# inspecting tools import asyncio import json import os import traceback import warnings from abc import abstractmethod from datetime import datetime from typing import Any, Callable, Dict, List, Optional, Tuple, Union from anthropic import AsyncAnthropic from composio.client import Composio from composio.client.collections import ActionModel, AppModel from fastapi import HTTPException from fastapi.responses import StreamingResponse import letta.constants as constants import letta.server.utils as server_utils import letta.system as system from letta.agent import Agent, save_agent from letta.config import LettaConfig from letta.data_sources.connectors import DataConnector, load_data from letta.errors import HandleNotFoundError from letta.functions.mcp_client.base_client import BaseMCPClient from letta.functions.mcp_client.sse_client import MCP_CONFIG_TOPLEVEL_KEY, SSEMCPClient from letta.functions.mcp_client.stdio_client import StdioMCPClient from letta.functions.mcp_client.types import MCPServerType, MCPTool, SSEServerConfig, StdioServerConfig from letta.groups.helpers import load_multi_agent from letta.helpers.datetime_helpers import get_utc_time from letta.helpers.json_helpers import json_dumps, json_loads # TODO use custom interface from letta.interface import AgentInterface # abstract from letta.interface import CLIInterface # for printing to terminal from letta.log import get_logger from letta.orm.errors import NoResultFound from letta.prompts.gpt_system import get_system_text from letta.schemas.agent import AgentState, AgentType, CreateAgent, UpdateAgent from letta.schemas.block import Block, BlockUpdate, CreateBlock from letta.schemas.embedding_config import EmbeddingConfig # openai schemas from letta.schemas.enums import JobStatus, MessageStreamStatus from letta.schemas.environment_variables import SandboxEnvironmentVariableCreate from letta.schemas.group import GroupCreate, SleeptimeManager from letta.schemas.job import Job, JobUpdate from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage, ToolReturnMessage from letta.schemas.letta_message_content import TextContent from letta.schemas.letta_response import LettaResponse from letta.schemas.llm_config import LLMConfig from letta.schemas.memory import ArchivalMemorySummary, ContextWindowOverview, Memory, RecallMemorySummary from letta.schemas.message import Message, MessageCreate, MessageUpdate from letta.schemas.organization import Organization from letta.schemas.passage import Passage, PassageUpdate from letta.schemas.providers import ( AnthropicBedrockProvider, AnthropicProvider, AzureProvider, DeepSeekProvider, GoogleAIProvider, GoogleVertexProvider, GroqProvider, LettaProvider, LMStudioOpenAIProvider, OllamaProvider, OpenAIProvider, Provider, TogetherProvider, VLLMChatCompletionsProvider, VLLMCompletionsProvider, XAIProvider, ) from letta.schemas.sandbox_config import SandboxType from letta.schemas.source import Source from letta.schemas.tool import Tool from letta.schemas.usage import LettaUsageStatistics from letta.schemas.user import User from letta.server.rest_api.chat_completions_interface import ChatCompletionsStreamingInterface from letta.server.rest_api.interface import StreamingServerInterface from letta.server.rest_api.utils import sse_async_generator from letta.services.agent_manager import AgentManager from letta.services.block_manager import BlockManager from letta.services.group_manager import GroupManager from letta.services.identity_manager import IdentityManager from letta.services.job_manager import JobManager from letta.services.llm_batch_manager import LLMBatchManager from letta.services.message_manager import MessageManager from letta.services.organization_manager import OrganizationManager from letta.services.passage_manager import PassageManager from letta.services.provider_manager import ProviderManager from letta.services.sandbox_config_manager import SandboxConfigManager from letta.services.source_manager import SourceManager from letta.services.step_manager import StepManager from letta.services.tool_executor.tool_execution_sandbox import ToolExecutionSandbox from letta.services.tool_manager import ToolManager from letta.services.user_manager import UserManager from letta.settings import model_settings, settings, tool_settings from letta.tracing import log_event, trace_method from letta.utils import get_friendly_error_msg, get_persona_text, make_key config = LettaConfig.load() logger = get_logger(__name__) class Server(object): """Abstract server class that supports multi-agent multi-user""" @abstractmethod def list_agents(self, user_id: str) -> dict: """List all available agents to a user""" raise NotImplementedError @abstractmethod def get_agent_memory(self, user_id: str, agent_id: str) -> dict: """Return the memory of an agent (core memory + non-core statistics)""" raise NotImplementedError @abstractmethod def get_server_config(self, user_id: str) -> dict: """Return the base config""" raise NotImplementedError @abstractmethod def update_agent_core_memory(self, user_id: str, agent_id: str, label: str, actor: User) -> Memory: """Update the agents core memory block, return the new state""" raise NotImplementedError @abstractmethod def create_agent( self, request: CreateAgent, actor: User, # interface interface: Union[AgentInterface, None] = None, ) -> AgentState: """Create a new agent using a config""" raise NotImplementedError @abstractmethod def user_message(self, user_id: str, agent_id: str, message: str) -> None: """Process a message from the user, internally calls step""" raise NotImplementedError @abstractmethod def system_message(self, user_id: str, agent_id: str, message: str) -> None: """Process a message from the system, internally calls step""" raise NotImplementedError @abstractmethod def send_messages(self, user_id: str, agent_id: str, input_messages: List[MessageCreate]) -> None: """Send a list of messages to the agent""" raise NotImplementedError @abstractmethod def run_command(self, user_id: str, agent_id: str, 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 SyncServer(Server): """Simple single-threaded / blocking server process""" def __init__( self, chaining: bool = True, max_chaining_steps: Optional[int] = 100, default_interface_factory: Callable[[], AgentInterface] = lambda: CLIInterface(), init_with_default_org_and_user: bool = True, # default_interface: AgentInterface = CLIInterface(), # default_persistence_manager_cls: PersistenceManager = LocalStateManager, # auth_mode: str = "none", # "none, "jwt", "external" ): """Server process holds in-memory agents that are being run""" # 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_factory = default_interface_factory # Initialize the metadata store config = LettaConfig.load() if settings.letta_pg_uri_no_default: config.recall_storage_type = "postgres" config.recall_storage_uri = settings.letta_pg_uri_no_default config.archival_storage_type = "postgres" config.archival_storage_uri = settings.letta_pg_uri_no_default config.save() self.config = config # Managers that interface with data models self.organization_manager = OrganizationManager() self.passage_manager = PassageManager() self.user_manager = UserManager() self.tool_manager = ToolManager() self.block_manager = BlockManager() self.source_manager = SourceManager() self.sandbox_config_manager = SandboxConfigManager() self.message_manager = MessageManager() self.job_manager = JobManager() self.agent_manager = AgentManager() self.provider_manager = ProviderManager() self.step_manager = StepManager() self.identity_manager = IdentityManager() self.group_manager = GroupManager() self.batch_manager = LLMBatchManager() # Make default user and org if init_with_default_org_and_user: self.default_org = self.organization_manager.create_default_organization() self.default_user = self.user_manager.create_default_user() self.block_manager.add_default_blocks(actor=self.default_user) self.tool_manager.upsert_base_tools(actor=self.default_user) # Add composio keys to the tool sandbox env vars of the org if tool_settings.composio_api_key: manager = SandboxConfigManager() sandbox_config = manager.get_or_create_default_sandbox_config(sandbox_type=SandboxType.LOCAL, actor=self.default_user) manager.create_sandbox_env_var( SandboxEnvironmentVariableCreate(key="COMPOSIO_API_KEY", value=tool_settings.composio_api_key), sandbox_config_id=sandbox_config.id, actor=self.default_user, ) # collect providers (always has Letta as a default) self._enabled_providers: List[Provider] = [LettaProvider()] if model_settings.openai_api_key: self._enabled_providers.append( OpenAIProvider( api_key=model_settings.openai_api_key, base_url=model_settings.openai_api_base, ) ) if model_settings.anthropic_api_key: self._enabled_providers.append( AnthropicProvider( api_key=model_settings.anthropic_api_key, ) ) if model_settings.ollama_base_url: self._enabled_providers.append( OllamaProvider( base_url=model_settings.ollama_base_url, api_key=None, default_prompt_formatter=model_settings.default_prompt_formatter, ) ) if model_settings.gemini_api_key: self._enabled_providers.append( GoogleAIProvider( api_key=model_settings.gemini_api_key, ) ) if model_settings.google_cloud_location and model_settings.google_cloud_project: self._enabled_providers.append( GoogleVertexProvider( google_cloud_project=model_settings.google_cloud_project, google_cloud_location=model_settings.google_cloud_location, ) ) if model_settings.azure_api_key and model_settings.azure_base_url: assert model_settings.azure_api_version, "AZURE_API_VERSION is required" self._enabled_providers.append( AzureProvider( api_key=model_settings.azure_api_key, base_url=model_settings.azure_base_url, api_version=model_settings.azure_api_version, ) ) if model_settings.groq_api_key: self._enabled_providers.append( GroqProvider( api_key=model_settings.groq_api_key, ) ) if model_settings.together_api_key: self._enabled_providers.append( TogetherProvider( api_key=model_settings.together_api_key, default_prompt_formatter=model_settings.default_prompt_formatter, ) ) if model_settings.vllm_api_base: # vLLM exposes both a /chat/completions and a /completions endpoint self._enabled_providers.append( VLLMCompletionsProvider( base_url=model_settings.vllm_api_base, default_prompt_formatter=model_settings.default_prompt_formatter, ) ) # NOTE: to use the /chat/completions endpoint, you need to specify extra flags on vLLM startup # see: https://docs.vllm.ai/en/latest/getting_started/examples/openai_chat_completion_client_with_tools.html # e.g. "... --enable-auto-tool-choice --tool-call-parser hermes" self._enabled_providers.append( VLLMChatCompletionsProvider( base_url=model_settings.vllm_api_base, ) ) if model_settings.aws_access_key and model_settings.aws_secret_access_key and model_settings.aws_region: self._enabled_providers.append( AnthropicBedrockProvider( aws_region=model_settings.aws_region, ) ) # Attempt to enable LM Studio by default if model_settings.lmstudio_base_url: # Auto-append v1 to the base URL lmstudio_url = ( model_settings.lmstudio_base_url if model_settings.lmstudio_base_url.endswith("/v1") else model_settings.lmstudio_base_url + "/v1" ) self._enabled_providers.append(LMStudioOpenAIProvider(base_url=lmstudio_url)) if model_settings.deepseek_api_key: self._enabled_providers.append(DeepSeekProvider(api_key=model_settings.deepseek_api_key)) if model_settings.xai_api_key: self._enabled_providers.append(XAIProvider(api_key=model_settings.xai_api_key)) # For MCP """Initialize the MCP clients (there may be multiple)""" mcp_server_configs = self.get_mcp_servers() self.mcp_clients: Dict[str, BaseMCPClient] = {} for server_name, server_config in mcp_server_configs.items(): if server_config.type == MCPServerType.SSE: self.mcp_clients[server_name] = SSEMCPClient(server_config) elif server_config.type == MCPServerType.STDIO: self.mcp_clients[server_name] = StdioMCPClient(server_config) else: raise ValueError(f"Invalid MCP server config: {server_config}") try: self.mcp_clients[server_name].connect_to_server() except Exception as e: logger.error(e) self.mcp_clients.pop(server_name) # Print out the tools that are connected for server_name, client in self.mcp_clients.items(): logger.info(f"Attempting to fetch tools from MCP server: {server_name}") mcp_tools = client.list_tools() logger.info(f"MCP tools connected: {', '.join([t.name for t in mcp_tools])}") logger.debug(f"MCP tools: {', '.join([str(t) for t in mcp_tools])}") # TODO: Remove these in memory caches self._llm_config_cache = {} self._embedding_config_cache = {} # TODO: Replace this with the Anthropic client we have in house self.anthropic_async_client = AsyncAnthropic() def load_agent(self, agent_id: str, actor: User, interface: Union[AgentInterface, None] = None) -> Agent: """Updated method to load agents from persisted storage""" agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor) if agent_state.multi_agent_group: return load_multi_agent( group=agent_state.multi_agent_group, agent_state=agent_state, actor=actor, interface=interface, mcp_clients=self.mcp_clients ) interface = interface or self.default_interface_factory() return Agent(agent_state=agent_state, interface=interface, user=actor, mcp_clients=self.mcp_clients) def _step( self, actor: User, agent_id: str, input_messages: List[MessageCreate], interface: Union[AgentInterface, None] = None, # needed to getting responses put_inner_thoughts_first: bool = True, # timestamp: Optional[datetime], ) -> LettaUsageStatistics: """Send the input message through the agent""" # TODO: Thread actor directly through this function, since the top level caller most likely already retrieved the user logger.debug(f"Got input messages: {input_messages}") letta_agent = None try: letta_agent = self.load_agent(agent_id=agent_id, interface=interface, actor=actor) if letta_agent is None: raise KeyError(f"Agent (user={actor.id}, agent={agent_id}) is not loaded") # Determine whether or not to token stream based on the capability of the interface token_streaming = letta_agent.interface.streaming_mode if hasattr(letta_agent.interface, "streaming_mode") else False logger.debug(f"Starting agent step") if interface: metadata = interface.metadata if hasattr(interface, "metadata") else None else: metadata = None usage_stats = letta_agent.step( input_messages=input_messages, chaining=self.chaining, max_chaining_steps=self.max_chaining_steps, stream=token_streaming, skip_verify=True, metadata=metadata, put_inner_thoughts_first=put_inner_thoughts_first, ) except Exception as e: logger.error(f"Error in server._step: {e}") print(traceback.print_exc()) raise finally: logger.debug("Calling step_yield()") if letta_agent: letta_agent.interface.step_yield() return usage_stats def _command(self, user_id: str, agent_id: str, command: str) -> LettaUsageStatistics: """Process a CLI command""" # TODO: Thread actor directly through this function, since the top level caller most likely already retrieved the user actor = self.user_manager.get_user_or_default(user_id=user_id) logger.debug(f"Got command: {command}") # Get the agent object (loaded in memory) letta_agent = self.load_agent(agent_id=agent_id, actor=actor) usage = None if command.lower() == "exit": # exit not supported on server.py