import asyncio
from datetime import datetime, timezone
from typing import Any, Dict, List, Literal, Optional, Set, Tuple
from zoneinfo import ZoneInfo

import sqlalchemy as sa
from sqlalchemy import delete, func, insert, literal, or_, select, tuple_
from sqlalchemy.dialects.postgresql import insert as pg_insert

from letta.constants import (
    BASE_MEMORY_TOOLS,
    BASE_MEMORY_TOOLS_V2,
    BASE_MEMORY_TOOLS_V3,
    BASE_SLEEPTIME_CHAT_TOOLS,
    BASE_SLEEPTIME_TOOLS,
    BASE_TOOLS,
    BASE_VOICE_SLEEPTIME_CHAT_TOOLS,
    BASE_VOICE_SLEEPTIME_TOOLS,
    DEFAULT_CORE_MEMORY_SOURCE_CHAR_LIMIT,
    DEFAULT_MAX_FILES_OPEN,
    DEFAULT_TIMEZONE,
    DEPRECATED_LETTA_TOOLS,
    EXCLUDE_MODEL_KEYWORDS_FROM_BASE_TOOL_RULES,
    FILES_TOOLS,
    INCLUDE_MODEL_KEYWORDS_BASE_TOOL_RULES,
    RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE,
)
from letta.errors import LettaAgentNotFoundError
from letta.helpers import ToolRulesSolver
from letta.helpers.datetime_helpers import get_utc_time
from letta.llm_api.llm_client import LLMClient
from letta.log import get_logger
from letta.orm import (
    Agent as AgentModel,
    AgentsTags,
    ArchivalPassage,
    Block as BlockModel,
    BlocksAgents,
    Group as GroupModel,
    GroupsAgents,
    IdentitiesAgents,
    Source as SourceModel,
    SourcePassage,
    SourcesAgents,
    Tool as ToolModel,
    ToolsAgents,
)
from letta.orm.errors import NoResultFound
from letta.orm.sandbox_config import AgentEnvironmentVariable, AgentEnvironmentVariable as AgentEnvironmentVariableModel
from letta.orm.sqlalchemy_base import AccessType
from letta.otel.tracing import trace_method
from letta.prompts.prompt_generator import PromptGenerator
from letta.schemas.agent import (
    AgentRelationships,
    AgentState as PydanticAgentState,
    CreateAgent,
    InternalTemplateAgentCreate,
    UpdateAgent,
)
from letta.schemas.block import DEFAULT_BLOCKS, Block as PydanticBlock, BlockUpdate
from letta.schemas.embedding_config import EmbeddingConfig
from letta.schemas.enums import AgentType, PrimitiveType, ProviderType, TagMatchMode, ToolType, VectorDBProvider
from letta.schemas.file import FileMetadata as PydanticFileMetadata
from letta.schemas.group import Group as PydanticGroup, ManagerType
from letta.schemas.letta_stop_reason import StopReasonType
from letta.schemas.llm_config import LLMConfig
from letta.schemas.memory import ContextWindowOverview, Memory
from letta.schemas.message import Message, Message as PydanticMessage, MessageCreate, MessageUpdate
from letta.schemas.passage import Passage as PydanticPassage
from letta.schemas.secret import Secret
from letta.schemas.source import Source as PydanticSource
from letta.schemas.tool import Tool as PydanticTool
from letta.schemas.tool_rule import ContinueToolRule, RequiresApprovalToolRule, TerminalToolRule
from letta.schemas.user import User as PydanticUser
from letta.serialize_schemas import MarshmallowAgentSchema
from letta.serialize_schemas.marshmallow_message import SerializedMessageSchema
from letta.serialize_schemas.marshmallow_tool import SerializedToolSchema
from letta.serialize_schemas.pydantic_agent_schema import AgentSchema
from letta.server.db import db_registry
from letta.services.archive_manager import ArchiveManager
from letta.services.block_manager import BlockManager, validate_block_limit_constraint
from letta.services.context_window_calculator.context_window_calculator import ContextWindowCalculator
from letta.services.context_window_calculator.token_counter import AnthropicTokenCounter, TiktokenCounter
from letta.services.file_processor.chunker.line_chunker import LineChunker
from letta.services.files_agents_manager import FileAgentManager
from letta.services.helpers.agent_manager_helper import (
    _apply_filters,
    _apply_identity_filters,
    _apply_pagination,
    _apply_pagination_async,
    _apply_relationship_filters,
    _apply_tag_filter,
    _process_relationship,
    _process_relationship_async,
    build_agent_passage_query,
    build_passage_query,
    build_source_passage_query,
    calculate_base_tools,
    calculate_multi_agent_tools,
    check_supports_structured_output,
    compile_system_message,
    derive_system_message,
    initialize_message_sequence,
    initialize_message_sequence_async,
    package_initial_message_sequence,
    validate_agent_exists_async,
)
from letta.services.identity_manager import IdentityManager
from letta.services.message_manager import MessageManager
from letta.services.passage_manager import PassageManager
from letta.services.source_manager import SourceManager
from letta.services.tool_manager import ToolManager
from letta.settings import DatabaseChoice, model_settings, settings
from letta.utils import calculate_file_defaults_based_on_context_window, enforce_types, united_diff
from letta.validators import raise_on_invalid_id

logger = get_logger(__name__)


class AgentManager:
    """Manager class to handle business logic related to Agents."""

    def __init__(self):
        self.block_manager = BlockManager()
        self.tool_manager = ToolManager()
        self.source_manager = SourceManager()
        self.message_manager = MessageManager()
        self.passage_manager = PassageManager()
        self.identity_manager = IdentityManager()
        self.file_agent_manager = FileAgentManager()
        self.archive_manager = ArchiveManager()

    @staticmethod
    def _should_exclude_model_from_base_tool_rules(model: str) -> bool:
        """Check if a model should be excluded from base tool rules based on model keywords."""
        # First check if model contains any include keywords (overrides exclusion)
        for include_keyword in INCLUDE_MODEL_KEYWORDS_BASE_TOOL_RULES:
            if include_keyword in model:
                return False

        # Then check if model contains any exclude keywords
        for exclude_keyword in EXCLUDE_MODEL_KEYWORDS_FROM_BASE_TOOL_RULES:
            if exclude_keyword in model:
                return True

        return False

    @staticmethod
    def _resolve_tools(session, names: Set[str], ids: Set[str], org_id: str) -> Tuple[Dict[str, str], Dict[str, str]]:
        """
        Bulk‑fetch all ToolModel rows matching either name ∈ names or id ∈ ids
        (and scoped to this organization), and return two maps:
          name_to_id, id_to_name.
        Raises if any requested name or id was not found.
        """
        stmt = select(ToolModel.id, ToolModel.name).where(
            ToolModel.organization_id == org_id,
            or_(
                ToolModel.name.in_(names),
                ToolModel.id.in_(ids),
            ),
        )
        rows = session.execute(stmt).all()
        name_to_id = {name: tid for tid, name in rows}
        id_to_name = {tid: name for tid, name in rows}

        missing_names = names - set(name_to_id.keys())
        missing_ids = ids - set(id_to_name.keys())
        if missing_names:
            raise ValueError(f"Tools not found by name: {missing_names}")
        if missing_ids:
            raise ValueError(f"Tools not found by id:   {missing_ids}")

        return name_to_id, id_to_name

    @staticmethod
    async def _resolve_tools_async(
        session, names: Set[str], ids: Set[str], org_id: str, ignore_invalid_tools: bool = False
    ) -> Tuple[Dict[str, str], Dict[str, str], List[str]]:
        """
        Bulk‑fetch all ToolModel rows matching either name ∈ names or id ∈ ids
        (and scoped to this organization), and return two maps:
          name_to_id, id_to_name.
        Raises if any requested name or id was not found (unless ignore_invalid_tools is True).

        Args:
            session: Database session
            names: Set of tool names to resolve
            ids: Set of tool IDs to resolve
            org_id: Organization ID for scoping
            ignore_invalid_tools: If True, silently filters out missing tools instead of raising an error
        """
        stmt = select(ToolModel.id, ToolModel.name, ToolModel.default_requires_approval).where(
            ToolModel.organization_id == org_id,
            or_(
                ToolModel.name.in_(names),
                ToolModel.id.in_(ids),
            ),
        )
        result = await session.execute(stmt)
        rows = result.fetchall()  # Use fetchall()
        name_to_id = {row[1]: row[0] for row in rows}  # row[1] is name, row[0] is id
        id_to_name = {row[0]: row[1] for row in rows}  # row[0] is id, row[1] is name
        requires_approval = [row[1] for row in rows if row[2]]  # row[1] is name, row[2] is default_requires_approval

        missing_names = names - set(name_to_id.keys())
        missing_ids = ids - set(id_to_name.keys())

        if not ignore_invalid_tools:
            # Original behavior: raise errors for missing tools
            if missing_names:
                raise ValueError(f"Tools not found by name: {missing_names}")
            if missing_ids:
                raise ValueError(f"Tools not found by id:   {missing_ids}")
        else:
            # New behavior: log missing tools but don't raise errors
            if missing_names or missing_ids:
                logger = get_logger(__name__)
                if missing_names:
                    logger.warning(f"Ignoring tools not found by name: {missing_names}")
                if missing_ids:
                    logger.warning(f"Ignoring tools not found by id: {missing_ids}")

        return name_to_id, id_to_name, requires_approval

    @staticmethod
    def _bulk_insert_pivot(session, table, rows: list[dict]):
        if not rows:
            return

        dialect = session.bind.dialect.name
        if dialect == "postgresql":
            stmt = pg_insert(table).values(rows).on_conflict_do_nothing()
        elif dialect == "sqlite":
            stmt = sa.insert(table).values(rows).prefix_with("OR IGNORE")
        else:
            # fallback: filter out exact-duplicate dicts in Python
            seen = set()
            filtered = []
            for row in rows:
                key = tuple(sorted(row.items()))
                if key not in seen:
                    seen.add(key)
                    filtered.append(row)
            stmt = sa.insert(table).values(filtered)

        session.execute(stmt)

    @staticmethod
    async def _bulk_insert_pivot_async(session, table, rows: list[dict]):
        if not rows:
            return

        dialect = session.bind.dialect.name
        if dialect == "postgresql":
            stmt = pg_insert(table).values(rows).on_conflict_do_nothing()
        elif dialect == "sqlite":
            stmt = sa.insert(table).values(rows).prefix_with("OR IGNORE")
        else:
            # fallback: filter out exact-duplicate dicts in Python
            seen = set()
            filtered = []
            for row in rows:
                key = tuple(sorted(row.items()))
                if key not in seen:
                    seen.add(key)
                    filtered.append(row)
            stmt = sa.insert(table).values(filtered)

        await session.execute(stmt)

    @staticmethod
    def _replace_pivot_rows(session, table, agent_id: str, rows: list[dict]):
        """
        Replace all pivot rows for an agent with *exactly* the provided list.
        Uses two bulk statements (DELETE + INSERT ... ON CONFLICT DO NOTHING).
        """
        # delete all existing rows for this agent
        session.execute(delete(table).where(table.c.agent_id == agent_id))
        if rows:
            AgentManager._bulk_insert_pivot(session, table, rows)

    @staticmethod
    async def _replace_pivot_rows_async(session, table, agent_id: str, rows: list[dict]):
        """
        Replace all pivot rows for an agent atomically using MERGE pattern.
        """
        dialect = session.bind.dialect.name

        if dialect == "postgresql":
            if rows:
                # separate upsert and delete operations
                stmt = pg_insert(table).values(rows)
                stmt = stmt.on_conflict_do_nothing()
                await session.execute(stmt)

                # delete rows not in new set
                pk_names = [c.name for c in table.primary_key.columns]
                new_keys = [tuple(r[c] for c in pk_names) for r in rows]
                await session.execute(
                    delete(table).where(table.c.agent_id == agent_id, ~tuple_(*[table.c[c] for c in pk_names]).in_(new_keys))
                )
            else:
                # if no rows to insert, just delete all
                await session.execute(delete(table).where(table.c.agent_id == agent_id))

        elif dialect == "sqlite":
            if rows:
                stmt = sa.insert(table).values(rows).prefix_with("OR REPLACE")
                await session.execute(stmt)

            if rows:
                primary_key_cols = [table.c[c.name] for c in table.primary_key.columns]
                new_keys = [tuple(r[c.name] for c in table.primary_key.columns) for r in rows]
                await session.execute(delete(table).where(table.c.agent_id == agent_id, ~tuple_(*primary_key_cols).in_(new_keys)))
            else:
                await session.execute(delete(table).where(table.c.agent_id == agent_id))

        else:
            # fallback: use original DELETE + INSERT pattern
            await session.execute(delete(table).where(table.c.agent_id == agent_id))
            if rows:
                await AgentManager._bulk_insert_pivot_async(session, table, rows)

    # ======================================================================================================================
    # Basic CRUD operations
    # ======================================================================================================================

    @trace_method
    async def create_agent_async(
        self,
        agent_create: CreateAgent,
        actor: PydanticUser,
        _test_only_force_id: Optional[str] = None,
        _init_with_no_messages: bool = False,
        ignore_invalid_tools: bool = False,
    ) -> PydanticAgentState:
        # validate required configs
        if not agent_create.llm_config or not agent_create.embedding_config:
            raise ValueError("llm_config and embedding_config are required")

        # For v1 agents, enforce sane defaults even when reasoning is omitted
        if agent_create.agent_type == AgentType.letta_v1_agent:
            # Claude 3.7/4 or OpenAI o1/o3/o4/gpt-5
            default_reasoning = LLMConfig.is_anthropic_reasoning_model(agent_create.llm_config) or LLMConfig.is_openai_reasoning_model(
                agent_create.llm_config
            )
            agent_create.llm_config = LLMConfig.apply_reasoning_setting_to_config(
                agent_create.llm_config,
                agent_create.reasoning if agent_create.reasoning is not None else default_reasoning,
                agent_create.agent_type,
            )
        else:
            if agent_create.reasoning is not None:
                agent_create.llm_config = LLMConfig.apply_reasoning_setting_to_config(
                    agent_create.llm_config,
                    agent_create.reasoning,
                    agent_create.agent_type,
                )

        # blocks
        block_ids = list(agent_create.block_ids or [])
        if agent_create.memory_blocks:
            pydantic_blocks = [PydanticBlock(**b.model_dump(to_orm=True)) for b in agent_create.memory_blocks]

