
     )
 
-    session = await create_chat_session(user_id)
+    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,
+        user_id=session.user_id or None,
     )
 
 
@@ -175,88 +99,29 @@ async def get_session(
         SessionDetailResponse: Details for the requested session; raises NotFoundError if not found.
 
     """
-    session = await get_chat_session(session_id, user_id)
+    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.
-
-    """
-    session = await _validate_and_get_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()
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
-        },
+        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
@@ -272,7 +137,14 @@ async def stream_chat_get(
         StreamingResponse: SSE-formatted response chunks.
 
     """
-    session = await _validate_and_get_session(session_id, user_id)
+    # 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(
@@ -283,8 +155,6 @@ async def stream_chat_get(
             session=session,  # Pass pre-fetched session to avoid double-fetch
         ):
             yield chunk.to_sse()
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -293,7 +163,6 @@ async def stream_chat_get(
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
             "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -332,28 +201,16 @@ async def health_check() -> dict:
     """
     Health check endpoint for the chat service.
 
-    Performs a full cycle test of session creation and retrieval. Should always return healthy
+    Performs a full cycle test of session creation, assignment, and retrieval. Should always return healthy
     if the service and data layer are operational.
 
     Returns:
         dict: A status dictionary indicating health, service name, and API version.
 
     """
-    from backend.data.user import get_or_create_user
-
-    # Ensure health check user exists (required for FK constraint)
-    health_check_user_id = "health-check-user"
-    await get_or_create_user(
-        {
-            "sub": health_check_user_id,
-            "email": "health-check@system.local",
-            "user_metadata": {"name": "Health Check User"},
-        }
-    )
-
-    # Create and retrieve session to verify full data layer
-    session = await create_chat_session(health_check_user_id)
-    await get_chat_session(session.session_id, health_check_user_id)
+    session = await chat_service.create_chat_session(None)
+    await chat_service.assign_user_to_session(session.session_id, "test_user")
+    await chat_service.get_session(session.session_id, "test_user")
 
     return {
         "status": "healthy",

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

@@ -4,19 +4,18 @@ from os import getenv
 import pytest
 
 from . import service as chat_service
-from .model import create_chat_session, get_chat_session, upsert_chat_session
 from .response_model import (
+    StreamEnd,
     StreamError,
-    StreamFinish,
-    StreamTextDelta,
-    StreamToolOutputAvailable,
+    StreamTextChunk,
+    StreamToolExecutionResult,
 )
 
 logger = logging.getLogger(__name__)
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_stream_chat_completion(setup_test_user, test_user_id):
+async def test_stream_chat_completion():
     """
     Test the stream_chat_completion function.
     """
@@ -24,7 +23,7 @@ async def test_stream_chat_completion(setup_test_user, test_user_id):
     if not api_key:
         return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
 
-    session = await create_chat_session(test_user_id)
+    session = await chat_service.create_chat_session()
 
     has_errors = False
     has_ended = False
@@ -35,9 +34,9 @@ async def test_stream_chat_completion(setup_test_user, test_user_id):
         logger.info(chunk)
         if isinstance(chunk, StreamError):
             has_errors = True
-        if isinstance(chunk, StreamTextDelta):
-            assistant_message += chunk.delta
-        if isinstance(chunk, StreamFinish):
+        if isinstance(chunk, StreamTextChunk):
+            assistant_message += chunk.content
+        if isinstance(chunk, StreamEnd):
             has_ended = True
 
     assert has_ended, "Chat completion did not end"
@@ -46,7 +45,7 @@ async def test_stream_chat_completion(setup_test_user, test_user_id):
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user_id):
+async def test_stream_chat_completion_with_tool_calls():
     """
     Test the stream_chat_completion function.
     """
@@ -54,8 +53,8 @@ async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user
     if not api_key:
         return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
 
-    session = await create_chat_session(test_user_id)
-    session = await upsert_chat_session(session)
+    session = await chat_service.create_chat_session()
+    session = await chat_service.upsert_chat_session(session)
 
     has_errors = False
     has_ended = False
@@ -69,14 +68,14 @@ async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user
         if isinstance(chunk, StreamError):
             has_errors = True
 
-        if isinstance(chunk, StreamFinish):
+        if isinstance(chunk, StreamEnd):
             has_ended = True
-        if isinstance(chunk, StreamToolOutputAvailable):
+        if isinstance(chunk, StreamToolExecutionResult):
             had_tool_calls = True
 
     assert has_ended, "Chat completion did not end"
     assert not has_errors, "Error occurred while streaming chat completion"
     assert had_tool_calls, "Tool calls did not occur"
-    session = await get_chat_session(session.session_id)
+    session = await chat_service.get_session(session.session_id)
     assert session, "Session not found"
     assert session.usage, "Usage is empty"

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

@@ -4,40 +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_block import FindBlockTool
-from .find_library_agent import FindLibraryAgentTool
-from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
-from .run_block import RunBlockTool
-from .search_docs import SearchDocsTool
 
 if TYPE_CHECKING:
-    from backend.api.features.chat.response_model import StreamToolOutputAvailable
+    from backend.api.features.chat.response_model import StreamToolExecutionResult
 
-# Single source of truth for all tools
-TOOL_REGISTRY: dict[str, BaseTool] = {
-    "add_understanding": AddUnderstandingTool(),
-    "find_agent": FindAgentTool(),
-    "find_block": FindBlockTool(),
-    "find_library_agent": FindLibraryAgentTool(),
-    "run_agent": RunAgentTool(),
-    "run_block": RunBlockTool(),
-    "agent_output": AgentOutputTool(),
-    "search_docs": SearchDocsTool(),
-    "get_doc_page": GetDocPageTool(),
-}
+# Initialize tool instances
+find_agent_tool = FindAgentTool()
+run_agent_tool = RunAgentTool()
 
-# Export individual tool instances for backwards compatibility
-find_agent_tool = TOOL_REGISTRY["find_agent"]
-run_agent_tool = TOOL_REGISTRY["run_agent"]
-
-# Generated from registry for OpenAI API
+# Export tools as OpenAI format
 tools: list[ChatCompletionToolParam] = [
-    tool.as_openai_tool() for tool in TOOL_REGISTRY.values()
+    find_agent_tool.as_openai_tool(),
+    run_agent_tool.as_openai_tool(),
 ]
 
 
@@ -47,9 +28,14 @@ async def execute_tool(
     user_id: str | None,
     session: ChatSession,
     tool_call_id: str,
-) -> "StreamToolOutputAvailable":
-    """Execute a tool by name."""
-    tool = TOOL_REGISTRY.get(tool_name)
-    if not tool:
+) -> "StreamToolExecutionResult":
+
+    tool_map: dict[str, BaseTool] = {
+        "find_agent": find_agent_tool,
+        "run_agent": run_agent_tool,
+    }
+    if tool_name not in tool_map:
         raise ValueError(f"Tool {tool_name} not found")
-    return await tool.execute(user_id, session, tool_call_id, **parameters)
+    return await tool_map[tool_name].execute(
+        user_id, session, tool_call_id, **parameters
+    )

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
@@ -18,7 +17,7 @@ from backend.data.user import get_or_create_user
 from backend.integrations.credentials_store import IntegrationCredentialsStore
 
 
-def make_session(user_id: str):
+def make_session(user_id: str | None = None):
     return ChatSession(
         session_id=str(uuid.uuid4()),
         user_id=user_id,
@@ -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

@@ -1,119 +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]:
-        # Auto-generate from Pydantic model schema
-        schema = BusinessUnderstandingInput.model_json_schema()
-        properties = {}
-        for field_name, field_schema in schema.get("properties", {}).items():
-            prop: dict[str, Any] = {"description": field_schema.get("description", "")}
-            # Handle anyOf for Optional types
-            if "anyOf" in field_schema:
-                for option in field_schema["anyOf"]:
-                    if option.get("type") != "null":
-                        prop["type"] = option.get("type", "string")
-                        if "items" in option:
-                            prop["items"] = option["items"]
-                        break
-            else:
-                prop["type"] = field_schema.get("type", "string")
-                if "items" in field_schema:
-                    prop["items"] = field_schema["items"]
-            properties[field_name] = prop
-        return {"type": "object", "properties": properties, "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 from kwargs (only include fields defined in the model)
-        valid_fields = set(BusinessUnderstandingInput.model_fields.keys())
-        input_data = BusinessUnderstandingInput(
-            **{k: v for k, v in kwargs.items() if k in valid_fields}
-        )
-
-        # Track which fields were updated
-        updated_fields = [
-            k for k, v in kwargs.items() if k in valid_fields and v is not None
-        ]
-
-        # Upsert with merge
-        understanding = await upsert_business_understanding(user_id, input_data)
-
-        # Build current understanding summary (filter out empty values)
-        current_understanding = {
-            k: v
-            for k, v in understanding.model_dump(
-                exclude={"id", "user_id", "created_at", "updated_at"}
-            ).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

@@ -1,446 +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", "yesterday", "today", "last week", "last 7 days",
-    "last month", "last 30 days", ISO date "YYYY-MM-DD", ISO datetime.
-    """
-    if not time_expr or time_expr.lower() == "latest":
-        return None, None
-
-    now = datetime.now(timezone.utc)
-    today_start = now.replace(hour=0, minute=0, second=0, microsecond=0)
-    expr = time_expr.lower().strip()
-
-    # Relative time expressions lookup
-    relative_times: dict[str, tuple[datetime, datetime]] = {
-        "yesterday": (today_start - timedelta(days=1), today_start),
-        "today": (today_start, now),
-        "last week": (now - timedelta(days=7), now),
-        "last 7 days": (now - timedelta(days=7), now),
-        "last month": (now - timedelta(days=30), now),
-        "last 30 days": (now - timedelta(days=30), now),
-    }
-    if expr in relative_times:
-        return relative_times[expr]
-
-    # Try ISO date format (YYYY-MM-DD)
-    date_match = re.match(r"^(\d{4})-(\d{2})-(\d{2})$", expr)
-    if date_match:
-        try:
-            year, month, day = map(int, date_match.groups())
-            start = datetime(year, month, day, 0, 0, 0, tzinfo=timezone.utc)
-            return start, start + timedelta(days=1)
-        except ValueError:
-            # Invalid date components (e.g., month=13, day=32)
-            pass
-
-    # Try ISO datetime
-    try:
-        parsed = datetime.fromisoformat(expr.replace("Z", "+00:00"))
-        if parsed.tzinfo is None:
-            parsed = parsed.replace(tzinfo=timezone.utc)
-        return parsed - timedelta(hours=1), parsed + timedelta(hours=1)
-    except ValueError:
-        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/agent_search.py

@@ -1,151 +0,0 @@
-"""Shared agent search functionality for find_agent and find_library_agent tools."""
-
-import logging
-from typing import Literal
-
-from backend.api.features.library import db as library_db
-from backend.api.features.store import db as store_db
-from backend.util.exceptions import DatabaseError, NotFoundError
-
-from .models import (
-    AgentInfo,
-    AgentsFoundResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-SearchSource = Literal["marketplace", "library"]
-
-
-async def search_agents(
-    query: str,
-    source: SearchSource,
-    session_id: str | None,
-    user_id: str | None = None,
-) -> ToolResponseBase:
-    """
-    Search for agents in marketplace or user library.
-
-    Args:
-        query: Search query string
-        source: "marketplace" or "library"
-        session_id: Chat session ID
-        user_id: User ID (required for library search)
-
-    Returns:
-        AgentsFoundResponse, NoResultsResponse, or ErrorResponse
-    """
-    if not query:
-        return ErrorResponse(
-            message="Please provide a search query", session_id=session_id
-        )
-
-    if source == "library" and not user_id:
-        return ErrorResponse(
-            message="User authentication required to search library",
-            session_id=session_id,
-        )
-
-    agents: list[AgentInfo] = []
-    try:
-        if source == "marketplace":
-            logger.info(f"Searching marketplace for: {query}")
-            results = await store_db.get_store_agents(search_query=query, page_size=5)
-            for agent in results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=f"{agent.creator}/{agent.slug}",
-                        name=agent.agent_name,
-                        description=agent.description or "",
-                        source="marketplace",
-                        in_library=False,
-                        creator=agent.creator,
-                        category="general",
-                        rating=agent.rating,
-                        runs=agent.runs,
-                        is_featured=False,
-                    )
-                )
-        else:  # library
-            logger.info(f"Searching user library for: {query}")
-            results = await library_db.list_library_agents(
-                user_id=user_id,  # type: ignore[arg-type]
-                search_term=query,
-                page_size=10,
-            )
-            for agent in 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,
-                    )
-                )
-        logger.info(f"Found {len(agents)} agents in {source}")
-    except NotFoundError:
-        pass
-    except DatabaseError as e:
-        logger.error(f"Error searching {source}: {e}", exc_info=True)
-        return ErrorResponse(
-            message=f"Failed to search {source}. Please try again.",
-            error=str(e),
-            session_id=session_id,
-        )
-
-    if not agents:
-        suggestions = (
-            [
-                "Try more general terms",
-                "Browse categories in the marketplace",
-                "Check spelling",
-            ]
-            if source == "marketplace"
-            else [
-                "Try different keywords",
-                "Use find_agent to search the marketplace",
-                "Check your library at /library",
-            ]
-        )
-        no_results_msg = (
-            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
-            if source == "marketplace"
-            else f"No agents matching '{query}' found in your library."
-        )
-        return NoResultsResponse(
-            message=no_results_msg, session_id=session_id, suggestions=suggestions
-        )
-
-    title = f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} "
-    title += (
-        f"for '{query}'"
-        if source == "marketplace"
-        else f"in your library for '{query}'"
-    )
-
-    message = (
-        "Now you have found some options for the user to choose from. "
-        "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents."
-        if source == "marketplace"
-        else "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."
-    )
-
-    return AgentsFoundResponse(
-        message=message,
-        title=title,
-        agents=agents,
-        count=len(agents),
-        session_id=session_id,
-    )

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

@@ -6,7 +6,7 @@ from typing import Any
 from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.response_model import StreamToolOutputAvailable
+from backend.api.features.chat.response_model import StreamToolExecutionResult
 
 from .models import ErrorResponse, NeedLoginResponse, ToolResponseBase
 
@@ -53,7 +53,7 @@ class BaseTool:
         session: ChatSession,
         tool_call_id: str,
         **kwargs,
-    ) -> StreamToolOutputAvailable:
+    ) -> StreamToolExecutionResult:
         """Execute the tool with authentication check.
 
         Args:
@@ -69,10 +69,10 @@ class BaseTool:
             logger.error(
                 f"Attempted tool call for {self.name} but user not authenticated"
             )
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=NeedLoginResponse(
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=NeedLoginResponse(
                     message=f"Please sign in to use {self.name}",
                     session_id=session.session_id,
                 ).model_dump_json(),
@@ -81,17 +81,17 @@ class BaseTool:
 
         try:
             result = await self._execute(user_id, session, **kwargs)
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=result.model_dump_json(),
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=result.model_dump_json(),
             )
         except Exception as e:
             logger.error(f"Error in {self.name}: {e}", exc_info=True)
-            return StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=self.name,
-                output=ErrorResponse(
+            return StreamToolExecutionResult(
+                tool_id=tool_call_id,
+                tool_name=self.name,
+                result=ErrorResponse(
                     message=f"An error occurred while executing {self.name}",
                     error=str(e),
                     session_id=session.session_id,

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

@@ -1,16 +1,26 @@
-"""Tool for discovering agents from marketplace."""
+"""Tool for discovering agents from marketplace and user library."""
 
+import logging
 from typing import Any
 
 from backend.api.features.chat.model import ChatSession
+from backend.api.features.store import db as store_db
+from backend.util.exceptions import DatabaseError, NotFoundError
 
-from .agent_search import search_agents
 from .base import BaseTool
-from .models import ToolResponseBase
+from .models import (
+    AgentCarouselResponse,
+    AgentInfo,
+    ErrorResponse,
+    NoResultsResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
 
 
 class FindAgentTool(BaseTool):
-    """Tool for discovering agents from the marketplace."""
+    """Tool for discovering agents based on user needs."""
 
     @property
     def name(self) -> str:
@@ -36,11 +46,84 @@ class FindAgentTool(BaseTool):
         }
 
     async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
     ) -> ToolResponseBase:
-        return await search_agents(
-            query=kwargs.get("query", "").strip(),
-            source="marketplace",
-            session_id=session.session_id,
-            user_id=user_id,
+        """Search for agents in the marketplace.
+
+        Args:
+            user_id: User ID (may be anonymous)
+            session_id: Chat session ID
+            query: Search query
+
+        Returns:
+            AgentCarouselResponse: List of agents found in the marketplace
+            NoResultsResponse: No agents found in the marketplace
+            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,
+            )
+        agents = []
+        try:
+            logger.info(f"Searching marketplace for: {query}")
+            store_results = await store_db.get_store_agents(
+                search_query=query,
+                page_size=5,
+            )
+
+            logger.info(f"Find agents tool found {len(store_results.agents)} agents")
+            for agent in store_results.agents:
+                agent_id = f"{agent.creator}/{agent.slug}"
+                logger.info(f"Building agent ID = {agent_id}")
+                agents.append(
+                    AgentInfo(
+                        id=agent_id,
+                        name=agent.agent_name,
+                        description=agent.description or "",
+                        source="marketplace",
+                        in_library=False,
+                        creator=agent.creator,
+                        category="general",
+                        rating=agent.rating,
+                        runs=agent.runs,
+                        is_featured=False,
+                    ),
+                )
+        except NotFoundError:
+            pass
+        except DatabaseError as e:
+            logger.error(f"Error searching agents: {e}", exc_info=True)
+            return ErrorResponse(
+                message="Failed to search for agents. Please try again.",
+                error=str(e),
+                session_id=session_id,
+            )
+        if not agents:
+            return NoResultsResponse(
+                message=f"No agents found matching '{query}'. Try different keywords or browse the marketplace. If you have 3 consecutive find_agent tool calls results and found no agents. Please stop trying and ask the user if there is anything else you can help with.",
+                session_id=session_id,
+                suggestions=[
+                    "Try more general terms",
+                    "Browse categories in the marketplace",
+                    "Check spelling",
+                ],
+            )
+
+        # Return formatted carousel
+        title = (
+            f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} for '{query}'"
+        )
+        return AgentCarouselResponse(
+            message="Now you have found some options for the user to choose from. You can add a link to a recommended agent at: /marketplace/agent/agent_id Please ask the user if they would like to use any of these agents. If they do, please call the get_agent_details tool for this agent.",
+            title=title,
+            agents=agents,
+            count=len(agents),
+            session_id=session_id,
         )

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

@@ -1,192 +0,0 @@
-import logging
-from typing import Any
-
-from prisma.enums import ContentType
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
-from backend.api.features.chat.tools.models import (
-    BlockInfoSummary,
-    BlockInputFieldInfo,
-    BlockListResponse,
-    ErrorResponse,
-    NoResultsResponse,
-)
-from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import get_block
-
-logger = logging.getLogger(__name__)
-
-
-class FindBlockTool(BaseTool):
-    """Tool for searching available blocks."""
-
-    @property
-    def name(self) -> str:
-        return "find_block"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search for available blocks by name or description. "
-            "Blocks are reusable components that perform specific tasks like "
-            "sending emails, making API calls, processing text, etc. "
-            "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
-            "The response includes each block's id, required_inputs, and input_schema."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query to find blocks by name or description. "
-                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search for blocks matching the query.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            BlockListResponse: List of matching blocks
-            NoResultsResponse: No blocks 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,
-            )
-
-        try:
-            # Search for blocks using hybrid search
-            results, total = await unified_hybrid_search(
-                query=query,
-                content_types=[ContentType.BLOCK],
-                page=1,
-                page_size=10,
-            )
-
-            if not results:
-                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
-                    suggestions=[
-                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
-                        "Check spelling of technical terms",
-                    ],
-                    session_id=session_id,
-                )
-
-            # Enrich results with full block information
-            blocks: list[BlockInfoSummary] = []
-            for result in results:
-                block_id = result["content_id"]
-                block = get_block(block_id)
-
-                if block:
-                    # Get input/output schemas
-                    input_schema = {}
-                    output_schema = {}
-                    try:
-                        input_schema = block.input_schema.jsonschema()
-                    except Exception:
-                        pass
-                    try:
-                        output_schema = block.output_schema.jsonschema()
-                    except Exception:
-                        pass
-
-                    # Get categories from block instance
-                    categories = []
-                    if hasattr(block, "categories") and block.categories:
-                        categories = [cat.value for cat in block.categories]
-
-                    # Extract required inputs for easier use
-                    required_inputs: list[BlockInputFieldInfo] = []
-                    if input_schema:
-                        properties = input_schema.get("properties", {})
-                        required_fields = set(input_schema.get("required", []))
-                        # Get credential field names to exclude from required inputs
-                        credentials_fields = set(
-                            block.input_schema.get_credentials_fields().keys()
-                        )
-
-                        for field_name, field_schema in properties.items():
-                            # Skip credential fields - they're handled separately
-                            if field_name in credentials_fields:
-                                continue
-
-                            required_inputs.append(
-                                BlockInputFieldInfo(
-                                    name=field_name,
-                                    type=field_schema.get("type", "string"),
-                                    description=field_schema.get("description", ""),
-                                    required=field_name in required_fields,
-                                    default=field_schema.get("default"),
-                                )
-                            )
-
-                    blocks.append(
-                        BlockInfoSummary(
-                            id=block_id,
-                            name=block.name,
-                            description=block.description or "",
-                            categories=categories,
-                            input_schema=input_schema,
-                            output_schema=output_schema,
-                            required_inputs=required_inputs,
-                        )
-                    )
-
-            if not blocks:
-                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
-                    suggestions=[
-                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
-                    ],
-                    session_id=session_id,
-                )
-
-            return BlockListResponse(
-                message=(
-                    f"Found {len(blocks)} block(s) matching '{query}'. "
-                    "To execute a block, use run_block with the block's 'id' field "
-                    "and provide 'input_data' matching the block's input_schema."
-                ),
-                blocks=blocks,
-                count=len(blocks),
-                query=query,
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error searching blocks: {e}", exc_info=True)
-            return ErrorResponse(
-                message="Failed to search blocks",
-                error=str(e),
-                session_id=session_id,
-            )

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

@@ -1,52 +0,0 @@
-"""Tool for searching agents in the user's library."""
-
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-
-from .agent_search import search_agents
-from .base import BaseTool
-from .models import ToolResponseBase
-
-
-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.",
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
-    ) -> ToolResponseBase:
-        return await search_agents(
-            query=kwargs.get("query", "").strip(),
-            source="library",
-            session_id=session.session_id,
-            user_id=user_id,
-        )

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

@@ -1,148 +0,0 @@
-"""GetDocPageTool - Fetch full content of a documentation page."""
-
-import logging
-from pathlib import Path
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool
-from backend.api.features.chat.tools.models import (
-    DocPageResponse,
-    ErrorResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-# Base URL for documentation (can be configured)
-DOCS_BASE_URL = "https://docs.agpt.co"
-
-
-class GetDocPageTool(BaseTool):
-    """Tool for fetching full content of a documentation page."""
-
-    @property
-    def name(self) -> str:
-        return "get_doc_page"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Get the full content of a documentation page by its path. "
-            "Use this after search_docs to read the complete content of a relevant page."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "The path to the documentation file, as returned by search_docs. "
-                        "Example: 'platform/block-sdk-guide.md'"
-                    ),
-                },
-            },
-            "required": ["path"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False  # Documentation is public
-
-    def _get_docs_root(self) -> Path:
-        """Get the documentation root directory."""
-        this_file = Path(__file__)
-        project_root = this_file.parent.parent.parent.parent.parent.parent.parent.parent
-        return project_root / "docs"
-
-    def _extract_title(self, content: str, fallback: str) -> str:
-        """Extract title from markdown content."""
-        lines = content.split("\n")
-        for line in lines:
-            if line.startswith("# "):
-                return line[2:].strip()
-        return fallback
-
-    def _make_doc_url(self, path: str) -> str:
-        """Create a URL for a documentation page."""
-        url_path = path.rsplit(".", 1)[0] if "." in path else path
-        return f"{DOCS_BASE_URL}/{url_path}"
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Fetch full content of a documentation page.
-
-        Args:
-            user_id: User ID (not required for docs)
-            session: Chat session
-            path: Path to the documentation file
-
-        Returns:
-            DocPageResponse: Full document content
-            ErrorResponse: Error message
-        """
-        path = kwargs.get("path", "").strip()
-        session_id = session.session_id if session else None
-
-        if not path:
-            return ErrorResponse(
-                message="Please provide a documentation path.",
-                error="Missing path parameter",
-                session_id=session_id,
-            )
-
-        # Sanitize path to prevent directory traversal
-        if ".." in path or path.startswith("/"):
-            return ErrorResponse(
-                message="Invalid documentation path.",
-                error="invalid_path",
-                session_id=session_id,
-            )
-
-        docs_root = self._get_docs_root()
-        full_path = docs_root / path
-
-        if not full_path.exists():
-            return ErrorResponse(
-                message=f"Documentation page not found: {path}",
-                error="not_found",
-                session_id=session_id,
-            )
-
-        # Ensure the path is within docs root
-        try:
-            full_path.resolve().relative_to(docs_root.resolve())
-        except ValueError:
-            return ErrorResponse(
-                message="Invalid documentation path.",
-                error="invalid_path",
-                session_id=session_id,
-            )
-
-        try:
-            content = full_path.read_text(encoding="utf-8")
-            title = self._extract_title(content, path)
-
-            return DocPageResponse(
-                message=f"Retrieved documentation page: {title}",
-                title=title,
-                path=path,
-                content=content,
-                doc_url=self._make_doc_url(path),
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Failed to read documentation page {path}: {e}")
-            return ErrorResponse(
-                message=f"Failed to read documentation page: {str(e)}",
-                error="read_failed",
-                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
 
@@ -12,19 +11,14 @@ from backend.data.model import CredentialsMetaInput
 class ResponseType(str, Enum):
     """Types of tool responses."""
 
-    AGENTS_FOUND = "agents_found"
+    AGENT_CAROUSEL = "agent_carousel"
     AGENT_DETAILS = "agent_details"
     SETUP_REQUIREMENTS = "setup_requirements"
     EXECUTION_STARTED = "execution_started"
     NEED_LOGIN = "need_login"
     ERROR = "error"
     NO_RESULTS = "no_results"
-    AGENT_OUTPUT = "agent_output"
-    UNDERSTANDING_UPDATED = "understanding_updated"
-    BLOCK_LIST = "block_list"
-    BLOCK_OUTPUT = "block_output"
-    DOC_SEARCH_RESULTS = "doc_search_results"
-    DOC_PAGE = "doc_page"
+    SUCCESS = "success"
 
 
 # Base response model
@@ -57,14 +51,14 @@ class AgentInfo(BaseModel):
     graph_id: str | None = None
 
 
-class AgentsFoundResponse(ToolResponseBase):
+class AgentCarouselResponse(ToolResponseBase):
     """Response for find_agent tool."""
 
