import json import random import time from typing import List, Optional, Union import requests from letta.constants import CLI_WARNING_PREFIX from letta.errors import LettaConfigurationError, RateLimitExceededError from letta.llm_api.anthropic import ( anthropic_bedrock_chat_completions_request, anthropic_chat_completions_process_stream, anthropic_chat_completions_request, ) from letta.llm_api.aws_bedrock import has_valid_aws_credentials from letta.llm_api.azure_openai import azure_openai_chat_completions_request from letta.llm_api.deepseek import build_deepseek_chat_completions_request, convert_deepseek_response_to_chatcompletion from letta.llm_api.helpers import add_inner_thoughts_to_functions, unpack_all_inner_thoughts_from_kwargs from letta.llm_api.openai import ( build_openai_chat_completions_request, openai_chat_completions_process_stream, openai_chat_completions_request, ) from letta.local_llm.chat_completion_proxy import get_chat_completion from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages from letta.schemas.llm_config import LLMConfig from letta.schemas.message import Message from letta.schemas.openai.chat_completion_request import ChatCompletionRequest, cast_message_to_subtype from letta.schemas.openai.chat_completion_response import ChatCompletionResponse from letta.settings import ModelSettings from letta.streaming_interface import AgentChunkStreamingInterface, AgentRefreshStreamingInterface from letta.tracing import log_event, trace_method LLM_API_PROVIDER_OPTIONS = ["openai", "azure", "anthropic", "google_ai", "cohere", "local", "groq", "deepseek"] def retry_with_exponential_backoff( func, initial_delay: float = 1, exponential_base: float = 2, jitter: bool = True, max_retries: int = 20, # List of OpenAI error codes: https://github.com/openai/openai-python/blob/17ac6779958b2b74999c634c4ea4c7b74906027a/src/openai/_client.py#L227-L250 # 429 = rate limit error_codes: tuple = (429,), ): """Retry a function with exponential backoff.""" def wrapper(*args, **kwargs): pass # Initialize variables num_retries = 0 delay = initial_delay # Loop until a successful response or max_retries is hit or an exception is raised while True: try: return func(*args, **kwargs) except KeyboardInterrupt: # Stop retrying if user hits Ctrl-C raise KeyboardInterrupt("User intentionally stopped thread. Stopping...") except requests.exceptions.HTTPError as http_err: if not hasattr(http_err, "response") or not http_err.response: raise # Retry on specified errors if http_err.response.status_code in error_codes: # Increment retries num_retries += 1 log_event( "llm_retry_attempt", { "attempt": num_retries, "delay": delay, "status_code": http_err.response.status_code, "error_type": type(http_err).__name__, "error": str(http_err), }, ) # Check if max retries has been reached if num_retries > max_retries: log_event( "llm_max_retries_exceeded", { "max_retries": max_retries, "status_code": http_err.response.status_code, "error_type": type(http_err).__name__, "error": str(http_err), }, ) raise RateLimitExceededError("Maximum number of retries exceeded", max_retries=max_retries) # Increment the delay delay *= exponential_base * (1 + jitter * random.random()) # Sleep for the delay # printd(f"Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying...") print( f"{CLI_WARNING_PREFIX}Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying..." ) time.sleep(delay) else: # For other HTTP errors, re-raise the exception log_event( "llm_non_retryable_error", {"status_code": http_err.response.status_code, "error_type": type(http_err).__name__, "error": str(http_err)}, ) raise # Raise exceptions for any errors not specified except Exception as e: log_event("llm_unexpected_error", {"error_type": type(e).__name__, "error": str(e)}) raise e return wrapper @trace_method @retry_with_exponential_backoff def create( # agent_state: AgentState, llm_config: LLMConfig, messages: List[Message], user_id: Optional[str] = None, # option UUID to associate request with functions: Optional[list] = None, functions_python: Optional[dict] = None, function_call: Optional[str] = None, # see: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice # hint first_message: bool = False, force_tool_call: Optional[str] = None, # Force a specific tool to be called # use tool naming? # if false, will use deprecated 'functions' style use_tool_naming: bool = True, # streaming? stream: bool = False, stream_interface: Optional[Union[AgentRefreshStreamingInterface, AgentChunkStreamingInterface]] = None, model_settings: Optional[dict] = None, # TODO: eventually pass from server put_inner_thoughts_first: bool = True, name: Optional[str] = None, ) -> ChatCompletionResponse: """Return response to chat completion with backoff""" from letta.utils import printd # Count the tokens first, if there's an overflow exit early by throwing an error up the stack # NOTE: we want to include a specific substring in the error