            # Inject a description for the default blocks if the user didn't specify them
            # Used for `persona`, `human`, etc
            default_blocks = {block.label: block for block in DEFAULT_BLOCKS}
            for block in pydantic_blocks:
                if block.label in default_blocks:
                    if block.description is None:
                        block.description = default_blocks[block.label].description

            # Actually create the blocks
            created_blocks = await self.block_manager.batch_create_blocks_async(
                pydantic_blocks,
                actor=actor,
            )
            block_ids.extend([blk.id for blk in created_blocks])

        # tools
        tool_names = set(agent_create.tools or [])
        if agent_create.include_base_tools:
            if agent_create.agent_type == AgentType.voice_sleeptime_agent:
                tool_names |= set(BASE_VOICE_SLEEPTIME_TOOLS)
                # NOTE: also overwrite initial message sequence to empty by default
                if agent_create.initial_message_sequence is None:
                    agent_create.initial_message_sequence = []
            elif agent_create.agent_type == AgentType.voice_convo_agent:
                tool_names |= set(BASE_VOICE_SLEEPTIME_CHAT_TOOLS)
            elif agent_create.agent_type == AgentType.sleeptime_agent:
                tool_names |= set(BASE_SLEEPTIME_TOOLS)
                # NOTE: also overwrite initial message sequence to empty by default
                if agent_create.initial_message_sequence is None:
                    agent_create.initial_message_sequence = []
            elif agent_create.enable_sleeptime:
                tool_names |= set(BASE_SLEEPTIME_CHAT_TOOLS)
            elif agent_create.agent_type == AgentType.memgpt_v2_agent:
                tool_names |= calculate_base_tools(is_v2=True)
            elif agent_create.agent_type == AgentType.react_agent:
                pass  # no default tools
            elif agent_create.agent_type == AgentType.letta_v1_agent:
                tool_names |= calculate_base_tools(is_v2=True)
                # Remove `send_message` if it exists
                tool_names.discard("send_message")
                # NOTE: also overwriting inner_thoughts_in_kwargs to force False
                agent_create.llm_config.put_inner_thoughts_in_kwargs = False
                # NOTE: also overwrite initial message sequence to empty by default
                if agent_create.initial_message_sequence is None:
                    agent_create.initial_message_sequence = []
                # NOTE: default to no base tool rules unless explicitly provided
                if not agent_create.tool_rules and agent_create.include_base_tool_rules is None:
                    agent_create.include_base_tool_rules = False
            elif agent_create.agent_type == AgentType.workflow_agent:
                pass  # no default tools
            else:
                tool_names |= calculate_base_tools(is_v2=False)
        if agent_create.include_multi_agent_tools:
            tool_names |= calculate_multi_agent_tools()

        supplied_ids = set(agent_create.tool_ids or [])

        # Use folder_ids if provided, otherwise fall back to deprecated source_ids for backwards compatibility
        source_ids = agent_create.folder_ids if agent_create.folder_ids else (agent_create.source_ids or [])

        # Create default source if requested
        if agent_create.include_default_source:
            default_source = PydanticSource(
                name=f"{agent_create.name} External Data Source",
                embedding_config=agent_create.embedding_config,
            )
            created_source = await self.source_manager.create_source(default_source, actor)
            source_ids.append(created_source.id)

        identity_ids = agent_create.identity_ids or []
        tag_values = agent_create.tags or []

        # if the agent type is workflow, we set the autoclear to forced true
        if agent_create.agent_type == AgentType.workflow_agent:
            agent_create.message_buffer_autoclear = True

        async with db_registry.async_session() as session:
            async with session.begin():
                # Note: This will need to be modified if _resolve_tools needs an async version
                name_to_id, id_to_name, requires_approval = await self._resolve_tools_async(
                    session,
                    tool_names,
                    supplied_ids,
                    actor.organization_id,
                    ignore_invalid_tools=ignore_invalid_tools,
                )

                tool_ids = set(name_to_id.values()) | set(id_to_name.keys())
                tool_names = set(name_to_id.keys())  # now canonical
                tool_rules = list(agent_create.tool_rules or [])

                # Override include_base_tool_rules to False if model matches exclusion keywords and include_base_tool_rules is not explicitly set to True
                if (
                    (
                        self._should_exclude_model_from_base_tool_rules(agent_create.llm_config.model)
                        and agent_create.include_base_tool_rules is None
                    )
                    and agent_create.agent_type != AgentType.sleeptime_agent
                ) or agent_create.include_base_tool_rules is False:
                    agent_create.include_base_tool_rules = False
                    logger.info(f"Overriding include_base_tool_rules to False for model: {agent_create.llm_config.model}")
                else:
                    agent_create.include_base_tool_rules = True

                should_add_base_tool_rules = agent_create.include_base_tool_rules
                if should_add_base_tool_rules:
                    for tn in tool_names:
                        if tn in {"send_message", "send_message_to_agent_async", "memory_finish_edits"}:
                            tool_rules.append(TerminalToolRule(tool_name=tn))
                        elif tn in (BASE_TOOLS + BASE_MEMORY_TOOLS + BASE_MEMORY_TOOLS_V2 + BASE_MEMORY_TOOLS_V3 + BASE_SLEEPTIME_TOOLS):
                            tool_rules.append(ContinueToolRule(tool_name=tn))

                for tool_with_requires_approval in requires_approval:
                    tool_rules.append(RequiresApprovalToolRule(tool_name=tool_with_requires_approval))

                if tool_rules:
                    check_supports_structured_output(model=agent_create.llm_config.model, tool_rules=tool_rules)

                new_agent = AgentModel(
                    name=agent_create.name,
                    system=derive_system_message(
                        agent_type=agent_create.agent_type,
                        enable_sleeptime=agent_create.enable_sleeptime,
                        system=agent_create.system,
                    ),
                    agent_type=agent_create.agent_type,
                    llm_config=agent_create.llm_config,
                    embedding_config=agent_create.embedding_config,
                    organization_id=actor.organization_id,
                    description=agent_create.description,
                    metadata_=agent_create.metadata,
                    tool_rules=tool_rules,
                    hidden=agent_create.hidden,
                    project_id=agent_create.project_id,
                    template_id=agent_create.template_id,
                    base_template_id=agent_create.base_template_id,
                    message_buffer_autoclear=agent_create.message_buffer_autoclear,
                    enable_sleeptime=agent_create.enable_sleeptime,
                    response_format=agent_create.response_format,
                    created_by_id=actor.id,
                    last_updated_by_id=actor.id,
                    timezone=agent_create.timezone if agent_create.timezone else DEFAULT_TIMEZONE,
                    max_files_open=agent_create.max_files_open,
                    per_file_view_window_char_limit=agent_create.per_file_view_window_char_limit,
                )

                # Set template fields for InternalTemplateAgentCreate (similar to group creation)
                if isinstance(agent_create, InternalTemplateAgentCreate):
                    new_agent.base_template_id = agent_create.base_template_id
                    new_agent.template_id = agent_create.template_id
                    new_agent.deployment_id = agent_create.deployment_id
                    new_agent.entity_id = agent_create.entity_id

                if _test_only_force_id:
                    new_agent.id = _test_only_force_id

                session.add(new_agent)
                await session.flush()
                aid = new_agent.id

                # Note: These methods may need async versions if they perform database operations
                await self._bulk_insert_pivot_async(
                    session,
                    ToolsAgents.__table__,
                    [{"agent_id": aid, "tool_id": tid} for tid in tool_ids],
                )

                if block_ids:
                    result = await session.execute(select(BlockModel.id, BlockModel.label).where(BlockModel.id.in_(block_ids)))
                    rows = [{"agent_id": aid, "block_id": bid, "block_label": lbl} for bid, lbl in result.all()]
                    await self._bulk_insert_pivot_async(session, BlocksAgents.__table__, rows)

                await self._bulk_insert_pivot_async(
                    session,
                    SourcesAgents.__table__,
                    [{"agent_id": aid, "source_id": sid} for sid in source_ids],
                )
                await self._bulk_insert_pivot_async(
                    session,
                    AgentsTags.__table__,
                    [{"agent_id": aid, "tag": tag} for tag in tag_values],
                )
                await self._bulk_insert_pivot_async(
                    session,
                    IdentitiesAgents.__table__,
                    [{"agent_id": aid, "identity_id": iid} for iid in identity_ids],
                )

                env_rows = []
                agent_secrets = agent_create.secrets or agent_create.tool_exec_environment_variables

                if agent_secrets:
                    # Encrypt environment variable values
                    env_rows = []
                    for key, val in agent_secrets.items():
                        row = {
                            "agent_id": aid,
                            "key": key,
                            "value": val,
                            "organization_id": actor.organization_id,
                        }
                        # Encrypt value (Secret.from_plaintext handles missing encryption key internally)
                        value_secret = Secret.from_plaintext(val)
                        row["value_enc"] = value_secret.get_encrypted()
                        env_rows.append(row)

                    result = await session.execute(insert(AgentEnvironmentVariable).values(env_rows).returning(AgentEnvironmentVariable.id))
                    env_rows = [{**row, "id": env_var_id} for row, env_var_id in zip(env_rows, result.scalars().all())]

                include_relationships = []
                if tool_ids:
                    include_relationships.append("tools")
                if source_ids:
                    include_relationships.append("sources")
                if block_ids:
                    include_relationships.append("memory")
                if identity_ids:
                    include_relationships.append("identity_ids")
                if tag_values:
                    include_relationships.append("tags")

                result = await new_agent.to_pydantic_async(include_relationships=include_relationships)

                if agent_secrets and env_rows:
                    result.tool_exec_environment_variables = [AgentEnvironmentVariable(**row) for row in env_rows]
                    result.secrets = [AgentEnvironmentVariable(**row) for row in env_rows]

                # initial message sequence (skip if _init_with_no_messages is True)
                if not _init_with_no_messages:
                    init_messages = await self._generate_initial_message_sequence_async(
                        actor,
                        agent_state=result,
                        supplied_initial_message_sequence=agent_create.initial_message_sequence,
                    )
                    result.message_ids = [msg.id for msg in init_messages]
                    new_agent.message_ids = [msg.id for msg in init_messages]
                    await new_agent.update_async(session, no_refresh=True)
                else:
                    init_messages = []

        # Only create messages if we initialized with messages
        if not _init_with_no_messages:
            await self.message_manager.create_many_messages_async(
                pydantic_msgs=init_messages, actor=actor, project_id=result.project_id, template_id=result.template_id
            )

        # Attach files from sources if this is a template-based creation
        # Use the new agent's sources (already copied from template via source_ids)
        if isinstance(agent_create, InternalTemplateAgentCreate) and source_ids:
            try:
                from letta.services.file_manager import FileManager

                file_manager = FileManager()

                # Get all files from the new agent's sources
                all_files_metadata = []
                for source_id in source_ids:
                    try:
                        files_in_source = await file_manager.list_files(
                            source_id=source_id,
                            actor=actor,
                            limit=1000,
                        )
                        all_files_metadata.extend(files_in_source)
                    except Exception as e:
                        logger.warning(f"Failed to get files from source {source_id}: {e}")

                if all_files_metadata:
                    try:
                        await self.file_agent_manager.attach_files_bulk(
                            agent_id=result.id,
                            files_metadata=all_files_metadata,
                            visible_content_map={},  # Empty map - content generated on-demand
                            actor=actor,
                            max_files_open=result.max_files_open or DEFAULT_MAX_FILES_OPEN,
                        )
                    except Exception as e:
                        logger.error(f"Failed to attach files: {e}")
            except Exception as e:
                logger.error(f"Failed to attach files from sources: {e}")
                import traceback

                traceback.print_exc()

        return result

    @enforce_types
    def _generate_initial_message_sequence(
        self, actor: PydanticUser, agent_state: PydanticAgentState, supplied_initial_message_sequence: Optional[List[MessageCreate]] = None
    ) -> List[Message]:
        init_messages = initialize_message_sequence(
            agent_state=agent_state, memory_edit_timestamp=get_utc_time(), include_initial_boot_message=True
        )
        if supplied_initial_message_sequence is not None:
            # We always need the system prompt up front
            system_message_obj = PydanticMessage.dict_to_message(
                agent_id=agent_state.id,
                model=agent_state.llm_config.model,
                openai_message_dict=init_messages[0],
            )
            # Don't use anything else in the pregen sequence, instead use the provided sequence
            init_messages = [system_message_obj]
            init_messages.extend(
                package_initial_message_sequence(
                    agent_state.id, supplied_initial_message_sequence, agent_state.llm_config.model, agent_state.timezone, actor
                )
            )
        else:
            init_messages = [
                PydanticMessage.dict_to_message(agent_id=agent_state.id, model=agent_state.llm_config.model, openai_message_dict=msg)
                for msg in init_messages
            ]

        return init_messages

    @enforce_types
    async def _generate_initial_message_sequence_async(
        self, actor: PydanticUser, agent_state: PydanticAgentState, supplied_initial_message_sequence: Optional[List[MessageCreate]] = None
    ) -> List[Message]:
        init_messages = await initialize_message_sequence_async(
            agent_state=agent_state, memory_edit_timestamp=get_utc_time(), include_initial_boot_message=True
        )
        if supplied_initial_message_sequence is not None:
            # We always need the system prompt up front
            system_message_obj = PydanticMessage.dict_to_message(
                agent_id=agent_state.id,
                model=agent_state.llm_config.model,
                openai_message_dict=init_messages[0],
            )
            # Don't use anything else in the pregen sequence, instead use the provided sequence
            init_messages = [system_message_obj]
            init_messages.extend(
                package_initial_message_sequence(
                    agent_state.id, supplied_initial_message_sequence, agent_state.llm_config.model, agent_state.timezone, actor
                )
            )
        else:
            init_messages = [
                PydanticMessage.dict_to_message(agent_id=agent_state.id, model=agent_state.llm_config.model, openai_message_dict=msg)
                for msg in init_messages
            ]

