e0a23f7039
* feat: add usage columns to steps table Adds denormalized usage fields to the steps table for easier querying: - model_handle: The model handle (e.g., "openai/gpt-4o-mini") - cached_input_tokens: Tokens served from cache - cache_write_tokens: Tokens written to cache (Anthropic) - reasoning_tokens: Reasoning/thinking tokens These fields mirror LettaUsageStatistics and are extracted from the existing prompt_tokens_details and completion_tokens_details JSON columns. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: regenerate OpenAPI specs and SDK for usage columns 🤖 Generated with [Letta Code](https://letta.com) Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com> --------- Co-authored-by: Letta <noreply@letta.com> Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com> Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
75 lines
4.5 KiB
Python
75 lines
4.5 KiB
Python
from enum import Enum, auto
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from typing import Dict, List, Literal, Optional
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from pydantic import Field
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from letta.schemas.enums import PrimitiveType, StepStatus
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from letta.schemas.letta_base import LettaBase
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from letta.schemas.letta_stop_reason import StopReasonType
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from letta.schemas.message import Message
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class StepBase(LettaBase):
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__id_prefix__ = PrimitiveType.STEP.value
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class Step(StepBase):
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id: str = Field(..., description="The id of the step. Assigned by the database.")
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origin: Optional[str] = Field(None, description="The surface that this agent step was initiated from.")
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organization_id: Optional[str] = Field(None, description="The unique identifier of the organization associated with the step.")
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provider_id: Optional[str] = Field(None, description="The unique identifier of the provider that was configured for this step")
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run_id: Optional[str] = Field(
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None, description="The unique identifier of the run that this step belongs to. Only included for async calls."
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)
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agent_id: Optional[str] = Field(None, description="The ID of the agent that performed the step.")
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provider_name: Optional[str] = Field(None, description="The name of the provider used for this step.")
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provider_category: Optional[str] = Field(None, description="The category of the provider used for this step.")
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model: Optional[str] = Field(None, description="The name of the model used for this step.")
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model_handle: Optional[str] = Field(None, description="The model handle (e.g., 'openai/gpt-4o-mini') used for this step.")
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model_endpoint: Optional[str] = Field(None, description="The model endpoint url used for this step.")
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context_window_limit: Optional[int] = Field(None, description="The context window limit configured for this step.")
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completion_tokens: Optional[int] = Field(None, description="The number of tokens generated by the agent during this step.")
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prompt_tokens: Optional[int] = Field(None, description="The number of tokens in the prompt during this step.")
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total_tokens: Optional[int] = Field(None, description="The total number of tokens processed by the agent during this step.")
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cached_input_tokens: Optional[int] = Field(
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None, description="The number of input tokens served from cache. None if not reported by provider."
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)
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cache_write_tokens: Optional[int] = Field(
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None, description="The number of input tokens written to cache (Anthropic only). None if not reported by provider."
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)
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reasoning_tokens: Optional[int] = Field(
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None, description="The number of reasoning/thinking tokens generated. None if not reported by provider."
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)
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completion_tokens_details: Optional[Dict] = Field(None, description="Detailed completion token breakdown (e.g., reasoning_tokens).")
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prompt_tokens_details: Optional[Dict] = Field(
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None, description="Detailed prompt token breakdown (e.g., cached_tokens, cache_read_tokens, cache_creation_tokens)."
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)
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stop_reason: Optional[StopReasonType] = Field(None, description="The stop reason associated with the step.")
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tags: List[str] = Field([], description="Metadata tags.")
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tid: Optional[str] = Field(None, description="The unique identifier of the transaction that processed this step.")
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trace_id: Optional[str] = Field(None, description="The trace id of the agent step.")
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request_id: Optional[str] = Field(None, description="The API request log ID from cloud-api for correlating steps with API requests.")
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messages: List[Message] = Field(
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[],
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description="The messages generated during this step. Deprecated: use `GET /v1/steps/{step_id}/messages` endpoint instead",
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deprecated=True,
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)
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feedback: Optional[Literal["positive", "negative"]] = Field(
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None, description="The feedback for this step. Must be either 'positive' or 'negative'."
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)
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project_id: Optional[str] = Field(None, description="The project that the agent that executed this step belongs to (cloud only).")
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# error tracking fields
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error_type: Optional[str] = Field(None, description="The type/class of the error that occurred")
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error_data: Optional[Dict] = Field(None, description="Error details including message, traceback, and additional context")
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status: Optional[StepStatus] = Field(StepStatus.PENDING, description="Step status: pending, success, or failed")
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class StepProgression(int, Enum):
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START = auto()
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STREAM_RECEIVED = auto()
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RESPONSE_RECEIVED = auto()
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STEP_LOGGED = auto()
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LOGGED_TRACE = auto()
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FINISHED = auto()
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