b8e9a80d93
* wait I forgot to comit locally * cp the entire core directory and then rm the .git subdir
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
from abc import ABC, abstractmethod
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from typing import AsyncGenerator
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from letta.constants import DEFAULT_MAX_STEPS
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from letta.log import get_logger
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import MessageStreamStatus
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from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage, MessageType
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from letta.schemas.letta_response import LettaResponse
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from letta.schemas.message import MessageCreate
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from letta.schemas.user import User
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class BaseAgentV2(ABC):
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"""
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Abstract base class for the main agent execution loop for letta agents, handling
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message management, llm api request, tool execution, and context tracking.
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"""
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def __init__(self, agent_state: AgentState, actor: User):
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self.agent_state = agent_state
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self.actor = actor
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self.logger = get_logger(agent_state.id)
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@abstractmethod
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async def build_request(
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self,
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input_messages: list[MessageCreate],
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) -> dict:
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"""
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Execute the agent loop in dry_run mode, returning just the generated request
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payload sent to the underlying llm provider.
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"""
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raise NotImplementedError
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@abstractmethod
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async def step(
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self,
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input_messages: list[MessageCreate],
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max_steps: int = DEFAULT_MAX_STEPS,
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run_id: str | None = None,
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use_assistant_message: bool = True,
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include_return_message_types: list[MessageType] | None = None,
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request_start_timestamp_ns: int | None = None,
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) -> LettaResponse:
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"""
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Execute the agent loop in blocking mode, returning all messages at once.
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"""
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raise NotImplementedError
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@abstractmethod
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async def stream(
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self,
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input_messages: list[MessageCreate],
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max_steps: int = DEFAULT_MAX_STEPS,
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stream_tokens: bool = False,
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run_id: str | None = None,
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use_assistant_message: bool = True,
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include_return_message_types: list[MessageType] | None = None,
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request_start_timestamp_ns: int | None = None,
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) -> AsyncGenerator[LettaMessage | LegacyLettaMessage | MessageStreamStatus, None]:
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"""
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Execute the agent loop in streaming mode, yielding chunks as they become available.
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If stream_tokens is True, individual tokens are streamed as they arrive from the LLM,
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providing the lowest latency experience, otherwise each complete step (reasoning +
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tool call + tool return) is yielded as it completes.
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"""
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raise NotImplementedError
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