        return init_messages

    @enforce_types
    @trace_method
    async def append_initial_message_sequence_to_in_context_messages_async(
        self, actor: PydanticUser, agent_state: PydanticAgentState, initial_message_sequence: Optional[List[MessageCreate]] = None
    ) -> PydanticAgentState:
        init_messages = await self._generate_initial_message_sequence_async(actor, agent_state, initial_message_sequence)
        return await self.append_to_in_context_messages_async(init_messages, agent_id=agent_state.id, actor=actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def update_agent_async(
        self,
        agent_id: str,
        agent_update: UpdateAgent,
        actor: PydanticUser,
    ) -> PydanticAgentState:
        new_tools = set(agent_update.tool_ids or [])
        # Use folder_ids if provided, otherwise fall back to deprecated source_ids for backwards compatibility
        folder_ids_to_update = agent_update.folder_ids if agent_update.folder_ids is not None else agent_update.source_ids
        new_sources = set(folder_ids_to_update or [])
        new_blocks = set(agent_update.block_ids or [])
        new_idents = set(agent_update.identity_ids or [])
        new_tags = set(agent_update.tags or [])

        async with db_registry.async_session() as session, session.begin():
            agent: AgentModel = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            agent.updated_at = datetime.now(timezone.utc)
            agent.last_updated_by_id = actor.id

            if agent_update.reasoning is not None:
                llm_config = agent_update.llm_config or agent.llm_config
                agent_update.llm_config = LLMConfig.apply_reasoning_setting_to_config(
                    llm_config,
                    agent_update.reasoning,
                    agent.agent_type,
                )

            scalar_updates = {
                "name": agent_update.name,
                "system": agent_update.system,
                "llm_config": agent_update.llm_config,
                "embedding_config": agent_update.embedding_config,
                "message_ids": agent_update.message_ids,
                "tool_rules": agent_update.tool_rules,
                "description": agent_update.description,
                "project_id": agent_update.project_id,
                "template_id": agent_update.template_id,
                "base_template_id": agent_update.base_template_id,
                "message_buffer_autoclear": agent_update.message_buffer_autoclear,
                "enable_sleeptime": agent_update.enable_sleeptime,
                "response_format": agent_update.response_format,
                "last_run_completion": agent_update.last_run_completion,
                "last_run_duration_ms": agent_update.last_run_duration_ms,
                "last_stop_reason": agent_update.last_stop_reason,
                "timezone": agent_update.timezone,
                "max_files_open": agent_update.max_files_open,
                "per_file_view_window_char_limit": agent_update.per_file_view_window_char_limit,
            }
            for col, val in scalar_updates.items():
                if val is not None:
                    setattr(agent, col, val)

            if agent_update.metadata is not None:
                agent.metadata_ = agent_update.metadata

            aid = agent.id

            if agent_update.tool_ids is not None:
                await self._replace_pivot_rows_async(
                    session,
                    ToolsAgents.__table__,
                    aid,
                    [{"agent_id": aid, "tool_id": tid} for tid in new_tools],
                )
                session.expire(agent, ["tools"])

            # Update sources if either folder_ids or source_ids (deprecated) is provided
            if agent_update.folder_ids is not None or agent_update.source_ids is not None:
                await self._replace_pivot_rows_async(
                    session,
                    SourcesAgents.__table__,
                    aid,
                    [{"agent_id": aid, "source_id": sid} for sid in new_sources],
                )
                session.expire(agent, ["sources"])

            if agent_update.block_ids is not None:
                rows = []
                if new_blocks:
                    result = await session.execute(select(BlockModel.id, BlockModel.label).where(BlockModel.id.in_(new_blocks)))
                    label_map = {bid: lbl for bid, lbl in result.all()}
                    rows = [{"agent_id": aid, "block_id": bid, "block_label": label_map[bid]} for bid in new_blocks]

                await self._replace_pivot_rows_async(session, BlocksAgents.__table__, aid, rows)
                session.expire(agent, ["core_memory"])

            if agent_update.identity_ids is not None:
                await self._replace_pivot_rows_async(
                    session,
                    IdentitiesAgents.__table__,
                    aid,
                    [{"agent_id": aid, "identity_id": iid} for iid in new_idents],
                )
                session.expire(agent, ["identities"])

            if agent_update.tags is not None:
                await self._replace_pivot_rows_async(
                    session,
                    AgentsTags.__table__,
                    aid,
                    [{"agent_id": aid, "tag": tag} for tag in new_tags],
                )
                session.expire(agent, ["tags"])

            agent_secrets = agent_update.secrets or agent_update.tool_exec_environment_variables
            if agent_secrets is not None:
                # Fetch existing environment variables to check if values changed
                result = await session.execute(select(AgentEnvironmentVariable).where(AgentEnvironmentVariable.agent_id == aid))
                existing_env_vars = {env.key: env for env in result.scalars().all()}

                # TODO: do we need to delete each time or can we just upsert?
                await session.execute(delete(AgentEnvironmentVariable).where(AgentEnvironmentVariable.agent_id == aid))
                # Encrypt environment variable values
                # Only re-encrypt if the value has actually changed
                env_rows = []
                for k, v in agent_secrets.items():
                    row = {
                        "agent_id": aid,
                        "key": k,
                        "value": v,
                        "organization_id": agent.organization_id,
                    }

                    # Check if value changed to avoid unnecessary re-encryption
                    existing_env = existing_env_vars.get(k)
                    existing_value = None
                    if existing_env:
                        if existing_env.value_enc:
                            existing_secret = Secret.from_encrypted(existing_env.value_enc)
                            existing_value = existing_secret.get_plaintext()
                        elif existing_env.value:
                            existing_value = existing_env.value

                    # Encrypt value (reuse existing encrypted value if unchanged)
                    if existing_value == v and existing_env and existing_env.value_enc:
                        # Value unchanged, reuse existing encrypted value
                        row["value_enc"] = existing_env.value_enc
                    else:
                        # Value changed or new, encrypt
                        value_secret = Secret.from_plaintext(v)
                        row["value_enc"] = value_secret.get_encrypted()

                    env_rows.append(row)

                if env_rows:
                    await self._bulk_insert_pivot_async(session, AgentEnvironmentVariable.__table__, env_rows)
                session.expire(agent, ["tool_exec_environment_variables"])

            if agent_update.enable_sleeptime and agent_update.system is None:
                agent.system = derive_system_message(
                    agent_type=agent.agent_type,
                    enable_sleeptime=agent_update.enable_sleeptime,
                    system=agent.system,
                )

            await session.flush()
            await session.refresh(agent)

            return await agent.to_pydantic_async()

    @enforce_types
    @trace_method
    async def update_message_ids_async(
        self,
        agent_id: str,
        message_ids: List[str],
        actor: PydanticUser,
    ) -> None:
        async with db_registry.async_session() as session:
            query = select(AgentModel)
            query = AgentModel.apply_access_predicate(query, actor, ["read"], AccessType.ORGANIZATION)
            query = query.where(AgentModel.id == agent_id)
            query = _apply_relationship_filters(query, include_relationships=[])

            result = await session.execute(query)
            agent = result.scalar_one_or_none()

            agent.updated_at = datetime.now(timezone.utc)
            agent.last_updated_by_id = actor.id
            agent.message_ids = message_ids

            await agent.update_async(db_session=session, actor=actor, no_commit=True, no_refresh=True)
            await session.commit()

    @trace_method
    async def list_agents_async(
        self,
        actor: PydanticUser,
        name: Optional[str] = None,
        tags: Optional[List[str]] = None,
        match_all_tags: bool = False,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        project_id: Optional[str] = None,
        template_id: Optional[str] = None,
        base_template_id: Optional[str] = None,
        identity_id: Optional[str] = None,
        identifier_keys: Optional[List[str]] = None,
        include_relationships: Optional[List[str]] = None,
        include: List[str] = [],
        ascending: bool = True,
        sort_by: Optional[str] = "created_at",
        show_hidden_agents: Optional[bool] = None,
        last_stop_reason: Optional[StopReasonType] = None,
    ) -> List[PydanticAgentState]:
        """
        Retrieves agents with optimized filtering and optional field selection.

        Args:
            actor: The User requesting the list
            name (Optional[str]): Filter by agent name.
            tags (Optional[List[str]]): Filter agents by tags.
            match_all_tags (bool): If True, only return agents that match ALL given tags.
            before (Optional[str]): Cursor for pagination.
            after (Optional[str]): Cursor for pagination.
            limit (Optional[int]): Maximum number of agents to return.
            query_text (Optional[str]): Search agents by name.
            project_id (Optional[str]): Filter by project ID.
            template_id (Optional[str]): Filter by template ID.
            base_template_id (Optional[str]): Filter by base template ID.
            identity_id (Optional[str]): Filter by identifier ID.
            identifier_keys (Optional[List[str]]): Search agents by identifier keys.
            include_relationships (Optional[List[str]]): List of fields to load for performance optimization.
            ascending (bool): Sort agents in ascending order.
            sort_by (Optional[str]): Sort agents by this field.
            show_hidden_agents (bool): If True, include agents marked as hidden in the results.
            last_stop_reason (Optional[str]): Filter by the agent's last stop reason (e.g., 'requires_approval', 'error').

        Returns:
            List[PydanticAgentState]: The filtered list of matching agents.
        """
        async with db_registry.async_session() as session:
            query = select(AgentModel)
            query = AgentModel.apply_access_predicate(query, actor, ["read"], AccessType.ORGANIZATION)

            # Apply filters
            query = _apply_filters(query, name, query_text, project_id, template_id, base_template_id, last_stop_reason)
            query = _apply_identity_filters(query, identity_id, identifier_keys)
            query = _apply_tag_filter(query, tags, match_all_tags)
            query = _apply_relationship_filters(query, include_relationships, include)

            # Apply hidden filter
            if not show_hidden_agents:
                query = query.where((AgentModel.hidden.is_(None)) | (AgentModel.hidden == False))
            query = await _apply_pagination_async(query, before, after, session, ascending=ascending, sort_by=sort_by)

            if limit:
                query = query.limit(limit)
            result = await session.execute(query)
            agents = result.scalars().all()
            return await asyncio.gather(
                *[agent.to_pydantic_async(include_relationships=include_relationships, include=include) for agent in agents]
            )

    @trace_method
    async def count_agents_async(
        self,
        actor: PydanticUser,
        name: Optional[str] = None,
        tags: Optional[List[str]] = None,
        match_all_tags: bool = False,
        query_text: Optional[str] = None,
        project_id: Optional[str] = None,
        template_id: Optional[str] = None,
        base_template_id: Optional[str] = None,
        identity_id: Optional[str] = None,
        identifier_keys: Optional[List[str]] = None,
        show_hidden_agents: Optional[bool] = None,
        last_stop_reason: Optional[StopReasonType] = None,
    ) -> int:
        """
        Count agents matching the specified filters using an efficient database-level COUNT query.

        Args:
            actor: The User requesting the count
            name (Optional[str]): Filter by agent name.
            tags (Optional[List[str]]): Filter agents by tags.
            match_all_tags (bool): If True, only count agents that match ALL given tags.
            query_text (Optional[str]): Search agents by name.
            project_id (Optional[str]): Filter by project ID.
            template_id (Optional[str]): Filter by template ID.
            base_template_id (Optional[str]): Filter by base template ID.
            identity_id (Optional[str]): Filter by identifier ID.
            identifier_keys (Optional[List[str]]): Search agents by identifier keys.
            show_hidden_agents (bool): If True, include agents marked as hidden in the results.
            last_stop_reason (Optional[str]): Filter by the agent's last stop reason (e.g., 'requires_approval', 'error').

        Returns:
            int: The count of agents matching the filters.
        """
        async with db_registry.async_session() as session:
            query = select(func.count()).select_from(AgentModel)
            query = AgentModel.apply_access_predicate(query, actor, ["read"], AccessType.ORGANIZATION)

            # Apply filters
            query = _apply_filters(query, name, query_text, project_id, template_id, base_template_id, last_stop_reason)
            query = _apply_identity_filters(query, identity_id, identifier_keys)
            query = _apply_tag_filter(query, tags, match_all_tags)

            # Apply hidden filter
            if not show_hidden_agents:
                query = query.where((AgentModel.hidden.is_(None)) | (AgentModel.hidden == False))

            result = await session.execute(query)
            return result.scalar_one()

    @enforce_types
    @trace_method
    async def list_agents_matching_tags_async(
        self,
        actor: PydanticUser,
        match_all: List[str],
        match_some: List[str],
        limit: Optional[int] = 50,
    ) -> List[PydanticAgentState]:
        """
        Retrieves agents in the same organization that match all specified `match_all` tags
        and at least one tag from `match_some`. The query is optimized for efficiency by
        leveraging indexed filtering and aggregation.

        Args:
            actor (PydanticUser): The user requesting the agent list.
            match_all (List[str]): Agents must have all these tags.
            match_some (List[str]): Agents must have at least one of these tags.
            limit (Optional[int]): Maximum number of agents to return.

        Returns:
            List[PydanticAgentState: The filtered list of matching agents.
        """
        async with db_registry.async_session() as session:
            query = select(AgentModel).where(AgentModel.organization_id == actor.organization_id)

            if match_all:
                # Subquery to find agent IDs that contain all match_all tags
                subquery = (
                    select(AgentsTags.agent_id)
                    .where(AgentsTags.tag.in_(match_all))
                    .group_by(AgentsTags.agent_id)
                    .having(func.count(AgentsTags.tag) == literal(len(match_all)))
                )
                query = query.where(AgentModel.id.in_(subquery))

            if match_some:
                # Ensures agents match at least one tag in match_some
                query = query.join(AgentsTags).where(AgentsTags.tag.in_(match_some))

            query = query.distinct(AgentModel.id).order_by(AgentModel.id).limit(limit)
            result = await session.execute(query)
            return await asyncio.gather(*[agent.to_pydantic_async() for agent in result.scalars()])

    @trace_method
    async def size_async(
        self,
        actor: PydanticUser,
    ) -> int:
        """
        Get the total count of agents for the given user.
        """
        async with db_registry.async_session() as session:
            return await AgentModel.size_async(db_session=session, actor=actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_agent_by_id_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        include_relationships: Optional[List[str]] = None,
        include: List[str] = [],
    ) -> PydanticAgentState:
        """Fetch an agent by its ID."""

        async with db_registry.async_session() as session:
            try:
                query = select(AgentModel)
                query = AgentModel.apply_access_predicate(query, actor, ["read"], AccessType.ORGANIZATION)
                query = query.where(AgentModel.id == agent_id)
                query = _apply_relationship_filters(query, include_relationships, include)

                result = await session.execute(query)
                agent = result.scalar_one_or_none()

                if agent is None:
                    raise NoResultFound(f"Agent with ID {agent_id} not found")

                return await agent.to_pydantic_async(include_relationships=include_relationships, include=include)
            except NoResultFound:
                # Re-raise NoResultFound without logging to preserve 404 handling
                raise
            except Exception as e:
                logger.error(f"Error fetching agent {agent_id}: {str(e)}")
                raise

    @enforce_types
    @trace_method
    async def get_agents_by_ids_async(
        self,
        agent_ids: list[str],
        actor: PydanticUser,
        include_relationships: Optional[List[str]] = None,
    ) -> list[PydanticAgentState]:
        """Fetch a list of agents by their IDs."""
        async with db_registry.async_session() as session:
            try:
                query = select(AgentModel)
                query = AgentModel.apply_access_predicate(query, actor, ["read"], AccessType.ORGANIZATION)
                query = query.where(AgentModel.id.in_(agent_ids))
                query = _apply_relationship_filters(query, include_relationships)

                result = await session.execute(query)
                agents = result.scalars().all()

                if not agents:
                    logger.warning(f"No agents found with IDs: {agent_ids}")
                    return []

                return await asyncio.gather(*[agent.to_pydantic_async(include_relationships=include_relationships) for agent in agents])
            except Exception as e:
                logger.error(f"Error fetching agents with IDs {agent_ids}: {str(e)}")
                raise

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_agent_archive_ids_async(self, agent_id: str, actor: PydanticUser) -> List[str]:
        """Get all archive IDs associated with an agent."""
        from letta.orm import ArchivesAgents

        async with db_registry.async_session() as session:
            # Direct query to archives_agents table for performance
            query = select(ArchivesAgents.archive_id).where(ArchivesAgents.agent_id == agent_id)
            result = await session.execute(query)
            archive_ids = [row[0] for row in result.fetchall()]
            return archive_ids

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def validate_agent_exists_async(self, agent_id: str, actor: PydanticUser) -> None:
        """
        Validate that an agent exists and user has access to it.
        Lightweight method that doesn't load the full agent object.

