1265 lines
64 KiB
Python
1265 lines
64 KiB
Python
import asyncio
|
||
import json
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import uuid
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from typing import Any, AsyncGenerator, Dict, Optional
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||
|
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from opentelemetry.trace import Span
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from letta.adapters.letta_llm_adapter import LettaLLMAdapter
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from letta.adapters.simple_llm_request_adapter import SimpleLLMRequestAdapter
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from letta.adapters.simple_llm_stream_adapter import SimpleLLMStreamAdapter
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from letta.agents.helpers import (
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||
_build_rule_violation_result,
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||
_load_last_function_response,
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||
_maybe_get_approval_messages,
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||
_maybe_get_pending_tool_call_message,
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||
_prepare_in_context_messages_no_persist_async,
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_safe_load_tool_call_str,
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||
generate_step_id,
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merge_and_validate_prefilled_args,
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)
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from letta.agents.letta_agent_v2 import LettaAgentV2
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from letta.constants import DEFAULT_MAX_STEPS, NON_USER_MSG_PREFIX, REQUEST_HEARTBEAT_PARAM, SUMMARIZATION_TRIGGER_MULTIPLIER
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from letta.errors import ContextWindowExceededError, LLMError
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from letta.helpers import ToolRulesSolver
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from letta.helpers.datetime_helpers import get_utc_time, get_utc_timestamp_ns
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from letta.helpers.tool_execution_helper import enable_strict_mode
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG
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from letta.otel.tracing import trace_method
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import MessageRole
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from letta.schemas.letta_message import ApprovalReturn, LettaErrorMessage, LettaMessage, MessageType
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from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, RedactedReasoningContent, TextContent
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from letta.schemas.letta_response import LettaResponse
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from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
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from letta.schemas.message import Message, MessageCreate, ToolReturn
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from letta.schemas.openai.chat_completion_response import FunctionCall, ToolCall, ToolCallDenial, UsageStatistics
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from letta.schemas.step import StepProgression
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from letta.schemas.step_metrics import StepMetrics
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from letta.schemas.tool_execution_result import ToolExecutionResult
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from letta.schemas.usage import LettaUsageStatistics
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from letta.server.rest_api.utils import (
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create_approval_request_message_from_llm_response,
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create_letta_messages_from_llm_response,
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create_parallel_tool_messages_from_llm_response,
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)
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from letta.services.helpers.tool_parser_helper import runtime_override_tool_json_schema
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from letta.settings import settings, summarizer_settings
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from letta.system import package_function_response
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from letta.utils import log_telemetry, validate_function_response
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class LettaAgentV3(LettaAgentV2):
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"""
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Similar to V2, but stripped down / simplified, while also generalized:
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* Supports non-tool returns
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* No inner thoughts in kwargs
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* No heartbeats (loops happen on tool calls)
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TODOs:
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* Support tool rules
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* Support Gemini / OpenAI client
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"""
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def _initialize_state(self):
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super()._initialize_state()
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self._require_tool_call = False
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self.last_step_usage = None
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self.response_messages_for_metadata = [] # Separate accumulator for streaming job metadata
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def _compute_tool_return_truncation_chars(self) -> int:
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"""Compute a dynamic cap for tool returns in requests.
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Heuristic: ~20% of context window × 4 chars/token, minimum 5k chars.
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This prevents any single tool return from consuming too much context.
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"""
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try:
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cap = int(self.agent_state.llm_config.context_window * 0.2 * 4) # 20% of tokens → chars
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except Exception:
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cap = 5000
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return max(5000, cap)
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||
def _update_global_usage_stats(self, step_usage_stats: LettaUsageStatistics):
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||
"""Override to track per-step usage for context limit checks"""
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||
self.last_step_usage = step_usage_stats
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super()._update_global_usage_stats(step_usage_stats)
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||
@trace_method
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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, # NOTE: not used
|
||
include_return_message_types: list[MessageType] | None = None,
|
||
request_start_timestamp_ns: int | None = None,
|
||
) -> LettaResponse:
|
||
"""
|
||
Execute the agent loop in blocking mode, returning all messages at once.
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||
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||
Args:
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||
input_messages: List of new messages to process
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||
max_steps: Maximum number of agent steps to execute
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||
run_id: Optional job/run ID for tracking
|
||
use_assistant_message: Whether to use assistant message format
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||
include_return_message_types: Filter for which message types to return
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||
request_start_timestamp_ns: Start time for tracking request duration
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||
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||
Returns:
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LettaResponse: Complete response with all messages and metadata
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"""
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self._initialize_state()
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request_span = self._request_checkpoint_start(request_start_timestamp_ns=request_start_timestamp_ns)
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in_context_messages, input_messages_to_persist = await _prepare_in_context_messages_no_persist_async(
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input_messages, self.agent_state, self.message_manager, self.actor, run_id
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||
)
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follow_up_messages = []
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||
if len(input_messages_to_persist) > 1 and input_messages_to_persist[0].role == "approval":
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follow_up_messages = input_messages_to_persist[1:]
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input_messages_to_persist = [input_messages_to_persist[0]]
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in_context_messages = in_context_messages + input_messages_to_persist
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response_letta_messages = []
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||
for i in range(max_steps):
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if i == 1 and follow_up_messages:
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input_messages_to_persist = follow_up_messages
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follow_up_messages = []
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response = self._step(
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messages=in_context_messages + self.response_messages,
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input_messages_to_persist=input_messages_to_persist,
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# TODO need to support non-streaming adapter too
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llm_adapter=SimpleLLMRequestAdapter(llm_client=self.llm_client, llm_config=self.agent_state.llm_config),
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run_id=run_id,
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# use_assistant_message=use_assistant_message,
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include_return_message_types=include_return_message_types,
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request_start_timestamp_ns=request_start_timestamp_ns,
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)
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async for chunk in response:
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response_letta_messages.append(chunk)
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# Proactive summarization if approaching context limit
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if (
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self.last_step_usage
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and self.last_step_usage.total_tokens > self.agent_state.llm_config.context_window * SUMMARIZATION_TRIGGER_MULTIPLIER
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and not self.agent_state.message_buffer_autoclear
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||
):
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self.logger.warning(
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f"Step usage ({self.last_step_usage.total_tokens} tokens) approaching "
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f"context limit ({self.agent_state.llm_config.context_window}), triggering summarization."
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)
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in_context_messages = await self.summarize_conversation_history(
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in_context_messages=in_context_messages,
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new_letta_messages=self.response_messages,
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total_tokens=self.last_step_usage.total_tokens,
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force=True,
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)
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# Clear to avoid duplication in next iteration
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self.response_messages = []
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if not self.should_continue:
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break
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input_messages_to_persist = []
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# Rebuild context window after stepping (safety net)
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if not self.agent_state.message_buffer_autoclear:
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if self.last_step_usage:
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await self.summarize_conversation_history(
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in_context_messages=in_context_messages,
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new_letta_messages=self.response_messages,
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total_tokens=self.last_step_usage.total_tokens,
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force=False,
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||
)
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else:
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self.logger.warning(
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||
"Post-loop summarization skipped: last_step_usage is None. "
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"No step completed successfully or usage stats were not updated."