raise ValueError(command) elif command.lower() == "save" or command.lower() == "savechat": save_agent(letta_agent) 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) # attach data to agent from source letta_agent.attach_source( user=self.user_manager.get_user_by_id(user_id=user_id), source_id=data_source, source_manager=self.source_manager, agent_manager=self.agent_manager, ) 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: letta_agent.interface.print_messages(letta_agent.messages, dump=True) else: letta_agent.interface.print_messages(letta_agent.messages[-min(amount, len(letta_agent.messages)) :], dump=True) elif command.lower() == "dumpraw": letta_agent.interface.print_messages_raw(letta_agent.messages) elif command.lower() == "memory": ret_str = f"\nDumping memory contents:\n" + f"\n{str(letta_agent.agent_state.memory)}" + f"\n{str(letta_agent.passage_manager)}" 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(letta_agent.messages) MIN_MESSAGES = 2 if n_messages <= MIN_MESSAGES: logger.debug(f"Agent only has {n_messages} messages in stack, none left to pop") elif n_messages - pop_amount < MIN_MESSAGES: logger.debug(f"Agent only has {n_messages} messages in stack, cannot pop more than {n_messages - MIN_MESSAGES}") else: logger.debug(f"Popping last {pop_amount} messages from stack") for _ in range(min(pop_amount, len(letta_agent.messages))): letta_agent.messages.pop() elif command.lower() == "retry": # TODO this needs to also modify the persistence manager logger.debug(f"Retrying for another answer") while len(letta_agent.messages) > 0: if letta_agent.messages[-1].get("role") == "user": # we want to pop up to the last user message and send it again letta_agent.messages[-1].get("content") letta_agent.messages.pop() break letta_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(letta_agent.messages) - 1, 0, -1): if letta_agent.messages[x].get("role") == "assistant": text = command[len("rethink ") :].strip() letta_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(letta_agent.messages) - 1, 0, -1): if letta_agent.messages[x].get("role") == "assistant": text = command[len("rewrite ") :].strip() args = json_loads(letta_agent.messages[x].get("function_call").get("arguments")) args["message"] = text letta_agent.messages[x].get("function_call").update({"arguments": json_dumps(args)}) 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() usage = self._step(actor=actor, agent_id=agent_id, input_message=input_message) elif command.lower() == "memorywarning": input_message = system.get_token_limit_warning() usage = self._step(actor=actor, agent_id=agent_id, input_message=input_message) if not usage: usage = LettaUsageStatistics() return usage def user_message( self, user_id: str, agent_id: str, message: Union[str, Message], timestamp: Optional[datetime] = None, ) -> LettaUsageStatistics: """Process an incoming user message and feed it through the Letta agent""" try: actor = self.user_manager.get_user_by_id(user_id=user_id) except NoResultFound: raise ValueError(f"User user_id={user_id} does not exist") try: agent = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor) except NoResultFound: raise ValueError(f"Agent agent_id={agent_id} does not exist") # Basic input sanitization if isinstance(message, str): if 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}'") packaged_user_message = system.package_user_message( user_message=message, time=timestamp.isoformat() if timestamp else None, ) # NOTE: eventually deprecate and only allow passing Message types message = MessageCreate( agent_id=agent_id, role="user", content=[TextContent(text=packaged_user_message)], ) # Run the agent state forward usage = self._step(actor=actor, agent_id=agent_id, input_messages=[message]) return usage def system_message( self, user_id: str, agent_id: str, message: Union[str, Message], timestamp: Optional[datetime] = None, ) -> LettaUsageStatistics: """Process an incoming system message and feed it through the Letta agent""" try: actor = self.user_manager.get_user_by_id(user_id=user_id) except NoResultFound: raise ValueError(f"User user_id={user_id} does not exist") try: agent = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor) except NoResultFound: raise ValueError(f"Agent agent_id={agent_id} does not exist") # Basic input sanitization if isinstance(message, str): if 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}'") packaged_system_message = system.package_system_message(system_message=message) # NOTE: eventually deprecate and only allow passing Message types # Convert to a Message object if timestamp: message = Message( agent_id=agent_id, role="system", content=[TextContent(text=packaged_system_message)], created_at=timestamp, ) else: message = Message( agent_id=agent_id, role="system", content=[TextContent(text=packaged_system_message)], ) if isinstance(message, Message): # Can't have a null text field message_text = message.content[0].text if message_text is None or len(message_text) == 0: raise ValueError(f"Invalid input: '{message_text}'") # If the input begins with a command prefix, reject elif message_text.startswith("/"): raise ValueError(f"Invalid input: '{message_text}'") else: raise TypeError(f"Invalid input: '{message}' - type {type(message)}") if timestamp: # Override the timestamp with what the caller provided message.created_at = timestamp # Run the agent state forward return self._step(actor=actor, agent_id=agent_id, input_messages=message) def send_messages( self, actor: User, agent_id: str, input_messages: List[MessageCreate], wrap_user_message: bool = True, wrap_system_message: bool = True, interface: Union[AgentInterface, ChatCompletionsStreamingInterface, None] = None, # needed for responses metadata: Optional[dict] = None, # Pass through metadata to interface put_inner_thoughts_first: bool = True, ) -> LettaUsageStatistics: """Send a list of messages to the agent.""" # Store metadata in interface if provided if metadata and hasattr(interface, "metadata"): interface.metadata = metadata # Run the agent state forward return self._step( actor=actor, agent_id=agent_id, input_messages=input_messages, interface=interface, put_inner_thoughts_first=put_inner_thoughts_first, ) # @LockingServer.agent_lock_decorator def run_command(self, user_id: str, agent_id: str, command: str) -> LettaUsageStatistics: """Run a command on the agent""" # 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) @trace_method def get_cached_llm_config(self, **kwargs): key = make_key(**kwargs) if key not in self._llm_config_cache: self._llm_config_cache[key] = self.get_llm_config_from_handle(**kwargs) return self._llm_config_cache[key] @trace_method def get_cached_embedding_config(self, **kwargs): key = make_key(**kwargs) if key not in self._embedding_config_cache: self._embedding_config_cache[key] = self.get_embedding_config_from_handle(**kwargs) return self._embedding_config_cache[key] @trace_method def create_agent( self, request: CreateAgent, actor: User, # interface interface: Union[AgentInterface, None] = None, ) -> AgentState: if request.llm_config is None: if request.model is None: raise ValueError("Must specify either model or llm_config in request") config_params = { "handle": request.model, "context_window_limit": request.context_window_limit, "max_tokens": request.max_tokens, "max_reasoning_tokens": request.max_reasoning_tokens, "enable_reasoner": request.enable_reasoner, } log_event(name="start get_cached_llm_config", attributes=config_params) request.llm_config = self.get_cached_llm_config(**config_params) log_event(name="end get_cached_llm_config", attributes=config_params) if