import importlib
from typing import List, Optional, Set, Union

from pydantic import ValidationError
from sqlalchemy import and_, func, or_, select

from letta.constants import (
    BASE_FUNCTION_RETURN_CHAR_LIMIT,
    BASE_MEMORY_TOOLS,
    BASE_SLEEPTIME_TOOLS,
    BASE_TOOLS,
    BASE_VOICE_SLEEPTIME_CHAT_TOOLS,
    BASE_VOICE_SLEEPTIME_TOOLS,
    BUILTIN_TOOLS,
    FILES_TOOLS,
    LETTA_PARALLEL_SAFE_TOOLS,
    LETTA_TOOL_MODULE_NAMES,
    LETTA_TOOL_SET,
    LOCAL_ONLY_MULTI_AGENT_TOOLS,
    MAX_TOOL_NAME_LENGTH,
    MCP_TOOL_TAG_NAME_PREFIX,
    MODAL_DEFAULT_PYTHON_VERSION,
    MODAL_DEFAULT_TOOL_NAME,
    MODAL_SAFE_IMPORT_MODULES,
)
from letta.errors import LettaInvalidArgumentError, LettaToolNameConflictError, LettaToolNameSchemaMismatchError
from letta.functions.functions import load_function_set
from letta.helpers.tool_helpers import compute_tool_hash, generate_modal_function_name
from letta.log import get_logger

# TODO: Remove this once we translate all of these to the ORM
from letta.orm.errors import NoResultFound
from letta.orm.tool import Tool as ToolModel
from letta.otel.tracing import trace_method, tracer
from letta.schemas.enums import PrimitiveType, SandboxType, ToolType
from letta.schemas.tool import Tool as PydanticTool, ToolCreate, ToolUpdate
from letta.schemas.user import User as PydanticUser
from letta.server.db import db_registry
from letta.services.helpers.agent_manager_helper import calculate_multi_agent_tools
from letta.services.mcp.types import SSEServerConfig, StdioServerConfig
from letta.services.tool_schema_generator import generate_schema_for_tool_creation, generate_schema_for_tool_update
from letta.settings import settings, tool_settings
from letta.utils import enforce_types, fire_and_forget, printd
from letta.validators import raise_on_invalid_id

logger = get_logger(__name__)


# NOTE: function name and nested modal function decorator name must stay in sync with MODAL_DEFAULT_TOOL_NAME
def modal_tool_wrapper(tool: PydanticTool, actor: PydanticUser, sandbox_env_vars: dict | None = None, project_id: str = "default"):
    """Create a Modal function wrapper for a tool"""
    import contextlib
    import io
    import os
    import sys
    from typing import Optional

    import modal

    packages = [str(req) for req in tool.pip_requirements] if tool.pip_requirements else []
    for package in MODAL_SAFE_IMPORT_MODULES:
        packages.append(package)
    packages.append("letta_client>=1.1.1")
    packages.append("letta")  # Base letta without extras
    packages.append("asyncpg>=0.30.0")  # Fixes asyncpg import error
    packages.append("psycopg2-binary>=2.9.10")  # PostgreSQL adapter (pre-compiled, no build required)
    # packages.append("pgvector>=0.3.6")  # Vector operations support

    function_name = generate_modal_function_name(tool.name, actor.organization_id, project_id)
    modal_app = modal.App(function_name)
    logger.info(f"Creating Modal app {tool.id} with name {function_name}")

    # Create secrets dict with sandbox env vars
    secrets_dict = {"LETTA_API_KEY": None}
    if sandbox_env_vars:
        secrets_dict.update(sandbox_env_vars)

    @modal_app.function(
        image=modal.Image.debian_slim(python_version=MODAL_DEFAULT_PYTHON_VERSION).pip_install(packages),
        restrict_modal_access=True,
        timeout=10,
        secrets=[modal.Secret.from_dict(secrets_dict)],
        serialized=True,
    )
    def modal_function(
        tool_name: str, agent_state: Optional[dict], agent_id: Optional[str], env_vars: dict, letta_api_key: Optional[str] = None, **kwargs
    ):
        """Wrapper function for running untrusted code in a Modal function"""
        # Reconstruct AgentState from dict if passed (to avoid cloudpickle serialization issues)
        # This is done with extra safety to handle schema mismatches between environments
        reconstructed_agent_state = None
        if agent_state:
            try:
                from letta.schemas.agent import AgentState as AgentStateModel

                # Filter dict to only include fields that exist in Modal's version of AgentState
                # This prevents ValidationError from extra fields in newer schemas
                modal_agent_fields = set(AgentStateModel.model_fields.keys())
                filtered_agent_state = {key: value for key, value in agent_state.items() if key in modal_agent_fields}

                # Try to reconstruct with filtered data
                reconstructed_agent_state = AgentStateModel.model_validate(filtered_agent_state)

                # Log if we filtered out any fields
                filtered_out = set(agent_state.keys()) - modal_agent_fields
                if filtered_out:
                    print(f"Fields not in available in AgentState: {filtered_out}", file=sys.stderr)

            except ImportError as e:
                print(f"Cannot import AgentState: {e}", file=sys.stderr)
                print("Passing agent_state as dict to tool", file=sys.stderr)
                reconstructed_agent_state = agent_state
            except Exception as e:
                print(f"Warning: Could not reconstruct AgentState (schema mismatch?): {e}", file=sys.stderr)
                print("Passing agent_state as dict to tool", file=sys.stderr)
                reconstructed_agent_state = agent_state

        if env_vars:
            for key, value in env_vars.items():
                os.environ[key] = str(value)

        # TODO: directly instantiate the letta client once we upgrade to 1.0.0+ in core
        # Initialize the Letta client
        # if letta_api_key:
        #     letta_client = Letta(token=letta_api_key, base_url=os.environ.get("LETTA_API_URL", "https://api.letta.com"))
        # else:
        #     letta_client = None

        tool_namespace = {
            "__builtins__": __builtins__,  # Include built-in functions
            # "_letta_client": letta_client,  # Make letta_client available
            "os": os,  # Include os module for env vars access
            "agent_id": agent_id,
            "_LETTA_API_KEY": letta_api_key,
            # Add any other modules/variables the tool might need
        }

        # Initialize the tool code
        # Create a namespace for the tool
        # tool_namespace = {}
        exec(tool.source_code, tool_namespace)

        # Get the tool function
        if tool_name not in tool_namespace:
            raise Exception(f"Tool function {tool_name} not found in {tool.source_code}, globals: {tool_namespace}")
        tool_func = tool_namespace[tool_name]

        # Detect if the tool function is async
        import asyncio
        import inspect
        import traceback

        is_async = inspect.iscoroutinefunction(tool_func)