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)
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if self.stop_reason is None:
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self.stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
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result = LettaResponse(messages=response_letta_messages, stop_reason=self.stop_reason, usage=self.usage)
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if run_id:
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if self.job_update_metadata is None:
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||
self.job_update_metadata = {}
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self.job_update_metadata["result"] = result.model_dump(mode="json")
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await self._request_checkpoint_finish(
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request_span=request_span, request_start_timestamp_ns=request_start_timestamp_ns, run_id=run_id
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)
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return result
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||
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||
@trace_method
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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, # NOTE: not used
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||
include_return_message_types: list[MessageType] | None = None,
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||
request_start_timestamp_ns: int | None = None,
|
||
) -> AsyncGenerator[str, None]:
|
||
"""
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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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||
Args:
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||
input_messages: List of new messages to process
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||
max_steps: Maximum number of agent steps to execute
|
||
stream_tokens: Whether to stream back individual tokens. Not all llm
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||
providers offer native token streaming functionality; in these cases,
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||
this api streams back steps rather than individual tokens.
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||
run_id: Optional job/run ID for tracking
|
||
use_assistant_message: Whether to use assistant message format
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||
include_return_message_types: Filter for which message types to return
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||
request_start_timestamp_ns: Start time for tracking request duration
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||
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||
Yields:
|
||
str: JSON-formatted SSE data chunks for each completed step
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||
"""
|
||
self._initialize_state()
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||
request_span = self._request_checkpoint_start(request_start_timestamp_ns=request_start_timestamp_ns)
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||
first_chunk = True
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||
|
||
if stream_tokens:
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||
llm_adapter = SimpleLLMStreamAdapter(
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||
llm_client=self.llm_client,
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||
llm_config=self.agent_state.llm_config,
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||
run_id=run_id,
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||
)
|
||
else:
|
||
llm_adapter = SimpleLLMRequestAdapter(
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||
llm_client=self.llm_client,
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||
llm_config=self.agent_state.llm_config,
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||
)
|
||
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||
try:
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in_context_messages, input_messages_to_persist = await _prepare_in_context_messages_no_persist_async(
|
||
input_messages, self.agent_state, self.message_manager, self.actor, run_id
|
||
)
|
||
follow_up_messages = []
|
||
if len(input_messages_to_persist) > 1 and input_messages_to_persist[0].role == "approval":
|
||
follow_up_messages = input_messages_to_persist[1:]
|
||
input_messages_to_persist = [input_messages_to_persist[0]]
|
||
|
||
in_context_messages = in_context_messages + input_messages_to_persist
|
||
for i in range(max_steps):
|
||
if i == 1 and follow_up_messages:
|
||
input_messages_to_persist = follow_up_messages
|
||
follow_up_messages = []
|
||
response = self._step(
|
||
messages=in_context_messages + self.response_messages,
|
||
input_messages_to_persist=input_messages_to_persist,
|
||
llm_adapter=llm_adapter,
|
||
run_id=run_id,
|
||
# use_assistant_message=use_assistant_message,
|
||
include_return_message_types=include_return_message_types,
|
||
request_start_timestamp_ns=request_start_timestamp_ns,
|
||
)
|
||
async for chunk in response:
|
||
if first_chunk:
|
||
request_span = self._request_checkpoint_ttft(request_span, request_start_timestamp_ns)
|
||
yield f"data: {chunk.model_dump_json()}\n\n"
|
||
first_chunk = False
|
||
|
||
# Check if step was cancelled - break out of the step loop
|
||
if not self.should_continue:
|
||
break
|
||
|
||
# Proactive summarization if approaching context limit
|
||
if (
|
||
self.last_step_usage
|
||
and self.last_step_usage.total_tokens > self.agent_state.llm_config.context_window * SUMMARIZATION_TRIGGER_MULTIPLIER
|
||
and not self.agent_state.message_buffer_autoclear
|
||
):
|
||
self.logger.warning(
|
||
f"Step usage ({self.last_step_usage.total_tokens} tokens) approaching "
|
||
f"context limit ({self.agent_state.llm_config.context_window}), triggering summarization."
|
||
)
|
||
|
||
in_context_messages = await self.summarize_conversation_history(
|
||
in_context_messages=in_context_messages,
|
||
new_letta_messages=self.response_messages,
|
||
total_tokens=self.last_step_usage.total_tokens,
|
||
force=True,
|
||
)
|
||
|
||
# Clear to avoid duplication in next iteration
|
||
self.response_messages = []
|
||
|
||
if not self.should_continue:
|
||
break
|
||
|
||
input_messages_to_persist = []
|
||
|
||
if self.stop_reason is None:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.max_steps.value)
|
||
|
||
if not self.agent_state.message_buffer_autoclear:
|
||
if self.last_step_usage:
|
||
await self.summarize_conversation_history(
|
||
in_context_messages=in_context_messages,
|
||
new_letta_messages=self.response_messages,
|
||
total_tokens=self.last_step_usage.total_tokens,
|
||
force=False,
|
||
)
|
||
else:
|
||
self.logger.warning(
|
||
"Post-loop summarization skipped: last_step_usage is None. "
|
||
"No step completed successfully or usage stats were not updated."
|
||
)
|
||
|
||
except Exception as e:
|
||
self.logger.warning(f"Error during agent stream: {e}", exc_info=True)
|
||
|
||
# Set stop_reason if not already set
|
||
if self.stop_reason is None:
|
||
# Classify error type
|
||
if isinstance(e, LLMError):
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.llm_api_error.value)
|
||
else:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
|
||
|
||
if first_chunk:
|
||
# Raise if no chunks sent yet (response not started, can return error status code)
|
||
raise
|
||
else:
|
||
yield f"data: {self.stop_reason.model_dump_json()}\n\n"
|
||
|
||
# Mid-stream error: yield error event to client in SSE format
|
||
error_message = LettaErrorMessage(
|
||
run_id=run_id,
|
||
error_type="internal_error",
|
||
message="An error occurred during agent execution.",
|
||
detail=str(e),
|
||
)
|
||
yield f"event: error\ndata: {error_message.model_dump_json()}\n\n"
|
||
|
||
# Return immediately - don't fall through to finish chunks
|
||
# This prevents sending end_turn finish chunks after an error
|
||
return
|
||
|
||
# Cleanup and finalize (only runs if no exception occurred)
|
||
try:
|
||
if run_id:
|
||
letta_messages = Message.to_letta_messages_from_list(
|
||
self.response_messages_for_metadata, # Use separate accumulator to preserve all messages
|
||
use_assistant_message=False, # NOTE: set to false
|
||
reverse=False,
|
||
# text_is_assistant_message=(self.agent_state.agent_type == AgentType.react_agent),
|
||
text_is_assistant_message=True,
|
||
)
|
||
result = LettaResponse(messages=letta_messages, stop_reason=self.stop_reason, usage=self.usage)
|
||
if self.job_update_metadata is None:
|
||
self.job_update_metadata = {}
|
||
self.job_update_metadata["result"] = result.model_dump(mode="json")
|
||
|
||
await self._request_checkpoint_finish(
|
||
request_span=request_span, request_start_timestamp_ns=request_start_timestamp_ns, run_id=run_id
|
||
)
|
||
for finish_chunk in self.get_finish_chunks_for_stream(self.usage, self.stop_reason):
|
||
yield f"data: {finish_chunk}\n\n"
|
||
except Exception as cleanup_error:
|
||
# Error during cleanup/finalization - ensure we still send a terminal event
|
||
self.logger.error(f"Error during stream cleanup: {cleanup_error}", exc_info=True)
|
||
|
||
# Set stop_reason if not already set
|
||
if self.stop_reason is None:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
|
||
|
||
yield f"data: {self.stop_reason.model_dump_json()}\n\n"
|
||
|
||
# Send error event
|
||
error_message = LettaErrorMessage(
|
||
run_id=run_id,
|
||
error_type="cleanup_error",
|
||
message="An error occurred during stream finalization.",
|
||
detail=str(cleanup_error),
|
||
)
|
||
yield f"event: error\ndata: {error_message.model_dump_json()}\n\n"
|
||
# Note: we don't send finish chunks here since we already errored
|
||
|
||
@trace_method
|
||
async def _step(
|
||
self,
|
||
messages: list[Message],
|
||
llm_adapter: LettaLLMAdapter,
|
||
input_messages_to_persist: list[Message] | None = None,
|
||
run_id: str | None = None,
|
||
# use_assistant_message: bool = True,
|
||
include_return_message_types: list[MessageType] | None = None,
|
||
request_start_timestamp_ns: int | None = None,
|
||
remaining_turns: int = -1,
|
||
dry_run: bool = False,
|
||
enforce_run_id_set: bool = True,
|
||
) -> AsyncGenerator[LettaMessage | dict, None]:
|
||
"""
|
||
Execute a single agent step (one LLM call and tool execution).
