from abc import abstractmethod
from typing import Union, Callable, Optional, Tuple, List
import uuid
import json
import logging
from threading import Lock
from functools import wraps
from fastapi import HTTPException

from memgpt.agent_store.storage import StorageConnector
from memgpt.config import MemGPTConfig
from memgpt.credentials import MemGPTCredentials
from memgpt.agent import Agent
import memgpt.system as system
import memgpt.constants as constants
from memgpt.cli.cli import attach

# from memgpt.agent_store.storage import StorageConnector
from memgpt.metadata import MetadataStore, save_agent
import memgpt.presets.presets as presets
import memgpt.utils as utils
import memgpt.server.utils as server_utils
from memgpt.persistence_manager import PersistenceManager, LocalStateManager
from memgpt.data_types import (
    Source,
    Passage,
    Document,
    User,
    AgentState,
    LLMConfig,
    EmbeddingConfig,
    Message,
    ToolCall,
    LLMConfig,
    EmbeddingConfig,
    Message,
    ToolCall,
)
from memgpt.data_types import (
    Source,
    Passage,
    Document,
    User,
    AgentState,
    LLMConfig,
    EmbeddingConfig,
    Message,
    ToolCall,
    LLMConfig,
    EmbeddingConfig,
    Message,
    ToolCall,
)

# TODO use custom interface
from memgpt.interface import CLIInterface  # for printing to terminal
from memgpt.interface import AgentInterface  # abstract

logger = logging.getLogger(__name__)


class Server(object):
    """Abstract server class that supports multi-agent multi-user"""

    @abstractmethod
    def list_agents(self, user_id: uuid.UUID) -> dict:
        """List all available agents to a user"""
        raise NotImplementedError

    @abstractmethod
    def get_agent_messages(self, user_id: uuid.UUID, agent_id: uuid.UUID, start: int, count: int) -> list:
        """Paginated query of in-context messages in agent message queue"""
        raise NotImplementedError

    @abstractmethod
    def get_agent_memory(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> dict:
        """Return the memory of an agent (core memory + non-core statistics)"""
        raise NotImplementedError

    @abstractmethod
    def get_agent_config(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> dict:
        """Return the config of an agent"""
        raise NotImplementedError

    @abstractmethod
    def get_server_config(self, user_id: uuid.UUID) -> dict:
        """Return the base config"""
        raise NotImplementedError

    @abstractmethod
    def update_agent_core_memory(self, user_id: uuid.UUID, agent_id: uuid.UUID, new_memory_contents: dict) -> dict:
        """Update the agents core memory block, return the new state"""
        raise NotImplementedError

    @abstractmethod
    def create_agent(
        self,
        user_id: uuid.UUID,
        agent_config: Union[dict, AgentState],
        interface: Union[AgentInterface, None],
        # persistence_manager: Union[PersistenceManager, None],
    ) -> str:
        """Create a new agent using a config"""
        raise NotImplementedError

    @abstractmethod
    def user_message(self, user_id: uuid.UUID, agent_id: uuid.UUID, message: str) -> None:
        """Process a message from the user, internally calls step"""
        raise NotImplementedError

    @abstractmethod
    def system_message(self, user_id: uuid.UUID, agent_id: uuid.UUID, message: str) -> None:
        """Process a message from the system, internally calls step"""
        raise NotImplementedError

    @abstractmethod
    def run_command(self, user_id: uuid.UUID, agent_id: uuid.UUID, command: str) -> Union[str, None]:
        """Run a command on the agent, e.g. /memory

        May return a string with a message generated by the command
        """
        raise NotImplementedError


class LockingServer(Server):
    """Basic support for concurrency protections (all requests that modify an agent lock the agent until the operation is complete)"""

    # Locks for each agent
    _agent_locks = {}

    @staticmethod
    def agent_lock_decorator(func: Callable) -> Callable:
        @wraps(func)
        def wrapper(self, user_id: uuid.UUID, agent_id: uuid.UUID, *args, **kwargs):
            # logger.info("Locking check")

            # Initialize the lock for the agent_id if it doesn't exist
            if agent_id not in self._agent_locks:
                # logger.info(f"Creating lock for agent_id = {agent_id}")
                self._agent_locks[agent_id] = Lock()

            # Check if the agent is currently locked
            if not self._agent_locks[agent_id].acquire(blocking=False):
                # logger.info(f"agent_id = {agent_id} is busy")
                raise HTTPException(status_code=423, detail=f"Agent '{agent_id}' is currently busy.")