        # Capture stdout and stderr during tool execution
        stdout_capture = io.StringIO()
        stderr_capture = io.StringIO()
        result = None
        error_occurred = False

        with contextlib.redirect_stdout(stdout_capture), contextlib.redirect_stderr(stderr_capture):
            try:
                # if `agent_state` is in the tool function arguments, inject it
                # Pass reconstructed AgentState (or dict if reconstruction failed)
                if "agent_state" in tool_func.__code__.co_varnames:
                    kwargs["agent_state"] = reconstructed_agent_state

                try:
                    from letta.functions.ast_parsers import coerce_dict_args_by_annotations

                    annotations = getattr(tool_func, "__annotations__", {})
                    kwargs = coerce_dict_args_by_annotations(
                        kwargs,
                        annotations,
                        allow_unsafe_eval=True,
                        extra_globals=tool_func.__globals__,
                    )
                except Exception:
                    pass

                # Execute the tool function (async or sync)
                if is_async:
                    result = asyncio.run(tool_func(**kwargs))
                else:
                    result = tool_func(**kwargs)
            except Exception:
                # Capture the exception and write to stderr
                error_occurred = True
                traceback.print_exc(file=stderr_capture)

        # Get captured output
        stdout = stdout_capture.getvalue()
        stderr = stderr_capture.getvalue()

        return {
            "result": result,
            "stdout": stdout,
            "stderr": stderr,
            "agent_state": agent_state,  # TODO: deprecate (use letta_client instead)
            "error": error_occurred or bool(stderr),
        }

    return modal_app


class ToolManager:
    """Manager class to handle business logic related to Tools."""

    @enforce_types
    @trace_method
    async def create_or_update_tool_async(
        self, pydantic_tool: PydanticTool, actor: PydanticUser, bypass_name_check: bool = False, modal_sandbox_enabled: bool = False
    ) -> PydanticTool:
        """Create a new tool based on the ToolCreate schema.

        Uses atomic PostgreSQL ON CONFLICT DO UPDATE to prevent race conditions
        during concurrent upserts.
        """
        if pydantic_tool.tool_type == ToolType.CUSTOM and not pydantic_tool.json_schema:
            with tracer.start_as_current_span("generate_schema_for_tool_creation"):
                generated_schema = generate_schema_for_tool_creation(pydantic_tool)
            if generated_schema:
                pydantic_tool.json_schema = generated_schema
            else:
                raise ValueError("Failed to generate schema for tool", pydantic_tool.source_code)

        # make sure the name matches the json_schema
        if not pydantic_tool.name:
            pydantic_tool.name = pydantic_tool.json_schema.get("name")
        else:
            # if name is provided, make sure its less tahn the MAX_TOOL_NAME_LENGTH
            if len(pydantic_tool.name) > MAX_TOOL_NAME_LENGTH:
                raise LettaInvalidArgumentError(
                    f"Tool name {pydantic_tool.name} is too long. It must be less than {MAX_TOOL_NAME_LENGTH} characters."
                )

            if pydantic_tool.name != pydantic_tool.json_schema.get("name"):
                raise LettaToolNameSchemaMismatchError(
                    tool_name=pydantic_tool.name,
                    json_schema_name=pydantic_tool.json_schema.get("name"),
                    source_code=pydantic_tool.source_code,
                )

        # Use atomic PostgreSQL upsert if available
        if settings.letta_pg_uri_no_default:
            return await self._atomic_upsert_tool_postgresql(pydantic_tool, actor, modal_sandbox_enabled)

        # Fallback for SQLite: use non-atomic check-then-act pattern
        current_tool = await self.get_tool_by_name_async(tool_name=pydantic_tool.name, actor=actor)

        if current_tool:
            # Put to dict and remove fields that should not be reset
            update_data = pydantic_tool.model_dump(exclude_unset=True, exclude_none=True)

            # Check if any field in update_data actually differs from the current tool
            current_tool_data = current_tool.model_dump()
            needs_update = any(current_tool_data.get(key) != value for key, value in update_data.items())

            if needs_update:
                # In case we want to update the tool type
                # Useful if we are shuffling around base tools
                updated_tool_type = None
                if "tool_type" in update_data:
                    updated_tool_type = update_data.get("tool_type")

                tool_update = ToolUpdate(**update_data)
                tool = await self.update_tool_by_id_async(
                    current_tool.id,
                    tool_update,
                    actor,
                    updated_tool_type=updated_tool_type,
                    modal_sandbox_enabled=modal_sandbox_enabled,
                )
            else:
                printd(
                    f"`create_or_update_tool` was called with user_id={actor.id}, organization_id={actor.organization_id}, name={pydantic_tool.name}, but found existing tool with nothing to update."
                )
                return current_tool
            return tool

        return await self.create_tool_async(pydantic_tool, actor=actor, modal_sandbox_enabled=modal_sandbox_enabled)

    @enforce_types
    @trace_method
    async def _atomic_upsert_tool_postgresql(
        self, pydantic_tool: PydanticTool, actor: PydanticUser, modal_sandbox_enabled: bool = False
    ) -> PydanticTool:
        """Atomically upsert a single tool using PostgreSQL's ON CONFLICT DO UPDATE.

        This prevents race conditions when multiple concurrent requests try to
        create/update the same tool by name.
        """
        from sqlalchemy.dialects.postgresql import insert as pg_insert

        # Auto-generate description if not provided
        if pydantic_tool.description is None and pydantic_tool.json_schema:
            pydantic_tool.description = pydantic_tool.json_schema.get("description", None)

        # Add sandbox:modal to metadata if flag is enabled
        if modal_sandbox_enabled:
            if pydantic_tool.metadata_ is None:
                pydantic_tool.metadata_ = {}
            pydantic_tool.metadata_["sandbox"] = "modal"

        # Add tool hash to metadata for Modal deployment tracking
        tool_hash = compute_tool_hash(pydantic_tool)
        if pydantic_tool.metadata_ is None:
            pydantic_tool.metadata_ = {}
        pydantic_tool.metadata_["tool_hash"] = tool_hash

        async with db_registry.async_session() as session:
            table = ToolModel.__table__
            valid_columns = {col.name for col in table.columns}

            tool_dict = pydantic_tool.model_dump(to_orm=True)
            tool_dict["_created_by_id"] = actor.id
            tool_dict["_last_updated_by_id"] = actor.id
            tool_dict["organization_id"] = actor.organization_id

            # Filter to only include columns that exist in the table
            # Also exclude None values to let database defaults apply
            insert_data = {k: v for k, v in tool_dict.items() if k in valid_columns and v is not None}

            # Build the INSERT ... ON CONFLICT DO UPDATE statement
            stmt = pg_insert(table).values(**insert_data)

            # On conflict, update all columns except id, created_at, and _created_by_id
            excluded = stmt.excluded
            update_dict = {}
            for col in table.columns:
                if col.name not in ("id", "created_at", "_created_by_id"):
                    if col.name == "updated_at":
                        update_dict[col.name] = func.now()
                    elif col.name == "tags" and (insert_data["tags"] is None or len(insert_data["tags"]) == 0):
                        # TODO: intentional bug to avoid overriding with empty tags on every upsert
                        # means you cannot clear tags, only override them
                        if insert_data["tags"] is None or len(insert_data["tags"]) == 0:
                            continue
                        update_dict[col.name] = excluded[col.name]
                    else:
                        update_dict[col.name] = excluded[col.name]

            upsert_stmt = stmt.on_conflict_do_update(index_elements=["name", "organization_id"], set_=update_dict).returning(table.c.id)

            result = await session.execute(upsert_stmt)
            tool_id = result.scalar_one()
            await session.commit()

