"""The agent conversation loop — extracted from ``run_agent.AIAgent``. ``run_conversation(agent, ...)`` drives one user turn (model call, tool dispatch, retries, fallbacks, compression, post-turn hooks). Symbols that callers patch on ``run_agent`` (``handle_function_call``, ``_set_interrupt``, ``OpenAI``) resolve via ``_ra`` so those patches keep working.""" from __future__ import annotations import inspect import json import logging import re import time from dataclasses import dataclass, field, fields from typing import Any, Dict, List, Optional from agent.codex_responses_adapter import _summarize_user_message_for_log from agent.conversation_compression import ( conversation_history_after_compression, # noqa: F401 — resolved lazily by turn_overflow/turn_preflight/turn_recovery (tests patch it here) ) from agent.fast_mode import begin_turn as begin_fast_mode_turn from agent.message_metadata import append_message from agent.turn_context import ( PreflightCompressionTimedOut, build_turn_context, ) from agent.turn_retry_state import TurnRetryState from agent.runtime_cwd import resolve_agent_cwd from agent.message_sanitization import ( _repair_tool_call_arguments, _sanitize_surrogates, ) # Must mirror _STALE_TOOL_CALL_MARKER_RE in hermes_state.py; kept local so importing # hermes_state (module-level DEFAULT_DB_PATH) is not forced at load time. _STALE_MARKER_RE = re.compile(r"^\[[A-Za-z_][A-Za-z0-9_.-]*\]$") from agent.model_metadata import ( MINIMUM_CONTEXT_LENGTH, _estimate_tools_tokens_rough, estimate_messages_tokens_rough, # noqa: F401 — resolved lazily by agent.turn_request_assembly (tests patch it here) estimate_request_tokens_rough, # noqa: F401 — resolved lazily by turn_overflow/turn_preflight/turn_recovery (tests patch it here) save_context_length, # noqa: F401 — resolved lazily by agent.turn_overflow (tests patch it here) ) from agent.process_bootstrap import _install_safe_stdio from agent.prompt_caching import ( build_prompt_cache_plan, effective_cache_ttl, strip_anthropic_cache_control, strip_anthropic_tool_cache_control, ) from agent.retry_utils import ( # noqa: F401 — resolved lazily by agent.turn_* (tests patch them here) adaptive_rate_limit_backoff, jittered_backoff, ) from agent.turn_recovery import ( # noqa: F401 — resolved lazily by agent.turn_response_check describe_invalid_response, interruptible_backoff_sleep, validate_response_shape, ) # Bind before the turn starts so a source-tree swap cannot load a skewed # finalizer at turn end. from agent.turn_finalizer import finalize_turn from agent.turn_iteration_prep import ( announce_api_call, apply_retry_restarts, begin_iteration, prepare_iteration, ) from agent.turn_preflight_gate import run_preflight_gate from agent.turn_request_assembly import assemble_api_request from agent.turn_api_request import build_api_request from agent.turn_api_call import handle_api_interrupt, nous_rate_limit_guard, perform_api_call from agent.turn_response_check import check_api_response from agent.turn_api_error import handle_api_error from agent.turn_final_response import finish_text_response from agent.turn_tool_round import run_tool_round from agent.turn_response_intake import normalize_model_response from agent.turn_loop_errors import handle_outer_loop_error from hermes_logging import set_session_context from tools.skill_provenance import set_current_write_origin from utils import base_url_host_matches logger = logging.getLogger(__name__) # Scaffold marker used by _apply_active_turn_redirect and the ghost-row filter # in the api_messages loop. Module-level so both sites can never drift. _INTERRUPT_SCAFFOLD_MARKER = "[This response was interrupted by a user correction.]" # One-time wrap-up notice appended when a wall-clock run budget crosses 80% # (agent.run_budget_seconds / --run-budget): stop new work, deliver current state. RUN_BUDGET_WRAPUP_NOTICE = ( "[SYSTEM NOTICE — run time budget nearly exhausted] " "Run time budget nearly exhausted. Stop new discovery/verification work " "now. Produce the required final deliverable (answer/JSON/summary) from " "the state you already have, completing only mandatory writes." ) def _midturn_request_pressure_tokens( agent: Any, api_messages: List[Dict[str, Any]], effective_system: str, approx_tokens: int, ) -> int: """Token figure the mid-turn pre-API compression guard compares. Returns the pruned native-Responses estimate when native compaction eligibility is proven (the generic estimate overstates the wire on