|
||
|
||
This is the core execution method that all public methods (step, stream_steps,
|
||
stream_tokens) funnel through. It handles the complete flow of making an LLM
|
||
request, processing the response, executing tools, and persisting messages.
|
||
|
||
Args:
|
||
messages: Current in-context messages
|
||
llm_adapter: Adapter for LLM interaction (blocking or streaming)
|
||
input_messages_to_persist: New messages to persist after execution
|
||
run_id: Optional job/run ID for tracking
|
||
include_return_message_types: Filter for which message types to yield
|
||
request_start_timestamp_ns: Start time for tracking request duration
|
||
remaining_turns: Number of turns remaining (for max_steps enforcement)
|
||
dry_run: If true, only build and return the request without executing
|
||
|
||
Yields:
|
||
LettaMessage or dict: Chunks for streaming mode, or request data for dry_run
|
||
"""
|
||
if enforce_run_id_set and run_id is None:
|
||
raise AssertionError("run_id is required when enforce_run_id_set is True")
|
||
|
||
step_progression = StepProgression.START
|
||
# TODO(@caren): clean this up
|
||
tool_calls, content, agent_step_span, first_chunk, step_id, logged_step, step_start_ns, step_metrics = (
|
||
None,
|
||
None,
|
||
None,
|
||
None,
|
||
None,
|
||
None,
|
||
None,
|
||
None,
|
||
)
|
||
try:
|
||
self.last_function_response = _load_last_function_response(messages)
|
||
valid_tools = await self._get_valid_tools()
|
||
require_tool_call = self.tool_rules_solver.should_force_tool_call()
|
||
|
||
if self._require_tool_call != require_tool_call:
|
||
if require_tool_call:
|
||
self.logger.info("switching to constrained mode (forcing tool call)")
|
||
else:
|
||
self.logger.info("switching to unconstrained mode (allowing non-tool responses)")
|
||
self._require_tool_call = require_tool_call
|
||
|
||
# Always refresh messages at the start of each step to pick up external inputs
|
||
# (e.g., approval responses submitted by the client while this stream is running)
|
||
try:
|
||
messages = await self._refresh_messages(messages)
|
||
except Exception as e:
|
||
self.logger.warning(f"Failed to refresh messages at step start: {e}")
|
||
|
||
approval_request, approval_response = _maybe_get_approval_messages(messages)
|
||
tool_call_denials, tool_returns = [], []
|
||
if approval_request and approval_response:
|
||
content = approval_request.content
|
||
|
||
# Get tool calls that are pending
|
||
backfill_tool_call_id = approval_request.tool_calls[0].id # legacy case
|
||
if approval_response.approvals:
|
||
approved_tool_call_ids = {
|
||
backfill_tool_call_id if a.tool_call_id.startswith("message-") else a.tool_call_id
|
||
for a in approval_response.approvals
|
||
if isinstance(a, ApprovalReturn) and a.approve
|
||
}
|
||
else:
|
||
approved_tool_call_ids = {}
|
||
tool_calls = [tool_call for tool_call in approval_request.tool_calls if tool_call.id in approved_tool_call_ids]
|
||
pending_tool_call_message = _maybe_get_pending_tool_call_message(messages)
|
||
if pending_tool_call_message:
|
||
tool_calls.extend(pending_tool_call_message.tool_calls)
|
||
|
||
# Get tool calls that were denied
|
||
if approval_response.approvals:
|
||
denies = {d.tool_call_id: d for d in approval_response.approvals if isinstance(d, ApprovalReturn) and not d.approve}
|
||
else:
|
||
denies = {}
|
||
tool_call_denials = [
|
||
ToolCallDenial(**t.model_dump(), reason=denies.get(t.id).reason) for t in approval_request.tool_calls if t.id in denies
|
||
]
|
||
|
||
# Get tool calls that were executed client side
|
||
if approval_response.approvals:
|
||
tool_returns = [r for r in approval_response.approvals if isinstance(r, ToolReturn)]
|
||
|
||
# Validate that the approval response contains meaningful data
|
||
# If all three lists are empty, this is a malformed approval response
|
||
if not tool_calls and not tool_call_denials and not tool_returns:
|
||
self.logger.error(
|
||
f"Invalid approval response: approval_response.approvals is {approval_response.approvals} "
|
||
f"but no tool calls, denials, or returns were extracted. "
|
||
f"This likely indicates a corrupted or malformed approval payload."
