forked from Zakaria/hermes-agent
Hermes-agent
This commit is contained in:
Zakaria
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"""Per-turn setup for ``run_conversation`` (the turn prologue).
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``run_conversation`` opened with ~470 lines of straight-line setup before the
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tool-calling loop ever started: stdio guarding, runtime-main wiring, retry-counter
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resets, user-message sanitization, todo/nudge-counter hydration, system-prompt
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restore-or-build, crash-resilience persistence, preflight context compression, the
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``pre_llm_call`` plugin hook, and external-memory prefetch.
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All of that is *prologue* — it runs once per turn, has no back-references into the
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loop, and produces a fixed set of values the loop then consumes. ``TurnContext``
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captures those produced values; ``build_turn_context`` performs the setup work and
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returns one. ``run_conversation`` is left to unpack the context and run the loop,
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shrinking the orchestrator by the full prologue.
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The builder still mutates ``agent`` heavily (counters, thread id, cached prompt,
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session DB) exactly as the inline code did — those side effects are the point. The
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``TurnContext`` it returns carries only the *locals* the loop reads back.
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Behavior is identical to the original inline prologue; this is a pure
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move-and-name refactor with no semantic change.
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"""
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from __future__ import annotations
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import logging
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import threading
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import uuid
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional
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from agent.iteration_budget import IterationBudget
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from agent.model_metadata import estimate_request_tokens_rough
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logger = logging.getLogger(__name__)
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@dataclass
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class TurnContext:
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"""Values produced by the turn prologue and consumed by the turn loop."""
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# Sanitized inbound message (surrogates stripped).
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user_message: str
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# Clean message preserved for transcripts / memory queries (no nudge injection).
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original_user_message: Any
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# Working message list for this turn (loop appends to it).
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messages: List[Dict[str, Any]]
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# May be reset to None by preflight compression (new session created).
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conversation_history: Optional[List[Dict[str, Any]]]
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# Cached system prompt active for this turn (may be rebuilt by compression).
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active_system_prompt: Optional[str]
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# Task / turn identifiers.
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effective_task_id: str
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turn_id: str
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# Index of the current user turn within ``messages``.
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current_turn_user_idx: int
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# Whether the post-turn memory review should fire.
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should_review_memory: bool = False
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# Context contributed by ``pre_llm_call`` plugins (appended to user message).
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plugin_user_context: str = ""
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# External-memory prefetch result, reused across loop iterations.
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ext_prefetch_cache: str = ""
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def build_turn_context(
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agent,
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user_message: str,
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system_message: Optional[str],
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conversation_history: Optional[List[Dict[str, Any]]],
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task_id: Optional[str],
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stream_callback,
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persist_user_message: Optional[str],
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*,
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restore_or_build_system_prompt,
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install_safe_stdio,
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sanitize_surrogates,
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summarize_user_message_for_log,
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set_session_context,
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set_current_write_origin,
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ra,
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) -> TurnContext:
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"""Run the once-per-turn setup and return the loop's input context.
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The callables/helpers the original prologue referenced from the
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``conversation_loop`` module are passed in explicitly to keep this module
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free of an import cycle with ``agent.conversation_loop``.
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"""
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# Guard stdio against OSError from broken pipes (systemd/headless/daemon).
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install_safe_stdio()
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agent._ensure_db_session()
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# Tell auxiliary_client what the live main provider/model are for this turn.
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try:
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from agent.auxiliary_client import set_runtime_main
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set_runtime_main(
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getattr(agent, "provider", "") or "",
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getattr(agent, "model", "") or "",
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base_url=getattr(agent, "base_url", "") or "",
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api_key=getattr(agent, "api_key", "") or "",
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api_mode=getattr(agent, "api_mode", "") or "",
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)
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except Exception:
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pass
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# Tag log records on this thread with the session ID for ``hermes logs``.
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set_session_context(agent.session_id)
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# Bind the skill write-origin ContextVar for this thread.
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set_current_write_origin(getattr(agent, "_memory_write_origin", "assistant_tool"))
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# Restore the primary runtime if the previous turn activated fallback.
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agent._restore_primary_runtime()
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# Sanitize surrogate characters from user input.
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if isinstance(user_message, str):
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user_message = sanitize_surrogates(user_message)
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if isinstance(persist_user_message, str):
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persist_user_message = sanitize_surrogates(persist_user_message)
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# Store stream callback for _interruptible_api_call to pick up.
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agent._stream_callback = stream_callback
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agent._persist_user_message_idx = None
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agent._persist_user_message_override = persist_user_message
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# Generate unique task_id if not provided to isolate VMs between tasks.
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effective_task_id = task_id or str(uuid.uuid4())
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agent._current_task_id = effective_task_id
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turn_id = f"{agent.session_id or 'session'}:{effective_task_id}:{uuid.uuid4().hex[:8]}"
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agent._current_turn_id = turn_id
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agent._current_api_request_id = ""
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# Reset retry counters and iteration budget at the start of each turn.