compacted sessions, #96995), else the generic message+tools figure. System prompt is counted exactly once.""" try: from agent.codex_responses_adapter import ( estimate_native_responses_preflight_tokens, ) native = estimate_native_responses_preflight_tokens( agent, api_messages, system_prompt=effective_system or "", tools=getattr(agent, "tools", None) or None, ) if isinstance(native, int) and not isinstance(native, bool) and native >= 0: return native except Exception: logger.debug( "native Responses mid-turn estimate unavailable; " "using generic transcript estimate", exc_info=True, ) return approx_tokens + ( _estimate_tools_tokens_rough(agent.tools) if agent.tools else 0 ) def _review_input_budget_exhausted(agent: Any) -> bool: """True when a detached review fork has replayed its aggregate input budget. Only forks with an explicit ``_review_input_token_budget`` are gated (#93057). Fires at the top of the NEXT iteration, so the budget-crossing request completes first.""" budget = getattr(agent, "_review_input_token_budget", None) if not isinstance(budget, int) or isinstance(budget, bool) or budget <= 0: return False used = getattr(agent, "session_input_tokens", 0) return isinstance(used, int) and not isinstance(used, bool) and used >= budget def _maybe_inject_run_budget_wrapup(agent: Any, messages: List[Dict[str, Any]]) -> bool: """Inject the one-time wall-clock wrap-up notice when past 80% of budget. Appends to the NEWEST ``role:"tool"`` message (cache-safe, like /steer); latches ``_run_budget_wrapup_injected`` only on a successful append. Returns True when injected. Dormant unless ``run_budget_seconds`` + ``_run_budget_started_at`` set.""" budget = getattr(agent, "run_budget_seconds", None) started = getattr(agent, "_run_budget_started_at", None) if ( not budget or not started or getattr(agent, "_run_budget_wrapup_injected", False) or (time.time() - started) < 0.8 * float(budget) ): return False for i in range(len(messages) - 1, -1, -1): msg = messages[i] if isinstance(msg, dict) and msg.get("role") == "tool": existing = msg.get("content", "") if isinstance(existing, str): msg["content"] = existing + f"\n\n{RUN_BUDGET_WRAPUP_NOTICE}" else: # Multimodal content blocks — append a text block. try: blocks = list(existing) if existing else [] blocks.append({"type": "text", "text": RUN_BUDGET_WRAPUP_NOTICE}) msg["content"] = blocks except Exception: return False agent._run_budget_wrapup_injected = True logger.info( "Run budget wrap-up notice injected (budget=%.0fs, elapsed=%.0fs)", float(budget), time.time() - started, ) return True return False def _restore_user_after_reference_handoff( messages: List[Dict[str, Any]], user_message: Any ) -> bool: """Re-append this turn's real user ask when compaction left only a handoff. Returns True when a restore append happened; only decides whether a restorable ask exists (#80622).""" if isinstance(user_message, str): restorable = bool(user_message.strip()) else: restorable = isinstance(user_message, list) and bool(user_message) if not restorable: return False last = messages[-1] if messages else None if isinstance(last, dict) and last.get("role") == "user" and last.get("content") == user_message: return False append_message(messages, {"role": "user", "content": user_message}) return True def _should_skip_model_call_for_reference_handoff( messages: List[Dict[str, Any]], user_message: Any ) -> bool: """Guard post-compaction continues against sole-handoff active turns (#80622).""" from agent.context_compressor import reference_handoff_would_drive_next_model_call # A restored ask is an actionable non-synthetic user row appended after the # handoff — by construction the handoff no longer drives. return reference_handoff_would_drive_next_model_call(messages) and not ( _restore_user_after_reference_handoff(messages, user_message) ) # Fallback final_response for the sole-handoff skip (#80622). Not a replay of the # last assistant text: finalize_turn appends final_response as a fresh assistant row. _HANDOFF_SKIP_FINAL_RESPONSE = ( "Context was compacted. The previous response is complete — " "awaiting your next message." ) # Terminal final_response when compression hit its host timeout while the request # was still oversized; resending would only bounce off the overflow error (#98722). _COMPRESSION_TIMEOUT_FINAL_RESPONSE = ( "Context compression timed out without reducing this conversation. " "No messages were dropped. Start a fresh session with /new, or check " "auxiliary.compression