|
||
)
|
||
self.should_continue = False
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_tool_call.value)
|
||
return
|
||
|
||
step_id = approval_request.step_id
|
||
step_metrics = await self.step_manager.get_step_metrics_async(step_id=step_id, actor=self.actor)
|
||
else:
|
||
# Check for job cancellation at the start of each step
|
||
if run_id and await self._check_run_cancellation(run_id):
|
||
self.should_continue = False
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.cancelled.value)
|
||
self.logger.info(f"Agent execution cancelled for run {run_id}")
|
||
return
|
||
|
||
step_id = generate_step_id()
|
||
step_progression, logged_step, step_metrics, agent_step_span = await self._step_checkpoint_start(
|
||
step_id=step_id, run_id=run_id
|
||
)
|
||
|
||
force_tool_call = valid_tools[0]["name"] if len(valid_tools) == 1 and self._require_tool_call else None
|
||
for llm_request_attempt in range(summarizer_settings.max_summarizer_retries + 1):
|
||
try:
|
||
request_data = self.llm_client.build_request_data(
|
||
agent_type=self.agent_state.agent_type,
|
||
messages=messages,
|
||
llm_config=self.agent_state.llm_config,
|
||
tools=valid_tools,
|
||
force_tool_call=force_tool_call,
|
||
requires_subsequent_tool_call=self._require_tool_call,
|
||
tool_return_truncation_chars=self._compute_tool_return_truncation_chars(),
|
||
)
|
||
# TODO: Extend to more providers, and also approval tool rules
|
||
# Enable parallel tool use when no tool rules are attached
|
||
try:
|
||
no_tool_rules = (
|
||
not self.agent_state.tool_rules
|
||
or len([t for t in self.agent_state.tool_rules if t.type != "requires_approval"]) == 0
|
||
)
|
||
|
||
# Anthropic/Bedrock parallel tool use
|
||
if self.agent_state.llm_config.model_endpoint_type in ["anthropic", "bedrock"]:
|
||
if (
|
||
isinstance(request_data.get("tool_choice"), dict)
|
||
and "disable_parallel_tool_use" in request_data["tool_choice"]
|
||
):
|
||
# Gate parallel tool use on both: no tool rules and toggled on
|
||
if no_tool_rules and self.agent_state.llm_config.parallel_tool_calls:
|
||
request_data["tool_choice"]["disable_parallel_tool_use"] = False
|
||
else:
|
||
# Explicitly disable when tool rules present or llm_config toggled off
|
||
request_data["tool_choice"]["disable_parallel_tool_use"] = True
|
||
|
||
# OpenAI parallel tool use
|
||
elif self.agent_state.llm_config.model_endpoint_type == "openai":
|
||
# For OpenAI, we control parallel tool calling via parallel_tool_calls field
|
||
# Only allow parallel tool calls when no tool rules and enabled in config
|
||
if "parallel_tool_calls" in request_data:
|
||
if no_tool_rules and self.agent_state.llm_config.parallel_tool_calls:
|
||
request_data["parallel_tool_calls"] = True
|
||
else:
|
||
request_data["parallel_tool_calls"] = False
|
||
|
||
# Gemini (Google AI/Vertex) parallel tool use
|
||
elif self.agent_state.llm_config.model_endpoint_type in ["google_ai", "google_vertex"]:
|
||
# Gemini supports parallel tool calling natively through multiple parts in the response
|
||
# We just need to ensure the config flag is set for tracking purposes
|
||
# The actual handling happens in GoogleVertexClient.convert_response_to_chat_completion
|
||
pass # No specific request_data field needed for Gemini
|
||
except Exception:
|
||
# if this fails, we simply don't enable parallel tool use
|
||
pass
|
||
if dry_run:
|
||
yield request_data
|
||
return
|
||
|
||
step_progression, step_metrics = self._step_checkpoint_llm_request_start(step_metrics, agent_step_span)
|
||
invocation = llm_adapter.invoke_llm(
|
||
request_data=request_data,
|
||
messages=messages,
|
||
tools=valid_tools,
|
||
use_assistant_message=False, # NOTE: set to false
|
||
requires_approval_tools=self.tool_rules_solver.get_requires_approval_tools(
|
||
set([t["name"] for t in valid_tools])
|
||
),
|
||
step_id=step_id,
|
||
actor=self.actor,
|
||
)
|
||
async for chunk in invocation:
|
||
if llm_adapter.supports_token_streaming():
|
||
if include_return_message_types is None or chunk.message_type in include_return_message_types:
|
||
first_chunk = True
|
||
yield chunk
|
||
# If you've reached this point without an error, break out of retry loop
|
||
break
|
||
except ValueError as e:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_llm_response.value)
|
||
raise e
|
||
except LLMError as e:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.llm_api_error.value)
|
||
raise e
|
||
except Exception as e:
|
||
if isinstance(e, ContextWindowExceededError) and llm_request_attempt < summarizer_settings.max_summarizer_retries:
|
||
# Retry case
|
||
messages = await self.summarize_conversation_history(
|
||
in_context_messages=messages,
|
||
new_letta_messages=self.response_messages,
|
||
force=True,
|
||
)
|
||
else:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
|
||
self.logger.error(f"Unknown error occured for run {run_id}: {e}")
|
||
raise e
|
||
|
||
step_progression, step_metrics = self._step_checkpoint_llm_request_finish(
|
||
step_metrics, agent_step_span, llm_adapter.llm_request_finish_timestamp_ns
|
||
)
|
||
|
||
self._update_global_usage_stats(llm_adapter.usage)
|
||
|
||
# Handle the AI response with the extracted data (supports multiple tool calls)
|
||
# Gather tool calls - check for multi-call API first, then fall back to single
|
||
if hasattr(llm_adapter, "tool_calls") and llm_adapter.tool_calls:
|
||
tool_calls = llm_adapter.tool_calls
|
||
elif llm_adapter.tool_call is not None:
|
||
tool_calls = [llm_adapter.tool_call]
|
||
else:
|
||
tool_calls = []
|
||
|
||
aggregated_persisted: list[Message] = []
|
||
persisted_messages, self.should_continue, self.stop_reason = await self._handle_ai_response(
|
||
tool_calls=tool_calls,
|
||
valid_tool_names=[tool["name"] for tool in valid_tools],
|
||
agent_state=self.agent_state,
|
||
tool_rules_solver=self.tool_rules_solver,
|
||
usage=UsageStatistics(
|
||
completion_tokens=self.usage.completion_tokens,
|
||
prompt_tokens=self.usage.prompt_tokens,
|
||
total_tokens=self.usage.total_tokens,
|
||
),
|
||
content=content or llm_adapter.content,
|
||
pre_computed_assistant_message_id=llm_adapter.message_id,
|
||
step_id=step_id,
|
||
initial_messages=input_messages_to_persist,
|
||
agent_step_span=agent_step_span,
|
||
is_final_step=(remaining_turns == 0),
|
||
run_id=run_id,
|
||
step_metrics=step_metrics,
|
||
is_approval_response=approval_response is not None,
|
||
tool_call_denials=tool_call_denials,
|
||
tool_returns=tool_returns,
|
||
)
|
||
aggregated_persisted.extend(persisted_messages)
|
||
# NOTE: there is an edge case where persisted_messages is empty (the LLM did a "no-op")
|
||
|
||
new_message_idx = len(input_messages_to_persist) if input_messages_to_persist else 0
|
||
self.response_messages.extend(aggregated_persisted[new_message_idx:])
|
||
self.response_messages_for_metadata.extend(aggregated_persisted[new_message_idx:]) # Track for job metadata
|
||
|
||
if llm_adapter.supports_token_streaming():
|
||
if tool_calls:
|
||
# Stream each tool return if tools were executed
|
||
response_tool_returns = [msg for msg in aggregated_persisted if msg.role == "tool"]
|
||
for tr in response_tool_returns:
|
||
# Skip streaming for aggregated parallel tool returns (no per-call tool_call_id)
|
||
if tr.tool_call_id is None and tr.tool_returns:
|
||
continue
|
||
tool_return_letta = tr.to_letta_messages()[0]
|
||
if include_return_message_types is None or tool_return_letta.message_type in include_return_message_types:
|
||
yield tool_return_letta
|
||
else:
|
||
filter_user_messages = [m for m in aggregated_persisted[new_message_idx:] if m.role != "user"]
|
||
letta_messages = Message.to_letta_messages_from_list(
|
||
filter_user_messages,
|
||
use_assistant_message=False, # NOTE: set to false
|
||
reverse=False,
|
||
# text_is_assistant_message=(self.agent_state.agent_type == AgentType.react_agent),
|
||
text_is_assistant_message=True,
|
||
)
|
||
for message in letta_messages:
|
||
if include_return_message_types is None or message.message_type in include_return_message_types:
|
||
yield message
|
||
|
||
# Note: message_ids update for approval responses now happens immediately after
|
||
# persistence in _handle_ai_response (line ~1093-1107) to prevent desync when
|
||
# the stream is interrupted and this generator is abandoned before being fully consumed
|
||
step_progression, step_metrics = await self._step_checkpoint_finish(step_metrics, agent_step_span, logged_step)
|
||
except Exception as e:
|
||
self.logger.warning(f"Error during step processing: {e}")