request.embedding_config is None: if request.embedding is None: raise ValueError("Must specify either embedding or embedding_config in request") embedding_config_params = { "handle": request.embedding, "embedding_chunk_size": request.embedding_chunk_size or constants.DEFAULT_EMBEDDING_CHUNK_SIZE, } log_event(name="start get_cached_embedding_config", attributes=embedding_config_params) request.embedding_config = self.get_cached_embedding_config(**embedding_config_params) log_event(name="end get_cached_embedding_config", attributes=embedding_config_params) log_event(name="start create_agent db") main_agent = self.agent_manager.create_agent( agent_create=request, actor=actor, ) log_event(name="end create_agent db") if request.enable_sleeptime: main_agent = self.create_sleeptime_agent(main_agent=main_agent, actor=actor) return main_agent def update_agent( self, agent_id: str, request: UpdateAgent, actor: User, ) -> AgentState: if request.model is not None: request.llm_config = self.get_llm_config_from_handle(handle=request.model) if request.embedding is not None: request.embedding_config = self.get_embedding_config_from_handle(handle=request.embedding) if request.enable_sleeptime: agent = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor) if agent.multi_agent_group is None: self.create_sleeptime_agent(main_agent=agent, actor=actor) return self.agent_manager.update_agent( agent_id=agent_id, agent_update=request, actor=actor, ) def create_sleeptime_agent(self, main_agent: AgentState, actor: User) -> AgentState: request = CreateAgent( name=main_agent.name + "-sleeptime", agent_type=AgentType.sleeptime_agent, block_ids=[block.id for block in main_agent.memory.blocks], memory_blocks=[ CreateBlock( label="memory_persona", value=( "I am an expert conversation memory manager. " "I manage the memory blocks such that they " "contain everything that is important about " "the conversation." ), ), ], llm_config=main_agent.llm_config, embedding_config=main_agent.embedding_config, project_id=main_agent.project_id, ) sleeptime_agent = self.agent_manager.create_agent( agent_create=request, actor=actor, ) self.group_manager.create_group( group=GroupCreate( description="", agent_ids=[sleeptime_agent.id], manager_config=SleeptimeManager( manager_agent_id=main_agent.id, sleeptime_agent_frequency=5, ), ), actor=actor, ) return self.agent_manager.get_agent_by_id(agent_id=main_agent.id, actor=actor) # convert name->id # TODO: These can be moved to agent_manager def get_agent_memory(self, agent_id: str, actor: User) -> Memory: """Return the memory of an agent (core memory)""" return self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor).memory def get_archival_memory_summary(self, agent_id: str, actor: User) -> ArchivalMemorySummary: return ArchivalMemorySummary(size=self.agent_manager.passage_size(actor=actor, agent_id=agent_id)) def get_recall_memory_summary(self, agent_id: str, actor: User) -> RecallMemorySummary: return RecallMemorySummary(size=self.message_manager.size(actor=actor, agent_id=agent_id)) def get_agent_archival( self, user_id: str, agent_id: str, after: Optional[str] = None, before: Optional[str] = None, limit: Optional[int] = 100, order_by: Optional[str] = "created_at", reverse: Optional[bool] = False, query_text: Optional[str] = None, ascending: Optional[bool] = True, ) -> List[Passage]: # TODO: Thread actor directly through this function, since the top level caller most likely already retrieved the user actor = self.user_manager.get_user_or_default(user_id=user_id) # iterate over records records = self.agent_manager.list_passages( actor=actor, agent_id=agent_id, after=after, query_text=query_text, before=before, ascending=ascending, limit=limit, ) return records def insert_archival_memory(self, agent_id: str, memory_contents: str, actor: User) -> List[Passage]: # Get the agent object (loaded in memory) agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor) # Insert into archival memory # TODO: @mindy look at moving this to agent_manager to avoid above extra call passages = self.passage_manager.insert_passage(agent_state=agent_state, agent_id=agent_id, text=memory_contents, actor=actor) # rebuild agent system prompt - force since no archival change self.agent_manager.rebuild_system_prompt(agent_id=agent_id, actor=actor, force=True) return passages def modify_archival_memory(self, agent_id: str, memory_id: str, passage: PassageUpdate, actor: User) -> List[Passage]: passage = Passage(**passage.model_dump(exclude_unset=True, exclude_none=True)) passages = self.passage_manager.update_passage_by_id(passage_id=memory_id, passage=passage, actor=actor) return passages def delete_archival_memory(self, memory_id: str, actor: User): # TODO check if it exists first, and throw error if not # TODO: need to also rebuild the prompt here passage = self.passage_manager.get_passage_by_id(passage_id=memory_id, actor=actor) # delete the passage self.passage_manager.delete_passage_by_id(passage_id=memory_id, actor=actor) # rebuild system prompt and force self.agent_manager.rebuild_system_prompt(agent_id=passage.agent_id, actor=actor, force=True) def get_agent_recall( self, user_id: str, agent_id: str, after: Optional[str] = None, before: Optional[str] = None, limit: Optional[int] = 100, group_id: Optional[str] = None, reverse: Optional[bool] = False, return_message_object: bool = True, use_assistant_message: bool = True, assistant_message_tool_name: str = constants.DEFAULT_MESSAGE_TOOL, assistant_message_tool_kwarg: str = constants.DEFAULT_MESSAGE_TOOL_KWARG, ) -> Union[List[Message], List[LettaMessage]]: # TODO: Thread actor directly through this function, since the top level caller most likely already retrieved the user actor = self.user_manager.get_user_or_default(user_id=user_id) records = self.message_manager.list_messages_for_agent( agent_id=agent_id, actor=actor, after=after, before=before, limit=limit, ascending=not reverse, group_id=group_id, ) if not return_message_object: records = Message.to_letta_messages_from_list( messages=records, use_assistant_message=use_assistant_message, assistant_message_tool_name=assistant_message_tool_name, assistant_message_tool_kwarg=assistant_message_tool_kwarg, reverse=reverse, ) if reverse: records = records[::-1] return records def get_server_config(self, include_defaults: bool = False) -> dict: """Return the base 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 # TODO: do we need a separate server config? base_config = vars(self.config) clean_base_config = clean_keys(base_config) response = {"config": clean_base_config} if include_defaults: default_config = vars(LettaConfig()) clean_default_config = clean_keys(default_config) response["defaults"] = clean_default_config return response def update_agent_core_memory(self, agent_id: str, label: str, value: str, actor: User) -> Memory: """Update the value of a block in the agent's memory""" # get the block id block = self.agent_manager.get_block_with_label(agent_id=agent_id, block_label=label, actor=actor) # update the block self.block_manager.update_block(block_id=block.id, block_update=BlockUpdate(value=value), actor=actor) # rebuild system prompt for agent, potentially changed return self.agent_manager.rebuild_system_prompt(agent_id=agent_id, actor=actor).memory def delete_source(self, source_id: str, actor: User): """Delete a data source""" self.source_manager.delete_source(source_id=source_id, actor=actor) # delete data from passage store passages_to_be_deleted = self.agent_manager.list_passages(actor=actor, source_id=source_id, limit=None) self.passage_manager.delete_passages(actor=actor, passages=passages_to_be_deleted) # TODO: delete data from agent passage stores (?) def load_file_to_source(self, source_id: str, file_path: str, job_id: str, actor: User) -> Job: # update job job = self.job_manager.get_job_by_id(job_id, actor=actor) job.status = JobStatus.running self.job_manager.update_job_by_id(job_id=job_id, job_update=JobUpdate(**job.model_dump()), actor=actor) # try: from letta.data_sources.connectors import DirectoryConnector source = self.source_manager.get_source_by_id(source_id=source_id) if source is None: raise ValueError(f"Source {source_id} does not exist") connector = DirectoryConnector(input_files=[file_path]) num_passages, num_documents = self.load_data(user_id=source.created_by_id, source_name=source.name, connector=connector) # update all agents who have this source attached agent_states = self.source_manager.list_attached_agents(source_id=source_id, actor=actor) for agent_state in agent_states: agent_id = agent_state.id # Attach source to agent curr_passage_size = self.agent_manager.passage_size(actor=actor, agent_id=agent_id) agent_state = self.agent_manager.attach_source(agent_id=agent_state.id, source_id=source_id, actor=actor) new_passage_size = self.agent_manager.passage_size(actor=actor, agent_id=agent_id) assert new_passage_size >= curr_passage_size # in case empty files are added # rebuild system prompt and force agent_state = self.agent_manager.rebuild_system_prompt(agent_id=agent_id, actor=actor, force=True) # update job status job.status = JobStatus.completed job.metadata["num_passages"] = num_passages job.metadata["num_documents"] = num_documents self.job_manager.update_job_by_id(job_id=job_id, job_update=JobUpdate(**job.model_dump()), actor=actor) return job def sleeptime_document_ingest(self, main_agent: AgentState, source: Source, actor: User, clear_history: bool = False) -> None: sleeptime_agent = self.create_document_sleeptime_agent(main_agent, source, actor, clear_history) agent = self.load_agent(agent_id=sleeptime_agent.id, actor=actor) for passage in self.list_data_source_passages(source_id=source.id, user_id=actor.id): agent.step( input_messages=[ MessageCreate(role="user", content=passage.text), ] ) self.agent_manager.delete_agent(agent_id=sleeptime_agent.id, actor=actor) def create_document_sleeptime_agent( self, main_agent: AgentState, source: Source, actor: User, clear_history: bool = False ) -> AgentState: try: block = self.agent_manager.get_block_with_label(agent_id=main_agent.id, block_label=source.name, actor=actor) except: block = self.block_manager.create_or_update_block(Block(label=source.name, value=""), actor=actor) self.agent_manager.attach_block(agent_id=main_agent.id, block_id=block.id, actor=actor) if clear_history and block.value != "": block = self.block_manager.update_block(block_id=block.id, block=BlockUpdate(value="")) request = CreateAgent( name=main_agent.name + "-doc-sleeptime", system=get_system_text("sleeptime_doc_ingest"), agent_type=AgentType.sleeptime_agent, block_ids=[block.id], memory_blocks=[ CreateBlock( label="persona", value=get_persona_text("sleeptime_doc_persona"), ), CreateBlock( label="instructions", value=source.description, ), ], llm_config=main_agent.llm_config, embedding_config=main_agent.embedding_config, project_id=main_agent.project_id, include_base_tools=False, tools=constants.BASE_SLEEPTIME_TOOLS, ) return self.agent_manager.create_agent( agent_create=request, actor=actor, ) def load_data( self, user_id: str, connector: DataConnector, source_name: str, ) -> Tuple[int, int]: """Load data from a DataConnector into a source for a specified user_id""" # TODO: this should be implemented as a batch job or at least async, since it may take a long time # load data from a data source into the document store user = self.user_manager.get_user_by_id(user_id=user_id) source = self.source_manager.get_source_by_name(source_name=source_name, actor=user) if source is None: raise ValueError(f"Data source {source_name} does not exist for user {user_id}") # load data into the document store passage_count, document_count = load_data(connector, source, self.passage_manager, self.source_manager, actor=user) return passage_count, document_count def list_data_source_passages(self, user_id: str, source_id: str) -> List[Passage]: # TODO: move this query into PassageManager return self.agent_manager.list_passages(actor=self.user_manager.get_user_or_default(user_id=user_id), source_id=source_id) def list_all_sources(self, actor: User) -> List[Source]: """List all sources (w/ extra metadata) belonging to a user""" sources = self.source_manager.list_sources(actor=actor) # Add extra metadata to the sources sources_with_metadata = [] for source in sources: # count number of passages num_passages = self.agent_manager.passage_size(actor=actor, source_id=source.id) # TODO: add when files table implemented ## count number of files # document_conn = StorageConnector.get_storage_connector(TableType.FILES, self.config, user_id=user_id) # num_documents = document_conn.size({"data_source": source.name}) num_documents = 0 agents = self.source_manager.list_attached_agents(source_id=source.id, actor=actor) # add the agent name information attached_agents = [{"id": agent.id, "name": agent.name} for agent in agents] # Overwrite metadata field, should be empty anyways source.metadata = dict( num_documents=num_documents, num_passages=num_passages, attached_agents=attached_agents, ) sources_with_metadata.append(source) return sources_with_metadata def update_agent_message(self, message_id: str, request: MessageUpdate, actor: User) -> Message: """Update the details of a message associated with an agent""" # Get the current message return self.message_manager.update_message_by_id(message_id=message_id, message_update=request, actor=actor) def get_organization_or_default(self, org_id: Optional[str]) -> Organization: """Get the organization object for org_id if it exists, otherwise return the default organization object""" if org_id is None: org_id = self.organization_manager.DEFAULT_ORG_ID try: return self.organization_manager.get_organization_by_id(org_id=org_id) except NoResultFound: raise HTTPException(status_code=404, detail=f"Organization with id {org_id} not found") def list_llm_models(self) -> List[LLMConfig]: """List available models""" llm_models = [] for provider in self.get_enabled_providers(): try: llm_models.extend(provider.list_llm_models()) except Exception as e: warnings.warn(f"An error occurred while listing LLM models for provider {provider}: {e}") llm_models.extend(self.get_local_llm_configs()) return llm_models def list_embedding_models(self) -> List[EmbeddingConfig]: """List available embedding models""" embedding_models = [] for provider in self.get_enabled_providers(): try: embedding_models.extend(provider.list_embedding_models()) except Exception as e: warnings.warn(f"An error occurred while listing embedding models for provider {provider}: {e}") return embedding_models def get_enabled_providers(self): providers_from_env = {p.name: p for p in self._enabled_providers} providers_from_db = {p.name: p for p in self.provider_manager.list_providers()} # Merge the two dictionaries, keeping the values from providers_from_db where conflicts occur return {**providers_from_env, **providers_from_db}.values() @trace_method def get_llm_config_from_handle( self, handle: str, context_window_limit: Optional[int] = None, max_tokens: Optional[int] = None, max_reasoning_tokens: Optional[int] = None, enable_reasoner: Optional[bool] = None, ) -> LLMConfig: try: provider_name, model_name = handle.split("/", 1) provider = self.get_provider_from_name(provider_name) llm_configs = [config for config in provider.list_llm_models() if config.handle == handle] if not llm_configs: llm_configs = [config for config in provider.list_llm_models() if config.model == model_name] if not llm_configs: available_handles = [config.handle for config in provider.list_llm_models()] raise HandleNotFoundError(handle, available_handles) except ValueError as e: llm_configs = [config for config in self.get_local_llm_configs() if config.handle == handle] if not llm_configs: llm_configs = [config for config in self.get_local_llm_configs() if config.model == model_name] if not llm_configs: raise e if len(llm_configs) == 1: llm_config = llm_configs[0] elif len(llm_configs) > 1: raise ValueError(f"Multiple LLM models with name {model_name} supported by {provider_name}") else: llm_config = llm_configs[0] if context_window_limit is not None: if context_window_limit > llm_config.context_window: raise ValueError(f"Context window limit ({context_window_limit}) is greater than maximum of ({llm_config.context_window})") llm_config.context_window = context_window_limit else: llm_config.context_window = min(llm_config.context_window, model_settings.global_max_context_window_limit) if max_tokens is not None: llm_config.max_tokens = max_tokens if max_reasoning_tokens is not None: if not max_tokens or max_reasoning_tokens > max_tokens: raise ValueError(f"Max reasoning tokens ({max_reasoning_tokens}) must be less than max tokens ({max_tokens})") llm_config.max_reasoning_tokens = max_reasoning_tokens if enable_reasoner is not None: llm_config.enable_reasoner = enable_reasoner return llm_config @trace_method def get_embedding_config_from_handle( self, handle: str, embedding_chunk_size: int = constants.DEFAULT_EMBEDDING_CHUNK_SIZE ) -> EmbeddingConfig: try: provider_name, model_name = handle.split("/", 1) provider = self.get_provider_from_name(provider_name) embedding_configs = [config for config in provider.list_embedding_models() if config.handle == handle] if not embedding_configs: raise ValueError(f"Embedding model {model_name} is not supported by {provider_name}") except ValueError as e: # search local configs embedding_configs = [config for config in self.get_local_embedding_configs() if config.handle == handle] if not embedding_configs: raise e if len(embedding_configs) == 1: embedding_config = embedding_configs[0] elif len(embedding_configs) > 1: raise ValueError(f"Multiple embedding models with name {model_name} supported by {provider_name}") else: embedding_config = embedding_configs[0] if embedding_chunk_size: embedding_config.embedding_chunk_size = embedding_chunk_size return embedding_config def get_provider_from_name(self, provider_name: str) -> Provider: providers = [provider for provider in self._enabled_providers if provider.name == provider_name] if not providers: raise ValueError(f"Provider {provider_name} is not supported") elif len(providers) > 1: raise ValueError(f"Multiple providers with name {provider_name} supported") else: provider = providers[0] return provider def get_local_llm_configs(self): llm_models = [] try: llm_configs_dir = os.path.expanduser("~/.letta/llm_configs") if os.path.exists(llm_configs_dir): for filename in os.listdir(llm_configs_dir): if filename.endswith(".json"): filepath = os.path.join(llm_configs_dir, filename) try: with open(filepath, "r") as f: config_data = json.load(f) llm_config = LLMConfig(**config_data) llm_models.append(llm_config) except (json.JSONDecodeError, ValueError) as e: warnings.warn(f"Error parsing LLM config file {filename}: {e}") except Exception as e: warnings.warn(f"Error reading LLM configs directory: {e}") return llm_models def get_local_embedding_configs(self): embedding_models = [] try: embedding_configs_dir = os.path.expanduser("~/.letta/embedding_configs") if os.path.exists(embedding_configs_dir): for filename in os.listdir(embedding_configs_dir): if filename.endswith(".json"): filepath = os.path.join(embedding_configs_dir, filename) try: with open(filepath, "r") as f: config_data = json.load(f) embedding_config = EmbeddingConfig(**config_data) embedding_models.append(embedding_config) except (json.JSONDecodeError, ValueError) as e: warnings.warn(f"Error parsing embedding config file {filename}: {e}") except Exception as e: warnings.warn(f"Error reading embedding configs directory: {e}") return embedding_models def add_llm_model(self, request: LLMConfig) -> LLMConfig: """Add a new LLM model""" def add_embedding_model(self, request: EmbeddingConfig) -> EmbeddingConfig: """Add a new embedding model""" def get_agent_context_window(self, agent_id: str, actor: User) -> ContextWindowOverview: letta_agent = self.load_agent(agent_id=agent_id, actor=actor) return letta_agent.get_context_window() def run_tool_from_source( self, actor: User, tool_args: Dict[str, str], tool_source: str, tool_env_vars: Optional[Dict[str, str]] = None, tool_source_type: Optional[str] = None, tool_name: Optional[str] = None, tool_args_json_schema: Optional[Dict[str, Any]] = None, tool_json_schema: Optional[Dict[str, Any]] = None, ) -> ToolReturnMessage: """Run a tool from source code""" if tool_source_type is not None and tool_source_type != "python": raise ValueError("Only Python source code is supported at this time") # If tools_json_schema is explicitly passed in, override it on the created Tool object if tool_json_schema: tool = Tool(name=tool_name, source_code=tool_source, json_schema=tool_json_schema) else: # NOTE: we're creating a floating Tool object and NOT persisting to DB tool = Tool( name=tool_name, source_code=tool_source, args_json_schema=tool_args_json_schema, ) assert tool.name is not None, "Failed to create tool object" # TODO eventually allow using agent state in tools agent_state = None # Next, attempt to run the tool with the sandbox try: tool_execution_result = ToolExecutionSandbox(tool.name, tool_args, actor, tool_object=tool).run( agent_state=agent_state, additional_env_vars=tool_env_vars ) return ToolReturnMessage( id="null", tool_call_id="null", date=get_utc_time(), status=tool_execution_result.status, tool_return=str(tool_execution_result.func_return), stdout=tool_execution_result.stdout, stderr=tool_execution_result.stderr, ) except Exception as e: func_return = get_friendly_error_msg(function_name=tool.name, exception_name=type(e).__name__, exception_message=str(e)) return ToolReturnMessage( id="null", tool_call_id="null", date=get_utc_time(), status="error", tool_return=func_return, stdout=[], stderr=[traceback.format_exc()], ) # Composio wrappers def get_composio_client(self, api_key: Optional[str] = None): if api_key: return Composio(api_key=api_key) elif tool_settings.composio_api_key: return Composio(api_key=tool_settings.composio_api_key) else: return Composio() def get_composio_apps(self, api_key: Optional[str] = None) -> List["AppModel"]: """Get a list of all Composio apps with actions""" apps = self.get_composio_client(api_key=api_key).apps.get() apps_with_actions = [] for app in apps: # A bit of hacky logic until composio patches this if app.meta["actionsCount"] > 0 and not app.name.lower().endswith("_beta"): apps_with_actions.append(app) return apps_with_actions def get_composio_actions_from_app_name(self, composio_app_name: str, api_key: Optional[str] = None) -> List["ActionModel"]: actions = self.get_composio_client(api_key=api_key).actions.get(apps=[composio_app_name]) return actions # MCP wrappers # TODO support both command + SSE servers (via config) def get_mcp_servers(self) -> dict[str, Union[SSEServerConfig, StdioServerConfig]]: """List the MCP servers in the config (doesn't test that they are actually