-    type: ResponseType = ResponseType.AGENTS_FOUND
+    type: ResponseType = ResponseType.AGENT_CAROUSEL
     title: str = "Available Agents"
     agents: list[AgentInfo]
     count: int
-    name: str = "agents_found"
+    name: str = "agent_carousel"
 
 
 class NoResultsResponse(ToolResponseBase):
@@ -179,117 +173,3 @@ class ErrorResponse(ToolResponseBase):
     type: ResponseType = ResponseType.ERROR
     error: str | None = None
     details: dict[str, Any] | None = None
-
-
-# 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
-
-
-# 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)
-
-
-# Documentation search models
-class DocSearchResult(BaseModel):
-    """A single documentation search result."""
-
-    title: str
-    path: str
-    section: str
-    snippet: str  # Short excerpt for UI display
-    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
-
-
-class DocPageResponse(ToolResponseBase):
-    """Response for get_doc_page tool."""
-
-    type: ResponseType = ResponseType.DOC_PAGE
-    title: str
-    path: str
-    content: str  # Full document content
-    doc_url: str | None = None
-
-
-# Block models
-class BlockInputFieldInfo(BaseModel):
-    """Information about a block input field."""
-
-    name: str
-    type: str
-    description: str = ""
-    required: bool = False
-    default: Any | None = None
-
-
-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]
-    required_inputs: list[BlockInputFieldInfo] = Field(
-        default_factory=list,
-        description="List of required input fields for this block",
-    )
-
-
-class BlockListResponse(ToolResponseBase):
-    """Response for find_block tool."""
-
-    type: ResponseType = ResponseType.BLOCK_LIST
-    blocks: list[BlockInfoSummary]
-    count: int
-    query: str
-    usage_hint: str = Field(
-        default="To execute a block, call run_block with block_id set to the block's "
-        "'id' field and input_data containing the required fields from input_schema."
-    )
-
-
-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

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

@@ -1,5 +1,4 @@
 import uuid
-from unittest.mock import AsyncMock, patch
 
 import orjson
 import pytest
@@ -18,17 +17,6 @@ setup_test_data = setup_test_data
 setup_firecrawl_test_data = setup_firecrawl_test_data
 
 
-@pytest.fixture(scope="session", autouse=True)
-def mock_embedding_functions():
-    """Mock embedding functions for all tests to avoid database/API dependencies."""
-    with patch(
-        "backend.api.features.store.db.ensure_embedding",
-        new_callable=AsyncMock,
-        return_value=True,
-    ):
-        yield
-
-
 @pytest.mark.asyncio(scope="session")
 async def test_run_agent(setup_test_data):
     """Test that the run_agent tool successfully executes an approved agent"""
@@ -58,11 +46,11 @@ async def test_run_agent(setup_test_data):
 
     # Verify the response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
     # Parse the result JSON to verify the execution started
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "execution_id" in result_data
     assert "graph_id" in result_data
     assert result_data["graph_id"] == graph.id
@@ -98,11 +86,11 @@ async def test_run_agent_missing_inputs(setup_test_data):
 
     # Verify that we get an error response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
     # The tool should return an ErrorResponse when setup info indicates not ready
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "message" in result_data
 
 
@@ -130,10 +118,10 @@ async def test_run_agent_invalid_agent_id(setup_test_data):
 
     # Verify that we get an error response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
     assert "message" in result_data
     # Should get an error about failed setup or not found
     assert any(
@@ -170,12 +158,12 @@ async def test_run_agent_with_llm_credentials(setup_llm_test_data):
 
     # Verify the response
     assert response is not None
-    assert hasattr(response, "output")
+    assert hasattr(response, "result")
 
     # Parse the result JSON to verify the execution started
 
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should successfully start execution since credentials are available
     assert "execution_id" in result_data
@@ -207,9 +195,9 @@ async def test_run_agent_shows_available_inputs_when_none_provided(setup_test_da
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return agent_details type showing available inputs
     assert result_data.get("type") == "agent_details"
@@ -242,9 +230,9 @@ async def test_run_agent_with_use_defaults(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should execute successfully
     assert "execution_id" in result_data
@@ -272,9 +260,9 @@ async def test_run_agent_missing_credentials(setup_firecrawl_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return setup_requirements type with missing credentials
     assert result_data.get("type") == "setup_requirements"
@@ -304,9 +292,9 @@ async def test_run_agent_invalid_slug_format(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error
     assert result_data.get("type") == "error"
@@ -317,10 +305,9 @@ async def test_run_agent_invalid_slug_format(setup_test_data):
 async def test_run_agent_unauthenticated():
     """Test that run_agent returns need_login for unauthenticated users."""
     tool = RunAgentTool()
-    # Session has a user_id (session owner), but we test tool execution without user_id
-    session = make_session(user_id="test-session-owner")
+    session = make_session(user_id=None)
 
-    # Execute without user_id to test unauthenticated behavior
+    # Execute without user_id
     response = await tool.execute(
         user_id=None,
         session_id=str(uuid.uuid4()),
@@ -331,9 +318,9 @@ async def test_run_agent_unauthenticated():
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Base tool returns need_login type for unauthenticated users
     assert result_data.get("type") == "need_login"
@@ -363,9 +350,9 @@ async def test_run_agent_schedule_without_cron(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error about missing cron
     assert result_data.get("type") == "error"
@@ -395,9 +382,9 @@ async def test_run_agent_schedule_without_name(setup_test_data):
     )
 
     assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
+    assert hasattr(response, "result")
+    assert isinstance(response.result, str)
+    result_data = orjson.loads(response.result)
 
     # Should return error about missing schedule_name
     assert result_data.get("type") == "error"

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

@@ -1,297 +0,0 @@
-"""Tool for executing blocks directly."""
-
-import logging
-from collections import defaultdict
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.data.block import get_block
-from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsMetaInput
-from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.util.exceptions import BlockError
-
-from .base import BaseTool
-from .models import (
-    BlockOutputResponse,
-    ErrorResponse,
-    SetupInfo,
-    SetupRequirementsResponse,
-    ToolResponseBase,
-    UserReadiness,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class RunBlockTool(BaseTool):
-    """Tool for executing a block and returning its outputs."""
-
-    @property
-    def name(self) -> str:
-        return "run_block"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Execute a specific block with the provided input data. "
-            "IMPORTANT: You MUST call find_block first to get the block's 'id' - "
-            "do NOT guess or make up block IDs. "
-            "Use the 'id' from find_block results and provide input_data "
-            "matching the block's required_inputs."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "block_id": {
-                    "type": "string",
-                    "description": (
-                        "The block's 'id' field from find_block results. "
-                        "NEVER guess this - always get it from find_block first."
-                    ),
-                },
-                "input_data": {
-                    "type": "object",
-                    "description": (
-                        "Input values for the block. Use the 'required_inputs' field "
-                        "from find_block to see what fields are needed."
-                    ),
-                },
-            },
-            "required": ["block_id", "input_data"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _check_block_credentials(
-        self,
-        user_id: str,
-        block: Any,
-    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-        """
-        Check if user has required credentials for a block.
-
-        Returns:
-            tuple[matched_credentials, missing_credentials]
-        """
-        matched_credentials: dict[str, CredentialsMetaInput] = {}
-        missing_credentials: list[CredentialsMetaInput] = []
-
-        # Get credential field info from block's input schema
-        credentials_fields_info = block.input_schema.get_credentials_fields_info()
-
-        if not credentials_fields_info:
-            return matched_credentials, missing_credentials
-
-        # Get user's available credentials
-        creds_manager = IntegrationCredentialsManager()
-        available_creds = await creds_manager.store.get_all_creds(user_id)
-
-        for field_name, field_info in credentials_fields_info.items():
-            # field_info.provider is a frozenset of acceptable providers
-            # field_info.supported_types is a frozenset of acceptable types
-            matching_cred = next(
-                (
-                    cred
-                    for cred in available_creds
-                    if cred.provider in field_info.provider
-                    and cred.type in field_info.supported_types
-                ),
-                None,
-            )
-
-            if matching_cred:
-                matched_credentials[field_name] = CredentialsMetaInput(
-                    id=matching_cred.id,
-                    provider=matching_cred.provider,  # type: ignore
-                    type=matching_cred.type,
-                    title=matching_cred.title,
-                )
-            else:
-                # Create a placeholder for the missing credential
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing_credentials.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=field_name.replace("_", " ").title(),
-                    )
-                )
-
-        return matched_credentials, missing_credentials
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute a block with the given input data.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            block_id: Block UUID to execute
-            input_data: Input values for the block
-
-        Returns:
-            BlockOutputResponse: Block execution outputs
-            SetupRequirementsResponse: Missing credentials
-            ErrorResponse: Error message
-        """
-        block_id = kwargs.get("block_id", "").strip()
-        input_data = kwargs.get("input_data", {})
-        session_id = session.session_id
-
-        if not block_id:
-            return ErrorResponse(
-                message="Please provide a block_id",
-                session_id=session_id,
-            )
-
-        if not isinstance(input_data, dict):
-            return ErrorResponse(
-                message="input_data must be an object",
-                session_id=session_id,
-            )
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        # Get the block
-        block = get_block(block_id)
-        if not block:
-            return ErrorResponse(
-                message=f"Block '{block_id}' not found",
-                session_id=session_id,
-            )
-
-        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
-
-        # Check credentials
-        creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block
-        )
-
-        if missing_credentials:
-            # Return setup requirements response with missing credentials
-            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
-
-            return SetupRequirementsResponse(
-                message=(
-                    f"Block '{block.name}' requires credentials that are not configured. "
-                    "Please set up the required credentials before running this block."
-                ),
-                session_id=session_id,
-                setup_info=SetupInfo(
-                    agent_id=block_id,
-                    agent_name=block.name,
-                    user_readiness=UserReadiness(
-                        has_all_credentials=False,
-                        missing_credentials=missing_creds_dict,
-                        ready_to_run=False,
-                    ),
-                    requirements={
-                        "credentials": [c.model_dump() for c in missing_credentials],
-                        "inputs": self._get_inputs_list(block),
-                        "execution_modes": ["immediate"],
-                    },
-                ),
-                graph_id=None,
-                graph_version=None,
-            )
-
-        try:
-            # Fetch actual credentials and prepare kwargs for block execution
-            # Create execution context with defaults (blocks may require it)
-            exec_kwargs: dict[str, Any] = {
-                "user_id": user_id,
-                "execution_context": ExecutionContext(),
-            }
-
-            for field_name, cred_meta in matched_credentials.items():
-                # Inject metadata into input_data (for validation)
-                if field_name not in input_data:
-                    input_data[field_name] = cred_meta.model_dump()
-
-                # Fetch actual credentials and pass as kwargs (for execution)
-                actual_credentials = await creds_manager.get(
-                    user_id, cred_meta.id, lock=False
-                )
-                if actual_credentials:
-                    exec_kwargs[field_name] = actual_credentials
-                else:
-                    return ErrorResponse(
-                        message=f"Failed to retrieve credentials for {field_name}",
-                        session_id=session_id,
-                    )
-
-            # Execute the block and collect outputs
-            outputs: dict[str, list[Any]] = defaultdict(list)
-            async for output_name, output_data in block.execute(
-                input_data,
-                **exec_kwargs,
-            ):
-                outputs[output_name].append(output_data)
-
-            return BlockOutputResponse(
-                message=f"Block '{block.name}' executed successfully",
-                block_id=block_id,
-                block_name=block.name,
-                outputs=dict(outputs),
-                success=True,
-                session_id=session_id,
-            )
-
-        except BlockError as e:
-            logger.warning(f"Block execution failed: {e}")
-            return ErrorResponse(
-                message=f"Block execution failed: {e}",
-                error=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Unexpected error executing block: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to execute block: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
-        """Extract non-credential inputs from block schema."""
-        inputs_list = []
-        schema = block.input_schema.jsonschema()
-        properties = schema.get("properties", {})
-        required_fields = set(schema.get("required", []))
-
-        # Get credential field names to exclude
-        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-
-        for field_name, field_schema in properties.items():
-            # Skip credential fields
-            if field_name in credentials_fields:
-                continue
-
-            inputs_list.append(
-                {
-                    "name": field_name,
-                    "title": field_schema.get("title", field_name),
-                    "type": field_schema.get("type", "string"),
-                    "description": field_schema.get("description", ""),
-                    "required": field_name in required_fields,
-                }
-            )
-
-        return inputs_list

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

@@ -1,208 +0,0 @@
-"""SearchDocsTool - Search documentation using hybrid search."""
-
-import logging
-from typing import Any
-
-from prisma.enums import ContentType
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool
-from backend.api.features.chat.tools.models import (
-    DocSearchResult,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-from backend.api.features.store.hybrid_search import unified_hybrid_search
-
-logger = logging.getLogger(__name__)
-
-# Base URL for documentation (can be configured)
-DOCS_BASE_URL = "https://docs.agpt.co"
-
-# Maximum number of results to return
-MAX_RESULTS = 5
-
-# Snippet length for preview
-SNIPPET_LENGTH = 200
-
-
-class SearchDocsTool(BaseTool):
-    """Tool for searching AutoGPT platform documentation."""
-
-    @property
-    def name(self) -> str:
-        return "search_docs"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search the AutoGPT platform documentation for information about "
-            "how to use the platform, build agents, configure blocks, and more. "
-            "Returns relevant documentation sections. Use get_doc_page to read full content."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query to find relevant documentation. "
-                        "Use natural language to describe what you're looking for."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False  # Documentation is public
-
-    def _create_snippet(self, content: str, max_length: int = SNIPPET_LENGTH) -> str:
-        """Create a short snippet from content for preview."""
-        # Remove markdown formatting for cleaner snippet
-        clean_content = content.replace("#", "").replace("*", "").replace("`", "")
-        # Remove extra whitespace
-        clean_content = " ".join(clean_content.split())
-
-        if len(clean_content) <= max_length:
-            return clean_content
-
-        # Truncate at word boundary
-        truncated = clean_content[:max_length]
-        last_space = truncated.rfind(" ")
-        if last_space > max_length // 2:
-            truncated = truncated[:last_space]
-
-        return truncated + "..."
-
-    def _make_doc_url(self, path: str) -> str:
-        """Create a URL for a documentation page."""
-        # Remove file extension for URL
-        url_path = path.rsplit(".", 1)[0] if "." in path else path
-        return f"{DOCS_BASE_URL}/{url_path}"
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search documentation and return relevant sections.
-
-        Args:
-            user_id: User ID (not required for docs)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            DocSearchResultsResponse: List of matching documentation sections
-            NoResultsResponse: No results found
-            ErrorResponse: Error message
-        """
-        query = kwargs.get("query", "").strip()
-        session_id = session.session_id if session else None
-
-        if not query:
-            return ErrorResponse(
-                message="Please provide a search query.",
-                error="Missing query parameter",
-                session_id=session_id,
-            )
-
-        try:
-            # Search using hybrid search for DOCUMENTATION content type only
-            results, total = await unified_hybrid_search(
-                query=query,
-                content_types=[ContentType.DOCUMENTATION],
-                page=1,
-                page_size=MAX_RESULTS * 2,  # Fetch extra for deduplication
-                min_score=0.1,  # Lower threshold for docs
-            )
-
-            if not results:
-                return NoResultsResponse(
-                    message=f"No documentation found for '{query}'.",
-                    suggestions=[
-                        "Try different keywords",
-                        "Use more general terms",
-                        "Check for typos in your query",
-                    ],
-                    session_id=session_id,
-                )
-
-            # Deduplicate by document path (keep highest scoring section per doc)
-            seen_docs: dict[str, dict[str, Any]] = {}
-            for result in results:
-                metadata = result.get("metadata", {})
-                doc_path = metadata.get("path", "")
-
-                if not doc_path:
-                    continue
-
-                # Keep the highest scoring result for each document
-                if doc_path not in seen_docs:
-                    seen_docs[doc_path] = result
-                elif result.get("combined_score", 0) > seen_docs[doc_path].get(
-                    "combined_score", 0
-                ):
-                    seen_docs[doc_path] = result
-
-            # Sort by score and take top MAX_RESULTS
-            deduplicated = sorted(
-                seen_docs.values(),
-                key=lambda x: x.get("combined_score", 0),
-                reverse=True,
-            )[:MAX_RESULTS]
-
-            if not deduplicated:
-                return NoResultsResponse(
-                    message=f"No documentation found for '{query}'.",
-                    suggestions=[
-                        "Try different keywords",
-                        "Use more general terms",
-                    ],
-                    session_id=session_id,
-                )
-
-            # Build response
-            doc_results: list[DocSearchResult] = []
-            for result in deduplicated:
-                metadata = result.get("metadata", {})
-                doc_path = metadata.get("path", "")
-                doc_title = metadata.get("doc_title", "")
-                section_title = metadata.get("section_title", "")
-                searchable_text = result.get("searchable_text", "")
-                score = result.get("combined_score", 0)
-
-                doc_results.append(
-                    DocSearchResult(
-                        title=doc_title or section_title or doc_path,
-                        path=doc_path,
-                        section=section_title,
-                        snippet=self._create_snippet(searchable_text),
-                        score=round(score, 3),
-                        doc_url=self._make_doc_url(doc_path),
-                    )
-                )
-
-            return DocSearchResultsResponse(
-                message=f"Found {len(doc_results)} relevant documentation sections.",
-                results=doc_results,
-                count=len(doc_results),
-                query=query,
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Documentation search failed: {e}")
-            return ErrorResponse(
-                message=f"Failed to search documentation: {str(e)}",
-                error="search_failed",
-                session_id=session_id,
-            )

autogpt_platform/backend/backend/api/features/integrations/router.py

@@ -35,7 +35,11 @@ from backend.data.model import (
     OAuth2Credentials,
     UserIntegrations,
 )
-from backend.data.onboarding import OnboardingStep, complete_onboarding_step
+from backend.data.onboarding import (
+    OnboardingStep,
+    complete_onboarding_step,
+    increment_runs,
+)
 from backend.data.user import get_user_integrations
 from backend.executor.utils import add_graph_execution
 from backend.integrations.ayrshare import AyrshareClient, SocialPlatform
@@ -171,7 +175,6 @@ async def callback(
         f"Successfully processed OAuth callback for user {user_id} "
         f"and provider {provider.value}"
     )
-
     return CredentialsMetaResponse(
         id=credentials.id,
         provider=credentials.provider,
@@ -190,7 +193,6 @@ async def list_credentials(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[CredentialsMetaResponse]:
     credentials = await creds_manager.store.get_all_creds(user_id)
-
     return [
         CredentialsMetaResponse(
             id=cred.id,
@@ -213,7 +215,6 @@ async def list_credentials_by_provider(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> list[CredentialsMetaResponse]:
     credentials = await creds_manager.store.get_creds_by_provider(user_id, provider)
-
     return [
         CredentialsMetaResponse(
             id=cred.id,
@@ -377,6 +378,7 @@ async def webhook_ingress_generic(
         return
 
     await complete_onboarding_step(user_id, OnboardingStep.TRIGGER_WEBHOOK)
+    await increment_runs(user_id)
 
     # Execute all triggers concurrently for better performance
     tasks = []
@@ -829,18 +831,6 @@ async def list_providers() -> List[str]:
     return all_providers
 
 
-@router.get("/providers/system", response_model=List[str])
-async def list_system_providers() -> List[str]:
-    """
-    Get a list of providers that have platform credits (system credentials) available.
-
-    These providers can be used without the user providing their own API keys.
-    """
-    from backend.integrations.credentials_store import SYSTEM_PROVIDERS
-
-    return list(SYSTEM_PROVIDERS)
-
-
 @router.get("/providers/names", response_model=ProviderNamesResponse)
 async def get_provider_names() -> ProviderNamesResponse:
     """

autogpt_platform/backend/backend/api/features/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 for any agent owned by the user.
+    Updates the agent version in the library if useGraphIsActiveVersion is True.
 
     Args:
         user_id: Owner of the LibraryAgent.
@@ -498,31 +498,20 @@ 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}"
     )
-    async with transaction() as tx:
-        library_agent = await prisma.models.LibraryAgent.prisma(tx).find_first_or_raise(
+    try:
+        library_agent = await prisma.models.LibraryAgent.prisma().find_first_or_raise(
             where={
                 "userId": user_id,
                 "agentGraphId": agent_graph_id,
+                "useGraphIsActiveVersion": True,
             },
         )
-
-        # 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(
+        lib = await prisma.models.LibraryAgent.prisma().update(
             where={"id": library_agent.id},
             data={
                 "AgentGraph": {
@@ -536,13 +525,13 @@ async def update_agent_version_in_library(
             },
             include={"AgentGraph": True},
         )
+        if lib is None:
+            raise NotFoundError(f"Library agent {library_agent.id} not found")
 
-    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)
+        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
 
 
 async def update_library_agent(
@@ -836,7 +825,6 @@ 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

@@ -48,7 +48,6 @@ 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
 
@@ -164,7 +163,6 @@ 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/presets.py

@@ -8,6 +8,7 @@ from backend.data.execution import GraphExecutionMeta
 from backend.data.graph import get_graph
 from backend.data.integrations import get_webhook
 from backend.data.model import CredentialsMetaInput
+from backend.data.onboarding import increment_runs
 from backend.executor.utils import add_graph_execution, make_node_credentials_input_map
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks import get_webhook_manager
@@ -402,6 +403,8 @@ async def execute_preset(
     merged_node_input = preset.inputs | inputs
     merged_credential_inputs = preset.credentials | credential_inputs
 
+    await increment_runs(user_id)
+
     return await add_graph_execution(
         user_id=user_id,
         graph_id=preset.graph_id,

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

@@ -42,7 +42,6 @@ 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,
@@ -65,7 +64,6 @@ 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,
@@ -140,7 +138,6 @@ 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,
@@ -208,7 +205,6 @@ 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

