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feat: split up handle and model_settings (#6022)

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This commit is contained in:
Sarah Wooders
2025-11-07 15:33:48 -08:00
committed by Caren Thomas
parent 19e6b09da5
commit 0b1fe096ec
8 changed files with 154 additions and 114 deletions
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137
fern/openapi.json
View File

@@ -18932,6 +18932,30 @@
"deprecated": true
},
"model": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Model",
"description": "The model handle used by the agent (format: provider/model-name)."
},
"embedding": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding model handle used by the agent (format: provider/model-name)."
},
"model_settings": {
"anyOf": [
{
"$ref": "#/components/schemas/ModelSettings"
@@ -18940,18 +18964,7 @@
"type": "null"
}
],
"description": "The model used by the agent."
},
"embedding": {
"anyOf": [
{
"$ref": "#/components/schemas/EmbeddingModelSettings"
},
{
"type": "null"
}
],
"description": "The embedding model used by the agent."
"description": "The model settings used by the agent."
},
"response_format": {
"anyOf": [
@@ -22943,15 +22956,12 @@
{
"type": "string"
},
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Model",
"description": "The model handle or model settings for the agent to use, specified either by a handle or an object. See the model schema for more information."
"description": "The model handle for the agent to use (format: provider/model-name)."
},
"embedding": {
"anyOf": [
@@ -22959,14 +22969,22 @@
"type": "string"
},
{
"$ref": "#/components/schemas/EmbeddingModelSettings"
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding model handle used by the agent (format: provider/model-name)."
},
"model_settings": {
"anyOf": [
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding configuration handle used by the agent, specified in the format provider/model-name."
"description": "The model settings for the agent."
},
"context_window_limit": {
"anyOf": [
@@ -24248,24 +24266,6 @@
],
"title": "EmbeddingModel"
},
"EmbeddingModelSettings": {
"properties": {
"model": {
"type": "string",
"title": "Model",
"description": "The name of the model."
},
"provider": {
"type": "string",
"enum": ["openai", "ollama"],
"title": "Provider",
"description": "The provider of the model."
}
},
"type": "object",
"required": ["model", "provider"],
"title": "EmbeddingModelSettings"
},
"EventMessage": {
"properties": {
"id": {
@@ -26794,15 +26794,12 @@
{
"type": "string"
},
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Model",
"description": "The model handle or model settings for the agent to use, specified either by a handle or an object. See the model schema for more information."
"description": "The model handle for the agent to use (format: provider/model-name)."
},
"embedding": {
"anyOf": [
@@ -26810,14 +26807,22 @@
"type": "string"
},
{
"$ref": "#/components/schemas/EmbeddingModelSettings"
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding model handle used by the agent (format: provider/model-name)."
},
"model_settings": {
"anyOf": [
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding configuration handle used by the agent, specified in the format provider/model-name."
"description": "The model settings for the agent."
},
"context_window_limit": {
"anyOf": [
@@ -30017,11 +30022,6 @@
},
"ModelSettings": {
"properties": {
"model": {
"type": "string",
"title": "Model",
"description": "The name of the model."
},
"max_output_tokens": {
"type": "integer",
"title": "Max Output Tokens",
@@ -30030,7 +30030,6 @@
}
},
"type": "object",
"required": ["model"],
"title": "ModelSettings",
"description": "Schema for defining settings for a model"
},
@@ -35289,15 +35288,12 @@
{
"type": "string"
},
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Model",
"description": "The model used by the agent, specified either by a handle or an object. See the model schema for more information."
"description": "The model handle used by the agent (format: provider/model-name)."
},
"embedding": {
"anyOf": [
@@ -35305,14 +35301,22 @@
"type": "string"
},
{
"$ref": "#/components/schemas/EmbeddingModelSettings"
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding model handle used by the agent (format: provider/model-name)."
},
"model_settings": {
"anyOf": [
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding configuration handle used by the agent, specified in the format provider/model-name."
"description": "The model settings for the agent."
},
"context_window_limit": {
"anyOf": [
@@ -36297,15 +36301,12 @@
{
"type": "string"
},
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Model",
"description": "The model handle or model settings for the agent to use, specified either by a handle or an object. See the model schema for more information."
"description": "The model handle for the agent to use (format: provider/model-name)."
},
"embedding": {
"anyOf": [
@@ -36313,14 +36314,22 @@
"type": "string"
},
{
"$ref": "#/components/schemas/EmbeddingModelSettings"
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding model handle used by the agent (format: provider/model-name)."
},
"model_settings": {
"anyOf": [
{
"$ref": "#/components/schemas/ModelSettings"
},
{
"type": "null"
}
],
"title": "Embedding",
"description": "The embedding configuration handle used by the agent, specified in the format provider/model-name."
"description": "The model settings for the agent."
},
"context_window_limit": {
"anyOf": [

