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

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

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

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

autogpt_platform/Makefile

@@ -6,10 +6,9 @@ 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
@@ -61,4 +60,4 @@ help:
 	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
 	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  load-store-agents - Load store agents from agents/ folder into test database"

autogpt_platform/backend/.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

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

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 | None,
-) -> PrismaChatSession:
-    """Create a new chat session in the database."""
-    data = ChatSessionCreateInput(
-        id=session_id,
-        userId=user_id,
-        credentials=SafeJson({}),
-        successfulAgentRuns=SafeJson({}),
-        successfulAgentSchedules=SafeJson({}),
-    )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
-
-
-async def update_chat_session(
-    session_id: str,
-    credentials: dict[str, Any] | None = None,
-    successful_agent_runs: dict[str, Any] | None = None,
-    successful_agent_schedules: dict[str, Any] | None = None,
-    total_prompt_tokens: int | None = None,
-    total_completion_tokens: int | None = None,
-    title: str | None = None,
-) -> PrismaChatSession | None:
-    """Update a chat session's metadata."""
-    data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
-
-    if credentials is not None:
-        data["credentials"] = SafeJson(credentials)
-    if successful_agent_runs is not None:
-        data["successfulAgentRuns"] = SafeJson(successful_agent_runs)
-    if successful_agent_schedules is not None:
-        data["successfulAgentSchedules"] = SafeJson(successful_agent_schedules)
-    if total_prompt_tokens is not None:
-        data["totalPromptTokens"] = total_prompt_tokens
-    if total_completion_tokens is not None:
-        data["totalCompletionTokens"] = total_completion_tokens
-    if title is not None:
-        data["title"] = title
-
-    session = await PrismaChatSession.prisma().update(
-        where={"id": session_id},
-        data=data,
-        include={"Messages": True},
-    )
-    if session and session.Messages:
-        # 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
@@ -95,7 +46,6 @@ class Usage(BaseModel):
 class ChatSession(BaseModel):
     session_id: str
     user_id: str | None
-    title: str | None = None
     messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
@@ -109,7 +59,6 @@ class ChatSession(BaseModel):
         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:
@@ -261,332 +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.
-    """
-    # Try cache first
-    try:
-        session = await _get_session_from_cache(session_id)
-        if session:
-            # Verify user ownership
-            if session.user_id is not None and session.user_id != user_id:
-                logger.warning(
-                    f"Session {session_id} user id mismatch: {session.user_id} != {user_id}"
-                )
-                return None
-            return session
-    except RedisError:
-        logger.warning(f"Cache error for session {session_id}, trying database")
-    except Exception as e:
-        logger.warning(f"Unexpected cache error for session {session_id}: {e}")
-
-    # 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 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 | None = None) -> 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

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

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

@@ -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

@@ -13,27 +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 assign user if needed."""
-    session = await get_chat_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)
-    return session
-
-
 router = APIRouter(
     tags=["chat"],
 )
@@ -41,14 +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."""
 
@@ -67,64 +44,9 @@ class SessionDetailResponse(BaseModel):
     messages: list[dict]
 
 
-class SessionSummaryResponse(BaseModel):
-    """Response model for a session summary (without messages)."""
-
-    id: str
-    created_at: str
-    updated_at: str
-    title: str | None = None
-
-
-class ListSessionsResponse(BaseModel):
-    """Response model for listing chat sessions."""
-
-    sessions: list[SessionSummaryResponse]
-    total: int
-
-
 # ========== Routes ==========
 
 
-@router.get(
-    "/sessions",
-    dependencies=[Security(auth.requires_user)],
-)
-async def list_sessions(
-    user_id: Annotated[str, Security(auth.get_user_id)],
-    limit: int = Query(default=50, ge=1, le=100),
-    offset: int = Query(default=0, ge=0),
-) -> ListSessionsResponse:
-    """
-    List chat sessions for the authenticated user.
-
-    Returns a paginated list of chat sessions belonging to the current user,
-    ordered by most recently updated.
-
-    Args:
-        user_id: The authenticated user's ID.
-        limit: Maximum number of sessions to return (1-100).
-        offset: Number of sessions to skip for pagination.
-
-    Returns:
-        ListSessionsResponse: List of session summaries and total count.
-    """
-    sessions, 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",
 )
@@ -148,7 +70,7 @@ async def create_session(
         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,
@@ -177,88 +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
@@ -274,7 +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(
@@ -285,8 +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(),
@@ -295,7 +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
         },
     )
 
@@ -341,9 +208,9 @@ async def health_check() -> dict:
         dict: A status dictionary indicating health, service name, and API version.
 
     """
-    session = await create_chat_session(None)
+    session = await chat_service.create_chat_session(None)
     await chat_service.assign_user_to_session(session.session_id, "test_user")
-    await get_chat_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

@@ -4,12 +4,11 @@ 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__)
@@ -24,7 +23,7 @@ async def test_stream_chat_completion():
     if not api_key:
         return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
 
-    session = await create_chat_session()
+    session = await chat_service.create_chat_session()
 
     has_errors = False
     has_ended = False
@@ -35,9 +34,9 @@ async def test_stream_chat_completion():
         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"
@@ -54,8 +53,8 @@ async def test_stream_chat_completion_with_tool_calls():
     if not api_key:
         return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
 
-    session = await create_chat_session()
-    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():
         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

@@ -4,32 +4,21 @@ from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
 
-from .add_understanding import AddUnderstandingTool
-from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .find_agent import FindAgentTool
-from .find_library_agent import FindLibraryAgentTool
 from .run_agent import RunAgentTool
 
 if TYPE_CHECKING:
-    from backend.api.features.chat.response_model import 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_library_agent": FindLibraryAgentTool(),
-    "run_agent": RunAgentTool(),
-    "agent_output": AgentOutputTool(),
-}
+# 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(),
 ]
 
 
@@ -39,9 +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
@@ -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_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/models.py

@@ -1,6 +1,5 @@
 """Pydantic models for tool responses."""
 
-from datetime import datetime
 from enum import Enum
 from typing import Any
 
@@ -12,15 +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"
+    SUCCESS = "success"
 
 
 # Base response model
@@ -53,14 +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):
@@ -175,37 +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)

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

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

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

@@ -1,4 +1,5 @@
 import uuid
+from unittest.mock import AsyncMock, patch
 
 import orjson
 import pytest
@@ -17,6 +18,17 @@ setup_test_data = setup_test_data
 setup_firecrawl_test_data = setup_firecrawl_test_data
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(scope="session")
 async def test_run_agent(setup_test_data):
     """Test that the run_agent tool successfully executes an approved agent"""
@@ -46,11 +58,11 @@ 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
@@ -86,11 +98,11 @@ 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
 
 
@@ -118,10 +130,10 @@ 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(
@@ -158,12 +170,12 @@ 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
@@ -195,9 +207,9 @@ 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"
@@ -230,9 +242,9 @@ 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
@@ -260,9 +272,9 @@ 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"
@@ -292,9 +304,9 @@ 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"
@@ -318,9 +330,9 @@ 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"
@@ -350,9 +362,9 @@ 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"
@@ -382,9 +394,9 @@ 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/store/content_handlers.py

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

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

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

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

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

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

@@ -1,8 +1,7 @@
 import asyncio
 import logging
-import typing
 from datetime import datetime, timezone
-from typing import Literal
+from typing import Any, Literal
 
 import fastapi
 import prisma.enums
@@ -10,7 +9,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import query_raw_with_schema, transaction
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -30,6 +29,8 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
+from .embeddings import ensure_embedding
+from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -50,128 +51,77 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view
+    Get PUBLIC store agents from the StoreAgent view.
+
+    Search behavior:
+    - With search_query: Uses hybrid search (semantic + lexical)
+    - Fallback: If embeddings unavailable, gracefully degrades to lexical-only
+    - Rationale: User-facing endpoint prioritizes availability over accuracy
+
+    Note: Admin operations (approval) use fail-fast to prevent inconsistent state.
     """
     logger.debug(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
+    search_used_hybrid = False
+    store_agents: list[store_model.StoreAgent] = []
+    agents: list[dict[str, Any]] = []
+    total = 0
+    total_pages = 0
+
     try:
-        # If search_query is provided, use full-text search
+        # If search_query is provided, use hybrid search (embeddings + tsvector)
         if search_query:
-            offset = (page - 1) * page_size
+            # Try hybrid search combining semantic and lexical signals
+            # Falls back to lexical-only if OpenAI unavailable (user-facing, high SLA)
+            try:
+                agents, total = await hybrid_search(
+                    query=search_query,
+                    featured=featured,
+                    creators=creators,
+                    category=category,
+                    sorted_by="relevance",  # Use hybrid scoring for relevance
+                    page=page,
+                    page_size=page_size,
+                )
+                search_used_hybrid = True
+            except Exception as e:
+                # Log error but fall back to lexical search for better UX
+                logger.error(
+                    f"Hybrid search failed (likely OpenAI unavailable), "
+                    f"falling back to lexical search: {e}"
+                )
+                # search_used_hybrid remains False, will use fallback path below
 
-            # Whitelist allowed order_by columns
-            ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
-            }
+            # Convert hybrid search results (dict format) if hybrid succeeded
+            if search_used_hybrid:
+                total_pages = (total + page_size - 1) // page_size
+                store_agents: list[store_model.StoreAgent] = []
+                for agent in agents:
+                    try:
+                        store_agent = store_model.StoreAgent(
+                            slug=agent["slug"],
+                            agent_name=agent["agent_name"],
+                            agent_image=(
+                                agent["agent_image"][0] if agent["agent_image"] else ""
+                            ),
+                            creator=agent["creator_username"] or "Needs Profile",
+                            creator_avatar=agent["creator_avatar"] or "",
+                            sub_heading=agent["sub_heading"],
+                            description=agent["description"],
+                            runs=agent["runs"],
+                            rating=agent["rating"],
+                        )
+                        store_agents.append(store_agent)
+                    except Exception as e:
+                        logger.error(
+                            f"Error parsing Store agent from hybrid search results: {e}"
+                        )
+                        continue
 
-            # Validate and get order clause
-            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
-                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
-            else:
-                order_by_clause = "updated_at DESC, rank DESC"
-
-            # Build WHERE conditions and parameters list
-            where_parts: list[str] = []
-            params: list[typing.Any] = [search_query]  # $1 - search term
-            param_index = 2  # Start at $2 for next parameter
-
-            # Always filter for available agents
-            where_parts.append("is_available = true")
-
-            if featured:
-                where_parts.append("featured = true")
-
-            if creators and creators:
-                # Use ANY with array parameter
-                where_parts.append(f"creator_username = ANY(${param_index})")
-                params.append(creators)
-                param_index += 1
-
-            if category and category:
-                where_parts.append(f"${param_index} = ANY(categories)")
-                params.append(category)
-                param_index += 1
-
-            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
-
-            # Add pagination params
-            params.extend([page_size, offset])
-            limit_param = f"${param_index}"
-            offset_param = f"${param_index + 1}"
-
-            # Execute full-text search query with parameterized values
-            sql_query = f"""
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    ts_rank_cd(search, query) AS rank
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-                ORDER BY {order_by_clause}
-                LIMIT {limit_param} OFFSET {offset_param}
-            """
-
-            # Count query for pagination - only uses search term parameter
-            count_query = f"""
-                SELECT COUNT(*) as count
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-            """
-
-            # Execute both queries with parameters
-            agents = await query_raw_with_schema(sql_query, *params)
-
-            # For count, use params without pagination (last 2 params)
-            count_params = params[:-2]
-            count_result = await query_raw_with_schema(count_query, *count_params)
-
-            total = count_result[0]["count"] if count_result else 0
-            total_pages = (total + page_size - 1) // page_size
-
-            # Convert raw results to StoreAgent models
-            store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
-                try:
-                    store_agent = store_model.StoreAgent(
-                        slug=agent["slug"],
-                        agent_name=agent["agent_name"],
-                        agent_image=(
-                            agent["agent_image"][0] if agent["agent_image"] else ""
-                        ),
-                        creator=agent["creator_username"] or "Needs Profile",
-                        creator_avatar=agent["creator_avatar"] or "",
-                        sub_heading=agent["sub_heading"],
-                        description=agent["description"],
-                        runs=agent["runs"],
-                        rating=agent["rating"],
-                    )
-                    store_agents.append(store_agent)
-                except Exception as e:
-                    logger.error(f"Error parsing Store agent from search results: {e}")
-                    continue
-
-        else:
-            # Non-search query path (original logic)
+        if not search_used_hybrid:
+            # Fallback path - use basic search or no search
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -180,6 +130,14 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
+            # Add basic text search if search_query provided but hybrid failed
+            if search_query:
+                where_clause["OR"] = [
+                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
+                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
+                    {"description": {"contains": search_query, "mode": "insensitive"}},
+                ]
+
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -188,7 +146,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            agents = await prisma.models.StoreAgent.prisma().find_many(
+            db_agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -199,7 +157,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
+            for agent in db_agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -1577,7 +1535,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma().update(
+                await prisma.models.AgentGraph.prisma(tx).update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1592,6 +1550,23 @@ async def review_store_submission(
                     },
                 )
 
+                # Generate embedding for approved listing (blocking - admin operation)
+                # Inside transaction: if embedding fails, entire transaction rolls back
+                embedding_success = await ensure_embedding(
+                    version_id=store_listing_version_id,
+                    name=store_listing_version.name,
+                    description=store_listing_version.description,
+                    sub_heading=store_listing_version.subHeading,
+                    categories=store_listing_version.categories or [],
+                    tx=tx,
+                )
+                if not embedding_success:
+                    raise ValueError(
+                        f"Failed to generate embedding for listing {store_listing_version_id}. "
+                        "This is likely due to OpenAI API being unavailable. "
+                        "Please try again later or contact support if the issue persists."
+                    )
+
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},
                     data={

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

@@ -0,0 +1,628 @@
+"""
+Unified Content Embeddings Service
+
+Handles generation and storage of OpenAI embeddings for all content types
+(store listings, blocks, documentation, library agents) to enable semantic/hybrid search.
+"""
+
+import asyncio
+import logging
+import time
+from typing import Any
+
+import prisma
+from prisma.enums import ContentType
+from tiktoken import encoding_for_model
+
+from backend.api.features.store.content_handlers import CONTENT_HANDLERS
+from backend.data.db import execute_raw_with_schema, query_raw_with_schema
+from backend.util.clients import get_openai_client
+from backend.util.json import dumps
+
+logger = logging.getLogger(__name__)
+
+
+# OpenAI embedding model configuration
+EMBEDDING_MODEL = "text-embedding-3-small"
+# OpenAI embedding token limit (8,191 with 1 token buffer for safety)
+EMBEDDING_MAX_TOKENS = 8191
+
+
+def build_searchable_text(
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+) -> str:
+    """
+    Build searchable text from listing version fields.
+
+    Combines relevant fields into a single string for embedding.
+    """
+    parts = []
+
+    # Name is important - include it
+    if name:
+        parts.append(name)
+
+    # Sub-heading provides context
+    if sub_heading:
+        parts.append(sub_heading)
+
+    # Description is the main content
+    if description:
+        parts.append(description)
+
+    # Categories help with semantic matching
+    if categories:
+        parts.append(" ".join(categories))
+
+    return " ".join(parts)
+
+
+async def generate_embedding(text: str) -> list[float] | None:
+    """
+    Generate embedding for text using OpenAI API.
+
+    Returns None if embedding generation fails.
+    Fail-fast: no retries to maintain consistency with approval flow.
+    """
+    try:
+        client = get_openai_client()
+        if not client:
+            logger.error("openai_internal_api_key not set, cannot generate embedding")
+            return None
+
+        # Truncate text to token limit using tiktoken
+        # Character-based truncation is insufficient because token ratios vary by content type
+        enc = encoding_for_model(EMBEDDING_MODEL)
+        tokens = enc.encode(text)
+        if len(tokens) > EMBEDDING_MAX_TOKENS:
+            tokens = tokens[:EMBEDDING_MAX_TOKENS]
+            truncated_text = enc.decode(tokens)
+            logger.info(
+                f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
+            )
+        else:
+            truncated_text = text
+
+        start_time = time.time()
+        response = await client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=truncated_text,
+        )
+        latency_ms = (time.time() - start_time) * 1000
+
+        embedding = response.data[0].embedding
+        logger.info(
+            f"Generated embedding: {len(embedding)} dims, "
+            f"{len(tokens)} tokens, {latency_ms:.0f}ms"
+        )
+        return embedding
+
+    except Exception as e:
+        logger.error(f"Failed to generate embedding: {e}")
+        return None
+
+
+async def store_embedding(
+    version_id: str,
+    embedding: list[float],
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the database.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    DEPRECATED: Use ensure_embedding() instead (includes searchable_text).
+    """
+    return await store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=version_id,
+        embedding=embedding,
+        searchable_text="",  # Empty for backward compat; ensure_embedding() populates this
+        metadata=None,
+        user_id=None,  # Store agents are public
+        tx=tx,
+    )
+
+
+async def store_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    embedding: list[float],
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the unified content embeddings table.
+
+    New function for unified content embedding storage.
+    Uses raw SQL since Prisma doesn't natively support pgvector.
+    """
+    try:
+        client = tx if tx else prisma.get_client()
+
+        # Convert embedding to PostgreSQL vector format
+        embedding_str = embedding_to_vector_string(embedding)
+        metadata_json = dumps(metadata or {})
+
+        # Upsert the embedding
+        # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        await execute_raw_with_schema(
+            """
+            INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
+                "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
+            )
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            ON CONFLICT ("contentType", "contentId", "userId")
+            DO UPDATE SET
+                "embedding" = $4::vector,
+                "searchableText" = $5,
+                "metadata" = $6::jsonb,
+                "updatedAt" = NOW()
+            WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
+                AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
+                AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            embedding_str,
+            searchable_text,
+            metadata_json,
+            client=client,
+            set_public_search_path=True,
+        )
+
+        logger.info(f"Stored embedding for {content_type}:{content_id}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding(version_id: str) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Returns dict with storeListingVersionId, embedding, timestamps or None if not found.
+    """
+    result = await get_content_embedding(
+        ContentType.STORE_AGENT, version_id, user_id=None
+    )
+    if result:
+        # Transform to old format for backward compatibility
+        return {
+            "storeListingVersionId": result["contentId"],
+            "embedding": result["embedding"],
+            "createdAt": result["createdAt"],
+            "updatedAt": result["updatedAt"],
+        }
+    return None
+
+
+async def get_content_embedding(
+    content_type: ContentType, content_id: str, user_id: str | None = None
+) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for any content type.
+
+    New function for unified content embedding retrieval.
+    Returns dict with contentType, contentId, embedding, timestamps or None if not found.
+    """
+    try:
+        result = await query_raw_with_schema(
+            """
+            SELECT
+                "contentType",
+                "contentId",
+                "userId",
+                "embedding"::text as "embedding",
+                "searchableText",
+                "metadata",
+                "createdAt",
+                "updatedAt"
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            set_public_search_path=True,
+        )
+
+        if result and len(result) > 0:
+            return result[0]
+        return None
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
+        return None
+
+
+async def ensure_embedding(
+    version_id: str,
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for the listing version.
+
+    Creates embedding if missing. Use force=True to regenerate.
+    Backward-compatible wrapper for store listings.
+
+    Args:
+        version_id: The StoreListingVersion ID
+        name: Agent name
+        description: Agent description
+        sub_heading: Agent sub-heading
+        categories: Agent categories
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_embedding(version_id)
+            if existing and existing.get("embedding"):
+                logger.debug(f"Embedding for version {version_id} already exists")
+                return True
+
+        # Build searchable text for embedding
+        searchable_text = build_searchable_text(
+            name, description, sub_heading, categories
+        )
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(f"Could not generate embedding for version {version_id}")
+            return False
+
+        # Store the embedding with metadata using new function
+        metadata = {
+            "name": name,
+            "subHeading": sub_heading,
+            "categories": categories,
+        }
+        return await store_content_embedding(
+            content_type=ContentType.STORE_AGENT,
+            content_id=version_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata,
+            user_id=None,  # Store agents are public
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
+        return False
+
+
+async def delete_embedding(version_id: str) -> bool:
+    """
+    Delete embedding for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+    """
+    return await delete_content_embedding(ContentType.STORE_AGENT, version_id)
+
+
+async def delete_content_embedding(
+    content_type: ContentType, content_id: str, user_id: str | None = None
+) -> bool:
+    """
+    Delete embedding for any content type.
+
+    New function for unified content embedding deletion.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+
+    Args:
+        content_type: The type of content (STORE_AGENT, LIBRARY_AGENT, etc.)
+        content_id: The unique identifier for the content
+        user_id: Optional user ID. For public content (STORE_AGENT, BLOCK), pass None.
+                 For user-scoped content (LIBRARY_AGENT), pass the user's ID to avoid
+                 deleting embeddings belonging to other users.
+
+    Returns:
+        True if deletion succeeded, False otherwise
+    """
+    try:
+        client = prisma.get_client()
+
+        await execute_raw_with_schema(
+            """
+            DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType"
+              AND "contentId" = $2
+              AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            client=client,
+        )
+
+        user_str = f" (user: {user_id})" if user_id else ""
+        logger.info(f"Deleted embedding for {content_type}:{content_id}{user_str}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to delete embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding_stats() -> dict[str, Any]:
+    """
+    Get statistics about embedding coverage for all content types.
+
+    Returns stats per content type and overall totals.
+    """
+    try:
+        stats_by_type = {}
+        total_items = 0
+        total_with_embeddings = 0
+        total_without_embeddings = 0
+
+        # Aggregate stats from all handlers
+        for content_type, handler in CONTENT_HANDLERS.items():
+            try:
+                stats = await handler.get_stats()
+                stats_by_type[content_type.value] = {
+                    "total": stats["total"],
+                    "with_embeddings": stats["with_embeddings"],
+                    "without_embeddings": stats["without_embeddings"],
+                    "coverage_percent": (
+                        round(stats["with_embeddings"] / stats["total"] * 100, 1)
+                        if stats["total"] > 0
+                        else 0
+                    ),
+                }
+
+                total_items += stats["total"]
+                total_with_embeddings += stats["with_embeddings"]
+                total_without_embeddings += stats["without_embeddings"]
+
+            except Exception as e:
+                logger.error(f"Failed to get stats for {content_type.value}: {e}")
+                stats_by_type[content_type.value] = {
+                    "total": 0,
+                    "with_embeddings": 0,
+                    "without_embeddings": 0,
+                    "coverage_percent": 0,
+                    "error": str(e),
+                }
+
+        return {
+            "by_type": stats_by_type,
+            "totals": {
+                "total": total_items,
+                "with_embeddings": total_with_embeddings,
+                "without_embeddings": total_without_embeddings,
+                "coverage_percent": (
+                    round(total_with_embeddings / total_items * 100, 1)
+                    if total_items > 0
+                    else 0
+                ),
+            },
+        }
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding stats: {e}")
+        return {
+            "by_type": {},
+            "totals": {
+                "total": 0,
+                "with_embeddings": 0,
+                "without_embeddings": 0,
+                "coverage_percent": 0,
+            },
+            "error": str(e),
+        }
+
+
+async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
+    """
+    Generate embeddings for approved listings that don't have them.
+
+    BACKWARD COMPATIBILITY: Maintained for existing usage.
+    This now delegates to backfill_all_content_types() to process all content types.
+
+    Args:
+        batch_size: Number of embeddings to generate per content type
+
+    Returns:
+        Dict with success/failure counts aggregated across all content types
+    """
+    # Delegate to the new generic backfill system
+    result = await backfill_all_content_types(batch_size)
+
+    # Return in the old format for backward compatibility
+    return result["totals"]
+
+
+async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
+    """
+    Generate embeddings for all content types using registered handlers.
+
+    Processes content types in order: BLOCK → STORE_AGENT → DOCUMENTATION.
+    This ensures foundational content (blocks) are searchable first.
+
+    Args:
+        batch_size: Number of embeddings to generate per content type
+
+    Returns:
+        Dict with stats per content type and overall totals
+    """
+    results_by_type = {}
+    total_processed = 0
+    total_success = 0
+    total_failed = 0
+
+    # Process content types in explicit order
+    processing_order = [
+        ContentType.BLOCK,
+        ContentType.STORE_AGENT,
+        ContentType.DOCUMENTATION,
+    ]
+
+    for content_type in processing_order:
+        handler = CONTENT_HANDLERS.get(content_type)
+        if not handler:
+            logger.warning(f"No handler registered for {content_type.value}")
+            continue
+        try:
+            logger.info(f"Processing {content_type.value} content type...")
+
+            # Get missing items from handler
+            missing_items = await handler.get_missing_items(batch_size)
+
+            if not missing_items:
+                results_by_type[content_type.value] = {
+                    "processed": 0,
+                    "success": 0,
+                    "failed": 0,
+                    "message": "No missing embeddings",
+                }
+                continue
+
+            # Process embeddings concurrently for better performance
+            embedding_tasks = [
+                ensure_content_embedding(
+                    content_type=item.content_type,
+                    content_id=item.content_id,
+                    searchable_text=item.searchable_text,
+                    metadata=item.metadata,
+                    user_id=item.user_id,
+                )
+                for item in missing_items
+            ]
+
+            results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
+
+            success = sum(1 for result in results if result is True)
+            failed = len(results) - success
+
+            results_by_type[content_type.value] = {
+                "processed": len(missing_items),
+                "success": success,
+                "failed": failed,
+                "message": f"Backfilled {success} embeddings, {failed} failed",
+            }
+
+            total_processed += len(missing_items)
+            total_success += success
+            total_failed += failed
+
+            logger.info(
+                f"{content_type.value}: processed {len(missing_items)}, "
+                f"success {success}, failed {failed}"
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to process {content_type.value}: {e}")
+            results_by_type[content_type.value] = {
+                "processed": 0,
+                "success": 0,
+                "failed": 0,
+                "error": str(e),
+            }
+
+    return {
+        "by_type": results_by_type,
+        "totals": {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+            "message": f"Overall: {total_success} succeeded, {total_failed} failed",
+        },
+    }
+
+
+async def embed_query(query: str) -> list[float] | None:
+    """
+    Generate embedding for a search query.
+
+    Same as generate_embedding but with clearer intent.
+    """
+    return await generate_embedding(query)
+
+
+def embedding_to_vector_string(embedding: list[float]) -> str:
+    """Convert embedding list to PostgreSQL vector string format."""
+    return "[" + ",".join(str(x) for x in embedding) + "]"
+
+
+async def ensure_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for any content type.
+
+    Generic function for creating embeddings for store agents, blocks, docs, etc.
+
+    Args:
+        content_type: ContentType enum value (STORE_AGENT, BLOCK, etc.)
+        content_id: Unique identifier for the content
+        searchable_text: Combined text for embedding generation
+        metadata: Optional metadata to store with embedding
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_content_embedding(content_type, content_id, user_id)
+            if existing and existing.get("embedding"):
+                logger.debug(
+                    f"Embedding for {content_type}:{content_id} already exists"
+                )
+                return True
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(
+                f"Could not generate embedding for {content_type}:{content_id}"
+            )
+            return False
+
+        # Store the embedding
+        return await store_content_embedding(
+            content_type=content_type,
+            content_id=content_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata or {},
+            user_id=user_id,
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
+        return False