before retrying /compress." ) # Stable prefix of the local interrupt status string; surfaces (ACP, TUI) match on # it to treat the text as cancellation metadata rather than assistant prose. INTERRUPT_WAITING_FOR_MODEL_PREFIX = "Operation interrupted: waiting for model response (" def _should_rearm_compression_budget( compression_attempts: int, *, completed_compaction_pending: bool, prompt_tokens: int, threshold_tokens: int, ) -> bool: """Return True after a provider proves a completed compaction worked. Rough estimates cannot rearm the anti-thrash budget; require the completed- compaction latch and a positive normalized prompt count below the threshold.""" return bool( compression_attempts and completed_compaction_pending and threshold_tokens > 0 and 0 < prompt_tokens < threshold_tokens ) # Modules whose presence in a traceback (without any API-call module) marks a # deterministic local bug not worth retrying. NEVER add "conversation_loop" or # "run_agent": every exception passes through them; _hit_local would be True (#66267) _LOCAL_PROCESSING_MODULES = frozenset({ "agent_runtime_helpers", "message_content", "message_sanitization", "chat_completion_helpers", # only local when NOT also an API-call module }) _API_CALL_MODULES = frozenset({ "chat_completion_helpers", }) # Max outer-loop exceptions per user turn before giving up; only exceptions that # ESCAPE the inner retry/fallback machinery count, so this can be small (#92450). _MAX_OUTER_LOOP_ERRORS = 8 def _is_interpreter_shutdown_error(exc: Exception) -> bool: """Check if *exc* is a fatal interpreter-shutdown failure. Delegates to ``tools.interpreter_shutdown`` (one text-matching site for the shutdown-race bug class) but keeps the RuntimeError type gate: a ValueError carrying similar text must not match (#93269).""" if isinstance(exc, RuntimeError): from tools.interpreter_shutdown import interpreter_shutting_down return interpreter_shutting_down(exc) return False def _moa_client_consumes_prepared_request(client: Any) -> bool: """True when ``client`` is the in-process MoA facade. Only ``MoAChatCompletions`` exposes ``prepare()``; other clients raise TypeError on ``_moa_prepared_request`` even while ``agent.provider`` stays ``"moa"``.""" completions = getattr(getattr(client, "chat", None), "completions", None) return callable(getattr(completions, "prepare", None)) def _join_truncated_parts(parts: List[str]) -> str: """Join continuation fragments, adding a newline where two would glue together (#78577).""" joined = "" for part in parts: if joined and not joined[-1].isspace() and part and not part[0].isspace(): joined += "\n" joined += part return joined def _moa_reference_metrics_for_hook(agent: Any) -> Any: """Per-advisor metrics for post_api_request, or None off the MoA path. MoA returns only the aggregator response, so a plugin sees one generation for the whole fan-out; this carries the per-slot advisor spend across the hook boundary.""" client = getattr(agent, "client", None) getter = getattr(client, "last_reference_metrics", None) if not callable(getter): return None try: return getter() except Exception: return None def _apply_active_turn_redirect(agent: Any, messages: List[Dict[str, Any]], text: str) -> None: """Append a provider-safe checkpoint and correction to the live turn. Keeps only the *visible* text (demoted to plain text) then adds the correction as a real user message, so role alternation holds and cached messages stay byte-identical. INVARIANT: raw chain-of-thought never enters replayable content — inlined CoT reads as a prefill jailbreak and bricks the session with empty-response storms. INVARIANT: the interruption scaffold is replay text, carried only in the user correction's ``api_content``; an on-screen-empty placeholder is ``display_kind=hidden``.""" visible = agent._strip_think_blocks( getattr(agent, "_current_streamed_assistant_text", "") or "" ).strip() checkpoint_parts = [_INTERRUPT_SCAFFOLD_MARKER] if visible: checkpoint_parts.extend( ["Visible response before the interruption:", visible] ) checkpoint = "\n\n".join(checkpoint_parts) correction = ( "[Context from the interrupted assistant response]\n" f"{checkpoint}\n\n" f"{text}" ) # The live tail is normally user or tool, so an assistant placeholder + correction # keeps strict alternation; if the tail is already assistant, fold the checkpoint # into the user correction instead of creating assistant→assistant. if messages and messages[-1].get("role") == "assistant": # Transcript