8
letta/orm/agent.py
View File

@@ -285,7 +285,9 @@ class Agent(SqlalchemyBase, OrganizationMixin, ProjectMixin, TemplateEntityMixin
if resolver:
state[field_name] = resolver()
state["model"] = self.llm_config._to_model() if self.llm_config else None
state["model"] = self.llm_config.handle if self.llm_config else None
state["model_settings"] = self.llm_config._to_model_settings() if self.llm_config else None
state["embedding"] = self.embedding_config.handle if self.embedding_config else None
return self.__pydantic_model__(**state)
@@ -425,6 +427,8 @@ class Agent(SqlalchemyBase, OrganizationMixin, ProjectMixin, TemplateEntityMixin
state["managed_group"] = multi_agent_group
state["tool_exec_environment_variables"] = tool_exec_environment_variables
state["secrets"] = tool_exec_environment_variables
state["model"] = self.llm_config._to_model() if self.llm_config else None
state["model"] = self.llm_config.handle if self.llm_config else None
state["model_settings"] = self.llm_config._to_model_settings() if self.llm_config else None
state["embedding"] = self.embedding_config.handle if self.embedding_config else None
return self.__pydantic_model__(**state)

37
letta/schemas/agent.py
View File

@@ -5,7 +5,7 @@ from typing import Dict, List, Literal, Optional
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from letta.constants import CORE_MEMORY_LINE_NUMBER_WARNING, DEFAULT_EMBEDDING_CHUNK_SIZE
from letta.errors import AgentExportProcessingError
from letta.errors import AgentExportProcessingError, LettaInvalidArgumentError
from letta.schemas.block import Block, CreateBlock
from letta.schemas.embedding_config import EmbeddingConfig
from letta.schemas.enums import PrimitiveType
@@ -18,7 +18,7 @@ from letta.schemas.letta_stop_reason import StopReasonType
from letta.schemas.llm_config import LLMConfig
from letta.schemas.memory import Memory
from letta.schemas.message import Message, MessageCreate
from letta.schemas.model import EmbeddingModelSettings, ModelSettings
from letta.schemas.model import ModelSettings
from letta.schemas.openai.chat_completion_response import UsageStatistics
from letta.schemas.response_format import ResponseFormatUnion
from letta.schemas.source import Source
@@ -83,8 +83,9 @@ class AgentState(OrmMetadataBase, validate_assignment=True):
embedding_config: EmbeddingConfig = Field(
..., description="Deprecated: Use `embedding` field instead. The embedding configuration used by the agent.", deprecated=True
)
model: Optional[ModelSettings] = Field(None, description="The model used by the agent.")
embedding: Optional[EmbeddingModelSettings] = Field(None, description="The embedding model used by the agent.")
model: Optional[str] = Field(None, description="The model handle used by the agent (format: provider/model-name).")
embedding: Optional[str] = Field(None, description="The embedding model handle used by the agent (format: provider/model-name).")
model_settings: Optional[ModelSettings] = Field(None, description="The model settings used by the agent.")
response_format: Optional[ResponseFormatUnion] = Field(
None,
@@ -229,13 +230,12 @@ class CreateAgent(BaseModel, validate_assignment=True): #
embedding_config: Optional[EmbeddingConfig] = Field(
None, description="Deprecated: Use `embedding` field instead. The embedding configuration used by the agent.", deprecated=True
)
model: Optional[str | ModelSettings] = Field( # TODO: make this required (breaking change)
model: Optional[str] = Field( # TODO: make this required (breaking change)
None,
description="The model handle or model settings for the agent to use, specified either by a handle or an object. See the model schema for more information.",
)
embedding: Optional[str | EmbeddingModelSettings] = Field(
None, description="The embedding configuration handle used by the agent, specified in the format provider/model-name."
description="The model handle for the agent to use (format: provider/model-name).",
)
embedding: Optional[str] = Field(None, description="The embedding model handle used by the agent (format: provider/model-name).")
model_settings: Optional[ModelSettings] = Field(None, description="The model settings for the agent.")
context_window_limit: Optional[int] = Field(None, description="The context window limit used by the agent.")
embedding_chunk_size: Optional[int] = Field(
@@ -348,9 +348,12 @@ class CreateAgent(BaseModel, validate_assignment=True): #
if not model:
return model
if "/" not in model:
raise LettaInvalidArgumentError("The model handle should be in the format provider/model-name", argument_name="model")
provider_name, model_name = model.split("/", 1)
if not provider_name or not model_name:
raise ValueError("The llm config handle should be in the format provider/model-name")
raise LettaInvalidArgumentError("The model handle should be in the format provider/model-name", argument_name="model")
return model
@@ -360,9 +363,12 @@ class CreateAgent(BaseModel, validate_assignment=True): #
if not embedding:
return embedding
if "/" not in embedding:
raise ValueError("The embedding handle should be in the format provider/model-name")
provider_name, embedding_name = embedding.split("/", 1)
if not provider_name or not embedding_name:
raise ValueError("The embedding config handle should be in the format provider/model-name")
raise ValueError("The embedding handle should be in the format provider/model-name")
return embedding
@@ -410,13 +416,12 @@ class UpdateAgent(BaseModel):
)
# model configuration
model: Optional[str | ModelSettings] = Field(
model: Optional[str] = Field(
None,
description="The model used by the agent, specified either by a handle or an object. See the model schema for more information.",
)
embedding: Optional[str | EmbeddingModelSettings] = Field(
None, description="The embedding configuration handle used by the agent, specified in the format provider/model-name."
description="The model handle used by the agent (format: provider/model-name).",
)
embedding: Optional[str] = Field(None, description="The embedding model handle used by the agent (format: provider/model-name).")
model_settings: Optional[ModelSettings] = Field(None, description="The model settings for the agent.")
context_window_limit: Optional[int] = Field(None, description="The context window limit used by the agent.")
reasoning: Optional[bool] = Field(
None,