@@ -1,610 +0,0 @@
-"""
-Content Type Handlers for Unified Embeddings
-
-Pluggable system for different content sources (store agents, blocks, docs).
-Each handler knows how to fetch and process its content type for embedding.
-"""
-
-import logging
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-from prisma.enums import ContentType
-
-from backend.data.db import query_raw_with_schema
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ContentItem:
-    """Represents a piece of content to be embedded."""
-
-    content_id: str  # Unique identifier (DB ID or file path)
-    content_type: ContentType
-    searchable_text: str  # Combined text for embedding
-    metadata: dict[str, Any]  # Content-specific metadata
-    user_id: str | None = None  # For user-scoped content
-
-
-class ContentHandler(ABC):
-    """Base handler for fetching and processing content for embeddings."""
-
-    @property
-    @abstractmethod
-    def content_type(self) -> ContentType:
-        """The ContentType this handler manages."""
-        pass
-
-    @abstractmethod
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """
-        Fetch items that don't have embeddings yet.
-
-        Args:
-            batch_size: Maximum number of items to return
-
-        Returns:
-            List of ContentItem objects ready for embedding
-        """
-        pass
-
-    @abstractmethod
-    async def get_stats(self) -> dict[str, int]:
-        """
-        Get statistics about embedding coverage.
-
-        Returns:
-            Dict with keys: total, with_embeddings, without_embeddings
-        """
-        pass
-
-
-class StoreAgentHandler(ContentHandler):
-    """Handler for marketplace store agent listings."""
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.STORE_AGENT
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch approved store listings without embeddings."""
-        from backend.api.features.store.embeddings import build_searchable_text
-
-        missing = await query_raw_with_schema(
-            """
-            SELECT
-                slv.id,
-                slv.name,
-                slv.description,
-                slv."subHeading",
-                slv.categories
-            FROM {schema_prefix}"StoreListingVersion" slv
-            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
-                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            AND uce."contentId" IS NULL
-            LIMIT $1
-            """,
-            batch_size,
-        )
-
-        return [
-            ContentItem(
-                content_id=row["id"],
-                content_type=ContentType.STORE_AGENT,
-                searchable_text=build_searchable_text(
-                    name=row["name"],
-                    description=row["description"],
-                    sub_heading=row["subHeading"],
-                    categories=row["categories"] or [],
-                ),
-                metadata={
-                    "name": row["name"],
-                    "categories": row["categories"] or [],
-                },
-                user_id=None,  # Store agents are public
-            )
-            for row in missing
-        ]
-
-    async def get_stats(self) -> dict[str, int]:
-        """Get statistics about store agent embedding coverage."""
-        # Count approved versions
-        approved_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion"
-            WHERE "submissionStatus" = 'APPROVED'
-            AND "isDeleted" = false
-            """
-        )
-        total_approved = approved_result[0]["count"] if approved_result else 0
-
-        # Count versions with embeddings
-        embedded_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion" slv
-            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            """
-        )
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-        }
-
-
-class BlockHandler(ContentHandler):
-    """Handler for block definitions (Python classes)."""
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.BLOCK
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch blocks without embeddings."""
-        from backend.data.block import get_blocks
-
-        # Get all available blocks
-        all_blocks = get_blocks()
-
-        # Check which ones have embeddings
-        if not all_blocks:
-            return []
-
-        block_ids = list(all_blocks.keys())
-
-        # Query for existing embeddings
-        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
-        existing_result = await query_raw_with_schema(
-            f"""
-            SELECT "contentId"
-            FROM {{schema_prefix}}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
-            AND "contentId" = ANY(ARRAY[{placeholders}])
-            """,
-            *block_ids,
-        )
-
-        existing_ids = {row["contentId"] for row in existing_result}
-        missing_blocks = [
-            (block_id, block_cls)
-            for block_id, block_cls in all_blocks.items()
-            if block_id not in existing_ids
-        ]
-
-        # Convert to ContentItem
-        items = []
-        for block_id, block_cls in missing_blocks[:batch_size]:
-            try:
-                block_instance = block_cls()
-
-                # Build searchable text from block metadata
-                parts = []
-                if hasattr(block_instance, "name") and block_instance.name:
-                    parts.append(block_instance.name)
-                if (
-                    hasattr(block_instance, "description")
-                    and block_instance.description
-                ):
-                    parts.append(block_instance.description)
-                if hasattr(block_instance, "categories") and block_instance.categories:
-                    # Convert BlockCategory enum to strings
-                    parts.append(
-                        " ".join(str(cat.value) for cat in block_instance.categories)
-                    )
-
-                # Add input/output schema info
-                if hasattr(block_instance, "input_schema"):
-                    schema = block_instance.input_schema
-                    if hasattr(schema, "model_json_schema"):
-                        schema_dict = schema.model_json_schema()
-                        if "properties" in schema_dict:
-                            for prop_name, prop_info in schema_dict[
-                                "properties"
-                            ].items():
-                                if "description" in prop_info:
-                                    parts.append(
-                                        f"{prop_name}: {prop_info['description']}"
-                                    )
-
-                searchable_text = " ".join(parts)
-
-                # Convert categories set of enums to list of strings for JSON serialization
-                categories = getattr(block_instance, "categories", set())
-                categories_list = (
-                    [cat.value for cat in categories] if categories else []
-                )
-
-                items.append(
-                    ContentItem(
-                        content_id=block_id,
-                        content_type=ContentType.BLOCK,
-                        searchable_text=searchable_text,
-                        metadata={
-                            "name": getattr(block_instance, "name", ""),
-                            "categories": categories_list,
-                        },
-                        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,
-        }
-
-
-@dataclass
-class MarkdownSection:
-    """Represents a section of a markdown document."""
-
-    title: str  # Section heading text
-    content: str  # Section content (including the heading line)
-    level: int  # Heading level (1 for #, 2 for ##, etc.)
-    index: int  # Section index within the document
-
-
-class DocumentationHandler(ContentHandler):
-    """Handler for documentation files (.md/.mdx).
-
-    Chunks documents by markdown headings to create multiple embeddings per file.
-    Each section (## heading) becomes a separate embedding for better retrieval.
-    """
-
-    @property
-    def content_type(self) -> ContentType:
-        return ContentType.DOCUMENTATION
-
-    def _get_docs_root(self) -> Path:
-        """Get the documentation root directory."""
-        # content_handlers.py is at: backend/backend/api/features/store/content_handlers.py
-        # Need to go up to project root then into docs/
-        # In container: /app/autogpt_platform/backend/backend/api/features/store -> /app/docs
-        # In development: /repo/autogpt_platform/backend/backend/api/features/store -> /repo/docs
-        this_file = Path(
-            __file__
-        )  # .../backend/backend/api/features/store/content_handlers.py
-        project_root = (
-            this_file.parent.parent.parent.parent.parent.parent.parent
-        )  # -> /app or /repo
-        docs_root = project_root / "docs"
-        return docs_root
-
-    def _extract_doc_title(self, file_path: Path) -> str:
-        """Extract the document title from a markdown file."""
-        try:
-            content = file_path.read_text(encoding="utf-8")
-            lines = content.split("\n")
-
-            # Try to extract title from first # heading
-            for line in lines:
-                if line.startswith("# "):
-                    return line[2:].strip()
-
-            # If no title found, use filename
-            return file_path.stem.replace("-", " ").replace("_", " ").title()
-        except Exception as e:
-            logger.warning(f"Failed to read title from {file_path}: {e}")
-            return file_path.stem.replace("-", " ").replace("_", " ").title()
-
-    def _chunk_markdown_by_headings(
-        self, file_path: Path, min_heading_level: int = 2
-    ) -> list[MarkdownSection]:
-        """
-        Split a markdown file into sections based on headings.
-
-        Args:
-            file_path: Path to the markdown file
-            min_heading_level: Minimum heading level to split on (default: 2 for ##)
-
-        Returns:
-            List of MarkdownSection objects, one per section.
-            If no headings found, returns a single section with all content.
-        """
-        try:
-            content = file_path.read_text(encoding="utf-8")
-        except Exception as e:
-            logger.warning(f"Failed to read {file_path}: {e}")
-            return []
-
-        lines = content.split("\n")
-        sections: list[MarkdownSection] = []
-        current_section_lines: list[str] = []
-        current_title = ""
-        current_level = 0
-        section_index = 0
-        doc_title = ""
-
-        for line in lines:
-            # Check if line is a heading
-            if line.startswith("#"):
-                # Count heading level
-                level = 0
-                for char in line:
-                    if char == "#":
-                        level += 1
-                    else:
-                        break
-
-                heading_text = line[level:].strip()
-
-                # Track document title (level 1 heading)
-                if level == 1 and not doc_title:
-                    doc_title = heading_text
-                    # Don't create a section for just the title - add it to first section
-                    current_section_lines.append(line)
-                    continue
-
-                # Check if this heading should start a new section
-                if level >= min_heading_level:
-                    # Save previous section if it has content
-                    if current_section_lines:
-                        section_content = "\n".join(current_section_lines).strip()
-                        if section_content:
-                            # Use doc title for first section if no specific title
-                            title = current_title if current_title else doc_title
-                            if not title:
-                                title = file_path.stem.replace("-", " ").replace(
-                                    "_", " "
-                                )
-                            sections.append(
-                                MarkdownSection(
-                                    title=title,
-                                    content=section_content,
-                                    level=current_level if current_level else 1,
-                                    index=section_index,
-                                )
-                            )
-                            section_index += 1
-
-                    # Start new section
-                    current_section_lines = [line]
-                    current_title = heading_text
-                    current_level = level
-                else:
-                    # Lower level heading (e.g., # when splitting on ##)
-                    current_section_lines.append(line)
-            else:
-                current_section_lines.append(line)
-
-        # Don't forget the last section
-        if current_section_lines:
-            section_content = "\n".join(current_section_lines).strip()
-            if section_content:
-                title = current_title if current_title else doc_title
-                if not title:
-                    title = file_path.stem.replace("-", " ").replace("_", " ")
-                sections.append(
-                    MarkdownSection(
-                        title=title,
-                        content=section_content,
-                        level=current_level if current_level else 1,
-                        index=section_index,
-                    )
-                )
-
-        # If no sections were created (no headings found), create one section with all content
-        if not sections and content.strip():
-            title = (
-                doc_title
-                if doc_title
-                else file_path.stem.replace("-", " ").replace("_", " ")
-            )
-            sections.append(
-                MarkdownSection(
-                    title=title,
-                    content=content.strip(),
-                    level=1,
-                    index=0,
-                )
-            )
-
-        return sections
-
-    def _make_section_content_id(self, doc_path: str, section_index: int) -> str:
-        """Create a unique content ID for a document section.
-
-        Format: doc_path::section_index
-        Example: 'platform/getting-started.md::0'
-        """
-        return f"{doc_path}::{section_index}"
-
-    def _parse_section_content_id(self, content_id: str) -> tuple[str, int]:
-        """Parse a section content ID back into doc_path and section_index.
-
-        Returns: (doc_path, section_index)
-        """
-        if "::" in content_id:
-            parts = content_id.rsplit("::", 1)
-            return parts[0], int(parts[1])
-        # Legacy format (whole document)
-        return content_id, 0
-
-    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
-        """Fetch documentation sections without embeddings.
-
-        Chunks each document by markdown headings and creates embeddings for each section.
-        Content IDs use the format: 'path/to/doc.md::section_index'
-        """
-        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"))
-
-        if not all_docs:
-            return []
-
-        # Build list of all sections from all documents
-        all_sections: list[tuple[str, Path, MarkdownSection]] = []
-        for doc_file in all_docs:
-            doc_path = str(doc_file.relative_to(docs_root))
-            sections = self._chunk_markdown_by_headings(doc_file)
-            for section in sections:
-                all_sections.append((doc_path, doc_file, section))
-
-        if not all_sections:
-            return []
-
-        # Generate content IDs for all sections
-        section_content_ids = [
-            self._make_section_content_id(doc_path, section.index)
-            for doc_path, _, section in all_sections
-        ]
-
-        # Check which ones have embeddings
-        placeholders = ",".join([f"${i+1}" for i in range(len(section_content_ids))])
-        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}])
-            """,
-            *section_content_ids,
-        )
-
-        existing_ids = {row["contentId"] for row in existing_result}
-
-        # Filter to missing sections
-        missing_sections = [
-            (doc_path, doc_file, section, content_id)
-            for (doc_path, doc_file, section), content_id in zip(
-                all_sections, section_content_ids
-            )
-            if content_id not in existing_ids
-        ]
-
-        # Convert to ContentItem (up to batch_size)
-        items = []
-        for doc_path, doc_file, section, content_id in missing_sections[:batch_size]:
-            try:
-                # Get document title for context
-                doc_title = self._extract_doc_title(doc_file)
-
-                # Build searchable text with context
-                # Include doc title and section title for better search relevance
-                searchable_text = f"{doc_title} - {section.title}\n\n{section.content}"
-
-                items.append(
-                    ContentItem(
-                        content_id=content_id,
-                        content_type=ContentType.DOCUMENTATION,
-                        searchable_text=searchable_text,
-                        metadata={
-                            "doc_title": doc_title,
-                            "section_title": section.title,
-                            "section_index": section.index,
-                            "heading_level": section.level,
-                            "path": doc_path,
-                        },
-                        user_id=None,  # Documentation is public
-                    )
-                )
-            except Exception as e:
-                logger.warning(f"Failed to process section {content_id}: {e}")
-                continue
-
-        return items
-
-    def _get_all_section_content_ids(self, docs_root: Path) -> set[str]:
-        """Get all current section content IDs from the docs directory.
-
-        Used for stats and cleanup to know what sections should exist.
-        """
-        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
-        content_ids = set()
-
-        for doc_file in all_docs:
-            doc_path = str(doc_file.relative_to(docs_root))
-            sections = self._chunk_markdown_by_headings(doc_file)
-            for section in sections:
-                content_ids.add(self._make_section_content_id(doc_path, section.index))
-
-        return content_ids
-
-    async def get_stats(self) -> dict[str, int]:
-        """Get statistics about documentation embedding coverage.
-
-        Counts sections (not documents) since each section gets its own embedding.
-        """
-        docs_root = self._get_docs_root()
-
-        if not docs_root.exists():
-            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
-
-        # Get all section content IDs
-        all_section_ids = self._get_all_section_content_ids(docs_root)
-        total_sections = len(all_section_ids)
-
-        if total_sections == 0:
-            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
-
-        # Count embeddings in database for DOCUMENTATION type
-        embedded_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"UnifiedContentEmbedding"
-            WHERE "contentType" = 'DOCUMENTATION'::{schema_prefix}"ContentType"
-            """
-        )
-
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total": total_sections,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_sections - with_embeddings,
-        }
-
-
-# Content handler registry
-CONTENT_HANDLERS: dict[ContentType, ContentHandler] = {
-    ContentType.STORE_AGENT: StoreAgentHandler(),
-    ContentType.BLOCK: BlockHandler(),
-    ContentType.DOCUMENTATION: DocumentationHandler(),
-}

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

@@ -1,215 +0,0 @@
-"""
-Integration tests for content handlers using real DB.
-
-Run with: poetry run pytest backend/api/features/store/content_handlers_integration_test.py -xvs
-
-These tests use the real database but mock OpenAI calls.
-"""
-
-from unittest.mock import patch
-
-import pytest
-
-from backend.api.features.store.content_handlers import (
-    CONTENT_HANDLERS,
-    BlockHandler,
-    DocumentationHandler,
-    StoreAgentHandler,
-)
-from backend.api.features.store.embeddings import (
-    EMBEDDING_DIM,
-    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] * EMBEDDING_DIM
-
-    # 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] * EMBEDDING_DIM
-
-    # Run backfill with batch_size=1 to process max 1 per type
-    result = await backfill_all_content_types(batch_size=1)
-
-    # Should have results for all content types
-    assert "by_type" in result
-    assert "totals" in result
-
-    by_type = result["by_type"]
-    assert "BLOCK" in by_type
-    assert "STORE_AGENT" in by_type
-    assert "DOCUMENTATION" in by_type
-
-    # Each type should have correct structure
-    for content_type, type_result in by_type.items():
-        assert "processed" in type_result
-        assert "success" in type_result
-        assert "failed" in type_result
-
-    # Totals should aggregate
-    totals = result["totals"]
-    assert totals["processed"] >= 0
-    assert totals["success"] >= 0
-    assert totals["failed"] >= 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_content_handler_registry():
-    """Test all handlers are registered in correct order."""
-    from prisma.enums import ContentType
-
-    # All three types should be registered
-    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
-    assert ContentType.BLOCK in CONTENT_HANDLERS
-    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
-
-    # Check handler types
-    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
-    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)

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

@@ -1,381 +0,0 @@
-"""
-E2E tests for content handlers (blocks, store agents, documentation).
-
-Tests the full flow: discovering content → generating embeddings → storing.
-"""
-
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store.content_handlers import (
-    CONTENT_HANDLERS,
-    BlockHandler,
-    DocumentationHandler,
-    StoreAgentHandler,
-)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_agent_handler_get_missing_items(mocker):
-    """Test StoreAgentHandler fetches approved agents without embeddings."""
-    handler = StoreAgentHandler()
-
-    # Mock database query
-    mock_missing = [
-        {
-            "id": "agent-1",
-            "name": "Test Agent",
-            "description": "A test agent",
-            "subHeading": "Test heading",
-            "categories": ["AI", "Testing"],
-        }
-    ]
-
-    with patch(
-        "backend.api.features.store.content_handlers.query_raw_with_schema",
-        return_value=mock_missing,
-    ):
-        items = await handler.get_missing_items(batch_size=10)
-
-        assert len(items) == 1
-        assert items[0].content_id == "agent-1"
-        assert items[0].content_type == ContentType.STORE_AGENT
-        assert "Test Agent" in items[0].searchable_text
-        assert "A test agent" in items[0].searchable_text
-        assert items[0].metadata["name"] == "Test Agent"
-        assert items[0].user_id is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_agent_handler_get_stats(mocker):
-    """Test StoreAgentHandler returns correct stats."""
-    handler = StoreAgentHandler()
-
-    # Mock approved count query
-    mock_approved = [{"count": 50}]
-    # Mock embedded count query
-    mock_embedded = [{"count": 30}]
-
-    with patch(
-        "backend.api.features.store.content_handlers.query_raw_with_schema",
-        side_effect=[mock_approved, mock_embedded],
-    ):
-        stats = await handler.get_stats()
-
-        assert stats["total"] == 50
-        assert stats["with_embeddings"] == 30
-        assert stats["without_embeddings"] == 20
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_block_handler_get_missing_items(mocker):
-    """Test BlockHandler discovers blocks without embeddings."""
-    handler = BlockHandler()
-
-    # Mock get_blocks to return test blocks
-    mock_block_class = MagicMock()
-    mock_block_instance = MagicMock()
-    mock_block_instance.name = "Calculator Block"
-    mock_block_instance.description = "Performs calculations"
-    mock_block_instance.categories = [MagicMock(value="MATH")]
-    mock_block_instance.input_schema.model_json_schema.return_value = {
-        "properties": {"expression": {"description": "Math expression to evaluate"}}
-    }
-    mock_block_class.return_value = mock_block_instance
-
-    mock_blocks = {"block-uuid-1": mock_block_class}
-
-    # Mock existing embeddings query (no embeddings exist)
-    mock_existing = []
-
-    with patch(
-        "backend.data.block.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.data.block.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 (content_id format: doc_path::section_index)
-            guide_item = next(
-                (item for item in items if item.content_id == "guide.md::0"), 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["doc_title"] == "Getting Started"
-            assert guide_item.user_id is None
-
-            # Check api.mdx (content_id format: doc_path::section_index)
-            api_item = next(
-                (item for item in items if item.content_id == "api.mdx::0"), 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 = handler._extract_doc_title(doc_with_heading)
-    assert title == "My Title"
-
-    # Test without heading
-    doc_without_heading = tmp_path / "no-heading.md"
-    doc_without_heading.write_text("Just content, no heading")
-    title = handler._extract_doc_title(doc_without_heading)
-    assert title == "No Heading"  # Uses filename
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_markdown_chunking(tmp_path):
-    """Test DocumentationHandler chunks markdown by headings."""
-    handler = DocumentationHandler()
-
-    # Test document with multiple sections
-    doc_with_sections = tmp_path / "sections.md"
-    doc_with_sections.write_text(
-        "# Document Title\n\n"
-        "Intro paragraph.\n\n"
-        "## Section One\n\n"
-        "Content for section one.\n\n"
-        "## Section Two\n\n"
-        "Content for section two.\n"
-    )
-    sections = handler._chunk_markdown_by_headings(doc_with_sections)
-
-    # Should have 3 sections: intro (with doc title), section one, section two
-    assert len(sections) == 3
-    assert sections[0].title == "Document Title"
-    assert sections[0].index == 0
-    assert "Intro paragraph" in sections[0].content
-
-    assert sections[1].title == "Section One"
-    assert sections[1].index == 1
-    assert "Content for section one" in sections[1].content
-
-    assert sections[2].title == "Section Two"
-    assert sections[2].index == 2
-    assert "Content for section two" in sections[2].content
-
-    # Test document without headings
-    doc_no_sections = tmp_path / "no-sections.md"
-    doc_no_sections.write_text("Just plain content without any headings.")
-    sections = handler._chunk_markdown_by_headings(doc_no_sections)
-    assert len(sections) == 1
-    assert sections[0].index == 0
-    assert "Just plain content" in sections[0].content
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_section_content_ids():
-    """Test DocumentationHandler creates and parses section content IDs."""
-    handler = DocumentationHandler()
-
-    # Test making content ID
-    content_id = handler._make_section_content_id("docs/guide.md", 2)
-    assert content_id == "docs/guide.md::2"
-
-    # Test parsing content ID
-    doc_path, section_index = handler._parse_section_content_id("docs/guide.md::2")
-    assert doc_path == "docs/guide.md"
-    assert section_index == 2
-
-    # Test parsing legacy format (no section index)
-    doc_path, section_index = handler._parse_section_content_id("docs/old-format.md")
-    assert doc_path == "docs/old-format.md"
-    assert section_index == 0
-
-
-@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.data.block.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.data.block.get_blocks",
-        return_value=mock_blocks,
-    ):
-        with patch(
-            "backend.api.features.store.content_handlers.query_raw_with_schema",
-            return_value=[],
-        ):
-            items = await handler.get_missing_items(batch_size=10)
-
-            # Should only get the good block
-            assert len(items) == 1
-            assert items[0].content_id == "good-block"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_documentation_handler_missing_docs_directory():
-    """Test DocumentationHandler handles missing docs directory gracefully."""
-    handler = DocumentationHandler()
-
-    # Mock _get_docs_root to return non-existent path
-    fake_path = Path("/nonexistent/docs")
-    with patch.object(handler, "_get_docs_root", return_value=fake_path):
-        items = await handler.get_missing_items(batch_size=10)
-        assert items == []
-
-        stats = await handler.get_stats()
-        assert stats["total"] == 0
-        assert stats["with_embeddings"] == 0
-        assert stats["without_embeddings"] == 0

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

@@ -1,7 +1,8 @@
 import asyncio
 import logging
+import typing
 from datetime import datetime, timezone
-from typing import Any, Literal
+from typing import Literal
 
 import fastapi
 import prisma.enums
@@ -9,7 +10,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import transaction
+from backend.data.db import query_raw_with_schema, transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -29,8 +30,6 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
-from .embeddings import ensure_embedding
-from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -51,77 +50,128 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view.
-
-    Search behavior:
-    - With search_query: Uses hybrid search (semantic + lexical)
-    - Fallback: If embeddings unavailable, gracefully degrades to lexical-only
-    - Rationale: User-facing endpoint prioritizes availability over accuracy
-
-    Note: Admin operations (approval) use fail-fast to prevent inconsistent state.
+    Get PUBLIC store agents from the StoreAgent view
     """
     logger.debug(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
-    search_used_hybrid = False
-    store_agents: list[store_model.StoreAgent] = []
-    agents: list[dict[str, Any]] = []
-    total = 0
-    total_pages = 0
-
     try:
-        # If search_query is provided, use hybrid search (embeddings + tsvector)
+        # If search_query is provided, use full-text search
         if search_query:
-            # Try hybrid search combining semantic and lexical signals
-            # Falls back to lexical-only if OpenAI unavailable (user-facing, high SLA)
-            try:
-                agents, total = await hybrid_search(
-                    query=search_query,
-                    featured=featured,
-                    creators=creators,
-                    category=category,
-                    sorted_by="relevance",  # Use hybrid scoring for relevance
-                    page=page,
-                    page_size=page_size,
-                )
-                search_used_hybrid = True
-            except Exception as e:
-                # Log error but fall back to lexical search for better UX
-                logger.error(
-                    f"Hybrid search failed (likely OpenAI unavailable), "
-                    f"falling back to lexical search: {e}"
-                )
-                # search_used_hybrid remains False, will use fallback path below
+            offset = (page - 1) * page_size
 
-            # Convert hybrid search results (dict format) if hybrid succeeded
-            if search_used_hybrid:
-                total_pages = (total + page_size - 1) // page_size
-                store_agents: list[store_model.StoreAgent] = []
-                for agent in agents:
-                    try:
-                        store_agent = store_model.StoreAgent(
-                            slug=agent["slug"],
-                            agent_name=agent["agent_name"],
-                            agent_image=(
-                                agent["agent_image"][0] if agent["agent_image"] else ""
-                            ),
-                            creator=agent["creator_username"] or "Needs Profile",
-                            creator_avatar=agent["creator_avatar"] or "",
-                            sub_heading=agent["sub_heading"],
-                            description=agent["description"],
-                            runs=agent["runs"],
-                            rating=agent["rating"],
-                        )
-                        store_agents.append(store_agent)
-                    except Exception as e:
-                        logger.error(
-                            f"Error parsing Store agent from hybrid search results: {e}"
-                        )
-                        continue
+            # Whitelist allowed order_by columns
+            ALLOWED_ORDER_BY = {
+                "rating": "rating DESC, rank DESC",
+                "runs": "runs DESC, rank DESC",
+                "name": "agent_name ASC, rank ASC",
+                "updated_at": "updated_at DESC, rank DESC",
+            }
 
-        if not search_used_hybrid:
-            # Fallback path - use basic search or no search
+            # Validate and get order clause
+            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
+                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
+            else:
+                order_by_clause = "updated_at DESC, rank DESC"
+
+            # Build WHERE conditions and parameters list
+            where_parts: list[str] = []
+            params: list[typing.Any] = [search_query]  # $1 - search term
+            param_index = 2  # Start at $2 for next parameter
+
+            # Always filter for available agents
+            where_parts.append("is_available = true")
+
+            if featured:
+                where_parts.append("featured = true")
+
+            if creators and creators:
+                # Use ANY with array parameter
+                where_parts.append(f"creator_username = ANY(${param_index})")
+                params.append(creators)
+                param_index += 1
+
+            if category and category:
+                where_parts.append(f"${param_index} = ANY(categories)")
+                params.append(category)
+                param_index += 1
+
+            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
+
+            # Add pagination params
+            params.extend([page_size, offset])
+            limit_param = f"${param_index}"
+            offset_param = f"${param_index + 1}"
+
+            # Execute full-text search query with parameterized values
+            sql_query = f"""
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    ts_rank_cd(search, query) AS rank
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+                ORDER BY {order_by_clause}
+                LIMIT {limit_param} OFFSET {offset_param}
+            """
+
+            # Count query for pagination - only uses search term parameter
+            count_query = f"""
+                SELECT COUNT(*) as count
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+            """
+
+            # Execute both queries with parameters
+            agents = await query_raw_with_schema(sql_query, *params)
+
+            # For count, use params without pagination (last 2 params)
+            count_params = params[:-2]
+            count_result = await query_raw_with_schema(count_query, *count_params)
+
+            total = count_result[0]["count"] if count_result else 0
+            total_pages = (total + page_size - 1) // page_size
+
+            # Convert raw results to StoreAgent models
+            store_agents: list[store_model.StoreAgent] = []
+            for agent in agents:
+                try:
+                    store_agent = store_model.StoreAgent(
+                        slug=agent["slug"],
+                        agent_name=agent["agent_name"],
+                        agent_image=(
+                            agent["agent_image"][0] if agent["agent_image"] else ""
+                        ),
+                        creator=agent["creator_username"] or "Needs Profile",
+                        creator_avatar=agent["creator_avatar"] or "",
+                        sub_heading=agent["sub_heading"],
+                        description=agent["description"],
+                        runs=agent["runs"],
+                        rating=agent["rating"],
+                    )
+                    store_agents.append(store_agent)
+                except Exception as e:
+                    logger.error(f"Error parsing Store agent from search results: {e}")
+                    continue
+
+        else:
+            # Non-search query path (original logic)
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -130,14 +180,6 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
-            # Add basic text search if search_query provided but hybrid failed
-            if search_query:
-                where_clause["OR"] = [
-                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
-                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
-                    {"description": {"contains": search_query, "mode": "insensitive"}},
-                ]
-
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -146,7 +188,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            db_agents = await prisma.models.StoreAgent.prisma().find_many(
+            agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -157,7 +199,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in db_agents:
+            for agent in agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -417,6 +459,9 @@ async def get_store_creators(
     # Build where clause with sanitized inputs
     where = {}
 
+    # Only return creators with approved agents
+    where["num_agents"] = {"gt": 0}
+
     if featured:
         where["is_featured"] = featured
 
@@ -572,7 +617,6 @@ 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,
@@ -626,48 +670,35 @@ async def delete_store_submission(
     submission_id: str,
 ) -> bool:
     """
-    Delete a store submission version as the submitting user.
+    Delete a store listing submission as the submitting user.
 
     Args:
         user_id: ID of the authenticated user
-        submission_id: StoreListingVersion ID to delete
+        submission_id: ID of the submission to be deleted
 
     Returns:
-        bool: True if successfully deleted
+        bool: True if the submission was successfully deleted, False otherwise
     """
+    logger.debug(f"Deleting store submission {submission_id} for user {user_id}")
+
     try:
-        # Find the submission version with ownership check
-        version = await prisma.models.StoreListingVersion.prisma().find_first(
-            where={"id": submission_id}, include={"StoreListing": True}
+        # Verify the submission belongs to this user
+        submission = await prisma.models.StoreListing.prisma().find_first(
+            where={"agentGraphId": submission_id, "owningUserId": user_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"
+        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}"
             )
 
-        # Delete the version
-        await prisma.models.StoreListingVersion.prisma().delete(
-            where={"id": version.id}
-        )
+        # Delete the submission
+        await prisma.models.StoreListing.prisma().delete(where={"id": submission.id})
 
-        # Clean up empty listing if this was the last version
-        remaining = await prisma.models.StoreListingVersion.prisma().count(
-            where={"storeListingId": version.storeListingId}
+        logger.debug(
+            f"Successfully deleted submission {submission_id} for user {user_id}"
         )
-        if remaining == 0:
-            await prisma.models.StoreListing.prisma().delete(
-                where={"id": version.storeListingId}
-            )
-
         return True
 
     except Exception as e:
@@ -731,15 +762,9 @@ async def create_store_submission(
             logger.warning(
                 f"Agent not found for user {user_id}: {agent_id} v{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}"
-                )
+            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(
@@ -811,7 +836,6 @@ 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,
@@ -923,56 +947,81 @@ 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.
 