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

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

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

@@ -0,0 +1,387 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import prisma
+import pytest
+from prisma import Prisma
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+
+
+@pytest.fixture(autouse=True)
+async def setup_prisma():
+    """Setup Prisma client for tests."""
+    try:
+        Prisma()
+    except prisma.errors.ClientAlreadyRegisteredError:
+        pass
+    yield
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text():
+    """Test searchable text building from listing fields."""
+    result = embeddings.build_searchable_text(
+        name="AI Assistant",
+        description="A helpful AI assistant for productivity",
+        sub_heading="Boost your productivity",
+        categories=["AI", "Productivity"],
+    )
+
+    expected = "AI Assistant Boost your productivity A helpful AI assistant for productivity AI Productivity"
+    assert result == expected
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text_empty_fields():
+    """Test searchable text building with empty fields."""
+    result = embeddings.build_searchable_text(
+        name="", description="Test description", sub_heading="", categories=[]
+    )
+
+    assert result == "Test description"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_success():
+    """Test successful embedding generation."""
+    # Mock OpenAI response
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1, 0.2, 0.3] * 512  # 1536 dimensions
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is not None
+        assert len(result) == 1536
+        assert result[0] == 0.1
+
+        mock_client.embeddings.create.assert_called_once_with(
+            model="text-embedding-3-small", input="test text"
+        )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_no_api_key():
+    """Test embedding generation without API key."""
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = None
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_api_error():
+    """Test embedding generation with API error."""
+    mock_client = MagicMock()
+    mock_client.embeddings.create = AsyncMock(side_effect=Exception("API Error"))
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_text_truncation():
+    """Test that long text is properly truncated using tiktoken."""
+    from tiktoken import encoding_for_model
+
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1] * 1536
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        # Create text that will exceed 8191 tokens
+        # Use varied characters to ensure token-heavy text: each word is ~1 token
+        words = [f"word{i}" for i in range(10000)]
+        long_text = " ".join(words)  # ~10000 tokens
+
+        await embeddings.generate_embedding(long_text)
+
+        # Verify text was truncated to 8191 tokens
+        call_args = mock_client.embeddings.create.call_args
+        truncated_text = call_args.kwargs["input"]
+
+        # Count actual tokens in truncated text
+        enc = encoding_for_model("text-embedding-3-small")
+        actual_tokens = len(enc.encode(truncated_text))
+
+        # Should be at or just under 8191 tokens
+        assert actual_tokens <= 8191
+        # Should be close to the limit (not over-truncated)
+        assert actual_tokens >= 8100
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_success(mocker):
+    """Test successful embedding storage."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw = mocker.AsyncMock()
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is True
+    # execute_raw is called twice: once for SET search_path, once for INSERT
+    assert mock_client.execute_raw.call_count == 2
+
+    # First call: SET search_path
+    first_call_args = mock_client.execute_raw.call_args_list[0][0]
+    assert "SET search_path" in first_call_args[0]
+
+    # Second call: INSERT query with the actual data
+    second_call_args = mock_client.execute_raw.call_args_list[1][0]
+    assert "test-version-id" in second_call_args
+    assert "[0.1,0.2,0.3]" in second_call_args
+    assert None in second_call_args  # userId should be None for store agents
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_database_error(mocker):
+    """Test embedding storage with database error."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw.side_effect = Exception("Database error")
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is False
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_success():
+    """Test successful embedding retrieval."""
+    mock_result = [
+        {
+            "contentType": "STORE_AGENT",
+            "contentId": "test-version-id",
+            "userId": None,
+            "embedding": "[0.1,0.2,0.3]",
+            "searchableText": "Test text",
+            "metadata": {},
+            "createdAt": "2024-01-01T00:00:00Z",
+            "updatedAt": "2024-01-01T00:00:00Z",
+        }
+    ]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_result,
+    ):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is not None
+        assert result["storeListingVersionId"] == "test-version-id"
+        assert result["embedding"] == "[0.1,0.2,0.3]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_not_found():
+    """Test embedding retrieval when not found."""
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    ):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_already_exists(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding when embedding already exists."""
+    mock_get.return_value = {"embedding": "[0.1,0.2,0.3]"}
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_not_called()
+    mock_store.assert_not_called()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_content_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding creating new embedding."""
+    mock_get.return_value = None
+    mock_generate.return_value = [0.1, 0.2, 0.3]
+    mock_store.return_value = True
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_called_once_with("Test Test heading Test description test")
+    mock_store.assert_called_once_with(
+        content_type=ContentType.STORE_AGENT,
+        content_id="test-id",
+        embedding=[0.1, 0.2, 0.3],
+        searchable_text="Test Test heading Test description test",
+        metadata={"name": "Test", "subHeading": "Test heading", "categories": ["test"]},
+        user_id=None,
+        tx=None,
+    )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
+    """Test ensure_embedding when generation fails."""
+    mock_get.return_value = None
+    mock_generate.return_value = None
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is False
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_stats():
+    """Test embedding statistics retrieval."""
+    # Mock approved count query and embedded count query
+    mock_approved_result = [{"count": 100}]
+    mock_embedded_result = [{"count": 75}]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        side_effect=[mock_approved_result, mock_embedded_result],
+    ):
+        result = await embeddings.get_embedding_stats()
+
+        assert result["total_approved"] == 100
+        assert result["with_embeddings"] == 75
+        assert result["without_embeddings"] == 25
+        assert result["coverage_percent"] == 75.0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.ensure_embedding")
+async def test_backfill_missing_embeddings_success(mock_ensure):
+    """Test backfill with successful embedding generation."""
+    # Mock missing embeddings query
+    mock_missing = [
+        {
+            "id": "version-1",
+            "name": "Agent 1",
+            "description": "Description 1",
+            "subHeading": "Heading 1",
+            "categories": ["AI"],
+        },
+        {
+            "id": "version-2",
+            "name": "Agent 2",
+            "description": "Description 2",
+            "subHeading": "Heading 2",
+            "categories": ["Productivity"],
+        },
+    ]
+
+    # Mock ensure_embedding to succeed for first, fail for second
+    mock_ensure.side_effect = [True, False]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_missing,
+    ):
+        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+
+        assert result["processed"] == 2
+        assert result["success"] == 1
+        assert result["failed"] == 1
+        assert mock_ensure.call_count == 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_backfill_missing_embeddings_no_missing():
+    """Test backfill when no embeddings are missing."""
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    ):
+        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+
+        assert result["processed"] == 0
+        assert result["success"] == 0
+        assert result["failed"] == 0
+        assert result["message"] == "No missing embeddings"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_embedding_to_vector_string():
+    """Test embedding to PostgreSQL vector string conversion."""
+    embedding = [0.1, 0.2, 0.3, -0.4]
+    result = embeddings.embedding_to_vector_string(embedding)
+    assert result == "[0.1,0.2,0.3,-0.4]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_embed_query():
+    """Test embed_query function (alias for generate_embedding)."""
+    with patch(
+        "backend.api.features.store.embeddings.generate_embedding"
+    ) as mock_generate:
+        mock_generate.return_value = [0.1, 0.2, 0.3]
+
+        result = await embeddings.embed_query("test query")
+
+        assert result == [0.1, 0.2, 0.3]
+        mock_generate.assert_called_once_with("test query")

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

@@ -0,0 +1,393 @@
+"""
+Hybrid Search for Store Agents
+
+Combines semantic (embedding) search with lexical (tsvector) search
+for improved relevance in marketplace agent discovery.
+"""
+
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Literal
+
+from backend.api.features.store.embeddings import (
+    embed_query,
+    embedding_to_vector_string,
+)
+from backend.data.db import query_raw_with_schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HybridSearchWeights:
+    """Weights for combining search signals."""
+
+    semantic: float = 0.30  # Embedding cosine similarity
+    lexical: float = 0.30  # tsvector ts_rank_cd score
+    category: float = 0.20  # Category match boost
+    recency: float = 0.10  # Newer agents ranked higher
+    popularity: float = 0.10  # Agent usage/runs (PageRank-like)
+
+    def __post_init__(self):
+        """Validate weights are non-negative and sum to approximately 1.0."""
+        total = (
+            self.semantic
+            + self.lexical
+            + self.category
+            + self.recency
+            + self.popularity
+        )
+
+        if any(
+            w < 0
+            for w in [
+                self.semantic,
+                self.lexical,
+                self.category,
+                self.recency,
+                self.popularity,
+            ]
+        ):
+            raise ValueError("All weights must be non-negative")
+
+        if not (0.99 <= total <= 1.01):
+            raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
+
+
+DEFAULT_WEIGHTS = HybridSearchWeights()
+
+# Minimum relevance score threshold - agents below this are filtered out
+# With weights (0.30 semantic + 0.30 lexical + 0.20 category + 0.10 recency + 0.10 popularity):
+# - 0.20 means at least ~60% semantic match OR strong lexical match required
+# - Ensures only genuinely relevant results are returned
+# - Recency/popularity alone (0.10 each) won't pass the threshold
+DEFAULT_MIN_SCORE = 0.20
+
+
+@dataclass
+class HybridSearchResult:
+    """A single search result with score breakdown."""
+
+    slug: str
+    agent_name: str
+    agent_image: str
+    creator_username: str
+    creator_avatar: str
+    sub_heading: str
+    description: str
+    runs: int
+    rating: float
+    categories: list[str]
+    featured: bool
+    is_available: bool
+    updated_at: datetime
+
+    # Score breakdown (for debugging/tuning)
+    combined_score: float
+    semantic_score: float = 0.0
+    lexical_score: float = 0.0
+    category_score: float = 0.0
+    recency_score: float = 0.0
+    popularity_score: float = 0.0
+
+
+async def hybrid_search(
+    query: str,
+    featured: bool = False,
+    creators: list[str] | None = None,
+    category: str | None = None,
+    sorted_by: (
+        Literal["relevance", "rating", "runs", "name", "updated_at"] | None
+    ) = None,
+    page: int = 1,
+    page_size: int = 20,
+    weights: HybridSearchWeights | None = None,
+    min_score: float | None = None,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Perform hybrid search combining semantic and lexical signals.
+
+    Args:
+        query: Search query string
+        featured: Filter for featured agents only
+        creators: Filter by creator usernames
+        category: Filter by category
+        sorted_by: Sort order (relevance uses hybrid scoring)
+        page: Page number (1-indexed)
+        page_size: Results per page
+        weights: Custom weights for search signals
+        min_score: Minimum relevance score threshold (0-1). Results below
+                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
+
+    Returns:
+        Tuple of (results list, total count). Returns empty list if no
+        results meet the minimum relevance threshold.
+    """
+    # Validate inputs
+    query = query.strip()
+    if not query:
+        return [], 0  # Empty query returns no results
+
+    if page < 1:
+        page = 1
+    if page_size < 1:
+        page_size = 1
+    if page_size > 100:  # Cap at reasonable limit to prevent performance issues
+        page_size = 100
+
+    if weights is None:
+        weights = DEFAULT_WEIGHTS
+    if min_score is None:
+        min_score = DEFAULT_MIN_SCORE
+
+    offset = (page - 1) * page_size
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Build WHERE clause conditions
+    where_parts: list[str] = ["sa.is_available = true"]
+    params: list[Any] = []
+    param_index = 1
+
+    # Add search query for lexical matching
+    params.append(query)
+    query_param = f"${param_index}"
+    param_index += 1
+
+    # Add lowercased query for category matching
+    params.append(query.lower())
+    query_lower_param = f"${param_index}"
+    param_index += 1
+
+    if featured:
+        where_parts.append("sa.featured = true")
+
+    if creators:
+        where_parts.append(f"sa.creator_username = ANY(${param_index})")
+        params.append(creators)
+        param_index += 1
+
+    if category:
+        where_parts.append(f"${param_index} = ANY(sa.categories)")
+        params.append(category)
+        param_index += 1
+
+    # Safe: where_parts only contains hardcoded strings with $N parameter placeholders
+    # No user input is concatenated directly into the SQL string
+    where_clause = " AND ".join(where_parts)
+
+    # Embedding is required for hybrid search - fail fast if unavailable
+    if query_embedding is None or not query_embedding:
+        # Log detailed error server-side
+        logger.error(
+            "Failed to generate query embedding. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+        )
+        # Raise generic error to client
+        raise ValueError("Search service temporarily unavailable")
+
+    # Add embedding parameter
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_index}"
+    param_index += 1
+
+    # Add weight parameters for SQL calculation
+    params.append(weights.semantic)
+    weight_semantic_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.lexical)
+    weight_lexical_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.category)
+    weight_category_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.recency)
+    weight_recency_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.popularity)
+    weight_popularity_param = f"${param_index}"
+    param_index += 1
+
+    # Add min_score parameter
+    params.append(min_score)
+    min_score_param = f"${param_index}"
+    param_index += 1
+
+    # Optimized hybrid search query:
+    # 1. Direct join to UnifiedContentEmbedding via contentId=storeListingVersionId (no redundant JOINs)
+    # 2. UNION approach (deduplicates agents matching both branches)
+    # 3. COUNT(*) OVER() to get total count in single query
+    # 4. Optimized category matching with EXISTS + unnest
+    # 5. Pre-calculated max values for lexical and popularity normalization
+    # 6. Simplified recency calculation with linear decay
+    # 7. Logarithmic popularity scaling to prevent viral agents from dominating
+    sql_query = f"""
+            WITH candidates AS (
+                -- Lexical matches (uses GIN index on search column)
+                SELECT sa."storeListingVersionId"
+                FROM {{schema_prefix}}"StoreAgent" sa
+                WHERE {where_clause}
+                AND sa.search @@ plainto_tsquery('english', {query_param})
+
+                UNION
+
+                -- Semantic matches (uses HNSW index on embedding with KNN)
+                SELECT "storeListingVersionId"
+                FROM (
+                    SELECT sa."storeListingVersionId", uce.embedding
+                    FROM {{schema_prefix}}"StoreAgent" sa
+                    INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                        ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+                    WHERE {where_clause}
+                    ORDER BY uce.embedding <=> {embedding_param}::vector
+                    LIMIT 200
+                ) semantic_results
+            ),
+            search_scores AS (
+                SELECT
+                    sa.slug,
+                    sa.agent_name,
+                    sa.agent_image,
+                    sa.creator_username,
+                    sa.creator_avatar,
+                    sa.sub_heading,
+                    sa.description,
+                    sa.runs,
+                    sa.rating,
+                    sa.categories,
+                    sa.featured,
+                    sa.is_available,
+                    sa.updated_at,
+                    -- Semantic score: cosine similarity (1 - distance)
+                    COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                    -- Lexical score: ts_rank_cd (will be normalized later)
+                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                    -- Category match: optimized with unnest for better performance
+                    CASE
+                        WHEN EXISTS (
+                            SELECT 1 FROM unnest(sa.categories) cat
+                            WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                        )
+                        THEN 1.0
+                        ELSE 0.0
+                    END as category_score,
+                    -- Recency score: linear decay over 90 days (simpler than exponential)
+                    GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
+                    -- Popularity raw: agent runs count (will be normalized with log scaling)
+                    sa.runs as popularity_raw
+                FROM candidates c
+                INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                    ON c."storeListingVersionId" = sa."storeListingVersionId"
+                LEFT JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+            ),
+            max_lexical AS (
+                SELECT MAX(lexical_raw) as max_val FROM search_scores
+            ),
+            max_popularity AS (
+                SELECT MAX(popularity_raw) as max_val FROM search_scores
+            ),
+            normalized AS (
+                SELECT
+                    ss.*,
+                    -- Normalize lexical score by pre-calculated max
+                    CASE
+                        WHEN ml.max_val > 0
+                        THEN ss.lexical_raw / ml.max_val
+                        ELSE 0
+                    END as lexical_score,
+                    -- Normalize popularity with logarithmic scaling to prevent viral agents from dominating
+                    -- LOG(1 + runs) / LOG(1 + max_runs) ensures score is 0-1 range
+                    CASE
+                        WHEN mp.max_val > 0 AND ss.popularity_raw > 0
+                        THEN LN(1 + ss.popularity_raw) / LN(1 + mp.max_val)
+                        ELSE 0
+                    END as popularity_score
+                FROM search_scores ss
+                CROSS JOIN max_lexical ml
+                CROSS JOIN max_popularity mp
+            ),
+            scored AS (
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    semantic_score,
+                    lexical_score,
+                    category_score,
+                    recency_score,
+                    popularity_score,
+                    (
+                        {weight_semantic_param} * semantic_score +
+                        {weight_lexical_param} * lexical_score +
+                        {weight_category_param} * category_score +
+                        {weight_recency_param} * recency_score +
+                        {weight_popularity_param} * popularity_score
+                    ) as combined_score
+                FROM normalized
+            ),
+            filtered AS (
+                SELECT
+                    *,
+                    COUNT(*) OVER () as total_count
+                FROM scored
+                WHERE combined_score >= {min_score_param}
+            )
+            SELECT * FROM filtered
+            ORDER BY combined_score DESC
+            LIMIT ${param_index} OFFSET ${param_index + 1}
+    """
+
+    # Add pagination params
+    params.extend([page_size, offset])
+
+    # Execute search query - includes total_count via window function
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
+
+    # Extract total count from first result (all rows have same count)
+    total = results[0]["total_count"] if results else 0
+
+    # Remove total_count from results before returning
+    for result in results:
+        result.pop("total_count", None)
+
+    # Log without sensitive query content
+    logger.info(f"Hybrid search: {len(results)} results, {total} total")
+
+    return results, total
+
+
+async def hybrid_search_simple(
+    query: str,
+    page: int = 1,
+    page_size: int = 20,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Simplified hybrid search for common use cases.
+
+    Uses default weights and no filters.
+    """
+    return await hybrid_search(
+        query=query,
+        page=page,
+        page_size=page_size,
+    )

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

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

autogpt_platform/backend/backend/data/db.py

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

autogpt_platform/backend/backend/data/graph_test.py

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

autogpt_platform/backend/backend/data/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

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

autogpt_platform/backend/backend/executor/manager_test.py

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

autogpt_platform/backend/backend/executor/scheduler.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import os
 import threading
+import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -37,7 +38,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_scheduler_client
+from backend.util.clients import get_database_manager_client, get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -254,6 +255,88 @@ def execution_accuracy_alerts():
     return report_execution_accuracy_alerts()
 
 
+def ensure_embeddings_coverage():
+    """
+    Ensure all content types (store agents, blocks, docs) have embeddings for search.
+
+    Processes ALL missing embeddings in batches of 10 per content type until 100% coverage.
+    Missing embeddings = content invisible in hybrid search.
+
+    Schedule: Runs every 6 hours (balanced between coverage and API costs).
+    - Catches new content added between scheduled runs
+    - Batch size 10 per content type: gradual processing to avoid rate limits
+    - Manual trigger available via execute_ensure_embeddings_coverage endpoint
+    """
+    db_client = get_database_manager_client()
+    stats = db_client.get_embedding_stats()
+
+    # Check for error from get_embedding_stats() first
+    if "error" in stats:
+        logger.error(
+            f"Failed to get embedding stats: {stats['error']} - skipping backfill"
+        )
+        return {"processed": 0, "success": 0, "failed": 0, "error": stats["error"]}
+
+    # Extract totals from new stats structure
+    totals = stats.get("totals", {})
+    without_embeddings = totals.get("without_embeddings", 0)
+    coverage_percent = totals.get("coverage_percent", 0)
+
+    if without_embeddings == 0:
+        logger.info("All content has embeddings, skipping backfill")
+        return {"processed": 0, "success": 0, "failed": 0}
+
+    # Log per-content-type stats for visibility
+    by_type = stats.get("by_type", {})
+    for content_type, type_stats in by_type.items():
+        if type_stats.get("without_embeddings", 0) > 0:
+            logger.info(
+                f"{content_type}: {type_stats['without_embeddings']} items without embeddings "
+                f"({type_stats['coverage_percent']}% coverage)"
+            )
+
+    logger.info(
+        f"Total: {without_embeddings} items without embeddings "
+        f"({coverage_percent}% coverage) - processing all"
+    )
+
+    total_processed = 0
+    total_success = 0
+    total_failed = 0
+
+    # Process in batches until no more missing embeddings
+    while True:
+        result = db_client.backfill_missing_embeddings(batch_size=10)
+
+        total_processed += result["processed"]
+        total_success += result["success"]
+        total_failed += result["failed"]
+
+        if result["processed"] == 0:
+            # No more missing embeddings
+            break
+
+        if result["success"] == 0 and result["processed"] > 0:
+            # All attempts in this batch failed - stop to avoid infinite loop
+            logger.error(
+                f"All {result['processed']} embedding attempts failed - stopping backfill"
+            )
+            break
+
+        # Small delay between batches to avoid rate limits
+        time.sleep(1)
+
+    logger.info(
+        f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
+        f"{total_failed} failed"
+    )
+    return {
+        "processed": total_processed,
+        "success": total_success,
+        "failed": total_failed,
+    }
+
+
 # Monitoring functions are now imported from monitoring module
 
 
@@ -475,6 +558,19 @@ class Scheduler(AppService):
                 jobstore=Jobstores.EXECUTION.value,
             )
 
+            # Embedding Coverage - Every 6 hours
+            # Ensures all approved agents have embeddings for hybrid search
+            # Critical: missing embeddings = agents invisible in search
+            self.scheduler.add_job(
+                ensure_embeddings_coverage,
+                id="ensure_embeddings_coverage",
+                trigger="interval",
+                hours=6,
+                replace_existing=True,
+                max_instances=1,  # Prevent overlapping runs
+                jobstore=Jobstores.EXECUTION.value,
+            )
+
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.add_listener(job_missed_listener, EVENT_JOB_MISSED)
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
@@ -632,6 +728,11 @@ class Scheduler(AppService):
         """Manually trigger execution accuracy alert checking."""
         return execution_accuracy_alerts()
 
+    @expose
+    def execute_ensure_embeddings_coverage(self):
+        """Manually trigger embedding backfill for approved store agents."""
+        return ensure_embeddings_coverage()
+
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/util/clients.py

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

autogpt_platform/backend/backend/util/settings.py

@@ -658,14 +658,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
 
     ayrshare_api_key: str = Field(default="", description="Ayrshare API Key")
     ayrshare_jwt_key: str = Field(default="", description="Ayrshare private Key")
-
-    # Langfuse prompt management
-    langfuse_public_key: str = Field(default="", description="Langfuse public key")
-    langfuse_secret_key: str = Field(default="", description="Langfuse secret key")
-    langfuse_host: str = Field(
-        default="https://cloud.langfuse.com", description="Langfuse host URL"
-    )
-
     # Add more secret fields as needed
     model_config = SettingsConfigDict(
         env_file=".env",