            try:
                # Execute the function
                # logger.info(f"running function on agent_id = {agent_id}")
                print("USERID", user_id)
                return func(self, user_id, agent_id, *args, **kwargs)
            finally:
                # Release the lock
                # logger.info(f"releasing lock on agent_id = {agent_id}")
                self._agent_locks[agent_id].release()

        return wrapper

    @agent_lock_decorator
    def user_message(self, user_id: uuid.UUID, agent_id: uuid.UUID, message: str) -> None:
        raise NotImplementedError

    @agent_lock_decorator
    def run_command(self, user_id: uuid.UUID, agent_id: uuid.UUID, command: str) -> Union[str, None]:
        raise NotImplementedError


# TODO actually use "user_id" for something
class SyncServer(LockingServer):
    """Simple single-threaded / blocking server process"""

    def __init__(
        self,
        chaining: bool = True,
        max_chaining_steps: bool = None,
        # default_interface_cls: AgentInterface = CLIInterface,
        default_interface: AgentInterface = CLIInterface(),
        # default_persistence_manager_cls: PersistenceManager = LocalStateManager,
    ):
        """Server process holds in-memory agents that are being run"""

        # List of {'user_id': user_id, 'agent_id': agent_id, 'agent': agent_obj} dicts
        self.active_agents = []

        # chaining = whether or not to run again if request_heartbeat=true
        self.chaining = chaining

        # if chaining == true, what's the max number of times we'll chain before yielding?
        # none = no limit, can go on forever
        self.max_chaining_steps = max_chaining_steps

        # The default interface that will get assigned to agents ON LOAD
        # self.default_interface_cls = default_interface_cls
        self.default_interface = default_interface

        # The default persistence manager that will get assigned to agents ON CREATION
        # self.default_persistence_manager_cls = default_persistence_manager_cls

        # Initialize the connection to the DB
        self.config = MemGPTConfig.load()

        # TODO figure out how to handle credentials for the server
        self.credentials = MemGPTCredentials.load()

        # Ensure valid database configuration
        # TODO: add back once tests are matched
        # assert (
        #    self.config.metadata_storage_type == "postgres"
        # ), f"Invalid metadata_storage_type for server: {self.config.metadata_storage_type}"
        # assert (
        #    self.config.archival_storage_type == "postgres"
        # ), f"Invalid archival_storage_type for server: {self.config.archival_storage_type}"
        # assert self.config.recall_storage_type == "postgres", f"Invalid recall_storage_type for server: {self.config.recall_storage_type}"

        # Generate default LLM/Embedding configs for the server
        # TODO: we may also want to do the same thing with default persona/human/etc.
        self.server_llm_config = LLMConfig(
            model=self.config.default_llm_config.model,
            model_endpoint_type=self.config.default_llm_config.model_endpoint_type,
            model_endpoint=self.config.default_llm_config.model_endpoint,
            model_wrapper=self.config.default_llm_config.model_wrapper,
            context_window=self.config.default_llm_config.context_window,
            # openai_key=self.credentials.openai_key,
            # azure_key=self.credentials.azure_key,
            # azure_endpoint=self.credentials.azure_endpoint,
            # azure_version=self.credentials.azure_version,
            # azure_deployment=self.credentials.azure_deployment,
        )
        self.server_embedding_config = EmbeddingConfig(
            embedding_endpoint_type=self.config.default_embedding_config.embedding_endpoint_type,
            embedding_endpoint=self.config.default_embedding_config.embedding_endpoint,
            embedding_dim=self.config.default_embedding_config.embedding_dim,
            # openai_key=self.credentials.openai_key,
        )

        # Initialize the metadata store
        self.ms = MetadataStore(self.config)

        # NOTE: removed, since server should be multi-user
        ## Create the default user
        # base_user_id = uuid.UUID(self.config.anon_clientid)
        # if not self.ms.get_user(user_id=base_user_id):
        #    base_user = User(id=base_user_id)
        #    self.ms.create_user(base_user)

    def save_agents(self):
        """Saves all the agents that are in the in-memory object store"""
        for agent_d in self.active_agents:
            try:
                # agent_d["agent"].save()
                save_agent(agent_d["agent"], self.ms)
                logger.info(f"Saved agent {agent_d['agent_id']}")
            except Exception as e:
                logger.exception(f"Error occurred while trying to save agent {agent_d['agent_id']}:\n{e}")