-        # Only allow editing of PENDING submissions
-        if current_version.submissionStatus != prisma.enums.SubmissionStatus.PENDING:
+        # Check if we can edit this submission
+        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
             raise store_exceptions.InvalidOperationError(
-                f"Cannot edit a {current_version.submissionStatus.value.lower()} submission. Only pending submissions can be edited."
+                "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,
             )
 
         # For PENDING submissions, we can update the existing version
-        # Update the existing version
-        updated_version = await prisma.models.StoreListingVersion.prisma().update(
-            where={"id": store_listing_version_id},
-            data=prisma.types.StoreListingVersionUpdateInput(
+        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,
                 name=name,
-                videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
-                imageUrls=image_urls,
+                sub_heading=sub_heading,
+                slug=current_version.StoreListing.slug,
                 description=description,
-                categories=categories,
-                subHeading=sub_heading,
-                changesSummary=changes_summary,
-                recommendedScheduleCron=recommended_schedule_cron,
                 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,
+            )
 
-        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,
-        )
+        else:
+            raise store_exceptions.InvalidOperationError(
+                f"Cannot edit submission with status: {current_version.submissionStatus}"
+            )
 
     except (
         store_exceptions.SubmissionNotFoundError,
@@ -1051,78 +1100,38 @@ async def create_store_version(
                 f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
             )
 
-        # 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,
-                )
+        # 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,
             )
         )
 
-        # 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,
@@ -1535,7 +1544,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma(tx).update(
+                await prisma.models.AgentGraph.prisma().update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1550,23 +1559,6 @@ async def review_store_submission(
                     },
                 )
 
-                # Generate embedding for approved listing (blocking - admin operation)
-                # Inside transaction: if embedding fails, entire transaction rolls back
-                embedding_success = await ensure_embedding(
-                    version_id=store_listing_version_id,
-                    name=store_listing_version.name,
-                    description=store_listing_version.description,
-                    sub_heading=store_listing_version.subHeading,
-                    categories=store_listing_version.categories or [],
-                    tx=tx,
-                )
-                if not embedding_success:
-                    raise ValueError(
-                        f"Failed to generate embedding for listing {store_listing_version_id}. "
-                        "This is likely due to OpenAI API being unavailable. "
-                        "Please try again later or contact support if the issue persists."
-                    )
-
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},
                     data={
@@ -1719,12 +1711,15 @@ 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 submission.StoreListing else ""),
+            slug=(
+                submission.StoreListing.slug
+                if hasattr(submission, "storeListing") and submission.StoreListing
+                else ""
+            ),
             description=submission.description,
             instructions=submission.instructions,
             image_urls=submission.imageUrls or [],
@@ -1826,7 +1821,9 @@ async def get_admin_listings_with_versions(
         where = prisma.types.StoreListingWhereInput(**where_dict)
         include = prisma.types.StoreListingInclude(
             Versions=prisma.types.FindManyStoreListingVersionArgsFromStoreListing(
-                order_by={"version": "desc"}
+                order_by=prisma.types._StoreListingVersion_version_OrderByInput(
+                    version="desc"
+                )
             ),
             OwningUser=True,
         )
@@ -1851,7 +1848,6 @@ 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/db_test.py

@@ -411,3 +411,36 @@ async def test_get_store_agents_search_category_array_injection():
     # Verify the query executed without error
     # Category should be parameterized, preventing SQL injection
     assert isinstance(result.agents, list)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_store_creators_only_returns_approved(mocker):
+    mock_creators = [
+        prisma.models.Creator(
+            name="Creator One",
+            username="creator1",
+            description="desc",
+            avatar_url="avatar.jpg",
+            num_agents=1,
+            agent_rating=4.5,
+            agent_runs=10,
+            is_featured=False,
+        )
+    ]
+
+    mock_creator = mocker.patch("prisma.models.Creator.prisma")
+    mock_creator.return_value.find_many = mocker.AsyncMock(return_value=mock_creators)
+    mock_creator.return_value.count = mocker.AsyncMock(return_value=1)
+
+    result = await db.get_store_creators()
+
+    assert len(result.creators) == 1
+    assert result.creators[0].username == "creator1"
+
+    mock_creator.return_value.find_many.assert_called_once()
+    mock_creator.return_value.count.assert_called_once()
+
+    _, find_kwargs = mock_creator.return_value.find_many.call_args
+    _, count_kwargs = mock_creator.return_value.count.call_args
+    assert find_kwargs["where"].num_agents == {"gt": 0}
+    assert count_kwargs["where"].num_agents == {"gt": 0}

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

@@ -1,962 +0,0 @@
-"""
-Unified Content Embeddings Service
-
-Handles generation and storage of OpenAI embeddings for all content types
-(store listings, blocks, documentation, library agents) to enable semantic/hybrid search.
-"""
-
-import asyncio
-import logging
-import time
-from typing import Any
-
-import prisma
-from prisma.enums import ContentType
-from tiktoken import encoding_for_model
-
-from backend.api.features.store.content_handlers import CONTENT_HANDLERS
-from backend.data.db import execute_raw_with_schema, query_raw_with_schema
-from backend.util.clients import get_openai_client
-from backend.util.json import dumps
-
-logger = logging.getLogger(__name__)
-
-
-# OpenAI embedding model configuration
-EMBEDDING_MODEL = "text-embedding-3-small"
-# Embedding dimension for the model above
-# text-embedding-3-small: 1536, text-embedding-3-large: 3072
-EMBEDDING_DIM = 1536
-# 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
-
-
-async def cleanup_orphaned_embeddings() -> dict[str, Any]:
-    """
-    Clean up embeddings for content that no longer exists or is no longer valid.
-
-    Compares current content with embeddings in database and removes orphaned records:
-    - STORE_AGENT: Removes embeddings for rejected/deleted store listings
-    - BLOCK: Removes embeddings for blocks no longer registered
-    - DOCUMENTATION: Removes embeddings for deleted doc files
-
-    Returns:
-        Dict with cleanup statistics per content type
-    """
-    results_by_type = {}
-    total_deleted = 0
-
-    # Cleanup orphaned embeddings for all content types
-    cleanup_types = [
-        ContentType.STORE_AGENT,
-        ContentType.BLOCK,
-        ContentType.DOCUMENTATION,
-    ]
-
-    for content_type in cleanup_types:
-        try:
-            handler = CONTENT_HANDLERS.get(content_type)
-            if not handler:
-                logger.warning(f"No handler registered for {content_type}")
-                results_by_type[content_type.value] = {
-                    "deleted": 0,
-                    "error": "No handler registered",
-                }
-                continue
-
-            # Get all current content IDs from handler
-            if content_type == ContentType.STORE_AGENT:
-                # Get IDs of approved store listing versions from non-deleted listings
-                valid_agents = await query_raw_with_schema(
-                    """
-                    SELECT slv.id
-                    FROM {schema_prefix}"StoreListingVersion" slv
-                    JOIN {schema_prefix}"StoreListing" sl ON slv."storeListingId" = sl.id
-                    WHERE slv."submissionStatus" = 'APPROVED'
-                      AND slv."isDeleted" = false
-                      AND sl."isDeleted" = false
-                    """,
-                )
-                current_ids = {row["id"] for row in valid_agents}
-            elif content_type == ContentType.BLOCK:
-                from backend.data.block import get_blocks
-
-                current_ids = set(get_blocks().keys())
-            elif content_type == ContentType.DOCUMENTATION:
-                # Use DocumentationHandler to get section-based content IDs
-                from backend.api.features.store.content_handlers import (
-                    DocumentationHandler,
-                )
-
-                doc_handler = CONTENT_HANDLERS.get(ContentType.DOCUMENTATION)
-                if isinstance(doc_handler, DocumentationHandler):
-                    docs_root = doc_handler._get_docs_root()
-                    if docs_root.exists():
-                        current_ids = doc_handler._get_all_section_content_ids(
-                            docs_root
-                        )
-                    else:
-                        current_ids = set()
-                else:
-                    current_ids = set()
-            else:
-                # Skip unknown content types to avoid accidental deletion
-                logger.warning(
-                    f"Skipping cleanup for unknown content type: {content_type}"
-                )
-                results_by_type[content_type.value] = {
-                    "deleted": 0,
-                    "error": "Unknown content type - skipped for safety",
-                }
-                continue
-
-            # Get all embedding IDs from database
-            db_embeddings = await query_raw_with_schema(
-                """
-                SELECT "contentId"
-                FROM {schema_prefix}"UnifiedContentEmbedding"
-                WHERE "contentType" = $1::{schema_prefix}"ContentType"
-                """,
-                content_type,
-            )
-
-            db_ids = {row["contentId"] for row in db_embeddings}
-
-            # Find orphaned embeddings (in DB but not in current content)
-            orphaned_ids = db_ids - current_ids
-
-            if not orphaned_ids:
-                logger.info(f"{content_type.value}: No orphaned embeddings found")
-                results_by_type[content_type.value] = {
-                    "deleted": 0,
-                    "message": "No orphaned embeddings",
-                }
-                continue
-
-            # Delete orphaned embeddings in batch for better performance
-            orphaned_list = list(orphaned_ids)
-            try:
-                await execute_raw_with_schema(
-                    """
-                    DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
-                    WHERE "contentType" = $1::{schema_prefix}"ContentType"
-                      AND "contentId" = ANY($2::text[])
-                    """,
-                    content_type,
-                    orphaned_list,
-                )
-                deleted = len(orphaned_list)
-            except Exception as e:
-                logger.error(f"Failed to batch delete orphaned embeddings: {e}")
-                deleted = 0
-
-            logger.info(
-                f"{content_type.value}: Deleted {deleted}/{len(orphaned_ids)} orphaned embeddings"
-            )
-            results_by_type[content_type.value] = {
-                "deleted": deleted,
-                "orphaned": len(orphaned_ids),
-                "message": f"Deleted {deleted} orphaned embeddings",
-            }
-
-            total_deleted += deleted
-
-        except Exception as e:
-            logger.error(f"Failed to cleanup {content_type.value}: {e}")
-            results_by_type[content_type.value] = {
-                "deleted": 0,
-                "error": str(e),
-            }
-
-    return {
-        "by_type": results_by_type,
-        "totals": {
-            "deleted": total_deleted,
-            "message": f"Deleted {total_deleted} orphaned embeddings",
-        },
-    }
-
-
-async def semantic_search(
-    query: str,
-    content_types: list[ContentType] | None = None,
-    user_id: str | None = None,
-    limit: int = 20,
-    min_similarity: float = 0.5,
-) -> list[dict[str, Any]]:
-    """
-    Semantic search across content types using embeddings.
-
-    Performs vector similarity search on UnifiedContentEmbedding table.
-    Used directly for blocks/docs/library agents, or as the semantic component
-    within hybrid_search for store agents.
-
-    If embedding generation fails, falls back to lexical search on searchableText.
-
-    Args:
-        query: Search query string
-        content_types: List of ContentType to search. Defaults to [BLOCK, STORE_AGENT, DOCUMENTATION]
-        user_id: Optional user ID for searching private content (library agents)
-        limit: Maximum number of results to return (default: 20)
-        min_similarity: Minimum cosine similarity threshold (0-1, default: 0.5)
-
-    Returns:
-        List of search results with the following structure:
-        [
-            {
-                "content_id": str,
-                "content_type": str,  # "BLOCK", "STORE_AGENT", "DOCUMENTATION", or "LIBRARY_AGENT"
-                "searchable_text": str,
-                "metadata": dict,
-                "similarity": float,  # Cosine similarity score (0-1)
-            },
-            ...
-        ]
-
-    Examples:
-        # Search blocks only
-        results = await semantic_search("calculate", content_types=[ContentType.BLOCK])
-
-        # Search blocks and documentation
-        results = await semantic_search(
-            "how to use API",
-            content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION]
-        )
-
-        # Search all public content (default)
-        results = await semantic_search("AI agent")
-
-        # Search user's library agents
-        results = await semantic_search(
-            "my custom agent",
-            content_types=[ContentType.LIBRARY_AGENT],
-            user_id="user123"
-        )
-    """
-    # Default to searching all public content types
-    if content_types is None:
-        content_types = [
-            ContentType.BLOCK,
-            ContentType.STORE_AGENT,
-            ContentType.DOCUMENTATION,
-        ]
-
-    # Validate inputs
-    if not content_types:
-        return []  # Empty content_types would cause invalid SQL (IN ())
-
-    query = query.strip()
-    if not query:
-        return []
-
-    if limit < 1:
-        limit = 1
-    if limit > 100:
-        limit = 100
-
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    if query_embedding is not None:
-        # Semantic search with embeddings
-        embedding_str = embedding_to_vector_string(query_embedding)
-
-        # Build params in order: limit, then user_id (if provided), then content types
-        params: list[Any] = [limit]
-        user_filter = ""
-        if user_id is not None:
-            user_filter = 'AND "userId" = ${}'.format(len(params) + 1)
-            params.append(user_id)
-
-        # Add content type parameters and build placeholders dynamically
-        content_type_start_idx = len(params) + 1
-        content_type_placeholders = ", ".join(
-            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
-            for i in range(len(content_types))
-        )
-        params.extend([ct.value for ct in content_types])
-
-        sql = f"""
-            SELECT
-                "contentId" as content_id,
-                "contentType" as content_type,
-                "searchableText" as searchable_text,
-                metadata,
-                1 - (embedding <=> '{embedding_str}'::vector) as similarity
-            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-            WHERE "contentType" IN ({content_type_placeholders})
-            {user_filter}
-            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
-            ORDER BY similarity DESC
-            LIMIT $1
-        """
-        params.append(min_similarity)
-
-        try:
-            results = await query_raw_with_schema(
-                sql, *params, set_public_search_path=True
-            )
-            return [
-                {
-                    "content_id": row["content_id"],
-                    "content_type": row["content_type"],
-                    "searchable_text": row["searchable_text"],
-                    "metadata": row["metadata"],
-                    "similarity": float(row["similarity"]),
-                }
-                for row in results
-            ]
-        except Exception as e:
-            logger.error(f"Semantic search failed: {e}")
-            # Fall through to lexical search below
-
-    # Fallback to lexical search if embeddings unavailable
-    logger.warning("Falling back to lexical search (embeddings unavailable)")
-
-    params_lexical: list[Any] = [limit]
-    user_filter = ""
-    if user_id is not None:
-        user_filter = 'AND "userId" = ${}'.format(len(params_lexical) + 1)
-        params_lexical.append(user_id)
-
-    # Add content type parameters and build placeholders dynamically
-    content_type_start_idx = len(params_lexical) + 1
-    content_type_placeholders_lexical = ", ".join(
-        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
-        for i in range(len(content_types))
-    )
-    params_lexical.extend([ct.value for ct in content_types])
-
-    sql_lexical = f"""
-        SELECT
-            "contentId" as content_id,
-            "contentType" as content_type,
-            "searchableText" as searchable_text,
-            metadata,
-            0.0 as similarity
-        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
-        WHERE "contentType" IN ({content_type_placeholders_lexical})
-        {user_filter}
-        AND "searchableText" ILIKE ${len(params_lexical) + 1}
-        ORDER BY "updatedAt" DESC
-        LIMIT $1
-    """
-    params_lexical.append(f"%{query}%")
-
-    try:
-        results = await query_raw_with_schema(
-            sql_lexical, *params_lexical, set_public_search_path=True
-        )
-        return [
-            {
-                "content_id": row["content_id"],
-                "content_type": row["content_type"],
-                "searchable_text": row["searchable_text"],
-                "metadata": row["metadata"],
-                "similarity": 0.0,  # Lexical search doesn't provide similarity
-            }
-            for row in results
-        ]
-    except Exception as e:
-        logger.error(f"Lexical search failed: {e}")
-        return []

autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py

@@ -1,666 +0,0 @@
-"""
-End-to-end database tests for embeddings and hybrid search.
-
-These tests hit the actual database to verify SQL queries work correctly.
-Tests cover:
-1. Embedding storage (store_content_embedding)
-2. Embedding retrieval (get_content_embedding)
-3. Embedding deletion (delete_content_embedding)
-4. Unified hybrid search across content types
-5. Store agent hybrid search
-"""
-
-import uuid
-from typing import AsyncGenerator
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store import embeddings
-from backend.api.features.store.embeddings import EMBEDDING_DIM
-from backend.api.features.store.hybrid_search import (
-    hybrid_search,
-    unified_hybrid_search,
-)
-
-# ============================================================================
-# Test Fixtures
-# ============================================================================
-
-
-@pytest.fixture
-def test_content_id() -> str:
-    """Generate unique content ID for test isolation."""
-    return f"test-content-{uuid.uuid4()}"
-
-
-@pytest.fixture
-def test_user_id() -> str:
-    """Generate unique user ID for test isolation."""
-    return f"test-user-{uuid.uuid4()}"
-
-
-@pytest.fixture
-def mock_embedding() -> list[float]:
-    """Generate a mock embedding vector."""
-    # Create a normalized embedding vector
-    import math
-
-    raw = [float(i % 10) / 10.0 for i in range(EMBEDDING_DIM)]
-    # Normalize to unit length (required for cosine similarity)
-    magnitude = math.sqrt(sum(x * x for x in raw))
-    return [x / magnitude for x in raw]
-
-
-@pytest.fixture
-def similar_embedding() -> list[float]:
-    """Generate an embedding similar to mock_embedding."""
-    import math
-
-    # Similar but slightly different values
-    raw = [float(i % 10) / 10.0 + 0.01 for i in range(EMBEDDING_DIM)]
-    magnitude = math.sqrt(sum(x * x for x in raw))
-    return [x / magnitude for x in raw]
-
-
-@pytest.fixture
-def different_embedding() -> list[float]:
-    """Generate an embedding very different from mock_embedding."""
-    import math
-
-    # Reversed pattern to be maximally different
-    raw = [float((EMBEDDING_DIM - i) % 10) / 10.0 for i in range(EMBEDDING_DIM)]
-    magnitude = math.sqrt(sum(x * x for x in raw))
-    return [x / magnitude for x in raw]
-
-
-@pytest.fixture
-async def cleanup_embeddings(
-    server,
-) -> AsyncGenerator[list[tuple[ContentType, str, str | None]], None]:
-    """
-    Fixture that tracks created embeddings and cleans them up after tests.
-
-    Yields a list to which tests can append (content_type, content_id, user_id) tuples.
-    """
-    created_embeddings: list[tuple[ContentType, str, str | None]] = []
-    yield created_embeddings
-
-    # Cleanup all created embeddings
-    for content_type, content_id, user_id in created_embeddings:
-        try:
-            await embeddings.delete_content_embedding(content_type, content_id, user_id)
-        except Exception:
-            pass  # Ignore cleanup errors
-
-
-# ============================================================================
-# store_content_embedding Tests
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_content_embedding_store_agent(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test storing embedding for STORE_AGENT content type."""
-    # Track for cleanup
-    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
-
-    result = await embeddings.store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="AI assistant for productivity tasks",
-        metadata={"name": "Test Agent", "categories": ["productivity"]},
-        user_id=None,  # Store agents are public
-    )
-
-    assert result is True
-
-    # Verify it was stored
-    stored = await embeddings.get_content_embedding(
-        ContentType.STORE_AGENT, test_content_id, user_id=None
-    )
-    assert stored is not None
-    assert stored["contentId"] == test_content_id
-    assert stored["contentType"] == "STORE_AGENT"
-    assert stored["searchableText"] == "AI assistant for productivity tasks"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_content_embedding_block(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test storing embedding for BLOCK content type."""
-    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
-
-    result = await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="HTTP request block for API calls",
-        metadata={"name": "HTTP Request Block"},
-        user_id=None,  # Blocks are public
-    )
-
-    assert result is True
-
-    stored = await embeddings.get_content_embedding(
-        ContentType.BLOCK, test_content_id, user_id=None
-    )
-    assert stored is not None
-    assert stored["contentType"] == "BLOCK"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_content_embedding_documentation(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test storing embedding for DOCUMENTATION content type."""
-    cleanup_embeddings.append((ContentType.DOCUMENTATION, test_content_id, None))
-
-    result = await embeddings.store_content_embedding(
-        content_type=ContentType.DOCUMENTATION,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="Getting started guide for AutoGPT platform",
-        metadata={"title": "Getting Started", "url": "/docs/getting-started"},
-        user_id=None,  # Docs are public
-    )
-
-    assert result is True
-
-    stored = await embeddings.get_content_embedding(
-        ContentType.DOCUMENTATION, test_content_id, user_id=None
-    )
-    assert stored is not None
-    assert stored["contentType"] == "DOCUMENTATION"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_content_embedding_upsert(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test that storing embedding twice updates instead of duplicates."""
-    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
-
-    # Store first time
-    result1 = await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="Original text",
-        metadata={"version": 1},
-        user_id=None,
-    )
-    assert result1 is True
-
-    # Store again with different text (upsert)
-    result2 = await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="Updated text",
-        metadata={"version": 2},
-        user_id=None,
-    )
-    assert result2 is True
-
-    # Verify only one record with updated text
-    stored = await embeddings.get_content_embedding(
-        ContentType.BLOCK, test_content_id, user_id=None
-    )
-    assert stored is not None
-    assert stored["searchableText"] == "Updated text"
-
-
-# ============================================================================
-# get_content_embedding Tests
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_content_embedding_not_found(server):
-    """Test retrieving non-existent embedding returns None."""
-    result = await embeddings.get_content_embedding(
-        ContentType.STORE_AGENT, "non-existent-id", user_id=None
-    )
-    assert result is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_content_embedding_with_metadata(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test that metadata is correctly stored and retrieved."""
-    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
-
-    metadata = {
-        "name": "Test Agent",
-        "subHeading": "A test agent",
-        "categories": ["ai", "productivity"],
-        "customField": 123,
-    }
-
-    await embeddings.store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="test",
-        metadata=metadata,
-        user_id=None,
-    )
-
-    stored = await embeddings.get_content_embedding(
-        ContentType.STORE_AGENT, test_content_id, user_id=None
-    )
-
-    assert stored is not None
-    assert stored["metadata"]["name"] == "Test Agent"
-    assert stored["metadata"]["categories"] == ["ai", "productivity"]
-    assert stored["metadata"]["customField"] == 123
-
-
-# ============================================================================
-# delete_content_embedding Tests
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_delete_content_embedding(
-    server,
-    test_content_id: str,
-    mock_embedding: list[float],
-):
-    """Test deleting embedding removes it from database."""
-    # Store embedding
-    await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=test_content_id,
-        embedding=mock_embedding,
-        searchable_text="To be deleted",
-        metadata=None,
-        user_id=None,
-    )
-
-    # Verify it exists
-    stored = await embeddings.get_content_embedding(
-        ContentType.BLOCK, test_content_id, user_id=None
-    )
-    assert stored is not None
-
-    # Delete it
-    result = await embeddings.delete_content_embedding(
-        ContentType.BLOCK, test_content_id, user_id=None
-    )
-    assert result is True
-
-    # Verify it's gone
-    stored = await embeddings.get_content_embedding(
-        ContentType.BLOCK, test_content_id, user_id=None
-    )
-    assert stored is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_delete_content_embedding_not_found(server):
-    """Test deleting non-existent embedding doesn't error."""
-    result = await embeddings.delete_content_embedding(
-        ContentType.BLOCK, "non-existent-id", user_id=None
-    )
-    # Should succeed even if nothing to delete
-    assert result is True
-
-
-# ============================================================================
-# unified_hybrid_search Tests
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_unified_hybrid_search_finds_matching_content(
-    server,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test unified search finds content matching the query."""
-    # Create unique content IDs
-    agent_id = f"test-agent-{uuid.uuid4()}"
-    block_id = f"test-block-{uuid.uuid4()}"
-    doc_id = f"test-doc-{uuid.uuid4()}"
-
-    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
-    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
-    cleanup_embeddings.append((ContentType.DOCUMENTATION, doc_id, None))
-
-    # Store embeddings for different content types
-    await embeddings.store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=agent_id,
-        embedding=mock_embedding,
-        searchable_text="AI writing assistant for blog posts",
-        metadata={"name": "Writing Assistant"},
-        user_id=None,
-    )
-
-    await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=block_id,
-        embedding=mock_embedding,
-        searchable_text="Text generation block for creative writing",
-        metadata={"name": "Text Generator"},
-        user_id=None,
-    )
-
-    await embeddings.store_content_embedding(
-        content_type=ContentType.DOCUMENTATION,
-        content_id=doc_id,
-        embedding=mock_embedding,
-        searchable_text="How to use writing blocks in AutoGPT",
-        metadata={"title": "Writing Guide"},
-        user_id=None,
-    )
-
-    # Search for "writing" - should find all three
-    results, total = await unified_hybrid_search(
-        query="writing",
-        page=1,
-        page_size=20,
-    )
-
-    # Should find at least our test content (may find others too)
-    content_ids = [r["content_id"] for r in results]
-    assert agent_id in content_ids or total >= 1  # Lexical search should find it
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_unified_hybrid_search_filter_by_content_type(
-    server,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test unified search can filter by content type."""
-    agent_id = f"test-agent-{uuid.uuid4()}"
-    block_id = f"test-block-{uuid.uuid4()}"
-
-    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
-    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
-
-    # Store both types with same searchable text
-    await embeddings.store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=agent_id,
-        embedding=mock_embedding,
-        searchable_text="unique_search_term_xyz123",
-        metadata={},
-        user_id=None,
-    )
-
-    await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=block_id,
-        embedding=mock_embedding,
-        searchable_text="unique_search_term_xyz123",
-        metadata={},
-        user_id=None,
-    )
-
-    # Search only for BLOCK type
-    results, total = await unified_hybrid_search(
-        query="unique_search_term_xyz123",
-        content_types=[ContentType.BLOCK],
-        page=1,
-        page_size=20,
-    )
-
-    # All results should be BLOCK type
-    for r in results:
-        assert r["content_type"] == "BLOCK"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_unified_hybrid_search_empty_query(server):
-    """Test unified search with empty query returns empty results."""
-    results, total = await unified_hybrid_search(
-        query="",
-        page=1,
-        page_size=20,
-    )
-
-    assert results == []
-    assert total == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_unified_hybrid_search_pagination(
-    server,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test unified search pagination works correctly."""
-    # Create multiple items
-    content_ids = []
-    for i in range(5):
-        content_id = f"test-pagination-{uuid.uuid4()}"
-        content_ids.append(content_id)
-        cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
-
-        await embeddings.store_content_embedding(
-            content_type=ContentType.BLOCK,
-            content_id=content_id,
-            embedding=mock_embedding,
-            searchable_text=f"pagination test item number {i}",
-            metadata={"index": i},
-            user_id=None,
-        )
-
-    # Get first page
-    page1_results, total1 = await unified_hybrid_search(
-        query="pagination test",
-        content_types=[ContentType.BLOCK],
-        page=1,
-        page_size=2,
-    )
-
-    # Get second page
-    page2_results, total2 = await unified_hybrid_search(
-        query="pagination test",
-        content_types=[ContentType.BLOCK],
-        page=2,
-        page_size=2,
-    )
-
-    # Total should be consistent
-    assert total1 == total2
-
-    # Pages should have different content (if we have enough results)
-    if len(page1_results) > 0 and len(page2_results) > 0:
-        page1_ids = {r["content_id"] for r in page1_results}
-        page2_ids = {r["content_id"] for r in page2_results}
-        # No overlap between pages
-        assert page1_ids.isdisjoint(page2_ids)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_unified_hybrid_search_min_score_filtering(
-    server,
-    mock_embedding: list[float],
-    cleanup_embeddings: list,
-):
-    """Test unified search respects min_score threshold."""
-    content_id = f"test-minscore-{uuid.uuid4()}"
-    cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
-
-    await embeddings.store_content_embedding(
-        content_type=ContentType.BLOCK,
-        content_id=content_id,
-        embedding=mock_embedding,
-        searchable_text="completely unrelated content about bananas",
-        metadata={},
-        user_id=None,
-    )
-
-    # Search with very high min_score - should filter out low relevance
-    results_high, _ = await unified_hybrid_search(
-        query="quantum computing algorithms",
-        content_types=[ContentType.BLOCK],
-        min_score=0.9,  # Very high threshold
-        page=1,
-        page_size=20,
-    )
-
-    # Search with low min_score
-    results_low, _ = await unified_hybrid_search(
-        query="quantum computing algorithms",
-        content_types=[ContentType.BLOCK],
-        min_score=0.01,  # Very low threshold
-        page=1,
-        page_size=20,
-    )
-
-    # High threshold should have fewer or equal results
-    assert len(results_high) <= len(results_low)
-
-
-# ============================================================================
-# hybrid_search (Store Agents) Tests
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_hybrid_search_store_agents_sql_valid(server):
-    """Test that hybrid_search SQL executes without errors."""
-    # This test verifies the SQL is syntactically correct
-    # even if no results are found
-    results, total = await hybrid_search(
-        query="test agent",
-        page=1,
-        page_size=20,
-    )
-
-    # Should not raise - verifies SQL is valid
-    assert isinstance(results, list)
-    assert isinstance(total, int)
-    assert total >= 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_hybrid_search_with_filters(server):
-    """Test hybrid_search with various filter options."""
-    # Test with all filter types
-    results, total = await hybrid_search(
-        query="productivity",
-        featured=True,
-        creators=["test-creator"],
-        category="productivity",
-        page=1,
-        page_size=10,
-    )
-
-    # Should not raise - verifies filter SQL is valid
-    assert isinstance(results, list)
-    assert isinstance(total, int)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_hybrid_search_pagination(server):
-    """Test hybrid_search pagination."""
-    # Page 1
-    results1, total1 = await hybrid_search(
-        query="agent",
-        page=1,
-        page_size=5,
-    )
-
-    # Page 2
-    results2, total2 = await hybrid_search(
-        query="agent",
-        page=2,
-        page_size=5,
-    )
-
-    # Verify SQL executes without error
-    assert isinstance(results1, list)
-    assert isinstance(results2, list)
-    assert isinstance(total1, int)
-    assert isinstance(total2, int)
-
-    # If page 1 has results, total should be > 0
-    # Note: total from page 2 may be 0 if no results on that page (COUNT(*) OVER limitation)
-    if results1:
-        assert total1 > 0
-
-
-# ============================================================================
-# SQL Validity Tests (verify queries don't break)
-# ============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_all_content_types_searchable(server):
-    """Test that all content types can be searched without SQL errors."""
-    for content_type in [
-        ContentType.STORE_AGENT,
-        ContentType.BLOCK,
-        ContentType.DOCUMENTATION,
-    ]:
-        results, total = await unified_hybrid_search(
-            query="test",
-            content_types=[content_type],
-            page=1,
-            page_size=10,
-        )
-
-        # Should not raise
-        assert isinstance(results, list)
-        assert isinstance(total, int)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_multiple_content_types_searchable(server):
-    """Test searching multiple content types at once."""
-    results, total = await unified_hybrid_search(
-        query="test",
-        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
-        page=1,
-        page_size=20,
-    )
-
-    # Should not raise
-    assert isinstance(results, list)
-    assert isinstance(total, int)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_search_all_content_types_default(server):
-    """Test searching all content types (default behavior)."""
-    results, total = await unified_hybrid_search(
-        query="test",
-        content_types=None,  # Should search all
-        page=1,
-        page_size=20,
-    )
-
-    # Should not raise
-    assert isinstance(results, list)
-    assert isinstance(total, int)
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v", "-s"])