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

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

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

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

autogpt_platform/backend/migrations/20260114084601_add_chat_tables/migration.sql

@@ -1,64 +0,0 @@
--- DropIndex
-DROP INDEX "StoreListingVersion_storeListingId_version_key";
-
--- CreateTable
-CREATE TABLE "CoPilotUnderstanding" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT NOT NULL,
-    "data" JSONB,
-
-    CONSTRAINT "CoPilotUnderstanding_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatSession" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "userId" TEXT,
-    "title" TEXT,
-    "credentials" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentRuns" JSONB NOT NULL DEFAULT '{}',
-    "successfulAgentSchedules" JSONB NOT NULL DEFAULT '{}',
-    "totalPromptTokens" INTEGER NOT NULL DEFAULT 0,
-    "totalCompletionTokens" INTEGER NOT NULL DEFAULT 0,
-
-    CONSTRAINT "ChatSession_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "ChatMessage" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "sessionId" TEXT NOT NULL,
-    "role" TEXT NOT NULL,
-    "content" TEXT,
-    "name" TEXT,
-    "toolCallId" TEXT,
-    "refusal" TEXT,
-    "toolCalls" JSONB,
-    "functionCall" JSONB,
-    "sequence" INTEGER NOT NULL,
-
-    CONSTRAINT "ChatMessage_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "CoPilotUnderstanding_userId_key" ON "CoPilotUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "CoPilotUnderstanding_userId_idx" ON "CoPilotUnderstanding"("userId");
-
--- CreateIndex
-CREATE INDEX "ChatSession_userId_updatedAt_idx" ON "ChatSession"("userId", "updatedAt");
-
--- CreateIndex
-CREATE UNIQUE INDEX "ChatMessage_sessionId_sequence_key" ON "ChatMessage"("sessionId", "sequence");
-
--- AddForeignKey
-ALTER TABLE "CoPilotUnderstanding" ADD CONSTRAINT "CoPilotUnderstanding_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "ChatMessage" ADD CONSTRAINT "ChatMessage_sessionId_fkey" FOREIGN KEY ("sessionId") REFERENCES "ChatSession"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/poetry.lock

@@ -2777,30 +2777,6 @@ enabler = ["pytest-enabler (>=2.2)"]
 test = ["pyfakefs", "pytest (>=6,!=8.1.*)"]
 type = ["pygobject-stubs", "pytest-mypy", "shtab", "types-pywin32"]
 
-[[package]]
-name = "langfuse"
-version = "3.11.2"
-description = "A client library for accessing langfuse"
-optional = false
-python-versions = "<4.0,>=3.10"
-groups = ["main"]
-files = [
-    {file = "langfuse-3.11.2-py3-none-any.whl", hash = "sha256:84faea9f909694023cc7f0eb45696be190248c8790424f22af57ca4cd7a29f2d"},
-    {file = "langfuse-3.11.2.tar.gz", hash = "sha256:ab5f296a8056815b7288c7f25bc308a5e79f82a8634467b25daffdde99276e09"},
-]
-
-[package.dependencies]
-backoff = ">=1.10.0"
-httpx = ">=0.15.4,<1.0"
-openai = ">=0.27.8"
-opentelemetry-api = ">=1.33.1,<2.0.0"
-opentelemetry-exporter-otlp-proto-http = ">=1.33.1,<2.0.0"
-opentelemetry-sdk = ">=1.33.1,<2.0.0"
-packaging = ">=23.2,<26.0"
-pydantic = ">=1.10.7,<3.0"
-requests = ">=2,<3"
-wrapt = ">=1.14,<2.0"
-
 [[package]]
 name = "launchdarkly-eventsource"
 version = "1.3.0"
@@ -3492,90 +3468,6 @@ files = [
 importlib-metadata = ">=6.0,<8.8.0"
 typing-extensions = ">=4.5.0"
 
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.35.0"
-description = "OpenTelemetry Protobuf encoding"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.35.0-py3-none-any.whl", hash = "sha256:863465de697ae81279ede660f3918680b4480ef5f69dcdac04f30722ed7b74cc"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.35.0.tar.gz", hash = "sha256:6f6d8c39f629b9fa5c79ce19a2829dbd93034f8ac51243cdf40ed2196f00d7eb"},
-]
-
-[package.dependencies]
-opentelemetry-proto = "1.35.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.35.0"
-description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_http-1.35.0-py3-none-any.whl", hash = "sha256:9a001e3df3c7f160fb31056a28ed7faa2de7df68877ae909516102ae36a54e1d"},
-    {file = "opentelemetry_exporter_otlp_proto_http-1.35.0.tar.gz", hash = "sha256:cf940147f91b450ef5f66e9980d40eb187582eed399fa851f4a7a45bb880de79"},
-]
-
-[package.dependencies]
-googleapis-common-protos = ">=1.52,<2.0"
-opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.35.0"
-opentelemetry-proto = "1.35.0"
-opentelemetry-sdk = ">=1.35.0,<1.36.0"
-requests = ">=2.7,<3.0"
-typing-extensions = ">=4.5.0"
-
-[[package]]
-name = "opentelemetry-proto"
-version = "1.35.0"
-description = "OpenTelemetry Python Proto"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_proto-1.35.0-py3-none-any.whl", hash = "sha256:98fffa803164499f562718384e703be8d7dfbe680192279a0429cb150a2f8809"},
-    {file = "opentelemetry_proto-1.35.0.tar.gz", hash = "sha256:532497341bd3e1c074def7c5b00172601b28bb83b48afc41a4b779f26eb4ee05"},
-]
-
-[package.dependencies]
-protobuf = ">=5.0,<7.0"
-
-[[package]]
-name = "opentelemetry-sdk"
-version = "1.35.0"
-description = "OpenTelemetry Python SDK"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_sdk-1.35.0-py3-none-any.whl", hash = "sha256:223d9e5f5678518f4842311bb73966e0b6db5d1e0b74e35074c052cd2487f800"},
-    {file = "opentelemetry_sdk-1.35.0.tar.gz", hash = "sha256:2a400b415ab68aaa6f04e8a6a9f6552908fb3090ae2ff78d6ae0c597ac581954"},
-]
-
-[package.dependencies]
-opentelemetry-api = "1.35.0"
-opentelemetry-semantic-conventions = "0.56b0"
-typing-extensions = ">=4.5.0"
-
-[[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.56b0"
-description = "OpenTelemetry Semantic Conventions"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "opentelemetry_semantic_conventions-0.56b0-py3-none-any.whl", hash = "sha256:df44492868fd6b482511cc43a942e7194be64e94945f572db24df2e279a001a2"},
-    {file = "opentelemetry_semantic_conventions-0.56b0.tar.gz", hash = "sha256:c114c2eacc8ff6d3908cb328c811eaf64e6d68623840be9224dc829c4fd6c2ea"},
-]
-
-[package.dependencies]
-opentelemetry-api = "1.35.0"
-typing-extensions = ">=4.5.0"
-
 [[package]]
 name = "orjson"
 version = "3.11.3"
@@ -7030,97 +6922,6 @@ files = [
     {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
 ]
 
-[[package]]
-name = "wrapt"
-version = "1.17.3"
-description = "Module for decorators, wrappers and monkey patching."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-files = [
-    {file = "wrapt-1.17.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88bbae4d40d5a46142e70d58bf664a89b6b4befaea7b2ecc14e03cedb8e06c04"},
-    {file = "wrapt-1.17.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6b13af258d6a9ad602d57d889f83b9d5543acd471eee12eb51f5b01f8eb1bc2"},
-    {file = "wrapt-1.17.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd341868a4b6714a5962c1af0bd44f7c404ef78720c7de4892901e540417111c"},
-    {file = "wrapt-1.17.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:f9b2601381be482f70e5d1051a5965c25fb3625455a2bf520b5a077b22afb775"},
-    {file = "wrapt-1.17.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:343e44b2a8e60e06a7e0d29c1671a0d9951f59174f3709962b5143f60a2a98bd"},
-    {file = "wrapt-1.17.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:33486899acd2d7d3066156b03465b949da3fd41a5da6e394ec49d271baefcf05"},
-    {file = "wrapt-1.17.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:e6f40a8aa5a92f150bdb3e1c44b7e98fb7113955b2e5394122fa5532fec4b418"},
-    {file = "wrapt-1.17.3-cp310-cp310-win32.whl", hash = "sha256:a36692b8491d30a8c75f1dfee65bef119d6f39ea84ee04d9f9311f83c5ad9390"},
-    {file = "wrapt-1.17.3-cp310-cp310-win_amd64.whl", hash = "sha256:afd964fd43b10c12213574db492cb8f73b2f0826c8df07a68288f8f19af2ebe6"},
-    {file = "wrapt-1.17.3-cp310-cp310-win_arm64.whl", hash = "sha256:af338aa93554be859173c39c85243970dc6a289fa907402289eeae7543e1ae18"},
-    {file = "wrapt-1.17.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:273a736c4645e63ac582c60a56b0acb529ef07f78e08dc6bfadf6a46b19c0da7"},
-    {file = "wrapt-1.17.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5531d911795e3f935a9c23eb1c8c03c211661a5060aab167065896bbf62a5f85"},
-    {file = "wrapt-1.17.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0610b46293c59a3adbae3dee552b648b984176f8562ee0dba099a56cfbe4df1f"},
-    {file = "wrapt-1.17.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b32888aad8b6e68f83a8fdccbf3165f5469702a7544472bdf41f582970ed3311"},
-    {file = "wrapt-1.17.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cccf4f81371f257440c88faed6b74f1053eef90807b77e31ca057b2db74edb1"},
-    {file = "wrapt-1.17.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d8a210b158a34164de8bb68b0e7780041a903d7b00c87e906fb69928bf7890d5"},
-    {file = "wrapt-1.17.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:79573c24a46ce11aab457b472efd8d125e5a51da2d1d24387666cd85f54c05b2"},
-    {file = "wrapt-1.17.3-cp311-cp311-win32.whl", hash = "sha256:c31eebe420a9a5d2887b13000b043ff6ca27c452a9a22fa71f35f118e8d4bf89"},
-    {file = "wrapt-1.17.3-cp311-cp311-win_amd64.whl", hash = "sha256:0b1831115c97f0663cb77aa27d381237e73ad4f721391a9bfb2fe8bc25fa6e77"},
-    {file = "wrapt-1.17.3-cp311-cp311-win_arm64.whl", hash = "sha256:5a7b3c1ee8265eb4c8f1b7d29943f195c00673f5ab60c192eba2d4a7eae5f46a"},
-    {file = "wrapt-1.17.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ab232e7fdb44cdfbf55fc3afa31bcdb0d8980b9b95c38b6405df2acb672af0e0"},
-    {file = "wrapt-1.17.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:9baa544e6acc91130e926e8c802a17f3b16fbea0fd441b5a60f5cf2cc5c3deba"},
-    {file = "wrapt-1.17.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6b538e31eca1a7ea4605e44f81a48aa24c4632a277431a6ed3f328835901f4fd"},
-    {file = "wrapt-1.17.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:042ec3bb8f319c147b1301f2393bc19dba6e176b7da446853406d041c36c7828"},
-    {file = "wrapt-1.17.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3af60380ba0b7b5aeb329bc4e402acd25bd877e98b3727b0135cb5c2efdaefe9"},
-    {file = "wrapt-1.17.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0b02e424deef65c9f7326d8c19220a2c9040c51dc165cddb732f16198c168396"},
-    {file = "wrapt-1.17.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:74afa28374a3c3a11b3b5e5fca0ae03bef8450d6aa3ab3a1e2c30e3a75d023dc"},
-    {file = "wrapt-1.17.3-cp312-cp312-win32.whl", hash = "sha256:4da9f45279fff3543c371d5ababc57a0384f70be244de7759c85a7f989cb4ebe"},
-    {file = "wrapt-1.17.3-cp312-cp312-win_amd64.whl", hash = "sha256:e71d5c6ebac14875668a1e90baf2ea0ef5b7ac7918355850c0908ae82bcb297c"},
-    {file = "wrapt-1.17.3-cp312-cp312-win_arm64.whl", hash = "sha256:604d076c55e2fdd4c1c03d06dc1a31b95130010517b5019db15365ec4a405fc6"},
-    {file = "wrapt-1.17.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a47681378a0439215912ef542c45a783484d4dd82bac412b71e59cf9c0e1cea0"},
-    {file = "wrapt-1.17.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a30837587c6ee3cd1a4d1c2ec5d24e77984d44e2f34547e2323ddb4e22eb77"},
-    {file = "wrapt-1.17.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:16ecf15d6af39246fe33e507105d67e4b81d8f8d2c6598ff7e3ca1b8a37213f7"},
-    {file = "wrapt-1.17.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6fd1ad24dc235e4ab88cda009e19bf347aabb975e44fd5c2fb22a3f6e4141277"},
-    {file = "wrapt-1.17.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ed61b7c2d49cee3c027372df5809a59d60cf1b6c2f81ee980a091f3afed6a2d"},
-    {file = "wrapt-1.17.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:423ed5420ad5f5529db9ce89eac09c8a2f97da18eb1c870237e84c5a5c2d60aa"},
-    {file = "wrapt-1.17.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e01375f275f010fcbf7f643b4279896d04e571889b8a5b3f848423d91bf07050"},
-    {file = "wrapt-1.17.3-cp313-cp313-win32.whl", hash = "sha256:53e5e39ff71b3fc484df8a522c933ea2b7cdd0d5d15ae82e5b23fde87d44cbd8"},
-    {file = "wrapt-1.17.3-cp313-cp313-win_amd64.whl", hash = "sha256:1f0b2f40cf341ee8cc1a97d51ff50dddb9fcc73241b9143ec74b30fc4f44f6cb"},
-    {file = "wrapt-1.17.3-cp313-cp313-win_arm64.whl", hash = "sha256:7425ac3c54430f5fc5e7b6f41d41e704db073309acfc09305816bc6a0b26bb16"},
-    {file = "wrapt-1.17.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cf30f6e3c077c8e6a9a7809c94551203c8843e74ba0c960f4a98cd80d4665d39"},
-    {file = "wrapt-1.17.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:e228514a06843cae89621384cfe3a80418f3c04aadf8a3b14e46a7be704e4235"},
-    {file = "wrapt-1.17.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:5ea5eb3c0c071862997d6f3e02af1d055f381b1d25b286b9d6644b79db77657c"},
-    {file = "wrapt-1.17.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:281262213373b6d5e4bb4353bc36d1ba4084e6d6b5d242863721ef2bf2c2930b"},
-    {file = "wrapt-1.17.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc4a8d2b25efb6681ecacad42fca8859f88092d8732b170de6a5dddd80a1c8fa"},
-    {file = "wrapt-1.17.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:373342dd05b1d07d752cecbec0c41817231f29f3a89aa8b8843f7b95992ed0c7"},
-    {file = "wrapt-1.17.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d40770d7c0fd5cbed9d84b2c3f2e156431a12c9a37dc6284060fb4bec0b7ffd4"},
-    {file = "wrapt-1.17.3-cp314-cp314-win32.whl", hash = "sha256:fbd3c8319de8e1dc79d346929cd71d523622da527cca14e0c1d257e31c2b8b10"},
-    {file = "wrapt-1.17.3-cp314-cp314-win_amd64.whl", hash = "sha256:e1a4120ae5705f673727d3253de3ed0e016f7cd78dc463db1b31e2463e1f3cf6"},
-    {file = "wrapt-1.17.3-cp314-cp314-win_arm64.whl", hash = "sha256:507553480670cab08a800b9463bdb881b2edeed77dc677b0a5915e6106e91a58"},
-    {file = "wrapt-1.17.3-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:ed7c635ae45cfbc1a7371f708727bf74690daedc49b4dba310590ca0bd28aa8a"},
-    {file = "wrapt-1.17.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:249f88ed15503f6492a71f01442abddd73856a0032ae860de6d75ca62eed8067"},
-    {file = "wrapt-1.17.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5a03a38adec8066d5a37bea22f2ba6bbf39fcdefbe2d91419ab864c3fb515454"},
-    {file = "wrapt-1.17.3-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5d4478d72eb61c36e5b446e375bbc49ed002430d17cdec3cecb36993398e1a9e"},
-    {file = "wrapt-1.17.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:223db574bb38637e8230eb14b185565023ab624474df94d2af18f1cdb625216f"},
-    {file = "wrapt-1.17.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e405adefb53a435f01efa7ccdec012c016b5a1d3f35459990afc39b6be4d5056"},
-    {file = "wrapt-1.17.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:88547535b787a6c9ce4086917b6e1d291aa8ed914fdd3a838b3539dc95c12804"},
-    {file = "wrapt-1.17.3-cp314-cp314t-win32.whl", hash = "sha256:41b1d2bc74c2cac6f9074df52b2efbef2b30bdfe5f40cb78f8ca22963bc62977"},
-    {file = "wrapt-1.17.3-cp314-cp314t-win_amd64.whl", hash = "sha256:73d496de46cd2cdbdbcce4ae4bcdb4afb6a11234a1df9c085249d55166b95116"},
-    {file = "wrapt-1.17.3-cp314-cp314t-win_arm64.whl", hash = "sha256:f38e60678850c42461d4202739f9bf1e3a737c7ad283638251e79cc49effb6b6"},
-    {file = "wrapt-1.17.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:70d86fa5197b8947a2fa70260b48e400bf2ccacdcab97bb7de47e3d1e6312225"},
-    {file = "wrapt-1.17.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:df7d30371a2accfe4013e90445f6388c570f103d61019b6b7c57e0265250072a"},
-    {file = "wrapt-1.17.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:caea3e9c79d5f0d2c6d9ab96111601797ea5da8e6d0723f77eabb0d4068d2b2f"},
-    {file = "wrapt-1.17.3-cp38-cp38-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:758895b01d546812d1f42204bd443b8c433c44d090248bf22689df673ccafe00"},
-    {file = "wrapt-1.17.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:02b551d101f31694fc785e58e0720ef7d9a10c4e62c1c9358ce6f63f23e30a56"},
-    {file = "wrapt-1.17.3-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:656873859b3b50eeebe6db8b1455e99d90c26ab058db8e427046dbc35c3140a5"},
-    {file = "wrapt-1.17.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:a9a2203361a6e6404f80b99234fe7fb37d1fc73487b5a78dc1aa5b97201e0f22"},
-    {file = "wrapt-1.17.3-cp38-cp38-win32.whl", hash = "sha256:55cbbc356c2842f39bcc553cf695932e8b30e30e797f961860afb308e6b1bb7c"},
-    {file = "wrapt-1.17.3-cp38-cp38-win_amd64.whl", hash = "sha256:ad85e269fe54d506b240d2d7b9f5f2057c2aa9a2ea5b32c66f8902f768117ed2"},
-    {file = "wrapt-1.17.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:30ce38e66630599e1193798285706903110d4f057aab3168a34b7fdc85569afc"},
-    {file = "wrapt-1.17.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:65d1d00fbfb3ea5f20add88bbc0f815150dbbde3b026e6c24759466c8b5a9ef9"},
-    {file = "wrapt-1.17.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a7c06742645f914f26c7f1fa47b8bc4c91d222f76ee20116c43d5ef0912bba2d"},
-    {file = "wrapt-1.17.3-cp39-cp39-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:7e18f01b0c3e4a07fe6dfdb00e29049ba17eadbc5e7609a2a3a4af83ab7d710a"},
-    {file = "wrapt-1.17.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f5f51a6466667a5a356e6381d362d259125b57f059103dd9fdc8c0cf1d14139"},
-    {file = "wrapt-1.17.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:59923aa12d0157f6b82d686c3fd8e1166fa8cdfb3e17b42ce3b6147ff81528df"},
-    {file = "wrapt-1.17.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:46acc57b331e0b3bcb3e1ca3b421d65637915cfcd65eb783cb2f78a511193f9b"},
-    {file = "wrapt-1.17.3-cp39-cp39-win32.whl", hash = "sha256:3e62d15d3cfa26e3d0788094de7b64efa75f3a53875cdbccdf78547aed547a81"},
-    {file = "wrapt-1.17.3-cp39-cp39-win_amd64.whl", hash = "sha256:1f23fa283f51c890eda8e34e4937079114c74b4c81d2b2f1f1d94948f5cc3d7f"},
-    {file = "wrapt-1.17.3-cp39-cp39-win_arm64.whl", hash = "sha256:24c2ed34dc222ed754247a2702b1e1e89fdbaa4016f324b4b8f1a802d4ffe87f"},
-    {file = "wrapt-1.17.3-py3-none-any.whl", hash = "sha256:7171ae35d2c33d326ac19dd8facb1e82e5fd04ef8c6c0e394d7af55a55051c22"},
-    {file = "wrapt-1.17.3.tar.gz", hash = "sha256:f66eb08feaa410fe4eebd17f2a2c8e2e46d3476e9f8c783daa8e09e0faa666d0"},
-]
-
 [[package]]
 name = "xattr"
 version = "1.2.0"
@@ -7494,4 +7295,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "86838b5ae40d606d6e01a14dad8a56c389d890d7a6a0c274a6602cca80f0df84"
+content-hash = "a93ba0cea3b465cb6ec3e3f258b383b09f84ea352ccfdbfa112902cde5653fc6"

autogpt_platform/backend/pyproject.toml

@@ -33,7 +33,6 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^3.11.0"
 launchdarkly-server-sdk = "^9.12.0"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"

autogpt_platform/backend/schema.prisma

@@ -1,14 +1,15 @@
 datasource db {
-  provider  = "postgresql"
-  url       = env("DATABASE_URL")
-  directUrl = env("DIRECT_URL")
+  provider   = "postgresql"
+  url        = env("DATABASE_URL")
+  directUrl  = env("DIRECT_URL")
+  extensions = [pgvector(map: "vector")]
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch"]
+  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -53,7 +54,6 @@ model User {
 
   Profile               Profile[]
   UserOnboarding        UserOnboarding?
-  CoPilotUnderstanding  CoPilotUnderstanding?
   BuilderSearchHistory  BuilderSearchHistory[]
   StoreListings         StoreListing[]
   StoreListingReviews   StoreListingReview[]
@@ -122,19 +122,6 @@ model UserOnboarding {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-model CoPilotUnderstanding {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String @unique
-  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
-
-  data Json?
-
-  @@index([userId])
-}
-
 model BuilderSearchHistory {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
@@ -148,58 +135,6 @@ model BuilderSearchHistory {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-////////////////   CHAT SESSION TABLES   ///////////////////
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-
-model ChatSession {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  userId String?
-
-  // Session metadata
-  title       String?
-  credentials Json    @default("{}") // Map of provider -> credential metadata
-
-  // Rate limiting counters (stored as JSON maps)
-  successfulAgentRuns      Json @default("{}") // Map of graph_id -> count
-  successfulAgentSchedules Json @default("{}") // Map of graph_id -> count
-
-  // Usage tracking
-  totalPromptTokens     Int @default(0)
-  totalCompletionTokens Int @default(0)
-
-  Messages ChatMessage[]
-
-  @@index([userId, updatedAt])
-}
-
-model ChatMessage {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-
-  sessionId String
-  Session   ChatSession @relation(fields: [sessionId], references: [id], onDelete: Cascade)
-
-  // Message content
-  role         String // "user", "assistant", "system", "tool", "function"
-  content      String?
-  name         String?
-  toolCallId   String?
-  refusal      String?
-  toolCalls    Json? // List of tool calls for assistant messages
-  functionCall Json? // Deprecated but kept for compatibility
-
-  // Ordering within session
-  sequence Int
-
-  @@unique([sessionId, sequence])
-}
-
 // This model describes the Agent Graph/Flow (Multi Agent System).
 model AgentGraph {
   id        String    @default(uuid())
@@ -793,20 +728,19 @@ view StoreAgent {
   agent_output_demo String?
   agent_image       String[]
 
-  featured           Boolean                  @default(false)
+  featured           Boolean  @default(false)
   creator_username   String?
   creator_avatar     String?
   sub_heading        String
   description        String
   categories         String[]
-  search             Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
   runs               Int
   rating             Float
   versions           String[]
   agentGraphVersions String[]
   agentGraphId       String
-  is_available       Boolean                  @default(true)
-  useForOnboarding   Boolean                  @default(false)
+  is_available       Boolean  @default(true)
+  useForOnboarding   Boolean  @default(false)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId
@@ -965,12 +899,52 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
+  // Note: Embeddings now stored in UnifiedContentEmbedding table
+  // Use contentType=STORE_AGENT and contentId=storeListingVersionId
+
+  @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
   @@index([reviewerId])
   @@index([agentGraphId, agentGraphVersion]) // Non-unique index for efficient lookups
 }
 
+// Content type enum for unified search across store agents, blocks, docs
+// Note: BLOCK/INTEGRATION are file-based (Python classes), not DB records
+// DOCUMENTATION are file-based (.md files), not DB records  
+// Only STORE_AGENT and LIBRARY_AGENT are stored in database
+enum ContentType {
+  STORE_AGENT // Database: StoreListingVersion
+  BLOCK // File-based: Python classes in /backend/blocks/
+  INTEGRATION // File-based: Python classes (blocks with credentials)
+  DOCUMENTATION // File-based: .md/.mdx files
+  LIBRARY_AGENT // Database: User's personal agents
+}
+
+// Unified embeddings table for all searchable content types
+// Supports both public content (userId=null) and user-specific content (userId=userID)
+model UnifiedContentEmbedding {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  // Content identification
+  contentType ContentType
+  contentId   String // DB ID (storeListingVersionId) or file identifier (block.id, file_path)
+  userId      String? // NULL for public content (store, blocks, docs), userId for private content (library agents)
+
+  // Search data
+  embedding      Unsupported("vector(1536)") // pgvector embedding (extension in platform schema)
+  searchableText String // Combined text for search and fallback
+  metadata       Json                        @default("{}") // Content-specific metadata
+
+  @@unique([contentType, contentId, userId], map: "UnifiedContentEmbedding_contentType_contentId_userId_key")
+  @@index([contentType])
+  @@index([userId])
+  @@index([contentType, userId])
+  @@index([embedding], map: "UnifiedContentEmbedding_embedding_idx")
+}
+
 model StoreListingReview {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())