            # Fetch the upserted tool
            tool = await ToolModel.read_async(db_session=session, identifier=tool_id, actor=actor)
            upserted_tool = tool.to_pydantic()

        # Deploy Modal app if needed (both Modal credentials configured AND tool metadata must indicate Modal)
        # TODO: dont have such duplicated code
        tool_requests_modal = upserted_tool.metadata_ and upserted_tool.metadata_.get("sandbox") == "modal"
        modal_configured = tool_settings.modal_sandbox_enabled

        if upserted_tool.tool_type == ToolType.CUSTOM and tool_requests_modal and modal_configured:
            await self.create_or_update_modal_app(upserted_tool, actor)

        # Embed tool in Turbopuffer if enabled
        from letta.helpers.tpuf_client import should_use_tpuf_for_tools

        if should_use_tpuf_for_tools():
            fire_and_forget(
                self._embed_tool_background(upserted_tool, actor),
                task_name=f"embed_tool_{upserted_tool.id}",
            )

        return upserted_tool

    @enforce_types
    async def create_mcp_server(
        self, server_config: Union[StdioServerConfig, SSEServerConfig], actor: PydanticUser
    ) -> List[Union[StdioServerConfig, SSEServerConfig]]:
        pass

    @enforce_types
    async def create_mcp_tool_async(
        self, tool_create: ToolCreate, mcp_server_name: str, mcp_server_id: str, actor: PydanticUser
    ) -> PydanticTool:
        metadata = {MCP_TOOL_TAG_NAME_PREFIX: {"server_name": mcp_server_name, "server_id": mcp_server_id}}
        return await self.create_or_update_tool_async(
            PydanticTool(
                tool_type=ToolType.EXTERNAL_MCP, name=tool_create.json_schema["name"], metadata_=metadata, **tool_create.model_dump()
            ),
            actor,
        )

    @enforce_types
    @trace_method
    async def create_or_update_mcp_tool_async(
        self, tool_create: ToolCreate, mcp_server_name: str, mcp_server_id: str, actor: PydanticUser
    ) -> PydanticTool:
        metadata = {MCP_TOOL_TAG_NAME_PREFIX: {"server_name": mcp_server_name, "server_id": mcp_server_id}}
        return await self.create_or_update_tool_async(
            PydanticTool(
                tool_type=ToolType.EXTERNAL_MCP, name=tool_create.json_schema["name"], metadata_=metadata, **tool_create.model_dump()
            ),
            actor,
        )

    @enforce_types
    @trace_method
    async def create_tool_async(
        self, pydantic_tool: PydanticTool, actor: PydanticUser, modal_sandbox_enabled: bool = False
    ) -> PydanticTool:
        """Create a new tool based on the ToolCreate schema."""
        # Generate schema only if not provided (only for custom tools)

        async with db_registry.async_session() as session:
            # Auto-generate description if not provided
            if pydantic_tool.description is None and pydantic_tool.json_schema:
                pydantic_tool.description = pydantic_tool.json_schema.get("description", None)

            # Add sandbox:modal to metadata if flag is enabled
            if modal_sandbox_enabled:
                if pydantic_tool.metadata_ is None:
                    pydantic_tool.metadata_ = {}
                pydantic_tool.metadata_["sandbox"] = "modal"

            # Add tool hash to metadata for Modal deployment tracking
            tool_hash = compute_tool_hash(pydantic_tool)
            if pydantic_tool.metadata_ is None:
                pydantic_tool.metadata_ = {}
            pydantic_tool.metadata_["tool_hash"] = tool_hash

            tool_data = pydantic_tool.model_dump(to_orm=True)
            # Set the organization id at the ORM layer
            tool_data["organization_id"] = actor.organization_id

            tool = ToolModel(**tool_data)

            # Log tool creation with memory footprint
            import sys

            tool_size_kb = sys.getsizeof(tool_data) / 1024
            source_code_size_kb = len(pydantic_tool.source_code or "") / 1024
            schema_size_kb = len(str(pydantic_tool.json_schema or "")) / 1024
            logger.info(
                f"Creating tool '{pydantic_tool.name}': total {tool_size_kb:.2f} KB (source: {source_code_size_kb:.2f} KB, schema: {schema_size_kb:.2f} KB)"
            )

            await tool.create_async(session, actor=actor)  # Re-raise other database-related errors
            created_tool = tool.to_pydantic()

        # TODO: dont have such duplicated code
        # Deploy Modal app for the new tool
        # Both Modal credentials configured AND tool metadata must indicate Modal
        tool_requests_modal = created_tool.metadata_ and created_tool.metadata_.get("sandbox") == "modal"
        modal_configured = tool_settings.modal_sandbox_enabled

        if created_tool.tool_type == ToolType.CUSTOM and tool_requests_modal and modal_configured:
            await self.create_or_update_modal_app(created_tool, actor)

        # Embed tool in Turbopuffer if enabled
        from letta.helpers.tpuf_client import should_use_tpuf_for_tools

        if should_use_tpuf_for_tools():
            fire_and_forget(
                self._embed_tool_background(created_tool, actor),
                task_name=f"embed_tool_{created_tool.id}",
            )

        return created_tool

    @enforce_types
    @trace_method
    async def bulk_upsert_tools_async(
        self, pydantic_tools: List[PydanticTool], actor: PydanticUser, override_existing_tools: bool = True
    ) -> List[PydanticTool]:
        """
        Bulk create or update multiple tools in a single database transaction.

        Uses optimized PostgreSQL bulk upsert when available, falls back to individual
        upserts for SQLite. This is much more efficient than calling create_or_update_tool_async
        in a loop.

        IMPORTANT BEHAVIOR NOTES:
        - Tools are matched by (name, organization_id) unique constraint, NOT by ID
        - If a tool with the same name already exists for the organization, it will be updated
          regardless of any ID provided in the input tool
        - The existing tool's ID is preserved during updates
        - If you provide a tool with an explicit ID but a name that matches an existing tool,
          the existing tool will be updated and the provided ID will be ignored
        - This matches the behavior of create_or_update_tool_async which also matches by name

        PostgreSQL optimization:
        - Uses native ON CONFLICT (name, organization_id) DO UPDATE for atomic upserts
        - All tools are processed in a single SQL statement for maximum efficiency

        SQLite fallback:
        - Falls back to individual create_or_update_tool_async calls
        - Still benefits from batched transaction handling

        Args:
            pydantic_tools: List of tools to create or update
            actor: User performing the action

        Returns:
            List of created/updated tools
        """
        if not pydantic_tools:
            return []

        # get schemas if not provided
        for tool in pydantic_tools:
            if tool.json_schema is None:
                tool.json_schema = generate_schema_for_tool_creation(tool)

        # auto-generate descriptions if not provided
        for tool in pydantic_tools:
            if tool.description is None:
                tool.description = tool.json_schema.get("description", None)