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

@@ -1,315 +0,0 @@
-"""
-Integration tests for embeddings with schema handling.
-
-These tests verify that embeddings operations work correctly across different database schemas.
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store import embeddings
-from backend.api.features.store.embeddings import EMBEDDING_DIM
-
-# 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] * EMBEDDING_DIM,
-                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 via content handlers."""
-    # Mock handler to return stats
-    mock_handler = MagicMock()
-    mock_handler.get_stats = AsyncMock(
-        return_value={
-            "total": 100,
-            "with_embeddings": 80,
-            "without_embeddings": 20,
-        }
-    )
-
-    with patch(
-        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
-        {ContentType.STORE_AGENT: mock_handler},
-    ):
-        result = await embeddings.get_embedding_stats()
-
-        # Verify handler was called
-        mock_handler.get_stats.assert_called_once()
-
-        # Verify new result structure
-        assert "by_type" in result
-        assert "totals" in result
-        assert result["totals"]["total"] == 100
-        assert result["totals"]["with_embeddings"] == 80
-        assert result["totals"]["without_embeddings"] == 20
-        assert result["totals"]["coverage_percent"] == 80.0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_backfill_missing_embeddings_with_schema():
-    """Test backfilling embeddings via content handlers."""
-    from backend.api.features.store.content_handlers import ContentItem
-
-    # Create mock content item
-    mock_item = ContentItem(
-        content_id="version-1",
-        content_type=ContentType.STORE_AGENT,
-        searchable_text="Test Agent Test description",
-        metadata={"name": "Test Agent"},
-    )
-
-    # Mock handler
-    mock_handler = MagicMock()
-    mock_handler.get_missing_items = AsyncMock(return_value=[mock_item])
-
-    with patch(
-        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
-        {ContentType.STORE_AGENT: mock_handler},
-    ):
-        with patch(
-            "backend.api.features.store.embeddings.generate_embedding",
-            return_value=[0.1] * EMBEDDING_DIM,
-        ):
-            with patch(
-                "backend.api.features.store.embeddings.store_content_embedding",
-                return_value=True,
-            ):
-                result = await embeddings.backfill_missing_embeddings(batch_size=10)
-
-                # Verify handler was called
-                mock_handler.get_missing_items.assert_called_once_with(10)
-
-                # 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] * EMBEDDING_DIM
-
-                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] * EMBEDDING_DIM,
-            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] * EMBEDDING_DIM,
-                searchable_text="test",
-                metadata=None,
-                user_id=None,
-            )
-
-            # Should return False on error, not raise
-            assert result is False
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v", "-s"])

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

@@ -1,407 +0,0 @@
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import prisma
-import pytest
-from prisma import Prisma
-from prisma.enums import ContentType
-
-from backend.api.features.store import embeddings
-
-
-@pytest.fixture(autouse=True)
-async def setup_prisma():
-    """Setup Prisma client for tests."""
-    try:
-        Prisma()
-    except prisma.errors.ClientAlreadyRegisteredError:
-        pass
-    yield
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_build_searchable_text():
-    """Test searchable text building from listing fields."""
-    result = embeddings.build_searchable_text(
-        name="AI Assistant",
-        description="A helpful AI assistant for productivity",
-        sub_heading="Boost your productivity",
-        categories=["AI", "Productivity"],
-    )
-
-    expected = "AI Assistant Boost your productivity A helpful AI assistant for productivity AI Productivity"
-    assert result == expected
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_build_searchable_text_empty_fields():
-    """Test searchable text building with empty fields."""
-    result = embeddings.build_searchable_text(
-        name="", description="Test description", sub_heading="", categories=[]
-    )
-
-    assert result == "Test description"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_generate_embedding_success():
-    """Test successful embedding generation."""
-    # Mock OpenAI response
-    mock_client = MagicMock()
-    mock_response = MagicMock()
-    mock_response.data = [MagicMock()]
-    mock_response.data[0].embedding = [0.1, 0.2, 0.3] * 512  # 1536 dimensions
-
-    # Use AsyncMock for async embeddings.create method
-    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
-
-    # Patch at the point of use in embeddings.py
-    with patch(
-        "backend.api.features.store.embeddings.get_openai_client"
-    ) as mock_get_client:
-        mock_get_client.return_value = mock_client
-
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is not None
-        assert len(result) == embeddings.EMBEDDING_DIM
-        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] * embeddings.EMBEDDING_DIM
-
-    # 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 handler stats for each content type
-    mock_handler = MagicMock()
-    mock_handler.get_stats = AsyncMock(
-        return_value={
-            "total": 100,
-            "with_embeddings": 75,
-            "without_embeddings": 25,
-        }
-    )
-
-    # Patch the CONTENT_HANDLERS where it's used (in embeddings module)
-    with patch(
-        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
-        {ContentType.STORE_AGENT: mock_handler},
-    ):
-        result = await embeddings.get_embedding_stats()
-
-        assert "by_type" in result
-        assert "totals" in result
-        assert result["totals"]["total"] == 100
-        assert result["totals"]["with_embeddings"] == 75
-        assert result["totals"]["without_embeddings"] == 25
-        assert result["totals"]["coverage_percent"] == 75.0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.store_content_embedding")
-async def test_backfill_missing_embeddings_success(mock_store):
-    """Test backfill with successful embedding generation."""
-    # Mock ContentItem from handlers
-    from backend.api.features.store.content_handlers import ContentItem
-
-    mock_items = [
-        ContentItem(
-            content_id="version-1",
-            content_type=ContentType.STORE_AGENT,
-            searchable_text="Agent 1 Description 1",
-            metadata={"name": "Agent 1"},
-        ),
-        ContentItem(
-            content_id="version-2",
-            content_type=ContentType.STORE_AGENT,
-            searchable_text="Agent 2 Description 2",
-            metadata={"name": "Agent 2"},
-        ),
-    ]
-
-    # Mock handler to return missing items
-    mock_handler = MagicMock()
-    mock_handler.get_missing_items = AsyncMock(return_value=mock_items)
-
-    # Mock store_content_embedding to succeed for first, fail for second
-    mock_store.side_effect = [True, False]
-
-    with patch(
-        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
-        {ContentType.STORE_AGENT: mock_handler},
-    ):
-        with patch(
-            "backend.api.features.store.embeddings.generate_embedding",
-            return_value=[0.1] * embeddings.EMBEDDING_DIM,
-        ):
-            result = await embeddings.backfill_missing_embeddings(batch_size=5)
-
-            assert result["processed"] == 2
-            assert result["success"] == 1
-            assert result["failed"] == 1
-            assert mock_store.call_count == 2
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_backfill_missing_embeddings_no_missing():
-    """Test backfill when no embeddings are missing."""
-    # Mock handler to return no missing items
-    mock_handler = MagicMock()
-    mock_handler.get_missing_items = AsyncMock(return_value=[])
-
-    with patch(
-        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
-        {ContentType.STORE_AGENT: mock_handler},
-    ):
-        result = await embeddings.backfill_missing_embeddings(batch_size=5)
-
-        assert result["processed"] == 0
-        assert result["success"] == 0
-        assert result["failed"] == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_embedding_to_vector_string():
-    """Test embedding to PostgreSQL vector string conversion."""
-    embedding = [0.1, 0.2, 0.3, -0.4]
-    result = embeddings.embedding_to_vector_string(embedding)
-    assert result == "[0.1,0.2,0.3,-0.4]"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_embed_query():
-    """Test embed_query function (alias for generate_embedding)."""
-    with patch(
-        "backend.api.features.store.embeddings.generate_embedding"
-    ) as mock_generate:
-        mock_generate.return_value = [0.1, 0.2, 0.3]
-
-        result = await embeddings.embed_query("test query")
-
-        assert result == [0.1, 0.2, 0.3]
-        mock_generate.assert_called_once_with("test query")

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

@@ -1,727 +0,0 @@
-"""
-Unified Hybrid Search
-
-Combines semantic (embedding) search with lexical (tsvector) search
-for improved relevance across all content types (agents, blocks, docs).
-Includes BM25 reranking for improved lexical relevance.
-"""
-
-import logging
-import re
-from dataclasses import dataclass
-from typing import Any, Literal
-
-from prisma.enums import ContentType
-from rank_bm25 import BM25Okapi
-
-from backend.api.features.store.embeddings import (
-    EMBEDDING_DIM,
-    embed_query,
-    embedding_to_vector_string,
-)
-from backend.data.db import query_raw_with_schema
-
-logger = logging.getLogger(__name__)
-
-
-# ============================================================================
-# BM25 Reranking
-# ============================================================================
-
-
-def tokenize(text: str) -> list[str]:
-    """Simple tokenizer for BM25 - lowercase and split on non-alphanumeric."""
-    if not text:
-        return []
-    # Lowercase and split on non-alphanumeric characters
-    tokens = re.findall(r"\b\w+\b", text.lower())
-    return tokens
-
-
-def bm25_rerank(
-    query: str,
-    results: list[dict[str, Any]],
-    text_field: str = "searchable_text",
-    bm25_weight: float = 0.3,
-    original_score_field: str = "combined_score",
-) -> list[dict[str, Any]]:
-    """
-    Rerank search results using BM25.
-
-    Combines the original combined_score with BM25 score for improved
-    lexical relevance, especially for exact term matches.
-
-    Args:
-        query: The search query
-        results: List of result dicts with text_field and original_score_field
-        text_field: Field name containing the text to score
-        bm25_weight: Weight for BM25 score (0-1). Original score gets (1 - bm25_weight)
-        original_score_field: Field name containing the original score
-
-    Returns:
-        Results list sorted by combined score (BM25 + original)
-    """
-    if not results or not query:
-        return results
-
-    # Extract texts and tokenize
-    corpus = [tokenize(r.get(text_field, "") or "") for r in results]
-
-    # Handle edge case where all documents are empty
-    if all(len(doc) == 0 for doc in corpus):
-        return results
-
-    # Build BM25 index
-    bm25 = BM25Okapi(corpus)
-
-    # Score query against corpus
-    query_tokens = tokenize(query)
-    if not query_tokens:
-        return results
-
-    bm25_scores = bm25.get_scores(query_tokens)
-
-    # Normalize BM25 scores to 0-1 range
-    max_bm25 = max(bm25_scores) if max(bm25_scores) > 0 else 1.0
-    normalized_bm25 = [s / max_bm25 for s in bm25_scores]
-
-    # Combine scores
-    original_weight = 1.0 - bm25_weight
-    for i, result in enumerate(results):
-        original_score = result.get(original_score_field, 0) or 0
-        result["bm25_score"] = normalized_bm25[i]
-        final_score = (
-            original_weight * original_score + bm25_weight * normalized_bm25[i]
-        )
-        result["final_score"] = final_score
-        result["relevance"] = final_score
-
-    # Sort by relevance descending
-    results.sort(key=lambda x: x.get("relevance", 0), reverse=True)
-
-    return results
-
-
-@dataclass
-class UnifiedSearchWeights:
-    """Weights for unified search (no popularity signal)."""
-
-    semantic: float = 0.40  # Embedding cosine similarity
-    lexical: float = 0.40  # tsvector ts_rank_cd score
-    category: float = 0.10  # Category match boost (for types that have categories)
-    recency: float = 0.10  # Newer content ranked higher
-
-    def __post_init__(self):
-        """Validate weights are non-negative and sum to approximately 1.0."""
-        total = self.semantic + self.lexical + self.category + self.recency
-
-        if any(
-            w < 0 for w in [self.semantic, self.lexical, self.category, self.recency]
-        ):
-            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 for unified search
-DEFAULT_UNIFIED_WEIGHTS = UnifiedSearchWeights()
-
-# Minimum relevance score thresholds
-DEFAULT_MIN_SCORE = 0.15  # For unified search (more permissive)
-DEFAULT_STORE_AGENT_MIN_SCORE = 0.20  # For store agent search (original threshold)
-
-
-async def unified_hybrid_search(
-    query: str,
-    content_types: list[ContentType] | None = None,
-    category: str | None = None,
-    page: int = 1,
-    page_size: int = 20,
-    weights: UnifiedSearchWeights | None = None,
-    min_score: float | None = None,
-    user_id: str | None = None,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Unified hybrid search across all content types.
-
-    Searches UnifiedContentEmbedding using both semantic (vector) and lexical (tsvector) signals.
-
-    Args:
-        query: Search query string
-        content_types: List of content types to search. Defaults to all public types.
-        category: Filter by category (for content types that support it)
-        page: Page number (1-indexed)
-        page_size: Results per page
-        weights: Custom weights for search signals
-        min_score: Minimum relevance score threshold (0-1)
-        user_id: User ID for searching private content (library agents)
-
-    Returns:
-        Tuple of (results list, total count)
-    """
-    # Validate inputs
-    query = query.strip()
-    if not query:
-        return [], 0
-
-    if page < 1:
-        page = 1
-    if page_size < 1:
-        page_size = 1
-    if page_size > 100:
-        page_size = 100
-
-    if content_types is None:
-        content_types = [
-            ContentType.STORE_AGENT,
-            ContentType.BLOCK,
-            ContentType.DOCUMENTATION,
-        ]
-
-    if weights is None:
-        weights = DEFAULT_UNIFIED_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)
-
-    # Graceful degradation if embedding unavailable
-    if query_embedding is None or not query_embedding:
-        logger.warning(
-            "Failed to generate query embedding - falling back to lexical-only search. "
-            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
-        )
-        query_embedding = [0.0] * EMBEDDING_DIM
-        # Redistribute semantic weight to lexical
-        total_non_semantic = weights.lexical + weights.category + weights.recency
-        if total_non_semantic > 0:
-            factor = 1.0 / total_non_semantic
-            weights = UnifiedSearchWeights(
-                semantic=0.0,
-                lexical=weights.lexical * factor,
-                category=weights.category * factor,
-                recency=weights.recency * factor,
-            )
-        else:
-            weights = UnifiedSearchWeights(
-                semantic=0.0, lexical=1.0, category=0.0, recency=0.0
-            )
-
-    # Build parameters
-    params: list[Any] = []
-    param_idx = 1
-
-    # Query for lexical search
-    params.append(query)
-    query_param = f"${param_idx}"
-    param_idx += 1
-
-    # Query lowercase for category matching
-    params.append(query.lower())
-    query_lower_param = f"${param_idx}"
-    param_idx += 1
-
-    # Embedding
-    embedding_str = embedding_to_vector_string(query_embedding)
-    params.append(embedding_str)
-    embedding_param = f"${param_idx}"
-    param_idx += 1
-
-    # Content types
-    content_type_values = [ct.value for ct in content_types]
-    params.append(content_type_values)
-    content_types_param = f"${param_idx}"
-    param_idx += 1
-
-    # User ID filter (for private content)
-    user_filter = ""
-    if user_id is not None:
-        params.append(user_id)
-        user_filter = f'AND (uce."userId" = ${param_idx} OR uce."userId" IS NULL)'
-        param_idx += 1
-    else:
-        user_filter = 'AND uce."userId" IS NULL'
-
-    # Weights
-    params.append(weights.semantic)
-    w_semantic = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.lexical)
-    w_lexical = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.category)
-    w_category = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.recency)
-    w_recency = f"${param_idx}"
-    param_idx += 1
-
-    # Min score
-    params.append(min_score)
-    min_score_param = f"${param_idx}"
-    param_idx += 1
-
-    # Pagination
-    params.append(page_size)
-    limit_param = f"${param_idx}"
-    param_idx += 1
-
-    params.append(offset)
-    offset_param = f"${param_idx}"
-    param_idx += 1
-
-    # Unified search query on UnifiedContentEmbedding
-    sql_query = f"""
-        WITH candidates AS (
-            -- Lexical matches (uses GIN index on search column)
-            SELECT uce.id, uce."contentType", uce."contentId"
-            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
-            WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
-            {user_filter}
-            AND uce.search @@ plainto_tsquery('english', {query_param})
-
-            UNION
-
-            -- Semantic matches (uses HNSW index on embedding)
-            (
-                SELECT uce.id, uce."contentType", uce."contentId"
-                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
-                WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
-                {user_filter}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
-                LIMIT 200
-            )
-        ),
-        search_scores AS (
-            SELECT
-                uce."contentType" as content_type,
-                uce."contentId" as content_id,
-                uce."searchableText" as searchable_text,
-                uce.metadata,
-                uce."updatedAt" as updated_at,
-                -- Semantic score: cosine similarity (1 - distance)
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                -- Lexical score: ts_rank_cd
-                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                -- Category match from metadata
-                CASE
-                    WHEN uce.metadata ? 'categories' AND EXISTS (
-                        SELECT 1 FROM jsonb_array_elements_text(uce.metadata->'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
-                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - uce."updatedAt")) / (90 * 24 * 3600)) as recency_score
-            FROM candidates c
-            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce ON c.id = uce.id
-        ),
-        max_lexical AS (
-            SELECT GREATEST(MAX(lexical_raw), 0.001) as max_val FROM search_scores
-        ),
-        normalized AS (
-            SELECT
-                ss.*,
-                ss.lexical_raw / ml.max_val as lexical_score
-            FROM search_scores ss
-            CROSS JOIN max_lexical ml
-        ),
-        scored AS (
-            SELECT
-                content_type,
-                content_id,
-                searchable_text,
-                metadata,
-                updated_at,
-                semantic_score,
-                lexical_score,
-                category_score,
-                recency_score,
-                (
-                    {w_semantic} * semantic_score +
-                    {w_lexical} * lexical_score +
-                    {w_category} * category_score +
-                    {w_recency} * recency_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 {limit_param} OFFSET {offset_param}
-    """
-
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
-
-    total = results[0]["total_count"] if results else 0
-    # Apply BM25 reranking
-    if results:
-        results = bm25_rerank(
-            query=query,
-            results=results,
-            text_field="searchable_text",
-            bm25_weight=0.3,
-            original_score_field="combined_score",
-        )
-
-    # Clean up results
-    for result in results:
-        result.pop("total_count", None)
-
-    logger.info(f"Unified hybrid search: {len(results)} results, {total} total")
-
-    return results, total
-
-
-# ============================================================================
-# Store Agent specific search (with full metadata)
-# ============================================================================
-
-
-@dataclass
-class StoreAgentSearchWeights:
-    """Weights for store agent search including popularity."""
-
-    semantic: float = 0.30
-    lexical: float = 0.30
-    category: float = 0.20
-    recency: float = 0.10
-    popularity: float = 0.10
-
-    def __post_init__(self):
-        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_STORE_AGENT_WEIGHTS = StoreAgentSearchWeights()
-
-
-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: StoreAgentSearchWeights | None = None,
-    min_score: float | None = None,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Hybrid search for store agents with full metadata.
-
-    Uses UnifiedContentEmbedding for search, joins to StoreAgent for metadata.
-    """
-    query = query.strip()
-    if not query:
-        return [], 0
-
-    if page < 1:
-        page = 1
-    if page_size < 1:
-        page_size = 1
-    if page_size > 100:
-        page_size = 100
-
-    if weights is None:
-        weights = DEFAULT_STORE_AGENT_WEIGHTS
-    if min_score is None:
-        min_score = (
-            DEFAULT_STORE_AGENT_MIN_SCORE  # Use original threshold for store agents
-        )
-
-    offset = (page - 1) * page_size
-
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    # Graceful degradation
-    if query_embedding is None or not query_embedding:
-        logger.warning(
-            "Failed to generate query embedding - falling back to lexical-only search."
-        )
-        query_embedding = [0.0] * EMBEDDING_DIM
-        total_non_semantic = (
-            weights.lexical + weights.category + weights.recency + weights.popularity
-        )
-        if total_non_semantic > 0:
-            factor = 1.0 / total_non_semantic
-            weights = StoreAgentSearchWeights(
-                semantic=0.0,
-                lexical=weights.lexical * factor,
-                category=weights.category * factor,
-                recency=weights.recency * factor,
-                popularity=weights.popularity * factor,
-            )
-        else:
-            weights = StoreAgentSearchWeights(
-                semantic=0.0, lexical=1.0, category=0.0, recency=0.0, popularity=0.0
-            )
-
-    # Build parameters
-    params: list[Any] = []
-    param_idx = 1
-
-    params.append(query)
-    query_param = f"${param_idx}"
-    param_idx += 1
-
-    params.append(query.lower())
-    query_lower_param = f"${param_idx}"
-    param_idx += 1
-
-    embedding_str = embedding_to_vector_string(query_embedding)
-    params.append(embedding_str)
-    embedding_param = f"${param_idx}"
-    param_idx += 1
-
-    # Build WHERE clause for StoreAgent filters
-    where_parts = ["sa.is_available = true"]
-
-    if featured:
-        where_parts.append("sa.featured = true")
-
-    if creators:
-        params.append(creators)
-        where_parts.append(f"sa.creator_username = ANY(${param_idx})")
-        param_idx += 1
-
-    if category:
-        params.append(category)
-        where_parts.append(f"${param_idx} = ANY(sa.categories)")
-        param_idx += 1
-
-    where_clause = " AND ".join(where_parts)
-
-    # Weights
-    params.append(weights.semantic)
-    w_semantic = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.lexical)
-    w_lexical = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.category)
-    w_category = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.recency)
-    w_recency = f"${param_idx}"
-    param_idx += 1
-
-    params.append(weights.popularity)
-    w_popularity = f"${param_idx}"
-    param_idx += 1
-
-    params.append(min_score)
-    min_score_param = f"${param_idx}"
-    param_idx += 1
-
-    params.append(page_size)
-    limit_param = f"${param_idx}"
-    param_idx += 1
-
-    params.append(offset)
-    offset_param = f"${param_idx}"
-    param_idx += 1
-
-    # Query using UnifiedContentEmbedding for search, StoreAgent for metadata
-    sql_query = f"""
-        WITH candidates AS (
-            -- Lexical matches via UnifiedContentEmbedding.search
-            SELECT uce."contentId" as "storeListingVersionId"
-            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
-            INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                ON uce."contentId" = sa."storeListingVersionId"
-            WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-            AND uce."userId" IS NULL
-            AND uce.search @@ plainto_tsquery('english', {query_param})
-            AND {where_clause}
-
-            UNION
-
-            -- Semantic matches via UnifiedContentEmbedding.embedding
-            SELECT uce."contentId" as "storeListingVersionId"
-            FROM (
-                SELECT uce."contentId", uce.embedding
-                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
-                INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                    ON uce."contentId" = sa."storeListingVersionId"
-                WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-                AND uce."userId" IS NULL
-                AND {where_clause}
-                ORDER BY uce.embedding <=> {embedding_param}::vector
-                LIMIT 200
-            ) uce
-        ),
-        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,
-                -- Searchable text for BM25 reranking
-                COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
-                -- Semantic score
-                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                -- Lexical score (raw, will normalize)
-                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                -- Category match
-                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
-                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
-                -- Popularity (raw)
-                sa.runs as popularity_raw
-            FROM candidates c
-            INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                ON c."storeListingVersionId" = sa."storeListingVersionId"
-            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                ON sa."storeListingVersionId" = uce."contentId"
-                AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-        ),
-        max_vals AS (
-            SELECT
-                GREATEST(MAX(lexical_raw), 0.001) as max_lexical,
-                GREATEST(MAX(popularity_raw), 1) as max_popularity
-            FROM search_scores
-        ),
-        normalized AS (
-            SELECT
-                ss.*,
-                ss.lexical_raw / mv.max_lexical as lexical_score,
-                CASE
-                    WHEN ss.popularity_raw > 0
-                    THEN LN(1 + ss.popularity_raw) / LN(1 + mv.max_popularity)
-                    ELSE 0
-                END as popularity_score
-            FROM search_scores ss
-            CROSS JOIN max_vals mv
-        ),
-        scored AS (
-            SELECT
-                slug,
-                agent_name,
-                agent_image,
-                creator_username,
-                creator_avatar,
-                sub_heading,
-                description,
-                runs,
-                rating,
-                categories,
-                featured,
-                is_available,
-                updated_at,
-                searchable_text,
-                semantic_score,
-                lexical_score,
-                category_score,
-                recency_score,
-                popularity_score,
-                (
-                    {w_semantic} * semantic_score +
-                    {w_lexical} * lexical_score +
-                    {w_category} * category_score +
-                    {w_recency} * recency_score +
-                    {w_popularity} * 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 {limit_param} OFFSET {offset_param}
-    """
-
-    results = await query_raw_with_schema(
-        sql_query, *params, set_public_search_path=True
-    )
-
-    total = results[0]["total_count"] if results else 0
-
-    # Apply BM25 reranking
-    if results:
-        results = bm25_rerank(
-            query=query,
-            results=results,
-            text_field="searchable_text",
-            bm25_weight=0.3,
-            original_score_field="combined_score",
-        )
-
-    for result in results:
-        result.pop("total_count", None)
-        result.pop("searchable_text", None)
-
-    logger.info(f"Hybrid search (store agents): {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 store agents."""
-    return await hybrid_search(query=query, page=page, page_size=page_size)
-
-
-# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
-# for existing code that expects the popularity parameter
-HybridSearchWeights = StoreAgentSearchWeights