    def _get_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> Union[Agent, None]:
        """Get the agent object from the in-memory object store"""
        for d in self.active_agents:
            if d["user_id"] == str(user_id) and d["agent_id"] == str(agent_id):
                return d["agent"]
        return None

    def _add_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID, agent_obj: Agent) -> None:
        """Put an agent object inside the in-memory object store"""
        # Make sure the agent doesn't already exist
        if self._get_agent(user_id=user_id, agent_id=agent_id) is not None:
            # Can be triggered on concucrent request, so don't throw a full error
            # raise KeyError(f"Agent (user={user_id}, agent={agent_id}) is already loaded")
            logger.exception(f"Agent (user={user_id}, agent={agent_id}) is already loaded")
            return
        # Add Agent instance to the in-memory list
        self.active_agents.append(
            {
                "user_id": str(user_id),
                "agent_id": str(agent_id),
                "agent": agent_obj,
            }
        )

    def _load_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID, interface: Union[AgentInterface, None] = None) -> Agent:
        """Loads a saved agent into memory (if it doesn't exist, throw an error)"""
        assert isinstance(user_id, uuid.UUID), user_id
        assert isinstance(agent_id, uuid.UUID), agent_id

        # If an interface isn't specified, use the default
        if interface is None:
            interface = self.default_interface

        try:
            logger.info(f"Grabbing agent user_id={user_id} agent_id={agent_id} from database")
            agent_state = self.ms.get_agent(agent_id=agent_id, user_id=user_id)
            if not agent_state:
                logger.exception(f"agent_id {agent_id} does not exist")
                raise ValueError(f"agent_id {agent_id} does not exist")
            # print(f"server._load_agent :: load got agent state {agent_id}, messages = {agent_state.state['messages']}")

            # Instantiate an agent object using the state retrieved
            logger.info(f"Creating an agent object")
            memgpt_agent = Agent(agent_state=agent_state, interface=interface)

            # Add the agent to the in-memory store and return its reference
            logger.info(f"Adding agent to the agent cache: user_id={user_id}, agent_id={agent_id}")
            self._add_agent(user_id=user_id, agent_id=agent_id, agent_obj=memgpt_agent)
            return memgpt_agent

        except Exception as e:
            logger.exception(f"Error occurred while trying to get agent {agent_id}:\n{e}")
            raise

    def _get_or_load_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> Agent:
        """Check if the agent is in-memory, then load"""
        logger.info(f"Checking for agent user_id={user_id} agent_id={agent_id}")
        memgpt_agent = self._get_agent(user_id=user_id, agent_id=agent_id)
        if not memgpt_agent:
            logger.info(f"Agent not loaded, loading agent user_id={user_id} agent_id={agent_id}")
            memgpt_agent = self._load_agent(user_id=user_id, agent_id=agent_id)
        return memgpt_agent

    def _step(self, user_id: uuid.UUID, agent_id: uuid.UUID, input_message: str) -> int:
        """Send the input message through the agent"""

        logger.debug(f"Got input message: {input_message}")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)
        if memgpt_agent is None:
            raise KeyError(f"Agent (user={user_id}, agent={agent_id}) is not loaded")

        logger.debug(f"Starting agent step")
        no_verify = True
        next_input_message = input_message
        counter = 0
        while True:
            new_messages, heartbeat_request, function_failed, token_warning, tokens_accumulated = memgpt_agent.step(
                next_input_message, first_message=False, skip_verify=no_verify
            )
            counter += 1

            # Chain stops
            if not self.chaining:
                logger.debug("No chaining, stopping after one step")
                break
            elif self.max_chaining_steps is not None and counter > self.max_chaining_steps:
                logger.debug(f"Hit max chaining steps, stopping after {counter} steps")
                break
            # Chain handlers
            elif token_warning:
                next_input_message = system.get_token_limit_warning()
                continue  # always chain
            elif function_failed:
                next_input_message = system.get_heartbeat(constants.FUNC_FAILED_HEARTBEAT_MESSAGE)
                continue  # always chain
            elif heartbeat_request:
                next_input_message = system.get_heartbeat(constants.REQ_HEARTBEAT_MESSAGE)
                continue  # always chain
            # MemGPT no-op / yield
            else:
                break

        memgpt_agent.interface.step_yield()
        logger.debug(f"Finished agent step")