|
||
self.job_update_metadata = {"error": str(e)}
|
||
|
||
# This indicates we failed after we decided to stop stepping, which indicates a bug with our flow.
|
||
if not self.stop_reason:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
|
||
elif self.stop_reason.stop_reason in (StopReasonType.end_turn, StopReasonType.max_steps, StopReasonType.tool_rule):
|
||
self.logger.warning("Error occurred during step processing, with valid stop reason: %s", self.stop_reason.stop_reason)
|
||
elif self.stop_reason.stop_reason not in (
|
||
StopReasonType.no_tool_call,
|
||
StopReasonType.invalid_tool_call,
|
||
StopReasonType.invalid_llm_response,
|
||
StopReasonType.llm_api_error,
|
||
):
|
||
self.logger.warning("Error occurred during step processing, with unexpected stop reason: %s", self.stop_reason.stop_reason)
|
||
raise e
|
||
finally:
|
||
self.logger.debug("Running cleanup for agent loop run: %s", run_id)
|
||
self.logger.info("Running final update. Step Progression: %s", step_progression)
|
||
try:
|
||
if step_progression == StepProgression.FINISHED:
|
||
if not self.should_continue:
|
||
if self.stop_reason is None:
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
|
||
if logged_step and step_id:
|
||
await self.step_manager.update_step_stop_reason(self.actor, step_id, self.stop_reason.stop_reason)
|
||
return
|
||
if step_progression < StepProgression.STEP_LOGGED:
|
||
# Error occurred before step was fully logged
|
||
import traceback
|
||
|
||
if logged_step:
|
||
await self.step_manager.update_step_error_async(
|
||
actor=self.actor,
|
||
step_id=step_id, # Use original step_id for telemetry
|
||
error_type=type(e).__name__ if "e" in locals() else "Unknown",
|
||
error_message=str(e) if "e" in locals() else "Unknown error",
|
||
error_traceback=traceback.format_exc(),
|
||
stop_reason=self.stop_reason,
|
||
)
|
||
if step_progression <= StepProgression.STREAM_RECEIVED:
|
||
if first_chunk and settings.track_errored_messages and input_messages_to_persist:
|
||
for message in input_messages_to_persist:
|
||
message.is_err = True
|
||
message.step_id = step_id
|
||
message.run_id = run_id
|
||
await self.message_manager.create_many_messages_async(
|
||
input_messages_to_persist,
|
||
actor=self.actor,
|
||
run_id=run_id,
|
||
project_id=self.agent_state.project_id,
|
||
template_id=self.agent_state.template_id,
|
||
)
|
||
elif step_progression <= StepProgression.LOGGED_TRACE:
|
||
if self.stop_reason is None:
|
||
self.logger.warning("Error in step after logging step")
|
||
self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
|
||
if logged_step:
|
||
await self.step_manager.update_step_stop_reason(self.actor, step_id, self.stop_reason.stop_reason)
|
||
else:
|
||
self.logger.warning("Invalid StepProgression value")
|
||
|
||
# Do tracking for failure cases. Can consolidate with success conditions later.
|
||
if settings.track_stop_reason:
|
||
await self._log_request(request_start_timestamp_ns, None, self.job_update_metadata, is_error=True, run_id=run_id)
|
||
|
||
# Record partial step metrics on failure (capture whatever timing data we have)
|
||
if logged_step and step_metrics and step_progression < StepProgression.FINISHED:
|
||
# Calculate total step time up to the failure point
|
||
step_metrics.step_ns = get_utc_timestamp_ns() - step_metrics.step_start_ns
|
||
|
||
await self._record_step_metrics(
|
||
step_id=step_id,
|
||
step_metrics=step_metrics,
|
||
run_id=run_id,
|
||
)
|
||
except Exception as e:
|
||
self.logger.warning(f"Error during post-completion step tracking: {e}")
|
||
|
||
@trace_method
|
||
async def _handle_ai_response(
|
||
self,
|
||
valid_tool_names: list[str],
|
||
agent_state: AgentState,
|
||
tool_rules_solver: ToolRulesSolver,
|
||
usage: UsageStatistics,
|
||
content: list[TextContent | ReasoningContent | RedactedReasoningContent | OmittedReasoningContent] | None = None,
|
||
pre_computed_assistant_message_id: str | None = None,
|
||
step_id: str | None = None,
|
||
initial_messages: list[Message] | None = None,
|
||
agent_step_span: Span | None = None,
|
||
is_final_step: bool | None = None,
|
||
run_id: str | None = None,
|
||
step_metrics: StepMetrics = None,
|
||
is_approval_response: bool | None = None,
|
||
tool_calls: list[ToolCall] = [],
|
||
tool_call_denials: list[ToolCallDenial] = [],
|
||
tool_returns: list[ToolReturn] = [],
|
||
) -> tuple[list[Message], bool, LettaStopReason | None]:
|
||
"""
|
||
Handle the final AI response once streaming completes, execute / validate tool calls,
|
||
decide whether we should keep stepping, and persist state.
|
||
|
||
Unified approach: treats single and multi-tool calls uniformly to reduce code duplication.
|
||
"""
|
||
# 1. Handle no-tool cases (content-only or no-op)
|
||
if not tool_calls and not tool_call_denials and not tool_returns:
|
||
# Case 1a: No tool call, no content (LLM no-op)
|
||
if content is None or len(content) == 0:
|
||
# Check if there are required-before-exit tools that haven't been called
|
||
uncalled = tool_rules_solver.get_uncalled_required_tools(available_tools=set([t.name for t in agent_state.tools]))
|
||
if uncalled:
|
||
heartbeat_reason = (
|
||
f"{NON_USER_MSG_PREFIX}ToolRuleViolated: You must call {', '.join(uncalled)} at least once to exit the loop."