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

@@ -81,16 +81,18 @@ export const RunInputDialog = ({
                     Inputs
                   </Text>
                 </div>
-                <FormRenderer
-                  jsonSchema={inputSchema as RJSFSchema}
-                  handleChange={(v) => handleInputChange(v.formData)}
-                  uiSchema={uiSchema}
-                  initialValues={{}}
-                  formContext={{
-                    showHandles: false,
-                    size: "large",
-                  }}
-                />
+                <div className="px-2">
+                  <FormRenderer
+                    jsonSchema={inputSchema as RJSFSchema}
+                    handleChange={(v) => handleInputChange(v.formData)}
+                    uiSchema={uiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                    }}
+                  />
+                </div>
               </div>
             )}
 

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

@@ -3,7 +3,6 @@ import { useGetV2GetSpecificBlocks } from "@/app/api/__generated__/endpoints/def
 import {
   useGetV1GetExecutionDetails,
   useGetV1GetSpecificGraph,
-  useGetV1ListUserGraphs,
 } from "@/app/api/__generated__/endpoints/graphs/graphs";
 import { BlockInfo } from "@/app/api/__generated__/models/blockInfo";
 import { GraphModel } from "@/app/api/__generated__/models/graphModel";
@@ -18,7 +17,6 @@ import { useReactFlow } from "@xyflow/react";
 import { useControlPanelStore } from "../../../stores/controlPanelStore";
 import { useHistoryStore } from "../../../stores/historyStore";
 import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
-import { okData } from "@/app/api/helpers";
 
 export const useFlow = () => {
   const [isLocked, setIsLocked] = useState(false);
@@ -38,9 +36,6 @@ export const useFlow = () => {
   const setGraphExecutionStatus = useGraphStore(
     useShallow((state) => state.setGraphExecutionStatus),
   );
-  const setAvailableSubGraphs = useGraphStore(
-    useShallow((state) => state.setAvailableSubGraphs),
-  );
   const updateEdgeBeads = useEdgeStore(
     useShallow((state) => state.updateEdgeBeads),
   );
@@ -67,11 +62,6 @@ export const useFlow = () => {
     },
   );
 
-  // Fetch all available graphs for sub-agent update detection
-  const { data: availableGraphs } = useGetV1ListUserGraphs({
-    query: { select: okData },
-  });
-
   const { data: graph, isLoading: isGraphLoading } = useGetV1GetSpecificGraph(
     flowID ?? "",
     flowVersion !== null ? { version: flowVersion } : {},
@@ -126,18 +116,10 @@ export const useFlow = () => {
     }
   }, [graph]);
 
-  // Update available sub-graphs in store for sub-agent update detection
-  useEffect(() => {
-    if (availableGraphs) {
-      setAvailableSubGraphs(availableGraphs);
-    }
-  }, [availableGraphs, setAvailableSubGraphs]);
-
   // adding nodes
   useEffect(() => {
     if (customNodes.length > 0) {
       useNodeStore.getState().setNodes([]);
-      useNodeStore.getState().clearResolutionState();
       addNodes(customNodes);
 
       // Sync hardcoded values with handle IDs.
@@ -221,7 +203,6 @@ export const useFlow = () => {
   useEffect(() => {
     return () => {
       useNodeStore.getState().setNodes([]);
-      useNodeStore.getState().clearResolutionState();
       useEdgeStore.getState().setEdges([]);
       useGraphStore.getState().reset();
       useEdgeStore.getState().resetEdgeBeads();

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

@@ -8,7 +8,6 @@ import {
   getBezierPath,
 } from "@xyflow/react";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { XIcon } from "@phosphor-icons/react";
 import { cn } from "@/lib/utils";
 import { NodeExecutionResult } from "@/lib/autogpt-server-api";
@@ -36,8 +35,6 @@ const CustomEdge = ({
   selected,
 }: EdgeProps<CustomEdge>) => {
   const removeConnection = useEdgeStore((state) => state.removeEdge);
-  // Subscribe to the brokenEdgeIDs map and check if this edge is broken across any node
-  const isBroken = useNodeStore((state) => state.isEdgeBroken(id));
   const [isHovered, setIsHovered] = useState(false);
 
   const [edgePath, labelX, labelY] = getBezierPath({
@@ -53,12 +50,6 @@ const CustomEdge = ({
   const beadUp = data?.beadUp ?? 0;
   const beadDown = data?.beadDown ?? 0;
 
-  const handleRemoveEdge = () => {
-    removeConnection(id);
-    // Note: broken edge tracking is cleaned up automatically by useSubAgentUpdateState
-    // when it detects the edge no longer exists
-  };
-
   return (
     <>
       <BaseEdge
@@ -66,11 +57,9 @@ const CustomEdge = ({
         markerEnd={markerEnd}
         className={cn(
           isStatic && "!stroke-[1.5px] [stroke-dasharray:6]",
-          isBroken
-            ? "!stroke-red-500 !stroke-[2px] [stroke-dasharray:4]"
-            : selected
-              ? "stroke-zinc-800"
-              : "stroke-zinc-500/50 hover:stroke-zinc-500",
+          selected
+            ? "stroke-zinc-800"
+            : "stroke-zinc-500/50 hover:stroke-zinc-500",
         )}
       />
       <JSBeads
@@ -81,16 +70,12 @@ const CustomEdge = ({
       />
       <EdgeLabelRenderer>
         <Button
-          onClick={handleRemoveEdge}
+          onClick={() => removeConnection(id)}
           className={cn(
             "absolute h-fit min-w-0 p-1 transition-opacity",
-            isBroken
-              ? "bg-red-500 opacity-100 hover:bg-red-600"
-              : isHovered
-                ? "opacity-100"
-                : "opacity-0",
+            isHovered ? "opacity-100" : "opacity-0",
           )}
-          variant={isBroken ? "primary" : "secondary"}
+          variant="secondary"
           style={{
             transform: `translate(-50%, -50%) translate(${labelX}px, ${labelY}px)`,
             pointerEvents: "all",

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

@@ -3,7 +3,6 @@ import { Handle, Position } from "@xyflow/react";
 import { useEdgeStore } from "../../../stores/edgeStore";
 import { cleanUpHandleId } from "@/components/renderers/InputRenderer/helpers";
 import { cn } from "@/lib/utils";
-import { useNodeStore } from "../../../stores/nodeStore";
 
 const InputNodeHandle = ({
   handleId,
@@ -16,9 +15,6 @@ const InputNodeHandle = ({
   const isInputConnected = useEdgeStore((state) =>
     state.isInputConnected(nodeId ?? "", cleanedHandleId),
   );
-  const isInputBroken = useNodeStore((state) =>
-    state.isInputBroken(nodeId, cleanedHandleId),
-  );
 
   return (
     <Handle
@@ -31,10 +27,7 @@ const InputNodeHandle = ({
         <CircleIcon
           size={16}
           weight={isInputConnected ? "fill" : "duotone"}
-          className={cn(
-            "text-gray-400 opacity-100",
-            isInputBroken && "text-red-500",
-          )}
+          className={"text-gray-400 opacity-100"}
         />
       </div>
     </Handle>
@@ -45,17 +38,14 @@ const OutputNodeHandle = ({
   field_name,
   nodeId,
   hexColor,
-  isBroken,
 }: {
   field_name: string;
   nodeId: string;
   hexColor: string;
-  isBroken: boolean;
 }) => {
   const isOutputConnected = useEdgeStore((state) =>
     state.isOutputConnected(nodeId, field_name),
   );
-
   return (
     <Handle
       type={"source"}
@@ -68,10 +58,7 @@ const OutputNodeHandle = ({
           size={16}
           weight={"duotone"}
           color={isOutputConnected ? hexColor : "gray"}
-          className={cn(
-            "text-gray-400 opacity-100",
-            isBroken && "text-red-500",
-          )}
+          className={cn("text-gray-400 opacity-100")}
         />
       </div>
     </Handle>

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

@@ -20,8 +20,6 @@ import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
 import { NodeRightClickMenu } from "./components/NodeRightClickMenu";
 import { StickyNoteBlock } from "./components/StickyNoteBlock";
 import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
-import { SubAgentUpdateFeature } from "./components/SubAgentUpdate/SubAgentUpdateFeature";
-import { useCustomNode } from "./useCustomNode";
 
 export type CustomNodeData = {
   hardcodedValues: {
@@ -47,10 +45,6 @@ export type CustomNode = XYNode<CustomNodeData, "custom">;
 
 export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
   ({ data, id: nodeId, selected }) => {
-    const { inputSchema, outputSchema } = useCustomNode({ data, nodeId });
-
-    const isAgent = data.uiType === BlockUIType.AGENT;
-
     if (data.uiType === BlockUIType.NOTE) {
       return (
         <StickyNoteBlock data={data} selected={selected} nodeId={nodeId} />
@@ -69,6 +63,16 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     const isAyrshare = data.uiType === BlockUIType.AYRSHARE;
 
+    const inputSchema =
+      data.uiType === BlockUIType.AGENT
+        ? (data.hardcodedValues.input_schema ?? {})
+        : data.inputSchema;
+
+    const outputSchema =
+      data.uiType === BlockUIType.AGENT
+        ? (data.hardcodedValues.output_schema ?? {})
+        : data.outputSchema;
+
     const hasConfigErrors =
       data.errors &&
       Object.values(data.errors).some(
@@ -83,11 +87,12 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     const hasErrors = hasConfigErrors || hasOutputError;
 
+    // Currently all blockTypes design are similar - that's why i am using the same component for all of them
+    // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
     const node = (
       <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
-          {isAgent && <SubAgentUpdateFeature nodeID={nodeId} nodeData={data} />}
           {isWebhook && <WebhookDisclaimer nodeId={nodeId} />}
           {isAyrshare && <AyrshareConnectButton />}
           <FormCreator

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

@@ -1,118 +0,0 @@
-import React from "react";
-import { ArrowUpIcon, WarningIcon } from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
-import { cn, beautifyString } from "@/lib/utils";
-import { CustomNodeData } from "../../CustomNode";
-import { useSubAgentUpdateState } from "./useSubAgentUpdateState";
-import { IncompatibleUpdateDialog } from "./components/IncompatibleUpdateDialog";
-import { ResolutionModeBar } from "./components/ResolutionModeBar";
-
-/**
- * Inline component for the update bar that can be placed after the header.
- * Use this inside the node content where you want the bar to appear.
- */
-type SubAgentUpdateFeatureProps = {
-  nodeID: string;
-  nodeData: CustomNodeData;
-};
-
-export function SubAgentUpdateFeature({
-  nodeID,
-  nodeData,
-}: SubAgentUpdateFeatureProps) {
-  const {
-    updateInfo,
-    isInResolutionMode,
-    handleUpdateClick,
-    showIncompatibilityDialog,
-    setShowIncompatibilityDialog,
-    handleConfirmIncompatibleUpdate,
-  } = useSubAgentUpdateState({ nodeID: nodeID, nodeData: nodeData });
-
-  const agentName = nodeData.title || "Agent";
-
-  if (!updateInfo.hasUpdate && !isInResolutionMode) {
-    return null;
-  }
-
-  return (
-    <>
-      {isInResolutionMode ? (
-        <ResolutionModeBar incompatibilities={updateInfo.incompatibilities} />
-      ) : (
-        <SubAgentUpdateAvailableBar
-          currentVersion={updateInfo.currentVersion}
-          latestVersion={updateInfo.latestVersion}
-          isCompatible={updateInfo.isCompatible}
-          onUpdate={handleUpdateClick}
-        />
-      )}
-      {/* Incompatibility dialog - rendered here since this component owns the state */}
-      {updateInfo.incompatibilities && (
-        <IncompatibleUpdateDialog
-          isOpen={showIncompatibilityDialog}
-          onClose={() => setShowIncompatibilityDialog(false)}
-          onConfirm={handleConfirmIncompatibleUpdate}
-          currentVersion={updateInfo.currentVersion}
-          latestVersion={updateInfo.latestVersion}
-          agentName={beautifyString(agentName)}
-          incompatibilities={updateInfo.incompatibilities}
-        />
-      )}
-    </>
-  );
-}
-
-type SubAgentUpdateAvailableBarProps = {
-  currentVersion: number;
-  latestVersion: number;
-  isCompatible: boolean;
-  onUpdate: () => void;
-};
-
-function SubAgentUpdateAvailableBar({
-  currentVersion,
-  latestVersion,
-  isCompatible,
-  onUpdate,
-}: SubAgentUpdateAvailableBarProps): React.ReactElement {
-  return (
-    <div className="flex items-center justify-between gap-2 rounded-t-xl bg-blue-50 px-3 py-2 dark:bg-blue-900/30">
-      <div className="flex items-center gap-2">
-        <ArrowUpIcon className="h-4 w-4 text-blue-600 dark:text-blue-400" />
-        <span className="text-sm text-blue-700 dark:text-blue-300">
-          Update available (v{currentVersion} → v{latestVersion})
-        </span>
-        {!isCompatible && (
-          <Tooltip>
-            <TooltipTrigger asChild>
-              <WarningIcon className="h-4 w-4 text-amber-500" />
-            </TooltipTrigger>
-            <TooltipContent className="max-w-xs">
-              <p className="font-medium">Incompatible changes detected</p>
-              <p className="text-xs text-gray-400">
-                Click Update to see details
-              </p>
-            </TooltipContent>
-          </Tooltip>
-        )}
-      </div>
-      <Button
-        size="small"
-        variant={isCompatible ? "primary" : "outline"}
-        onClick={onUpdate}
-        className={cn(
-          "h-7 text-xs",
-          !isCompatible && "border-amber-500 text-amber-600 hover:bg-amber-50",
-        )}
-      >
-        Update
-      </Button>
-    </div>
-  );
-}

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

@@ -1,274 +0,0 @@
-import React from "react";
-import {
-  WarningIcon,
-  XCircleIcon,
-  PlusCircleIcon,
-} from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { beautifyString } from "@/lib/utils";
-import { IncompatibilityInfo } from "@/app/(platform)/build/hooks/useSubAgentUpdate/types";
-
-type IncompatibleUpdateDialogProps = {
-  isOpen: boolean;
-  onClose: () => void;
-  onConfirm: () => void;
-  currentVersion: number;
-  latestVersion: number;
-  agentName: string;
-  incompatibilities: IncompatibilityInfo;
-};
-
-export function IncompatibleUpdateDialog({
-  isOpen,
-  onClose,
-  onConfirm,
-  currentVersion,
-  latestVersion,
-  agentName,
-  incompatibilities,
-}: IncompatibleUpdateDialogProps) {
-  const hasMissingInputs = incompatibilities.missingInputs.length > 0;
-  const hasMissingOutputs = incompatibilities.missingOutputs.length > 0;
-  const hasNewInputs = incompatibilities.newInputs.length > 0;
-  const hasNewOutputs = incompatibilities.newOutputs.length > 0;
-  const hasNewRequired = incompatibilities.newRequiredInputs.length > 0;
-  const hasTypeMismatches = incompatibilities.inputTypeMismatches.length > 0;
-
-  const hasInputChanges = hasMissingInputs || hasNewInputs;
-  const hasOutputChanges = hasMissingOutputs || hasNewOutputs;
-
-  return (
-    <Dialog
-      title={
-        <div className="flex items-center gap-2">
-          <WarningIcon className="h-5 w-5 text-amber-500" weight="fill" />
-          Incompatible Update
-        </div>
-      }
-      controlled={{
-        isOpen,
-        set: async (open) => {
-          if (!open) onClose();
-        },
-      }}
-      onClose={onClose}
-      styling={{ maxWidth: "32rem" }}
-    >
-      <Dialog.Content>
-        <div className="space-y-4">
-          <p className="text-sm text-gray-600 dark:text-gray-400">
-            Updating <strong>{beautifyString(agentName)}</strong> from v
-            {currentVersion} to v{latestVersion} will break some connections.
-          </p>
-
-          {/* Input changes - two column layout */}
-          {hasInputChanges && (
-            <TwoColumnSection
-              title="Input Changes"
-              leftIcon={
-                <XCircleIcon className="h-4 w-4 text-red-500" weight="fill" />
-              }
-              leftTitle="Removed"
-              leftItems={incompatibilities.missingInputs}
-              rightIcon={
-                <PlusCircleIcon
-                  className="h-4 w-4 text-green-500"
-                  weight="fill"
-                />
-              }
-              rightTitle="Added"
-              rightItems={incompatibilities.newInputs}
-            />
-          )}
-
-          {/* Output changes - two column layout */}
-          {hasOutputChanges && (
-            <TwoColumnSection
-              title="Output Changes"
-              leftIcon={
-                <XCircleIcon className="h-4 w-4 text-red-500" weight="fill" />
-              }
-              leftTitle="Removed"
-              leftItems={incompatibilities.missingOutputs}
-              rightIcon={
-                <PlusCircleIcon
-                  className="h-4 w-4 text-green-500"
-                  weight="fill"
-                />
-              }
-              rightTitle="Added"
-              rightItems={incompatibilities.newOutputs}
-            />
-          )}
-
-          {hasTypeMismatches && (
-            <SingleColumnSection
-              icon={
-                <XCircleIcon className="h-4 w-4 text-red-500" weight="fill" />
-              }
-              title="Type Changed"
-              description="These connected inputs have a different type:"
-              items={incompatibilities.inputTypeMismatches.map(
-                (m) => `${m.name} (${m.oldType} → ${m.newType})`,
-              )}
-            />
-          )}
-
-          {hasNewRequired && (
-            <SingleColumnSection
-              icon={
-                <PlusCircleIcon
-                  className="h-4 w-4 text-amber-500"
-                  weight="fill"
-                />
-              }
-              title="New Required Inputs"
-              description="These inputs are now required:"
-              items={incompatibilities.newRequiredInputs}
-            />
-          )}
-
-          <Alert variant="warning">
-            <AlertDescription>
-              If you proceed, you&apos;ll need to remove the broken connections
-              before you can save or run your agent.
-            </AlertDescription>
-          </Alert>
-
-          <Dialog.Footer>
-            <Button variant="ghost" size="small" onClick={onClose}>
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              onClick={onConfirm}
-              className="border-amber-700 bg-amber-600 hover:bg-amber-700"
-            >
-              Update Anyway
-            </Button>
-          </Dialog.Footer>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}
-
-type TwoColumnSectionProps = {
-  title: string;
-  leftIcon: React.ReactNode;
-  leftTitle: string;
-  leftItems: string[];
-  rightIcon: React.ReactNode;
-  rightTitle: string;
-  rightItems: string[];
-};
-
-function TwoColumnSection({
-  title,
-  leftIcon,
-  leftTitle,
-  leftItems,
-  rightIcon,
-  rightTitle,
-  rightItems,
-}: TwoColumnSectionProps) {
-  return (
-    <div className="rounded-md border border-gray-200 p-3 dark:border-gray-700">
-      <span className="font-medium">{title}</span>
-      <div className="mt-2 grid grid-cols-2 items-start gap-4">
-        {/* Left column - Breaking changes */}
-        <div className="min-w-0">
-          <div className="flex items-center gap-1.5 text-sm text-gray-500 dark:text-gray-400">
-            {leftIcon}
-            <span>{leftTitle}</span>
-          </div>
-          <ul className="mt-1.5 space-y-1">
-            {leftItems.length > 0 ? (
-              leftItems.map((item) => (
-                <li
-                  key={item}
-                  className="text-sm text-gray-700 dark:text-gray-300"
-                >
-                  <code className="rounded bg-red-50 px-1 py-0.5 font-mono text-xs text-red-700 dark:bg-red-900/30 dark:text-red-300">
-                    {item}
-                  </code>
-                </li>
-              ))
-            ) : (
-              <li className="text-sm italic text-gray-400 dark:text-gray-500">
-                None
-              </li>
-            )}
-          </ul>
-        </div>
-
-        {/* Right column - Possible solutions */}
-        <div className="min-w-0">
-          <div className="flex items-center gap-1.5 text-sm text-gray-500 dark:text-gray-400">
-            {rightIcon}
-            <span>{rightTitle}</span>
-          </div>
-          <ul className="mt-1.5 space-y-1">
-            {rightItems.length > 0 ? (
-              rightItems.map((item) => (
-                <li
-                  key={item}
-                  className="text-sm text-gray-700 dark:text-gray-300"
-                >
-                  <code className="rounded bg-green-50 px-1 py-0.5 font-mono text-xs text-green-700 dark:bg-green-900/30 dark:text-green-300">
-                    {item}
-                  </code>
-                </li>
-              ))
-            ) : (
-              <li className="text-sm italic text-gray-400 dark:text-gray-500">
-                None
-              </li>
-            )}
-          </ul>
-        </div>
-      </div>
-    </div>
-  );
-}
-
-type SingleColumnSectionProps = {
-  icon: React.ReactNode;
-  title: string;
-  description: string;
-  items: string[];
-};
-
-function SingleColumnSection({
-  icon,
-  title,
-  description,
-  items,
-}: SingleColumnSectionProps) {
-  return (
-    <div className="rounded-md border border-gray-200 p-3 dark:border-gray-700">
-      <div className="flex items-center gap-2">
-        {icon}
-        <span className="font-medium">{title}</span>
-      </div>
-      <p className="mt-1 text-sm text-gray-500 dark:text-gray-400">
-        {description}
-      </p>
-      <ul className="mt-2 space-y-1">
-        {items.map((item) => (
-          <li
-            key={item}
-            className="ml-4 list-disc text-sm text-gray-700 dark:text-gray-300"
-          >
-            <code className="rounded bg-gray-100 px-1 py-0.5 font-mono text-xs dark:bg-gray-800">
-              {item}
-            </code>
-          </li>
-        ))}
-      </ul>
-    </div>
-  );
-}

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

@@ -1,107 +0,0 @@
-import React from "react";
-import { InfoIcon, WarningIcon } from "@phosphor-icons/react";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
-import { IncompatibilityInfo } from "@/app/(platform)/build/hooks/useSubAgentUpdate/types";
-
-type ResolutionModeBarProps = {
-  incompatibilities: IncompatibilityInfo | null;
-};
-
-export function ResolutionModeBar({
-  incompatibilities,
-}: ResolutionModeBarProps): React.ReactElement {
-  const renderIncompatibilities = () => {
-    if (!incompatibilities) return <span>No incompatibilities</span>;
-
-    const sections: React.ReactNode[] = [];
-
-    if (incompatibilities.missingInputs.length > 0) {
-      sections.push(
-        <div key="missing-inputs" className="mb-1">
-          <span className="font-semibold">Missing inputs: </span>
-          {incompatibilities.missingInputs.map((name, i) => (
-            <React.Fragment key={name}>
-              <code className="font-mono">{name}</code>
-              {i < incompatibilities.missingInputs.length - 1 && ", "}
-            </React.Fragment>
-          ))}
-        </div>,
-      );
-    }
-    if (incompatibilities.missingOutputs.length > 0) {
-      sections.push(
-        <div key="missing-outputs" className="mb-1">
-          <span className="font-semibold">Missing outputs: </span>
-          {incompatibilities.missingOutputs.map((name, i) => (
-            <React.Fragment key={name}>
-              <code className="font-mono">{name}</code>
-              {i < incompatibilities.missingOutputs.length - 1 && ", "}
-            </React.Fragment>
-          ))}
-        </div>,
-      );
-    }
-    if (incompatibilities.newRequiredInputs.length > 0) {
-      sections.push(
-        <div key="new-required" className="mb-1">
-          <span className="font-semibold">New required inputs: </span>
-          {incompatibilities.newRequiredInputs.map((name, i) => (
-            <React.Fragment key={name}>
-              <code className="font-mono">{name}</code>
-              {i < incompatibilities.newRequiredInputs.length - 1 && ", "}
-            </React.Fragment>
-          ))}
-        </div>,
-      );
-    }
-    if (incompatibilities.inputTypeMismatches.length > 0) {
-      sections.push(
-        <div key="type-mismatches" className="mb-1">
-          <span className="font-semibold">Type changed: </span>
-          {incompatibilities.inputTypeMismatches.map((m, i) => (
-            <React.Fragment key={m.name}>
-              <code className="font-mono">{m.name}</code>
-              <span className="text-gray-400">
-                {" "}
-                ({m.oldType} → {m.newType})
-              </span>
-              {i < incompatibilities.inputTypeMismatches.length - 1 && ", "}
-            </React.Fragment>
-          ))}
-        </div>,
-      );
-    }
-
-    return <>{sections}</>;
-  };
-
-  return (
-    <div className="flex items-center justify-between gap-2 rounded-t-xl bg-amber-50 px-3 py-2 dark:bg-amber-900/30">
-      <div className="flex items-center gap-2">
-        <WarningIcon className="h-4 w-4 text-amber-600 dark:text-amber-400" />
-        <span className="text-sm text-amber-700 dark:text-amber-300">
-          Remove incompatible connections
-        </span>
-        <Tooltip>
-          <TooltipTrigger asChild>
-            <InfoIcon className="h-4 w-4 cursor-help text-amber-500" />
-          </TooltipTrigger>
-          <TooltipContent className="max-w-sm">
-            <p className="mb-2 font-semibold">Incompatible changes:</p>
-            <div className="text-xs">{renderIncompatibilities()}</div>
-            <p className="mt-2 text-xs text-gray-400">
-              {(incompatibilities?.newRequiredInputs.length ?? 0) > 0
-                ? "Replace / delete"
-                : "Delete"}{" "}
-              the red connections to continue
-            </p>
-          </TooltipContent>
-        </Tooltip>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/SubAgentUpdate/useSubAgentUpdateState.ts