message to trigger summarization messages_oai_format = [m.to_openai_dict() for m in messages] prompt_tokens = num_tokens_from_messages(messages=messages_oai_format, model=llm_config.model) function_tokens = num_tokens_from_functions(functions=functions, model=llm_config.model) if functions else 0 if prompt_tokens + function_tokens > llm_config.context_window: raise Exception(f"Request exceeds maximum context length ({prompt_tokens + function_tokens} > {llm_config.context_window} tokens)") if not model_settings: from letta.settings import model_settings model_settings = model_settings assert isinstance(model_settings, ModelSettings) printd(f"Using model {llm_config.model_endpoint_type}, endpoint: {llm_config.model_endpoint}") if function_call and not functions: printd("unsetting function_call because functions is None") function_call = None # openai if llm_config.model_endpoint_type == "openai": if model_settings.openai_api_key is None and llm_config.model_endpoint == "https://api.openai.com/v1": # only is a problem if we are *not* using an openai proxy raise LettaConfigurationError(message="OpenAI key is missing from letta config file", missing_fields=["openai_api_key"]) elif model_settings.openai_api_key is None: # the openai python client requires a dummy API key api_key = "DUMMY_API_KEY" else: api_key = model_settings.openai_api_key if function_call is None and functions is not None and len(functions) > 0: # force function calling for reliability, see https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice # TODO(matt) move into LLMConfig # TODO: This vllm checking is very brittle and is a patch at most if llm_config.model_endpoint == "https://inference.memgpt.ai" or (llm_config.handle and "vllm" in llm_config.handle): function_call = "auto" # TODO change to "required" once proxy supports it else: function_call = "required" data = build_openai_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming, put_inner_thoughts_first=put_inner_thoughts_first, use_structured_output=True, # NOTE: turn on all the time for OpenAI API ) if stream: # Client requested token streaming data.stream = True assert isinstance(stream_interface, AgentChunkStreamingInterface) or isinstance( stream_interface, AgentRefreshStreamingInterface ), type(stream_interface) response = openai_chat_completions_process_stream( url=llm_config.model_endpoint, api_key=api_key, chat_completion_request=data, stream_interface=stream_interface, name=name, ) else: # Client did not request token streaming (expect a blocking backend response) data.stream = False if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_start() try: response = openai_chat_completions_request( url=llm_config.model_endpoint, api_key=api_key, chat_completion_request=data, ) finally: if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_end() if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response elif llm_config.model_endpoint_type == "xai": api_key = model_settings.xai_api_key if function_call is None and functions is not None and len(functions) > 0: # force function calling for reliability, see https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice function_call = "required" data = build_openai_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming, put_inner_thoughts_first=put_inner_thoughts_first, use_structured_output=False, # NOTE: not supported atm for xAI ) if stream: # Client requested token streaming data.stream = True assert isinstance(stream_interface, AgentChunkStreamingInterface) or isinstance( stream_interface, AgentRefreshStreamingInterface ), type(stream_interface) response = openai_chat_completions_process_stream( url=llm_config.model_endpoint, api_key=api_key, chat_completion_request=data, stream_interface=stream_interface, name=name, ) else: # Client did not request token streaming (expect a blocking backend response) data.stream = False if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_start() try: response = openai_chat_completions_request( url=llm_config.model_endpoint, api_key=api_key, chat_completion_request=data, ) finally: if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_end() if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response # azure elif llm_config.model_endpoint_type == "azure": if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if model_settings.azure_api_key is None: raise LettaConfigurationError( message="Azure API key is missing. Did you set AZURE_API_KEY in your env?", missing_fields=["azure_api_key"] ) if model_settings.azure_base_url is None: raise LettaConfigurationError( message="Azure base url is missing. Did you set AZURE_BASE_URL in your env?", missing_fields=["azure_base_url"] ) if model_settings.azure_api_version is None: raise LettaConfigurationError( message="Azure API version is missing. Did you set AZURE_API_VERSION in your env?", missing_fields=["azure_api_version"] ) # Set the llm config model_endpoint