        Args:
            agent_id: ID of the agent to validate
            actor: User performing the action

        Raises:
            LettaAgentNotFoundError: If agent doesn't exist or user doesn't have access
        """
        async with db_registry.async_session() as session:
            await validate_agent_exists_async(session, agent_id, actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def delete_agent_async(self, agent_id: str, actor: PydanticUser) -> None:
        """
        Deletes an agent and its associated relationships.
        Ensures proper permission checks and cascades where applicable.

        Args:
            agent_id: ID of the agent to be deleted.
            actor: User performing the action.

        Raises:
            NoResultFound: If agent doesn't exist
        """
        async with db_registry.async_session() as session:
            # Retrieve the agent
            logger.debug(f"Hard deleting Agent with ID: {agent_id} with actor={actor}")
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            agents_to_delete = [agent]
            sleeptime_group_to_delete = None

            # Delete sleeptime agent and group (TODO this is flimsy pls fix)
            if agent.multi_agent_group:
                participant_agent_ids = agent.multi_agent_group.agent_ids
                if agent.multi_agent_group.manager_type in {ManagerType.sleeptime, ManagerType.voice_sleeptime} and participant_agent_ids:
                    for participant_agent_id in participant_agent_ids:
                        try:
                            sleeptime_agent = await AgentModel.read_async(db_session=session, identifier=participant_agent_id, actor=actor)
                            agents_to_delete.append(sleeptime_agent)
                        except NoResultFound:
                            pass  # agent already deleted
                    sleeptime_agent_group = await GroupModel.read_async(
                        db_session=session, identifier=agent.multi_agent_group.id, actor=actor
                    )
                    sleeptime_group_to_delete = sleeptime_agent_group

            try:
                if sleeptime_group_to_delete is not None:
                    await session.delete(sleeptime_group_to_delete)
                    await session.commit()
                for agent in agents_to_delete:
                    await session.delete(agent)
                    await session.commit()
            except Exception as e:
                await session.rollback()
                logger.exception(f"Failed to hard delete Agent with ID {agent_id}")
                raise ValueError(f"Failed to hard delete Agent with ID {agent_id}: {e}")
            else:
                logger.debug(f"Agent with ID {agent_id} successfully hard deleted")

    # ======================================================================================================================
    # Per Agent Environment Variable Management
    # ======================================================================================================================

    # ======================================================================================================================
    # In Context Messages Management
    # ======================================================================================================================
    # TODO: There are several assumptions here that are not explicitly checked
    # TODO: 1) These message ids are valid
    # TODO: 2) These messages are ordered from oldest to newest
    # TODO: This can be fixed by having an actual relationship in the ORM for message_ids
    # TODO: This can also be made more efficient, instead of getting, setting, we can do it all in one db session for one query.
    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_in_context_messages(self, agent_id: str, actor: PydanticUser) -> List[PydanticMessage]:
        agent_state = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor)
        return await self.message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=actor)

    @enforce_types
    @trace_method
    def get_system_message(self, agent_id: str, actor: PydanticUser) -> PydanticMessage:
        message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids
        return self.message_manager.get_message_by_id(message_id=message_ids[0], actor=actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_system_message_async(self, agent_id: str, actor: PydanticUser) -> PydanticMessage:
        agent = await self.get_agent_by_id_async(agent_id=agent_id, include_relationships=[], actor=actor)
        return await self.message_manager.get_message_by_id_async(message_id=agent.message_ids[0], actor=actor)

    # TODO: This is duplicated below
    # TODO: This is legacy code and should be cleaned up
    # TODO: A lot of the memory "compilation" should be offset to a separate class
    @enforce_types
    @trace_method
    def rebuild_system_prompt(self, agent_id: str, actor: PydanticUser, force=False, update_timestamp=True) -> PydanticAgentState:
        """Rebuilds the system message with the latest memory object and any shared memory block updates

        Updates to core memory blocks should trigger a "rebuild", which itself will create a new message object

        Updates to the memory header should *not* trigger a rebuild, since that will simply flood recall storage with excess messages
        """
        agent_state = self.get_agent_by_id(agent_id=agent_id, actor=actor)

        curr_system_message = self.get_system_message(
            agent_id=agent_id, actor=actor
        )  # this is the system + memory bank, not just the system prompt

        if curr_system_message is None:
            logger.warning(f"No system message found for agent {agent_state.id} and user {actor}")
            return agent_state

        curr_system_message_openai = curr_system_message.to_openai_dict()

        # note: we only update the system prompt if the core memory is changed
        # this means that the archival/recall memory statistics may be someout out of date
        curr_memory_str = agent_state.memory.compile(sources=agent_state.sources, llm_config=agent_state.llm_config)
        if curr_memory_str in curr_system_message_openai["content"] and not force:
            # NOTE: could this cause issues if a block is removed? (substring match would still work)
            logger.debug(
                f"Memory hasn't changed for agent id={agent_id} and actor=({actor.id}, {actor.name}), skipping system prompt rebuild"
            )
            return agent_state

        # If the memory didn't update, we probably don't want to update the timestamp inside
        # For example, if we're doing a system prompt swap, this should probably be False
        if update_timestamp:
            memory_edit_timestamp = get_utc_time()
        else:
            # NOTE: a bit of a hack - we pull the timestamp from the message created_by
            memory_edit_timestamp = curr_system_message.created_at

        num_messages = self.message_manager.size(actor=actor, agent_id=agent_id)
        num_archival_memories = self.passage_manager.size(actor=actor, agent_id=agent_id)

        # update memory (TODO: potentially update recall/archival stats separately)
        new_system_message_str = compile_system_message(
            system_prompt=agent_state.system,
            in_context_memory=agent_state.memory,
            in_context_memory_last_edit=memory_edit_timestamp,
            timezone=agent_state.timezone,
            previous_message_count=num_messages - len(agent_state.message_ids),
            archival_memory_size=num_archival_memories,
            sources=agent_state.sources,
            max_files_open=agent_state.max_files_open,
            llm_config=agent_state.llm_config,
        )

        diff = united_diff(curr_system_message_openai["content"], new_system_message_str)
        if len(diff) > 0:  # there was a diff
            logger.debug(f"Rebuilding system with new memory...\nDiff:\n{diff}")

            # Swap the system message out (only if there is a diff)
            message = PydanticMessage.dict_to_message(
                agent_id=agent_id,
                model=agent_state.llm_config.model,
                openai_message_dict={"role": "system", "content": new_system_message_str},
            )
            message = self.message_manager.update_message_by_id(
                message_id=curr_system_message.id,
                message_update=MessageUpdate(**message.model_dump()),
                actor=actor,
            )
            return self.set_in_context_messages(agent_id=agent_id, message_ids=agent_state.message_ids, actor=actor)
        else:
            return agent_state

    # Do not remove comment. (cliandy)
    # TODO: This is probably one of the worst pieces of code I've ever written please rip up as you see wish
    @enforce_types
    @trace_method
    async def rebuild_system_prompt_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        force=False,
        update_timestamp=True,
        dry_run: bool = False,
    ) -> Tuple[PydanticAgentState, Optional[PydanticMessage], int, int]:
        """Rebuilds the system message with the latest memory object and any shared memory block updates

        Updates to core memory blocks should trigger a "rebuild", which itself will create a new message object

        Updates to the memory header should *not* trigger a rebuild, since that will simply flood recall storage with excess messages
        """
        num_messages = await self.message_manager.size_async(actor=actor, agent_id=agent_id)
        num_archival_memories = await self.passage_manager.agent_passage_size_async(actor=actor, agent_id=agent_id)
        agent_state = await self.get_agent_by_id_async(agent_id=agent_id, include_relationships=["memory", "sources", "tools"], actor=actor)

        tool_rules_solver = ToolRulesSolver(agent_state.tool_rules)

        if agent_state.message_ids == []:
            curr_system_message = None
        else:
            curr_system_message = await self.message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)

        if curr_system_message is None:
            logger.warning(f"No system message found for agent {agent_state.id} and user {actor}")
            return agent_state, curr_system_message, num_messages, num_archival_memories

        curr_system_message_openai = curr_system_message.to_openai_dict()

        # note: we only update the system prompt if the core memory is changed
        # this means that the archival/recall memory statistics may be someout out of date
        curr_memory_str = agent_state.memory.compile(
            sources=agent_state.sources,
            tool_usage_rules=tool_rules_solver.compile_tool_rule_prompts(),
            max_files_open=agent_state.max_files_open,
            llm_config=agent_state.llm_config,
        )
        if curr_memory_str in curr_system_message_openai["content"] and not force:
            # NOTE: could this cause issues if a block is removed? (substring match would still work)
            logger.debug(
                f"Memory hasn't changed for agent id={agent_id} and actor=({actor.id}, {actor.name}), skipping system prompt rebuild"
            )
            return agent_state, curr_system_message, num_messages, num_archival_memories

        # If the memory didn't update, we probably don't want to update the timestamp inside
        # For example, if we're doing a system prompt swap, this should probably be False
        if update_timestamp:
            memory_edit_timestamp = get_utc_time()
        else:
            # NOTE: a bit of a hack - we pull the timestamp from the message created_by
            memory_edit_timestamp = curr_system_message.created_at

        # update memory (TODO: potentially update recall/archival stats separately)

        new_system_message_str = PromptGenerator.get_system_message_from_compiled_memory(
            system_prompt=agent_state.system,
            memory_with_sources=curr_memory_str,
            in_context_memory_last_edit=memory_edit_timestamp,
            timezone=agent_state.timezone,
            previous_message_count=num_messages - len(agent_state.message_ids),
            archival_memory_size=num_archival_memories,
        )

        diff = united_diff(curr_system_message_openai["content"], new_system_message_str)
        if len(diff) > 0:  # there was a diff
            logger.debug(f"Rebuilding system with new memory...\nDiff:\n{diff}")

            # Swap the system message out (only if there is a diff)
            temp_message = PydanticMessage.dict_to_message(
                agent_id=agent_id,
                model=agent_state.llm_config.model,
                openai_message_dict={"role": "system", "content": new_system_message_str},
            )
            temp_message.id = curr_system_message.id

            if not dry_run:
                await self.message_manager.update_message_by_id_async(
                    message_id=curr_system_message.id,
                    message_update=MessageUpdate(**temp_message.model_dump()),
                    actor=actor,
                    project_id=agent_state.project_id,
                )
            else:
                curr_system_message = temp_message

        return agent_state, curr_system_message, num_messages, num_archival_memories

    @enforce_types
    @trace_method
    def set_in_context_messages(self, agent_id: str, message_ids: List[str], actor: PydanticUser) -> PydanticAgentState:
        return self.update_agent(agent_id=agent_id, agent_update=UpdateAgent(message_ids=message_ids), actor=actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def set_in_context_messages_async(self, agent_id: str, message_ids: List[str], actor: PydanticUser) -> PydanticAgentState:
        return await self.update_agent_async(agent_id=agent_id, agent_update=UpdateAgent(message_ids=message_ids), actor=actor)

    @enforce_types
    @trace_method
    def trim_older_in_context_messages(self, num: int, agent_id: str, actor: PydanticUser) -> PydanticAgentState:
        message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids
        new_messages = [message_ids[0]] + message_ids[num:]  # 0 is system message
        return self.set_in_context_messages(agent_id=agent_id, message_ids=new_messages, actor=actor)

    @enforce_types
    @trace_method
    def trim_all_in_context_messages_except_system(self, agent_id: str, actor: PydanticUser) -> PydanticAgentState:
        message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids
        # TODO: How do we know this?
        new_messages = [message_ids[0]]  # 0 is system message
        return self.set_in_context_messages(agent_id=agent_id, message_ids=new_messages, actor=actor)

    @enforce_types
    @trace_method
    def prepend_to_in_context_messages(self, messages: List[PydanticMessage], agent_id: str, actor: PydanticUser) -> PydanticAgentState:
        message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids
        new_messages = self.message_manager.create_many_messages(messages, actor=actor)
        message_ids = [message_ids[0]] + [m.id for m in new_messages] + message_ids[1:]
        return self.set_in_context_messages(agent_id=agent_id, message_ids=message_ids, actor=actor)

    @enforce_types
    @trace_method
    def append_to_in_context_messages(self, messages: List[PydanticMessage], agent_id: str, actor: PydanticUser) -> PydanticAgentState:
        messages = self.message_manager.create_many_messages(messages, actor=actor)
        message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids or []
        message_ids += [m.id for m in messages]
        return self.set_in_context_messages(agent_id=agent_id, message_ids=message_ids, actor=actor)

    @enforce_types
    @trace_method
    async def append_to_in_context_messages_async(
        self, messages: List[PydanticMessage], agent_id: str, actor: PydanticUser
    ) -> PydanticAgentState:
        agent = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor)
        messages = await self.message_manager.create_many_messages_async(
            messages, actor=actor, project_id=agent.project_id, template_id=agent.template_id
        )
        message_ids = agent.message_ids or []
        message_ids += [m.id for m in messages]
        return await self.set_in_context_messages_async(agent_id=agent_id, message_ids=message_ids, actor=actor)

    @enforce_types
    @trace_method
    async def reset_messages_async(
        self, agent_id: str, actor: PydanticUser, add_default_initial_messages: bool = False, needs_agent_state: bool = True
    ) -> Optional[PydanticAgentState]:
        """
        Removes all in-context messages for the specified agent except the original system message by:
          1) Preserving the first message ID (original system message).
          2) Deleting all other messages for the agent.
          3) Updating the agent's message_ids to only contain the system message.
          4) Optionally adding default initial messages after the system message.