        return tokens_accumulated

    def _command(self, user_id: uuid.UUID, agent_id: uuid.UUID, command: str) -> Union[str, None]:
        """Process a CLI command"""

        logger.debug(f"Got command: {command}")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)
        # print("AGENT", memgpt_agent.agent_state.id, memgpt_agent.agent_state.user_id)

        if command.lower() == "exit":
            # exit not supported on server.py
            raise ValueError(command)

        elif command.lower() == "save" or command.lower() == "savechat":
            save_agent(memgpt_agent, self.ms)

        elif command.lower() == "attach":
            # Different from CLI, we extract the data source name from the command
            command = command.strip().split()
            try:
                data_source = int(command[1])
            except:
                raise ValueError(command)

            # TODO: check if agent already has it
            data_source_options = StorageConnector.list_loaded_data()
            if len(data_source_options) == 0:
                raise ValueError('No sources available. You must load a souce with "memgpt load ..." before running /attach.')
            elif data_source not in data_source_options:
                raise ValueError(f"Invalid data source name: {data_source} (options={data_source_options})")
            else:
                # attach new data
                attach(memgpt_agent.agent_state.name, data_source)

                # update agent config
                memgpt_agent.agent_state.attach_data_source(data_source)

                # reload agent with new data source
                # TODO: maybe make this less ugly...
                memgpt_agent.persistence_manager.archival_memory.storage = StorageConnector.get_storage_connector(
                    agent_config=memgpt_agent.agent_state
                )

        elif command.lower() == "dump" or command.lower().startswith("dump "):
            # Check if there's an additional argument that's an integer
            command = command.strip().split()
            amount = int(command[1]) if len(command) > 1 and command[1].isdigit() else 0
            if amount == 0:
                memgpt_agent.interface.print_messages(memgpt_agent.messages, dump=True)
            else:
                memgpt_agent.interface.print_messages(memgpt_agent.messages[-min(amount, len(memgpt_agent.messages)) :], dump=True)

        elif command.lower() == "dumpraw":
            memgpt_agent.interface.print_messages_raw(memgpt_agent.messages)

        elif command.lower() == "memory":
            ret_str = (
                f"\nDumping memory contents:\n"
                + f"\n{str(memgpt_agent.memory)}"
                + f"\n{str(memgpt_agent.persistence_manager.archival_memory)}"
                + f"\n{str(memgpt_agent.persistence_manager.recall_memory)}"
            )
            return ret_str

        elif command.lower() == "pop" or command.lower().startswith("pop "):
            # Check if there's an additional argument that's an integer
            command = command.strip().split()
            pop_amount = int(command[1]) if len(command) > 1 and command[1].isdigit() else 3
            n_messages = len(memgpt_agent.messages)
            MIN_MESSAGES = 2
            if n_messages <= MIN_MESSAGES:
                logger.info(f"Agent only has {n_messages} messages in stack, none left to pop")
            elif n_messages - pop_amount < MIN_MESSAGES:
                logger.info(f"Agent only has {n_messages} messages in stack, cannot pop more than {n_messages - MIN_MESSAGES}")
            else:
                logger.info(f"Popping last {pop_amount} messages from stack")
                for _ in range(min(pop_amount, len(memgpt_agent.messages))):
                    memgpt_agent.messages.pop()

        elif command.lower() == "retry":
            # TODO this needs to also modify the persistence manager
            logger.info(f"Retrying for another answer")
            while len(memgpt_agent.messages) > 0:
                if memgpt_agent.messages[-1].get("role") == "user":
                    # we want to pop up to the last user message and send it again
                    user_message = memgpt_agent.messages[-1].get("content")
                    memgpt_agent.messages.pop()
                    break
                memgpt_agent.messages.pop()

        elif command.lower() == "rethink" or command.lower().startswith("rethink "):
            # TODO this needs to also modify the persistence manager
            if len(command) < len("rethink "):
                logger.warning("Missing text after the command")
            else:
                for x in range(len(memgpt_agent.messages) - 1, 0, -1):
                    if memgpt_agent.messages[x].get("role") == "assistant":
                        text = command[len("rethink ") :].strip()
                        memgpt_agent.messages[x].update({"content": text})
                        break

        elif command.lower() == "rewrite" or command.lower().startswith("rewrite "):
            # TODO this needs to also modify the persistence manager
            if len(command) < len("rewrite "):
                logger.warning("Missing text after the command")
            else:
                for x in range(len(memgpt_agent.messages) - 1, 0, -1):
                    if memgpt_agent.messages[x].get("role") == "assistant":
                        text = command[len("rewrite ") :].strip()
                        args = json.loads(memgpt_agent.messages[x].get("function_call").get("arguments"))
                        args["message"] = text
                        memgpt_agent.messages[x].get("function_call").update(
                            {"arguments": json.dumps(args, ensure_ascii=constants.JSON_ENSURE_ASCII)}
                        )
                        break