15
letta/schemas/llm_config.py
View File

@@ -255,7 +255,7 @@ class LLMConfig(BaseModel):
+ (f" [ip={self.model_endpoint}]" if self.model_endpoint else "")
)
def _to_model(self) -> "ModelSettings":
def _to_model_settings(self) -> "ModelSettings":
"""
Convert LLMConfig back into a Model schema (OpenAIModelSettings, AnthropicModelSettings, etc.).
This is the inverse of the _to_legacy_config_params() methods in model.py.
@@ -279,7 +279,6 @@ class LLMConfig(BaseModel):
if self.model_endpoint_type == "openai":
return OpenAIModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
reasoning=OpenAIReasoning(reasoning_effort=self.reasoning_effort or "minimal"),
@@ -287,7 +286,6 @@ class LLMConfig(BaseModel):
elif self.model_endpoint_type == "anthropic":
thinking_type = "enabled" if self.enable_reasoner else "disabled"
return AnthropicModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
thinking=AnthropicThinking(type=thinking_type, budget_tokens=self.max_reasoning_tokens or 1024),
@@ -295,7 +293,6 @@ class LLMConfig(BaseModel):
)
elif self.model_endpoint_type == "google_ai":
return GoogleAIModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 65536,
temperature=self.temperature,
thinking_config=GeminiThinkingConfig(
@@ -304,7 +301,6 @@ class LLMConfig(BaseModel):
)
elif self.model_endpoint_type == "google_vertex":
return GoogleVertexModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 65536,
temperature=self.temperature,
thinking_config=GeminiThinkingConfig(
@@ -313,39 +309,34 @@ class LLMConfig(BaseModel):
)
elif self.model_endpoint_type == "azure":
return AzureModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
)
elif self.model_endpoint_type == "xai":
return XAIModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
)
elif self.model_endpoint_type == "groq":
return GroqModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
)
elif self.model_endpoint_type == "deepseek":
return DeepseekModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
)
elif self.model_endpoint_type == "together":
return TogetherModelSettings(
model=self.model,
max_output_tokens=self.max_tokens or 4096,
temperature=self.temperature,
)
elif self.model_endpoint_type == "bedrock":
return Model(model=self.model, max_output_tokens=self.max_tokens or 4096)
return Model(max_output_tokens=self.max_tokens or 4096)
else:
# If we don't know the model type, use the default Model schema
return Model(model=self.model, max_output_tokens=self.max_tokens or 4096)
return Model(max_output_tokens=self.max_tokens or 4096)
@classmethod
def is_openai_reasoning_model(cls, config: "LLMConfig") -> bool:

55
letta/schemas/model.py
View File

@@ -120,6 +120,25 @@ class Model(LLMConfig, ModelBase):
provider_category=llm_config.provider_category,
)
@property
def model_settings_schema(self) -> Optional[dict]:
"""Returns the JSON schema for the ModelSettings class corresponding to this model's provider."""
PROVIDER_SETTINGS_MAP = {
ProviderType.openai: OpenAIModelSettings,
ProviderType.anthropic: AnthropicModelSettings,
ProviderType.google_ai: GoogleAIModelSettings,
ProviderType.google_vertex: GoogleVertexModelSettings,
ProviderType.azure: AzureModelSettings,
ProviderType.xai: XAIModelSettings,
ProviderType.groq: GroqModelSettings,
ProviderType.deepseek: DeepseekModelSettings,
ProviderType.together: TogetherModelSettings,
ProviderType.bedrock: BedrockModelSettings,
}
settings_class = PROVIDER_SETTINGS_MAP.get(self.provider_type)
return settings_class.model_json_schema() if settings_class else None
class EmbeddingModel(EmbeddingConfig, ModelBase):
model_type: Literal["embedding"] = Field("embedding", description="Type of model (llm or embedding)")
@@ -184,8 +203,9 @@ class EmbeddingModel(EmbeddingConfig, ModelBase):
class ModelSettings(BaseModel):
"""Schema for defining settings for a model"""
model: str = Field(..., description="The name of the model.")
# model: str = Field(..., description="The name of the model.")
max_output_tokens: int = Field(4096, description="The maximum number of tokens the model can generate.")
parallel_tool_calls: bool = Field(False, description="Whether to enable parallel tool calling.")
class OpenAIReasoning(BaseModel):
@@ -201,7 +221,7 @@ class OpenAIReasoning(BaseModel):
class OpenAIModelSettings(ModelSettings):
provider: Literal["openai"] = Field("openai", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
reasoning: OpenAIReasoning = Field(OpenAIReasoning(reasoning_effort="high"), description="The reasoning configuration for the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
@@ -220,6 +240,7 @@ class OpenAIModelSettings(ModelSettings):
"max_tokens": self.max_output_tokens,
"reasoning_effort": self.reasoning.reasoning_effort,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -231,7 +252,7 @@ class OpenAIModelSettings(ModelSettings):
class AnthropicThinking(BaseModel):
type: Literal["enabled", "disabled"] = Field("enabled", description="The type of thinking to use.")
budget_tokens: int = Field(1024, description="The maximum number of tokens the model can use for extended thinking.")
budget_tokens: int = Field(1024, ge=0, le=1024, description="The maximum number of tokens the model can use for extended thinking.")
class AnthropicModelSettings(ModelSettings):
@@ -258,17 +279,18 @@ class AnthropicModelSettings(ModelSettings):
"extended_thinking": self.thinking.type == "enabled",
"thinking_budget_tokens": self.thinking.budget_tokens,
"verbosity": self.verbosity,
"parallel_tool_calls": self.parallel_tool_calls,
}
class GeminiThinkingConfig(BaseModel):
include_thoughts: bool = Field(True, description="Whether to include thoughts in the model's response.")
thinking_budget: int = Field(1024, description="The thinking budget for the model.")
thinking_budget: int = Field(1024, ge=0, le=1024, description="The thinking budget for the model.")
class GoogleAIModelSettings(ModelSettings):
provider: Literal["google_ai"] = Field("google_ai", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
thinking_config: GeminiThinkingConfig = Field(
GeminiThinkingConfig(include_thoughts=True, thinking_budget=1024), description="The thinking configuration for the model."
)
@@ -280,6 +302,7 @@ class GoogleAIModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"max_reasoning_tokens": self.thinking_config.thinking_budget if self.thinking_config.include_thoughts else 0,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -291,7 +314,7 @@ class AzureModelSettings(ModelSettings):
"""Azure OpenAI model configuration (OpenAI-compatible)."""
provider: Literal["azure"] = Field("azure", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -299,6 +322,7 @@ class AzureModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -306,7 +330,7 @@ class XAIModelSettings(ModelSettings):
"""xAI model configuration (OpenAI-compatible)."""
provider: Literal["xai"] = Field("xai", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -314,6 +338,7 @@ class XAIModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -321,7 +346,7 @@ class GroqModelSettings(ModelSettings):
"""Groq model configuration (OpenAI-compatible)."""
provider: Literal["groq"] = Field("groq", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -336,7 +361,7 @@ class DeepseekModelSettings(ModelSettings):
"""Deepseek model configuration (OpenAI-compatible)."""
provider: Literal["deepseek"] = Field("deepseek", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -344,6 +369,7 @@ class DeepseekModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -351,7 +377,7 @@ class TogetherModelSettings(ModelSettings):
"""Together AI model configuration (OpenAI-compatible)."""
provider: Literal["together"] = Field("together", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -359,6 +385,7 @@ class TogetherModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -366,7 +393,7 @@ class BedrockModelSettings(ModelSettings):
"""AWS Bedrock model configuration."""
provider: Literal["bedrock"] = Field("bedrock", description="The provider of the model.")
temperature: float = Field(0.7, description="The temperature of the model.")
temperature: float = Field(0.7, ge=0.0, le=1.0, description="The temperature of the model.")
response_format: Optional[ResponseFormatUnion] = Field(None, description="The response format for the model.")
def _to_legacy_config_params(self) -> dict:
@@ -374,6 +401,7 @@ class BedrockModelSettings(ModelSettings):
"temperature": self.temperature,
"max_tokens": self.max_output_tokens,
"response_format": self.response_format,
"parallel_tool_calls": self.parallel_tool_calls,
}
@@ -392,8 +420,3 @@ ModelSettingsUnion = Annotated[
],
Field(discriminator="provider"),
]
class EmbeddingModelSettings(BaseModel):
model: str = Field(..., description="The name of the model.")
provider: Literal["openai", "ollama"] = Field(..., description="The provider of the model.")