        This action is destructive and cannot be undone once committed.

        Args:
            add_default_initial_messages: If true, adds the default initial messages after resetting.
            agent_id (str): The ID of the agent whose messages will be reset.
            actor (PydanticUser): The user performing this action.
            needs_agent_state: If True, returns the updated agent state. If False, returns None (for performance optimization)

        Returns:
            Optional[PydanticAgentState]: The updated agent state with only the original system message preserved, or None if needs_agent_state=False.
        """
        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)

            if not agent.message_ids or len(agent.message_ids) == 0:
                logger.error(
                    f"Agent {agent_id} has no message_ids. Agent details: "
                    f"name={agent.name}, created_at={agent.created_at}, "
                    f"message_ids={agent.message_ids}, organization_id={actor.organization_id}"
                )
                raise ValueError(f"Agent {agent_id} has no message_ids - cannot preserve system message")

            system_message_id = agent.message_ids[0]

        await self.message_manager.delete_all_messages_for_agent_async(agent_id=agent_id, actor=actor, exclude_ids=[system_message_id])

        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            agent.message_ids = [system_message_id]
            await agent.update_async(db_session=session, actor=actor)

            # Only convert to pydantic if we need to return it or add initial messages
            if add_default_initial_messages or needs_agent_state:
                agent_state = await agent.to_pydantic_async(include_relationships=["sources"] if add_default_initial_messages else None)
            else:
                agent_state = None

        # Optionally add default initial messages after the system message
        if add_default_initial_messages:
            init_messages = await initialize_message_sequence_async(
                agent_state=agent_state, memory_edit_timestamp=get_utc_time(), include_initial_boot_message=True
            )
            # Skip index 0 (system message) since we preserved the original
            non_system_messages = [
                PydanticMessage.dict_to_message(
                    agent_id=agent_state.id,
                    model=agent_state.llm_config.model,
                    openai_message_dict=msg,
                )
                for msg in init_messages[1:]
            ]
            return await self.append_to_in_context_messages_async(non_system_messages, agent_id=agent_state.id, actor=actor)
        else:
            return agent_state

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def update_memory_if_changed_async(self, agent_id: str, new_memory: Memory, actor: PydanticUser) -> PydanticAgentState:
        """
        Update internal memory object and system prompt if there have been modifications.

        Args:
            actor:
            agent_id:
            new_memory (Memory): the new memory object to compare to the current memory object

        Returns:
            modified (bool): whether the memory was updated
        """
        agent_state = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor, include_relationships=["memory", "sources"])
        system_message = await self.message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)
        temp_tool_rules_solver = ToolRulesSolver(agent_state.tool_rules)
        new_memory_str = new_memory.compile(
            sources=agent_state.sources,
            tool_usage_rules=temp_tool_rules_solver.compile_tool_rule_prompts(),
            max_files_open=agent_state.max_files_open,
            llm_config=agent_state.llm_config,
        )
        if new_memory_str not in system_message.content[0].text:
            # update the blocks (LRW) in the DB
            for label in new_memory.list_block_labels():
                if label in agent_state.memory.list_block_labels():
                    # Block exists in both old and new memory - check if value changed
                    updated_value = new_memory.get_block(label).value
                    if updated_value != agent_state.memory.get_block(label).value:
                        # update the block if it's changed
                        block_id = agent_state.memory.get_block(label).id
                        await self.block_manager.update_block_async(
                            block_id=block_id, block_update=BlockUpdate(value=updated_value), actor=actor
                        )

                # Note: New blocks are already persisted in the creation methods,
                # so we don't need to handle them here

            # refresh memory from DB (using block ids from the new memory)
            blocks = await self.block_manager.get_all_blocks_by_ids_async(block_ids=[b.id for b in new_memory.get_blocks()], actor=actor)

            agent_state.memory = Memory(
                blocks=blocks,
                file_blocks=agent_state.memory.file_blocks,
                agent_type=agent_state.agent_type,
            )

            # NOTE: don't do this since re-buildin the memory is handled at the start of the step
            # rebuild memory - this records the last edited timestamp of the memory
            # TODO: pass in update timestamp from block edit time
            await self.rebuild_system_prompt_async(agent_id=agent_id, actor=actor)

        return agent_state

    @enforce_types
    @trace_method
    async def refresh_memory_async(self, agent_state: PydanticAgentState, actor: PydanticUser) -> PydanticAgentState:
        # TODO: This will NOT work for new blocks/file blocks added intra-step
        block_ids = [b.id for b in agent_state.memory.blocks]
        file_block_names = [b.label for b in agent_state.memory.file_blocks]

        if block_ids:
            blocks = await self.block_manager.get_all_blocks_by_ids_async(block_ids=[b.id for b in agent_state.memory.blocks], actor=actor)
            agent_state.memory.blocks = [b for b in blocks if b is not None]

        if file_block_names:
            file_blocks = await self.file_agent_manager.get_all_file_blocks_by_name(
                file_names=file_block_names,
                agent_id=agent_state.id,
                actor=actor,
                per_file_view_window_char_limit=agent_state.per_file_view_window_char_limit,
            )
            agent_state.memory.file_blocks = [b for b in file_blocks if b is not None]

        return agent_state

    @enforce_types
    @trace_method
    async def refresh_file_blocks(self, agent_state: PydanticAgentState, actor: PydanticUser) -> PydanticAgentState:
        """
        Refresh the file blocks in an agent's memory with current file content.

        This method synchronizes the agent's in-memory file blocks with the actual
        file content from attached sources. It respects the per-file view window
        limit to prevent excessive memory usage.

        Args:
            agent_state: The current agent state containing memory configuration
            actor: The user performing this action (for permission checking)

        Returns:
            Updated agent state with refreshed file blocks

        Important:
            - File blocks are truncated based on per_file_view_window_char_limit
            - None values are filtered out (files that couldn't be loaded)
            - This does NOT persist changes to the database, only updates the state object
            - Call this before agent interactions if files may have changed externally
        """
        file_blocks = await self.file_agent_manager.list_files_for_agent(
            agent_id=agent_state.id,
            per_file_view_window_char_limit=agent_state.per_file_view_window_char_limit,
            actor=actor,
            return_as_blocks=True,
        )
        agent_state.memory.file_blocks = [b for b in file_blocks if b is not None]
        return agent_state

    # ======================================================================================================================
    # Source Management
    # ======================================================================================================================
    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    @raise_on_invalid_id(param_name="source_id", expected_prefix=PrimitiveType.SOURCE)
    async def attach_source_async(self, agent_id: str, source_id: str, actor: PydanticUser) -> PydanticAgentState:
        """
        Attaches a source to an agent.

        Args:
            agent_id: ID of the agent to attach the source to
            source_id: ID of the source to attach
            actor: User performing the action

        Raises:
            NoResultFound: If either agent or source doesn't exist or actor lacks permission to access them
            IntegrityError: If the source is already attached to the agent
        """

        async with db_registry.async_session() as session:
            # Verify both agent and source exist and user has permission to access them
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)

            # Verify the actor has permission to access the source
            await SourceModel.read_async(db_session=session, identifier=source_id, actor=actor)

            # The _process_relationship helper already handles duplicate checking via unique constraint
            await _process_relationship_async(
                session=session,
                agent=agent,
                relationship_name="sources",
                model_class=SourceModel,
                item_ids=[source_id],
                replace=False,
            )

            # Commit the changes
            agent = await agent.update_async(session, actor=actor)
            # TODO: This refresh is expensive. If we can find out which fields are needed, we can save cost by only refreshing those fields.
            # or even better, not refresh at all.
            return await agent.to_pydantic_async()

    @enforce_types
    @trace_method
    def append_system_message(self, agent_id: str, content: str, actor: PydanticUser):
        """
        Append a system message to an agent's in-context message history.

        This method is typically used during agent initialization to add system prompts,
        instructions, or context that should be treated as system-level guidance.
        Unlike user messages, system messages directly influence the agent's behavior
        and understanding of its role.

        Args:
            agent_id: The ID of the agent to append the message to
            content: The system message content (e.g., instructions, context, role definition)
            actor: The user performing this action (for permission checking)

        Side Effects:
            - Creates a new Message object in the database
            - Updates the agent's in_context_message_ids list
            - The message becomes part of the agent's permanent context window

        Note:
            System messages consume tokens in the context window and cannot be
            removed without rebuilding the agent's message history.
        """

        # get the agent
        agent = self.get_agent_by_id(agent_id=agent_id, actor=actor)
        message = PydanticMessage.dict_to_message(
            agent_id=agent.id, model=agent.llm_config.model, openai_message_dict={"role": "system", "content": content}
        )

        # update agent in-context message IDs
        self.append_to_in_context_messages(messages=[message], agent_id=agent_id, actor=actor)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def append_system_message_async(self, agent_id: str, content: str, actor: PydanticUser):
        """
        Async version of append_system_message.

        Append a system message to an agent's in-context message history.
        See append_system_message for detailed documentation.

        This async version is preferred for high-throughput scenarios or when
        called within other async operations to avoid blocking the event loop.
        """

        # get the agent
        agent = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor)
        message = PydanticMessage.dict_to_message(
            agent_id=agent.id, model=agent.llm_config.model, openai_message_dict={"role": "system", "content": content}
        )

        # update agent in-context message IDs
        await self.append_to_in_context_messages_async(messages=[message], agent_id=agent_id, actor=actor)

    @enforce_types
    @trace_method
    async def list_attached_sources_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = None,
        ascending: bool = False,
    ) -> List[PydanticSource]:
        """
        Lists all sources attached to an agent with pagination.

        Args:
            agent_id: ID of the agent to list sources for
            actor: User performing the action
            before: Source ID cursor for pagination. Returns sources that come before this source ID.
            after: Source ID cursor for pagination. Returns sources that come after this source ID.
            limit: Maximum number of sources to return.
            ascending: Sort order by creation time.

        Returns:
            List[PydanticSource]: List of sources attached to the agent

        Raises:
            NoResultFound: If agent doesn't exist or user doesn't have access
        """
        async with db_registry.async_session() as session:
            # Validate agent exists and user has access
            await validate_agent_exists_async(session, agent_id, actor)

            # Use raw SQL to efficiently fetch sources - much faster than lazy loading
            # Fast query without relationship loading
            query = (
                select(SourceModel)
                .join(SourcesAgents, SourceModel.id == SourcesAgents.source_id)
                .where(
                    SourcesAgents.agent_id == agent_id,
                    SourceModel.organization_id == actor.organization_id,
                    SourceModel.is_deleted == False,
                )
            )

            # Apply cursor-based pagination
            if before:
                query = query.where(SourceModel.id < before)
            if after:
                query = query.where(SourceModel.id > after)

            # Apply sorting
            if ascending:
                query = query.order_by(SourceModel.created_at.asc(), SourceModel.id.asc())
            else:
                query = query.order_by(SourceModel.created_at.desc(), SourceModel.id.desc())

            # Apply limit
            if limit:
                query = query.limit(limit)

            result = await session.execute(query)
            sources = result.scalars().all()

            return [source.to_pydantic() for source in sources]

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    @raise_on_invalid_id(param_name="source_id", expected_prefix=PrimitiveType.SOURCE)
    async def detach_source_async(self, agent_id: str, source_id: str, actor: PydanticUser) -> PydanticAgentState:
        """
        Detaches a source from an agent.