from model_settings # For Azure, this model_endpoint is required to be configured via env variable, so users don't need to provide it in the LLM config llm_config.model_endpoint = model_settings.azure_base_url chat_completion_request = build_openai_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming ) response = azure_openai_chat_completions_request( model_settings=model_settings, llm_config=llm_config, chat_completion_request=chat_completion_request, ) if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response elif llm_config.model_endpoint_type == "anthropic": if not use_tool_naming: raise NotImplementedError("Only tool calling supported on Anthropic API requests") # Force tool calling tool_call = None if functions is None: # Special case for summarization path tools = None tool_choice = None elif force_tool_call is not None: # tool_call = {"type": "function", "function": {"name": force_tool_call}} tool_choice = {"type": "tool", "name": force_tool_call} tools = [{"type": "function", "function": f} for f in functions if f["name"] == force_tool_call] assert functions is not None # need to have this setting to be able to put inner thoughts in kwargs llm_config.put_inner_thoughts_in_kwargs = True else: if llm_config.put_inner_thoughts_in_kwargs: # tool_choice_type other than "auto" only plays nice if thinking goes inside the tool calls tool_choice = {"type": "any", "disable_parallel_tool_use": True} else: tool_choice = {"type": "auto", "disable_parallel_tool_use": True} tools = [{"type": "function", "function": f} for f in functions] if functions is not None else None chat_completion_request = ChatCompletionRequest( model=llm_config.model, messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages], tools=tools, tool_choice=tool_choice, max_tokens=llm_config.max_tokens, # Note: max_tokens is required for Anthropic API temperature=llm_config.temperature, stream=stream, ) # Handle streaming if stream: # Client requested token streaming assert isinstance(stream_interface, (AgentChunkStreamingInterface, AgentRefreshStreamingInterface)), type(stream_interface) response = anthropic_chat_completions_process_stream( chat_completion_request=chat_completion_request, put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs, stream_interface=stream_interface, extended_thinking=llm_config.enable_reasoner, max_reasoning_tokens=llm_config.max_reasoning_tokens, name=name, ) else: # Client did not request token streaming (expect a blocking backend response) response = anthropic_chat_completions_request( data=chat_completion_request, put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs, extended_thinking=llm_config.enable_reasoner, max_reasoning_tokens=llm_config.max_reasoning_tokens, ) if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response # elif llm_config.model_endpoint_type == "cohere": # if stream: # raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") # if not use_tool_naming: # raise NotImplementedError("Only tool calling supported on Cohere API requests") # # if functions is not None: # tools = [{"type": "function", "function": f} for f in functions] # tools = [Tool(**t) for t in tools] # else: # tools = None # # return cohere_chat_completions_request( # # url=llm_config.model_endpoint, # url="https://api.cohere.ai/v1", # TODO # api_key=os.getenv("COHERE_API_KEY"), # TODO remove # chat_completion_request=ChatCompletionRequest( # model="command-r-plus", # TODO # messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages], # tools=tools, # tool_choice=function_call, # # user=str(user_id), # # NOTE: max_tokens is required for Anthropic API # # max_tokens=1024, # TODO make dynamic # ), # ) elif llm_config.model_endpoint_type == "groq": if stream: raise NotImplementedError(f"Streaming not yet implemented for Groq.") if model_settings.groq_api_key is None and llm_config.model_endpoint == "https://api.groq.com/openai/v1/chat/completions": raise LettaConfigurationError(message="Groq key is missing from letta config file", missing_fields=["groq_api_key"]) # force to true for groq, since they don't support 'content' is non-null if llm_config.put_inner_thoughts_in_kwargs: functions = add_inner_thoughts_to_functions( functions=functions, inner_thoughts_key=INNER_THOUGHTS_KWARG, inner_thoughts_description=INNER_THOUGHTS_KWARG_DESCRIPTION, ) tools = [{"type": "function", "function": f} for f in functions] if functions is not None else None data = ChatCompletionRequest( model=llm_config.model, messages=[m.to_openai_dict(put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs) for m in messages], tools=tools, tool_choice=function_call, user=str(user_id), ) # https://console.groq.com/docs/openai # "The following fields are currently not supported and will result in a 400 error (yikes) if they are supplied:" assert data.top_logprobs is None assert data.logit_bias is None assert data.logprobs == False assert data.n == 1 # They