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

@@ -1,726 +0,0 @@
-"""
-Integration tests for hybrid search with schema handling.
-
-These tests verify that hybrid search works correctly across different database schemas.
-"""
-
-from unittest.mock import patch
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store import embeddings
-from backend.api.features.store.hybrid_search import (
-    HybridSearchWeights,
-    UnifiedSearchWeights,
-    hybrid_search,
-    unified_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] * embeddings.EMBEDDING_DIM  # 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] * embeddings.EMBEDDING_DIM
-
-                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] * embeddings.EMBEDDING_DIM
-
-                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 gracefully degrades when embeddings are unavailable."""
-    # Mock database to return some results
-    mock_results = [
-        {
-            "slug": "test-agent",
-            "agent_name": "Test Agent",
-            "agent_image": "test.png",
-            "creator_username": "creator",
-            "creator_avatar": "avatar.png",
-            "sub_heading": "Test heading",
-            "description": "Test description",
-            "runs": 100,
-            "rating": 4.5,
-            "categories": ["AI"],
-            "featured": False,
-            "is_available": True,
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.0,  # Zero because no embedding
-            "lexical_score": 0.5,
-            "category_score": 0.0,
-            "recency_score": 0.1,
-            "popularity_score": 0.2,
-            "combined_score": 0.3,
-            "total_count": 1,
-        }
-    ]
-
-    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
-        with patch(
-            "backend.api.features.store.hybrid_search.query_raw_with_schema"
-        ) as mock_query:
-            # Simulate embedding failure
-            mock_embed.return_value = None
-            mock_query.return_value = mock_results
-
-            # Should NOT raise - graceful degradation
-            results, total = await hybrid_search(
-                query="test",
-                page=1,
-                page_size=20,
-            )
-
-            # Verify it returns results even without embeddings
-            assert len(results) == 1
-            assert results[0]["slug"] == "test-agent"
-            assert total == 1
-
-
-@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] * embeddings.EMBEDDING_DIM
-
-            # 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] * embeddings.EMBEDDING_DIM
-
-            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 "$" in sql_template  # Verify parameterization is used
-
-            # Check that custom weights are in the params
-            assert 0.5 in params  # semantic weight
-            assert 0.3 in params  # lexical weight
-            assert 0.1 in params  # category and recency weights
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_hybrid_search_min_score_filtering():
-    """Test hybrid search minimum score threshold."""
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        # Return results with varying scores
-        mock_query.return_value = [
-            {
-                "slug": "high-score/agent",
-                "agent_name": "High Score Agent",
-                "combined_score": 0.8,
-                "total_count": 1,
-                # ... other fields
-            }
-        ]
-
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            # Test with custom min_score
-            results, total = await hybrid_search(
-                query="test",
-                min_score=0.5,  # High threshold
-                page=1,
-                page_size=20,
-            )
-
-            # Verify min_score was applied in query
-            call_args = mock_query.call_args
-            sql_template = call_args[0][0]
-            params = call_args[0][1:]  # Get all parameters
-
-            # Check that SQL uses parameterized min_score
-            assert "combined_score >=" in sql_template
-            assert "$" 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.
-
-    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
-    to the paginated results.
-    """
-    # Create mock results that SQL would return for a page
-    mock_results = [
-        {
-            "slug": f"agent-{i}",
-            "agent_name": f"Agent {i}",
-            "agent_image": "test.png",
-            "creator_username": "test",
-            "creator_avatar": "avatar.png",
-            "sub_heading": "Test",
-            "description": "Test description",
-            "runs": 100 - i,
-            "rating": 4.5,
-            "categories": ["test"],
-            "featured": False,
-            "is_available": True,
-            "updated_at": "2024-01-01T00:00:00Z",
-            "searchable_text": f"Agent {i} test description",
-            "combined_score": 0.9 - (i * 0.01),
-            "semantic_score": 0.7,
-            "lexical_score": 0.6,
-            "category_score": 0.5,
-            "recency_score": 0.4,
-            "popularity_score": 0.3,
-            "total_count": 25,
-        }
-        for i in range(10)  # SQL returns page_size results
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        mock_query.return_value = mock_results
-
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            # Test page 2 with page_size 10
-            results, total = await hybrid_search(
-                query="test",
-                page=2,
-                page_size=10,
-            )
-
-            # Verify results returned
-            assert len(results) == 10
-            assert total == 25  # Total from SQL COUNT(*) OVER()
-
-            # Verify the SQL query uses page_size and offset
-            call_args = mock_query.call_args
-            params = call_args[0]
-            # Last two params are page_size and offset
-            page_size_param = params[-2]
-            offset_param = params[-1]
-            assert page_size_param == 10
-            assert offset_param == 10  # (page 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] * embeddings.EMBEDDING_DIM
-
-            # 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)
-
-
-# =============================================================================
-# Unified Hybrid Search Tests
-# =============================================================================
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_basic():
-    """Test basic unified hybrid search across all content types."""
-    mock_results = [
-        {
-            "content_type": "STORE_AGENT",
-            "content_id": "agent-1",
-            "searchable_text": "Test Agent Description",
-            "metadata": {"name": "Test Agent"},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.7,
-            "lexical_score": 0.8,
-            "category_score": 0.5,
-            "recency_score": 0.3,
-            "combined_score": 0.6,
-            "total_count": 2,
-        },
-        {
-            "content_type": "BLOCK",
-            "content_id": "block-1",
-            "searchable_text": "Test Block Description",
-            "metadata": {"name": "Test Block"},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.6,
-            "lexical_score": 0.7,
-            "category_score": 0.4,
-            "recency_score": 0.2,
-            "combined_score": 0.5,
-            "total_count": 2,
-        },
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = mock_results
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            results, total = await unified_hybrid_search(
-                query="test",
-                page=1,
-                page_size=20,
-            )
-
-            assert len(results) == 2
-            assert total == 2
-            assert results[0]["content_type"] == "STORE_AGENT"
-            assert results[1]["content_type"] == "BLOCK"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_filter_by_content_type():
-    """Test unified search filtering by specific content types."""
-    mock_results = [
-        {
-            "content_type": "BLOCK",
-            "content_id": "block-1",
-            "searchable_text": "Test Block",
-            "metadata": {},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.7,
-            "lexical_score": 0.8,
-            "category_score": 0.0,
-            "recency_score": 0.3,
-            "combined_score": 0.5,
-            "total_count": 1,
-        },
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = mock_results
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            results, total = await unified_hybrid_search(
-                query="test",
-                content_types=[ContentType.BLOCK],
-                page=1,
-                page_size=20,
-            )
-
-            # Verify content_types parameter was passed correctly
-            call_args = mock_query.call_args
-            params = call_args[0][1:]
-            # The content types should be in the params as a list
-            assert ["BLOCK"] in params
-
-            assert len(results) == 1
-            assert total == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_with_user_id():
-    """Test unified search with user_id for private content."""
-    mock_results = [
-        {
-            "content_type": "STORE_AGENT",
-            "content_id": "agent-1",
-            "searchable_text": "My Private Agent",
-            "metadata": {},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.7,
-            "lexical_score": 0.8,
-            "category_score": 0.0,
-            "recency_score": 0.3,
-            "combined_score": 0.6,
-            "total_count": 1,
-        },
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = mock_results
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            results, total = await unified_hybrid_search(
-                query="test",
-                user_id="user-123",
-                page=1,
-                page_size=20,
-            )
-
-            # Verify SQL contains user_id filter
-            call_args = mock_query.call_args
-            sql_template = call_args[0][0]
-            params = call_args[0][1:]
-
-            assert 'uce."userId"' in sql_template
-            assert "user-123" in params
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_custom_weights():
-    """Test unified search with custom weights."""
-    custom_weights = UnifiedSearchWeights(
-        semantic=0.6,
-        lexical=0.2,
-        category=0.1,
-        recency=0.1,
-    )
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = []
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            results, total = await unified_hybrid_search(
-                query="test",
-                weights=custom_weights,
-                page=1,
-                page_size=20,
-            )
-
-            # Verify custom weights are in parameters
-            call_args = mock_query.call_args
-            params = call_args[0][1:]
-
-            assert 0.6 in params  # semantic weight
-            assert 0.2 in params  # lexical weight
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_graceful_degradation():
-    """Test unified search gracefully degrades when embeddings unavailable."""
-    mock_results = [
-        {
-            "content_type": "DOCUMENTATION",
-            "content_id": "doc-1",
-            "searchable_text": "API Documentation",
-            "metadata": {},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.0,  # Zero because no embedding
-            "lexical_score": 0.8,
-            "category_score": 0.0,
-            "recency_score": 0.2,
-            "combined_score": 0.5,
-            "total_count": 1,
-        },
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = mock_results
-            mock_embed.return_value = None  # Embedding failure
-
-            # Should NOT raise - graceful degradation
-            results, total = await unified_hybrid_search(
-                query="test",
-                page=1,
-                page_size=20,
-            )
-
-            assert len(results) == 1
-            assert total == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_empty_query():
-    """Test unified search with empty query returns empty results."""
-    results, total = await unified_hybrid_search(
-        query="",
-        page=1,
-        page_size=20,
-    )
-
-    assert results == []
-    assert total == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_pagination():
-    """Test unified search pagination with BM25 reranking.
-
-    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
-    to the paginated results.
-    """
-    # Create mock results that SQL would return for a page
-    mock_results = [
-        {
-            "content_type": "STORE_AGENT",
-            "content_id": f"agent-{i}",
-            "searchable_text": f"Agent {i} description",
-            "metadata": {"name": f"Agent {i}"},
-            "updated_at": "2025-01-01T00:00:00Z",
-            "semantic_score": 0.7,
-            "lexical_score": 0.8 - (i * 0.01),
-            "category_score": 0.5,
-            "recency_score": 0.3,
-            "combined_score": 0.6 - (i * 0.01),
-            "total_count": 50,
-        }
-        for i in range(15)  # SQL returns page_size results
-    ]
-
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = mock_results
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            results, total = await unified_hybrid_search(
-                query="test",
-                page=3,
-                page_size=15,
-            )
-
-            # Verify results returned
-            assert len(results) == 15
-            assert total == 50  # Total from SQL COUNT(*) OVER()
-
-            # Verify the SQL query uses page_size and offset
-            call_args = mock_query.call_args
-            params = call_args[0]
-            # Last two params are page_size and offset
-            page_size_param = params[-2]
-            offset_param = params[-1]
-            assert page_size_param == 15
-            assert offset_param == 30  # (page 3 - 1) * 15
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@pytest.mark.integration
-async def test_unified_hybrid_search_schema_prefix():
-    """Test unified search uses schema_prefix placeholder."""
-    with patch(
-        "backend.api.features.store.hybrid_search.query_raw_with_schema"
-    ) as mock_query:
-        with patch(
-            "backend.api.features.store.hybrid_search.embed_query"
-        ) as mock_embed:
-            mock_query.return_value = []
-            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
-
-            await unified_hybrid_search(
-                query="test",
-                page=1,
-                page_size=20,
-            )
-
-            call_args = mock_query.call_args
-            sql_template = call_args[0][0]
-
-            # Verify schema_prefix placeholder is used for table references
-            assert "{schema_prefix}" in sql_template
-            assert '"UnifiedContentEmbedding"' in sql_template
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v", "-s"])

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

@@ -110,7 +110,6 @@ class Profile(pydantic.BaseModel):
 
 
 class StoreSubmission(pydantic.BaseModel):
-    listing_id: str
     agent_id: str
     agent_version: int
     name: str
@@ -165,12 +164,8 @@ class StoreListingsWithVersionsResponse(pydantic.BaseModel):
 
 
 class StoreSubmissionRequest(pydantic.BaseModel):
-    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"
-    )
+    agent_id: str
+    agent_version: int
     slug: str
     name: str
     sub_heading: str
@@ -221,23 +216,3 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     is_approved: bool
     comments: str  # External comments visible to creator
     internal_comments: str | None = None  # Private admin notes
-
-
-class UnifiedSearchResult(pydantic.BaseModel):
-    """A single result from unified hybrid search across all content types."""
-
-    content_type: str  # STORE_AGENT, BLOCK, DOCUMENTATION
-    content_id: str
-    searchable_text: str
-    metadata: dict | None = None
-    updated_at: datetime.datetime | None = None
-    combined_score: float | None = None
-    semantic_score: float | None = None
-    lexical_score: float | None = None
-
-
-class UnifiedSearchResponse(pydantic.BaseModel):
-    """Response model for unified search across all content types."""
-
-    results: list[UnifiedSearchResult]
-    pagination: Pagination

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

@@ -138,7 +138,6 @@ 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",
@@ -160,7 +159,6 @@ 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.py

@@ -7,15 +7,12 @@ from typing import Literal
 import autogpt_libs.auth
 import fastapi
 import fastapi.responses
-import prisma.enums
 
 import backend.data.graph
 import backend.util.json
-from backend.util.models import Pagination
 
 from . import cache as store_cache
 from . import db as store_db
-from . import hybrid_search as store_hybrid_search
 from . import image_gen as store_image_gen
 from . import media as store_media
 from . import model as store_model
@@ -149,102 +146,6 @@ async def get_agents(
     return agents
 
 
-##############################################
-############### Search Endpoints #############
-##############################################
-
-
-@router.get(
-    "/search",
-    summary="Unified search across all content types",
-    tags=["store", "public"],
-    response_model=store_model.UnifiedSearchResponse,
-)
-async def unified_search(
-    query: str,
-    content_types: list[str] | None = fastapi.Query(
-        default=None,
-        description="Content types to search: STORE_AGENT, BLOCK, DOCUMENTATION. If not specified, searches all.",
-    ),
-    page: int = 1,
-    page_size: int = 20,
-    user_id: str | None = fastapi.Security(
-        autogpt_libs.auth.get_optional_user_id, use_cache=False
-    ),
-):
-    """
-    Search across all content types (store agents, blocks, documentation) using hybrid search.
-
-    Combines semantic (embedding-based) and lexical (text-based) search for best results.
-
-    Args:
-        query: The search query string
-        content_types: Optional list of content types to filter by (STORE_AGENT, BLOCK, DOCUMENTATION)
-        page: Page number for pagination (default 1)
-        page_size: Number of results per page (default 20)
-        user_id: Optional authenticated user ID (for user-scoped content in future)
-
-    Returns:
-        UnifiedSearchResponse: Paginated list of search results with relevance scores
-    """
-    if page < 1:
-        raise fastapi.HTTPException(
-            status_code=422, detail="Page must be greater than 0"
-        )
-
-    if page_size < 1:
-        raise fastapi.HTTPException(
-            status_code=422, detail="Page size must be greater than 0"
-        )
-
-    # Convert string content types to enum
-    content_type_enums: list[prisma.enums.ContentType] | None = None
-    if content_types:
-        try:
-            content_type_enums = [prisma.enums.ContentType(ct) for ct in content_types]
-        except ValueError as e:
-            raise fastapi.HTTPException(
-                status_code=422,
-                detail=f"Invalid content type. Valid values: STORE_AGENT, BLOCK, DOCUMENTATION. Error: {e}",
-            )
-
-    # Perform unified hybrid search
-    results, total = await store_hybrid_search.unified_hybrid_search(
-        query=query,
-        content_types=content_type_enums,
-        user_id=user_id,
-        page=page,
-        page_size=page_size,
-    )
-
-    # Convert results to response model
-    search_results = [
-        store_model.UnifiedSearchResult(
-            content_type=r["content_type"],
-            content_id=r["content_id"],
-            searchable_text=r.get("searchable_text", ""),
-            metadata=r.get("metadata"),
-            updated_at=r.get("updated_at"),
-            combined_score=r.get("combined_score"),
-            semantic_score=r.get("semantic_score"),
-            lexical_score=r.get("lexical_score"),
-        )
-        for r in results
-    ]
-
-    total_pages = (total + page_size - 1) // page_size if total > 0 else 0
-
-    return store_model.UnifiedSearchResponse(
-        results=search_results,
-        pagination=Pagination(
-            total_items=total,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
 @router.get(
     "/agents/{username}/{agent_name}",
     summary="Get specific agent",

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

@@ -521,7 +521,6 @@ 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/api/features/store/semantic_search_test.py

@@ -1,272 +0,0 @@
-"""Tests for the semantic_search function."""
-
-import pytest
-from prisma.enums import ContentType
-
-from backend.api.features.store.embeddings import EMBEDDING_DIM, semantic_search
-
-
-@pytest.mark.asyncio
-async def test_search_blocks_only(mocker):
-    """Test searching only BLOCK content type."""
-    # Mock embed_query to return a test embedding
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    # Mock query_raw_with_schema to return test results
-    mock_results = [
-        {
-            "content_id": "block-123",
-            "content_type": "BLOCK",
-            "searchable_text": "Calculator Block - Performs arithmetic operations",
-            "metadata": {"name": "Calculator", "categories": ["Math"]},
-            "similarity": 0.85,
-        }
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_results,
-    )
-
-    results = await semantic_search(
-        query="calculate numbers",
-        content_types=[ContentType.BLOCK],
-    )
-
-    assert len(results) == 1
-    assert results[0]["content_type"] == "BLOCK"
-    assert results[0]["content_id"] == "block-123"
-    assert results[0]["similarity"] == 0.85
-
-
-@pytest.mark.asyncio
-async def test_search_multiple_content_types(mocker):
-    """Test searching multiple content types simultaneously."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    mock_results = [
-        {
-            "content_id": "block-123",
-            "content_type": "BLOCK",
-            "searchable_text": "Calculator Block",
-            "metadata": {},
-            "similarity": 0.85,
-        },
-        {
-            "content_id": "doc-456",
-            "content_type": "DOCUMENTATION",
-            "searchable_text": "How to use Calculator",
-            "metadata": {},
-            "similarity": 0.75,
-        },
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_results,
-    )
-
-    results = await semantic_search(
-        query="calculator",
-        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
-    )
-
-    assert len(results) == 2
-    assert results[0]["content_type"] == "BLOCK"
-    assert results[1]["content_type"] == "DOCUMENTATION"
-
-
-@pytest.mark.asyncio
-async def test_search_with_min_similarity_threshold(mocker):
-    """Test that results below min_similarity are filtered out."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    # Only return results above 0.7 similarity
-    mock_results = [
-        {
-            "content_id": "block-123",
-            "content_type": "BLOCK",
-            "searchable_text": "Calculator Block",
-            "metadata": {},
-            "similarity": 0.85,
-        }
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_results,
-    )
-
-    results = await semantic_search(
-        query="calculate",
-        content_types=[ContentType.BLOCK],
-        min_similarity=0.7,
-    )
-
-    assert len(results) == 1
-    assert results[0]["similarity"] >= 0.7
-
-
-@pytest.mark.asyncio
-async def test_search_fallback_to_lexical(mocker):
-    """Test fallback to lexical search when embeddings fail."""
-    # Mock embed_query to return None (embeddings unavailable)
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=None,
-    )
-
-    mock_lexical_results = [
-        {
-            "content_id": "block-123",
-            "content_type": "BLOCK",
-            "searchable_text": "Calculator Block performs calculations",
-            "metadata": {},
-            "similarity": 0.0,
-        }
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_lexical_results,
-    )
-
-    results = await semantic_search(
-        query="calculator",
-        content_types=[ContentType.BLOCK],
-    )
-
-    assert len(results) == 1
-    assert results[0]["similarity"] == 0.0  # Lexical search returns 0 similarity
-
-
-@pytest.mark.asyncio
-async def test_search_empty_query():
-    """Test that empty query returns no results."""
-    results = await semantic_search(query="")
-    assert results == []
-
-    results = await semantic_search(query="   ")
-    assert results == []
-
-
-@pytest.mark.asyncio
-async def test_search_with_user_id_filter(mocker):
-    """Test searching with user_id filter for private content."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    mock_results = [
-        {
-            "content_id": "agent-789",
-            "content_type": "LIBRARY_AGENT",
-            "searchable_text": "My Custom Agent",
-            "metadata": {},
-            "similarity": 0.9,
-        }
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_results,
-    )
-
-    results = await semantic_search(
-        query="custom agent",
-        content_types=[ContentType.LIBRARY_AGENT],
-        user_id="user-123",
-    )
-
-    assert len(results) == 1
-    assert results[0]["content_type"] == "LIBRARY_AGENT"
-
-
-@pytest.mark.asyncio
-async def test_search_limit_parameter(mocker):
-    """Test that limit parameter correctly limits results."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    # Return 5 results
-    mock_results = [
-        {
-            "content_id": f"block-{i}",
-            "content_type": "BLOCK",
-            "searchable_text": f"Block {i}",
-            "metadata": {},
-            "similarity": 0.8,
-        }
-        for i in range(5)
-    ]
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_results,
-    )
-
-    results = await semantic_search(
-        query="block",
-        content_types=[ContentType.BLOCK],
-        limit=5,
-    )
-
-    assert len(results) == 5
-
-
-@pytest.mark.asyncio
-async def test_search_default_content_types(mocker):
-    """Test that default content_types includes BLOCK, STORE_AGENT, and DOCUMENTATION."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    mock_query_raw = mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=[],
-    )
-
-    await semantic_search(query="test")
-
-    # Check that the SQL query includes all three default content types
-    call_args = mock_query_raw.call_args
-    assert "BLOCK" in str(call_args)
-    assert "STORE_AGENT" in str(call_args)
-    assert "DOCUMENTATION" in str(call_args)
-
-
-@pytest.mark.asyncio
-async def test_search_handles_database_error(mocker):
-    """Test that database errors are handled gracefully."""
-    mock_embedding = [0.1] * EMBEDDING_DIM
-    mocker.patch(
-        "backend.api.features.store.embeddings.embed_query",
-        return_value=mock_embedding,
-    )
-
-    # Simulate database error
-    mocker.patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        side_effect=Exception("Database connection failed"),
-    )
-
-    results = await semantic_search(
-        query="test",
-        content_types=[ContentType.BLOCK],
-    )
-
-    # Should return empty list on error
-    assert results == []

autogpt_platform/backend/backend/api/features/v1.py

@@ -64,6 +64,7 @@ from backend.data.onboarding import (
     complete_re_run_agent,
     get_recommended_agents,
     get_user_onboarding,
+    increment_runs,
     onboarding_enabled,
     reset_user_onboarding,
     update_user_onboarding,
@@ -974,6 +975,7 @@ async def execute_graph(
         # Record successful graph execution
         record_graph_execution(graph_id=graph_id, status="success", user_id=user_id)
         record_graph_operation(operation="execute", status="success")
+        await increment_runs(user_id)
         await complete_re_run_agent(user_id, graph_id)
         if source == "library":
             await complete_onboarding_step(

autogpt_platform/backend/backend/api/rest_api.py

@@ -39,7 +39,7 @@ import backend.data.user
 import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
-from backend.blocks.llm import DEFAULT_LLM_MODEL
+from backend.blocks.llm import LlmModel
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.monitoring.instrumentation import instrument_fastapi
@@ -113,7 +113,7 @@ async def lifespan_context(app: fastapi.FastAPI):
 
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
-    await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
+    await backend.data.graph.migrate_llm_models(LlmModel.GPT4O)
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
     with launch_darkly_context():

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,7 +1,6 @@
 from typing import Any
 
 from backend.blocks.llm import (
-    DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
     AIBlockBase,
@@ -50,7 +49,7 @@ class AIConditionBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for evaluating the condition.",
             advanced=False,
         )
@@ -82,7 +81,7 @@ class AIConditionBlock(AIBlockBase):
                 "condition": "the input is an email address",
                 "yes_value": "Valid email",
                 "no_value": "Not an email",
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,

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

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

autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py

@@ -182,10 +182,13 @@ class DataForSeoRelatedKeywordsBlock(Block):
             if results and len(results) > 0:
                 # results is a list, get the first element
                 first_result = results[0] if isinstance(results, list) else results
-                # Handle missing key, null value, or valid list value
-                if isinstance(first_result, dict):
-                    items = first_result.get("items") or []
-                else:
+                items = (
+                    first_result.get("items", [])
+                    if isinstance(first_result, dict)
+                    else []
+                )
+                # Ensure items is never None
+                if items is None:
                     items = []
                 for item in items:
                     # Extract keyword_data from the item

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

@@ -1,184 +0,0 @@
-"""
-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

@@ -3,7 +3,6 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
-from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -12,9 +11,11 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
+from backend.data.execution import ExecutionContext, ExecutionStatus
 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__)
 
@@ -71,26 +72,32 @@ class HumanInTheLoopBlock(Block):
                 ("approved_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
-                "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",
-                        ),
-                    },
-                )(),
+                "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,
             },
         )
 
-    async def handle_review_decision(self, **kwargs):
-        return await HITLReviewHelper.handle_review_decision(**kwargs)
+    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 run(
         self,
@@ -102,7 +109,7 @@ class HumanInTheLoopBlock(Block):
         graph_id: str,
         graph_version: int,
         execution_context: ExecutionContext,
-        **_kwargs,
+        **kwargs,
     ) -> BlockOutput:
         if not execution_context.safe_mode:
             logger.info(
@@ -112,28 +119,48 @@ class HumanInTheLoopBlock(Block):
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
-        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,
-        )
+        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
 
-        if decision is None:
-            return
+        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
 
-        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 not result.processed:
+            await self.update_review_processed_status(
+                node_exec_id=node_exec_id, processed=True
+            )
 
-        if decision.message:
-            yield "review_message", decision.message
+            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

autogpt_platform/backend/backend/blocks/llm.py

@@ -92,9 +92,8 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     O1 = "o1"
     O1_MINI = "o1-mini"
     # GPT-5 models
-    GPT5_2 = "gpt-5.2-2025-12-11"
-    GPT5_1 = "gpt-5.1-2025-11-13"
     GPT5 = "gpt-5-2025-08-07"
+    GPT5_1 = "gpt-5.1-2025-11-13"
     GPT5_MINI = "gpt-5-mini-2025-08-07"
     GPT5_NANO = "gpt-5-nano-2025-08-07"
     GPT5_CHAT = "gpt-5-chat-latest"
@@ -195,9 +194,8 @@ MODEL_METADATA = {
     LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
     LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
     # GPT-5 models
-    LlmModel.GPT5_2: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
@@ -305,8 +303,6 @@ MODEL_METADATA = {
     LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
 }
 
-DEFAULT_LLM_MODEL = LlmModel.GPT5_2
-
 for model in LlmModel:
     if model not in MODEL_METADATA:
         raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
@@ -794,7 +790,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -859,7 +855,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
             test_input={
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "expected_format": {
                     "key1": "value1",
@@ -1225,7 +1221,7 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -1321,7 +1317,7 @@ class AITextSummarizerBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for summarizing the text.",
         )
         focus: str = SchemaField(
@@ -1538,7 +1534,7 @@ class AIConversationBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for the conversation.",
         )
         credentials: AICredentials = AICredentialsField()
@@ -1576,7 +1572,7 @@ class AIConversationBlock(AIBlockBase):
                     },
                     {"role": "user", "content": "Where was it played?"},
                 ],
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
@@ -1639,7 +1635,7 @@ class AIListGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for generating the list.",
             advanced=True,
         )
@@ -1696,7 +1692,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
                     "fictional worlds."
                 ),
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
                 "force_json_output": False,

autogpt_platform/backend/backend/blocks/search.py

@@ -18,7 +18,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.request import DEFAULT_USER_AGENT
 
 
 class GetWikipediaSummaryBlock(Block, GetRequest):
@@ -40,27 +39,17 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
             output_schema=GetWikipediaSummaryBlock.Output,
             test_input={"topic": "Artificial Intelligence"},
             test_output=("summary", "summary content"),
-            test_mock={
-                "get_request": lambda url, headers, json: {"extract": "summary content"}
-            },
+            test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.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",
-        }
+        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
 
+        # 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, headers=headers, json=True)
+            response = await self.get_request(url, 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

@@ -226,7 +226,7 @@ class SmartDecisionMakerBlock(Block):
         )
         model: llm.LlmModel = SchemaField(
             title="LLM Model",
-            default=llm.DEFAULT_LLM_MODEL,
+            default=llm.LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -391,12 +391,8 @@ 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(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(block.name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -493,24 +489,14 @@ 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(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.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, {}
@@ -520,7 +506,7 @@ class SmartDecisionMakerBlock(Block):
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[clean_field_name] = {
+            properties[link.sink_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -533,7 +519,7 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        tool_function["_field_mapping"] = field_mapping
+        # Store node info for later use in output processing
         tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
@@ -989,28 +975,10 @@ 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 []
@@ -1161,9 +1129,8 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_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}"
+                sanitized_arg_name = self.cleanup(original_field_name)
+                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -196,15 +196,6 @@ class TestXMLParserBlockSecurity:
             async for _ in block.run(XMLParserBlock.Input(input_xml=large_xml)):
                 pass
 
-    async def test_rejects_text_outside_root(self):
-        """Ensure parser surfaces readable errors for invalid root text."""
-        block = XMLParserBlock()
-        invalid_xml = "<root><child>value</child></root> trailing"
-
-        with pytest.raises(ValueError, match="text outside the root element"):
-            async for _ in block.run(XMLParserBlock.Input(input_xml=invalid_xml)):
-                pass
-
 
 class TestStoreMediaFileSecurity:
     """Test file storage security limits."""

autogpt_platform/backend/backend/blocks/test/test_llm.py

@@ -28,7 +28,7 @@ class TestLLMStatsTracking:
 
             response = await llm.llm_call(
                 credentials=llm.TEST_CREDENTIALS,
-                llm_model=llm.DEFAULT_LLM_MODEL,
+                llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
                 max_tokens=100,
             )
@@ -65,7 +65,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test prompt",
             expected_format={"key1": "desc1", "key2": "desc2"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore  # type: ignore
         )
 
@@ -109,7 +109,7 @@ class TestLLMStatsTracking:
         # Run the block
         input_data = llm.AITextGeneratorBlock.Input(
             prompt="Generate text",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -170,7 +170,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test prompt",
             expected_format={"key1": "desc1", "key2": "desc2"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
         )
@@ -228,7 +228,7 @@ class TestLLMStatsTracking:
 
         input_data = llm.AITextSummarizerBlock.Input(
             text=long_text,
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_tokens=100,  # Small chunks
             chunk_overlap=10,
@@ -299,7 +299,7 @@ class TestLLMStatsTracking:
             # Test with very short text (should only need 1 chunk + 1 final summary)
             input_data = llm.AITextSummarizerBlock.Input(
                 text="This is a short text.",
-                model=llm.DEFAULT_LLM_MODEL,
+                model=llm.LlmModel.GPT4O,
                 credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
                 max_tokens=1000,  # Large enough to avoid chunking
             )
@@ -346,7 +346,7 @@ class TestLLMStatsTracking:
                 {"role": "assistant", "content": "Hi there!"},
                 {"role": "user", "content": "How are you?"},
             ],
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -387,7 +387,7 @@ class TestLLMStatsTracking:
         # Run the block
         input_data = llm.AIListGeneratorBlock.Input(
             focus="test items",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_retries=3,
         )
@@ -469,7 +469,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test",
             expected_format={"result": "desc"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -513,7 +513,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             style=llm.SummaryStyle.BULLET_POINTS,
         )
@@ -558,7 +558,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             style=llm.SummaryStyle.BULLET_POINTS,
             max_tokens=1000,
@@ -593,7 +593,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -623,7 +623,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_tokens=1000,
         )
@@ -654,7 +654,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 