        # Log bulk tool operation
        total_source_code_kb = sum(len(t.source_code or "") for t in pydantic_tools) / 1024
        logger.info(f"Bulk upserting {len(pydantic_tools)} tools: total source code {total_source_code_kb:.2f} KB")

        if settings.letta_pg_uri_no_default:
            # use optimized postgresql bulk upsert
            async with db_registry.async_session() as session:
                return await self._bulk_upsert_postgresql(session, pydantic_tools, actor, override_existing_tools)
        else:
            # fallback to individual upserts for sqlite
            return await self._upsert_tools_individually(pydantic_tools, actor, override_existing_tools)

    @enforce_types
    @raise_on_invalid_id(param_name="tool_id", expected_prefix=PrimitiveType.TOOL)
    @trace_method
    async def get_tool_by_id_async(self, tool_id: str, actor: PydanticUser) -> PydanticTool:
        """Fetch a tool by its ID."""
        async with db_registry.async_session() as session:
            # Retrieve tool by id using the Tool model's read method
            tool = await ToolModel.read_async(db_session=session, identifier=tool_id, actor=actor)
            # Convert the SQLAlchemy Tool object to PydanticTool
            return tool.to_pydantic()

    @enforce_types
    @trace_method
    async def get_tool_by_name_async(self, tool_name: str, actor: PydanticUser) -> Optional[PydanticTool]:
        """Retrieve a tool by its name and a user. We derive the organization from the user, and retrieve that tool."""
        try:
            async with db_registry.async_session() as session:
                tool = await ToolModel.read_async(db_session=session, name=tool_name, actor=actor)
                return tool.to_pydantic()
        except NoResultFound:
            return None

    @enforce_types
    @trace_method
    async def get_tool_id_by_name_async(self, tool_name: str, actor: PydanticUser) -> Optional[str]:
        """Retrieve a tool by its name and a user. We derive the organization from the user, and retrieve that tool."""
        try:
            async with db_registry.async_session() as session:
                tool = await ToolModel.read_async(db_session=session, name=tool_name, actor=actor)
                return tool.id
        except NoResultFound:
            return None

    @enforce_types
    @raise_on_invalid_id(param_name="tool_id", expected_prefix=PrimitiveType.TOOL)
    @trace_method
    async def tool_exists_async(self, tool_id: str, actor: PydanticUser) -> bool:
        """Check if a tool exists and belongs to the user's organization (lightweight check)."""
        async with db_registry.async_session() as session:
            query = select(func.count(ToolModel.id)).where(ToolModel.id == tool_id, ToolModel.organization_id == actor.organization_id)
            result = await session.execute(query)
            count = result.scalar()
            return count > 0

    @enforce_types
    @trace_method
    async def tool_name_exists_async(self, tool_name: str, actor: PydanticUser) -> bool:
        """Check if a tool with the given name exists in the user's organization (lightweight check)."""
        async with db_registry.async_session() as session:
            query = select(func.count(ToolModel.id)).where(ToolModel.name == tool_name, ToolModel.organization_id == actor.organization_id)
            result = await session.execute(query)
            count = result.scalar()
            return count > 0

    @enforce_types
    async def _check_tool_name_conflict_with_lock_async(self, session, tool_name: str, exclude_tool_id: str, actor: PydanticUser) -> bool:
        """Check if a tool with the given name exists (excluding the current tool), with row locking.

        Uses SELECT FOR UPDATE to prevent race conditions when two concurrent updates
        try to rename tools to the same name.

        Args:
            session: The database session (must be part of an active transaction)
            tool_name: The name to check for conflicts
            exclude_tool_id: The ID of the current tool being updated (to exclude from check)
            actor: The user performing the action

        Returns:
            True if a conflicting tool exists, False otherwise
        """
        # Use SELECT FOR UPDATE to lock any existing row with this name
        # This prevents another concurrent transaction from also checking and then updating
        query = (
            select(ToolModel.id)
            .where(
                ToolModel.name == tool_name,
                ToolModel.organization_id == actor.organization_id,
                ToolModel.id != exclude_tool_id,
            )
            .with_for_update(nowait=False)  # Wait for lock if another transaction holds it
        )
        result = await session.execute(query)
        existing_tool = result.scalar()
        return existing_tool is not None

    @enforce_types
    @trace_method
    async def list_tools_async(
        self,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = 50,
        ascending: bool = False,
        upsert_base_tools: bool = True,
        tool_types: Optional[List[str]] = None,
        exclude_tool_types: Optional[List[str]] = None,
        names: Optional[List[str]] = None,
        tool_ids: Optional[List[str]] = None,
        search: Optional[str] = None,
        return_only_letta_tools: bool = False,
        project_id: Optional[str] = None,
    ) -> List[PydanticTool]:
        """List all tools with pagination support."""
        tools = await self._list_tools_async(
            actor=actor,
            before=before,
            after=after,
            limit=limit,
            ascending=ascending,
            tool_types=tool_types,
            exclude_tool_types=exclude_tool_types,
            names=names,
            tool_ids=tool_ids,
            search=search,
            return_only_letta_tools=return_only_letta_tools,
            project_id=project_id,
        )

        # Check if all base tools are present if we requested all the tools w/o cursor
        # TODO: This is a temporary hack to resolve this issue
        # TODO: This requires a deeper rethink about how we keep all our internal tools up-to-date
        if not after and upsert_base_tools:
            existing_tool_names = {tool.name for tool in tools}
            base_tool_names = LETTA_TOOL_SET - set(LOCAL_ONLY_MULTI_AGENT_TOOLS) if settings.environment == "prod" else LETTA_TOOL_SET
            missing_base_tools = base_tool_names - existing_tool_names

            # If any base tools are missing, upsert all base tools
            if missing_base_tools:
                logger.info(f"Missing base tools detected: {missing_base_tools}. Upserting all base tools.")
                await self.upsert_base_tools_async(actor=actor)
                # Re-fetch the tools list after upserting base tools
                tools = await self._list_tools_async(
                    actor=actor,
                    before=before,
                    after=after,
                    limit=limit,
                    ascending=ascending,
                    tool_types=tool_types,
                    exclude_tool_types=exclude_tool_types,
                    names=names,
                    tool_ids=tool_ids,
                    search=search,
                    return_only_letta_tools=return_only_letta_tools,
                    project_id=project_id,
                )

        return tools

    @enforce_types
    @trace_method
    async def _list_tools_async(
        self,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = 50,
        ascending: bool = False,
        tool_types: Optional[List[str]] = None,
        exclude_tool_types: Optional[List[str]] = None,
        names: Optional[List[str]] = None,
        tool_ids: Optional[List[str]] = None,
        search: Optional[str] = None,
        return_only_letta_tools: bool = False,
        project_id: Optional[str] = None,
    ) -> List[PydanticTool]:
        """List all tools with optional pagination."""
        tools_to_delete = []
        async with db_registry.async_session() as session:
            # Use SQLAlchemy directly for all cases - more control and consistency
            # Start with base query
            query = select(ToolModel).where(ToolModel.organization_id == actor.organization_id)