mention that none of the messages can have names, but it seems to not error out (for now) data.stream = False if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_start() try: # groq uses the openai chat completions API, so this component should be reusable response = openai_chat_completions_request( url=llm_config.model_endpoint, api_key=model_settings.groq_api_key, chat_completion_request=data, ) finally: if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_end() if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response elif llm_config.model_endpoint_type == "together": """TogetherAI endpoint that goes via /completions instead of /chat/completions""" if stream: raise NotImplementedError(f"Streaming not yet implemented for TogetherAI (via the /completions endpoint).") if model_settings.together_api_key is None and llm_config.model_endpoint == "https://api.together.ai/v1/completions": raise LettaConfigurationError(message="TogetherAI key is missing from letta config file", missing_fields=["together_api_key"]) return get_chat_completion( model=llm_config.model, messages=messages, functions=functions, functions_python=functions_python, function_call=function_call, context_window=llm_config.context_window, endpoint=llm_config.model_endpoint, endpoint_type="vllm", # NOTE: use the vLLM path through /completions wrapper=llm_config.model_wrapper, user=str(user_id), # hint first_message=first_message, # auth-related auth_type="bearer_token", # NOTE: Together expects bearer token auth auth_key=model_settings.together_api_key, ) elif llm_config.model_endpoint_type == "bedrock": """Anthropic endpoint that goes via /embeddings instead of /chat/completions""" if stream: raise NotImplementedError(f"Streaming not yet implemented for Anthropic (via the /embeddings endpoint).") if not use_tool_naming: raise NotImplementedError("Only tool calling supported on Anthropic API requests") if not has_valid_aws_credentials(): raise LettaConfigurationError(message="Invalid or missing AWS credentials. Please configure valid AWS credentials.") tool_call = None if force_tool_call is not None: tool_call = {"type": "function", "function": {"name": force_tool_call}} assert functions is not None return anthropic_bedrock_chat_completions_request( data=ChatCompletionRequest( model=llm_config.model, messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages], tools=[{"type": "function", "function": f} for f in functions] if functions else None, tool_choice=tool_call, # user=str(user_id), # NOTE: max_tokens is required for Anthropic API max_tokens=llm_config.max_tokens, ), ) elif llm_config.model_endpoint_type == "deepseek": if model_settings.deepseek_api_key is None and llm_config.model_endpoint == "": # only is a problem if we are *not* using an openai proxy raise LettaConfigurationError(message="DeepSeek key is missing from letta config file", missing_fields=["deepseek_api_key"]) data = build_deepseek_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming, llm_config.max_tokens, ) if stream: # Client requested token streaming data.stream = True assert isinstance(stream_interface, AgentChunkStreamingInterface) or isinstance( stream_interface, AgentRefreshStreamingInterface ), type(stream_interface) response = openai_chat_completions_process_stream( url=llm_config.model_endpoint, api_key=model_settings.deepseek_api_key, chat_completion_request=data, stream_interface=stream_interface, name=name, ) else: # Client did not request token streaming (expect a blocking backend response) data.stream = False if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_start() try: response = openai_chat_completions_request( url=llm_config.model_endpoint, api_key=model_settings.deepseek_api_key, chat_completion_request=data, ) finally: if isinstance(stream_interface, AgentChunkStreamingInterface): stream_interface.stream_end() """ if llm_config.put_inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) """ response = convert_deepseek_response_to_chatcompletion(response) return response # local model else: if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if "DeepSeek-R1".lower() in llm_config.model.lower(): # TODO: move this to the llm_config. messages[0].content[0].text += f" {''.join(json.dumps(f) for f in functions)} " messages[0].content[ 0 ].text += f'Select best function to call simply by responding with a single json block with the keys "function" and "params". Use double quotes around the arguments.' return get_chat_completion( model=llm_config.model, messages=messages, functions=functions, functions_python=functions_python, function_call=function_call, context_window=llm_config.context_window, endpoint=llm_config.model_endpoint, endpoint_type=llm_config.model_endpoint_type, wrapper=llm_config.model_wrapper, user=str(user_id), # hint first_message=first_message, # auth-related auth_type=model_settings.openllm_auth_type, auth_key=model_settings.openllm_api_key, )