        Args:
            agent_id: ID of the agent to detach the source from
            source_id: ID of the source to detach
            actor: User performing the action

        Raises:
            NoResultFound: If agent doesn't exist or user doesn't have access
        """
        async with db_registry.async_session() as session:
            # Validate agent exists and user has access
            await validate_agent_exists_async(session, agent_id, actor)

            # Check if the source is actually attached to this agent using junction table
            attachment_check_query = select(SourcesAgents).where(SourcesAgents.agent_id == agent_id, SourcesAgents.source_id == source_id)
            attachment_result = await session.execute(attachment_check_query)
            attachment = attachment_result.scalar_one_or_none()

            if not attachment:
                logger.warning(f"Attempted to remove unattached source id={source_id} from agent id={agent_id} by actor={actor}")
            else:
                # Delete the association directly from the junction table
                delete_query = delete(SourcesAgents).where(SourcesAgents.agent_id == agent_id, SourcesAgents.source_id == source_id)
                await session.execute(delete_query)
                await session.commit()

            # Get agent without loading relationships for return value
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            # TODO: This refresh is expensive. If we can find out which fields are needed, we can save cost by only refreshing those fields.
            # or even better, not refresh at all.
            return await agent.to_pydantic_async()

    # ======================================================================================================================
    # Block management
    # ======================================================================================================================
    @enforce_types
    @trace_method
    async def get_block_with_label_async(
        self,
        agent_id: str,
        block_label: str,
        actor: PydanticUser,
    ) -> PydanticBlock:
        """Gets a block attached to an agent by its label."""
        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            for block in agent.core_memory:
                if block.label == block_label:
                    return block.to_pydantic()
            raise NoResultFound(f"No block with label '{block_label}' found for agent '{agent_id}'")

    @enforce_types
    @trace_method
    async def modify_block_by_label_async(
        self,
        agent_id: str,
        block_label: str,
        block_update: BlockUpdate,
        actor: PydanticUser,
    ) -> PydanticBlock:
        """Gets a block attached to an agent by its label."""
        async with db_registry.async_session() as session:
            block = None
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            for block in agent.core_memory:
                if block.label == block_label:
                    block = block
                    break
            if not block:
                raise NoResultFound(f"No block with label '{block_label}' found for agent '{agent_id}'")

            update_data = block_update.model_dump(to_orm=True, exclude_unset=True, exclude_none=True)

            # Validate limit constraints before updating
            validate_block_limit_constraint(update_data, block)

            for key, value in update_data.items():
                setattr(block, key, value)

            await block.update_async(session, actor=actor)
            return block.to_pydantic()

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    @raise_on_invalid_id(param_name="block_id", expected_prefix=PrimitiveType.BLOCK)
    async def attach_block_async(self, agent_id: str, block_id: str, actor: PydanticUser) -> PydanticAgentState:
        """Attaches a block to an agent. For sleeptime agents, also attaches to paired agents in the same group."""
        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            block = await BlockModel.read_async(db_session=session, identifier=block_id, actor=actor)

            # Attach block to the main agent
            agent.core_memory.append(block)
            # await agent.update_async(session, actor=actor, no_commit=True)
            await agent.update_async(session)

            # If agent is part of a sleeptime group, attach block to the sleeptime_agent
            if agent.multi_agent_group and agent.multi_agent_group.manager_type == ManagerType.sleeptime:
                group = agent.multi_agent_group
                # Find the sleeptime_agent in the group
                for other_agent_id in group.agent_ids or []:
                    if other_agent_id != agent_id:
                        try:
                            other_agent = await AgentModel.read_async(db_session=session, identifier=other_agent_id, actor=actor)
                            if other_agent.agent_type == AgentType.sleeptime_agent and block not in other_agent.core_memory:
                                other_agent.core_memory.append(block)
                                # await other_agent.update_async(session, actor=actor, no_commit=True)
                                await other_agent.update_async(session, actor=actor)
                        except NoResultFound:
                            # Agent might not exist anymore, skip
                            continue

            # TODO: @andy/caren
            # TODO: Ideally we do two no commits on the update_async calls, and then commit here - but that errors for some reason?
            # TODO: I have too many things rn so lets look at this later
            # await session.commit()

            return await agent.to_pydantic_async()

    @enforce_types
    @trace_method
    async def detach_block_async(
        self,
        agent_id: str,
        block_id: str,
        actor: PydanticUser,
    ) -> PydanticAgentState:
        """Detaches a block from an agent."""
        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            original_length = len(agent.core_memory)

            agent.core_memory = [b for b in agent.core_memory if b.id != block_id]

            if len(agent.core_memory) == original_length:
                raise NoResultFound(f"No block with id '{block_id}' found for agent '{agent_id}' with actor id: '{actor.id}'")

            await agent.update_async(session, actor=actor)
            return await agent.to_pydantic_async()

    # ======================================================================================================================
    # Passage Management
    # ======================================================================================================================

    @enforce_types
    @trace_method
    async def list_passages(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        file_id: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        source_id: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
        agent_only: bool = False,
    ) -> List[PydanticPassage]:
        """Lists all passages attached to an agent."""
        async with db_registry.async_session() as session:
            main_query = await build_passage_query(
                actor=actor,
                agent_id=agent_id,
                file_id=file_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                source_id=source_id,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
                agent_only=agent_only,
            )

            # Add limit (enforce default if not provided)
            main_query = main_query.limit(limit)

            # Execute query
            result = await session.execute(main_query)

            passages = []
            for row in result:
                data = dict(row._mapping)
                if data.get("archive_id", None):
                    # This is an ArchivalPassage - remove source fields
                    data.pop("source_id", None)
                    data.pop("file_id", None)
                    data.pop("file_name", None)
                    passage = ArchivalPassage(**data)
                elif data.get("source_id", None):
                    # This is a SourcePassage - remove archive field
                    data.pop("archive_id", None)
                    data.pop("agent_id", None)  # For backward compatibility
                    passage = SourcePassage(**data)
                else:
                    raise ValueError(f"Passage data is malformed, is neither ArchivalPassage nor SourcePassage {data}")
                passages.append(passage)

            return [p.to_pydantic() for p in passages]

    @enforce_types
    @trace_method
    async def list_passages_async(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        file_id: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        source_id: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
        agent_only: bool = False,
    ) -> List[PydanticPassage]:
        """
        DEPRECATED: Use query_source_passages_async or query_agent_passages_async instead.
        This method is kept only for test compatibility and will be removed in a future version.

        Lists all passages attached to an agent (combines both source and agent passages).
        """
        import warnings

        logger.warning(
            "list_passages_async is deprecated. Use query_source_passages_async or query_agent_passages_async instead.",
            stacklevel=2,
        )

        async with db_registry.async_session() as session:
            main_query = await build_passage_query(
                actor=actor,
                agent_id=agent_id,
                file_id=file_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                source_id=source_id,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
                agent_only=agent_only,
            )

            # Add limit (enforce default if not provided)
            main_query = main_query.limit(limit)

            # Execute query
            result = await session.execute(main_query)

            passages = []
            for row in result:
                data = dict(row._mapping)
                if data.get("archive_id", None):
                    # This is an ArchivalPassage - remove source fields
                    data.pop("source_id", None)
                    data.pop("file_id", None)
                    data.pop("file_name", None)
                    passage = ArchivalPassage(**data)
                elif data.get("source_id", None):
                    # This is a SourcePassage - remove archive field
                    data.pop("archive_id", None)
                    data.pop("agent_id", None)  # For backward compatibility
                    passage = SourcePassage(**data)
                else:
                    raise ValueError(f"Passage data is malformed, is neither ArchivalPassage nor SourcePassage {data}")
                passages.append(passage)

            return [p.to_pydantic() for p in passages]

    @enforce_types
    @trace_method
    async def query_source_passages_async(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        file_id: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        source_id: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
    ) -> List[PydanticPassage]:
        """Lists all passages attached to an agent."""
        async with db_registry.async_session() as session:
            main_query = await build_source_passage_query(
                actor=actor,
                agent_id=agent_id,
                file_id=file_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                source_id=source_id,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
            )

            # Add limit (enforce default if not provided)
            main_query = main_query.limit(limit)

            # Execute query
            result = await session.execute(main_query)

            # Get ORM objects directly using scalars()
            passages = result.scalars().all()

            # Convert to Pydantic models
            return [p.to_pydantic() for p in passages]

    @enforce_types
    @trace_method
    async def query_agent_passages_async(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        archive_id: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
        tags: Optional[List[str]] = None,
        tag_match_mode: Optional[TagMatchMode] = None,
    ) -> List[Tuple[PydanticPassage, float, dict]]:
        """Lists all passages attached to an agent."""
        # Check if we should use Turbopuffer for vector search
        # Support searching by either agent_id or archive_id directly
        if embed_query and query_text and embedding_config:
            target_archive_id = None

            if agent_id:
                # Get archive IDs for the agent
                archive_ids = await self.get_agent_archive_ids_async(agent_id=agent_id, actor=actor)

                if archive_ids:
                    # TODO: Remove this restriction once we support multiple archives with mixed vector DB providers
                    if len(archive_ids) > 1:
                        raise ValueError(f"Agent {agent_id} has multiple archives, which is not yet supported for vector search")
                    target_archive_id = archive_ids[0]
            elif archive_id:
                # Use the provided archive_id directly
                target_archive_id = archive_id

            if target_archive_id:
                # Get archive to check vector_db_provider
                archive = await self.archive_manager.get_archive_by_id_async(archive_id=target_archive_id, actor=actor)

                # Use Turbopuffer for vector search if archive is configured for TPUF
                if archive.vector_db_provider == VectorDBProvider.TPUF:
                    from letta.helpers.tpuf_client import TurbopufferClient
                    from letta.llm_api.llm_client import LLMClient

                    # Generate embedding for query
                    embedding_client = LLMClient.create(
                        provider_type=embedding_config.embedding_endpoint_type,
                        actor=actor,
                    )
                    embeddings = await embedding_client.request_embeddings([query_text], embedding_config)
                    query_embedding = embeddings[0]

                    # Query Turbopuffer - use hybrid search when text is available
                    tpuf_client = TurbopufferClient()
                    # use hybrid search to combine vector and full-text search
                    passages_with_scores = await tpuf_client.query_passages(
                        archive_id=target_archive_id,
                        query_text=query_text,  # pass text for potential hybrid search
                        search_mode="hybrid",  # use hybrid mode for better results
                        top_k=limit,
                        tags=tags,
                        tag_match_mode=tag_match_mode or TagMatchMode.ANY,
                        start_date=start_date,
                        end_date=end_date,
                        actor=actor,
                    )

                    # Return full tuples with metadata
                    return passages_with_scores

        # Fall back to SQL-based search for non-vector queries or NATIVE archives
        async with db_registry.async_session() as session:
            main_query = await build_agent_passage_query(
                actor=actor,
                agent_id=agent_id,
                archive_id=archive_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
            )

            # Add limit
            if limit:
                main_query = main_query.limit(limit)

            # Execute query
            result = await session.execute(main_query)

            # Get ORM objects directly using scalars()
            passages = result.scalars().all()

            # Convert to Pydantic models
            pydantic_passages = [p.to_pydantic() for p in passages]

            # TODO: Integrate tag filtering directly into the SQL query for better performance.
            # Currently using post-filtering which is less efficient but simpler to implement.
            # Future optimization: Add JOIN with passage_tags table and WHERE clause for tag filtering.
            if tags:
                filtered_passages = []
                for passage in pydantic_passages:
                    if passage.tags:
                        passage_tags = set(passage.tags)
                        query_tags = set(tags)

                        if tag_match_mode == TagMatchMode.ALL:
                            # ALL mode: passage must have all query tags
                            if query_tags.issubset(passage_tags):
                                filtered_passages.append(passage)
                        else:
                            # ANY mode (default): passage must have at least one query tag
                            if query_tags.intersection(passage_tags):
                                filtered_passages.append(passage)

                # Return as tuples with empty metadata for SQL path
                return [(p, 0.0, {}) for p in filtered_passages]

            # Return as tuples with empty metadata for SQL path
            return [(p, 0.0, {}) for p in pydantic_passages]

    @enforce_types
    @trace_method
    async def search_agent_archival_memory_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        query: str,
        tags: Optional[List[str]] = None,
        tag_match_mode: Literal["any", "all"] = "any",
        top_k: Optional[int] = None,
        start_datetime: Optional[str] = None,
        end_datetime: Optional[str] = None,
    ) -> List[Dict[str, Any]]:
        """
        Search archival memory using semantic (embedding-based) search with optional temporal filtering.

        This is a shared method used by both the agent tool and API endpoint to ensure consistent behavior.

        Args:
            agent_id: ID of the agent whose archival memory to search
            actor: User performing the search
            query: String to search for using semantic similarity
            tags: Optional list of tags to filter search results
            tag_match_mode: How to match tags - "any" or "all"
            top_k: Maximum number of results to return
            start_datetime: Filter results after this datetime (ISO 8601 format)
            end_datetime: Filter results before this datetime (ISO 8601 format)

        Returns:
            List of formatted results with relevance metadata
        """
        # Handle empty or whitespace-only queries
        if not query or not query.strip():
            return []

        # Get the agent to access timezone and embedding config
        agent_state = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor)

        # Parse datetime parameters if provided
        start_date = None
        end_date = None

        if start_datetime:
            try:
                # Try parsing as full datetime first (with time)
                start_date = datetime.fromisoformat(start_datetime)
            except ValueError:
                try:
                    # Fall back to date-only format
                    start_date = datetime.strptime(start_datetime, "%Y-%m-%d")
                    # Set to beginning of day
                    start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
                except ValueError:
                    raise ValueError(
                        f"Invalid start_datetime format: {start_datetime}. Use ISO 8601 format (YYYY-MM-DD or YYYY-MM-DDTHH:MM)"
                    )

            # Apply agent's timezone if datetime is naive
            if start_date.tzinfo is None and agent_state.timezone:
                tz = ZoneInfo(agent_state.timezone)
                start_date = start_date.replace(tzinfo=tz)

        if end_datetime:
            try:
                # Try parsing as full datetime first (with time)
                end_date = datetime.fromisoformat(end_datetime)
            except ValueError:
                try:
                    # Fall back to date-only format
                    end_date = datetime.strptime(end_datetime, "%Y-%m-%d")
                    # Set to end of day for end dates
                    end_date = end_date.replace(hour=23, minute=59, second=59, microsecond=999999)
                except ValueError:
                    raise ValueError(f"Invalid end_datetime format: {end_datetime}. Use ISO 8601 format (YYYY-MM-DD or YYYY-MM-DDTHH:MM)")

            # Apply agent's timezone if datetime is naive
            if end_date.tzinfo is None and agent_state.timezone:
                tz = ZoneInfo(agent_state.timezone)
                end_date = end_date.replace(tzinfo=tz)

        # Convert string to TagMatchMode enum
        tag_mode = TagMatchMode.ANY if tag_match_mode == "any" else TagMatchMode.ALL

        # Get results using existing passage query method
        limit = top_k if top_k is not None else RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
        passages_with_metadata = await self.query_agent_passages_async(
            actor=actor,
            agent_id=agent_id,
            query_text=query,
            limit=limit,
            embedding_config=agent_state.embedding_config,
            embed_query=True,
            tags=tags,
            tag_match_mode=tag_mode,
            start_date=start_date,
            end_date=end_date,
        )

        # Format results to include tags with friendly timestamps and relevance metadata
        formatted_results = []
        for passage, score, metadata in passages_with_metadata:
            # Format timestamp in agent's timezone if available
            timestamp = passage.created_at
            if timestamp and agent_state.timezone:
                try:
                    # Convert to agent's timezone
                    tz = ZoneInfo(agent_state.timezone)
                    local_time = timestamp.astimezone(tz)
                    # Format as ISO string with timezone
                    formatted_timestamp = local_time.isoformat()
                except Exception:
                    # Fallback to ISO format if timezone conversion fails
                    formatted_timestamp = str(timestamp)
            else:
                # Use ISO format if no timezone is set
                formatted_timestamp = str(timestamp) if timestamp else "Unknown"

            result_dict = {"timestamp": formatted_timestamp, "content": passage.text, "tags": passage.tags or []}

            # Add relevance metadata if available
            if metadata:
                relevance_info = {
                    k: v
                    for k, v in {
                        "rrf_score": metadata.get("combined_score"),
                        "vector_rank": metadata.get("vector_rank"),
                        "fts_rank": metadata.get("fts_rank"),
                    }.items()
                    if v is not None
                }

                if relevance_info:  # Only add if we have metadata
                    result_dict["relevance"] = relevance_info

            formatted_results.append(result_dict)

        return formatted_results

    @enforce_types
    @trace_method
    async def passage_size(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        file_id: Optional[str] = None,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        source_id: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
        agent_only: bool = False,
    ) -> int:
        """Returns the count of passages matching the given criteria."""
        async with db_registry.async_session() as session:
            main_query = await build_passage_query(
                actor=actor,
                agent_id=agent_id,
                file_id=file_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                source_id=source_id,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
                agent_only=agent_only,
            )

            # Convert to count query
            count_query = select(func.count()).select_from(main_query.subquery())
            return (await session.scalar(count_query)) or 0

    @enforce_types
    async def passage_size_async(
        self,
        actor: PydanticUser,
        agent_id: Optional[str] = None,
        file_id: Optional[str] = None,
        query_text: Optional[str] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        source_id: Optional[str] = None,
        embed_query: bool = False,
        ascending: bool = True,
        embedding_config: Optional[EmbeddingConfig] = None,
        agent_only: bool = False,
    ) -> int:
        async with db_registry.async_session() as session:
            main_query = await build_passage_query(
                actor=actor,
                agent_id=agent_id,
                file_id=file_id,
                query_text=query_text,
                start_date=start_date,
                end_date=end_date,
                before=before,
                after=after,
                source_id=source_id,
                embed_query=embed_query,
                ascending=ascending,
                embedding_config=embedding_config,
                agent_only=agent_only,
            )

            # Convert to count query
            count_query = select(func.count()).select_from(main_query.subquery())
            return (await session.execute(count_query)).scalar() or 0

    # ======================================================================================================================
    # Tool Management
    # ======================================================================================================================
    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    @raise_on_invalid_id(param_name="tool_id", expected_prefix=PrimitiveType.TOOL)
    async def attach_tool_async(self, agent_id: str, tool_id: str, actor: PydanticUser) -> None:
        """
        Attaches a tool to an agent.

