371 lines
16 KiB
Python
371 lines
16 KiB
Python
from typing import TYPE_CHECKING, Literal, Optional
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from letta.constants import LETTA_MODEL_ENDPOINT
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from letta.log import get_logger
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from letta.schemas.enums import AgentType, ProviderCategory
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logger = get_logger(__name__)
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class LLMConfig(BaseModel):
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"""Configuration for Language Model (LLM) connection and generation parameters."""
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model: str = Field(..., description="LLM model name. ")
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display_name: Optional[str] = Field(None, description="A human-friendly display name for the model.")
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model_endpoint_type: Literal[
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"openai",
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"anthropic",
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"google_ai",
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"google_vertex",
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"azure",
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"groq",
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"ollama",
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"webui",
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"webui-legacy",
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"lmstudio",
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"lmstudio-legacy",
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"lmstudio-chatcompletions",
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"llamacpp",
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"koboldcpp",
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"vllm",
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"hugging-face",
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"mistral",
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"together", # completions endpoint
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"bedrock",
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"deepseek",
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"xai",
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] = Field(..., description="The endpoint type for the model.")
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model_endpoint: Optional[str] = Field(None, description="The endpoint for the model.")
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provider_name: Optional[str] = Field(None, description="The provider name for the model.")
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provider_category: Optional[ProviderCategory] = Field(None, description="The provider category for the model.")
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model_wrapper: Optional[str] = Field(None, description="The wrapper for the model.")
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context_window: int = Field(..., description="The context window size for the model.")
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put_inner_thoughts_in_kwargs: Optional[bool] = Field(
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True,
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description="Puts 'inner_thoughts' as a kwarg in the function call if this is set to True. This helps with function calling performance and also the generation of inner thoughts.",
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)
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handle: Optional[str] = Field(None, description="The handle for this config, in the format provider/model-name.")
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temperature: float = Field(
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0.7,
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description="The temperature to use when generating text with the model. A higher temperature will result in more random text.",
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)
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max_tokens: Optional[int] = Field(
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None,
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description="The maximum number of tokens to generate. If not set, the model will use its default value.",
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)
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enable_reasoner: bool = Field(
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True, description="Whether or not the model should use extended thinking if it is a 'reasoning' style model"
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)
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reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = Field(
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None,
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description="The reasoning effort to use when generating text reasoning models",
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)
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max_reasoning_tokens: int = Field(
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0,
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description="Configurable thinking budget for extended thinking. Used for enable_reasoner and also for Google Vertex models like Gemini 2.5 Flash. Minimum value is 1024 when used with enable_reasoner.",
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)
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frequency_penalty: Optional[float] = Field(
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None, # Can also deafult to 0.0?
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description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. From OpenAI: Number between -2.0 and 2.0.",
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)
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compatibility_type: Optional[Literal["gguf", "mlx"]] = Field(None, description="The framework compatibility type for the model.")
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verbosity: Optional[Literal["low", "medium", "high"]] = Field(
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None,
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description="Soft control for how verbose model output should be, used for GPT-5 models.",
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)
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tier: Optional[str] = Field(None, description="The cost tier for the model (cloud only).")
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# FIXME hack to silence pydantic protected namespace warning
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model_config = ConfigDict(protected_namespaces=())
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parallel_tool_calls: Optional[bool] = Field(False, description="If set to True, enables parallel tool calling. Defaults to False.")
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@model_validator(mode="before")
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@classmethod
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def set_model_specific_defaults(cls, values):
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"""
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Set model-specific default values for fields like max_tokens, context_window, etc.
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This ensures the same defaults from default_config are applied automatically.
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"""
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model = values.get("model")
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if model is None:
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return values
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# Set max_tokens defaults based on model
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if values.get("max_tokens") is None:
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if model == "gpt-5":
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values["max_tokens"] = 16384
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elif model == "gpt-4.1":
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values["max_tokens"] = 8192
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# For other models, the field default of 4096 will be used
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# Set context_window defaults if not provided
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if values.get("context_window") is None:
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if model == "gpt-5":
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values["context_window"] = 128000
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elif model == "gpt-4.1":
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values["context_window"] = 256000
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elif model == "gpt-4o" or model == "gpt-4o-mini":
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values["context_window"] = 128000
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elif model == "gpt-4":
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values["context_window"] = 8192
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# Set verbosity defaults for GPT-5 models
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if model == "gpt-5" and values.get("verbosity") is None:
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values["verbosity"] = "medium"
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return values
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@model_validator(mode="before")
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@classmethod
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def set_default_enable_reasoner(cls, values):
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# NOTE: this is really only applicable for models that can toggle reasoning on-and-off, like 3.7
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# We can also use this field to identify if a model is a "reasoning" model (o1/o3, etc.) if we want
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# if any(openai_reasoner_model in values.get("model", "") for openai_reasoner_model in ["o3-mini", "o1"]):
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# values["enable_reasoner"] = True
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# values["put_inner_thoughts_in_kwargs"] = False
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return values
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@model_validator(mode="before")
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@classmethod
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def set_default_put_inner_thoughts(cls, values):
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"""
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Dynamically set the default for put_inner_thoughts_in_kwargs based on the model field,
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falling back to True if no specific rule is defined.