|
||
)
|
||
from letta.server.rest_api.utils import create_heartbeat_system_message
|
||
|
||
heartbeat_msg = create_heartbeat_system_message(
|
||
agent_id=agent_state.id,
|
||
model=agent_state.llm_config.model,
|
||
function_call_success=True,
|
||
timezone=agent_state.timezone,
|
||
heartbeat_reason=heartbeat_reason,
|
||
run_id=run_id,
|
||
)
|
||
messages_to_persist = (initial_messages or []) + [heartbeat_msg]
|
||
continue_stepping, stop_reason = True, None
|
||
else:
|
||
# No required tools remaining, end turn without persisting no-op
|
||
continue_stepping = False
|
||
stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
|
||
messages_to_persist = initial_messages or []
|
||
|
||
# Case 1b: No tool call but has content
|
||
else:
|
||
continue_stepping, heartbeat_reason, stop_reason = self._decide_continuation(
|
||
agent_state=agent_state,
|
||
tool_call_name=None,
|
||
tool_rule_violated=False,
|
||
tool_rules_solver=tool_rules_solver,
|
||
is_final_step=is_final_step,
|
||
)
|
||
assistant_message = create_letta_messages_from_llm_response(
|
||
agent_id=agent_state.id,
|
||
model=agent_state.llm_config.model,
|
||
function_name=None,
|
||
function_arguments=None,
|
||
tool_execution_result=None,
|
||
tool_call_id=None,
|
||
function_response=None,
|
||
timezone=agent_state.timezone,
|
||
continue_stepping=continue_stepping,
|
||
heartbeat_reason=heartbeat_reason,
|
||
reasoning_content=content,
|
||
pre_computed_assistant_message_id=pre_computed_assistant_message_id,
|
||
step_id=step_id,
|
||
run_id=run_id,
|
||
is_approval_response=is_approval_response,
|
||
force_set_request_heartbeat=False,
|
||
add_heartbeat_on_continue=bool(heartbeat_reason),
|
||
)
|
||
messages_to_persist = (initial_messages or []) + assistant_message
|
||
|
||
# Persist messages for no-tool cases
|
||
for message in messages_to_persist:
|
||
if message.run_id is None:
|
||
message.run_id = run_id
|
||
if message.step_id is None:
|
||
message.step_id = step_id
|
||
|
||
persisted_messages = await self.message_manager.create_many_messages_async(
|
||
messages_to_persist, actor=self.actor, run_id=run_id, project_id=agent_state.project_id, template_id=agent_state.template_id
|
||
)
|
||
return persisted_messages, continue_stepping, stop_reason
|
||
|
||
# 2. Check whether tool call requires approval
|
||
if not is_approval_response:
|
||
requested_tool_calls = [t for t in tool_calls if tool_rules_solver.is_requires_approval_tool(t.function.name)]
|
||
allowed_tool_calls = [t for t in tool_calls if not tool_rules_solver.is_requires_approval_tool(t.function.name)]
|
||
if requested_tool_calls:
|
||
approval_messages = create_approval_request_message_from_llm_response(
|
||
agent_id=agent_state.id,
|
||
model=agent_state.llm_config.model,
|
||
requested_tool_calls=requested_tool_calls,
|
||
allowed_tool_calls=allowed_tool_calls,
|
||
reasoning_content=content,
|
||
pre_computed_assistant_message_id=pre_computed_assistant_message_id,
|
||
step_id=step_id,
|
||
run_id=run_id,
|
||
)
|
||
messages_to_persist = (initial_messages or []) + approval_messages
|
||
|
||
for message in messages_to_persist:
|
||
if message.run_id is None:
|
||
message.run_id = run_id
|
||
if message.step_id is None:
|
||
message.step_id = step_id
|
||
|
||
persisted_messages = await self.message_manager.create_many_messages_async(
|
||
messages_to_persist,
|
||
actor=self.actor,
|
||
run_id=run_id,
|
||
project_id=agent_state.project_id,
|
||
template_id=agent_state.template_id,
|
||
)
|
||
return persisted_messages, False, LettaStopReason(stop_reason=StopReasonType.requires_approval.value)
|
||
|
||
result_tool_returns = []
|
||
|
||
# 3. Handle client side tool execution
|
||
if tool_returns:
|
||
# Clamp client-side tool returns before persisting (JSON-aware: truncate only the 'message' field)
|
||
try:
|
||
cap = self._compute_tool_return_truncation_chars()
|
||
except Exception:
|
||
cap = 5000
|
||
|
||
for tr in tool_returns:
|
||
try:
|
||
if tr.func_response and isinstance(tr.func_response, str):
|
||
parsed = json.loads(tr.func_response)
|
||
if isinstance(parsed, dict) and "message" in parsed and isinstance(parsed["message"], str):
|
||
msg = parsed["message"]
|
||
if len(msg) > cap:
|
||
original_len = len(msg)
|
||
parsed["message"] = msg[:cap] + f"... [truncated {original_len - cap} chars]"
|
||
tr.func_response = json.dumps(parsed)
|
||
self.logger.warning(f"Truncated client-side tool return message from {original_len} to {cap} chars")
|
||
else:
|
||
# Fallback to raw string truncation if not a dict with 'message'
|
||
if len(tr.func_response) > cap:
|
||
original_len = len(tr.func_response)
|
||
tr.func_response = tr.func_response[:cap] + f"... [truncated {original_len - cap} chars]"
|
||
self.logger.warning(f"Truncated client-side tool return (raw) from {original_len} to {cap} chars")
|
||
except json.JSONDecodeError:
|
||
# Non-JSON or unexpected shape; truncate as raw string
|
||
if tr.func_response and len(tr.func_response) > cap:
|
||
original_len = len(tr.func_response)
|
||
tr.func_response = tr.func_response[:cap] + f"... [truncated {original_len - cap} chars]"
|
||
self.logger.warning(f"Truncated client-side tool return (non-JSON) from {original_len} to {cap} chars")
|
||
except Exception as e:
|
||
# Unexpected error; log and skip truncation for this return
|
||
self.logger.warning(f"Failed to truncate client-side tool return: {e}")
|
||
|
||
continue_stepping = True
|
||
stop_reason = None
|
||
result_tool_returns = tool_returns
|
||
|
||
# 4. Handle denial cases
|
||
if tool_call_denials:
|
||
for tool_call_denial in tool_call_denials:
|
||
tool_call_id = tool_call_denial.id or f"call_{uuid.uuid4().hex[:8]}"
|
||
packaged_function_response = package_function_response(
|
||
was_success=False,
|
||
response_string=f"Error: request to call tool denied. User reason: {tool_call_denial.reason}",
|
||
timezone=agent_state.timezone,
|
||
)
|
||
tool_return = ToolReturn(
|
||
tool_call_id=tool_call_id,
|
||
func_response=packaged_function_response,
|
||
status="error",
|
||
)
|
||
result_tool_returns.append(tool_return)
|
||
|
||
# 5. Unified tool execution path (works for both single and multiple tools)
|
||
|
||
# 5a. Validate parallel tool calling constraints
|
||
if len(tool_calls) > 1:
|
||
# No parallel tool calls with tool rules
|
||
if agent_state.tool_rules and len([r for r in agent_state.tool_rules if r.type != "requires_approval"]) > 0:
|
||
raise ValueError(
|
||
"Parallel tool calling is not allowed when tool rules are present. Disable tool rules to use parallel tool calls."