@@ -1,194 +0,0 @@
-import { useState, useCallback, useEffect } from "react";
-import { useShallow } from "zustand/react/shallow";
-import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
-import {
-  useNodeStore,
-  NodeResolutionData,
-} from "@/app/(platform)/build/stores/nodeStore";
-import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
-import {
-  useSubAgentUpdate,
-  createUpdatedAgentNodeInputs,
-  getBrokenEdgeIDs,
-} from "@/app/(platform)/build/hooks/useSubAgentUpdate";
-import { GraphInputSchema, GraphOutputSchema } from "@/lib/autogpt-server-api";
-import { CustomNodeData } from "../../CustomNode";
-
-// Stable empty set to avoid creating new references in selectors
-const EMPTY_SET: Set<string> = new Set();
-
-type UseSubAgentUpdateParams = {
-  nodeID: string;
-  nodeData: CustomNodeData;
-};
-
-export function useSubAgentUpdateState({
-  nodeID,
-  nodeData,
-}: UseSubAgentUpdateParams) {
-  const [showIncompatibilityDialog, setShowIncompatibilityDialog] =
-    useState(false);
-
-  // Get store actions
-  const updateNodeData = useNodeStore(
-    useShallow((state) => state.updateNodeData),
-  );
-  const setNodeResolutionMode = useNodeStore(
-    useShallow((state) => state.setNodeResolutionMode),
-  );
-  const isNodeInResolutionMode = useNodeStore(
-    useShallow((state) => state.isNodeInResolutionMode),
-  );
-  const setBrokenEdgeIDs = useNodeStore(
-    useShallow((state) => state.setBrokenEdgeIDs),
-  );
-  // Get this node's broken edge IDs from the per-node map
-  // Use EMPTY_SET as fallback to maintain referential stability
-  const brokenEdgeIDs = useNodeStore(
-    (state) => state.brokenEdgeIDs.get(nodeID) || EMPTY_SET,
-  );
-  const getNodeResolutionData = useNodeStore(
-    useShallow((state) => state.getNodeResolutionData),
-  );
-  const connectedEdges = useEdgeStore(
-    useShallow((state) => state.getNodeEdges(nodeID)),
-  );
-  const availableSubGraphs = useGraphStore(
-    useShallow((state) => state.availableSubGraphs),
-  );
-
-  // Extract agent-specific data
-  const graphID = nodeData.hardcodedValues?.graph_id as string | undefined;
-  const graphVersion = nodeData.hardcodedValues?.graph_version as
-    | number
-    | undefined;
-  const currentInputSchema = nodeData.hardcodedValues?.input_schema as
-    | GraphInputSchema
-    | undefined;
-  const currentOutputSchema = nodeData.hardcodedValues?.output_schema as
-    | GraphOutputSchema
-    | undefined;
-
-  // Use the sub-agent update hook
-  const updateInfo = useSubAgentUpdate(
-    nodeID,
-    graphID,
-    graphVersion,
-    currentInputSchema,
-    currentOutputSchema,
-    connectedEdges,
-    availableSubGraphs,
-  );
-
-  const isInResolutionMode = isNodeInResolutionMode(nodeID);
-
-  // Handle update button click
-  const handleUpdateClick = useCallback(() => {
-    if (!updateInfo.hasUpdate || !updateInfo.latestGraph) return;
-
-    if (updateInfo.isCompatible) {
-      // Compatible update - apply directly
-      const newHardcodedValues = createUpdatedAgentNodeInputs(
-        nodeData.hardcodedValues,
-        updateInfo.latestGraph,
-      );
-      updateNodeData(nodeID, { hardcodedValues: newHardcodedValues });
-    } else {
-      // Incompatible update - show dialog
-      setShowIncompatibilityDialog(true);
-    }
-  }, [
-    updateInfo.hasUpdate,
-    updateInfo.latestGraph,
-    updateInfo.isCompatible,
-    nodeData.hardcodedValues,
-    updateNodeData,
-    nodeID,
-  ]);
-
-  // Handle confirming an incompatible update
-  function handleConfirmIncompatibleUpdate() {
-    if (!updateInfo.latestGraph || !updateInfo.incompatibilities) return;
-
-    const latestGraph = updateInfo.latestGraph;
-
-    // Get the new schemas from the latest graph version
-    const newInputSchema =
-      (latestGraph.input_schema as Record<string, unknown>) || {};
-    const newOutputSchema =
-      (latestGraph.output_schema as Record<string, unknown>) || {};
-
-    // Create the updated hardcoded values but DON'T apply them yet
-    // We'll apply them when resolution is complete
-    const pendingHardcodedValues = createUpdatedAgentNodeInputs(
-      nodeData.hardcodedValues,
-      latestGraph,
-    );
-
-    // Get broken edge IDs and store them for this node
-    const brokenIds = getBrokenEdgeIDs(
-      connectedEdges,
-      updateInfo.incompatibilities,
-      nodeID,
-    );
-    setBrokenEdgeIDs(nodeID, brokenIds);
-
-    // Enter resolution mode with both old and new schemas
-    // DON'T apply the update yet - keep old schema so connections remain visible
-    const resolutionData: NodeResolutionData = {
-      incompatibilities: updateInfo.incompatibilities,
-      pendingUpdate: {
-        input_schema: newInputSchema,
-        output_schema: newOutputSchema,
-      },
-      currentSchema: {
-        input_schema: (currentInputSchema as Record<string, unknown>) || {},
-        output_schema: (currentOutputSchema as Record<string, unknown>) || {},
-      },
-      pendingHardcodedValues,
-    };
-    setNodeResolutionMode(nodeID, true, resolutionData);
-
-    setShowIncompatibilityDialog(false);
-  }
-
-  // Check if resolution is complete (all broken edges removed)
-  const resolutionData = getNodeResolutionData(nodeID);
-
-  // Auto-check resolution on edge changes
-  useEffect(() => {
-    if (!isInResolutionMode) return;
-
-    // Check if any broken edges still exist
-    const remainingBroken = Array.from(brokenEdgeIDs).filter((edgeId) =>
-      connectedEdges.some((e) => e.id === edgeId),
-    );
-
-    if (remainingBroken.length === 0) {
-      // Resolution complete - now apply the pending update
-      if (resolutionData?.pendingHardcodedValues) {
-        updateNodeData(nodeID, {
-          hardcodedValues: resolutionData.pendingHardcodedValues,
-        });
-      }
-      // setNodeResolutionMode will clean up this node's broken edges automatically
-      setNodeResolutionMode(nodeID, false);
-    }
-  }, [
-    isInResolutionMode,
-    brokenEdgeIDs,
-    connectedEdges,
-    resolutionData,
-    nodeID,
-  ]);
-
-  return {
-    updateInfo,
-    isInResolutionMode,
-    resolutionData,
-    showIncompatibilityDialog,
-    setShowIncompatibilityDialog,
-    handleUpdateClick,
-    handleConfirmIncompatibleUpdate,
-  };
-}

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

@@ -1,6 +1,4 @@
 import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
-import { NodeResolutionData } from "@/app/(platform)/build/stores/nodeStore";
-import { RJSFSchema } from "@rjsf/utils";
 
 export const nodeStyleBasedOnStatus: Record<AgentExecutionStatus, string> = {
   INCOMPLETE: "ring-slate-300 bg-slate-300",
@@ -11,48 +9,3 @@ export const nodeStyleBasedOnStatus: Record<AgentExecutionStatus, string> = {
   TERMINATED: "ring-orange-300 bg-orange-300 ",
   FAILED: "ring-red-300 bg-red-300",
 };
-
-/**
- * Merges schemas during resolution mode to include removed inputs/outputs
- * that still have connections, so users can see and delete them.
- */
-export function mergeSchemaForResolution(
-  currentSchema: Record<string, unknown>,
-  newSchema: Record<string, unknown>,
-  resolutionData: NodeResolutionData,
-  type: "input" | "output",
-): Record<string, unknown> {
-  const newProps = (newSchema.properties as RJSFSchema) || {};
-  const currentProps = (currentSchema.properties as RJSFSchema) || {};
-  const mergedProps = { ...newProps };
-  const incomp = resolutionData.incompatibilities;
-
-  if (type === "input") {
-    // Add back missing inputs that have connections
-    incomp.missingInputs.forEach((inputName: string) => {
-      if (currentProps[inputName]) {
-        mergedProps[inputName] = currentProps[inputName];
-      }
-    });
-    // Add back inputs with type mismatches (keep old type so connection works visually)
-    incomp.inputTypeMismatches.forEach(
-      (mismatch: { name: string; oldType: string; newType: string }) => {
-        if (currentProps[mismatch.name]) {
-          mergedProps[mismatch.name] = currentProps[mismatch.name];
-        }
-      },
-    );
-  } else {
-    // Add back missing outputs that have connections
-    incomp.missingOutputs.forEach((outputName: string) => {
-      if (currentProps[outputName]) {
-        mergedProps[outputName] = currentProps[outputName];
-      }
-    });
-  }
-
-  return {
-    ...newSchema,
-    properties: mergedProps,
-  };
-}

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

@@ -1,58 +0,0 @@
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { CustomNodeData } from "./CustomNode";
-import { BlockUIType } from "../../../types";
-import { useMemo } from "react";
-import { mergeSchemaForResolution } from "./helpers";
-
-export const useCustomNode = ({
-  data,
-  nodeId,
-}: {
-  data: CustomNodeData;
-  nodeId: string;
-}) => {
-  const isInResolutionMode = useNodeStore((state) =>
-    state.nodesInResolutionMode.has(nodeId),
-  );
-  const resolutionData = useNodeStore((state) =>
-    state.nodeResolutionData.get(nodeId),
-  );
-
-  const isAgent = data.uiType === BlockUIType.AGENT;
-
-  const currentInputSchema = isAgent
-    ? (data.hardcodedValues.input_schema ?? {})
-    : data.inputSchema;
-  const currentOutputSchema = isAgent
-    ? (data.hardcodedValues.output_schema ?? {})
-    : data.outputSchema;
-
-  const inputSchema = useMemo(() => {
-    if (isAgent && isInResolutionMode && resolutionData) {
-      return mergeSchemaForResolution(
-        resolutionData.currentSchema.input_schema,
-        resolutionData.pendingUpdate.input_schema,
-        resolutionData,
-        "input",
-      );
-    }
-    return currentInputSchema;
-  }, [isAgent, isInResolutionMode, resolutionData, currentInputSchema]);
-
-  const outputSchema = useMemo(() => {
-    if (isAgent && isInResolutionMode && resolutionData) {
-      return mergeSchemaForResolution(
-        resolutionData.currentSchema.output_schema,
-        resolutionData.pendingUpdate.output_schema,
-        resolutionData,
-        "output",
-      );
-    }
-    return currentOutputSchema;
-  }, [isAgent, isInResolutionMode, resolutionData, currentOutputSchema]);
-
-  return {
-    inputSchema,
-    outputSchema,
-  };
-};

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

@@ -5,16 +5,20 @@ import { useNodeStore } from "../../../stores/nodeStore";
 import { BlockUIType } from "../../types";
 import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
 
-interface FormCreatorProps {
-  jsonSchema: RJSFSchema;
-  nodeId: string;
-  uiType: BlockUIType;
-  showHandles?: boolean;
-  className?: string;
-}
-
-export const FormCreator: React.FC<FormCreatorProps> = React.memo(
-  ({ jsonSchema, nodeId, uiType, showHandles = true, className }) => {
+export const FormCreator = React.memo(
+  ({
+    jsonSchema,
+    nodeId,
+    uiType,
+    showHandles = true,
+    className,
+  }: {
+    jsonSchema: RJSFSchema;
+    nodeId: string;
+    uiType: BlockUIType;
+    showHandles?: boolean;
+    className?: string;
+  }) => {
     const updateNodeData = useNodeStore((state) => state.updateNodeData);
 
     const getHardCodedValues = useNodeStore(

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

@@ -14,8 +14,6 @@ import {
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { getTypeDisplayInfo } from "./helpers";
 import { BlockUIType } from "../../types";
-import { cn } from "@/lib/utils";
-import { useBrokenOutputs } from "./useBrokenOutputs";
 
 export const OutputHandler = ({
   outputSchema,
@@ -29,9 +27,6 @@ export const OutputHandler = ({
   const { isOutputConnected } = useEdgeStore();
   const properties = outputSchema?.properties || {};
   const [isOutputVisible, setIsOutputVisible] = useState(true);
-  const brokenOutputs = useBrokenOutputs(nodeId);
-
-  console.log("brokenOutputs", brokenOutputs);
 
   const showHandles = uiType !== BlockUIType.OUTPUT;
 
@@ -49,7 +44,6 @@ export const OutputHandler = ({
         const shouldShow = isConnected || isOutputVisible;
         const { displayType, colorClass, hexColor } =
           getTypeDisplayInfo(fieldSchema);
-        const isBroken = brokenOutputs.has(fullKey);
 
         return shouldShow ? (
           <div key={fullKey} className="flex flex-col items-end gap-2">
@@ -70,29 +64,15 @@ export const OutputHandler = ({
                   </Tooltip>
                 </TooltipProvider>
               )}
-              <Text
-                variant="body"
-                className={cn(
-                  "text-slate-700",
-                  isBroken && "text-red-500 line-through",
-                )}
-              >
+              <Text variant="body" className="text-slate-700">
                 {fieldTitle}
               </Text>
-              <Text
-                variant="small"
-                as="span"
-                className={cn(
-                  colorClass,
-                  isBroken && "!text-red-500 line-through",
-                )}
-              >
+              <Text variant="small" as="span" className={colorClass}>
                 ({displayType})
               </Text>
 
               {showHandles && (
                 <OutputNodeHandle
-                  isBroken={isBroken}
                   field_name={fullKey}
                   nodeId={nodeId}
                   hexColor={hexColor}

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

@@ -1,23 +0,0 @@
-import { useMemo } from "react";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-
-/**
- * Hook to get the set of broken output names for a node in resolution mode.
- */
-export function useBrokenOutputs(nodeID: string): Set<string> {
-  // Subscribe to the actual state values, not just methods
-  const isInResolution = useNodeStore((state) =>
-    state.nodesInResolutionMode.has(nodeID),
-  );
-  const resolutionData = useNodeStore((state) =>
-    state.nodeResolutionData.get(nodeID),
-  );
-
-  return useMemo(() => {
-    if (!isInResolution || !resolutionData) {
-      return new Set<string>();
-    }
-
-    return new Set(resolutionData.incompatibilities.missingOutputs);
-  }, [isInResolution, resolutionData]);
-}

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

@@ -25,7 +25,7 @@ export const RightSidebar = () => {
     >
       <div className="mb-4">
         <h2 className="text-lg font-semibold text-slate-800 dark:text-slate-200">
-          Graph Debug Panel
+          Flow Debug Panel
         </h2>
       </div>
 
@@ -65,7 +65,7 @@ export const RightSidebar = () => {
                 {l.source_id}[{l.source_name}] → {l.sink_id}[{l.sink_name}]
               </div>
               <div className="mt-1 text-slate-500 dark:text-slate-400">
-                edge.id: {l.id}
+                edge_id: {l.id}
               </div>
             </div>
           ))}

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

@@ -12,14 +12,7 @@ import {
   PopoverContent,
   PopoverTrigger,
 } from "@/components/__legacy__/ui/popover";
-import {
-  Block,
-  BlockIORootSchema,
-  BlockUIType,
-  GraphInputSchema,
-  GraphOutputSchema,
-  SpecialBlockID,
-} from "@/lib/autogpt-server-api";
+import { Block, BlockUIType, SpecialBlockID } from "@/lib/autogpt-server-api";
 import { MagnifyingGlassIcon, PlusIcon } from "@radix-ui/react-icons";
 import { IconToyBrick } from "@/components/__legacy__/ui/icons";
 import { getPrimaryCategoryColor } from "@/lib/utils";
@@ -31,10 +24,8 @@ import {
 import { GraphMeta } from "@/lib/autogpt-server-api";
 import jaro from "jaro-winkler";
 
-type _Block = Omit<Block, "inputSchema" | "outputSchema"> & {
+type _Block = Block & {
   uiKey?: string;
-  inputSchema: BlockIORootSchema | GraphInputSchema;
-  outputSchema: BlockIORootSchema | GraphOutputSchema;
   hardcodedValues?: Record<string, any>;
   _cached?: {
     blockName: string;

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

@@ -2,7 +2,7 @@ import React from "react";
 import { cn } from "@/lib/utils";
 import { Button } from "@/components/__legacy__/ui/button";
 import { LogOut } from "lucide-react";
-import { ClockIcon, WarningIcon } from "@phosphor-icons/react";
+import { ClockIcon } from "@phosphor-icons/react";
 import { IconPlay, IconSquare } from "@/components/__legacy__/ui/icons";
 
 interface Props {
@@ -13,7 +13,6 @@ interface Props {
   isRunning: boolean;
   isDisabled: boolean;
   className?: string;
-  resolutionModeActive?: boolean;
 }
 
 export const BuildActionBar: React.FC<Props> = ({
@@ -24,30 +23,9 @@ export const BuildActionBar: React.FC<Props> = ({
   isRunning,
   isDisabled,
   className,
-  resolutionModeActive = false,
 }) => {
   const buttonClasses =
     "flex items-center gap-2 text-sm font-medium md:text-lg";
-
-  // Show resolution mode message instead of action buttons
-  if (resolutionModeActive) {
-    return (
-      <div
-        className={cn(
-          "flex w-fit select-none items-center justify-center p-4",
-          className,
-        )}
-      >
-        <div className="flex items-center gap-3 rounded-lg border border-amber-300 bg-amber-50 px-4 py-3 dark:border-amber-700 dark:bg-amber-900/30">
-          <WarningIcon className="size-5 text-amber-600 dark:text-amber-400" />
-          <span className="text-sm font-medium text-amber-800 dark:text-amber-200">
-            Remove incompatible connections to continue
-          </span>
-        </div>
-      </div>
-    );
-  }
-
   return (
     <div
       className={cn(

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

@@ -60,16 +60,10 @@ export function CustomEdge({
     targetY - 5,
   );
   const { deleteElements } = useReactFlow<Node, CustomEdge>();
-  const builderContext = useContext(BuilderContext);
-  const { visualizeBeads } = builderContext ?? {
+  const { visualizeBeads } = useContext(BuilderContext) ?? {
     visualizeBeads: "no",
   };
 
-  // Check if this edge is broken (during resolution mode)
-  const isBroken =
-    builderContext?.resolutionMode?.active &&
-    builderContext?.resolutionMode?.brokenEdgeIds?.includes(id);
-
   const onEdgeRemoveClick = () => {
     deleteElements({ edges: [{ id }] });
   };
@@ -177,27 +171,12 @@ export function CustomEdge({
 
   const middle = getPointForT(0.5);
 
-  // Determine edge color - red for broken edges
-  const baseColor = data?.edgeColor ?? "#555555";
-  const edgeColor = isBroken ? "#ef4444" : baseColor;
-  // Add opacity to hex color (99 = 60% opacity, 80 = 50% opacity)
-  const strokeColor = isBroken
-    ? `${edgeColor}99`
-    : selected
-      ? edgeColor
-      : `${edgeColor}80`;
-
   return (
     <>
       <BaseEdge
         path={svgPath}
         markerEnd={markerEnd}
-        style={{
-          stroke: strokeColor,
-          strokeWidth: data?.isStatic ? 2.5 : 2,
-          strokeDasharray: data?.isStatic ? "5 3" : undefined,
-        }}
-        className="data-sentry-unmask transition-all duration-200"
+        className={`data-sentry-unmask transition-all duration-200 ${data?.isStatic ? "[stroke-dasharray:5_3]" : "[stroke-dasharray:0]"} [stroke-width:${data?.isStatic ? 2.5 : 2}px] hover:[stroke-width:${data?.isStatic ? 3.5 : 3}px] ${selected ? `[stroke:${data?.edgeColor ?? "#555555"}]` : `[stroke:${data?.edgeColor ?? "#555555"}80] hover:[stroke:${data?.edgeColor ?? "#555555"}]`}`}
       />
       <path
         d={svgPath}

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

@@ -18,8 +18,6 @@ import {
   BlockIOSubSchema,
   BlockUIType,
   Category,
-  GraphInputSchema,
-  GraphOutputSchema,
   NodeExecutionResult,
 } from "@/lib/autogpt-server-api";
 import {
@@ -64,21 +62,14 @@ import { NodeGenericInputField, NodeTextBoxInput } from "../NodeInputs";
 import NodeOutputs from "../NodeOutputs";
 import OutputModalComponent from "../OutputModalComponent";
 import "./customnode.css";
-import { SubAgentUpdateBar } from "./SubAgentUpdateBar";
-import { IncompatibilityDialog } from "./IncompatibilityDialog";
-import {
-  useSubAgentUpdate,
-  createUpdatedAgentNodeInputs,
-  getBrokenEdgeIDs,
-} from "../../../hooks/useSubAgentUpdate";
 
-export type ConnectedEdge = {
-  id: string;
+export type ConnectionData = Array<{
+  edge_id: string;
   source: string;
   sourceHandle: string;
   target: string;
   targetHandle: string;
-};
+}>;
 
 export type CustomNodeData = {
   blockType: string;
@@ -89,7 +80,7 @@ export type CustomNodeData = {
   inputSchema: BlockIORootSchema;
   outputSchema: BlockIORootSchema;
   hardcodedValues: { [key: string]: any };
-  connections: ConnectedEdge[];
+  connections: ConnectionData;
   isOutputOpen: boolean;
   status?: NodeExecutionResult["status"];
   /** executionResults contains outputs across multiple executions
@@ -136,199 +127,20 @@ export const CustomNode = React.memo(
 
     let subGraphID = "";
 
+    if (data.uiType === BlockUIType.AGENT) {
+      // Display the graph's schema instead AgentExecutorBlock's schema.
+      data.inputSchema = data.hardcodedValues?.input_schema || {};
+      data.outputSchema = data.hardcodedValues?.output_schema || {};
+      subGraphID = data.hardcodedValues?.graph_id || subGraphID;
+    }
+
     if (!builderContext) {
       throw new Error(
         "BuilderContext consumer must be inside FlowEditor component",
       );
     }
 