        # No skip options
        elif command.lower() == "wipe":
            # exit not supported on server.py
            raise ValueError(command)

        elif command.lower() == "heartbeat":
            input_message = system.get_heartbeat()
            self._step(user_id=user_id, agent_id=agent_id, input_message=input_message)

        elif command.lower() == "memorywarning":
            input_message = system.get_token_limit_warning()
            self._step(user_id=user_id, agent_id=agent_id, input_message=input_message)

    @LockingServer.agent_lock_decorator
    def user_message(self, user_id: uuid.UUID, agent_id: uuid.UUID, message: str) -> None:
        """Process an incoming user message and feed it through the MemGPT agent"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Basic input sanitization
        if not isinstance(message, str) or len(message) == 0:
            raise ValueError(f"Invalid input: '{message}'")

        # If the input begins with a command prefix, reject
        elif message.startswith("/"):
            raise ValueError(f"Invalid input: '{message}'")

        # Else, process it as a user message to be fed to the agent
        else:
            # Package the user message first
            packaged_user_message = system.package_user_message(user_message=message)
            # Run the agent state forward
            tokens_accumulated = self._step(user_id=user_id, agent_id=agent_id, input_message=packaged_user_message)

        return tokens_accumulated

    @LockingServer.agent_lock_decorator
    def system_message(self, user_id: uuid.UUID, agent_id: uuid.UUID, message: str) -> None:
        """Process an incoming system message and feed it through the MemGPT agent"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Basic input sanitization
        if not isinstance(message, str) or len(message) == 0:
            raise ValueError(f"Invalid input: '{message}'")

        # If the input begins with a command prefix, reject
        elif message.startswith("/"):
            raise ValueError(f"Invalid input: '{message}'")

        # Else, process it as a user message to be fed to the agent
        else:
            # Package the user message first
            packaged_system_message = system.package_system_message(system_message=message)
            # Run the agent state forward
            self._step(user_id=user_id, agent_id=agent_id, input_message=packaged_system_message)

    @LockingServer.agent_lock_decorator
    def run_command(self, user_id: uuid.UUID, agent_id: uuid.UUID, command: str) -> Union[str, None]:
        """Run a command on the agent"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # If the input begins with a command prefix, attempt to process it as a command
        if command.startswith("/"):
            if len(command) > 1:
                command = command[1:]  # strip the prefix
        return self._command(user_id=user_id, agent_id=agent_id, command=command)

    def create_user(
        self,
        user_config: Optional[Union[dict, User]] = {},
    ):
        """Create a new user using a config"""
        if not isinstance(user_config, dict):
            raise ValueError(f"user_config must be provided as a dictionary")

        user = User(
            id=user_config["id"] if "id" in user_config else None,
            default_preset=user_config["default_preset"] if "default_preset" in user_config else "memgpt_chat",
            default_persona=user_config["default_persona"] if "default_persona" in user_config else constants.DEFAULT_PERSONA,
            default_human=user_config["default_human"] if "default_human" in user_config else constants.DEFAULT_HUMAN,
        )
        self.ms.create_user(user)
        logger.info(f"Created new user from config: {user}")
        return user

    def create_agent(
        self,
        user_id: uuid.UUID,
        agent_config: Union[dict, AgentState],
        interface: Union[AgentInterface, None] = None,
        # persistence_manager: Union[PersistenceManager, None] = None,
    ) -> AgentState:
        """Create a new agent using a config"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")

        # Initialize the agent based on the provided configuration
        if not isinstance(agent_config, dict):
            raise ValueError(f"agent_config must be provided as a dictionary")

        if interface is None:
            # interface = self.default_interface_cls()
            interface = self.default_interface