            # Apply project_id filter - include tools where project_id matches OR project_id is None (global tools)
            if project_id is not None:
                query = query.where(or_(ToolModel.project_id == project_id, ToolModel.project_id.is_(None)))

            # Apply tool_types filter
            if tool_types is not None:
                query = query.where(ToolModel.tool_type.in_(tool_types))

            # Apply names filter
            if names is not None:
                query = query.where(ToolModel.name.in_(names))

            # Apply tool_ids filter
            if tool_ids is not None:
                query = query.where(ToolModel.id.in_(tool_ids))

            # Apply search filter (ILIKE for case-insensitive partial match)
            if search is not None:
                query = query.where(ToolModel.name.ilike(f"%{search}%"))

            # Apply exclude_tool_types filter at database level
            if exclude_tool_types is not None:
                query = query.where(~ToolModel.tool_type.in_(exclude_tool_types))

            # Apply return_only_letta_tools filter at database level
            if return_only_letta_tools:
                query = query.where(ToolModel.tool_type.like("letta_%"))

            # Handle pagination cursors
            if after is not None:
                after_tool = await session.get(ToolModel, after)
                if after_tool:
                    if ascending:
                        query = query.where(
                            or_(
                                ToolModel.created_at > after_tool.created_at,
                                and_(ToolModel.created_at == after_tool.created_at, ToolModel.id > after_tool.id),
                            )
                        )
                    else:
                        query = query.where(
                            or_(
                                ToolModel.created_at < after_tool.created_at,
                                and_(ToolModel.created_at == after_tool.created_at, ToolModel.id < after_tool.id),
                            )
                        )

            if before is not None:
                before_tool = await session.get(ToolModel, before)
                if before_tool:
                    if ascending:
                        query = query.where(
                            or_(
                                ToolModel.created_at < before_tool.created_at,
                                and_(ToolModel.created_at == before_tool.created_at, ToolModel.id < before_tool.id),
                            )
                        )
                    else:
                        query = query.where(
                            or_(
                                ToolModel.created_at > before_tool.created_at,
                                and_(ToolModel.created_at == before_tool.created_at, ToolModel.id > before_tool.id),
                            )
                        )

            # Apply limit
            if limit is not None:
                query = query.limit(limit)

            # Apply ordering based on ascending parameter
            if ascending:
                query = query.order_by(ToolModel.created_at.asc(), ToolModel.id.asc())
            else:
                query = query.order_by(ToolModel.created_at.desc(), ToolModel.id.desc())

            # Execute query
            result = await session.execute(query)
            tools = list(result.scalars())

            # Remove any malformed tools
            results = []
            for tool in tools:
                try:
                    pydantic_tool = tool.to_pydantic()
                    results.append(pydantic_tool)
                except (ValueError, ModuleNotFoundError, AttributeError) as e:
                    tools_to_delete.append(tool)
                    logger.warning(
                        "Deleting malformed tool with id=%s and name=%s. Error was:\n%s\nDeleted tool:%s",
                        tool.id,
                        tool.name,
                        e,
                        tool.pretty_print_columns(),
                    )

        for tool in tools_to_delete:
            await self.delete_tool_by_id_async(tool.id, actor=actor)

        return results

    @enforce_types
    @trace_method
    async def count_tools_async(
        self,
        actor: PydanticUser,
        tool_types: Optional[List[str]] = None,
        exclude_tool_types: Optional[List[str]] = None,
        names: Optional[List[str]] = None,
        tool_ids: Optional[List[str]] = None,
        search: Optional[str] = None,
        return_only_letta_tools: bool = False,
        exclude_letta_tools: bool = False,
        project_id: Optional[str] = None,
    ) -> int:
        """Count tools with the same filtering logic as list_tools_async."""
        async with db_registry.async_session() as session:
            # Use SQLAlchemy directly with COUNT query - same filtering logic as list_tools_async
            # Start with base query
            query = select(func.count(ToolModel.id)).where(ToolModel.organization_id == actor.organization_id)

            # Apply project_id filter - include tools where project_id matches OR project_id is None (global tools)
            if project_id is not None:
                query = query.where(or_(ToolModel.project_id == project_id, ToolModel.project_id.is_(None)))

            # Apply tool_types filter
            if tool_types is not None:
                query = query.where(ToolModel.tool_type.in_(tool_types))

            # Apply names filter
            if names is not None:
                query = query.where(ToolModel.name.in_(names))

            # Apply tool_ids filter
            if tool_ids is not None:
                query = query.where(ToolModel.id.in_(tool_ids))

            # Apply search filter (ILIKE for case-insensitive partial match)
            if search is not None:
                query = query.where(ToolModel.name.ilike(f"%{search}%"))

            # Apply exclude_tool_types filter at database level
            if exclude_tool_types is not None:
                query = query.where(~ToolModel.tool_type.in_(exclude_tool_types))

            # Apply return_only_letta_tools filter at database level
            if return_only_letta_tools:
                query = query.where(ToolModel.tool_type.like("letta_%"))

            # Handle exclude_letta_tools logic (if True, exclude Letta tools)
            if exclude_letta_tools:
                # Exclude tools that are in the LETTA_TOOL_SET
                letta_tool_names = list(LETTA_TOOL_SET)
                query = query.where(~ToolModel.name.in_(letta_tool_names))

            # Execute count query
            result = await session.execute(query)
            count = result.scalar()

            return count or 0

    @enforce_types
    @trace_method
    async def size_async(
        self,
        actor: PydanticUser,
        include_base_tools: bool,
    ) -> int:
        """
        Get the total count of tools for the given user.

        If include_builtin is True, it will also count the built-in tools.
        """
        async with db_registry.async_session() as session:
            if include_base_tools:
                return await ToolModel.size_async(db_session=session, actor=actor)
            return await ToolModel.size_async(db_session=session, actor=actor, name=LETTA_TOOL_SET)

    @enforce_types
    @raise_on_invalid_id(param_name="tool_id", expected_prefix=PrimitiveType.TOOL)
    @trace_method
    async def update_tool_by_id_async(
        self,
        tool_id: str,
        tool_update: ToolUpdate,
        actor: PydanticUser,
        updated_tool_type: Optional[ToolType] = None,
        bypass_name_check: bool = False,
        modal_sandbox_enabled: bool = False,
    ) -> PydanticTool:
        """Update a tool by its ID with the given ToolUpdate object."""
        # Fetch current tool early to allow conditional logic based on tool type
        current_tool = await self.get_tool_by_id_async(tool_id=tool_id, actor=actor)

        # Handle schema updates for custom tools
        new_schema = None
        if current_tool.tool_type == ToolType.CUSTOM:
            if tool_update.json_schema is not None:
                new_schema = tool_update.json_schema
            elif tool_update.args_json_schema is not None:
                # Generate full schema from args_json_schema
                generated_schema = generate_schema_for_tool_update(
                    current_tool=current_tool,
                    json_schema=None,
                    args_json_schema=tool_update.args_json_schema,
                    source_code=tool_update.source_code,
                    source_type=tool_update.source_type,
                )
                if generated_schema:
                    tool_update.json_schema = generated_schema
                    new_schema = generated_schema