-    const {
-      libraryAgent,
-      setIsAnyModalOpen,
-      getNextNodeId,
-      availableFlows,
-      resolutionMode,
-      enterResolutionMode,
-    } = builderContext;
-
-    // Check if this node is in resolution mode (moved up for schema merge logic)
-    const isInResolutionMode =
-      resolutionMode.active && resolutionMode.nodeId === id;
-
-    if (data.uiType === BlockUIType.AGENT) {
-      // Display the graph's schema instead AgentExecutorBlock's schema.
-      const currentInputSchema = data.hardcodedValues?.input_schema || {};
-      const currentOutputSchema = data.hardcodedValues?.output_schema || {};
-      subGraphID = data.hardcodedValues?.graph_id || subGraphID;
-
-      // During resolution mode, merge old connected inputs/outputs with new schema
-      if (isInResolutionMode && resolutionMode.pendingUpdate) {
-        const newInputSchema =
-          (resolutionMode.pendingUpdate.input_schema as BlockIORootSchema) ||
-          {};
-        const newOutputSchema =
-          (resolutionMode.pendingUpdate.output_schema as BlockIORootSchema) ||
-          {};
-
-        // Merge input schemas: start with new schema, add old connected inputs that are missing
-        const mergedInputProps = { ...newInputSchema.properties };
-        const incomp = resolutionMode.incompatibilities;
-        if (incomp && currentInputSchema.properties) {
-          // Add back missing inputs that have connections (so user can see/delete them)
-          incomp.missingInputs.forEach((inputName) => {
-            if (currentInputSchema.properties[inputName]) {
-              mergedInputProps[inputName] =
-                currentInputSchema.properties[inputName];
-            }
-          });
-          // Add back inputs with type mismatches (keep old type so connection still works visually)
-          incomp.inputTypeMismatches.forEach((mismatch) => {
-            if (currentInputSchema.properties[mismatch.name]) {
-              mergedInputProps[mismatch.name] =
-                currentInputSchema.properties[mismatch.name];
-            }
-          });
-        }
-
-        // Merge output schemas: start with new schema, add old connected outputs that are missing
-        const mergedOutputProps = { ...newOutputSchema.properties };
-        if (incomp && currentOutputSchema.properties) {
-          incomp.missingOutputs.forEach((outputName) => {
-            if (currentOutputSchema.properties[outputName]) {
-              mergedOutputProps[outputName] =
-                currentOutputSchema.properties[outputName];
-            }
-          });
-        }
-
-        data.inputSchema = {
-          ...newInputSchema,
-          properties: mergedInputProps,
-        };
-        data.outputSchema = {
-          ...newOutputSchema,
-          properties: mergedOutputProps,
-        };
-      } else {
-        data.inputSchema = currentInputSchema;
-        data.outputSchema = currentOutputSchema;
-      }
-    }
-
-    const setHardcodedValues = useCallback(
-      (values: any) => {
-        updateNodeData(id, { hardcodedValues: values });
-      },
-      [id, updateNodeData],
-    );
-
-    // Sub-agent update detection
-    const isAgentBlock = data.uiType === BlockUIType.AGENT;
-    const graphId = isAgentBlock ? data.hardcodedValues?.graph_id : undefined;
-    const graphVersion = isAgentBlock
-      ? data.hardcodedValues?.graph_version
-      : undefined;
-
-    const subAgentUpdate = useSubAgentUpdate(
-      id,
-      graphId,
-      graphVersion,
-      isAgentBlock
-        ? (data.hardcodedValues?.input_schema as GraphInputSchema)
-        : undefined,
-      isAgentBlock
-        ? (data.hardcodedValues?.output_schema as GraphOutputSchema)
-        : undefined,
-      data.connections,
-      availableFlows,
-    );
-
-    const [showIncompatibilityDialog, setShowIncompatibilityDialog] =
-      useState(false);
-
-    // Helper to check if a handle is broken (for resolution mode)
-    const isInputHandleBroken = useCallback(
-      (handleName: string): boolean => {
-        if (!isInResolutionMode || !resolutionMode.incompatibilities) {
-          return false;
-        }
-        const incomp = resolutionMode.incompatibilities;
-        return (
-          incomp.missingInputs.includes(handleName) ||
-          incomp.inputTypeMismatches.some((m) => m.name === handleName)
-        );
-      },
-      [isInResolutionMode, resolutionMode.incompatibilities],
-    );
-
-    const isOutputHandleBroken = useCallback(
-      (handleName: string): boolean => {
-        if (!isInResolutionMode || !resolutionMode.incompatibilities) {
-          return false;
-        }
-        return resolutionMode.incompatibilities.missingOutputs.includes(
-          handleName,
-        );
-      },
-      [isInResolutionMode, resolutionMode.incompatibilities],
-    );
-
-    // Handle update button click
-    const handleUpdateClick = useCallback(() => {
-      if (!subAgentUpdate.latestGraph) return;
-
-      if (subAgentUpdate.isCompatible) {
-        // Compatible update - directly apply
-        const updatedValues = createUpdatedAgentNodeInputs(
-          data.hardcodedValues,
-          subAgentUpdate.latestGraph,
-        );
-        setHardcodedValues(updatedValues);
-        toast({
-          title: "Agent updated",
-          description: `Updated to version ${subAgentUpdate.latestVersion}`,
-        });
-      } else {
-        // Incompatible update - show dialog
-        setShowIncompatibilityDialog(true);
-      }
-    }, [subAgentUpdate, data.hardcodedValues, setHardcodedValues]);
-
-    // Handle confirm incompatible update
-    const handleConfirmIncompatibleUpdate = useCallback(() => {
-      if (!subAgentUpdate.latestGraph || !subAgentUpdate.incompatibilities) {
-        return;
-      }
-
-      // Create the updated values but DON'T apply them yet
-      const updatedValues = createUpdatedAgentNodeInputs(
-        data.hardcodedValues,
-        subAgentUpdate.latestGraph,
-      );
-
-      // Get broken edge IDs
-      const brokenEdgeIds = getBrokenEdgeIDs(
-        data.connections,
-        subAgentUpdate.incompatibilities,
-        id,
-      );
-
-      // Enter resolution mode with pending update (don't apply schema yet)
-      enterResolutionMode(
-        id,
-        subAgentUpdate.incompatibilities,
-        brokenEdgeIds,
-        updatedValues,
-      );
-
-      setShowIncompatibilityDialog(false);
-    }, [
-      subAgentUpdate,
-      data.hardcodedValues,
-      data.connections,
-      id,
-      enterResolutionMode,
-    ]);
+    const { libraryAgent, setIsAnyModalOpen, getNextNodeId } = builderContext;
 
     useEffect(() => {
       if (data.executionResults || data.status) {
@@ -344,6 +156,13 @@ export const CustomNode = React.memo(
       setIsAnyModalOpen?.(isModalOpen || isOutputModalOpen);
     }, [isModalOpen, isOutputModalOpen, data, setIsAnyModalOpen]);
 
+    const setHardcodedValues = useCallback(
+      (values: any) => {
+        updateNodeData(id, { hardcodedValues: values });
+      },
+      [id, updateNodeData],
+    );
+
     const handleTitleEdit = useCallback(() => {
       setIsEditingTitle(true);
       setTimeout(() => {
@@ -436,7 +255,6 @@ export const CustomNode = React.memo(
                 isConnected={isOutputHandleConnected(propKey)}
                 schema={fieldSchema}
                 side="right"
-                isBroken={isOutputHandleBroken(propKey)}
               />
               {"properties" in fieldSchema &&
                 renderHandles(
@@ -567,7 +385,6 @@ export const CustomNode = React.memo(
                       isRequired={isRequired}
                       schema={propSchema}
                       side="left"
-                      isBroken={isInputHandleBroken(propKey)}
                     />
                   ) : (
                     propKey !== "credentials" &&
@@ -1056,22 +873,6 @@ export const CustomNode = React.memo(
           <ContextMenuContent />
         </div>
 
-        {/* Sub-agent Update Bar - shown below header */}
-        {isAgentBlock && (subAgentUpdate.hasUpdate || isInResolutionMode) && (
-          <SubAgentUpdateBar
-            currentVersion={subAgentUpdate.currentVersion}
-            latestVersion={subAgentUpdate.latestVersion}
-            isCompatible={subAgentUpdate.isCompatible}
-            incompatibilities={
-              isInResolutionMode
-                ? resolutionMode.incompatibilities
-                : subAgentUpdate.incompatibilities
-            }
-            onUpdate={handleUpdateClick}
-            isInResolutionMode={isInResolutionMode}
-          />
-        )}
-
         {/* Body */}
         <div className="mx-5 my-6 rounded-b-xl">
           {/* Input Handles */}
@@ -1243,24 +1044,9 @@ export const CustomNode = React.memo(
     );
 
     return (
-      <>
-        <ContextMenu.Root>
-          <ContextMenu.Trigger>{nodeContent()}</ContextMenu.Trigger>
-        </ContextMenu.Root>
-
-        {/* Incompatibility Dialog for sub-agent updates */}
-        {isAgentBlock && subAgentUpdate.incompatibilities && (
-          <IncompatibilityDialog
-            isOpen={showIncompatibilityDialog}
-            onClose={() => setShowIncompatibilityDialog(false)}
-            onConfirm={handleConfirmIncompatibleUpdate}
-            currentVersion={subAgentUpdate.currentVersion}
-            latestVersion={subAgentUpdate.latestVersion}
-            agentName={data.blockType || "Agent"}
-            incompatibilities={subAgentUpdate.incompatibilities}
-          />
-        )}
-      </>
+      <ContextMenu.Root>
+        <ContextMenu.Trigger>{nodeContent()}</ContextMenu.Trigger>
+      </ContextMenu.Root>
     );
   },
   (prevProps, nextProps) => {

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

@@ -1,244 +0,0 @@
-import React from "react";
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogFooter,
-  DialogHeader,
-  DialogTitle,
-} from "@/components/__legacy__/ui/dialog";
-import { Button } from "@/components/__legacy__/ui/button";
-import { AlertTriangle, XCircle, PlusCircle } from "lucide-react";
-import { IncompatibilityInfo } from "../../../hooks/useSubAgentUpdate/types";
-import { beautifyString } from "@/lib/utils";
-import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
-
-interface IncompatibilityDialogProps {
-  isOpen: boolean;
-  onClose: () => void;
-  onConfirm: () => void;
-  currentVersion: number;
-  latestVersion: number;
-  agentName: string;
-  incompatibilities: IncompatibilityInfo;
-}
-
-export const IncompatibilityDialog: React.FC<IncompatibilityDialogProps> = ({
-  isOpen,
-  onClose,
-  onConfirm,
-  currentVersion,
-  latestVersion,
-  agentName,
-  incompatibilities,
-}) => {
-  const hasMissingInputs = incompatibilities.missingInputs.length > 0;
-  const hasMissingOutputs = incompatibilities.missingOutputs.length > 0;
-  const hasNewInputs = incompatibilities.newInputs.length > 0;
-  const hasNewOutputs = incompatibilities.newOutputs.length > 0;
-  const hasNewRequired = incompatibilities.newRequiredInputs.length > 0;
-  const hasTypeMismatches = incompatibilities.inputTypeMismatches.length > 0;
-
-  const hasInputChanges = hasMissingInputs || hasNewInputs;
-  const hasOutputChanges = hasMissingOutputs || hasNewOutputs;
-
-  return (
-    <Dialog open={isOpen} onOpenChange={(open) => !open && onClose()}>
-      <DialogContent className="max-w-lg">
-        <DialogHeader>
-          <DialogTitle className="flex items-center gap-2">
-            <AlertTriangle className="h-5 w-5 text-amber-500" />
-            Incompatible Update
-          </DialogTitle>
-          <DialogDescription>
-            Updating <strong>{beautifyString(agentName)}</strong> from v
-            {currentVersion} to v{latestVersion} will break some connections.
-          </DialogDescription>
-        </DialogHeader>
-
-        <div className="space-y-4 py-2">
-          {/* Input changes - two column layout */}
-          {hasInputChanges && (
-            <TwoColumnSection
-              title="Input Changes"
-              leftIcon={<XCircle className="h-4 w-4 text-red-500" />}
-              leftTitle="Removed"
-              leftItems={incompatibilities.missingInputs}
-              rightIcon={<PlusCircle className="h-4 w-4 text-green-500" />}
-              rightTitle="Added"
-              rightItems={incompatibilities.newInputs}
-            />
-          )}
-
-          {/* Output changes - two column layout */}
-          {hasOutputChanges && (
-            <TwoColumnSection
-              title="Output Changes"
-              leftIcon={<XCircle className="h-4 w-4 text-red-500" />}
-              leftTitle="Removed"
-              leftItems={incompatibilities.missingOutputs}
-              rightIcon={<PlusCircle className="h-4 w-4 text-green-500" />}
-              rightTitle="Added"
-              rightItems={incompatibilities.newOutputs}
-            />
-          )}
-
-          {hasTypeMismatches && (
-            <SingleColumnSection
-              icon={<XCircle className="h-4 w-4 text-red-500" />}
-              title="Type Changed"
-              description="These connected inputs have a different type:"
-              items={incompatibilities.inputTypeMismatches.map(
-                (m) => `${m.name} (${m.oldType} → ${m.newType})`,
-              )}
-            />
-          )}
-
-          {hasNewRequired && (
-            <SingleColumnSection
-              icon={<PlusCircle className="h-4 w-4 text-amber-500" />}
-              title="New Required Inputs"
-              description="These inputs are now required:"
-              items={incompatibilities.newRequiredInputs}
-            />
-          )}
-        </div>
-
-        <Alert variant="warning">
-          <AlertDescription>
-            If you proceed, you&apos;ll need to remove the broken connections
-            before you can save or run your agent.
-          </AlertDescription>
-        </Alert>
-
-        <DialogFooter className="gap-2 sm:gap-0">
-          <Button variant="outline" onClick={onClose}>
-            Cancel
-          </Button>
-          <Button
-            variant="destructive"
-            onClick={onConfirm}
-            className="bg-amber-600 hover:bg-amber-700"
-          >
-            Update Anyway
-          </Button>
-        </DialogFooter>
-      </DialogContent>
-    </Dialog>
-  );
-};
-
-interface TwoColumnSectionProps {
-  title: string;
-  leftIcon: React.ReactNode;
-  leftTitle: string;
-  leftItems: string[];
-  rightIcon: React.ReactNode;
-  rightTitle: string;
-  rightItems: string[];
-}
-
-const TwoColumnSection: React.FC<TwoColumnSectionProps> = ({
-  title,
-  leftIcon,
-  leftTitle,
-  leftItems,
-  rightIcon,
-  rightTitle,
-  rightItems,
-}) => (
-  <div className="rounded-md border border-gray-200 p-3 dark:border-gray-700">
-    <span className="font-medium">{title}</span>
-    <div className="mt-2 grid grid-cols-2 items-start gap-4">
-      {/* Left column - Breaking changes */}
-      <div className="min-w-0">
-        <div className="flex items-center gap-1.5 text-sm text-gray-500 dark:text-gray-400">
-          {leftIcon}
-          <span>{leftTitle}</span>
-        </div>
-        <ul className="mt-1.5 space-y-1">
-          {leftItems.length > 0 ? (
-            leftItems.map((item) => (
-              <li
-                key={item}
-                className="text-sm text-gray-700 dark:text-gray-300"
-              >
-                <code className="rounded bg-red-50 px-1 py-0.5 font-mono text-xs text-red-700 dark:bg-red-900/30 dark:text-red-300">
-                  {item}
-                </code>
-              </li>
-            ))
-          ) : (
-            <li className="text-sm italic text-gray-400 dark:text-gray-500">
-              None
-            </li>
-          )}
-        </ul>
-      </div>
-
-      {/* Right column - Possible solutions */}
-      <div className="min-w-0">
-        <div className="flex items-center gap-1.5 text-sm text-gray-500 dark:text-gray-400">
-          {rightIcon}
-          <span>{rightTitle}</span>
-        </div>
-        <ul className="mt-1.5 space-y-1">
-          {rightItems.length > 0 ? (
-            rightItems.map((item) => (
-              <li
-                key={item}
-                className="text-sm text-gray-700 dark:text-gray-300"
-              >
-                <code className="rounded bg-green-50 px-1 py-0.5 font-mono text-xs text-green-700 dark:bg-green-900/30 dark:text-green-300">
-                  {item}
-                </code>
-              </li>
-            ))
-          ) : (
-            <li className="text-sm italic text-gray-400 dark:text-gray-500">
-              None
-            </li>
-          )}
-        </ul>
-      </div>
-    </div>
-  </div>
-);
-
-interface SingleColumnSectionProps {
-  icon: React.ReactNode;
-  title: string;
-  description: string;
-  items: string[];
-}
-
-const SingleColumnSection: React.FC<SingleColumnSectionProps> = ({
-  icon,
-  title,
-  description,
-  items,
-}) => (
-  <div className="rounded-md border border-gray-200 p-3 dark:border-gray-700">
-    <div className="flex items-center gap-2">
-      {icon}
-      <span className="font-medium">{title}</span>
-    </div>
-    <p className="mt-1 text-sm text-gray-500 dark:text-gray-400">
-      {description}
-    </p>
-    <ul className="mt-2 space-y-1">
-      {items.map((item) => (
-        <li
-          key={item}
-          className="ml-4 list-disc text-sm text-gray-700 dark:text-gray-300"
-        >
-          <code className="rounded bg-gray-100 px-1 py-0.5 font-mono text-xs dark:bg-gray-800">
-            {item}
-          </code>
-        </li>
-      ))}
-    </ul>
-  </div>
-);
-
-export default IncompatibilityDialog;

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

@@ -1,130 +0,0 @@
-import React from "react";
-import { Button } from "@/components/__legacy__/ui/button";
-import { ArrowUp, AlertTriangle, Info } from "lucide-react";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
-import { IncompatibilityInfo } from "../../../hooks/useSubAgentUpdate/types";
-import { cn } from "@/lib/utils";
-
-interface SubAgentUpdateBarProps {
-  currentVersion: number;
-  latestVersion: number;
-  isCompatible: boolean;
-  incompatibilities: IncompatibilityInfo | null;
-  onUpdate: () => void;
-  isInResolutionMode?: boolean;
-}
-
-export const SubAgentUpdateBar: React.FC<SubAgentUpdateBarProps> = ({
-  currentVersion,
-  latestVersion,
-  isCompatible,
-  incompatibilities,
-  onUpdate,
-  isInResolutionMode = false,
-}) => {
-  if (isInResolutionMode) {
-    return <ResolutionModeBar incompatibilities={incompatibilities} />;
-  }
-
-  return (
-    <div className="flex items-center justify-between gap-2 rounded-t-lg bg-blue-50 px-3 py-2 dark:bg-blue-900/30">
-      <div className="flex items-center gap-2">
-        <ArrowUp className="h-4 w-4 text-blue-600 dark:text-blue-400" />
-        <span className="text-sm text-blue-700 dark:text-blue-300">
-          Update available (v{currentVersion} → v{latestVersion})
-        </span>
-        {!isCompatible && (
-          <Tooltip>
-            <TooltipTrigger asChild>
-              <AlertTriangle className="h-4 w-4 text-amber-500" />
-            </TooltipTrigger>
-            <TooltipContent className="max-w-xs">
-              <p className="font-medium">Incompatible changes detected</p>
-              <p className="text-xs text-gray-400">
-                Click Update to see details
-              </p>
-            </TooltipContent>
-          </Tooltip>
-        )}
-      </div>
-      <Button
-        size="sm"
-        variant={isCompatible ? "default" : "outline"}
-        onClick={onUpdate}
-        className={cn(
-          "h-7 text-xs",
-          !isCompatible && "border-amber-500 text-amber-600 hover:bg-amber-50",
-        )}
-      >
-        Update
-      </Button>
-    </div>
-  );
-};
-
-interface ResolutionModeBarProps {
-  incompatibilities: IncompatibilityInfo | null;
-}
-
-const ResolutionModeBar: React.FC<ResolutionModeBarProps> = ({
-  incompatibilities,
-}) => {
-  const formatIncompatibilities = () => {
-    if (!incompatibilities) return "No incompatibilities";
-
-    const items: string[] = [];
-
-    if (incompatibilities.missingInputs.length > 0) {
-      items.push(
-        `Missing inputs: ${incompatibilities.missingInputs.join(", ")}`,
-      );
-    }
-    if (incompatibilities.missingOutputs.length > 0) {
-      items.push(
-        `Missing outputs: ${incompatibilities.missingOutputs.join(", ")}`,
-      );
-    }
-    if (incompatibilities.newRequiredInputs.length > 0) {
-      items.push(
-        `New required inputs: ${incompatibilities.newRequiredInputs.join(", ")}`,
-      );
-    }
-    if (incompatibilities.inputTypeMismatches.length > 0) {
-      const mismatches = incompatibilities.inputTypeMismatches
-        .map((m) => `${m.name} (${m.oldType} → ${m.newType})`)
-        .join(", ");
-      items.push(`Type changed: ${mismatches}`);
-    }
-
-    return items.join("\n");
-  };
-
-  return (
-    <div className="flex items-center justify-between gap-2 rounded-t-lg bg-amber-50 px-3 py-2 dark:bg-amber-900/30">
-      <div className="flex items-center gap-2">
-        <AlertTriangle className="h-4 w-4 text-amber-600 dark:text-amber-400" />
-        <span className="text-sm text-amber-700 dark:text-amber-300">
-          Remove incompatible connections
-        </span>
-        <Tooltip>
-          <TooltipTrigger asChild>
-            <Info className="h-4 w-4 cursor-help text-amber-500" />
-          </TooltipTrigger>
-          <TooltipContent className="max-w-sm whitespace-pre-line">
-            <p className="font-medium">Incompatible changes:</p>
-            <p className="mt-1 text-xs">{formatIncompatibilities()}</p>
-            <p className="mt-2 text-xs text-gray-400">
-              Delete the red connections to continue
-            </p>
-          </TooltipContent>
-        </Tooltip>
-      </div>
-    </div>
-  );
-};
-
-export default SubAgentUpdateBar;

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

@@ -26,17 +26,15 @@ import {
   applyNodeChanges,
 } from "@xyflow/react";
 import "@xyflow/react/dist/style.css";
-import { ConnectedEdge, CustomNode } from "../CustomNode/CustomNode";
+import { CustomNode } from "../CustomNode/CustomNode";
 import "./flow.css";
 import {
   BlockUIType,
   formatEdgeID,
   GraphExecutionID,
   GraphID,
-  GraphMeta,
   LibraryAgent,
 } from "@/lib/autogpt-server-api";
-import { IncompatibilityInfo } from "../../../hooks/useSubAgentUpdate/types";
 import { Key, storage } from "@/services/storage/local-storage";
 import { findNewlyAddedBlockCoordinates, getTypeColor } from "@/lib/utils";
 import { history } from "../history";
@@ -74,30 +72,12 @@ import { FloatingSafeModeToggle } from "../../FloatingSafeModeToogle";
 // It helps to prevent spamming the history with small movements especially when pressing on a input in a block
 const MINIMUM_MOVE_BEFORE_LOG = 50;
 
-export type ResolutionModeState = {
-  active: boolean;
-  nodeId: string | null;
-  incompatibilities: IncompatibilityInfo | null;
-  brokenEdgeIds: string[];
-  pendingUpdate: Record<string, unknown> | null; // The hardcoded values to apply after resolution
-};
-
 type BuilderContextType = {
   libraryAgent: LibraryAgent | null;
   visualizeBeads: "no" | "static" | "animate";
   setIsAnyModalOpen: (isOpen: boolean) => void;
   getNextNodeId: () => string;
   getNodeTitle: (nodeID: string) => string | null;
-  availableFlows: GraphMeta[];
-  resolutionMode: ResolutionModeState;
-  enterResolutionMode: (
-    nodeId: string,
-    incompatibilities: IncompatibilityInfo,
-    brokenEdgeIds: string[],
-    pendingUpdate: Record<string, unknown>,
-  ) => void;
-  exitResolutionMode: () => void;
-  applyPendingUpdate: () => void;
 };
 
 export type NodeDimension = {
@@ -192,92 +172,6 @@ const FlowEditor: React.FC<{
   // It stores the dimension of all nodes with position as well
   const [nodeDimensions, setNodeDimensions] = useState<NodeDimension>({});
 
