"""Per-turn setup for ``run_conversation`` (the turn prologue). ``run_conversation`` opened with ~470 lines of straight-line setup before the tool-calling loop ever started: stdio guarding, runtime-main wiring, retry-counter resets, user-message sanitization, todo/nudge-counter hydration, system-prompt restore-or-build, crash-resilience persistence, preflight context compression, the ``pre_llm_call`` plugin hook, and external-memory prefetch. All of that is *prologue* — it runs once per turn, has no back-references into the loop, and produces a fixed set of values the loop then consumes. ``TurnContext`` captures those produced values; ``build_turn_context`` performs the setup work and returns one. ``run_conversation`` is left to unpack the context and run the loop, shrinking the orchestrator by the full prologue. The builder still mutates ``agent`` heavily (counters, thread id, cached prompt, session DB) exactly as the inline code did — those side effects are the point. The ``TurnContext`` it returns carries only the *locals* the loop reads back. Behavior is identical to the original inline prologue; this is a pure move-and-name refactor with no semantic change. """ from __future__ import annotations import logging import threading import uuid from dataclasses import dataclass from typing import Any, Dict, List, Optional from agent.conversation_compression import conversation_history_after_compression from agent.iteration_budget import IterationBudget from agent.model_metadata import ( estimate_messages_tokens_rough, estimate_request_tokens_rough, ) logger = logging.getLogger(__name__) def _compression_made_progress( orig_len: int, new_len: int, orig_tokens: int, new_tokens: int ) -> bool: """Return ``True`` if a compression pass materially reduced the request. Compression can succeed by summarising message contents — reducing the estimated request token count — without reducing the message row count. Treating row count as the sole progress signal false-positives on size-only wins and surfaces a misleading "Cannot compress further" failure even when post-compression tokens are well below the model context window. See issue #39548 for an observed case: 220 → 220 messages, ~288k → ~183k tokens on a 1M-context model still triggered auto-reset. The token reduction must be *material* (>5%) to count as progress — the same floor the overflow-handler retry path uses (conversation_loop.py, #39550) — so a sub-5% wobble doesn't keep the multi-pass loop spinning. """ if new_len < orig_len: return True return orig_tokens > 0 and new_tokens < orig_tokens * 0.95 def _should_run_preflight_estimate( messages: List[Dict[str, Any]], protect_first_n: int, protect_last_n: int, threshold_tokens: int, ) -> bool: """Cheap gate for the (expensive) full preflight token estimate. Returns ``True`` when either: (a) message count exceeds the protected ranges (the historical gate), or (b) a cheap char-based estimate already crosses the configured threshold — the few-but-huge case from issue #27405 that the count-only gate would silently skip (a handful of very large messages never trips the count condition, so compression was never attempted and the turn hit a hard context-overflow error). Branch (b) uses ``estimate_messages_tokens_rough`` (the shared char-based estimator) so a single large base64 image isn't mistaken for ~250K tokens. It intentionally undercounts vs. the full request estimate — it omits the system prompt and tool schemas — because it is only a *hint* deciding whether to pay for the authoritative ``estimate_request_tokens_rough``, which (together with ``should_compress``) makes the real decision. """ if len(messages) > protect_first_n + protect_last_n + 1: return True return estimate_messages_tokens_rough(messages) >= threshold_tokens @dataclass class TurnContext: """Values produced by the turn prologue and consumed by the turn loop.""" # Sanitized inbound message (surrogates stripped). user_message: str # Clean message preserved for transcripts / memory queries (no nudge injection). original_user_message: Any # Working message list for this turn (loop appends to it). messages: List[Dict[str, Any]] # May be reset to None by preflight compression (new session created). conversation_history: Optional[List[Dict[str, Any]]] # Cached system prompt active for this turn (may be rebuilt by compression). active_system_prompt: Optional[str] # Task / turn identifiers. effective_task_id: str turn_id: str # Index of the current user turn within ``messages``. current_turn_user_idx: int # Whether the post-turn memory review should fire. should_review_memory: bool = False # Context contributed by ``pre_llm_call`` plugins (appended to user message). plugin_user_context: str = "" # External-memory prefetch result, reused across loop iterations. ext_prefetch_cache: str = "" def build_turn_context( agent, user_message: str, system_message: Optional[str], conversation_history: Optional[List[Dict[str, Any]]], task_id: Optional[str], stream_callback, persist_user_message: Optional[str], persist_user_timestamp: Optional[float] = None, *, restore_or_build_system_prompt, install_safe_stdio, sanitize_surrogates, summarize_user_message_for_log, set_session_context, set_current_write_origin, ra, ) -> TurnContext: """Run the once-per-turn setup and return the loop's input context. The callables/helpers the original prologue referenced from the ``conversation_loop`` module are passed in explicitly to keep this module free of an import cycle with ``agent.conversation_loop``. """ # Guard stdio against OSError from broken pipes (systemd/headless/daemon). install_safe_stdio() # NOTE: the DB session row is created later, AFTER the system prompt is # restored/built (see _ensure_db_session() below the system-prompt block). # Creating it here — before _cached_system_prompt is populated — inserts a # row with system_prompt=NULL on a fresh API/gateway agent that carries # client-managed history, which then trips the "stored system prompt is # null; rebuilding from scratch" warning and a needless first-turn prefix # cache miss. (Issue #45499.) # Tell auxiliary_client what the live main provider/model are for this turn. try: from agent.auxiliary_client import set_runtime_main set_runtime_main( getattr(agent, "provider", "") or "", getattr(agent, "model", "") or "", base_url=getattr(agent, "base_url", "") or "", api_key=getattr(agent, "api_key", "") or "", api_mode=getattr(agent, "api_mode", "") or "", ) except Exception: pass # Tag log records on this thread with the session ID for ``hermes logs``. set_session_context(agent.session_id) # Bind the skill write-origin ContextVar for this thread. set_current_write_origin(getattr(agent, "_memory_write_origin", "assistant_tool")) # Restore the primary runtime if the previous turn activated fallback. agent._restore_primary_runtime() # Between-turns MCP refresh: an MCP server that finished connecting since # the previous turn (slow HTTP/OAuth servers routinely take 2-6s on a cold # connect, missing the bounded startup wait) lands in THIS turn's tool # snapshot. This is cache-safe by construction: it runs in the per-turn # prologue, before this turn's first API call assembles ``tools=``, so it # only ever extends a fresh request prefix — it never mutates the cached # prefix of an in-flight turn. No-op when no MCP servers are registered # (the common case, gated by the cheap ``has_registered_mcp_tools`` check) # or when the tool set is unchanged (``refresh_agent_mcp_tools`` diffs by # name and leaves the snapshot untouched on no-change). try: if not getattr(agent, "_skip_mcp_refresh", False): from tools.mcp_tool import has_registered_mcp_tools, refresh_agent_mcp_tools if has_registered_mcp_tools(): refresh_agent_mcp_tools(agent, quiet_mode=True) except Exception: logger.debug("between-turns MCP tool refresh skipped", exc_info=True) # Sanitize surrogate characters from user input. if isinstance(user_message, str): user_message = sanitize_surrogates(user_message) if isinstance(persist_user_message, str): persist_user_message = sanitize_surrogates(persist_user_message) # Store stream callback for _interruptible_api_call to pick up. agent._stream_callback = stream_callback agent._persist_user_message_idx = None agent._persist_user_message_override = persist_user_message agent._persist_user_message_timestamp = persist_user_timestamp # Generate unique task_id if not provided to isolate VMs between tasks. effective_task_id = task_id or str(uuid.uuid4()) agent._current_task_id = effective_task_id turn_id = f"{agent.session_id or 'session'}:{effective_task_id}:{uuid.uuid4().hex[:8]}" agent._current_turn_id = turn_id agent._current_api_request_id = "" # Reset retry counters and iteration budget at the start of each turn. agent._invalid_tool_retries = 0 agent._invalid_json_retries = 0 agent._empty_content_retries = 0 agent._incomplete_scratchpad_retries = 0 agent._codex_incomplete_retries = 0 agent._thinking_prefill_retries = 0 agent._post_tool_empty_retried = False agent._last_content_with_tools = None agent._last_content_tools_all_housekeeping = False agent._mute_post_response = False agent._unicode_sanitization_passes = 0 agent._tool_guardrails.reset_for_turn() agent._tool_guardrail_halt_decision = None _reset_consol = getattr(agent._memory_store, "reset_consolidation_failures", None) if callable(_reset_consol): _reset_consol() agent._vision_supported = True # Pre-turn connection health check: clean up dead TCP connections. if agent.api_mode != "anthropic_messages": try: if agent._cleanup_dead_connections(): agent._emit_status( "🔌 Detected stale connections from a previous provider " "issue — cleaned up automatically. Proceeding with fresh " "connection." ) except Exception: pass # Replay compression warning through status_callback for gateway platforms. if agent._compression_warning: agent._replay_compression_warning() agent._compression_warning = None # send once # NOTE: _turns_since_memory and _iters_since_skill are NOT reset here. agent.iteration_budget = IterationBudget(agent.max_iterations) # Log conversation turn start for debugging/observability. _preview_text = summarize_user_message_for_log(user_message) _msg_preview = (_preview_text[:80] + "...") if len(_preview_text) > 80 else _preview_text _msg_preview = _msg_preview.replace("\n", " ") logger.info( "conversation turn: session=%s model=%s provider=%s platform=%s history=%d msg=%r", agent.session_id or "none", agent.model, agent.provider or "unknown", agent.platform or "unknown", len(conversation_history or []), _msg_preview, ) # Initialize conversation (copy to avoid mutating the caller's list). messages = list(conversation_history) if conversation_history else [] # Hydrate todo store from conversation history. if conversation_history and not agent._todo_store.has_items(): agent._hydrate_todo_store(conversation_history) # Hydrate per-session nudge counters from persisted history (issue #22357). if conversation_history and agent._user_turn_count == 0: prior_user_turns = sum( 1 for m in conversation_history if m.get("role") == "user" ) if prior_user_turns > 0: agent._user_turn_count = prior_user_turns if agent._memory_nudge_interval > 0 and agent._turns_since_memory == 0: agent._turns_since_memory = prior_user_turns % agent._memory_nudge_interval # Track user turns for memory flush and periodic nudge logic. agent._user_turn_count += 1 # Copilot x-initiator: the first API call of this user turn is # user-initiated; tool-loop follow-ups revert to "agent" (#3040). agent._is_user_initiated_turn = True # Reset the streaming context scrubber at the top of each turn. scrubber = getattr(agent, "_stream_context_scrubber", None) if scrubber is not None: scrubber.reset() # Reset the think scrubber for the same reason. think_scrubber = getattr(agent, "_stream_think_scrubber", None) if think_scrubber is not None: think_scrubber.reset() # Preserve the original user message (no nudge injection). original_user_message = persist_user_message if persist_user_message is not None else user_message # Track memory nudge trigger (turn-based, checked here). should_review_memory = False if (agent._memory_nudge_interval > 0 and "memory" in agent.valid_tool_names and agent._memory_store): agent._turns_since_memory += 1 if agent._turns_since_memory >= agent._memory_nudge_interval: should_review_memory = True agent._turns_since_memory = 0 # Add user message. user_msg = {"role": "user", "content": user_message} messages.append(user_msg) current_turn_user_idx = len(messages) - 1 agent._persist_user_message_idx = current_turn_user_idx if not agent.quiet_mode: _print_preview = summarize_user_message_for_log(user_message) agent._safe_print( f"💬 Starting conversation: '{_print_preview[:60]}" f"{'...' if len(_print_preview) > 60 else ''}'" ) # ── System prompt (cached per session for prefix caching) ── if agent._cached_system_prompt is None: restore_or_build_system_prompt(agent, system_message, conversation_history) active_system_prompt = agent._cached_system_prompt # Create the DB session row now that _cached_system_prompt is populated, so # the persisted snapshot is written non-NULL on the first turn (Issue # #45499). Idempotent: _ensure_db_session() no-ops once the row exists. agent._ensure_db_session() # Crash-resilience: persist the inbound user turn as soon as the session row exists. try: agent._persist_session(messages, conversation_history) except Exception: logger.warning( "Early turn-start session persistence failed for session=%s", agent.session_id or "none", exc_info=True, ) # ── Preflight context compression ── # Gate the (expensive) full token estimate behind a cheap pre-check. # See ``_should_run_preflight_estimate`` for the OR semantics that fix # issue #27405 (a few very large messages slipping past the count gate). if agent.compression_enabled and _should_run_preflight_estimate( messages, agent.context_compressor.protect_first_n, agent.context_compressor.protect_last_n, agent.context_compressor.threshold_tokens, ): _preflight_tokens = estimate_request_tokens_rough( messages, system_prompt=active_system_prompt or "", tools=agent.tools or None, ) _compressor = agent.context_compressor _defer_preflight = getattr( _compressor, "should_defer_preflight_to_real_usage", lambda _tokens: False, ) _preflight_deferred = _defer_preflight(_preflight_tokens) if not _preflight_deferred: _last = _compressor.last_prompt_tokens # Do NOT overwrite the -1 sentinel (#36718). if _last >= 0 and _preflight_tokens > _last: _compressor.last_prompt_tokens = _preflight_tokens _compression_cooldown = getattr( _compressor, "get_active_compression_failure_cooldown", lambda: None, )() if _preflight_deferred: logger.info( "Skipping preflight compression: rough estimate ~%s >= %s, " "but last real provider prompt was %s after compression", f"{_preflight_tokens:,}", f"{_compressor.threshold_tokens:,}", f"{_compressor.last_real_prompt_tokens:,}", ) elif _compression_cooldown: logger.info( "Skipping preflight compression: same-session cooldown active " "(~%s seconds remaining, session %s)", int(_compression_cooldown.get("remaining_seconds", 0.0)), agent.session_id or "none", ) elif _compressor.should_compress(_preflight_tokens): logger.info( "Preflight compression: ~%s tokens >= %s threshold (model %s, ctx %s)", f"{_preflight_tokens:,}", f"{_compressor.threshold_tokens:,}", agent.model, f"{_compressor.context_length:,}", ) agent._emit_status( f"📦 Preflight compression: ~{_preflight_tokens:,} tokens " f">= {_compressor.threshold_tokens:,} threshold. " "This may take a moment." ) for _pass in range(3): _orig_len = len(messages) _orig_tokens = _preflight_tokens messages, active_system_prompt = agent._compress_context( messages, system_message, approx_tokens=_preflight_tokens, task_id=effective_task_id, ) # Re-estimate now so size-only compression (same row count, # lower token count — e.g. summarising tool outputs) is # recognised as progress instead of being misread as # "Cannot compress further". Fixes #39548. _preflight_tokens = estimate_request_tokens_rough( messages, system_prompt=active_system_prompt or "", tools=agent.tools or None, ) if not _compression_made_progress( _orig_len, len(messages), _orig_tokens, _preflight_tokens ): break # Cannot compress further: neither rows nor tokens moved conversation_history = conversation_history_after_compression( agent, messages ) agent._empty_content_retries = 0 agent._thinking_prefill_retries = 0 agent._last_content_with_tools = None agent._last_content_tools_all_housekeeping = False agent._mute_post_response = False if not _compressor.should_compress(_preflight_tokens): break # Plugin hook: pre_llm_call (context injected into user message, not system prompt). plugin_user_context = "" try: from hermes_cli.plugins import invoke_hook as _invoke_hook _pre_results = _invoke_hook( "pre_llm_call", session_id=agent.session_id, task_id=effective_task_id, turn_id=turn_id, user_message=original_user_message, conversation_history=list(messages), is_first_turn=(not bool(conversation_history)), model=agent.model, platform=getattr(agent, "platform", None) or "", sender_id=getattr(agent, "_user_id", None) or "", ) _ctx_parts: list[str] = [] # Spill oversized per-hook context to disk so a runaway plugin # can't inflate every subsequent turn's prompt. Ported from # openai/codex PR #21069 ("Spill large hook outputs from context"). try: from tools.hook_output_spill import ( get_spill_config as _spill_cfg, spill_if_oversized as _spill_if_oversized, ) _spill_config_cached = _spill_cfg() except Exception: _spill_if_oversized = None # type: ignore[assignment] _spill_config_cached = None for r in _pre_results: _piece: str = "" if isinstance(r, dict) and r.get("context"): _piece = str(r["context"]) elif isinstance(r, str) and r.strip(): _piece = r else: continue if _spill_if_oversized is not None: try: _piece = _spill_if_oversized( _piece, session_id=agent.session_id, source="plugin hook", config=_spill_config_cached, ) except Exception as _spill_exc: logger.warning("hook context spill failed: %s", _spill_exc) _ctx_parts.append(_piece) if _ctx_parts: plugin_user_context = "\n\n".join(_ctx_parts) except Exception as exc: logger.warning("pre_llm_call hook failed: %s", exc) # Per-turn file-mutation verifier state. agent._turn_failed_file_mutations = {} agent._turn_file_mutation_paths = set() agent._verification_stop_nudges = 0 agent._pre_verify_nudges = 0 # Record the execution thread so interrupt()/clear_interrupt() can scope # the tool-level interrupt signal to THIS agent's thread only. agent._execution_thread_id = threading.current_thread().ident # Clear stale per-thread interrupt state, preserving a pending interrupt. ra()._set_interrupt(False, agent._execution_thread_id) if agent._interrupt_requested: ra()._set_interrupt(True, agent._execution_thread_id) agent._interrupt_thread_signal_pending = False else: agent._interrupt_message = None agent._interrupt_thread_signal_pending = False # Notify memory providers of the new turn (BEFORE prefetch_all). if agent._memory_manager: try: _turn_msg = original_user_message if isinstance(original_user_message, str) else "" agent._memory_manager.on_turn_start(agent._user_turn_count, _turn_msg) except Exception: pass # External memory provider: prefetch once before the tool loop. ext_prefetch_cache = "" if agent._memory_manager: try: _query = original_user_message if isinstance(original_user_message, str) else "" ext_prefetch_cache = agent._memory_manager.prefetch_all(_query) or "" except Exception: pass return TurnContext( user_message=user_message, original_user_message=original_user_message, messages=messages, conversation_history=conversation_history, active_system_prompt=active_system_prompt, effective_task_id=effective_task_id, turn_id=turn_id, current_turn_user_idx=current_turn_user_idx, should_review_memory=should_review_memory, plugin_user_context=plugin_user_context, ext_prefetch_cache=ext_prefetch_cache, )