        Args:
            agent_id: ID of the agent to attach the tool to.
            tool_id: ID of the tool to attach.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent or tool is not found.

        Returns:
            PydanticAgentState: The updated agent state.
        """
        async with db_registry.async_session() as session:
            # Verify the agent exists and user has permission to access it
            await validate_agent_exists_async(session, agent_id, actor)

            # verify tool exists and belongs to organization in a single query with the insert
            # first, check if tool exists with correct organization
            tool_check_query = select(ToolModel.name, ToolModel.default_requires_approval).where(
                ToolModel.id == tool_id, ToolModel.organization_id == actor.organization_id
            )
            result = await session.execute(tool_check_query)
            tool_rows = result.fetchall()

            if len(tool_rows) == 0:
                raise NoResultFound(f"Tool with id={tool_id} not found in organization={actor.organization_id}")
            tool_name, default_requires_approval = tool_rows[0]

            # use postgresql on conflict or mysql on duplicate key update for atomic operation
            if settings.letta_pg_uri_no_default:
                from sqlalchemy.dialects.postgresql import insert as pg_insert

                insert_stmt = pg_insert(ToolsAgents).values(agent_id=agent_id, tool_id=tool_id)
                # on conflict do nothing - silently ignore if already exists
                insert_stmt = insert_stmt.on_conflict_do_nothing(index_elements=["agent_id", "tool_id"])
                result = await session.execute(insert_stmt)
                if result.rowcount == 0:
                    logger.info(f"Tool id={tool_id} is already attached to agent id={agent_id}")
            else:
                # for sqlite/mysql, check then insert
                existing_query = (
                    select(func.count()).select_from(ToolsAgents).where(ToolsAgents.agent_id == agent_id, ToolsAgents.tool_id == tool_id)
                )
                existing_result = await session.execute(existing_query)
                if existing_result.scalar() == 0:
                    insert_stmt = insert(ToolsAgents).values(agent_id=agent_id, tool_id=tool_id)
                    await session.execute(insert_stmt)
                else:
                    logger.info(f"Tool id={tool_id} is already attached to agent id={agent_id}")

            if default_requires_approval:
                agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
                existing_rules = [rule for rule in agent.tool_rules if rule.tool_name == tool_name and rule.type == "requires_approval"]
                if len(existing_rules) == 0:
                    # Create a new list to ensure SQLAlchemy detects the change
                    # This is critical for JSON columns - modifying in place doesn't trigger change detection
                    tool_rules = list(agent.tool_rules) if agent.tool_rules else []
                    tool_rules.append(RequiresApprovalToolRule(tool_name=tool_name))
                    agent.tool_rules = tool_rules
                    session.add(agent)

            await session.commit()

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def bulk_attach_tools_async(self, agent_id: str, tool_ids: List[str], actor: PydanticUser) -> None:
        """
        Efficiently attaches multiple tools to an agent in a single operation.

        Args:
            agent_id: ID of the agent to attach the tools to.
            tool_ids: List of tool IDs to attach.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent or any tool is not found.
        """
        if not tool_ids:
            # no tools to attach, nothing to do
            return

        async with db_registry.async_session() as session:
            # Verify the agent exists and user has permission to access it
            await validate_agent_exists_async(session, agent_id, actor)

            # verify all tools exist and belong to organization in a single query
            tool_check_query = select(func.count(ToolModel.id)).where(
                ToolModel.id.in_(tool_ids), ToolModel.organization_id == actor.organization_id
            )
            tool_result = await session.execute(tool_check_query)
            found_count = tool_result.scalar()

            if found_count != len(tool_ids):
                # find which tools are missing for better error message
                existing_query = select(ToolModel.id).where(ToolModel.id.in_(tool_ids), ToolModel.organization_id == actor.organization_id)
                existing_result = await session.execute(existing_query)
                existing_ids = {row[0] for row in existing_result}
                missing_ids = set(tool_ids) - existing_ids
                raise NoResultFound(f"Tools with ids={missing_ids} not found in organization={actor.organization_id}")

            if settings.letta_pg_uri_no_default:
                from sqlalchemy.dialects.postgresql import insert as pg_insert

                # prepare bulk values
                values = [{"agent_id": agent_id, "tool_id": tool_id} for tool_id in tool_ids]

                # bulk insert with on conflict do nothing
                insert_stmt = pg_insert(ToolsAgents).values(values)
                insert_stmt = insert_stmt.on_conflict_do_nothing(index_elements=["agent_id", "tool_id"])
                result = await session.execute(insert_stmt)
                logger.info(
                    f"Attached {result.rowcount} new tools to agent {agent_id} (skipped {len(tool_ids) - result.rowcount} already attached)"
                )
            else:
                # for sqlite/mysql, first check which tools are already attached
                existing_query = select(ToolsAgents.tool_id).where(ToolsAgents.agent_id == agent_id, ToolsAgents.tool_id.in_(tool_ids))
                existing_result = await session.execute(existing_query)
                already_attached = {row[0] for row in existing_result}

                # only insert tools that aren't already attached
                new_tool_ids = [tid for tid in tool_ids if tid not in already_attached]

                if new_tool_ids:
                    # bulk insert new attachments
                    values = [{"agent_id": agent_id, "tool_id": tool_id} for tool_id in new_tool_ids]
                    insert_stmt = insert(ToolsAgents).values(values)
                    await session.execute(insert_stmt)
                    logger.info(
                        f"Attached {len(new_tool_ids)} new tools to agent {agent_id} (skipped {len(already_attached)} already attached)"
                    )
                else:
                    logger.info(f"All {len(tool_ids)} tools already attached to agent {agent_id}")

            await session.commit()

    @enforce_types
    @trace_method
    async def attach_missing_files_tools_async(self, agent_state: PydanticAgentState, actor: PydanticUser) -> PydanticAgentState:
        """
        Attaches missing core file tools to an agent.

        Args:
            agent_state: The current agent state with tools already loaded.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent or tool is not found.

        Returns:
            PydanticAgentState: The updated agent state.
        """
        # get current file tools attached to the agent
        attached_file_tool_names = {tool.name for tool in agent_state.tools if tool.tool_type == ToolType.LETTA_FILES_CORE}

        # determine which file tools are missing
        missing_tool_names = set(FILES_TOOLS) - attached_file_tool_names

        if not missing_tool_names:
            # agent already has all file tools
            return agent_state

        # get full tool objects for all missing file tools in one query
        async with db_registry.async_session() as session:
            query = select(ToolModel).where(
                ToolModel.name.in_(missing_tool_names),
                ToolModel.organization_id == actor.organization_id,
                ToolModel.tool_type == ToolType.LETTA_FILES_CORE,
            )
            result = await session.execute(query)
            found_tool_models = result.scalars().all()

        if not found_tool_models:
            logger.warning(f"No file tools found for organization {actor.organization_id}. Expected tools: {missing_tool_names}")
            return agent_state

        # convert to pydantic tools
        found_tools = [tool.to_pydantic() for tool in found_tool_models]
        found_tool_names = {tool.name for tool in found_tools}

        # log if any expected tools weren't found
        still_missing = missing_tool_names - found_tool_names
        if still_missing:
            logger.warning(f"File tools {still_missing} not found in organization {actor.organization_id}")

        # extract tool IDs for bulk attach
        tool_ids_to_attach = [tool.id for tool in found_tools]

        # bulk attach all found file tools
        await self.bulk_attach_tools_async(agent_id=agent_state.id, tool_ids=tool_ids_to_attach, actor=actor)

        # create a shallow copy with updated tools list to avoid modifying input
        agent_state_dict = agent_state.model_dump()
        agent_state_dict["tools"] = agent_state.tools + found_tools

        return PydanticAgentState(**agent_state_dict)

    @enforce_types
    @trace_method
    async def detach_all_files_tools_async(self, agent_state: PydanticAgentState, actor: PydanticUser) -> PydanticAgentState:
        """
        Detach all core file tools from an agent.

        Args:
            agent_state: The current agent state with tools already loaded.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent is not found.

        Returns:
            PydanticAgentState: The updated agent state.
        """
        # extract file tool IDs directly from agent_state.tools
        file_tool_ids = [tool.id for tool in agent_state.tools if tool.tool_type == ToolType.LETTA_FILES_CORE]

        if not file_tool_ids:
            # no file tools to detach
            return agent_state

        # bulk detach all file tools in one operation
        await self.bulk_detach_tools_async(agent_id=agent_state.id, tool_ids=file_tool_ids, actor=actor)

        # create a shallow copy with updated tools list to avoid modifying input
        agent_state_dict = agent_state.model_dump()
        agent_state_dict["tools"] = [tool for tool in agent_state.tools if tool.tool_type != ToolType.LETTA_FILES_CORE]

        return PydanticAgentState(**agent_state_dict)

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    @raise_on_invalid_id(param_name="tool_id", expected_prefix=PrimitiveType.TOOL)
    async def detach_tool_async(self, agent_id: str, tool_id: str, actor: PydanticUser) -> None:
        """
        Detaches a tool from an agent.

        Args:
            agent_id: ID of the agent to detach the tool from.
            tool_id: ID of the tool to detach.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent is not found.
        """
        async with db_registry.async_session() as session:
            # Verify the agent exists and user has permission to access it
            await validate_agent_exists_async(session, agent_id, actor)

            # Delete the association directly - if it doesn't exist, rowcount will be 0
            delete_query = delete(ToolsAgents).where(ToolsAgents.agent_id == agent_id, ToolsAgents.tool_id == tool_id)
            result = await session.execute(delete_query)

            if result.rowcount == 0:
                logger.warning(f"Attempted to remove unattached tool id={tool_id} from agent id={agent_id} by actor={actor}")
            else:
                logger.debug(f"Detached tool id={tool_id} from agent id={agent_id}")

            await session.commit()

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def bulk_detach_tools_async(self, agent_id: str, tool_ids: List[str], actor: PydanticUser) -> None:
        """
        Efficiently detaches multiple tools from an agent in a single operation.

        Args:
            agent_id: ID of the agent to detach the tools from.
            tool_ids: List of tool IDs to detach.
            actor: User performing the action.

        Raises:
            NoResultFound: If the agent is not found.
        """
        if not tool_ids:
            # no tools to detach, nothing to do
            return

        async with db_registry.async_session() as session:
            # Verify the agent exists and user has permission to access it
            await validate_agent_exists_async(session, agent_id, actor)

            # Delete all associations in a single query
            delete_query = delete(ToolsAgents).where(ToolsAgents.agent_id == agent_id, ToolsAgents.tool_id.in_(tool_ids))
            result = await session.execute(delete_query)

            detached_count = result.rowcount
            if detached_count == 0:
                logger.warning(f"No tools from list {tool_ids} were attached to agent id={agent_id}")
            elif detached_count < len(tool_ids):
                logger.info(f"Detached {detached_count} tools from agent {agent_id} ({len(tool_ids) - detached_count} were not attached)")
            else:
                logger.info(f"Detached all {detached_count} tools from agent {agent_id}")

            await session.commit()

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def modify_approvals_async(self, agent_id: str, tool_name: str, requires_approval: bool, actor: PydanticUser) -> None:
        def is_target_rule(rule):
            return rule.tool_name == tool_name and rule.type == "requires_approval"

        async with db_registry.async_session() as session:
            agent = await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)
            existing_rules = [rule for rule in agent.tool_rules if is_target_rule(rule)]

            if len(existing_rules) == 1 and not requires_approval:
                tool_rules = [rule for rule in agent.tool_rules if not is_target_rule(rule)]
            elif len(existing_rules) == 0 and requires_approval:
                # Create a new list to ensure SQLAlchemy detects the change
                # This is critical for JSON columns - modifying in place doesn't trigger change detection
                tool_rules = list(agent.tool_rules) if agent.tool_rules else []
                tool_rules.append(RequiresApprovalToolRule(tool_name=tool_name))
            else:
                tool_rules = None

            if tool_rules is None:
                return

            agent.tool_rules = tool_rules
            session.add(agent)
            await session.commit()

    @enforce_types
    @trace_method
    async def list_attached_tools_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = None,
        ascending: bool = False,
    ) -> List[PydanticTool]:
        """
        List all tools attached to an agent (async version with optimized performance).
        Uses direct SQL queries to avoid SqlAlchemyBase overhead.

        Args:
            agent_id: ID of the agent to list tools for.
            actor: User performing the action.
            before: Tool ID cursor for pagination. Returns tools that come before this tool ID.
            after: Tool ID cursor for pagination. Returns tools that come after this tool ID.
            limit: Maximum number of tools to return.
            ascending: Sort order by creation time.