-  // Resolution mode state for sub-agent incompatible updates
-  const [resolutionMode, setResolutionMode] = useState<ResolutionModeState>({
-    active: false,
-    nodeId: null,
-    incompatibilities: null,
-    brokenEdgeIds: [],
-    pendingUpdate: null,
-  });
-
-  const enterResolutionMode = useCallback(
-    (
-      nodeId: string,
-      incompatibilities: IncompatibilityInfo,
-      brokenEdgeIds: string[],
-      pendingUpdate: Record<string, unknown>,
-    ) => {
-      setResolutionMode({
-        active: true,
-        nodeId,
-        incompatibilities,
-        brokenEdgeIds,
-        pendingUpdate,
-      });
-    },
-    [],
-  );
-
-  const exitResolutionMode = useCallback(() => {
-    setResolutionMode({
-      active: false,
-      nodeId: null,
-      incompatibilities: null,
-      brokenEdgeIds: [],
-      pendingUpdate: null,
-    });
-  }, []);
-
-  // Apply pending update after resolution mode completes
-  const applyPendingUpdate = useCallback(() => {
-    if (!resolutionMode.nodeId || !resolutionMode.pendingUpdate) return;
-
-    const node = nodes.find((n) => n.id === resolutionMode.nodeId);
-    if (node) {
-      const pendingUpdate = resolutionMode.pendingUpdate as {
-        [key: string]: any;
-      };
-      setNodes((nds) =>
-        nds.map((n) =>
-          n.id === resolutionMode.nodeId
-            ? { ...n, data: { ...n.data, hardcodedValues: pendingUpdate } }
-            : n,
-        ),
-      );
-    }
-    exitResolutionMode();
-    toast({
-      title: "Update complete",
-      description: "Agent has been updated to the new version.",
-    });
-  }, [resolutionMode, nodes, setNodes, exitResolutionMode, toast]);
-
-  // Check if all broken edges have been removed and auto-apply pending update
-  useEffect(() => {
-    if (!resolutionMode.active || resolutionMode.brokenEdgeIds.length === 0) {
-      return;
-    }
-
-    const currentEdgeIds = new Set(edges.map((e) => e.id));
-    const remainingBrokenEdges = resolutionMode.brokenEdgeIds.filter((id) =>
-      currentEdgeIds.has(id),
-    );
-
-    if (remainingBrokenEdges.length === 0) {
-      // All broken edges have been removed, apply pending update
-      applyPendingUpdate();
-    } else if (
-      remainingBrokenEdges.length !== resolutionMode.brokenEdgeIds.length
-    ) {
-      // Update the list of broken edges
-      setResolutionMode((prev) => ({
-        ...prev,
-        brokenEdgeIds: remainingBrokenEdges,
-      }));
-    }
-  }, [edges, resolutionMode, applyPendingUpdate]);
-
   // Set page title with or without graph name
   useEffect(() => {
     document.title = savedAgent
@@ -537,19 +431,17 @@ const FlowEditor: React.FC<{
                 ...node.data.connections.filter(
                   (conn) =>
                     !removedEdges.some(
-                      (removedEdge) => removedEdge.id === conn.id,
+                      (removedEdge) => removedEdge.id === conn.edge_id,
                     ),
                 ),
                 // Add node connections for added edges
-                ...addedEdges.map(
-                  (addedEdge): ConnectedEdge => ({
-                    id: addedEdge.item.id,
-                    source: addedEdge.item.source,
-                    target: addedEdge.item.target,
-                    sourceHandle: addedEdge.item.sourceHandle!,
-                    targetHandle: addedEdge.item.targetHandle!,
-                  }),
-                ),
+                ...addedEdges.map((addedEdge) => ({
+                  edge_id: addedEdge.item.id,
+                  source: addedEdge.item.source,
+                  target: addedEdge.item.target,
+                  sourceHandle: addedEdge.item.sourceHandle!,
+                  targetHandle: addedEdge.item.targetHandle!,
+                })),
               ],
             },
           }));
@@ -575,15 +467,13 @@ const FlowEditor: React.FC<{
             data: {
               ...node.data,
               connections: [
-                ...replaceEdges.map(
-                  (replaceEdge): ConnectedEdge => ({
-                    id: replaceEdge.item.id,
-                    source: replaceEdge.item.source,
-                    target: replaceEdge.item.target,
-                    sourceHandle: replaceEdge.item.sourceHandle!,
-                    targetHandle: replaceEdge.item.targetHandle!,
-                  }),
-                ),
+                ...replaceEdges.map((replaceEdge) => ({
+                  edge_id: replaceEdge.item.id,
+                  source: replaceEdge.item.source,
+                  target: replaceEdge.item.target,
+                  sourceHandle: replaceEdge.item.sourceHandle!,
+                  targetHandle: replaceEdge.item.targetHandle!,
+                })),
               ],
             },
           })),
@@ -1000,23 +890,8 @@ const FlowEditor: React.FC<{
       setIsAnyModalOpen,
       getNextNodeId,
       getNodeTitle,
-      availableFlows,
-      resolutionMode,
-      enterResolutionMode,
-      exitResolutionMode,
-      applyPendingUpdate,
     }),
-    [
-      libraryAgent,
-      visualizeBeads,
-      getNextNodeId,
-      getNodeTitle,
-      availableFlows,
-      resolutionMode,
-      enterResolutionMode,
-      applyPendingUpdate,
-      exitResolutionMode,
-    ],
+    [libraryAgent, visualizeBeads, getNextNodeId, getNodeTitle],
   );
 
   return (
@@ -1116,7 +991,6 @@ const FlowEditor: React.FC<{
               onClickScheduleButton={handleScheduleButton}
               isDisabled={!savedAgent}
               isRunning={isRunning}
-              resolutionModeActive={resolutionMode.active}
             />
           ) : (
             <Alert className="absolute bottom-4 left-1/2 z-20 w-auto -translate-x-1/2 select-none">

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

@@ -1,11 +1,6 @@
 import { BlockIOSubSchema } from "@/lib/autogpt-server-api/types";
-import {
-  cn,
-  beautifyString,
-  getTypeBgColor,
-  getTypeTextColor,
-  getEffectiveType,
-} from "@/lib/utils";
+import { cn } from "@/lib/utils";
+import { beautifyString, getTypeBgColor, getTypeTextColor } from "@/lib/utils";
 import { FC, memo, useCallback } from "react";
 import { Handle, Position } from "@xyflow/react";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
@@ -18,7 +13,6 @@ type HandleProps = {
   side: "left" | "right";
   title?: string;
   className?: string;
-  isBroken?: boolean;
 };
 
 // Move the constant out of the component to avoid re-creation on every render.
@@ -33,23 +27,18 @@ const TYPE_NAME: Record<string, string> = {
 };
 
 // Extract and memoize the Dot component so that it doesn't re-render unnecessarily.
-const Dot: FC<{ isConnected: boolean; type?: string; isBroken?: boolean }> =
-  memo(({ isConnected, type, isBroken }) => {
-    const color = isBroken
-      ? "border-red-500 bg-red-100 dark:bg-red-900/30"
-      : isConnected
-        ? getTypeBgColor(type || "any")
-        : "border-gray-300 dark:border-gray-600";
+const Dot: FC<{ isConnected: boolean; type?: string }> = memo(
+  ({ isConnected, type }) => {
+    const color = isConnected
+      ? getTypeBgColor(type || "any")
+      : "border-gray-300 dark:border-gray-600";
     return (
       <div
-        className={cn(
-          "m-1 h-4 w-4 rounded-full border-2 bg-white transition-colors duration-100 group-hover:bg-gray-300 dark:bg-slate-800 dark:group-hover:bg-gray-700",
-          color,
-          isBroken && "opacity-50",
-        )}
+        className={`${color} m-1 h-4 w-4 rounded-full border-2 bg-white transition-colors duration-100 group-hover:bg-gray-300 dark:bg-slate-800 dark:group-hover:bg-gray-700`}
       />
     );
-  });
+  },
+);
 Dot.displayName = "Dot";
 
 const NodeHandle: FC<HandleProps> = ({
@@ -60,34 +49,24 @@ const NodeHandle: FC<HandleProps> = ({
   side,
   title,
   className,
-  isBroken = false,
 }) => {
-  // Extract effective type from schema (handles anyOf/oneOf/allOf wrappers)
-  const effectiveType = getEffectiveType(schema);
-
-  const typeClass = `text-sm ${getTypeTextColor(effectiveType || "any")} ${
+  const typeClass = `text-sm ${getTypeTextColor(schema.type || "any")} ${
     side === "left" ? "text-left" : "text-right"
   }`;
 
   const label = (
-    <div className={cn("flex flex-grow flex-row", isBroken && "opacity-50")}>
+    <div className="flex flex-grow flex-row">
       <span
         className={cn(
           "data-sentry-unmask text-m green flex items-end pr-2 text-gray-900 dark:text-gray-100",
           className,
-          isBroken && "text-red-500 line-through",
         )}
       >
         {title || schema.title || beautifyString(keyName.toLowerCase())}
         {isRequired ? "*" : ""}
       </span>
-      <span
-        className={cn(
-          `${typeClass} data-sentry-unmask flex items-end`,
-          isBroken && "text-red-400",
-        )}
-      >
-        ({TYPE_NAME[effectiveType as keyof typeof TYPE_NAME] || "any"})
+      <span className={`${typeClass} data-sentry-unmask flex items-end`}>
+        ({TYPE_NAME[schema.type as keyof typeof TYPE_NAME] || "any"})
       </span>
     </div>
   );
@@ -105,7 +84,7 @@ const NodeHandle: FC<HandleProps> = ({
     return (
       <div
         key={keyName}
-        className={cn("handle-container", isBroken && "pointer-events-none")}
+        className="handle-container"
         onContextMenu={handleContextMenu}
       >
         <Handle
@@ -113,15 +92,10 @@ const NodeHandle: FC<HandleProps> = ({
           data-testid={`input-handle-${keyName}`}
           position={Position.Left}
           id={keyName}
-          className={cn("group -ml-[38px]", isBroken && "cursor-not-allowed")}
-          isConnectable={!isBroken}
+          className="group -ml-[38px]"
         >
           <div className="pointer-events-none flex items-center">
-            <Dot
-              isConnected={isConnected}
-              type={effectiveType}
-              isBroken={isBroken}
-            />
+            <Dot isConnected={isConnected} type={schema.type} />
             {label}
           </div>
         </Handle>
@@ -132,10 +106,7 @@ const NodeHandle: FC<HandleProps> = ({
     return (
       <div
         key={keyName}
-        className={cn(
-          "handle-container justify-end",
-          isBroken && "pointer-events-none",
-        )}
+        className="handle-container justify-end"
         onContextMenu={handleContextMenu}
       >
         <Handle
@@ -143,16 +114,11 @@ const NodeHandle: FC<HandleProps> = ({
           data-testid={`output-handle-${keyName}`}
           position={Position.Right}
           id={keyName}
-          className={cn("group -mr-[38px]", isBroken && "cursor-not-allowed")}
-          isConnectable={!isBroken}
+          className="group -mr-[38px]"
         >
           <div className="pointer-events-none flex items-center">
             {label}
-            <Dot
-              isConnected={isConnected}
-              type={effectiveType}
-              isBroken={isBroken}
-            />
+            <Dot isConnected={isConnected} type={schema.type} />
           </div>
         </Handle>
       </div>

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

@@ -1,5 +1,5 @@
 import {
-  ConnectedEdge,
+  ConnectionData,
   CustomNodeData,
 } from "@/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode";
 import { NodeTableInput } from "@/app/(platform)/build/components/legacy-builder/NodeTableInput";
@@ -65,7 +65,7 @@ type NodeObjectInputTreeProps = {
   selfKey?: string;
   schema: BlockIORootSchema | BlockIOObjectSubSchema;
   object?: { [key: string]: any };
-  connections: ConnectedEdge[];
+  connections: ConnectionData;
   handleInputClick: (key: string) => void;
   handleInputChange: (key: string, value: any) => void;
   errors: { [key: string]: string | undefined };
@@ -585,7 +585,7 @@ const NodeOneOfDiscriminatorField: FC<{
   currentValue?: any;
   defaultValue?: any;
   errors: { [key: string]: string | undefined };
-  connections: ConnectedEdge[];
+  connections: ConnectionData;
   handleInputChange: (key: string, value: any) => void;
   handleInputClick: (key: string) => void;
   className?: string;

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

@@ -1,16 +1,15 @@
 import { FC, useCallback, useEffect, useState } from "react";
 
 import NodeHandle from "@/app/(platform)/build/components/legacy-builder/NodeHandle";
-import type {
+import {
   BlockIOTableSubSchema,
   TableCellValue,
   TableRow,
 } from "@/lib/autogpt-server-api/types";
-import type { ConnectedEdge } from "./CustomNode/CustomNode";
 import { cn } from "@/lib/utils";
 import { PlusIcon, XIcon } from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "../../../../../components/atoms/Button/Button";
+import { Input } from "../../../../../components/atoms/Input/Input";
 
 interface NodeTableInputProps {
   /** Unique identifier for the node in the builder graph */
@@ -26,7 +25,13 @@ interface NodeTableInputProps {
   /** Validation errors mapped by field key */
   errors: { [key: string]: string | undefined };
   /** Graph connections between nodes in the builder */
-  connections: ConnectedEdge[];
+  connections: {
+    edge_id: string;
+    source: string;
+    sourceHandle: string;
+    target: string;
+    targetHandle: string;
+  }[];
   /** Callback when table data changes */
   handleInputChange: (key: string, value: TableRow[]) => void;
   /** Callback when input field is clicked (for builder selection) */

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/useCopyPaste.ts

@@ -1,7 +1,6 @@
 import { useCallback } from "react";
 import { Node, Edge, useReactFlow } from "@xyflow/react";
 import { Key, storage } from "@/services/storage/local-storage";
-import { ConnectedEdge } from "./CustomNode/CustomNode";
 
 interface CopyableData {
   nodes: Node[];
@@ -112,15 +111,13 @@ export function useCopyPaste(getNextNodeId: () => string) {
                     (edge: Edge) =>
                       edge.source === node.id || edge.target === node.id,
                   )
-                  .map(
-                    (edge: Edge): ConnectedEdge => ({
-                      id: edge.id,
-                      source: edge.source,
-                      target: edge.target,
-                      sourceHandle: edge.sourceHandle!,
-                      targetHandle: edge.targetHandle!,
-                    }),
-                  );
+                  .map((edge: Edge) => ({
+                    edge_id: edge.id,
+                    source: edge.source,
+                    target: edge.target,
+                    sourceHandle: edge.sourceHandle,
+                    targetHandle: edge.targetHandle,
+                  }));
 
                 return {
                   ...node,

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/helpers.ts

@@ -1,104 +0,0 @@
-import { GraphInputSchema } from "@/lib/autogpt-server-api";
-import { GraphMetaLike, IncompatibilityInfo } from "./types";
-
-// Helper type for schema properties - the generated types are too loose
-type SchemaProperties = Record<string, GraphInputSchema["properties"][string]>;
-type SchemaRequired = string[];
-
-// Helper to safely extract schema properties
-export function getSchemaProperties(schema: unknown): SchemaProperties {
-  if (
-    schema &&
-    typeof schema === "object" &&
-    "properties" in schema &&
-    typeof schema.properties === "object" &&
-    schema.properties !== null
-  ) {
-    return schema.properties as SchemaProperties;
-  }
-  return {};
-}
-
-export function getSchemaRequired(schema: unknown): SchemaRequired {
-  if (
-    schema &&
-    typeof schema === "object" &&
-    "required" in schema &&
-    Array.isArray(schema.required)
-  ) {
-    return schema.required as SchemaRequired;
-  }
-  return [];
-}
-
-/**
- * Creates the updated agent node inputs for a sub-agent node
- */
-export function createUpdatedAgentNodeInputs(
-  currentInputs: Record<string, unknown>,
-  latestSubGraphVersion: GraphMetaLike,
-): Record<string, unknown> {
-  return {
-    ...currentInputs,
-    graph_version: latestSubGraphVersion.version,
-    input_schema: latestSubGraphVersion.input_schema,
-    output_schema: latestSubGraphVersion.output_schema,
-  };
-}
-
-/** Generic edge type that works with both builders:
- *  - New builder uses CustomEdge with (formally) optional handles
- *  - Legacy builder uses ConnectedEdge type with required handles */
-export type EdgeLike = {
-  id: string;
-  source: string;
-  target: string;
-  sourceHandle?: string | null;
-  targetHandle?: string | null;
-};
-
-/**
- * Determines which edges are broken after an incompatible update.
- * Works with both legacy ConnectedEdge and new CustomEdge.
- */
-export function getBrokenEdgeIDs(
-  connections: EdgeLike[],
-  incompatibilities: IncompatibilityInfo,
-  nodeID: string,
-): string[] {
-  const brokenEdgeIDs: string[] = [];
-  const typeMismatchInputNames = new Set(
-    incompatibilities.inputTypeMismatches.map((m) => m.name),
-  );
-
-  connections.forEach((conn) => {
-    // Check if this connection uses a missing input (node is target)
-    if (
-      conn.target === nodeID &&
-      conn.targetHandle &&
-      incompatibilities.missingInputs.includes(conn.targetHandle)
-    ) {
-      brokenEdgeIDs.push(conn.id);
-    }
-
-    // Check if this connection uses an input with a type mismatch (node is target)
-    if (
-      conn.target === nodeID &&
-      conn.targetHandle &&
-      typeMismatchInputNames.has(conn.targetHandle)
-    ) {
-      brokenEdgeIDs.push(conn.id);
-    }
-
-    // Check if this connection uses a missing output (node is source)
-    if (
-      conn.source === nodeID &&
-      conn.sourceHandle &&
-      incompatibilities.missingOutputs.includes(conn.sourceHandle)
-    ) {
-      brokenEdgeIDs.push(conn.id);
-    }
-  });
-
-  return brokenEdgeIDs;
-}

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/index.ts

@@ -1,2 +0,0 @@
-export { useSubAgentUpdate } from "./useSubAgentUpdate";
-export { createUpdatedAgentNodeInputs, getBrokenEdgeIDs } from "./helpers";

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/types.ts

@@ -1,27 +0,0 @@
-import type { GraphMeta as LegacyGraphMeta } from "@/lib/autogpt-server-api";
-import type { GraphMeta as GeneratedGraphMeta } from "@/app/api/__generated__/models/graphMeta";
-
-export type SubAgentUpdateInfo<T extends GraphMetaLike = GraphMetaLike> = {
-  hasUpdate: boolean;
-  currentVersion: number;
-  latestVersion: number;
-  latestGraph: T | null;
-  isCompatible: boolean;
-  incompatibilities: IncompatibilityInfo | null;
-};
-
-// Union type for GraphMeta that works with both legacy and new builder
-export type GraphMetaLike = LegacyGraphMeta | GeneratedGraphMeta;
-
-export type IncompatibilityInfo = {
-  missingInputs: string[]; // Connected inputs that no longer exist
-  missingOutputs: string[]; // Connected outputs that no longer exist
-  newInputs: string[]; // Inputs that exist in new version but not in current
-  newOutputs: string[]; // Outputs that exist in new version but not in current
-  newRequiredInputs: string[]; // New required inputs not in current version or not required
-  inputTypeMismatches: Array<{
-    name: string;
-    oldType: string;
-    newType: string;
-  }>; // Connected inputs where the type has changed
-};

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSubAgentUpdate/useSubAgentUpdate.ts

@@ -1,160 +0,0 @@
-import { useMemo } from "react";
-import { GraphInputSchema, GraphOutputSchema } from "@/lib/autogpt-server-api";
-import { getEffectiveType } from "@/lib/utils";
-import { EdgeLike, getSchemaProperties, getSchemaRequired } from "./helpers";
-import {
-  GraphMetaLike,
-  IncompatibilityInfo,
-  SubAgentUpdateInfo,
-} from "./types";
-
-/**
- * Checks if a newer version of a sub-agent is available and determines compatibility
- */
-export function useSubAgentUpdate<T extends GraphMetaLike>(
-  nodeID: string,
-  graphID: string | undefined,
-  graphVersion: number | undefined,
-  currentInputSchema: GraphInputSchema | undefined,
-  currentOutputSchema: GraphOutputSchema | undefined,
-  connections: EdgeLike[],
-  availableGraphs: T[],
-): SubAgentUpdateInfo<T> {
-  // Find the latest version of the same graph
-  const latestGraph = useMemo(() => {
-    if (!graphID) return null;
-    return availableGraphs.find((graph) => graph.id === graphID) || null;
-  }, [graphID, availableGraphs]);
-
-  // Check if there's an update available
-  const hasUpdate = useMemo(() => {
-    if (!latestGraph || graphVersion === undefined) return false;
-    return latestGraph.version! > graphVersion;
-  }, [latestGraph, graphVersion]);
-
-  // Get connected input and output handles for this specific node
-  const connectedHandles = useMemo(() => {
-    const inputHandles = new Set<string>();
-    const outputHandles = new Set<string>();
-
-    connections.forEach((conn) => {
-      // If this node is the target, the targetHandle is an input on this node
-      if (conn.target === nodeID && conn.targetHandle) {
-        inputHandles.add(conn.targetHandle);
-      }
-      // If this node is the source, the sourceHandle is an output on this node
-      if (conn.source === nodeID && conn.sourceHandle) {
-        outputHandles.add(conn.sourceHandle);
-      }
-    });
-
-    return { inputHandles, outputHandles };
-  }, [connections, nodeID]);
-
-  // Check schema compatibility
-  const compatibilityResult = useMemo((): {
-    isCompatible: boolean;
-    incompatibilities: IncompatibilityInfo | null;
-  } => {
-    if (!hasUpdate || !latestGraph) {
-      return { isCompatible: true, incompatibilities: null };
-    }
-
-    const newInputProps = getSchemaProperties(latestGraph.input_schema);
-    const newOutputProps = getSchemaProperties(latestGraph.output_schema);
-    const newRequiredInputs = getSchemaRequired(latestGraph.input_schema);
-
-    const currentInputProps = getSchemaProperties(currentInputSchema);
-    const currentOutputProps = getSchemaProperties(currentOutputSchema);
-    const currentRequiredInputs = getSchemaRequired(currentInputSchema);
-
-    const incompatibilities: IncompatibilityInfo = {
-      missingInputs: [],
-      missingOutputs: [],
-      newInputs: [],
-      newOutputs: [],
-      newRequiredInputs: [],
-      inputTypeMismatches: [],
-    };
-
-    // Check for missing connected inputs and type mismatches
-    connectedHandles.inputHandles.forEach((inputHandle) => {
-      if (!(inputHandle in newInputProps)) {
-        incompatibilities.missingInputs.push(inputHandle);
-      } else {
-        // Check for type mismatch on connected inputs
-        const currentProp = currentInputProps[inputHandle];
-        const newProp = newInputProps[inputHandle];
-        const currentType = getEffectiveType(currentProp);
-        const newType = getEffectiveType(newProp);
-
-        if (currentType && newType && currentType !== newType) {
-          incompatibilities.inputTypeMismatches.push({
-            name: inputHandle,
-            oldType: currentType,
-            newType: newType,
-          });
-        }
-      }
-    });
-
-    // Check for missing connected outputs
-    connectedHandles.outputHandles.forEach((outputHandle) => {
-      if (!(outputHandle in newOutputProps)) {
-        incompatibilities.missingOutputs.push(outputHandle);
-      }
-    });
-
-    // Check for new required inputs that didn't exist or weren't required before
-    newRequiredInputs.forEach((requiredInput) => {
-      const existedBefore = requiredInput in currentInputProps;
-      const wasRequiredBefore = currentRequiredInputs.includes(
-        requiredInput as string,
-      );
-
-      if (!existedBefore || !wasRequiredBefore) {
-        incompatibilities.newRequiredInputs.push(requiredInput as string);
-      }
-    });
-
-    // Check for new inputs that don't exist in the current version
-    Object.keys(newInputProps).forEach((inputName) => {
-      if (!(inputName in currentInputProps)) {
-        incompatibilities.newInputs.push(inputName);
-      }
-    });
-
-    // Check for new outputs that don't exist in the current version
-    Object.keys(newOutputProps).forEach((outputName) => {
-      if (!(outputName in currentOutputProps)) {
-        incompatibilities.newOutputs.push(outputName);
-      }
-    });
-
-    const hasIncompatibilities =
-      incompatibilities.missingInputs.length > 0 ||
-      incompatibilities.missingOutputs.length > 0 ||
-      incompatibilities.newRequiredInputs.length > 0 ||
-      incompatibilities.inputTypeMismatches.length > 0;
-
-    return {
-      isCompatible: !hasIncompatibilities,
-      incompatibilities: hasIncompatibilities ? incompatibilities : null,
-    };
-  }, [
-    hasUpdate,
-    latestGraph,
-    currentInputSchema,
-    currentOutputSchema,
-    connectedHandles,
-  ]);
-
-  return {
-    hasUpdate,
-    currentVersion: graphVersion || 0,
-    latestVersion: latestGraph?.version || 0,
-    latestGraph,
-    isCompatible: compatibilityResult.isCompatible,
-    incompatibilities: compatibilityResult.incompatibilities,
-  };
-}

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

@@ -1,6 +1,5 @@
 import { create } from "zustand";
 import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
-import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
 
 interface GraphStore {
   graphExecutionStatus: AgentExecutionStatus | undefined;
@@ -18,10 +17,6 @@ interface GraphStore {
     outputSchema: Record<string, any> | null,
   ) => void;
 
-  // Available graphs; used for sub-graph updates
-  availableSubGraphs: GraphMeta[];
-  setAvailableSubGraphs: (graphs: GraphMeta[]) => void;
-
   hasInputs: () => boolean;
   hasCredentials: () => boolean;
   hasOutputs: () => boolean;
@@ -34,7 +29,6 @@ export const useGraphStore = create<GraphStore>((set, get) => ({
   inputSchema: null,
   credentialsInputSchema: null,
   outputSchema: null,
-  availableSubGraphs: [],
 
   setGraphExecutionStatus: (status: AgentExecutionStatus | undefined) => {
     set({
@@ -52,8 +46,6 @@ export const useGraphStore = create<GraphStore>((set, get) => ({
   setGraphSchemas: (inputSchema, credentialsInputSchema, outputSchema) =>
     set({ inputSchema, credentialsInputSchema, outputSchema }),
 
-  setAvailableSubGraphs: (graphs) => set({ availableSubGraphs: graphs }),
-
   hasOutputs: () => {
     const { outputSchema } = get();
     return Object.keys(outputSchema?.properties ?? {}).length > 0;

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

@@ -17,25 +17,6 @@ import {
   ensurePathExists,
   parseHandleIdToPath,
 } from "@/components/renderers/InputRenderer/helpers";
-import { IncompatibilityInfo } from "../hooks/useSubAgentUpdate/types";
-
-// Resolution mode data stored per node
-export type NodeResolutionData = {
-  incompatibilities: IncompatibilityInfo;
-  // The NEW schema from the update (what we're updating TO)
-  pendingUpdate: {
-    input_schema: Record<string, unknown>;
-    output_schema: Record<string, unknown>;
-  };
-  // The OLD schema before the update (what we're updating FROM)
-  // Needed to merge and show removed inputs during resolution
-  currentSchema: {
-    input_schema: Record<string, unknown>;
-    output_schema: Record<string, unknown>;
-  };
-  // The full updated hardcoded values to apply when resolution completes
-  pendingHardcodedValues: Record<string, unknown>;
-};
 
 // Minimum movement (in pixels) required before logging position change to history
 // Prevents spamming history with small movements when clicking on inputs inside blocks
@@ -84,32 +65,12 @@ type NodeStore = {
     backendId: string,
     errors: { [key: string]: string },
   ) => void;
+  clearAllNodeErrors: () => void; // Add this
 
   syncHardcodedValuesWithHandleIds: (nodeId: string) => void;
 