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"""
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model = values.get("model")
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if model is None:
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return values
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# Define models where we want put_inner_thoughts_in_kwargs to be False
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avoid_put_inner_thoughts_in_kwargs = ["gpt-4"]
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if values.get("put_inner_thoughts_in_kwargs") is None:
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values["put_inner_thoughts_in_kwargs"] = False if model in avoid_put_inner_thoughts_in_kwargs else True
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# For the o1/o3 series from OpenAI, set to False by default
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# We can set this flag to `true` if desired, which will enable "double-think"
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from letta.llm_api.openai_client import is_openai_reasoning_model
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if is_openai_reasoning_model(model):
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values["put_inner_thoughts_in_kwargs"] = False
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if values.get("model_endpoint_type") == "anthropic" and (
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model.startswith("claude-3-7-sonnet")
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or model.startswith("claude-sonnet-4")
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or model.startswith("claude-opus-4")
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or model.startswith("claude-haiku-4-5")
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):
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values["put_inner_thoughts_in_kwargs"] = False
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return values
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@classmethod
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def default_config(cls, model_name: str):
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"""
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Convenience function to generate a default `LLMConfig` from a model name. Only some models are supported in this function.
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Args:
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model_name (str): The name of the model (gpt-4, gpt-4o-mini, letta).
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"""
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if model_name == "gpt-4":
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return cls(
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model="gpt-4",
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model_wrapper=None,
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context_window=8192,
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put_inner_thoughts_in_kwargs=True,
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)
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elif model_name == "gpt-4o-mini":
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return cls(
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model="gpt-4o-mini",
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model_wrapper=None,
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context_window=128000,
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)
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elif model_name == "gpt-4o":
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return cls(
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model="gpt-4o",
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model_wrapper=None,
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context_window=128000,
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)
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elif model_name == "gpt-4.1":
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return cls(
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model="gpt-4.1",
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model_wrapper=None,
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context_window=256000,
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max_tokens=8192,
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)
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elif model_name == "gpt-5":
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return cls(
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model="gpt-5",
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model_wrapper=None,
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context_window=128000,
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reasoning_effort="minimal",
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verbosity="medium",
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max_tokens=16384,
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)
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elif model_name == "letta":
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return cls(
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model="memgpt-openai",
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model_endpoint_type="openai",
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model_endpoint=LETTA_MODEL_ENDPOINT,
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context_window=30000,
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)
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else:
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raise ValueError(f"Model {model_name} not supported.")
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def pretty_print(self) -> str:
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return (
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f"{self.model}"
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+ (f" [type={self.model_endpoint_type}]" if self.model_endpoint_type else "")
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+ (f" [ip={self.model_endpoint}]" if self.model_endpoint else "")
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)
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@classmethod
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def is_openai_reasoning_model(cls, config: "LLMConfig") -> bool:
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from letta.llm_api.openai_client import is_openai_reasoning_model
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return config.model_endpoint_type == "openai" and is_openai_reasoning_model(config.model)
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@classmethod
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def is_anthropic_reasoning_model(cls, config: "LLMConfig") -> bool:
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return config.model_endpoint_type == "anthropic" and (
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config.model.startswith("claude-opus-4")
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or config.model.startswith("claude-sonnet-4")
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or config.model.startswith("claude-3-7-sonnet")
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or config.model.startswith("claude-haiku-4-5")
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)
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@classmethod
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def is_google_vertex_reasoning_model(cls, config: "LLMConfig") -> bool:
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return config.model_endpoint_type == "google_vertex" and (
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config.model.startswith("gemini-2.5-flash") or config.model.startswith("gemini-2.5-pro")
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)
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@classmethod
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def is_google_ai_reasoning_model(cls, config: "LLMConfig") -> bool:
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return config.model_endpoint_type == "google_ai" and (
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config.model.startswith("gemini-2.5-flash") or config.model.startswith("gemini-2.5-pro")
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)
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@classmethod
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def supports_verbosity(cls, config: "LLMConfig") -> bool:
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"""Check if the model supports verbosity control."""