7
letta/server/server.py
View File

@@ -436,6 +436,8 @@ class SyncServer(object):
handle = f"{request.model.provider}/{request.model.model}"
# TODO: figure out how to override various params
additional_config_params = request.model._to_legacy_config_params()
additional_config_params["model"] = request.model.model
additional_config_params["provider_name"] = request.model.provider
config_params = {
"handle": handle,
@@ -525,6 +527,11 @@ class SyncServer(object):
request.llm_config = await self.get_cached_llm_config_async(actor=actor, **config_params)
log_event(name="end get_cached_llm_config", attributes=config_params)
# update with model_settings
if request.model_settings is not None:
update_llm_config_params = request.model_settings._to_legacy_config_params()
request.llm_config.update(update_llm_config_params)
# Copy parallel_tool_calls from request to llm_config if provided
if request.parallel_tool_calls is not None:
if request.llm_config is None:

2
tests/configs/llm_model_configs/gemini-2.5-flash.json
View File

@@ -6,5 +6,5 @@
"model_wrapper": null,
"put_inner_thoughts_in_kwargs": true,
"enable_reasoner": true,
"max_reasoning_tokens": 20000
"max_reasoning_tokens": 1000
}

7
tests/managers/test_agent_manager.py
View File

@@ -1250,7 +1250,7 @@ async def test_agent_state_schema_unchanged(server: SyncServer):
from letta.schemas.group import Group
from letta.schemas.llm_config import LLMConfig
from letta.schemas.memory import Memory
from letta.schemas.model import EmbeddingModelSettings, ModelSettings
from letta.schemas.model import ModelSettings
from letta.schemas.response_format import ResponseFormatUnion
from letta.schemas.source import Source
from letta.schemas.tool import Tool
@@ -1271,9 +1271,10 @@ async def test_agent_state_schema_unchanged(server: SyncServer):
"agent_type": AgentType,
# LLM information
"llm_config": LLMConfig,
"model": ModelSettings,
"embedding": EmbeddingModelSettings,
"model": str,
"embedding": str,
"embedding_config": EmbeddingConfig,
"model_settings": ModelSettings,
"response_format": (ResponseFormatUnion, type(None)),
# State fields
"description": (str, type(None)),