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

@@ -233,7 +233,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         # Create test input
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Should I continue with this task?",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -335,7 +335,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,  # Set retry to 2 for testing
             agent_mode_max_iterations=0,
@@ -402,7 +402,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -462,7 +462,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -526,7 +526,7 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -648,7 +648,7 @@ async def test_smart_decision_maker_raw_response_conversion():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Test prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
             agent_mode_max_iterations=0,
@@ -722,7 +722,7 @@ async def test_smart_decision_maker_raw_response_conversion():
     ):
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Simple prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -778,7 +778,7 @@ async def test_smart_decision_maker_raw_response_conversion():
     ):
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Another test",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,
         )
@@ -931,7 +931,7 @@ async def test_smart_decision_maker_agent_mode():
         # Test agent mode with max_iterations = 3
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Complete this task using tools",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=3,  # Enable agent mode with 3 max iterations
         )
@@ -1020,7 +1020,7 @@ async def test_smart_decision_maker_traditional_mode_default():
         # Test default behavior (traditional mode)
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Test prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             agent_mode_max_iterations=0,  # Traditional mode
         )
@@ -1057,153 +1057,3 @@ 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

@@ -15,7 +15,6 @@ 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 = [
@@ -78,7 +77,6 @@ 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

@@ -44,7 +44,6 @@ 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 = [
@@ -107,7 +106,6 @@ 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 = [
@@ -161,7 +159,6 @@ 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 = [
@@ -211,13 +208,11 @@ 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(
@@ -378,7 +373,7 @@ async def test_output_yielding_with_dynamic_fields():
             input_data = block.input_schema(
                 prompt="Create a user dictionary",
                 credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.DEFAULT_LLM_MODEL,
+                model=llm.LlmModel.GPT4O,
                 agent_mode_max_iterations=0,  # Use traditional mode to test output yielding
             )
 
@@ -428,7 +423,6 @@ 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):
@@ -600,7 +594,7 @@ async def test_validation_errors_dont_pollute_conversation():
                 input_data = block.input_schema(
                     prompt="Test prompt",
                     credentials=llm.TEST_CREDENTIALS_INPUT,
-                    model=llm.DEFAULT_LLM_MODEL,
+                    model=llm.LlmModel.GPT4O,
                     retry=3,  # Allow retries
                     agent_mode_max_iterations=1,
                 )

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

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

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(raise_for_status=False).post(
+    response = await Requests().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(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests(raise_for_status=False).get(
+    response = await Requests().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(raise_for_status=False), method.lower())
+    request_method = getattr(Requests(), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,7 +476,6 @@ 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 = {
@@ -484,7 +483,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -500,132 +499,3 @@ 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

@@ -9,15 +9,7 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import (
-    CreatePostRequest,
-    Post,
-    PostResponse,
-    PostsResponse,
-    PostStatus,
-    create_post,
-    get_posts,
-)
+from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
 from ._config import wordpress
 
 
@@ -57,15 +49,8 @@ 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")
@@ -93,9 +78,7 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=(
-                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
-            ),
+            status=PostStatus.PUBLISH,
         )
 
         post_response: PostResponse = await create_post(
@@ -104,69 +87,7 @@ 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/blocks/xml_parser.py

@@ -1,5 +1,5 @@
 from gravitasml.parser import Parser
-from gravitasml.token import Token, tokenize
+from gravitasml.token import tokenize
 
 from backend.data.block import Block, BlockOutput, BlockSchemaInput, BlockSchemaOutput
 from backend.data.model import SchemaField
@@ -25,38 +25,6 @@ class XMLParserBlock(Block):
             ],
         )
 
-    @staticmethod
-    def _validate_tokens(tokens: list[Token]) -> None:
-        """Ensure the XML has a single root element and no stray text."""
-        if not tokens:
-            raise ValueError("XML input is empty.")
-
-        depth = 0
-        root_seen = False
-
-        for token in tokens:
-            if token.type == "TAG_OPEN":
-                if depth == 0 and root_seen:
-                    raise ValueError("XML must have a single root element.")
-                depth += 1
-                if depth == 1:
-                    root_seen = True
-            elif token.type == "TAG_CLOSE":
-                depth -= 1
-                if depth < 0:
-                    raise SyntaxError("Unexpected closing tag in XML input.")
-            elif token.type in {"TEXT", "ESCAPE"}:
-                if depth == 0 and token.value:
-                    raise ValueError(
-                        "XML contains text outside the root element; "
-                        "wrap content in a single root tag."
-                    )
-
-        if depth != 0:
-            raise SyntaxError("Unclosed tag detected in XML input.")
-        if not root_seen:
-            raise ValueError("XML must include a root element.")
-
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         # Security fix: Add size limits to prevent XML bomb attacks
         MAX_XML_SIZE = 10 * 1024 * 1024  # 10MB limit for XML input
@@ -67,9 +35,7 @@ class XMLParserBlock(Block):
             )
 
         try:
-            tokens = list(tokenize(input_data.input_xml))
-            self._validate_tokens(tokens)
-
+            tokens = tokenize(input_data.input_xml)
             parser = Parser(tokens)
             parsed_result = parser.parse()
             yield "parsed_xml", parsed_result

autogpt_platform/backend/backend/blocks/youtube.py

@@ -111,8 +111,6 @@ class TranscribeYoutubeVideoBlock(Block):
                 return parsed_url.path.split("/")[2]
             if parsed_url.path[:3] == "/v/":
                 return parsed_url.path.split("/")[2]
-            if parsed_url.path.startswith("/shorts/"):
-                return parsed_url.path.split("/")[2]
         raise ValueError(f"Invalid YouTube URL: {url}")
 
     def get_transcript(

autogpt_platform/backend/backend/data/block.py

@@ -50,8 +50,6 @@ from .model import (
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from backend.data.execution import ExecutionContext
-
     from .graph import Link
 
 app_config = Config()
@@ -474,7 +472,6 @@ 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):
@@ -617,88 +614,7 @@ 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 only if running within a graph execution context
-        # Direct block execution (e.g., from chat) skips the review process
-        has_graph_context = all(
-            key in kwargs
-            for key in (
-                "node_exec_id",
-                "graph_exec_id",
-                "graph_id",
-                "execution_context",
-            )
-        )
-        if has_graph_context:
-            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}",
@@ -706,7 +622,6 @@ 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/block_cost_config.py

@@ -59,13 +59,12 @@ from backend.integrations.credentials_store import (
 
 MODEL_COST: dict[LlmModel, int] = {
     LlmModel.O3: 4,
-    LlmModel.O3_MINI: 2,
-    LlmModel.O1: 16,
+    LlmModel.O3_MINI: 2,  # $1.10 / $4.40
+    LlmModel.O1: 16,  # $15 / $60
     LlmModel.O1_MINI: 4,
     # GPT-5 models
-    LlmModel.GPT5_2: 6,
-    LlmModel.GPT5_1: 5,
     LlmModel.GPT5: 2,
+    LlmModel.GPT5_1: 5,
     LlmModel.GPT5_MINI: 1,
     LlmModel.GPT5_NANO: 1,
     LlmModel.GPT5_CHAT: 5,
@@ -88,7 +87,7 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.AIML_API_LLAMA3_3_70B: 1,
     LlmModel.AIML_API_META_LLAMA_3_1_70B: 1,
     LlmModel.AIML_API_LLAMA_3_2_3B: 1,
-    LlmModel.LLAMA3_3_70B: 1,
+    LlmModel.LLAMA3_3_70B: 1,  # $0.59 / $0.79
     LlmModel.LLAMA3_1_8B: 1,
     LlmModel.OLLAMA_LLAMA3_3: 1,
     LlmModel.OLLAMA_LLAMA3_2: 1,

autogpt_platform/backend/backend/data/credit.py

@@ -341,19 +341,6 @@ class UserCreditBase(ABC):
 
         if result:
             # UserBalance is already updated by the CTE
-
-            # Clear insufficient funds notification flags when credits are added
-            # so user can receive alerts again if they run out in the future.
-            if transaction.amount > 0 and transaction.type in [
-                CreditTransactionType.GRANT,
-                CreditTransactionType.TOP_UP,
-            ]:
-                from backend.executor.manager import (
-                    clear_insufficient_funds_notifications,
-                )
-
-                await clear_insufficient_funds_notifications(user_id)
-
             return result[0]["balance"]
 
     async def _add_transaction(
@@ -543,22 +530,6 @@ class UserCreditBase(ABC):
         if result:
             new_balance, tx_key = result[0]["balance"], result[0]["transactionKey"]
             # UserBalance is already updated by the CTE
-
-            # Clear insufficient funds notification flags when credits are added
-            # so user can receive alerts again if they run out in the future.
-            if (
-                amount > 0
-                and is_active
-                and transaction_type
-                in [CreditTransactionType.GRANT, CreditTransactionType.TOP_UP]
-            ):
-                # Lazy import to avoid circular dependency with executor.manager
-                from backend.executor.manager import (
-                    clear_insufficient_funds_notifications,
-                )
-
-                await clear_insufficient_funds_notifications(user_id)
-
             return new_balance, tx_key
 
         # If no result, either user doesn't exist or insufficient balance

autogpt_platform/backend/backend/data/db.py

@@ -38,20 +38,6 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
-# Add public schema to search_path for pgvector type access
-# The vector extension is in public schema, but search_path is determined by schema parameter
-# Extract the schema from DATABASE_URL or default to 'public' (matching get_database_schema())
-parsed_url = urlparse(DATABASE_URL)
-url_params = dict(parse_qsl(parsed_url.query))
-db_schema = url_params.get("schema", "public")
-# Build search_path, avoiding duplicates if db_schema is already 'public'
-search_path_schemas = list(
-    dict.fromkeys([db_schema, "public"])
-)  # Preserves order, removes duplicates
-search_path = ",".join(search_path_schemas)
-# This allows using ::vector without schema qualification
-DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
-
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -122,102 +108,21 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
-async def _raw_with_schema(
-    query_template: str,
-    *args,
-    execute: bool = False,
-    client: Prisma | None = None,
-    set_public_search_path: bool = False,
-) -> list[dict] | int:
-    """Internal: Execute raw SQL with proper schema handling.
-
-    Use query_raw_with_schema() or execute_raw_with_schema() instead.
-
-    Args:
-        query_template: SQL query with {schema_prefix} placeholder
-        *args: Query parameters
-        execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
-        client: Optional Prisma client for transactions (only used when execute=True).
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    Returns:
-        - list[dict] if execute=False (query results)
-        - int if execute=True (number of affected rows)
-    """
+async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
+    """Execute raw SQL query with proper schema handling."""
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
     formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
-    db_client = client if client else prisma_module.get_client()
-
-    # Set search_path to include public schema if requested
-    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
-    # This is idempotent and safe to call multiple times
-    if set_public_search_path:
-        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
-
-    if execute:
-        result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
-    else:
-        result = await db_client.query_raw(formatted_query, *args)  # type: ignore
+    result = await prisma_module.get_client().query_raw(
+        formatted_query, *args  # type: ignore
+    )
 
     return result
 
 
-async def query_raw_with_schema(
-    query_template: str, *args, set_public_search_path: bool = False
-) -> list[dict]:
-    """Execute raw SQL SELECT query with proper schema handling.
-
-    Args:
-        query_template: SQL query with {schema_prefix} placeholder
-        *args: Query parameters
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    Returns:
-        List of result rows as dictionaries
-
-    Example:
-        results = await query_raw_with_schema(
-            'SELECT * FROM {schema_prefix}"User" WHERE id = $1',
-            user_id
-        )
-    """
-    return await _raw_with_schema(query_template, *args, execute=False, set_public_search_path=set_public_search_path)  # type: ignore
-
-
-async def execute_raw_with_schema(
-    query_template: str,
-    *args,
-    client: Prisma | None = None,
-    set_public_search_path: bool = False,
-) -> int:
-    """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
-
-    Args:
-        query_template: SQL query with {schema_prefix} placeholder
-        *args: Query parameters
-        client: Optional Prisma client for transactions
-        set_public_search_path: If True, sets search_path to include public schema.
-                                Needed for pgvector types and other public schema objects.
-
-    Returns:
-        Number of affected rows
-
-    Example:
-        await execute_raw_with_schema(
-            'INSERT INTO {schema_prefix}"User" (id, name) VALUES ($1, $2)',
-            user_id, name,
-            client=tx  # Optional transaction client
-        )
-    """
-    return await _raw_with_schema(query_template, *args, execute=True, client=client, set_public_search_path=set_public_search_path)  # type: ignore
-
-
 class BaseDbModel(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid4()))
 

autogpt_platform/backend/backend/data/execution.py

@@ -383,7 +383,6 @@ 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,
@@ -391,7 +390,6 @@ 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,
         )
 
@@ -1147,8 +1145,6 @@ 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

@@ -94,15 +94,6 @@ 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."""
@@ -244,10 +235,7 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if (
-                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-                or node.block.requires_human_review
-            )
+            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
         )
 
     @property
@@ -338,35 +326,7 @@ class Graph(BaseGraph):
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        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
+        return self._credentials_input_schema.jsonschema()
 
     @property
     def _credentials_input_schema(self) -> type[BlockSchema]:

autogpt_platform/backend/backend/data/graph_test.py

@@ -1,6 +1,5 @@
 import json
 from typing import Any
-from unittest.mock import AsyncMock, patch
 from uuid import UUID
 
 import fastapi.exceptions
@@ -19,17 +18,6 @@ from backend.usecases.sample import create_test_user
 from backend.util.test import SpinTestServer
 
 
-@pytest.fixture(scope="session", autouse=True)
-def mock_embedding_functions():
-    """Mock embedding functions for all tests to avoid database/API dependencies."""
-    with patch(
-        "backend.api.features.store.db.ensure_embedding",
-        new_callable=AsyncMock,
-        return_value=True,
-    ):
-        yield
-
-
 @pytest.mark.asyncio(loop_scope="session")
 async def test_graph_creation(server: SpinTestServer, snapshot: Snapshot):
     """
@@ -408,58 +396,3 @@ 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/onboarding.py

@@ -334,7 +334,7 @@ async def _get_user_timezone(user_id: str) -> str:
     return get_user_timezone_or_utc(user.timezone if user else None)
 
 
-async def increment_onboarding_runs(user_id: str):
+async def increment_runs(user_id: str):
     """
     Increment a user's run counters and trigger any onboarding milestones.
     """

autogpt_platform/backend/backend/data/understanding.py

@@ -1,404 +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 CoPilotUnderstanding
-
-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: CoPilotUnderstanding) -> "BusinessUnderstanding":
-        """Convert database record to Pydantic model."""
-        data = db_record.data if isinstance(db_record.data, dict) else {}
-        business = (
-            data.get("business", {}) if isinstance(data.get("business"), dict) else {}
-        )
-        return cls(
-            id=db_record.id,
-            user_id=db_record.userId,
-            created_at=db_record.createdAt,
-            updated_at=db_record.updatedAt,
-            user_name=data.get("name"),
-            job_title=business.get("job_title"),
-            business_name=business.get("business_name"),
-            industry=business.get("industry"),
-            business_size=business.get("business_size"),
-            user_role=business.get("user_role"),
-            key_workflows=_json_to_list(business.get("key_workflows")),
-            daily_activities=_json_to_list(business.get("daily_activities")),
-            pain_points=_json_to_list(business.get("pain_points")),
-            bottlenecks=_json_to_list(business.get("bottlenecks")),
-            manual_tasks=_json_to_list(business.get("manual_tasks")),
-            automation_goals=_json_to_list(business.get("automation_goals")),
-            current_software=_json_to_list(business.get("current_software")),
-            existing_automation=_json_to_list(business.get("existing_automation")),
-            additional_notes=business.get("additional_notes"),
-        )
-
-
-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 CoPilotUnderstanding.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,
-    input_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)
-
-    Data is stored as: {name: ..., business: {version: 1, ...}}
-    """
-    # Get existing record for merge
-    existing = await CoPilotUnderstanding.prisma().find_unique(
-        where={"userId": user_id}
-    )
-
-    # Get existing data structure or start fresh
-    existing_data: dict[str, Any] = {}
-    if existing and isinstance(existing.data, dict):
-        existing_data = dict(existing.data)
-
-    existing_business: dict[str, Any] = {}
-    if isinstance(existing_data.get("business"), dict):
-        existing_business = dict(existing_data["business"])
-
-    # Business fields (stored inside business object)
-    business_string_fields = [
-        "job_title",
-        "business_name",
-        "industry",
-        "business_size",
-        "user_role",
-        "additional_notes",
-    ]
-    business_list_fields = [
-        "key_workflows",
-        "daily_activities",
-        "pain_points",
-        "bottlenecks",
-        "manual_tasks",
-        "automation_goals",
-        "current_software",
-        "existing_automation",
-    ]
-
-    # Handle top-level name field
-    if input_data.user_name is not None:
-        existing_data["name"] = input_data.user_name
-
-    # Business string fields - overwrite if provided
-    for field in business_string_fields:
-        value = getattr(input_data, field)
-        if value is not None:
-            existing_business[field] = value
-
-    # Business list fields - merge with existing
-    for field in business_list_fields:
-        value = getattr(input_data, field)
-        if value is not None:
-            existing_list = _json_to_list(existing_business.get(field))
-            merged = _merge_lists(existing_list, value)
-            existing_business[field] = merged
-
-    # Set version and nest business data
-    existing_business["version"] = 1
-    existing_data["business"] = existing_business
-
-    # Upsert with the merged data
-    record = await CoPilotUnderstanding.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": {"userId": user_id, "data": SafeJson(existing_data)},
-            "update": {"data": SafeJson(existing_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 CoPilotUnderstanding.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

@@ -7,11 +7,6 @@ from backend.api.features.library.db import (
     list_library_agents,
 )
 from backend.api.features.store.db import get_store_agent_details, get_store_agents
-from backend.api.features.store.embeddings import (
-    backfill_missing_embeddings,
-    cleanup_orphaned_embeddings,
-    get_embedding_stats,
-)
 from backend.data import db
 from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
@@ -25,7 +20,6 @@ from backend.data.execution import (
     get_execution_kv_data,
     get_execution_outputs_by_node_exec_id,
     get_frequently_executed_graphs,
-    get_graph_execution,
     get_graph_execution_meta,
     get_graph_executions,
     get_graph_executions_count,
@@ -63,7 +57,6 @@ from backend.data.notifications import (
     get_user_notification_oldest_message_in_batch,
     remove_notifications_from_batch,
 )
-from backend.data.onboarding import increment_onboarding_runs
 from backend.data.user import (
     get_active_user_ids_in_timerange,
     get_user_by_id,
@@ -147,7 +140,6 @@ class DatabaseManager(AppService):
     get_child_graph_executions = _(get_child_graph_executions)
     get_graph_executions = _(get_graph_executions)
     get_graph_executions_count = _(get_graph_executions_count)
-    get_graph_execution = _(get_graph_execution)
     get_graph_execution_meta = _(get_graph_execution_meta)
     create_graph_execution = _(create_graph_execution)
     get_node_execution = _(get_node_execution)
@@ -212,18 +204,10 @@ class DatabaseManager(AppService):
     add_store_agent_to_library = _(add_store_agent_to_library)
     validate_graph_execution_permissions = _(validate_graph_execution_permissions)
 
-    # Onboarding
-    increment_onboarding_runs = _(increment_onboarding_runs)
-
     # Store
     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)
-    cleanup_orphaned_embeddings = _(cleanup_orphaned_embeddings)
-
     # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
 
@@ -275,11 +259,6 @@ class DatabaseManagerClient(AppServiceClient):
     get_store_agents = _(d.get_store_agents)
     get_store_agent_details = _(d.get_store_agent_details)
 
-    # Store Embeddings
-    get_embedding_stats = _(d.get_embedding_stats)
-    backfill_missing_embeddings = _(d.backfill_missing_embeddings)
-    cleanup_orphaned_embeddings = _(d.cleanup_orphaned_embeddings)
-
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager
@@ -295,7 +274,6 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_graph = d.get_graph
     get_graph_metadata = d.get_graph_metadata
     get_graph_settings = d.get_graph_settings
-    get_graph_execution = d.get_graph_execution
     get_graph_execution_meta = d.get_graph_execution_meta
     get_node = d.get_node
     get_node_execution = d.get_node_execution
@@ -340,9 +318,6 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     add_store_agent_to_library = d.add_store_agent_to_library
     validate_graph_execution_permissions = d.validate_graph_execution_permissions
 
-    # Onboarding
-    increment_onboarding_runs = d.increment_onboarding_runs
-
     # Store
     get_store_agents = d.get_store_agents
     get_store_agent_details = d.get_store_agent_details

autogpt_platform/backend/backend/executor/manager.py

@@ -114,40 +114,6 @@ utilization_gauge = Gauge(
     "Ratio of active graph runs to max graph workers",
 )
 
-# Redis key prefix for tracking insufficient funds Discord notifications.
-# We only send one notification per user per agent until they top up credits.
-INSUFFICIENT_FUNDS_NOTIFIED_PREFIX = "insufficient_funds_discord_notified"
-# TTL for the notification flag (30 days) - acts as a fallback cleanup
-INSUFFICIENT_FUNDS_NOTIFIED_TTL_SECONDS = 30 * 24 * 60 * 60
-
-
-async def clear_insufficient_funds_notifications(user_id: str) -> int:
-    """
-    Clear all insufficient funds notification flags for a user.
-
-    This should be called when a user tops up their credits, allowing
-    Discord notifications to be sent again if they run out of funds.
-
-    Args:
-        user_id: The user ID to clear notifications for.
-
-    Returns:
-        The number of keys that were deleted.
-    """
-    try:
-        redis_client = await redis.get_redis_async()
-        pattern = f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:*"
-        keys = [key async for key in redis_client.scan_iter(match=pattern)]
-        if keys:
-            return await redis_client.delete(*keys)
-        return 0
-    except Exception as e:
-        logger.warning(
-            f"Failed to clear insufficient funds notification flags for user "
-            f"{user_id}: {e}"
-        )
-        return 0
-
 
 # Thread-local storage for ExecutionProcessor instances
 _tls = threading.local()
@@ -178,7 +144,6 @@ 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,
@@ -246,7 +211,6 @@ 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
@@ -544,7 +508,6 @@ 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,
@@ -567,7 +530,6 @@ 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
@@ -613,7 +575,6 @@ 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
 
@@ -650,7 +611,6 @@ 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)
 
@@ -962,21 +922,6 @@ 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}",
@@ -1037,7 +982,6 @@ class ExecutionProcessor:
                             execution_stats,
                             execution_stats_lock,
                         ),
-                        nodes_to_skip=graph_exec.nodes_to_skip,
                     ),
                     self.node_execution_loop,
                 )
@@ -1317,40 +1261,12 @@ class ExecutionProcessor:
         graph_id: str,
         e: InsufficientBalanceError,
     ):
-        # Check if we've already sent a notification for this user+agent combo.
-        # We only send one notification per user per agent until they top up credits.
-        redis_key = f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:{graph_id}"
-        try:
-            redis_client = redis.get_redis()
-            # SET NX returns True only if the key was newly set (didn't exist)
-            is_new_notification = redis_client.set(
-                redis_key,
-                "1",
-                nx=True,
-                ex=INSUFFICIENT_FUNDS_NOTIFIED_TTL_SECONDS,
-            )
-            if not is_new_notification:
-                # Already notified for this user+agent, skip all notifications
-                logger.debug(
-                    f"Skipping duplicate insufficient funds notification for "
-                    f"user={user_id}, graph={graph_id}"
-                )
-                return
-        except Exception as redis_error:
-            # If Redis fails, log and continue to send the notification
-            # (better to occasionally duplicate than to never notify)
-            logger.warning(
-                f"Failed to check/set insufficient funds notification flag in Redis: "
-                f"{redis_error}"
-            )
-
         shortfall = abs(e.amount) - e.balance
         metadata = db_client.get_graph_metadata(graph_id)
         base_url = (
             settings.config.frontend_base_url or settings.config.platform_base_url
         )
 
-        # Queue user email notification
         queue_notification(
             NotificationEventModel(
                 user_id=user_id,
@@ -1364,7 +1280,6 @@ class ExecutionProcessor:
             )
         )
 