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agent._invalid_tool_retries = 0
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agent._invalid_json_retries = 0
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agent._empty_content_retries = 0
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agent._incomplete_scratchpad_retries = 0
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agent._codex_incomplete_retries = 0
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agent._thinking_prefill_retries = 0
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agent._post_tool_empty_retried = False
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agent._last_content_with_tools = None
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agent._last_content_tools_all_housekeeping = False
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agent._mute_post_response = False
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agent._unicode_sanitization_passes = 0
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agent._tool_guardrails.reset_for_turn()
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agent._tool_guardrail_halt_decision = None
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agent._vision_supported = True
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# Pre-turn connection health check: clean up dead TCP connections.
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if agent.api_mode != "anthropic_messages":
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try:
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if agent._cleanup_dead_connections():
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agent._emit_status(
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"🔌 Detected stale connections from a previous provider "
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"issue — cleaned up automatically. Proceeding with fresh "
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"connection."
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)
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except Exception:
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pass
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# Replay compression warning through status_callback for gateway platforms.
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if agent._compression_warning:
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agent._replay_compression_warning()
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agent._compression_warning = None # send once
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# NOTE: _turns_since_memory and _iters_since_skill are NOT reset here.
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agent.iteration_budget = IterationBudget(agent.max_iterations)
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# Log conversation turn start for debugging/observability.
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_preview_text = summarize_user_message_for_log(user_message)
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_msg_preview = (_preview_text[:80] + "...") if len(_preview_text) > 80 else _preview_text
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_msg_preview = _msg_preview.replace("\n", " ")
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logger.info(
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"conversation turn: session=%s model=%s provider=%s platform=%s history=%d msg=%r",
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agent.session_id or "none", agent.model, agent.provider or "unknown",
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agent.platform or "unknown", len(conversation_history or []),
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_msg_preview,
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)
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# Initialize conversation (copy to avoid mutating the caller's list).
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messages = list(conversation_history) if conversation_history else []
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# Hydrate todo store from conversation history.
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if conversation_history and not agent._todo_store.has_items():
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agent._hydrate_todo_store(conversation_history)
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# Hydrate per-session nudge counters from persisted history (issue #22357).
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if conversation_history and agent._user_turn_count == 0:
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prior_user_turns = sum(
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1 for m in conversation_history if m.get("role") == "user"
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)
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if prior_user_turns > 0:
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agent._user_turn_count = prior_user_turns
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if agent._memory_nudge_interval > 0 and agent._turns_since_memory == 0:
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agent._turns_since_memory = prior_user_turns % agent._memory_nudge_interval
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# Track user turns for memory flush and periodic nudge logic.
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agent._user_turn_count += 1
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# Reset the streaming context scrubber at the top of each turn.
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scrubber = getattr(agent, "_stream_context_scrubber", None)
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if scrubber is not None:
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scrubber.reset()
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# Reset the think scrubber for the same reason.
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think_scrubber = getattr(agent, "_stream_think_scrubber", None)
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if think_scrubber is not None:
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think_scrubber.reset()
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# Preserve the original user message (no nudge injection).
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original_user_message = persist_user_message if persist_user_message is not None else user_message
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# Track memory nudge trigger (turn-based, checked here).
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should_review_memory = False
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if (agent._memory_nudge_interval > 0
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and "memory" in agent.valid_tool_names
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and agent._memory_store):
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agent._turns_since_memory += 1
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if agent._turns_since_memory >= agent._memory_nudge_interval:
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should_review_memory = True
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agent._turns_since_memory = 0
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# Add user message.
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user_msg = {"role": "user", "content": user_message}
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messages.append(user_msg)
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current_turn_user_idx = len(messages) - 1
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agent._persist_user_message_idx = current_turn_user_idx
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if not agent.quiet_mode:
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_print_preview = summarize_user_message_for_log(user_message)
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agent._safe_print(
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f"💬 Starting conversation: '{_print_preview[:60]}"
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f"{'...' if len(_print_preview) > 60 else ''}'"
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)
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# ── System prompt (cached per session for prefix caching) ──
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if agent._cached_system_prompt is None:
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restore_or_build_system_prompt(agent, system_message, conversation_history)
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active_system_prompt = agent._cached_system_prompt
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# Crash-resilience: persist the inbound user turn as soon as the session row exists.
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try:
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agent._persist_session(messages, conversation_history)
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except Exception:
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logger.warning(
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"Early turn-start session persistence failed for session=%s",
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agent.session_id or "none",
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exc_info=True,
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)
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# ── Preflight context compression ──
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if (
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agent.compression_enabled
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and len(messages) > agent.context_compressor.protect_first_n
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+ agent.context_compressor.protect_last_n + 1
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):
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_preflight_tokens = estimate_request_tokens_rough(
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messages,
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system_prompt=active_system_prompt or "",
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tools=agent.tools or None,
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)
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_compressor = agent.context_compressor
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_defer_preflight = getattr(
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_compressor,
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"should_defer_preflight_to_real_usage",
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lambda _tokens: False,
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)
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_preflight_deferred = _defer_preflight(_preflight_tokens)
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if not _preflight_deferred:
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_last = _compressor.last_prompt_tokens
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# Do NOT overwrite the -1 sentinel (#36718).