        # Now model_dump with the potentially updated schema
        update_data = tool_update.model_dump(to_orm=True, exclude_none=True)

        # Determine the final schema and name
        if new_schema:
            new_name = new_schema.get("name", current_tool.name)
        elif "json_schema" in update_data:
            new_schema = update_data["json_schema"]
            new_name = new_schema.get("name", current_tool.name)
        else:
            # Keep existing schema
            new_schema = current_tool.json_schema
            new_name = current_tool.name

        # Handle explicit name updates
        if "name" in update_data and update_data["name"] != current_tool.name:
            # Name is being explicitly changed
            new_name = update_data["name"]
            # Update the json_schema name to match if there's a schema
            if new_schema:
                new_schema = new_schema.copy()
                new_schema["name"] = new_name
                update_data["json_schema"] = new_schema
        elif new_schema and new_name != current_tool.name:
            # Schema provides a different name but name wasn't explicitly changed
            update_data["name"] = new_name
            # raise ValueError(
            #    f"JSON schema name '{new_name}' conflicts with current tool name '{current_tool.name}'. Update the name field explicitly if you want to rename the tool."
            # )

        # Track if we need to check name uniqueness (check is done inside session with lock)
        needs_name_conflict_check = new_name != current_tool.name

        # Definitive checker for source code type
        if "source_code" in update_data:
            source_code = update_data.get("source_code", "")
            source_type = update_data.get("source_type", current_tool.source_type)

            # Check for function name based on source type
            if source_type == "typescript":
                # TypeScript: check for "function name" or "export function name"
                if f"function {new_name}" not in source_code:
                    raise LettaToolNameSchemaMismatchError(
                        tool_name=new_name,
                        json_schema_name=new_schema.get("name") if new_schema else None,
                        source_code=source_code,
                    )
            else:
                # Python: check for "def name"
                if f"def {new_name}" not in source_code:
                    raise LettaToolNameSchemaMismatchError(
                        tool_name=new_name,
                        json_schema_name=new_schema.get("name") if new_schema else None,
                        source_code=source_code,
                    )

        # Create a preview of the updated tool by merging current tool with updates
        # This allows us to compute the hash before the database session
        updated_tool_pydantic = current_tool.model_copy(deep=True)
        for key, value in update_data.items():
            setattr(updated_tool_pydantic, key, value)
        if new_schema is not None:
            updated_tool_pydantic.json_schema = new_schema
            updated_tool_pydantic.name = new_name
        if updated_tool_type:
            updated_tool_pydantic.tool_type = updated_tool_type

        # Handle sandbox:modal metadata based on flag
        if modal_sandbox_enabled:
            # Add sandbox:modal to metadata if flag is enabled
            if updated_tool_pydantic.metadata_ is None:
                updated_tool_pydantic.metadata_ = {}
            updated_tool_pydantic.metadata_["sandbox"] = "modal"
            # Update the update_data to reflect this change if metadata was in the update
            if "metadata_" not in update_data:
                update_data["metadata_"] = updated_tool_pydantic.metadata_
            else:
                update_data["metadata_"]["sandbox"] = "modal"
        else:
            # Remove sandbox:modal from metadata if flag is not enabled
            if updated_tool_pydantic.metadata_ and updated_tool_pydantic.metadata_.get("sandbox") == "modal":
                updated_tool_pydantic.metadata_ = {k: v for k, v in updated_tool_pydantic.metadata_.items() if k != "sandbox"}
                if not updated_tool_pydantic.metadata_:  # If metadata becomes empty, set to None
                    updated_tool_pydantic.metadata_ = None
                # Update the update_data to reflect this change
                update_data["metadata_"] = updated_tool_pydantic.metadata_

        # Check if we need to redeploy the Modal app due to changes
        # Compute this before the session to avoid issues
        tool_requests_modal = updated_tool_pydantic.metadata_ and updated_tool_pydantic.metadata_.get("sandbox") == "modal"
        modal_configured = tool_settings.modal_sandbox_enabled
        should_check_modal = tool_requests_modal and modal_configured and updated_tool_pydantic.tool_type == ToolType.CUSTOM

        # Compute hash before session if needed
        new_hash = None
        old_hash = None
        needs_modal_deployment = False

        if should_check_modal:
            new_hash = compute_tool_hash(updated_tool_pydantic)
            old_hash = current_tool.metadata_.get("tool_hash") if current_tool.metadata_ else None
            needs_modal_deployment = new_hash != old_hash

        # Now perform the update within the session
        async with db_registry.async_session() as session:
            # Check name uniqueness with lock INSIDE the session to prevent race conditions
            # This uses SELECT FOR UPDATE to ensure no other transaction can rename to this name
            if needs_name_conflict_check:
                name_conflict = await self._check_tool_name_conflict_with_lock_async(
                    session=session,
                    tool_name=new_name,
                    exclude_tool_id=tool_id,
                    actor=actor,
                )
                if name_conflict:
                    raise LettaToolNameConflictError(tool_name=new_name)

            # Fetch the tool by ID
            tool = await ToolModel.read_async(db_session=session, identifier=tool_id, actor=actor)

            # Update tool attributes with only the fields that were explicitly set
            for key, value in update_data.items():
                setattr(tool, key, value)

            # If we already computed the new schema, apply it
            if new_schema is not None:
                tool.json_schema = new_schema
                tool.name = new_name

            if updated_tool_type:
                tool.tool_type = updated_tool_type

            # Save the updated tool to the database
            tool = await tool.update_async(db_session=session, actor=actor)
            updated_tool = tool.to_pydantic()

            # Update Modal hash in metadata if needed (inside session context)
            if needs_modal_deployment:
                if updated_tool.metadata_ is None:
                    updated_tool.metadata_ = {}
                updated_tool.metadata_["tool_hash"] = new_hash

                # Update the metadata in the database (still inside session)
                tool.metadata_ = updated_tool.metadata_
                tool = await tool.update_async(db_session=session, actor=actor)
                updated_tool = tool.to_pydantic()

        # Deploy Modal app outside of session (it creates its own sessions)
        if needs_modal_deployment:
            logger.info(f"Deploying Modal app for tool {updated_tool.id} with new hash: {new_hash}")
            await self.create_or_update_modal_app(updated_tool, actor)

        # Update embedding in Turbopuffer if enabled (delete old, insert new)
        from letta.helpers.tpuf_client import should_use_tpuf_for_tools

        if should_use_tpuf_for_tools():

            async def update_tool_embedding():
                try:
                    from letta.helpers.tpuf_client import TurbopufferClient

                    tpuf_client = TurbopufferClient()
                    # Delete old and re-insert (simpler than update)
                    await tpuf_client.delete_tools(actor.organization_id, [updated_tool.id])
                    await tpuf_client.insert_tools([updated_tool], actor.organization_id, actor)
                    logger.info(f"Successfully updated tool {updated_tool.id} in Turbopuffer")
                except Exception as e:
                    logger.error(f"Failed to update tool {updated_tool.id} in Turbopuffer: {e}")

            fire_and_forget(
                update_tool_embedding(),
                task_name=f"update_tool_embedding_{updated_tool.id}",
            )

        return updated_tool

    @enforce_types
    # @raise_on_invalid_id This is commented out bc it's called by _list_tools_async, when it encounters malformed tools (i.e. if id is invalid will fail validation on deletion)
    @trace_method
    async def delete_tool_by_id_async(self, tool_id: str, actor: PydanticUser) -> None:
        """Delete a tool by its ID."""
        async with db_registry.async_session() as session:
            try:
                tool = await ToolModel.read_async(db_session=session, identifier=tool_id, actor=actor)