        # if persistence_manager is None:
        # persistence_manager = self.default_persistence_manager_cls(agent_config=agent_config)

        logger.debug(f"Attempting to find user: {user_id}")
        user = self.ms.get_user(user_id=user_id)
        if not user:
            raise ValueError(f"cannot find user with associated client id: {user_id}")

        agent_state = AgentState(
            user_id=user.id,
            name=agent_config["name"] if "name" in agent_config else utils.create_random_username(),
            preset=agent_config["preset"] if "preset" in agent_config else user.default_preset,
            # TODO we need to allow passing raw persona/human text via the server request
            persona=agent_config["persona"] if "persona" in agent_config else user.default_persona,
            human=agent_config["human"] if "human" in agent_config else user.default_human,
            llm_config=agent_config["llm_config"] if "llm_config" in agent_config else self.server_llm_config,
            embedding_config=agent_config["embedding_config"] if "embedding_config" in agent_config else self.server_embedding_config,
        )
        # NOTE: you MUST add to the metadata store before creating the agent, otherwise the storage connectors will error on creation
        # TODO: fix this db dependency and remove
        self.ms.create_agent(agent_state)

        logger.debug(f"Attempting to create agent from agent_state:\n{agent_state}")
        try:
            agent = presets.create_agent_from_preset(agent_state=agent_state, interface=interface)
            save_agent(agent=agent, ms=self.ms)

            # FIXME: this is a hacky way to get the system prompts injected into agent into the DB
            # self.ms.update_agent(agent.agent_state)
        except Exception as e:
            logger.exception(e)
            self.ms.delete_agent(agent_id=agent_state.id)
            raise

        save_agent(agent, self.ms)

        logger.info(f"Created new agent from config: {agent}")

        return agent.agent_state

    def delete_agent(
        self,
        user_id: uuid.UUID,
        agent_id: uuid.UUID,
    ):
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # TODO: Make sure the user owns the agent
        agent = self.ms.get_agent(agent_id=agent_id, user_id=user_id)
        if agent is not None:
            self.ms.delete_agent(agent_id=agent_id)

    def list_agents(self, user_id: uuid.UUID) -> dict:
        """List all available agents to a user"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")

        agents_states = self.ms.list_agents(user_id=user_id)
        logger.info(f"Retrieved {len(agents_states)} agents for user {user_id}:\n{[vars(s) for s in agents_states]}")
        return {
            "num_agents": len(agents_states),
            "agents": [
                {
                    "id": state.id,
                    "name": state.name,
                    "human": state.human,
                    "persona": state.persona,
                    "created_at": state.created_at.isoformat(),
                }
                for state in agents_states
            ],
        }

    def get_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID):
        """Get the agent state"""
        return self.ms.get_agent(agent_id=agent_id, user_id=user_id)

    def get_user(self, user_id: uuid.UUID) -> User:
        """Get the user"""
        return self.ms.get_user(user_id=user_id)

    def get_agent_memory(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> dict:
        """Return the memory of an agent (core memory + non-core statistics)"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        core_memory = memgpt_agent.memory
        recall_memory = memgpt_agent.persistence_manager.recall_memory
        archival_memory = memgpt_agent.persistence_manager.archival_memory

        memory_obj = {
            "core_memory": {
                "persona": core_memory.persona,
                "human": core_memory.human,
            },
            "recall_memory": len(recall_memory) if recall_memory is not None else None,
            "archival_memory": len(archival_memory) if archival_memory is not None else None,
        }

        return memory_obj

    def get_in_context_message_ids(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> List[uuid.UUID]:
        """Get the message ids of the in-context messages in the agent's memory"""
        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)
        return [m.id for m in memgpt_agent._messages]

    def get_agent_messages(self, user_id: uuid.UUID, agent_id: uuid.UUID, start: int, count: int) -> list:
        """Paginated query of all messages in agent message queue"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        if start < 0 or count < 0:
            raise ValueError("Start and count values should be non-negative")

        if start + count < len(memgpt_agent._messages):  # messages can be returned from whats in memory
            # Reverse the list to make it in reverse chronological order
            reversed_messages = memgpt_agent._messages[::-1]
            # Check if start is within the range of the list
            if start >= len(reversed_messages):
                raise IndexError("Start index is out of range")

            # Calculate the end index, ensuring it does not exceed the list length
            end_index = min(start + count, len(reversed_messages))

            # Slice the list for pagination
            messages = reversed_messages[start:end_index]

        else:
            # need to access persistence manager for additional messages
            db_iterator = memgpt_agent.persistence_manager.recall_memory.storage.get_all_paginated(page_size=count, offset=start)