+  // Credentials optional helpers
   setCredentialsOptional: (nodeId: string, optional: boolean) => void;
-  clearAllNodeErrors: () => void;
-
-  nodesInResolutionMode: Set<string>;
-  brokenEdgeIDs: Map<string, Set<string>>;
-  nodeResolutionData: Map<string, NodeResolutionData>;
-  setNodeResolutionMode: (
-    nodeID: string,
-    inResolution: boolean,
-    resolutionData?: NodeResolutionData,
-  ) => void;
-  isNodeInResolutionMode: (nodeID: string) => boolean;
-  getNodeResolutionData: (nodeID: string) => NodeResolutionData | undefined;
-  setBrokenEdgeIDs: (nodeID: string, edgeIDs: string[]) => void;
-  removeBrokenEdgeID: (nodeID: string, edgeID: string) => void;
-  isEdgeBroken: (edgeID: string) => boolean;
-  clearResolutionState: () => void;
-
-  isInputBroken: (nodeID: string, handleID: string) => boolean;
-  getInputTypeMismatch: (
-    nodeID: string,
-    handleID: string,
-  ) => string | undefined;
 };
 
 export const useNodeStore = create<NodeStore>((set, get) => ({
@@ -413,99 +374,4 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
 
     useHistoryStore.getState().pushState(newState);
   },
-
-  // Sub-agent resolution mode state
-  nodesInResolutionMode: new Set<string>(),
-  brokenEdgeIDs: new Map<string, Set<string>>(),
-  nodeResolutionData: new Map<string, NodeResolutionData>(),
-
-  setNodeResolutionMode: (
-    nodeID: string,
-    inResolution: boolean,
-    resolutionData?: NodeResolutionData,
-  ) => {
-    set((state) => {
-      const newNodesSet = new Set(state.nodesInResolutionMode);
-      const newResolutionDataMap = new Map(state.nodeResolutionData);
-      const newBrokenEdgeIDs = new Map(state.brokenEdgeIDs);
-
-      if (inResolution) {
-        newNodesSet.add(nodeID);
-        if (resolutionData) {
-          newResolutionDataMap.set(nodeID, resolutionData);
-        }
-      } else {
-        newNodesSet.delete(nodeID);
-        newResolutionDataMap.delete(nodeID);
-        newBrokenEdgeIDs.delete(nodeID); // Clean up broken edges when exiting resolution mode
-      }
-
-      return {
-        nodesInResolutionMode: newNodesSet,
-        nodeResolutionData: newResolutionDataMap,
-        brokenEdgeIDs: newBrokenEdgeIDs,
-      };
-    });
-  },
-
-  isNodeInResolutionMode: (nodeID: string) => {
-    return get().nodesInResolutionMode.has(nodeID);
-  },
-
-  getNodeResolutionData: (nodeID: string) => {
-    return get().nodeResolutionData.get(nodeID);
-  },
-
-  setBrokenEdgeIDs: (nodeID: string, edgeIDs: string[]) => {
-    set((state) => {
-      const newMap = new Map(state.brokenEdgeIDs);
-      newMap.set(nodeID, new Set(edgeIDs));
-      return { brokenEdgeIDs: newMap };
-    });
-  },
-
-  removeBrokenEdgeID: (nodeID: string, edgeID: string) => {
-    set((state) => {
-      const newMap = new Map(state.brokenEdgeIDs);
-      const nodeSet = new Set(newMap.get(nodeID) || []);
-      nodeSet.delete(edgeID);
-      newMap.set(nodeID, nodeSet);
-      return { brokenEdgeIDs: newMap };
-    });
-  },
-
-  isEdgeBroken: (edgeID: string) => {
-    // Check across all nodes
-    const brokenEdgeIDs = get().brokenEdgeIDs;
-    for (const edgeSet of brokenEdgeIDs.values()) {
-      if (edgeSet.has(edgeID)) {
-        return true;
-      }
-    }
-    return false;
-  },
-
-  clearResolutionState: () => {
-    set({
-      nodesInResolutionMode: new Set<string>(),
-      brokenEdgeIDs: new Map<string, Set<string>>(),
-      nodeResolutionData: new Map<string, NodeResolutionData>(),
-    });
-  },
-
-  // Helper functions for input renderers
-  isInputBroken: (nodeID: string, handleID: string) => {
-    const resolutionData = get().nodeResolutionData.get(nodeID);
-    if (!resolutionData) return false;
-    return resolutionData.incompatibilities.missingInputs.includes(handleID);
-  },
-
-  getInputTypeMismatch: (nodeID: string, handleID: string) => {
-    const resolutionData = get().nodeResolutionData.get(nodeID);
-    if (!resolutionData) return undefined;
-    const mismatch = resolutionData.incompatibilities.inputTypeMismatches.find(
-      (m) => m.name === handleID,
-    );
-    return mismatch?.newType;
-  },
 }));

autogpt_platform/frontend/src/app/(platform)/error.tsx

@@ -18,7 +18,7 @@ function ErrorPageContent() {
     ) {
       window.location.href = "/login";
     } else {
-      window.document.location.reload();
+      window.location.href = "/marketplace";
     }
   }
 

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

@@ -940,67 +940,11 @@
       }
     },
     "/api/chat/sessions": {
-      "get": {
-        "tags": ["v2", "chat", "chat"],
-        "summary": "List Sessions",
-        "description": "List chat sessions for the authenticated user.\n\nReturns a paginated list of chat sessions belonging to the current user,\nordered by most recently updated.\n\nArgs:\n    user_id: The authenticated user's ID.\n    limit: Maximum number of sessions to return (1-100).\n    offset: Number of sessions to skip for pagination.\n\nReturns:\n    ListSessionsResponse: List of session summaries and total count.",
-        "operationId": "getV2ListSessions",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "limit",
-            "in": "query",
-            "required": false,
-            "schema": {
-              "type": "integer",
-              "maximum": 100,
-              "minimum": 1,
-              "default": 50,
-              "title": "Limit"
-            }
-          },
-          {
-            "name": "offset",
-            "in": "query",
-            "required": false,
-            "schema": {
-              "type": "integer",
-              "minimum": 0,
-              "default": 0,
-              "title": "Offset"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ListSessionsResponse"
-                }
-              }
-            }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
-      },
       "post": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Create Session",
         "description": "Create a new chat session.\n\nInitiates a new chat session for either an authenticated or anonymous user.\n\nArgs:\n    user_id: The optional authenticated user ID parsed from the JWT. If missing, creates an anonymous session.\n\nReturns:\n    CreateSessionResponse: Details of the created session.",
         "operationId": "postV2CreateSession",
-        "security": [{ "HTTPBearerJWT": [] }],
         "responses": {
           "200": {
             "description": "Successful Response",
@@ -1015,7 +959,8 @@
           "401": {
             "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
           }
-        }
+        },
+        "security": [{ "HTTPBearerJWT": [] }]
       }
     },
     "/api/chat/sessions/{session_id}": {
@@ -1103,9 +1048,9 @@
     "/api/chat/sessions/{session_id}/stream": {
       "get": {
         "tags": ["v2", "chat", "chat"],
-        "summary": "Stream Chat Get",
-        "description": "Stream chat responses for a session (GET - legacy endpoint).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    message: The user's new message to process.\n    user_id: Optional authenticated user ID.\n    is_user_message: Whether the message is a user message.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
-        "operationId": "getV2StreamChatGet",
+        "summary": "Stream Chat",
+        "description": "Stream chat responses for a session.\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    message: The user's new message to process.\n    user_id: Optional authenticated user ID.\n    is_user_message: Whether the message is a user message.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
+        "operationId": "getV2StreamChat",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
           {
@@ -1153,46 +1098,6 @@
             }
           }
         }
-      },
-      "post": {
-        "tags": ["v2", "chat", "chat"],
-        "summary": "Stream Chat Post",
-        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
-        "operationId": "postV2StreamChatPost",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "session_id",
-            "in": "path",
-            "required": true,
-            "schema": { "type": "string", "title": "Session Id" }
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": { "$ref": "#/components/schemas/StreamChatRequest" }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": { "application/json": { "schema": {} } }
-          },
-          "401": {
-            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
-              }
-            }
-          }
-        }
       }
     },
     "/api/credits": {
@@ -8114,20 +8019,6 @@
         "required": ["source_id", "sink_id", "source_name", "sink_name"],
         "title": "Link"
       },
-      "ListSessionsResponse": {
-        "properties": {
-          "sessions": {
-            "items": { "$ref": "#/components/schemas/SessionSummaryResponse" },
-            "type": "array",
-            "title": "Sessions"
-          },
-          "total": { "type": "integer", "title": "Total" }
-        },
-        "type": "object",
-        "required": ["sessions", "total"],
-        "title": "ListSessionsResponse",
-        "description": "Response model for listing chat sessions."
-      },
       "LogRawMetricRequest": {
         "properties": {
           "metric_name": {
@@ -9457,21 +9348,6 @@
         "title": "SessionDetailResponse",
         "description": "Response model providing complete details for a chat session, including messages."
       },
-      "SessionSummaryResponse": {
-        "properties": {
-          "id": { "type": "string", "title": "Id" },
-          "created_at": { "type": "string", "title": "Created At" },
-          "updated_at": { "type": "string", "title": "Updated At" },
-          "title": {
-            "anyOf": [{ "type": "string" }, { "type": "null" }],
-            "title": "Title"
-          }
-        },
-        "type": "object",
-        "required": ["id", "created_at", "updated_at"],
-        "title": "SessionSummaryResponse",
-        "description": "Response model for a session summary (without messages)."
-      },
       "SetGraphActiveVersion": {
         "properties": {
           "active_graph_version": {
@@ -10023,30 +9899,6 @@
         "required": ["submissions", "pagination"],
         "title": "StoreSubmissionsResponse"
       },
-      "StreamChatRequest": {
-        "properties": {
-          "message": { "type": "string", "title": "Message" },
-          "is_user_message": {
-            "type": "boolean",
-            "title": "Is User Message",
-            "default": true
-          },
-          "context": {
-            "anyOf": [
-              {
-                "additionalProperties": { "type": "string" },
-                "type": "object"
-              },
-              { "type": "null" }
-            ],
-            "title": "Context"
-          }
-        },
-        "type": "object",
-        "required": ["message"],
-        "title": "StreamChatRequest",
-        "description": "Request model for streaming chat with optional context."
-      },
       "SubmissionStatus": {
         "type": "string",
         "enum": ["DRAFT", "PENDING", "APPROVED", "REJECTED"],

autogpt_platform/frontend/src/components/atoms/Button/helpers.ts

@@ -32,7 +32,7 @@ export const extendedButtonVariants = cva(
         ),
       },
       size: {
-        small: "px-3 py-2 text-sm gap-1.5 h-[2.25rem] min-w-[5.5rem]",
+        small: "px-3 py-2 text-sm gap-1.5 h-[2.25rem]",
         large: "px-4 py-3 text-sm gap-2 h-[3.25rem]",
         icon: "p-3 !min-w-0",
       },

autogpt_platform/frontend/src/components/renderers/InputRenderer/FormRenderer.tsx

@@ -30,6 +30,8 @@ export const FormRenderer = ({
     return generateUiSchemaForCustomFields(preprocessedSchema, uiSchema);
   }, [preprocessedSchema, uiSchema]);
 
+  console.log("preprocessedSchema", preprocessedSchema);
+
   return (
     <div className={"mb-6 mt-4"}>
       <Form

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/anyof/AnyOfField.tsx

@@ -2,12 +2,10 @@ import { FieldProps, getUiOptions, getWidget } from "@rjsf/utils";
 import { AnyOfFieldTitle } from "./components/AnyOfFieldTitle";
 import { isEmpty } from "lodash";
 import { useAnyOfField } from "./useAnyOfField";
-import { cleanUpHandleId, getHandleId, updateUiOption } from "../../helpers";
+import { getHandleId, updateUiOption } from "../../helpers";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { ANY_OF_FLAG } from "../../constants";
 import { findCustomFieldId } from "../../registry";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { cn } from "@/lib/utils";
 
 export const AnyOfField = (props: FieldProps) => {
   const { registry, schema } = props;
@@ -23,8 +21,6 @@ export const AnyOfField = (props: FieldProps) => {
     field_id,
   } = useAnyOfField(props);
 
-  const isInputBroken = useNodeStore((state) => state.isInputBroken);
-
   const parentCustomFieldId = findCustomFieldId(schema);
   if (parentCustomFieldId) {
     return null;
@@ -47,7 +43,6 @@ export const AnyOfField = (props: FieldProps) => {
   });
 
   const isHandleConnected = isInputConnected(nodeId, handleId);
-  const isAnyOfInputBroken = isInputBroken(nodeId, cleanUpHandleId(handleId));
 
   // Now anyOf can render - custom fields if the option schema matches a custom field
   const optionCustomFieldId = optionSchema
@@ -83,11 +78,7 @@ export const AnyOfField = (props: FieldProps) => {
       registry={registry}
       placeholder={props.placeholder}
       autocomplete={props.autocomplete}
-      className={cn(
-        "-ml-1 h-[22px] w-fit gap-1 px-1 pl-2 text-xs font-medium",
-        isAnyOfInputBroken &&
-          "border-red-500 bg-red-100 text-red-600 line-through",
-      )}
+      className="-ml-1 h-[22px] w-fit gap-1 px-1 pl-2 text-xs font-medium"
       autofocus={props.autofocus}
       label=""
       hideLabel={true}
@@ -102,7 +93,7 @@ export const AnyOfField = (props: FieldProps) => {
         selector={selector}
         uiSchema={updatedUiSchema}
       />
-      {!isHandleConnected && !isAnyOfInputBroken && optionsSchemaField}
+      {!isHandleConnected && optionsSchemaField}
     </div>
   );
 };

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/anyof/components/AnyOfFieldTitle.tsx

@@ -13,7 +13,6 @@ import { Text } from "@/components/atoms/Text/Text";
 import { isOptionalType } from "../../../utils/schema-utils";
 import { getTypeDisplayInfo } from "@/app/(platform)/build/components/FlowEditor/nodes/helpers";
 import { cn } from "@/lib/utils";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 
 interface customFieldProps extends FieldProps {
   selector: JSX.Element;
@@ -52,13 +51,6 @@ export const AnyOfFieldTitle = (props: customFieldProps) => {
     shouldShowTypeSelector(schema) && !isArrayItem && !isHandleConnected;
   const shoudlShowType = isHandleConnected || (isOptional && type);
 
-  const isInputBroken = useNodeStore((state) =>
-    state.isInputBroken(nodeId, cleanUpHandleId(uiOptions.handleId)),
-  );
-  const inputMismatch = useNodeStore((state) =>
-    state.getInputTypeMismatch(nodeId, cleanUpHandleId(uiOptions.handleId)),
-  );
-
   return (
     <div className="flex items-center gap-2">
       <TitleFieldTemplate
@@ -70,16 +62,8 @@ export const AnyOfFieldTitle = (props: customFieldProps) => {
         uiSchema={uiSchema}
       />
       {shoudlShowType && (
-        <Text
-          variant="small"
-          className={cn(
-            "text-zinc-700",
-            isInputBroken && "line-through",
-            colorClass,
-            inputMismatch && "rounded-md bg-red-100 px-1 !text-red-500",
-          )}
-        >
-          {isOptional ? `(${inputMismatch || displayType})` : "(any)"}
+        <Text variant="small" className={cn("text-zinc-700", colorClass)}>
+          {isOptional ? `(${displayType})` : "(any)"}
         </Text>
       )}
       {shouldShowSelector && selector}

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/standard/TitleField.tsx

@@ -9,9 +9,8 @@ import { Text } from "@/components/atoms/Text/Text";
 import { getTypeDisplayInfo } from "@/app/(platform)/build/components/FlowEditor/nodes/helpers";
 import { isAnyOfSchema } from "../../utils/schema-utils";
 import { cn } from "@/lib/utils";
-import { cleanUpHandleId, isArrayItem } from "../../helpers";
+import { isArrayItem } from "../../helpers";
 import { InputNodeHandle } from "@/app/(platform)/build/components/FlowEditor/handlers/NodeHandle";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 
 export default function TitleField(props: TitleFieldProps) {
   const { id, title, required, schema, registry, uiSchema } = props;
@@ -27,11 +26,6 @@ export default function TitleField(props: TitleFieldProps) {
   const smallText = isArrayItemFlag || additional;
 
   const showHandle = uiOptions.showHandles ?? showHandles;
-
-  const isInputBroken = useNodeStore((state) =>
-    state.isInputBroken(nodeId, cleanUpHandleId(uiOptions.handleId)),
-  );
-
   return (
     <div className="flex items-center">
       {showHandle !== false && (
@@ -40,11 +34,7 @@ export default function TitleField(props: TitleFieldProps) {
       <Text
         variant={isArrayItemFlag ? "small" : "body"}
         id={id}
-        className={cn(
-          "line-clamp-1",
-          smallText && "text-sm text-zinc-700",
-          isInputBroken && "text-red-500 line-through",
-        )}
+        className={cn("line-clamp-1", smallText && "text-sm text-zinc-700")}
       >
         {title}
       </Text>
@@ -54,7 +44,7 @@ export default function TitleField(props: TitleFieldProps) {
       {!isAnyOf && (
         <Text
           variant="small"
-          className={cn("ml-2", isInputBroken && "line-through", colorClass)}
+          className={cn("ml-2", colorClass)}
           id={description_id}
         >
           ({displayType})

autogpt_platform/frontend/src/components/renderers/InputRenderer/helpers.ts

@@ -30,8 +30,6 @@ export function updateUiOption<T extends Record<string, any>>(
 }
 
 export const cleanUpHandleId = (handleId: string) => {
-  if (!handleId) return "";
-
   let newHandleId = handleId;
   if (handleId.includes(ANY_OF_FLAG)) {
     newHandleId = newHandleId.replace(ANY_OF_FLAG, "");

autogpt_platform/frontend/src/hooks/useAgentGraph.tsx

@@ -233,14 +233,13 @@ export default function useAgentGraph(
         title: `${block.name} ${node.id}`,
         inputSchema: block.inputSchema,
         outputSchema: block.outputSchema,
-        isOutputStatic: block.staticOutput,
         hardcodedValues: node.input_default,
         uiType: block.uiType,
         metadata: metadata,
         connections: graph.links
           .filter((l) => [l.source_id, l.sink_id].includes(node.id))
           .map((link) => ({
-            id: formatEdgeID(link),
+            edge_id: formatEdgeID(link),
             source: link.source_id,
             sourceHandle: link.source_name,
             target: link.sink_id,

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

@@ -245,8 +245,8 @@ export type BlockIONullSubSchema = BlockIOSubSchemaMeta & {
 // At the time of writing, combined schemas only occur on the first nested level in a
 // block schema. It is typed this way to make the use of these objects less tedious.
 type BlockIOCombinedTypeSubSchema = BlockIOSubSchemaMeta & {
-  type?: never;
-  const?: never;
+  type: never;
+  const: never;
 } & (
     | {
         allOf: [BlockIOSimpleTypeSubSchema];
@@ -368,8 +368,8 @@ export type GraphMeta = {
   recommended_schedule_cron: string | null;
   forked_from_id?: GraphID | null;
   forked_from_version?: number | null;
-  input_schema: GraphInputSchema;
-  output_schema: GraphOutputSchema;
+  input_schema: GraphIOSchema;
+  output_schema: GraphIOSchema;
   credentials_input_schema: CredentialsInputSchema;
 } & (
   | {
@@ -385,51 +385,19 @@ export type GraphMeta = {
 export type GraphID = Brand<string, "GraphID">;
 
 /* Derived from backend/data/graph.py:Graph._generate_schema() */
-export type GraphInputSchema = {
+export type GraphIOSchema = {
   type: "object";
-  properties: Record<string, GraphInputSubSchema>;
-  required: (keyof GraphInputSchema["properties"])[];
+  properties: Record<string, GraphIOSubSchema>;
+  required: (keyof BlockIORootSchema["properties"])[];
 };
-export type GraphInputSubSchema = GraphOutputSubSchema &
-  (
-    | { type?: never; default: any | null } // AgentInputBlock (generic Any type)
-    | { type: "string"; format: "short-text"; default: string | null } // AgentShortTextInputBlock
-    | { type: "string"; format: "long-text"; default: string | null } // AgentLongTextInputBlock
-    | { type: "integer"; default: number | null } // AgentNumberInputBlock
-    | { type: "string"; format: "date"; default: string | null } // AgentDateInputBlock
-    | { type: "string"; format: "time"; default: string | null } // AgentTimeInputBlock
-    | { type: "string"; format: "file"; default: string | null } // AgentFileInputBlock
-    | { type: "string"; enum: string[]; default: string | null } // AgentDropdownInputBlock
-    | { type: "boolean"; default: boolean } // AgentToggleInputBlock
-    | {
-        // AgentTableInputBlock
-        type: "array";
-        format: "table";
-        items: {
-          type: "object";
-          properties: Record<string, { type: "string" }>;
-        };
-        default: Array<Record<string, string>> | null;
-      }
-    | {
-        // AgentGoogleDriveFileInputBlock
-        type: "object";
-        format: "google-drive-picker";
-        google_drive_picker_config?: GoogleDrivePickerConfig;
-        default: GoogleDriveFile | null;
-      }
-  );
-export type GraphOutputSchema = {
-  type: "object";
-  properties: Record<string, GraphOutputSubSchema>;
-  required: (keyof GraphOutputSchema["properties"])[];
-};
-export type GraphOutputSubSchema = {
-  // TODO: typed outputs based on the incoming edges?
-  title: string;
-  description?: string;
-  advanced: boolean;
+export type GraphIOSubSchema = Omit<
+  BlockIOSubSchemaMeta,
+  "placeholder" | "depends_on" | "hidden"
+> & {
+  type: never; // bodge to avoid type checking hell; doesn't exist at runtime
+  default?: string;
   secret: boolean;
+  metadata?: any;
 };
 
 export type CredentialsInputSchema = {
@@ -472,8 +440,8 @@ export type GraphUpdateable = Omit<
   is_active?: boolean;
   nodes: NodeCreatable[];
   links: LinkCreatable[];
-  input_schema?: GraphInputSchema;
-  output_schema?: GraphOutputSchema;
+  input_schema?: GraphIOSchema;
+  output_schema?: GraphIOSchema;
 };
 
 export type GraphCreatable = _GraphCreatableInner & {
@@ -529,8 +497,8 @@ export type LibraryAgent = {
   name: string;
   description: string;
   instructions?: string | null;
-  input_schema: GraphInputSchema;
-  output_schema: GraphOutputSchema;
+  input_schema: GraphIOSchema;
+  output_schema: GraphIOSchema;
   credentials_input_schema: CredentialsInputSchema;
   new_output: boolean;
   can_access_graph: boolean;

autogpt_platform/frontend/src/lib/utils.ts

@@ -6,10 +6,7 @@ import { NodeDimension } from "@/app/(platform)/build/components/legacy-builder/
 import {
   BlockIOObjectSubSchema,
   BlockIORootSchema,
-  BlockIOSubSchema,
   Category,
-  GraphInputSubSchema,
-  GraphOutputSubSchema,
 } from "@/lib/autogpt-server-api/types";
 
 export function cn(...inputs: ClassValue[]) {
@@ -79,8 +76,8 @@ export function getTypeBgColor(type: string | null): string {
   );
 }
 
-export function getTypeColor(type: string | undefined): string {
-  if (!type) return "#6b7280";
+export function getTypeColor(type: string | null): string {
+  if (type === null) return "#6b7280";
   return (
     {
       string: "#22c55e",
@@ -91,59 +88,11 @@ export function getTypeColor(type: string | undefined): string {
       array: "#6366f1",
       null: "#6b7280",
       any: "#6b7280",
+      "": "#6b7280",
     }[type] || "#6b7280"
   );
 }
 
-/**
- * Extracts the effective type from a JSON schema, handling anyOf/oneOf/allOf wrappers.
- * Returns the first non-null type found in the schema structure.
- */
-export function getEffectiveType(
-  schema:
-    | BlockIOSubSchema
-    | GraphInputSubSchema
-    | GraphOutputSubSchema
-    | null
-    | undefined,
-): string | undefined {
-  if (!schema) return undefined;
-
-  // Direct type property
-  if ("type" in schema && schema.type) {
-    return String(schema.type);
-  }
-
-  // Handle allOf - typically a single-item wrapper
-  if (
-    "allOf" in schema &&
-    Array.isArray(schema.allOf) &&
-    schema.allOf.length > 0
-  ) {
-    return getEffectiveType(schema.allOf[0]);
-  }
-
-  // Handle anyOf - e.g. [{ type: "string" }, { type: "null" }]
-  if ("anyOf" in schema && Array.isArray(schema.anyOf)) {
-    for (const item of schema.anyOf) {
-      if ("type" in item && item.type !== "null") {
-        return String(item.type);
-      }
-    }
-  }
-
-  // Handle oneOf
-  if ("oneOf" in schema && Array.isArray(schema.oneOf)) {
-    for (const item of schema.oneOf) {
-      if ("type" in item && item.type !== "null") {
-        return String(item.type);
-      }
-    }
-  }
-
-  return undefined;
-}
-
 export function beautifyString(name: string): string {
   // Regular expression to identify places to split, considering acronyms
   const result = name