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return config.model_endpoint_type == "openai" and config.model.startswith("gpt-5")
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@classmethod
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def apply_reasoning_setting_to_config(cls, config: "LLMConfig", reasoning: bool, agent_type: Optional["AgentType"] = None):
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"""
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Normalize reasoning-related flags on the config based on the requested
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"reasoning" setting, model capabilities, and optionally the agent type.
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For AgentType.letta_v1_agent, we enforce stricter semantics:
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- OpenAI native reasoning (o1/o3/o4/gpt-5): force enabled (non-togglable)
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- Anthropic (claude 3.7 / 4): toggle honored (default on elsewhere)
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- Google Gemini (2.5 family): force disabled until native reasoning supported
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- All others: disabled (no simulated reasoning via kwargs)
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"""
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# V1 agent policy: do not allow simulated reasoning for non-native models
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if agent_type is not None and agent_type == AgentType.letta_v1_agent:
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# OpenAI native reasoning models: always on
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if cls.is_openai_reasoning_model(config):
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config.put_inner_thoughts_in_kwargs = False
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config.enable_reasoner = True
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if config.reasoning_effort is None:
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if config.model.startswith("gpt-5"):
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config.reasoning_effort = "minimal"
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else:
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config.reasoning_effort = "medium"
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if config.model.startswith("gpt-5") and config.verbosity is None:
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config.verbosity = "medium"
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return config
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# Anthropic 3.7/4 and Gemini: toggle honored
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is_google_reasoner_with_configurable_thinking = (
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cls.is_google_vertex_reasoning_model(config) or cls.is_google_ai_reasoning_model(config)
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) and not config.model.startswith("gemini-2.5-pro")
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if cls.is_anthropic_reasoning_model(config) or is_google_reasoner_with_configurable_thinking:
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config.enable_reasoner = bool(reasoning)
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config.put_inner_thoughts_in_kwargs = False
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if config.enable_reasoner and config.max_reasoning_tokens == 0:
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config.max_reasoning_tokens = 1024
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return config
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# Google Gemini 2.5 Pro: not possible to disable
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if config.model.startswith("gemini-2.5-pro"):
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config.put_inner_thoughts_in_kwargs = False
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config.enable_reasoner = True
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if config.max_reasoning_tokens == 0:
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config.max_reasoning_tokens = 1024
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return config
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# Everything else: disabled (no inner_thoughts-in-kwargs simulation)
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config.put_inner_thoughts_in_kwargs = False
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config.enable_reasoner = False
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config.max_reasoning_tokens = 0
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return config
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if not reasoning:
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if cls.is_openai_reasoning_model(config):
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logger.warning("Reasoning cannot be disabled for OpenAI o1/o3/gpt-5 models")
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config.put_inner_thoughts_in_kwargs = False
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config.enable_reasoner = True
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if config.reasoning_effort is None:
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# GPT-5 models default to minimal, others to medium
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if config.model.startswith("gpt-5"):
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config.reasoning_effort = "minimal"
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else:
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config.reasoning_effort = "medium"
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# Set verbosity for GPT-5 models
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if config.model.startswith("gpt-5") and config.verbosity is None:
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config.verbosity = "medium"
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elif config.model.startswith("gemini-2.5-pro"):
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logger.warning("Reasoning cannot be disabled for Gemini 2.5 Pro model")
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# Handle as non-reasoner until we support summary
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config.put_inner_thoughts_in_kwargs = True
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config.enable_reasoner = True
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if config.max_reasoning_tokens == 0:
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config.max_reasoning_tokens = 1024
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else:
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config.put_inner_thoughts_in_kwargs = False
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config.enable_reasoner = False
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else:
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config.enable_reasoner = True
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if cls.is_anthropic_reasoning_model(config):
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config.put_inner_thoughts_in_kwargs = False
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if config.max_reasoning_tokens == 0:
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config.max_reasoning_tokens = 1024
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elif cls.is_google_vertex_reasoning_model(config) or cls.is_google_ai_reasoning_model(config):
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# Handle as non-reasoner until we support summary
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config.put_inner_thoughts_in_kwargs = True
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if config.max_reasoning_tokens == 0:
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config.max_reasoning_tokens = 1024
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elif cls.is_openai_reasoning_model(config):
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config.put_inner_thoughts_in_kwargs = False
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if config.reasoning_effort is None:
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# GPT-5 models default to minimal, others to medium
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if config.model.startswith("gpt-5"):
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config.reasoning_effort = "minimal"
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else:
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config.reasoning_effort = "medium"
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# Set verbosity for GPT-5 models
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if config.model.startswith("gpt-5") and config.verbosity is None:
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config.verbosity = "medium"
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else:
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config.put_inner_thoughts_in_kwargs = True
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return config
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