        Returns:
            List[PydanticTool]: List of tools attached to the agent.
        """
        async with db_registry.async_session() as session:
            # lightweight check for agent access
            await validate_agent_exists_async(session, agent_id, actor)

            # direct query for tools via join - much more performant
            query = (
                select(ToolModel)
                .join(ToolsAgents, ToolModel.id == ToolsAgents.tool_id)
                .where(ToolsAgents.agent_id == agent_id, ToolModel.organization_id == actor.organization_id)
            )

            # Apply cursor-based pagination
            if before:
                query = query.where(ToolModel.id < before)
            if after:
                query = query.where(ToolModel.id > after)

            # Apply sorting
            if ascending:
                query = query.order_by(ToolModel.created_at.asc())
            else:
                query = query.order_by(ToolModel.created_at.desc())

            # Apply limit
            if limit:
                query = query.limit(limit)

            result = await session.execute(query)
            tools = result.scalars().all()
            return [tool.to_pydantic() for tool in tools]

    @enforce_types
    @trace_method
    async def list_agent_blocks_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = None,
        ascending: bool = False,
    ) -> List[PydanticBlock]:
        """
        List all blocks for a specific agent with pagination.

        Args:
            agent_id: ID of the agent to find blocks for.
            actor: User performing the action.
            before: Block ID cursor for pagination. Returns blocks that come before this block ID.
            after: Block ID cursor for pagination. Returns blocks that come after this block ID.
            limit: Maximum number of blocks to return.
            ascending: Sort order by creation time.

        Returns:
            List[PydanticBlock]: List of blocks for the agent.
        """
        async with db_registry.async_session() as session:
            # First verify agent exists and user has access
            await AgentModel.read_async(db_session=session, identifier=agent_id, actor=actor)

            # Build query to get blocks for this agent with pagination
            query = (
                select(BlockModel)
                .join(BlocksAgents, BlockModel.id == BlocksAgents.block_id)
                .where(BlocksAgents.agent_id == agent_id, BlockModel.organization_id == actor.organization_id)
            )

            # Apply cursor-based pagination
            if before:
                query = query.where(BlockModel.id < before)
            if after:
                query = query.where(BlockModel.id > after)

            # Apply sorting - use id instead of created_at for core memory blocks
            if ascending:
                query = query.order_by(BlockModel.id.asc())
            else:
                query = query.order_by(BlockModel.id.desc())

            # Apply limit
            if limit:
                query = query.limit(limit)

            result = await session.execute(query)
            blocks = result.scalars().all()

            return [block.to_pydantic() for block in blocks]

    @enforce_types
    @trace_method
    async def list_groups_async(
        self,
        agent_id: str,
        actor: PydanticUser,
        manager_type: Optional[str] = None,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = None,
        ascending: bool = False,
    ) -> List[PydanticGroup]:
        """
        List all groups that contain the specified agent.

        Args:
            agent_id: ID of the agent to find groups for.
            actor: User performing the action.
            manager_type: Optional manager type to filter by.
            before: Group ID cursor for pagination. Returns groups that come before this group ID.
            after: Group ID cursor for pagination. Returns groups that come after this group ID.
            limit: Maximum number of groups to return.
            ascending: Sort order by creation time.

        Returns:
            List[PydanticGroup]: List of groups containing the agent.
        """
        async with db_registry.async_session() as session:
            query = (
                select(GroupModel)
                .join(GroupsAgents, GroupModel.id == GroupsAgents.group_id)
                .where(GroupsAgents.agent_id == agent_id, GroupModel.organization_id == actor.organization_id)
            )

            if manager_type:
                query = query.where(GroupModel.manager_type == manager_type)

            # Apply cursor-based pagination
            if before:
                query = query.where(GroupModel.id < before)
            if after:
                query = query.where(GroupModel.id > after)

            # Apply sorting
            if ascending:
                query = query.order_by(GroupModel.created_at.asc())
            else:
                query = query.order_by(GroupModel.created_at.desc())

            # Apply limit
            if limit:
                query = query.limit(limit)

            result = await session.execute(query)
            groups = result.scalars().all()
            return [group.to_pydantic() for group in groups]

    # ======================================================================================================================
    # File Management
    # ======================================================================================================================
    async def insert_file_into_context_windows(
        self,
        source_id: str,
        file_metadata_with_content: PydanticFileMetadata,
        actor: PydanticUser,
        agent_states: Optional[List[PydanticAgentState]] = None,
    ) -> List[PydanticAgentState]:
        """
        Insert the uploaded document into the context window of all agents
        attached to the given source.
        """
        agent_states = agent_states or await self.source_manager.list_attached_agents(source_id=source_id, actor=actor)

        # Return early
        if not agent_states:
            return []

        logger.info(f"Inserting document into context window for source: {source_id}")
        logger.info(f"Attached agents: {[a.id for a in agent_states]}")

        # Generate visible content for the file
        line_chunker = LineChunker()
        content_lines = line_chunker.chunk_text(file_metadata=file_metadata_with_content)
        visible_content = "\n".join(content_lines)
        visible_content_map = {file_metadata_with_content.file_name: visible_content}

        # Attach file to each agent using bulk method (one file per agent, but atomic per agent)
        all_closed_files = await asyncio.gather(
            *(
                self.file_agent_manager.attach_files_bulk(
                    agent_id=agent_state.id,
                    files_metadata=[file_metadata_with_content],
                    visible_content_map=visible_content_map,
                    actor=actor,
                    max_files_open=agent_state.max_files_open,
                )
                for agent_state in agent_states
            )
        )
        # Flatten and log if any files were closed
        closed_files = [file for closed_list in all_closed_files for file in closed_list]
        if closed_files:
            logger.info(f"LRU eviction closed {len(closed_files)} files during bulk attach: {closed_files}")

        return agent_states

    async def insert_files_into_context_window(
        self, agent_state: PydanticAgentState, file_metadata_with_content: List[PydanticFileMetadata], actor: PydanticUser
    ) -> None:
        """
        Insert the uploaded documents into the context window of an agent
        attached to the given source.
        """
        logger.info(f"Inserting {len(file_metadata_with_content)} documents into context window for agent_state: {agent_state.id}")

        # Generate visible content for each file
        line_chunker = LineChunker()
        visible_content_map = {}
        for file_metadata in file_metadata_with_content:
            content_lines = line_chunker.chunk_text(file_metadata=file_metadata)
            visible_content_map[file_metadata.file_name] = "\n".join(content_lines)

        # Use bulk attach to avoid race conditions and duplicate LRU eviction decisions
        closed_files = await self.file_agent_manager.attach_files_bulk(
            agent_id=agent_state.id,
            files_metadata=file_metadata_with_content,
            visible_content_map=visible_content_map,
            actor=actor,
            max_files_open=agent_state.max_files_open,
        )

        if closed_files:
            logger.info(f"LRU eviction closed {len(closed_files)} files during bulk insert: {closed_files}")

    # ======================================================================================================================
    # Tag Management
    # ======================================================================================================================

    @enforce_types
    @trace_method
    async def list_tags_async(
        self,
        actor: PydanticUser,
        before: Optional[str] = None,
        after: Optional[str] = None,
        limit: Optional[int] = 50,
        query_text: Optional[str] = None,
        ascending: bool = True,
    ) -> List[str]:
        """
        Get all tags a user has created, ordered alphabetically.

        Args:
            actor: User performing the action.
            before: Cursor for backward pagination (tags before this tag).
            after: Cursor for forward pagination (tags after this tag).
            limit: Maximum number of tags to return (default: 50).
            query_text: Filter tags by text search.
            ascending: Sort order - True for alphabetical, False for reverse (default: True).

        Returns:
            List[str]: List of all tags matching the criteria.
        """
        async with db_registry.async_session() as session:
            # Build the query using select() for async SQLAlchemy
            query = (
                select(AgentsTags.tag)
                .join(AgentModel, AgentModel.id == AgentsTags.agent_id)
                .where(AgentModel.organization_id == actor.organization_id)
                .distinct()
            )

            if query_text:
                if settings.database_engine is DatabaseChoice.POSTGRES:
                    # PostgreSQL: Use ILIKE for case-insensitive search
                    query = query.where(AgentsTags.tag.ilike(f"%{query_text}%"))
                else:
                    # SQLite: Use LIKE with LOWER for case-insensitive search
                    query = query.where(func.lower(AgentsTags.tag).like(func.lower(f"%{query_text}%")))

            # Handle pagination cursors
            if after:
                if ascending:
                    query = query.where(AgentsTags.tag > after)
                else:
                    query = query.where(AgentsTags.tag < after)

            if before:
                if ascending:
                    query = query.where(AgentsTags.tag < before)
                else:
                    query = query.where(AgentsTags.tag > before)

            # Apply ordering based on ascending parameter
            if ascending:
                query = query.order_by(AgentsTags.tag.asc())
            else:
                query = query.order_by(AgentsTags.tag.desc())

            query = query.limit(limit)

            # Execute the query asynchronously
            result = await session.execute(query)
            # Extract the tag values from the result
            results = [row[0] for row in result.all()]
            return results

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_agent_files_config_async(self, agent_id: str, actor: PydanticUser) -> Tuple[int, int]:
        """Get per_file_view_window_char_limit and max_files_open for an agent.

        This is a performant query that only fetches the specific fields needed.

        Args:
            agent_id: The ID of the agent
            actor: The user making the request

        Returns:
            Tuple of per_file_view_window_char_limit, max_files_open values
        """
        async with db_registry.async_session() as session:
            result = await session.execute(
                select(AgentModel.per_file_view_window_char_limit, AgentModel.max_files_open)
                .where(AgentModel.id == agent_id)
                .where(AgentModel.organization_id == actor.organization_id)
                .where(AgentModel.is_deleted == False)
            )
            row = result.one_or_none()

            if row is None:
                raise ValueError(f"Agent {agent_id} not found")

            per_file_limit, max_files = row[0], row[1]

            # Handle None values by calculating defaults based on context window
            if per_file_limit is None or max_files is None:
                # Get the agent's model context window to calculate appropriate defaults
                model_result = await session.execute(
                    select(AgentModel.llm_config)
                    .where(AgentModel.id == agent_id)
                    .where(AgentModel.organization_id == actor.organization_id)
                    .where(AgentModel.is_deleted == False)
                )
                model_row = model_result.one_or_none()
                context_window = model_row[0].context_window if model_row and model_row[0] else None

                default_max_files, default_per_file_limit = calculate_file_defaults_based_on_context_window(context_window)

                # Use calculated defaults for None values
                if per_file_limit is None:
                    per_file_limit = default_per_file_limit
                if max_files is None:
                    max_files = default_max_files

            # FINAL fallback: ensure neither is None (should never happen, but just in case)
            if per_file_limit is None:
                per_file_limit = DEFAULT_CORE_MEMORY_SOURCE_CHAR_LIMIT
            if max_files is None:
                max_files = DEFAULT_MAX_FILES_OPEN

            return per_file_limit, max_files

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_agent_max_files_open_async(self, agent_id: str, actor: PydanticUser) -> int:
        """Get max_files_open for an agent.

        This is a performant query that only fetches the specific field needed.

        Args:
            agent_id: The ID of the agent
            actor: The user making the request

        Returns:
            max_files_open value
        """
        async with db_registry.async_session() as session:
            result = await session.execute(
                select(AgentModel.max_files_open)
                .where(AgentModel.id == agent_id)
                .where(AgentModel.organization_id == actor.organization_id)
                .where(AgentModel.is_deleted == False)
            )
            row = result.scalar_one_or_none()

            if row is None:
                raise ValueError(f"Agent {agent_id} not found")

            return row

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_agent_per_file_view_window_char_limit_async(self, agent_id: str, actor: PydanticUser) -> int:
        """Get per_file_view_window_char_limit for an agent.

        This is a performant query that only fetches the specific field needed.

        Args:
            agent_id: The ID of the agent
            actor: The user making the request

        Returns:
            per_file_view_window_char_limit value
        """
        async with db_registry.async_session() as session:
            result = await session.execute(
                select(AgentModel.per_file_view_window_char_limit)
                .where(AgentModel.id == agent_id)
                .where(AgentModel.organization_id == actor.organization_id)
                .where(AgentModel.is_deleted == False)
            )
            row = result.scalar_one_or_none()

            if row is None:
                raise ValueError(f"Agent {agent_id} not found")

            return row

    @enforce_types
    @trace_method
    @raise_on_invalid_id(param_name="agent_id", expected_prefix=PrimitiveType.AGENT)
    async def get_context_window(self, agent_id: str, actor: PydanticUser) -> ContextWindowOverview:
        agent_state, system_message, num_messages, num_archival_memories = await self.rebuild_system_prompt_async(
            agent_id=agent_id, actor=actor, force=True, dry_run=True
        )
        calculator = ContextWindowCalculator()

        # Use Anthropic token counter if:
        # 1. The model endpoint type is anthropic, OR
        # 2. We're in PRODUCTION and anthropic_api_key is available
        use_anthropic = agent_state.llm_config.model_endpoint_type == "anthropic" or (
            settings.environment == "PRODUCTION" and model_settings.anthropic_api_key is not None
        )

        if use_anthropic:
            anthropic_client = LLMClient.create(provider_type=ProviderType.anthropic, actor=actor)
            model = agent_state.llm_config.model if agent_state.llm_config.model_endpoint_type == "anthropic" else None

            token_counter = AnthropicTokenCounter(anthropic_client, model)  # noqa
            logger.info(
                f"Using AnthropicTokenCounter for agent_id={agent_id}, model={model}, "
                f"model_endpoint_type={agent_state.llm_config.model_endpoint_type}, "
                f"environment={settings.environment}"
            )
        else:
            token_counter = TiktokenCounter(agent_state.llm_config.model)
            logger.info(
                f"Using TiktokenCounter for agent_id={agent_id}, model={agent_state.llm_config.model}, "
                f"model_endpoint_type={agent_state.llm_config.model_endpoint_type}, "
                f"environment={settings.environment}"
            )

        return await calculator.calculate_context_window(
            agent_state=agent_state,
            actor=actor,
            token_counter=token_counter,
            message_manager=self.message_manager,
            system_message_compiled=system_message,
            num_archival_memories=num_archival_memories,
            num_messages=num_messages,
        )