                # Try to convert to Pydantic to check for Modal cleanup
                # If this fails (corrupted tool), skip Modal cleanup and just delete
                try:
                    tool_pydantic = tool.to_pydantic()

                    # Check if tool had Modal deployment and delete it
                    tool_requests_modal = tool_pydantic.metadata_ and tool_pydantic.metadata_.get("sandbox") == "modal"
                    modal_configured = tool_settings.modal_sandbox_enabled

                    if tool_pydantic.tool_type == ToolType.CUSTOM and tool_requests_modal and modal_configured:
                        try:
                            await self.delete_modal_app(tool_pydantic, actor)
                        except Exception as e:
                            logger.warning(f"Failed to delete Modal app for tool {tool_pydantic.name}: {e}")
                            # Continue with tool deletion even if Modal cleanup fails
                except (ValueError, ValidationError) as e:
                    # Tool is corrupted and can't be converted to Pydantic
                    # Skip Modal cleanup and just delete the tool from database
                    logger.warning(f"Skipping Modal cleanup for corrupted tool {tool_id}: {e}")

                await tool.hard_delete_async(db_session=session, actor=actor)

                # Delete from Turbopuffer if enabled
                from letta.helpers.tpuf_client import should_use_tpuf_for_tools

                if should_use_tpuf_for_tools():
                    try:
                        from letta.helpers.tpuf_client import TurbopufferClient

                        tpuf_client = TurbopufferClient()
                        await tpuf_client.delete_tools(actor.organization_id, [tool_id])
                        logger.info(f"Successfully deleted tool {tool_id} from Turbopuffer")
                    except Exception as e:
                        logger.warning(f"Failed to delete tool {tool_id} from Turbopuffer: {e}")

            except NoResultFound:
                raise ValueError(f"Tool with id {tool_id} not found.")

    @enforce_types
    @trace_method
    async def upsert_base_tools_async(
        self,
        actor: PydanticUser,
        allowed_types: Optional[Set[ToolType]] = None,
    ) -> List[PydanticTool]:
        """Add default tools defined in the various function_sets modules, optionally filtered by ToolType.

        Optimized bulk implementation using single database session and batch operations.
        """

        functions_to_schema = {}
        for module_name in LETTA_TOOL_MODULE_NAMES:
            try:
                module = importlib.import_module(module_name)
                functions_to_schema.update(load_function_set(module))
            except ValueError as e:
                logger.warning(f"Error loading function set '{module_name}': {e}")
            except Exception as e:
                raise e

        # prepare tool data for bulk operations
        tool_data_list = []
        for name, schema in functions_to_schema.items():
            if name not in LETTA_TOOL_SET:
                continue

            if name in BASE_TOOLS:
                tool_type = ToolType.LETTA_CORE
            elif name in BASE_MEMORY_TOOLS:
                tool_type = ToolType.LETTA_MEMORY_CORE
            elif name in BASE_SLEEPTIME_TOOLS:
                tool_type = ToolType.LETTA_SLEEPTIME_CORE
            elif name in calculate_multi_agent_tools():
                tool_type = ToolType.LETTA_MULTI_AGENT_CORE
            elif name in BASE_VOICE_SLEEPTIME_TOOLS or name in BASE_VOICE_SLEEPTIME_CHAT_TOOLS:
                tool_type = ToolType.LETTA_VOICE_SLEEPTIME_CORE
            elif name in BUILTIN_TOOLS:
                tool_type = ToolType.LETTA_BUILTIN
            elif name in FILES_TOOLS:
                tool_type = ToolType.LETTA_FILES_CORE
            else:
                logger.warning(f"Tool name {name} is not in any known base tool set, skipping")
                continue

            if allowed_types is not None and tool_type not in allowed_types:
                continue

            # create pydantic tool for validation and conversion
            parallel_safe = name in LETTA_PARALLEL_SAFE_TOOLS
            pydantic_tool = PydanticTool(
                name=name,
                tags=[tool_type.value],
                source_type="python",
                tool_type=tool_type,
                return_char_limit=BASE_FUNCTION_RETURN_CHAR_LIMIT,
                enable_parallel_execution=parallel_safe,
            )

            # auto-generate description if not provided
            if pydantic_tool.description is None:
                pydantic_tool.description = pydantic_tool.json_schema.get("description", None)

            tool_data_list.append(pydantic_tool)

        if not tool_data_list:
            return []

        if settings.letta_pg_uri_no_default:
            async with db_registry.async_session() as session:
                return await self._bulk_upsert_postgresql(session, tool_data_list, actor)
        else:
            return await self._upsert_tools_individually(tool_data_list, actor)

    @trace_method
    async def _bulk_upsert_postgresql(
        self, session, tool_data_list: List[PydanticTool], actor: PydanticUser, override_existing_tools: bool = True
    ) -> List[PydanticTool]:
        """hyper-optimized postgresql bulk upsert using on_conflict_do_update or on_conflict_do_nothing."""
        from sqlalchemy import func, select
        from sqlalchemy.dialects.postgresql import insert

        # Sort tools by name to prevent deadlocks.
        # When multiple concurrent transactions try to upsert the same tools,
        # they must acquire row locks in a consistent order to avoid deadlocks.
        # Without sorting, Transaction A might lock (a, b, c) while Transaction B
        # locks (b, c, a), causing each to wait for the other (deadlock).
        sorted_tool_data_list = sorted(tool_data_list, key=lambda t: t.name)

        # prepare data for bulk insert
        table = ToolModel.__table__
        valid_columns = {col.name for col in table.columns}

        insert_data = []
        for tool in sorted_tool_data_list:
            tool_dict = tool.model_dump(to_orm=True)
            # set created/updated by fields
            if actor:
                tool_dict["_created_by_id"] = actor.id
                tool_dict["_last_updated_by_id"] = actor.id
                tool_dict["organization_id"] = actor.organization_id

            # filter to only include columns that exist in the table
            filtered_dict = {k: v for k, v in tool_dict.items() if k in valid_columns}
            insert_data.append(filtered_dict)

        # use postgresql's native bulk upsert
        stmt = insert(table).values(insert_data)

        if override_existing_tools:
            # on conflict, update all columns except id, created_at, and _created_by_id
            excluded = stmt.excluded
            update_dict = {}
            for col in table.columns:
                if col.name not in ("id", "created_at", "_created_by_id"):
                    if col.name == "updated_at":
                        update_dict[col.name] = func.now()
                    else:
                        update_dict[col.name] = excluded[col.name]

            upsert_stmt = stmt.on_conflict_do_update(index_elements=["name", "organization_id"], set_=update_dict)
        else:
            # on conflict, do nothing (skip existing tools)
            upsert_stmt = stmt.on_conflict_do_nothing(index_elements=["name", "organization_id"])

        await session.execute(upsert_stmt)
        # context manager now handles commits
        # await session.commit()