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if _last >= 0 and _preflight_tokens > _last:
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_compressor.last_prompt_tokens = _preflight_tokens
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if _preflight_deferred:
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logger.info(
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"Skipping preflight compression: rough estimate ~%s >= %s, "
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"but last real provider prompt was %s after compression",
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f"{_preflight_tokens:,}",
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f"{_compressor.threshold_tokens:,}",
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f"{_compressor.last_real_prompt_tokens:,}",
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)
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elif _compressor.should_compress(_preflight_tokens):
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logger.info(
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"Preflight compression: ~%s tokens >= %s threshold (model %s, ctx %s)",
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f"{_preflight_tokens:,}",
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f"{_compressor.threshold_tokens:,}",
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agent.model,
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f"{_compressor.context_length:,}",
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)
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agent._emit_status(
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f"📦 Preflight compression: ~{_preflight_tokens:,} tokens "
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f">= {_compressor.threshold_tokens:,} threshold. "
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"This may take a moment."
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)
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for _pass in range(3):
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_orig_len = len(messages)
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messages, active_system_prompt = agent._compress_context(
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messages, system_message, approx_tokens=_preflight_tokens,
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task_id=effective_task_id,
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)
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if len(messages) >= _orig_len:
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break # Cannot compress further
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conversation_history = None
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agent._empty_content_retries = 0
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agent._thinking_prefill_retries = 0
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agent._last_content_with_tools = None
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agent._last_content_tools_all_housekeeping = False
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agent._mute_post_response = False
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_preflight_tokens = estimate_request_tokens_rough(
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messages,
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system_prompt=active_system_prompt or "",
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tools=agent.tools or None,
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)
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if not _compressor.should_compress(_preflight_tokens):
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break
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# Plugin hook: pre_llm_call (context injected into user message, not system prompt).
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plugin_user_context = ""
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try:
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from hermes_cli.plugins import invoke_hook as _invoke_hook
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_pre_results = _invoke_hook(
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"pre_llm_call",
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session_id=agent.session_id,
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task_id=effective_task_id,
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turn_id=turn_id,
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user_message=original_user_message,
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conversation_history=list(messages),
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is_first_turn=(not bool(conversation_history)),
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model=agent.model,
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platform=getattr(agent, "platform", None) or "",
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sender_id=getattr(agent, "_user_id", None) or "",
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)
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_ctx_parts: list[str] = []
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for r in _pre_results:
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if isinstance(r, dict) and r.get("context"):
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_ctx_parts.append(str(r["context"]))
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elif isinstance(r, str) and r.strip():
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_ctx_parts.append(r)
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if _ctx_parts:
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plugin_user_context = "\n\n".join(_ctx_parts)
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except Exception as exc:
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logger.warning("pre_llm_call hook failed: %s", exc)
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# Per-turn file-mutation verifier state.
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agent._turn_failed_file_mutations = {}
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# Record the execution thread so interrupt()/clear_interrupt() can scope
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# the tool-level interrupt signal to THIS agent's thread only.
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agent._execution_thread_id = threading.current_thread().ident
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# Clear stale per-thread interrupt state, preserving a pending interrupt.
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ra()._set_interrupt(False, agent._execution_thread_id)
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if agent._interrupt_requested:
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ra()._set_interrupt(True, agent._execution_thread_id)
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agent._interrupt_thread_signal_pending = False
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else:
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agent._interrupt_message = None
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agent._interrupt_thread_signal_pending = False
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# Notify memory providers of the new turn (BEFORE prefetch_all).
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if agent._memory_manager:
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try:
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_turn_msg = original_user_message if isinstance(original_user_message, str) else ""
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agent._memory_manager.on_turn_start(agent._user_turn_count, _turn_msg)
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except Exception:
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pass
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# External memory provider: prefetch once before the tool loop.
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ext_prefetch_cache = ""
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if agent._memory_manager:
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try:
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_query = original_user_message if isinstance(original_user_message, str) else ""
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ext_prefetch_cache = agent._memory_manager.prefetch_all(_query) or ""
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except Exception:
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pass
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return TurnContext(
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user_message=user_message,
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original_user_message=original_user_message,
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messages=messages,
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conversation_history=conversation_history,
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active_system_prompt=active_system_prompt,
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effective_task_id=effective_task_id,
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turn_id=turn_id,
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current_turn_user_idx=current_turn_user_idx,
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should_review_memory=should_review_memory,
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plugin_user_context=plugin_user_context,
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ext_prefetch_cache=ext_prefetch_cache,
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)
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