|
||
)
|
||
|
||
# 5b. Prepare execution specs for all tools
|
||
exec_specs = []
|
||
for tc in tool_calls:
|
||
call_id = tc.id or f"call_{uuid.uuid4().hex[:8]}"
|
||
name = tc.function.name
|
||
args = _safe_load_tool_call_str(tc.function.arguments)
|
||
args.pop(REQUEST_HEARTBEAT_PARAM, None)
|
||
args.pop(INNER_THOUGHTS_KWARG, None)
|
||
|
||
# Validate against allowed tools
|
||
tool_rule_violated = name not in valid_tool_names and not is_approval_response
|
||
|
||
# Handle prefilled args if present
|
||
if not tool_rule_violated:
|
||
prefill_args = tool_rules_solver.last_prefilled_args_by_tool.get(name)
|
||
if prefill_args:
|
||
target_tool = next((t for t in agent_state.tools if t.name == name), None)
|
||
provenance = tool_rules_solver.last_prefilled_args_provenance.get(name)
|
||
try:
|
||
args = merge_and_validate_prefilled_args(
|
||
tool=target_tool,
|
||
llm_args=args,
|
||
prefilled_args=prefill_args,
|
||
)
|
||
except ValueError as ve:
|
||
# Invalid prefilled args - create error result
|
||
error_prefix = "Invalid prefilled tool arguments from tool rules"
|
||
prov_suffix = f" (source={provenance})" if provenance else ""
|
||
err_msg = f"{error_prefix}{prov_suffix}: {str(ve)}"
|
||
|
||
exec_specs.append(
|
||
{
|
||
"id": call_id,
|
||
"name": name,
|
||
"args": args,
|
||
"violated": False,
|
||
"error": err_msg,
|
||
}
|
||
)
|
||
continue
|
||
|
||
exec_specs.append(
|
||
{
|
||
"id": call_id,
|
||
"name": name,
|
||
"args": args,
|
||
"violated": tool_rule_violated,
|
||
"error": None,
|
||
}
|
||
)
|
||
|
||
# 5c. Execute tools (sequentially for single, parallel for multiple)
|
||
async def _run_one(spec: Dict[str, Any]):
|
||
if spec.get("error"):
|
||
return ToolExecutionResult(status="error", func_return=spec["error"]), 0
|
||
if spec["violated"]:
|
||
result = _build_rule_violation_result(spec["name"], valid_tool_names, tool_rules_solver)
|
||
return result, 0
|
||
t0 = get_utc_timestamp_ns()
|
||
target_tool = next((x for x in agent_state.tools if x.name == spec["name"]), None)
|
||
res = await self._execute_tool(
|
||
target_tool=target_tool,
|
||
tool_args=spec["args"],
|
||
agent_state=agent_state,
|
||
agent_step_span=agent_step_span,
|
||
step_id=step_id,
|
||
)
|
||
dt = get_utc_timestamp_ns() - t0
|
||
return res, dt
|
||
|
||
if len(exec_specs) == 1:
|
||
results = [await _run_one(exec_specs[0])]
|
||
else:
|
||
# separate tools by parallel execution capability
|
||
parallel_items = []
|
||
serial_items = []
|
||
|
||
for idx, spec in enumerate(exec_specs):
|
||
target_tool = next((x for x in agent_state.tools if x.name == spec["name"]), None)
|
||
if target_tool and target_tool.enable_parallel_execution:
|
||
parallel_items.append((idx, spec))
|
||
else:
|
||
serial_items.append((idx, spec))
|
||
|
||
# execute all parallel tools concurrently and all serial tools sequentially
|
||
results = [None] * len(exec_specs)
|
||
|
||
parallel_results = await asyncio.gather(*[_run_one(spec) for _, spec in parallel_items]) if parallel_items else []
|
||
for (idx, _), result in zip(parallel_items, parallel_results):
|
||
results[idx] = result
|
||
|
||
for idx, spec in serial_items:
|
||
results[idx] = await _run_one(spec)
|
||
|
||
# 5d. Update metrics with execution time
|
||
if step_metrics is not None and results:
|
||
step_metrics.tool_execution_ns = max(dt for _, dt in results)
|
||
|
||
# 5e. Process results and compute function responses
|
||
function_responses: list[Optional[str]] = []
|
||
persisted_continue_flags: list[bool] = []
|
||
persisted_stop_reasons: list[LettaStopReason | None] = []
|
||
|
||
for idx, spec in enumerate(exec_specs):
|
||
tool_execution_result, _ = results[idx]
|
||
has_prefill_error = bool(spec.get("error"))
|
||
|
||
# Validate and format function response
|
||
truncate = spec["name"] not in {"conversation_search", "conversation_search_date", "archival_memory_search"}
|
||
return_char_limit = next((t.return_char_limit for t in agent_state.tools if t.name == spec["name"]), None)
|
||
function_response_string = validate_function_response(
|
||
tool_execution_result.func_return,
|
||
return_char_limit=return_char_limit,
|
||
truncate=truncate,
|
||
)
|
||
function_responses.append(function_response_string)
|
||
|
||
# Update last function response (for tool rules)
|
||
self.last_function_response = package_function_response(
|
||
was_success=tool_execution_result.success_flag,
|
||
response_string=function_response_string,
|
||
timezone=agent_state.timezone,
|
||
)
|
||
|
||
# Register successful tool call with solver
|
||
if not spec["violated"] and not has_prefill_error:
|
||
tool_rules_solver.register_tool_call(spec["name"])
|
||
|
||
# Decide continuation for this tool
|
||
if has_prefill_error:
|
||
cont = False
|
||
hb_reason = None
|
||
sr = LettaStopReason(stop_reason=StopReasonType.invalid_tool_call.value)
|
||
else:
|
||
cont, hb_reason, sr = self._decide_continuation(
|
||
agent_state=agent_state,
|
||
tool_call_name=spec["name"],
|
||
tool_rule_violated=spec["violated"],
|
||
tool_rules_solver=tool_rules_solver,
|
||
is_final_step=(is_final_step and idx == len(exec_specs) - 1),
|
||
)
|
||
persisted_continue_flags.append(cont)
|
||
persisted_stop_reasons.append(sr)
|
||
|
||
# 5f. Create messages using parallel message creation (works for both single and multi)
|
||
tool_call_specs = [{"name": s["name"], "arguments": s["args"], "id": s["id"]} for s in exec_specs]
|
||
tool_execution_results = [res for (res, _) in results]
|
||
|
||
# Use the parallel message creation function for both single and multiple tools
|
||
parallel_messages = create_parallel_tool_messages_from_llm_response(
|
||
agent_id=agent_state.id,
|
||
model=agent_state.llm_config.model,
|
||
tool_call_specs=tool_call_specs,
|
||
tool_execution_results=tool_execution_results,
|
||
function_responses=function_responses,
|
||
timezone=agent_state.timezone,
|
||
run_id=run_id,
|
||
step_id=step_id,
|
||
reasoning_content=content,
|
||
pre_computed_assistant_message_id=pre_computed_assistant_message_id,
|
||
is_approval_response=is_approval_response,