working)""" # TODO implement non-flatfile mechanism if not tool_settings.mcp_read_from_config: raise RuntimeError("MCP config file disabled. Enable it in settings.") mcp_server_list = {} # Attempt to read from ~/.letta/mcp_config.json mcp_config_path = os.path.join(constants.LETTA_DIR, constants.MCP_CONFIG_NAME) if os.path.exists(mcp_config_path): with open(mcp_config_path, "r") as f: try: mcp_config = json.load(f) except Exception as e: logger.error(f"Failed to parse MCP config file ({mcp_config_path}) as json: {e}") return mcp_server_list # Proper formatting is "mcpServers" key at the top level, # then a dict with the MCP server name as the key, # with the value being the schema from StdioServerParameters if MCP_CONFIG_TOPLEVEL_KEY in mcp_config: for server_name, server_params_raw in mcp_config[MCP_CONFIG_TOPLEVEL_KEY].items(): # No support for duplicate server names if server_name in mcp_server_list: logger.error(f"Duplicate MCP server name found (skipping): {server_name}") continue if "url" in server_params_raw: # Attempt to parse the server params as an SSE server try: server_params = SSEServerConfig( server_name=server_name, server_url=server_params_raw["url"], ) mcp_server_list[server_name] = server_params except Exception as e: logger.error(f"Failed to parse server params for MCP server {server_name} (skipping): {e}") continue else: # Attempt to parse the server params as a StdioServerParameters try: server_params = StdioServerConfig( server_name=server_name, command=server_params_raw["command"], args=server_params_raw.get("args", []), ) mcp_server_list[server_name] = server_params except Exception as e: logger.error(f"Failed to parse server params for MCP server {server_name} (skipping): {e}") continue # If the file doesn't exist, return empty dictionary return mcp_server_list def get_tools_from_mcp_server(self, mcp_server_name: str) -> List[MCPTool]: """List the tools in an MCP server. Requires a client to be created.""" if mcp_server_name not in self.mcp_clients: raise ValueError(f"No client was created for MCP server: {mcp_server_name}") return self.mcp_clients[mcp_server_name].list_tools() def add_mcp_server_to_config( self, server_config: Union[SSEServerConfig, StdioServerConfig], allow_upsert: bool = True ) -> List[Union[SSEServerConfig, StdioServerConfig]]: """Add a new server config to the MCP config file""" # TODO implement non-flatfile mechanism if not tool_settings.mcp_read_from_config: raise RuntimeError("MCP config file disabled. Enable it in settings.") # If the config file doesn't exist, throw an error. mcp_config_path = os.path.join(constants.LETTA_DIR, constants.MCP_CONFIG_NAME) if not os.path.exists(mcp_config_path): # Create the file if it doesn't exist logger.debug(f"MCP config file not found, creating new file at: {mcp_config_path}") # If the file does exist, attempt to parse it get calling get_mcp_servers try: current_mcp_servers = self.get_mcp_servers() except Exception as e: # Raise an error telling the user to fix the config file logger.error(f"Failed to parse MCP config file at {mcp_config_path}: {e}") raise ValueError(f"Failed to parse MCP config file {mcp_config_path}") # Check if the server name is already in the config if server_config.server_name in current_mcp_servers and not allow_upsert: raise ValueError(f"Server name {server_config.server_name} is already in the config file") # Attempt to initialize the connection to the server if server_config.type == MCPServerType.SSE: new_mcp_client = SSEMCPClient(server_config) elif server_config.type == MCPServerType.STDIO: new_mcp_client = StdioMCPClient(server_config) else: raise ValueError(f"Invalid MCP server config: {server_config}") try: new_mcp_client.connect_to_server() except: logger.exception(f"Failed to connect to MCP server: {server_config.server_name}") raise RuntimeError(f"Failed to connect to MCP server: {server_config.server_name}") # Print out the tools that are connected logger.info(f"Attempting to fetch tools from MCP server: {server_config.server_name}") new_mcp_tools = new_mcp_client.list_tools() logger.info(f"MCP tools connected: {', '.join([t.name for t in new_mcp_tools])}") logger.debug(f"MCP tools: {', '.join([str(t) for t in new_mcp_tools])}") # Now that we've confirmed the config is working, let's add it to the client list self.mcp_clients[server_config.server_name] = new_mcp_client # Add to the server file current_mcp_servers[server_config.server_name] = server_config # Write out the file, and make sure to in include the top-level mcpConfig try: new_mcp_file = {MCP_CONFIG_TOPLEVEL_KEY: {k: v.to_dict() for k, v in current_mcp_servers.items()}} with open(mcp_config_path, "w") as f: json.dump(new_mcp_file, f, indent=4) except Exception as e: logger.error(f"Failed to write MCP config file at {mcp_config_path}: {e}") raise ValueError(f"Failed to write MCP config file {mcp_config_path}") return list(current_mcp_servers.values()) def delete_mcp_server_from_config(self, server_name: str) -> dict[str, Union[SSEServerConfig, StdioServerConfig]]: """Delete a server config from the MCP config file""" # TODO implement non-flatfile mechanism if not tool_settings.mcp_read_from_config: raise RuntimeError("MCP config file disabled. Enable it in settings.") # If the config file doesn't exist, throw an error. mcp_config_path = os.path.join(constants.LETTA_DIR, constants.MCP_CONFIG_NAME) if not os.path.exists(mcp_config_path): # If the file doesn't exist, raise an error raise FileNotFoundError(f"MCP config file not found: {mcp_config_path}") # If the file does exist, attempt to parse it get calling get_mcp_servers try: current_mcp_servers = self.get_mcp_servers() except Exception as e: # Raise an error telling the user to fix the config file logger.error(f"Failed to parse MCP config file at {mcp_config_path}: {e}") raise ValueError(f"Failed to parse MCP config file {mcp_config_path}") # Check if the server name is already in the config # If it's not, throw an error if server_name not in current_mcp_servers: raise ValueError(f"Server name {server_name} not found in MCP config file") # Remove from the server file del current_mcp_servers[server_name] # Write out the file, and make sure to in include the top-level mcpConfig try: new_mcp_file = {MCP_CONFIG_TOPLEVEL_KEY: {k: v.to_dict() for k, v in current_mcp_servers.items()}} with open(mcp_config_path, "w") as f: json.dump(new_mcp_file, f, indent=4) except Exception as e: logger.error(f"Failed to write MCP config file at {mcp_config_path}: {e}") raise ValueError(f"Failed to write MCP config file {mcp_config_path}") return list(current_mcp_servers.values()) @trace_method async def send_message_to_agent( self, agent_id: str, actor: User, # role: MessageRole, input_messages: List[MessageCreate], stream_steps: bool, stream_tokens: bool, # related to whether or not we return `LettaMessage`s or `Message`s chat_completion_mode: bool = False, # Support for AssistantMessage use_assistant_message: bool = True, assistant_message_tool_name: str = constants.DEFAULT_MESSAGE_TOOL, assistant_message_tool_kwarg: str = constants.DEFAULT_MESSAGE_TOOL_KWARG, metadata: Optional[dict] = None, ) -> Union[StreamingResponse, LettaResponse]: """Split off into a separate function so that it can be imported in the /chat/completion proxy.""" # TODO: @charles is this the correct way to handle? include_final_message = True if not stream_steps and stream_tokens: raise HTTPException(status_code=400, detail="stream_steps must be 'true' if stream_tokens is 'true'") # For streaming response try: # TODO: move this logic into server.py # Get the generator object off of the agent's streaming interface # This will be attached to the POST SSE request used under-the-hood letta_agent = self.load_agent(agent_id=agent_id, actor=actor) # Disable token streaming if not OpenAI or Anthropic # TODO: cleanup this logic llm_config = letta_agent.agent_state.llm_config # supports_token_streaming = ["openai", "anthropic", "xai", "deepseek"] supports_token_streaming = ["openai", "anthropic", "deepseek"] # TODO re-enable xAI once streaming is patched if stream_tokens and ( llm_config.model_endpoint_type not in supports_token_streaming or "inference.memgpt.ai" in llm_config.model_endpoint ): warnings.warn( f"Token streaming is only supported for models with type {' or '.join(supports_token_streaming)} in the model_endpoint: agent has endpoint type {llm_config.model_endpoint_type} and {llm_config.model_endpoint}. Setting stream_tokens to False." ) stream_tokens = False # Create a new interface per request letta_agent.interface = StreamingServerInterface( # multi_step=True, # would we ever want to disable this? use_assistant_message=use_assistant_message, assistant_message_tool_name=assistant_message_tool_name, assistant_message_tool_kwarg=assistant_message_tool_kwarg, inner_thoughts_in_kwargs=( llm_config.put_inner_thoughts_in_kwargs if llm_config.put_inner_thoughts_in_kwargs is not None else False ), # inner_thoughts_kwarg=INNER_THOUGHTS_KWARG, ) streaming_interface = letta_agent.interface if not isinstance(streaming_interface, StreamingServerInterface): raise ValueError(f"Agent has wrong type of interface: {type(streaming_interface)}") # Enable token-streaming within the request if desired streaming_interface.streaming_mode = stream_tokens # "chatcompletion mode" does some remapping and ignores inner thoughts streaming_interface.streaming_chat_completion_mode = chat_completion_mode # streaming_interface.allow_assistant_message = stream # streaming_interface.function_call_legacy_mode = stream # Allow AssistantMessage is desired by client # streaming_interface.use_assistant_message = use_assistant_message # streaming_interface.assistant_message_tool_name = assistant_message_tool_name # streaming_interface.assistant_message_tool_kwarg = assistant_message_tool_kwarg # Related to JSON buffer reader # streaming_interface.inner_thoughts_in_kwargs = ( # llm_config.put_inner_thoughts_in_kwargs if llm_config.put_inner_thoughts_in_kwargs is not None else False # ) # Offload the synchronous message_func to a separate thread streaming_interface.stream_start() task = asyncio.create_task( asyncio.to_thread( self.send_messages, actor=actor, agent_id=agent_id, input_messages=input_messages, interface=streaming_interface, metadata=metadata, ) ) if stream_steps: # return a stream return StreamingResponse( sse_async_generator( streaming_interface.get_generator(), usage_task=task, finish_message=include_final_message, ), media_type="text/event-stream", ) else: # buffer the stream, then return the list generated_stream = [] async for message in streaming_interface.get_generator(): assert ( isinstance(message, LettaMessage) or isinstance(message, LegacyLettaMessage) or isinstance(message, MessageStreamStatus) ), type(message) generated_stream.append(message) if message == MessageStreamStatus.done: break # Get rid of the stream status messages filtered_stream = [d for d in generated_stream if not isinstance(d, MessageStreamStatus)] usage = await task # By default the stream will be messages of type LettaMessage or LettaLegacyMessage # If we want to convert these to Message, we can use the attached IDs # NOTE: we will need to de-duplicate the Messsage IDs though (since Assistant->Inner+Func_Call) # TODO: eventually update the interface to use `Message` and `MessageChunk` (new) inside the deque instead return LettaResponse(messages=filtered_stream, usage=usage) except HTTPException: raise except Exception as e: print(e) import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"{e}") @trace_method async def send_group_message_to_agent( self, group_id: str, actor: User, input_messages: Union[List[Message], List[MessageCreate]], stream_steps: bool, stream_tokens: bool, chat_completion_mode: bool = False, # Support for AssistantMessage use_assistant_message: bool = True, assistant_message_tool_name: str = constants.DEFAULT_MESSAGE_TOOL, assistant_message_tool_kwarg: str = constants.DEFAULT_MESSAGE_TOOL_KWARG, metadata: Optional[dict] = None, ) -> Union[StreamingResponse, LettaResponse]: include_final_message = True if not stream_steps and stream_tokens: raise ValueError("stream_steps must be 'true' if stream_tokens is 'true'") group = self.group_manager.retrieve_group(group_id=group_id, actor=actor) agent_state_id = group.manager_agent_id or (group.agent_ids[0] if len(group.agent_ids) > 0 else None) agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_state_id, actor=actor) if agent_state_id else None letta_multi_agent = load_multi_agent(group=group, agent_state=agent_state, actor=actor) llm_config = letta_multi_agent.agent_state.llm_config supports_token_streaming = ["openai", "anthropic", "deepseek"] if stream_tokens and ( llm_config.model_endpoint_type not in supports_token_streaming or "inference.memgpt.ai" in llm_config.model_endpoint ): warnings.warn( f"Token streaming is only supported for models with type {' or '.join(supports_token_streaming)} in the model_endpoint: agent has endpoint type {llm_config.model_endpoint_type} and {llm_config.model_endpoint}. Setting stream_tokens to False." ) stream_tokens = False # Create a new interface per request letta_multi_agent.interface = StreamingServerInterface( use_assistant_message=use_assistant_message, assistant_message_tool_name=assistant_message_tool_name, assistant_message_tool_kwarg=assistant_message_tool_kwarg, inner_thoughts_in_kwargs=( llm_config.put_inner_thoughts_in_kwargs if llm_config.put_inner_thoughts_in_kwargs is not None else False ), ) streaming_interface = letta_multi_agent.interface if not isinstance(streaming_interface, StreamingServerInterface): raise ValueError(f"Agent has wrong type of interface: {type(streaming_interface)}") streaming_interface.streaming_mode = stream_tokens streaming_interface.streaming_chat_completion_mode = chat_completion_mode if metadata and hasattr(streaming_interface, "metadata"): streaming_interface.metadata = metadata streaming_interface.stream_start() task = asyncio.create_task( asyncio.to_thread( letta_multi_agent.step, input_messages=input_messages, chaining=self.chaining, max_chaining_steps=self.max_chaining_steps, ) ) if stream_steps: # return a stream return StreamingResponse( sse_async_generator( streaming_interface.get_generator(), usage_task=task, finish_message=include_final_message, ), media_type="text/event-stream", ) else: # buffer the stream, then return the list generated_stream = [] async for message in streaming_interface.get_generator(): assert ( isinstance(message, LettaMessage) or isinstance(message, LegacyLettaMessage) or isinstance(message, MessageStreamStatus) ), type(message) generated_stream.append(message) if message == MessageStreamStatus.done: break # Get rid of the stream status messages filtered_stream = [d for d in generated_stream if not isinstance(d, MessageStreamStatus)] usage = await task # By default the stream will be messages of type LettaMessage or LettaLegacyMessage # If we want to convert these to Message, we can use the attached IDs # NOTE: we will need to de-duplicate the Messsage IDs though (since Assistant->Inner+Func_Call) # TODO: eventually update the interface to use `Message` and `MessageChunk` (new) inside the deque instead return LettaResponse(messages=filtered_stream, usage=usage)