-        # Send Discord system alert
         try:
             user_email = db_client.get_user_email_by_id(user_id)
 

autogpt_platform/backend/backend/executor/manager_insufficient_funds_test.py

@@ -1,560 +0,0 @@
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from prisma.enums import NotificationType
-
-from backend.data.notifications import ZeroBalanceData
-from backend.executor.manager import (
-    INSUFFICIENT_FUNDS_NOTIFIED_PREFIX,
-    ExecutionProcessor,
-    clear_insufficient_funds_notifications,
-)
-from backend.util.exceptions import InsufficientBalanceError
-from backend.util.test import SpinTestServer
-
-
-async def async_iter(items):
-    """Helper to create an async iterator from a list."""
-    for item in items:
-        yield item
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_insufficient_funds_sends_discord_alert_first_time(
-    server: SpinTestServer,
-):
-    """Test that the first insufficient funds notification sends a Discord alert."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    graph_id = "test-graph-456"
-    error = InsufficientBalanceError(
-        message="Insufficient balance",
-        user_id=user_id,
-        balance=72,  # $0.72
-        amount=-714,  # Attempting to spend $7.14
-    )
-
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.frontend_base_url = "https://test.com"
-
-        # Mock Redis to simulate first-time notification (set returns True)
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis.return_value = mock_redis_client
-        mock_redis_client.set.return_value = True  # Key was newly set
-
-        # Create mock database client
-        mock_db_client = MagicMock()
-        mock_graph_metadata = MagicMock()
-        mock_graph_metadata.name = "Test Agent"
-        mock_db_client.get_graph_metadata.return_value = mock_graph_metadata
-        mock_db_client.get_user_email_by_id.return_value = "test@example.com"
-
-        # Test the insufficient funds handler
-        execution_processor._handle_insufficient_funds_notif(
-            db_client=mock_db_client,
-            user_id=user_id,
-            graph_id=graph_id,
-            e=error,
-        )
-
-        # Verify notification was queued
-        mock_queue_notif.assert_called_once()
-        notification_call = mock_queue_notif.call_args[0][0]
-        assert notification_call.type == NotificationType.ZERO_BALANCE
-        assert notification_call.user_id == user_id
-        assert isinstance(notification_call.data, ZeroBalanceData)
-        assert notification_call.data.current_balance == 72
-
-        # Verify Redis was checked with correct key pattern
-        expected_key = f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:{graph_id}"
-        mock_redis_client.set.assert_called_once()
-        call_args = mock_redis_client.set.call_args
-        assert call_args[0][0] == expected_key
-        assert call_args[1]["nx"] is True
-
-        # Verify Discord alert was sent
-        mock_client.discord_system_alert.assert_called_once()
-        discord_message = mock_client.discord_system_alert.call_args[0][0]
-        assert "Insufficient Funds Alert" in discord_message
-        assert "test@example.com" in discord_message
-        assert "Test Agent" in discord_message
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_insufficient_funds_skips_duplicate_notifications(
-    server: SpinTestServer,
-):
-    """Test that duplicate insufficient funds notifications skip both email and Discord."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    graph_id = "test-graph-456"
-    error = InsufficientBalanceError(
-        message="Insufficient balance",
-        user_id=user_id,
-        balance=72,
-        amount=-714,
-    )
-
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.frontend_base_url = "https://test.com"
-
-        # Mock Redis to simulate duplicate notification (set returns False/None)
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis.return_value = mock_redis_client
-        mock_redis_client.set.return_value = None  # Key already existed
-
-        # Create mock database client
-        mock_db_client = MagicMock()
-        mock_db_client.get_graph_metadata.return_value = MagicMock(name="Test Agent")
-
-        # Test the insufficient funds handler
-        execution_processor._handle_insufficient_funds_notif(
-            db_client=mock_db_client,
-            user_id=user_id,
-            graph_id=graph_id,
-            e=error,
-        )
-
-        # Verify email notification was NOT queued (deduplication worked)
-        mock_queue_notif.assert_not_called()
-
-        # Verify Discord alert was NOT sent (deduplication worked)
-        mock_client.discord_system_alert.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_insufficient_funds_different_agents_get_separate_alerts(
-    server: SpinTestServer,
-):
-    """Test that different agents for the same user get separate Discord alerts."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    graph_id_1 = "test-graph-111"
-    graph_id_2 = "test-graph-222"
-
-    error = InsufficientBalanceError(
-        message="Insufficient balance",
-        user_id=user_id,
-        balance=72,
-        amount=-714,
-    )
-
-    with patch("backend.executor.manager.queue_notification"), patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.frontend_base_url = "https://test.com"
-
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis.return_value = mock_redis_client
-        # Both calls return True (first time for each agent)
-        mock_redis_client.set.return_value = True
-
-        mock_db_client = MagicMock()
-        mock_graph_metadata = MagicMock()
-        mock_graph_metadata.name = "Test Agent"
-        mock_db_client.get_graph_metadata.return_value = mock_graph_metadata
-        mock_db_client.get_user_email_by_id.return_value = "test@example.com"
-
-        # First agent notification
-        execution_processor._handle_insufficient_funds_notif(
-            db_client=mock_db_client,
-            user_id=user_id,
-            graph_id=graph_id_1,
-            e=error,
-        )
-
-        # Second agent notification
-        execution_processor._handle_insufficient_funds_notif(
-            db_client=mock_db_client,
-            user_id=user_id,
-            graph_id=graph_id_2,
-            e=error,
-        )
-
-        # Verify Discord alerts were sent for both agents
-        assert mock_client.discord_system_alert.call_count == 2
-
-        # Verify Redis was called with different keys
-        assert mock_redis_client.set.call_count == 2
-        calls = mock_redis_client.set.call_args_list
-        assert (
-            calls[0][0][0]
-            == f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:{graph_id_1}"
-        )
-        assert (
-            calls[1][0][0]
-            == f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:{graph_id_2}"
-        )
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_clear_insufficient_funds_notifications(server: SpinTestServer):
-    """Test that clearing notifications removes all keys for a user."""
-
-    user_id = "test-user-123"
-
-    with patch("backend.executor.manager.redis") as mock_redis_module:
-
-        mock_redis_client = MagicMock()
-        # get_redis_async is an async function, so we need AsyncMock for it
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-
-        # Mock scan_iter to return some keys as an async iterator
-        mock_keys = [
-            f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:graph-1",
-            f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:graph-2",
-            f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:graph-3",
-        ]
-        mock_redis_client.scan_iter.return_value = async_iter(mock_keys)
-        # delete is awaited, so use AsyncMock
-        mock_redis_client.delete = AsyncMock(return_value=3)
-
-        # Clear notifications
-        result = await clear_insufficient_funds_notifications(user_id)
-
-        # Verify correct pattern was used
-        expected_pattern = f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:*"
-        mock_redis_client.scan_iter.assert_called_once_with(match=expected_pattern)
-
-        # Verify delete was called with all keys
-        mock_redis_client.delete.assert_called_once_with(*mock_keys)
-
-        # Verify return value
-        assert result == 3
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_clear_insufficient_funds_notifications_no_keys(server: SpinTestServer):
-    """Test clearing notifications when there are no keys to clear."""
-
-    user_id = "test-user-no-notifications"
-
-    with patch("backend.executor.manager.redis") as mock_redis_module:
-
-        mock_redis_client = MagicMock()
-        # get_redis_async is an async function, so we need AsyncMock for it
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-
-        # Mock scan_iter to return no keys as an async iterator
-        mock_redis_client.scan_iter.return_value = async_iter([])
-
-        # Clear notifications
-        result = await clear_insufficient_funds_notifications(user_id)
-
-        # Verify delete was not called
-        mock_redis_client.delete.assert_not_called()
-
-        # Verify return value
-        assert result == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_clear_insufficient_funds_notifications_handles_redis_error(
-    server: SpinTestServer,
-):
-    """Test that clearing notifications handles Redis errors gracefully."""
-
-    user_id = "test-user-redis-error"
-
-    with patch("backend.executor.manager.redis") as mock_redis_module:
-
-        # Mock get_redis_async to raise an error
-        mock_redis_module.get_redis_async = AsyncMock(
-            side_effect=Exception("Redis connection failed")
-        )
-
-        # Clear notifications should not raise, just return 0
-        result = await clear_insufficient_funds_notifications(user_id)
-
-        # Verify it returned 0 (graceful failure)
-        assert result == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_insufficient_funds_continues_on_redis_error(
-    server: SpinTestServer,
-):
-    """Test that both email and Discord notifications are still sent when Redis fails."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    graph_id = "test-graph-456"
-    error = InsufficientBalanceError(
-        message="Insufficient balance",
-        user_id=user_id,
-        balance=72,
-        amount=-714,
-    )
-
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.frontend_base_url = "https://test.com"
-
-        # Mock Redis to raise an error
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis.return_value = mock_redis_client
-        mock_redis_client.set.side_effect = Exception("Redis connection error")
-
-        mock_db_client = MagicMock()
-        mock_graph_metadata = MagicMock()
-        mock_graph_metadata.name = "Test Agent"
-        mock_db_client.get_graph_metadata.return_value = mock_graph_metadata
-        mock_db_client.get_user_email_by_id.return_value = "test@example.com"
-
-        # Test the insufficient funds handler
-        execution_processor._handle_insufficient_funds_notif(
-            db_client=mock_db_client,
-            user_id=user_id,
-            graph_id=graph_id,
-            e=error,
-        )
-
-        # Verify email notification was still queued despite Redis error
-        mock_queue_notif.assert_called_once()
-
-        # Verify Discord alert was still sent despite Redis error
-        mock_client.discord_system_alert.assert_called_once()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_add_transaction_clears_notifications_on_grant(server: SpinTestServer):
-    """Test that _add_transaction clears notification flags when adding GRANT credits."""
-    from prisma.enums import CreditTransactionType
-
-    from backend.data.credit import UserCredit
-
-    user_id = "test-user-grant-clear"
-
-    with patch("backend.data.credit.query_raw_with_schema") as mock_query, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Mock the query to return a successful transaction
-        mock_query.return_value = [{"balance": 1000, "transactionKey": "test-tx-key"}]
-
-        # Mock async Redis for notification clearing
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-        mock_redis_client.scan_iter.return_value = async_iter(
-            [f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:graph-1"]
-        )
-        mock_redis_client.delete = AsyncMock(return_value=1)
-
-        # Create a concrete instance
-        credit_model = UserCredit()
-
-        # Call _add_transaction with GRANT type (should clear notifications)
-        await credit_model._add_transaction(
-            user_id=user_id,
-            amount=500,  # Positive amount
-            transaction_type=CreditTransactionType.GRANT,
-            is_active=True,  # Active transaction
-        )
-
-        # Verify notification clearing was called
-        mock_redis_module.get_redis_async.assert_called_once()
-        mock_redis_client.scan_iter.assert_called_once_with(
-            match=f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:*"
-        )
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_add_transaction_clears_notifications_on_top_up(server: SpinTestServer):
-    """Test that _add_transaction clears notification flags when adding TOP_UP credits."""
-    from prisma.enums import CreditTransactionType
-
-    from backend.data.credit import UserCredit
-
-    user_id = "test-user-topup-clear"
-
-    with patch("backend.data.credit.query_raw_with_schema") as mock_query, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Mock the query to return a successful transaction
-        mock_query.return_value = [{"balance": 2000, "transactionKey": "test-tx-key-2"}]
-
-        # Mock async Redis for notification clearing
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-        mock_redis_client.scan_iter.return_value = async_iter([])
-        mock_redis_client.delete = AsyncMock(return_value=0)
-
-        credit_model = UserCredit()
-
-        # Call _add_transaction with TOP_UP type (should clear notifications)
-        await credit_model._add_transaction(
-            user_id=user_id,
-            amount=1000,  # Positive amount
-            transaction_type=CreditTransactionType.TOP_UP,
-            is_active=True,
-        )
-
-        # Verify notification clearing was attempted
-        mock_redis_module.get_redis_async.assert_called_once()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_add_transaction_skips_clearing_for_inactive_transaction(
-    server: SpinTestServer,
-):
-    """Test that _add_transaction does NOT clear notifications for inactive transactions."""
-    from prisma.enums import CreditTransactionType
-
-    from backend.data.credit import UserCredit
-
-    user_id = "test-user-inactive"
-
-    with patch("backend.data.credit.query_raw_with_schema") as mock_query, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Mock the query to return a successful transaction
-        mock_query.return_value = [{"balance": 500, "transactionKey": "test-tx-key-3"}]
-
-        # Mock async Redis
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-
-        credit_model = UserCredit()
-
-        # Call _add_transaction with is_active=False (should NOT clear notifications)
-        await credit_model._add_transaction(
-            user_id=user_id,
-            amount=500,
-            transaction_type=CreditTransactionType.TOP_UP,
-            is_active=False,  # Inactive - pending Stripe payment
-        )
-
-        # Verify notification clearing was NOT called
-        mock_redis_module.get_redis_async.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_add_transaction_skips_clearing_for_usage_transaction(
-    server: SpinTestServer,
-):
-    """Test that _add_transaction does NOT clear notifications for USAGE transactions."""
-    from prisma.enums import CreditTransactionType
-
-    from backend.data.credit import UserCredit
-
-    user_id = "test-user-usage"
-
-    with patch("backend.data.credit.query_raw_with_schema") as mock_query, patch(
-        "backend.executor.manager.redis"
-    ) as mock_redis_module:
-
-        # Mock the query to return a successful transaction
-        mock_query.return_value = [{"balance": 400, "transactionKey": "test-tx-key-4"}]
-
-        # Mock async Redis
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-
-        credit_model = UserCredit()
-
-        # Call _add_transaction with USAGE type (spending, should NOT clear)
-        await credit_model._add_transaction(
-            user_id=user_id,
-            amount=-100,  # Negative - spending credits
-            transaction_type=CreditTransactionType.USAGE,
-            is_active=True,
-        )
-
-        # Verify notification clearing was NOT called
-        mock_redis_module.get_redis_async.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_enable_transaction_clears_notifications(server: SpinTestServer):
-    """Test that _enable_transaction clears notification flags when enabling a TOP_UP."""
-    from prisma.enums import CreditTransactionType
-
-    from backend.data.credit import UserCredit
-
-    user_id = "test-user-enable"
-
-    with patch("backend.data.credit.CreditTransaction") as mock_credit_tx, patch(
-        "backend.data.credit.query_raw_with_schema"
-    ) as mock_query, patch("backend.executor.manager.redis") as mock_redis_module:
-
-        # Mock finding the pending transaction
-        mock_transaction = MagicMock()
-        mock_transaction.amount = 1000
-        mock_transaction.type = CreditTransactionType.TOP_UP
-        mock_credit_tx.prisma.return_value.find_first = AsyncMock(
-            return_value=mock_transaction
-        )
-
-        # Mock the query to return updated balance
-        mock_query.return_value = [{"balance": 1500}]
-
-        # Mock async Redis for notification clearing
-        mock_redis_client = MagicMock()
-        mock_redis_module.get_redis_async = AsyncMock(return_value=mock_redis_client)
-        mock_redis_client.scan_iter.return_value = async_iter(
-            [f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:graph-1"]
-        )
-        mock_redis_client.delete = AsyncMock(return_value=1)
-
-        credit_model = UserCredit()
-
-        # Call _enable_transaction (simulates Stripe checkout completion)
-        from backend.util.json import SafeJson
-
-        await credit_model._enable_transaction(
-            transaction_key="cs_test_123",
-            user_id=user_id,
-            metadata=SafeJson({"payment": "completed"}),
-        )
-
-        # Verify notification clearing was called
-        mock_redis_module.get_redis_async.assert_called_once()
-        mock_redis_client.scan_iter.assert_called_once_with(
-            match=f"{INSUFFICIENT_FUNDS_NOTIFIED_PREFIX}:{user_id}:*"
-        )

autogpt_platform/backend/backend/executor/manager_test.py

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

autogpt_platform/backend/backend/executor/scheduler.py

@@ -2,7 +2,6 @@ import asyncio
 import logging
 import os
 import threading
-import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -28,7 +27,7 @@ from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_onboarding_runs
+from backend.data.onboarding import increment_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -38,7 +37,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_database_manager_client, get_scheduler_client
+from backend.util.clients import get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -157,7 +156,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
-        await increment_onboarding_runs(args.user_id)
+        await increment_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -255,114 +254,6 @@ def execution_accuracy_alerts():
     return report_execution_accuracy_alerts()
 
 
-def ensure_embeddings_coverage():
-    """
-    Ensure all content types (store agents, blocks, docs) have embeddings for search.
-
-    Processes ALL missing embeddings in batches of 10 per content type until 100% coverage.
-    Missing embeddings = content invisible in hybrid search.
-
-    Schedule: Runs every 6 hours (balanced between coverage and API costs).
-    - Catches new content added between scheduled runs
-    - Batch size 10 per content type: gradual processing to avoid rate limits
-    - Manual trigger available via execute_ensure_embeddings_coverage endpoint
-    """
-    db_client = get_database_manager_client()
-    stats = db_client.get_embedding_stats()
-
-    # Check for error from get_embedding_stats() first
-    if "error" in stats:
-        logger.error(
-            f"Failed to get embedding stats: {stats['error']} - skipping backfill"
-        )
-        return {
-            "backfill": {"processed": 0, "success": 0, "failed": 0},
-            "cleanup": {"deleted": 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)
-
-    total_processed = 0
-    total_success = 0
-    total_failed = 0
-
-    if without_embeddings == 0:
-        logger.info("All content has embeddings, skipping backfill")
-    else:
-        # 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"
-        )
-
-        # 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"
-        )
-
-    # Clean up orphaned embeddings for blocks and docs
-    logger.info("Running cleanup for orphaned embeddings (blocks/docs)...")
-    cleanup_result = db_client.cleanup_orphaned_embeddings()
-    cleanup_totals = cleanup_result.get("totals", {})
-    cleanup_deleted = cleanup_totals.get("deleted", 0)
-
-    if cleanup_deleted > 0:
-        logger.info(f"Cleanup completed: deleted {cleanup_deleted} orphaned embeddings")
-        by_type = cleanup_result.get("by_type", {})
-        for content_type, type_result in by_type.items():
-            if type_result.get("deleted", 0) > 0:
-                logger.info(
-                    f"{content_type}: deleted {type_result['deleted']} orphaned embeddings"
-                )
-    else:
-        logger.info("Cleanup completed: no orphaned embeddings found")
-
-    return {
-        "backfill": {
-            "processed": total_processed,
-            "success": total_success,
-            "failed": total_failed,
-        },
-        "cleanup": {
-            "deleted": cleanup_deleted,
-        },
-    }
-
-
 # Monitoring functions are now imported from monitoring module
 
 
@@ -584,36 +475,11 @@ 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)
         self.scheduler.start()
 
-        # Run embedding backfill immediately on startup
-        # This ensures blocks/docs are searchable right away, not after 6 hours
-        # Safe to run on multiple pods - uses upserts and checks for existing embeddings
-        if self.register_system_tasks:
-            logger.info("Running embedding backfill on startup...")
-            try:
-                result = ensure_embeddings_coverage()
-                logger.info(f"Startup embedding backfill complete: {result}")
-            except Exception as e:
-                logger.error(f"Startup embedding backfill failed: {e}")
-                # Don't fail startup - the scheduled job will retry later
-
         # Keep the service running since BackgroundScheduler doesn't block
         super().run_service()
 
@@ -766,11 +632,6 @@ class Scheduler(AppService):
         """Manually trigger execution accuracy alert checking."""
         return execution_accuracy_alerts()
 
-    @expose
-    def execute_ensure_embeddings_coverage(self):
-        """Manually trigger embedding backfill for approved store agents."""
-        return ensure_embeddings_coverage()
-
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/executor/utils.py

@@ -10,7 +10,6 @@ from pydantic import BaseModel, JsonValue, ValidationError
 
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
-from backend.data import onboarding as onboarding_db
 from backend.data import user as user_db
 from backend.data.block import (
     Block,
@@ -32,6 +31,7 @@ from backend.data.execution import (
     GraphExecutionStats,
     GraphExecutionWithNodes,
     NodesInputMasks,
+    get_graph_execution,
 )
 from backend.data.graph import GraphModel, Node
 from backend.data.model import USER_TIMEZONE_NOT_SET, CredentialsMetaInput
@@ -239,19 +239,14 @@ async def _validate_node_input_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[dict[str, dict[str, str]], set[str]]:
+) -> dict[str, dict[str, str]]:
     """
-    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.
+    Checks all credentials for all nodes of the graph and returns structured errors.
 
     Returns:
-        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)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Credential validation errors per node
     """
     credential_errors: dict[str, dict[str, str]] = defaultdict(dict)
-    nodes_to_skip: set[str] = set()
 
     for node in graph.nodes:
         block = node.block
@@ -261,46 +256,27 @@ 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
                 ):
-                    field_value = node_input_mask[field_name]
+                    credentials_meta = credentials_meta_type.model_validate(
+                        node_input_mask[field_name]
+                    )
                 elif field_name in node.input_default:
-                    # 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)
-
+                    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
             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
 
@@ -311,7 +287,6 @@ 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}"
@@ -338,19 +313,7 @@ async def _validate_node_input_credentials(
                 ] = "Invalid credentials: type/provider mismatch"
                 continue
 
-        # 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
+    return credential_errors
 
 
 def make_node_credentials_input_map(
@@ -392,25 +355,21 @@ async def validate_graph_with_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[Mapping[str, Mapping[str, str]], set[str]]:
+) -> Mapping[str, Mapping[str, str]]:
     """
-    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.
+    Validate graph including credentials and return structured errors per node.
 
     Returns:
-        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)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Validation errors per node
     """
     # 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 and nodes to skip
-    node_credential_input_errors, nodes_to_skip = (
-        await _validate_node_input_credentials(graph, user_id, nodes_input_masks)
+    # Get credential input/availability/validation errors
+    node_credential_input_errors = await _validate_node_input_credentials(
+        graph, user_id, nodes_input_masks
     )
 
     # Merge credential errors with structural errors
@@ -419,7 +378,7 @@ async def validate_graph_with_credentials(
             node_input_errors[node_id] = {}
         node_input_errors[node_id].update(field_errors)
 
-    return node_input_errors, nodes_to_skip
+    return node_input_errors
 
 
 async def _construct_starting_node_execution_input(
@@ -427,7 +386,7 @@ async def _construct_starting_node_execution_input(
     user_id: str,
     graph_inputs: BlockInput,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[list[tuple[str, BlockInput]], set[str]]:
+) -> list[tuple[str, BlockInput]]:
     """
     Validates and prepares the input data for executing a graph.
     This function checks the graph for starting nodes, validates the input data
@@ -441,14 +400,11 @@ async def _construct_starting_node_execution_input(
         node_credentials_map: `dict[node_id, dict[input_name, CredentialsMetaInput]]`
 
     Returns:
-        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)
-        ]
+        list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID and
+            the corresponding input data for that node.
     """
     # Use new validation function that includes credentials
-    validation_errors, nodes_to_skip = await validate_graph_with_credentials(
+    validation_errors = await validate_graph_with_credentials(
         graph, user_id, nodes_input_masks
     )
     n_error_nodes = len(validation_errors)
@@ -489,7 +445,7 @@ 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, nodes_to_skip
+    return nodes_input
 
 
 async def validate_and_construct_node_execution_input(
@@ -500,7 +456,7 @@ 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, set[str]]:
+) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -517,7 +473,6 @@ 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.
@@ -559,16 +514,14 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks or {},
     )
 
-    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,
-        )
+    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,
     )
 
-    return graph, starting_nodes_input, nodes_input_masks, nodes_to_skip
+    return graph, starting_nodes_input, nodes_input_masks
 
 
 def _merge_nodes_input_masks(
@@ -809,14 +762,13 @@ async def add_graph_execution(
         edb = execution_db
         udb = user_db
         gdb = graph_db
-        odb = onboarding_db
     else:
-        edb = udb = gdb = odb = get_database_manager_async_client()
+        edb = udb = gdb = get_database_manager_async_client()
 
     # Get or create the graph execution
     if graph_exec_id:
         # Resume existing execution
-        graph_exec = await edb.get_graph_execution(
+        graph_exec = await get_graph_execution(
             user_id=user_id,
             execution_id=graph_exec_id,
             include_node_executions=True,
@@ -827,9 +779,6 @@ 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:
@@ -838,7 +787,7 @@ async def add_graph_execution(
         )
 
         # Create new execution
-        graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip = (
+        graph, starting_nodes_input, compiled_nodes_input_masks = (
             await validate_and_construct_node_execution_input(
                 graph_id=graph_id,
                 user_id=user_id,
@@ -887,12 +836,10 @@ 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")
 
-        # Publish to execution queue for executor to pick up
         exec_queue = await get_async_execution_queue()
         await exec_queue.publish_message(
             routing_key=GRAPH_EXECUTION_ROUTING_KEY,
@@ -901,12 +848,14 @@ async def add_graph_execution(
         )
         logger.info(f"Published execution {graph_exec.id} to RabbitMQ queue")
 
-        # Update execution status to QUEUED
         graph_exec.status = ExecutionStatus.QUEUED
         await edb.update_graph_execution_stats(
             graph_exec_id=graph_exec.id,
             status=graph_exec.status,
         )
+        await get_async_execution_event_bus().publish(graph_exec)
+
+        return graph_exec
     except BaseException as e:
         err = str(e) or type(e).__name__
         if not graph_exec:
@@ -927,24 +876,6 @@ async def add_graph_execution(
         )
         raise
 
-    try:
-        await get_async_execution_event_bus().publish(graph_exec)
-        logger.info(f"Published update for execution #{graph_exec.id} to event bus")
-    except Exception as e:
-        logger.error(
-            f"Failed to publish execution event for graph exec #{graph_exec.id}: {e}"
-        )
-
-    try:
-        await odb.increment_onboarding_runs(user_id)
-        logger.info(
-            f"Incremented user #{user_id} onboarding runs for exec #{graph_exec.id}"
-        )
-    except Exception as e:
-        logger.error(f"Failed to increment onboarding runs for user #{user_id}: {e}")
-
-    return graph_exec
-
 
 # ============ Execution Output Helpers ============ #
 

autogpt_platform/backend/backend/executor/utils_test.py

@@ -367,13 +367,10 @@ 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)
@@ -459,212 +456,3 @@ 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/credentials_store.py

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

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

@@ -8,7 +8,6 @@ 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:
@@ -21,7 +20,6 @@ _ORIGINAL_HANDLERS = [
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,
-    RedditOAuthHandler,
     TwitterOAuthHandler,
     TodoistOAuthHandler,
 ]

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

@@ -1,208 +0,0 @@
-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/cache.py

@@ -16,7 +16,7 @@ import pickle
 import threading
 import time
 from dataclasses import dataclass
-from functools import cache, wraps
+from functools import wraps
 from typing import Any, Callable, ParamSpec, Protocol, TypeVar, cast, runtime_checkable
 
 from redis import ConnectionPool, Redis
@@ -38,34 +38,29 @@ settings = Settings()
 #   maxmemory 2gb                   # Set memory limit (adjust based on your needs)
 #   save ""                         # Disable persistence if using Redis purely for caching
 
+# Create a dedicated Redis connection pool for caching (binary mode for pickle)
+_cache_pool: ConnectionPool | None = None
 
-@cache
+
+@conn_retry("Redis", "Acquiring cache connection pool")
 def _get_cache_pool() -> ConnectionPool:
-    """Get or create a connection pool for cache operations (lazy, thread-safe)."""
-    return ConnectionPool(
-        host=settings.config.redis_host,
-        port=settings.config.redis_port,
-        password=settings.config.redis_password or None,
-        decode_responses=False,  # Binary mode for pickle
-        max_connections=50,
-        socket_keepalive=True,
-        socket_connect_timeout=5,
-        retry_on_timeout=True,
-    )
+    """Get or create a connection pool for cache operations."""
+    global _cache_pool
+    if _cache_pool is None:
+        _cache_pool = ConnectionPool(
+            host=settings.config.redis_host,
+            port=settings.config.redis_port,
+            password=settings.config.redis_password or None,
+            decode_responses=False,  # Binary mode for pickle
+            max_connections=50,
+            socket_keepalive=True,
+            socket_connect_timeout=5,
+            retry_on_timeout=True,
+        )
+    return _cache_pool
 
 
-@cache
-@conn_retry("Redis", "Acquiring cache connection")
-def _get_redis() -> Redis:
-    """
-    Get the lazily-initialized Redis client for shared cache operations.
-    Uses @cache for thread-safe singleton behavior - connection is only
-    established when first accessed, allowing services that only use
-    in-memory caching to work without Redis configuration.
-    """
-    r = Redis(connection_pool=_get_cache_pool())
-    r.ping()  # Verify connection
-    return r
+redis = Redis(connection_pool=_get_cache_pool())
 
 
 @dataclass
@@ -184,9 +179,9 @@ def cached(
             try:
                 if refresh_ttl_on_get:
                     # Use GETEX to get value and refresh expiry atomically
-                    cached_bytes = _get_redis().getex(redis_key, ex=ttl_seconds)
+                    cached_bytes = redis.getex(redis_key, ex=ttl_seconds)
                 else:
-                    cached_bytes = _get_redis().get(redis_key)
+                    cached_bytes = redis.get(redis_key)
 
                 if cached_bytes and isinstance(cached_bytes, bytes):
                     return pickle.loads(cached_bytes)
@@ -200,7 +195,7 @@ def cached(
             """Set value in Redis with TTL."""
             try:
                 pickled_value = pickle.dumps(value, protocol=pickle.HIGHEST_PROTOCOL)
-                _get_redis().setex(redis_key, ttl_seconds, pickled_value)
+                redis.setex(redis_key, ttl_seconds, pickled_value)
             except Exception as e:
                 logger.error(
                     f"Redis error storing cache for {target_func.__name__}: {e}"
@@ -338,18 +333,14 @@ def cached(
                 if pattern:
                     # Clear entries matching pattern
                     keys = list(
-                        _get_redis().scan_iter(
-                            f"cache:{target_func.__name__}:{pattern}"
-                        )
+                        redis.scan_iter(f"cache:{target_func.__name__}:{pattern}")
                     )
                 else:
                     # Clear all cache keys
-                    keys = list(
-                        _get_redis().scan_iter(f"cache:{target_func.__name__}:*")
-                    )
+                    keys = list(redis.scan_iter(f"cache:{target_func.__name__}:*"))
 
                 if keys:
-                    pipeline = _get_redis().pipeline()
+                    pipeline = redis.pipeline()
                     for key in keys:
                         pipeline.delete(key)
                     pipeline.execute()
@@ -364,9 +355,7 @@ def cached(
 
         def cache_info() -> dict[str, int | None]:
             if shared_cache:
-                cache_keys = list(
-                    _get_redis().scan_iter(f"cache:{target_func.__name__}:*")
-                )
+                cache_keys = list(redis.scan_iter(f"cache:{target_func.__name__}:*"))
                 return {
                     "size": len(cache_keys),
                     "maxsize": None,  # Redis manages its own size
@@ -384,8 +373,10 @@ def cached(
             key = _make_hashable_key(args, kwargs)
             if shared_cache:
                 redis_key = _make_redis_key(key, target_func.__name__)
-                deleted_count = cast(int, _get_redis().delete(redis_key))
-                return deleted_count > 0
+                if redis.exists(redis_key):
+                    redis.delete(redis_key)
+                    return True
+                return False
             else:
                 if key in cache_storage:
                     del cache_storage[key]

autogpt_platform/backend/backend/util/clients.py

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