|
||
tool_returns=result_tool_returns,
|
||
)
|
||
|
||
messages_to_persist: list[Message] = (initial_messages or []) + parallel_messages
|
||
|
||
# Set run_id and step_id on all messages before persisting
|
||
for message in messages_to_persist:
|
||
if message.run_id is None:
|
||
message.run_id = run_id
|
||
if message.step_id is None:
|
||
message.step_id = step_id
|
||
|
||
# Persist all messages
|
||
persisted_messages = await self.message_manager.create_many_messages_async(
|
||
messages_to_persist,
|
||
actor=self.actor,
|
||
run_id=run_id,
|
||
project_id=agent_state.project_id,
|
||
template_id=agent_state.template_id,
|
||
)
|
||
|
||
# Update message_ids immediately after persistence to prevent desync
|
||
# This handles approval responses where we need to keep message_ids in sync
|
||
if (
|
||
is_approval_response
|
||
and initial_messages
|
||
and len(initial_messages) == 1
|
||
and initial_messages[0].role == "approval"
|
||
and len(persisted_messages) >= 2
|
||
and persisted_messages[0].role == "approval"
|
||
and persisted_messages[1].role == "tool"
|
||
):
|
||
agent_state.message_ids = agent_state.message_ids + [m.id for m in persisted_messages[:2]]
|
||
await self.agent_manager.update_message_ids_async(
|
||
agent_id=agent_state.id, message_ids=agent_state.message_ids, actor=self.actor
|
||
)
|
||
|
||
# 5g. Aggregate continuation decisions
|
||
aggregate_continue = any(persisted_continue_flags) if persisted_continue_flags else False
|
||
aggregate_continue = aggregate_continue or tool_call_denials or tool_returns
|
||
|
||
# Determine aggregate stop reason
|
||
aggregate_stop_reason = None
|
||
for sr in persisted_stop_reasons:
|
||
if sr is not None:
|
||
aggregate_stop_reason = sr
|
||
|
||
# For parallel tool calls, always continue to allow the agent to process/summarize results
|
||
# unless a terminal tool was called or we hit max steps
|
||
if len(exec_specs) > 1:
|
||
has_terminal = any(sr and sr.stop_reason == StopReasonType.tool_rule.value for sr in persisted_stop_reasons)
|
||
is_max_steps = any(sr and sr.stop_reason == StopReasonType.max_steps.value for sr in persisted_stop_reasons)
|
||
|
||
if not has_terminal and not is_max_steps:
|
||
# Force continuation for parallel tool execution
|
||
aggregate_continue = True
|
||
aggregate_stop_reason = None
|
||
return persisted_messages, aggregate_continue, aggregate_stop_reason
|
||
|
||
@trace_method
|
||
def _decide_continuation(
|
||
self,
|
||
agent_state: AgentState,
|
||
tool_call_name: Optional[str],
|
||
tool_rule_violated: bool,
|
||
tool_rules_solver: ToolRulesSolver,
|
||
is_final_step: bool | None,
|
||
) -> tuple[bool, str | None, LettaStopReason | None]:
|
||
"""
|
||
In v3 loop, we apply the following rules:
|
||
|
||
1. Did not call a tool? Loop ends
|
||
|
||
2. Called a tool? Loop continues. This can be:
|
||
2a. Called tool, tool executed successfully
|
||
2b. Called tool, tool failed to execute
|
||
2c. Called tool + tool rule violation (did not execute)
|
||
|
||
"""
|
||
continue_stepping = True # Default continue
|
||
continuation_reason: str | None = None
|
||
stop_reason: LettaStopReason | None = None
|
||
|
||
if tool_call_name is None:
|
||
# No tool call – if there are required-before-exit tools uncalled, keep stepping
|
||
# and provide explicit feedback to the model; otherwise end the loop.
|
||
uncalled = tool_rules_solver.get_uncalled_required_tools(available_tools=set([t.name for t in agent_state.tools]))
|
||
if uncalled and not is_final_step:
|
||
reason = f"{NON_USER_MSG_PREFIX}ToolRuleViolated: You must call {', '.join(uncalled)} at least once to exit the loop."
|
||
return True, reason, None
|
||
# No required tools remaining → end turn
|
||
return False, None, LettaStopReason(stop_reason=StopReasonType.end_turn.value)
|
||
else:
|
||
if tool_rule_violated:
|
||
continue_stepping = True
|
||
continuation_reason = f"{NON_USER_MSG_PREFIX}Continuing: tool rule violation."
|
||
else:
|
||
tool_rules_solver.register_tool_call(tool_call_name)
|
||
|
||
if tool_rules_solver.is_terminal_tool(tool_call_name):
|
||
stop_reason = LettaStopReason(stop_reason=StopReasonType.tool_rule.value)
|
||
continue_stepping = False
|
||
|
||
elif tool_rules_solver.has_children_tools(tool_call_name):
|
||
continue_stepping = True
|
||
continuation_reason = f"{NON_USER_MSG_PREFIX}Continuing: child tool rule."
|
||
|
||
elif tool_rules_solver.is_continue_tool(tool_call_name):
|
||
continue_stepping = True
|
||
continuation_reason = f"{NON_USER_MSG_PREFIX}Continuing: continue tool rule."
|
||
|
||
# – hard stop overrides –
|
||
if is_final_step:
|
||
continue_stepping = False
|
||
stop_reason = LettaStopReason(stop_reason=StopReasonType.max_steps.value)
|
||
else:
|
||
uncalled = tool_rules_solver.get_uncalled_required_tools(available_tools=set([t.name for t in agent_state.tools]))
|
||
if uncalled:
|
||
continue_stepping = True
|
||
continuation_reason = (
|
||
f"{NON_USER_MSG_PREFIX}Continuing, user expects these tools: [{', '.join(uncalled)}] to be called still."
|
||
)
|
||
|
||
stop_reason = None # reset – we’re still going
|
||
|
||
return continue_stepping, continuation_reason, stop_reason
|
||
|
||
@trace_method
|
||
async def _get_valid_tools(self):
|
||
tools = self.agent_state.tools
|
||
valid_tool_names = self.tool_rules_solver.get_allowed_tool_names(
|
||
available_tools=set([t.name for t in tools]),
|
||
last_function_response=self.last_function_response,
|
||
error_on_empty=False, # Return empty list instead of raising error
|
||
) or list(set(t.name for t in tools))
|
||
allowed_tools = [enable_strict_mode(t.json_schema) for t in tools if t.name in set(valid_tool_names)]
|
||
terminal_tool_names = {rule.tool_name for rule in self.tool_rules_solver.terminal_tool_rules}
|
||
allowed_tools = runtime_override_tool_json_schema(
|
||
tool_list=allowed_tools,
|
||
response_format=self.agent_state.response_format,
|
||
request_heartbeat=False, # NOTE: difference for v3 (don't add request heartbeat)
|
||
terminal_tools=terminal_tool_names,
|
||
)
|
||
return allowed_tools
|