        # fetch results (includes both inserted and skipped tools)
        tool_names = [tool.name for tool in tool_data_list]
        result_query = select(ToolModel).where(ToolModel.name.in_(tool_names), ToolModel.organization_id == actor.organization_id)
        result = await session.execute(result_query)
        return [tool.to_pydantic() for tool in result.scalars()]

    @trace_method
    async def _upsert_tools_individually(
        self, tool_data_list: List[PydanticTool], actor: PydanticUser, override_existing_tools: bool = True
    ) -> List[PydanticTool]:
        """fallback to individual upserts for sqlite (original approach)."""
        tools = []
        for tool in tool_data_list:
            if override_existing_tools:
                # update existing tools if they exist
                upserted_tool = await self.create_or_update_tool_async(tool, actor)
                tools.append(upserted_tool)
            else:
                # skip existing tools, only create new ones
                existing_tool_id = await self.get_tool_id_by_name_async(tool_name=tool.name, actor=actor)
                if existing_tool_id:
                    # tool exists, fetch and return it without updating
                    existing_tool = await self.get_tool_by_id_async(existing_tool_id, actor=actor)
                    tools.append(existing_tool)
                else:
                    # tool doesn't exist, create it
                    created_tool = await self.create_tool_async(tool, actor=actor)
                    tools.append(created_tool)
        return tools

    @trace_method
    async def create_or_update_modal_app(self, tool: PydanticTool, actor: PydanticUser):
        """Create a Modal app with the tool function registered"""

        import modal

        from letta.services.sandbox_config_manager import SandboxConfigManager

        # Load sandbox env vars to bake them into the Modal secrets
        sandbox_env_vars = {}
        try:
            sandbox_config_manager = SandboxConfigManager()
            sandbox_config = await sandbox_config_manager.get_or_create_default_sandbox_config_async(
                sandbox_type=SandboxType.MODAL, actor=actor
            )
            if sandbox_config:
                sandbox_env_vars = await sandbox_config_manager.get_sandbox_env_vars_as_dict_async(
                    sandbox_config_id=sandbox_config.id, actor=actor, limit=None
                )
                logger.info(f"Loaded {len(sandbox_env_vars)} sandbox env vars for Modal app {tool.id}")
        except Exception as e:
            logger.warning(f"Could not load sandbox env vars for Modal app {tool.id}: {e}")

        # Create the Modal app using the global function with sandbox env vars
        modal_app = modal_tool_wrapper(tool, actor, sandbox_env_vars)

        # Deploy the app first
        with modal.enable_output(show_progress=False):
            try:
                deploy = modal_app.deploy()
            except Exception as e:
                raise LettaInvalidArgumentError(f"Failed to deploy tool {tool.id} with name {tool.name} to Modal: {e}")

        # After deployment, look up the function to configure autoscaler
        try:
            import asyncio

            func = modal.Function.from_name(modal_app.name, MODAL_DEFAULT_TOOL_NAME)
            func.update_autoscaler(scaledown_window=2)  # drain inactive old containers
            await asyncio.sleep(5)
            func.update_autoscaler(scaledown_window=60)
        except Exception as e:
            logger.warning(f"Failed to configure autoscaler for Modal function {modal_app.name}: {e}")

        return deploy

    async def delete_modal_app(self, tool: PydanticTool, actor: PydanticUser):
        """Delete a Modal app deployment for the tool"""
        try:
            # Generate the app name for this tool
            generate_modal_function_name(tool.id, actor.organization_id)

            # Try to delete the app
            # TODO: we need to soft delete, and then potentially stop via the CLI, no programmatic way to delete currently
            # try:
            #     app = modal.App.from_name(modal_app_name)
            #     await app.delete_async()
            #     logger.info(f"Deleted Modal app {modal_app_name} for tool {tool.name}")
            # except modal.exception.NotFoundError:
            #     logger.info(f"Modal app {modal_app_name} not found, may have been already deleted")
            # except Exception as e:
            #     logger.warning(f"Failed to delete Modal app {modal_app_name}: {e}")

        except Exception as e:
            logger.error(f"Error during Modal app deletion for tool {tool.name}: {e}")
            raise

    async def _embed_tool_background(
        self,
        tool: PydanticTool,
        actor: PydanticUser,
    ) -> None:
        """Background task to embed a tool in Turbopuffer.

        Args:
            tool: The tool to embed
            actor: User performing the action
        """
        try:
            from letta.helpers.tpuf_client import TurbopufferClient

            tpuf_client = TurbopufferClient()
            await tpuf_client.insert_tools(
                tools=[tool],
                organization_id=actor.organization_id,
                actor=actor,
            )
            logger.info(f"Successfully embedded tool {tool.id} in Turbopuffer")
        except Exception as e:
            logger.error(f"Failed to embed tool {tool.id} in Turbopuffer: {e}")

    @enforce_types
    @trace_method
    async def search_tools_async(
        self,
        actor: PydanticUser,
        query_text: Optional[str] = None,
        search_mode: str = "hybrid",
        tool_types: Optional[List[str]] = None,
        tags: Optional[List[str]] = None,
        limit: int = 50,
    ) -> List[tuple[PydanticTool, dict]]:
        """
        Search tools using Turbopuffer semantic search.

        Args:
            actor: User performing the search
            query_text: Text query for semantic search
            search_mode: "vector", "fts", or "hybrid" (default: "hybrid")
            tool_types: Optional list of tool types to filter by
            tags: Optional list of tags to filter by
            limit: Maximum number of results to return

        Returns:
            List of (tool, metadata) tuples where metadata contains search scores

        Raises:
            ValueError: If Turbopuffer is not enabled for tools
        """
        from letta.helpers.tpuf_client import TurbopufferClient, should_use_tpuf_for_tools

        if not should_use_tpuf_for_tools():
            raise ValueError("Tool semantic search requires tool embedding to be enabled (embed_tools=True).")

        tpuf_client = TurbopufferClient()
        results = await tpuf_client.query_tools(
            organization_id=actor.organization_id,
            actor=actor,
            query_text=query_text,
            search_mode=search_mode,
            top_k=limit,
            tool_types=tool_types,
            tags=tags,
        )

        if not results:
            return []

        # Fetch full tool objects from database
        tool_ids = [tool_dict["id"] for tool_dict, _, _ in results]
        tools = []
        for tool_id in tool_ids:
            try:
                tool = await self.get_tool_by_id_async(tool_id, actor=actor)
                tools.append(tool)
            except Exception:
                pass  # Tool may have been deleted

        tool_map = {tool.id: tool for tool in tools}

        # Build result list preserving order and including metadata
        result_list = []
        for tool_dict, _, metadata in results:
            tool_id = tool_dict["id"]
            if tool_id in tool_map:
                result_list.append((tool_map[tool_id], metadata))

        return result_list