            # get a single page of messages
            # TODO: handle stop iteration
            page = next(db_iterator, [])

            # return messages in reverse chronological order
            messages = sorted(page, key=lambda x: x.created_at, reverse=True)

        # convert to json
        json_messages = [vars(record) for record in messages]
        return json_messages

    def get_agent_archival(self, user_id: uuid.UUID, agent_id: uuid.UUID, start: int, count: int) -> list:
        """Paginated query of all messages in agent archival memory"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        # iterate over records
        db_iterator = memgpt_agent.persistence_manager.archival_memory.storage.get_all_paginated(page_size=count, offset=start)

        # get a single page of messages
        page = next(db_iterator, [])
        json_passages = [vars(record) for record in page]
        return json_passages

    def get_agent_archival_cursor(
        self,
        user_id: uuid.UUID,
        agent_id: uuid.UUID,
        after: Optional[uuid.UUID] = None,
        before: Optional[uuid.UUID] = None,
        limit: Optional[int] = 100,
        order_by: Optional[str] = "created_at",
        reverse: Optional[bool] = False,
    ):
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        # iterate over recorde
        cursor, records = memgpt_agent.persistence_manager.archival_memory.storage.get_all_cursor(
            after=after, before=before, limit=limit, order_by=order_by, reverse=reverse
        )
        json_records = [vars(record) for record in records]
        return cursor, json_records

    def get_agent_recall_cursor(
        self,
        user_id: uuid.UUID,
        agent_id: uuid.UUID,
        after: Optional[uuid.UUID] = None,
        before: Optional[uuid.UUID] = None,
        limit: Optional[int] = 100,
        order_by: Optional[str] = "created_at",
        reverse: Optional[bool] = False,
    ):
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        # iterate over records
        cursor, records = memgpt_agent.persistence_manager.recall_memory.storage.get_all_cursor(
            after=after, before=before, limit=limit, order_by=order_by, reverse=reverse
        )
        json_records = [vars(record) for record in records]

        # TODO: mark what is in-context versus not
        return cursor, json_records

    def get_agent_config(self, user_id: uuid.UUID, agent_id: uuid.UUID) -> dict:
        """Return the config of an agent"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)
        agent_config = vars(memgpt_agent.agent_state)

        return agent_config

    def get_server_config(self) -> dict:
        """Return the base config"""
        # TODO: do we need a seperate server config?
        base_config = vars(self.config)

        def clean_keys(config):
            config_copy = config.copy()
            for k, v in config.items():
                if k == "key" or "_key" in k:
                    config_copy[k] = server_utils.shorten_key_middle(v, chars_each_side=5)
            return config_copy

        clean_base_config = clean_keys(base_config)
        return clean_base_config

    def update_agent_core_memory(self, user_id: uuid.UUID, agent_id: uuid.UUID, new_memory_contents: dict) -> dict:
        """Update the agents core memory block, return the new state"""
        if self.ms.get_user(user_id=user_id) is None:
            raise ValueError(f"User user_id={user_id} does not exist")
        if self.ms.get_agent(agent_id=agent_id, user_id=user_id) is None:
            raise ValueError(f"Agent agent_id={agent_id} does not exist")

        # Get the agent object (loaded in memory)
        memgpt_agent = self._get_or_load_agent(user_id=user_id, agent_id=agent_id)

        old_core_memory = self.get_agent_memory(user_id=user_id, agent_id=agent_id)["core_memory"]
        new_core_memory = old_core_memory.copy()

        modified = False
        if "persona" in new_memory_contents and new_memory_contents["persona"] is not None:
            new_persona = new_memory_contents["persona"]
            if old_core_memory["persona"] != new_persona:
                new_core_memory["persona"] = new_persona
                memgpt_agent.memory.edit_persona(new_persona)
                modified = True

        if "human" in new_memory_contents and new_memory_contents["human"] is not None:
            new_human = new_memory_contents["human"]
            if old_core_memory["human"] != new_human:
                new_core_memory["human"] = new_human
                memgpt_agent.memory.edit_human(new_human)
                modified = True

        # If we modified the memory contents, we need to rebuild the memory block inside the system message
        if modified:
            memgpt_agent.rebuild_memory()

        return {
            "old_core_memory": old_core_memory,
            "new_core_memory": new_core_memory,
            "modified": modified,
        }

    def authenticate_user(self) -> uuid.UUID:
        # TODO: Implement actual authentication to enable multi user setup
        return uuid.UUID(int=uuid.getnode())