shows the user's own words; the provider replays the # scaffolded form so it still sees the interrupted context. append_message( messages, {"role": "user", "content": text, "api_content": correction}, ) else: # Placeholder preserves role alternation only. Scaffold bytes must never land # here: api_content is substituted back into content on replay (#81841). placeholder: Dict[str, Any] = { "role": "assistant", "content": visible or "", } if not visible: placeholder["display_kind"] = "hidden" # Hidden row, but a non-empty neutral api_content so the pre-call # sanitizer does not re-heal it every call (#88955). Never # _INTERRUPT_SCAFFOLD_MARKER: as assistant text the model echoes it (#81841) from agent.agent_runtime_helpers import _INTERRUPTED_PLACEHOLDER placeholder["api_content"] = _INTERRUPTED_PLACEHOLDER append_message(messages, placeholder) append_message( messages, {"role": "user", "content": text, "api_content": correction}, ) agent._current_streamed_assistant_text = "" agent._stream_needs_break = True def _is_copilot_provider(agent: Any) -> bool: """Delegate to ``AIAgent._is_copilot_provider`` (single owner of the check). ``agent.provider`` may hold the aliases ``github-copilot`` / ``github``; a bare ``provider == "copilot"`` gate would skip credential recovery for them.""" try: return bool(agent._is_copilot_provider()) except Exception: return (getattr(agent, "provider", "") or "").strip().lower() in { "copilot", "github-copilot", "github", } def _is_stale_copilot_credential_error(status_code: Optional[int], error_message: str) -> bool: """Detect a Copilot 400 that is really a STALE / DEGRADED credential. Matches status 400 AND ``model_not_available_for_integrator`` or ``model_not_supported`` / "the requested model is not supported", so a wrong model name never triggers the single-shot re-exchange. Caller enforces scoping/guard.""" lowered = (error_message or "").lower() if status_code != 400 and "error code: 400" not in lowered: return False return any(marker in lowered for marker in ( "model_not_available_for_integrator", "not available for integrator", "model_not_supported", "the requested model is not supported", )) def _ollama_context_limit_error(agent: Any, request_tokens: int) -> Optional[str]: """Return a user-facing error when Ollama is loaded with too little context.""" if not getattr(agent, "tools", None): return None runtime_ctx = getattr(agent, "_ollama_num_ctx", None) if not isinstance(runtime_ctx, int) or runtime_ctx <= 0: return None if runtime_ctx >= MINIMUM_CONTEXT_LENGTH: return None model = getattr(agent, "model", "") or "the selected model" base_url = getattr(agent, "base_url", "") or "unknown base URL" provider = getattr(agent, "provider", "") or "unknown" tool_count = len(getattr(agent, "tools", None) or []) logger.warning( "Ollama runtime context too small for Hermes tool use: " "model=%s provider=%s base_url=%s runtime_context=%d " "minimum_context=%d estimated_request_tokens=%d tool_count=%d " "session=%s", model, provider, base_url, runtime_ctx, MINIMUM_CONTEXT_LENGTH, request_tokens, tool_count, getattr(agent, "session_id", None) or "none", ) return ( f"Ollama loaded `{model}` with only {runtime_ctx:,} tokens of runtime " f"context, but Hermes needs at least {MINIMUM_CONTEXT_LENGTH:,} tokens " "for reliable tool use.\n\n" "Increase the Ollama context for this model and restart/reload the " "model before trying again. A known-good starting point is 65,536 " "tokens. In Hermes config, set `model.ollama_num_ctx: 65536` " "(and `model.context_length: 65536` if you also override the displayed " "model context). If you manage the model through an Ollama Modelfile, " "set `PARAMETER num_ctx 65536` there instead." ) def _maybe_grow_local_window(agent: Any, compressor: Any, request_tokens: int) -> Optional[int]: """Try growing the managed local model's context window before compressing. Returns the new window when the ladder granted one, else None (hold / at native / not a managed local session). Cheap for non-local providers: one compare.""" provider = (getattr(agent, "provider", "") or "").strip().lower() if provider not in ("llamacpp", "llama.cpp", "llama-cpp", "custom"): return None base_url = getattr(agent, "base_url", "") or "" if "127.0.0.1" not in base_url and "localhost" not in base_url: return None try: from hermes_cli.local_runtime.growth import maybe_grow_window current_window = int(getattr(compressor, "context_length", 0) or 0) if current_window <= 0: return None return maybe_grow_window( getattr(agent, "model", "") or "", base_url=base_url, session_tokens=int(request_tokens), current_window=current_window, ) except Exception as exc: # noqa: BLE001 — growth must never break a turn logger.debug("local window growth check failed: %s", exc) return None def _ra(): """Lazy ``run_agent`` reference so patches on ``run_agent.handle_function_call`` / ``run_agent._set_interrupt`` / ``run_agent.OpenAI`` reach this code path.""" import run_agent return run_agent def _nous_entitlement_message(capability: str) -> str: try: from hermes_cli.nous_account import ( format_nous_portal_entitlement_message, get_nous_portal_account_info, ) account_info = get_nous_portal_account_info(force_fresh=True) message = format_nous_portal_entitlement_message( account_info, capability=capability, ) return message or "" except Exception: return "" def _print_guidance(agent, message: str) -> bool: """Print each line of ``message`` as a 💡 hint; False when there is nothing to print.""" if not message: return False for line in message.splitlines(): agent._vprint(f"{agent.log_prefix} 💡 {line}", force=True) return True def _print_nous_entitlement_guidance(agent, capability: str) -> bool: return _print_guidance(agent, _nous_entitlement_message(capability)) def _system_prompt_for_hooks(api_kwargs: Any, request_messages: Any) -> Any: """System prompt as actually sent to the provider, for observability hooks. Checks ``system`` (Anthropic), ``instructions`` (Responses/Codex), then ``messages[0]``. Returns None when the request carries no system prompt.""" system_prompt = api_kwargs.get("system") if system_prompt is None: system_prompt = api_kwargs.get("instructions") if system_prompt is None and isinstance(request_messages, list) and request_messages: first = request_messages[0] if isinstance(first, dict) and first.get("role") == "system": system_prompt = first.get("content") return system_prompt def _is_nous_inference_route(provider: str, base_url: str) -> bool: provider = (provider or "").strip().lower() if provider == "nous": return True base = str(base_url or "") return ( base_url_host_matches(base, "inference-api.nousresearch.com") ) def _billing_or_entitlement_message( *, capability: str, provider: str, base_url: str, model: str, unverified: bool = False, ) -> str: if _is_nous_inference_route(provider, base_url): return _nous_entitlement_message(capability) provider_label = (provider or "").strip() or "the selected provider" model_label = (model or "").strip() or "the selected model" # Anthropic Pro/Max OAuth surfaces exhaustion of the "extra usage" bucket as a hard # 400; point at the settings page and cycle reset — "add credits" does not apply. if (provider or "").strip().lower() == "anthropic": # ``unverified`` (#82154): the "out of extra usage" 400 is also returned for a # server-side content-filter rejection, so hedge and name the other cause. if unverified: lines = [ ( f"{provider_label} reported that your Claude subscription usage may be " f"exhausted for {model_label} (included quota + extra-usage credits) — " "but this specific error is not proof of a billing problem." ), "If https://claude.ai/settings/usage still shows quota remaining, this is " "probably NOT a billing problem: on a Claude subscription (OAuth) token " "Anthropic returns this same message when its content filter rejects part " "of the request — typically a phrase in the system prompt.", "If usage really is exhausted: wait for the billing cycle to reset, or add " "extra usage at https://claude.ai/settings/usage", "You can also switch to an Anthropic API key or another provider with " "/model --provider .", # The exhaustion latch replays the stored error without issuing # a request, so a real fix looks like it didn't work. "Retry with a fresh credential state: `hermes auth reset anthropic`. Until " "that cooldown clears, this error can be replayed from cache without " "contacting the API.", ] else: lines = [ ( f"{provider_label} reported that your Claude subscription usage is " f"exhausted for {model_label} (included quota + extra-usage credits)." ), "Options: wait for the billing cycle to reset, or add extra usage at " "https://claude.ai/settings/usage", "You can also switch to an Anthropic API key or another provider with " "/model --provider .", ] return "\n".join(lines) # Provider-agnostic billing URL so every text surface (CLI, gateway, TUI) shows the # same actionable link, not just OpenRouter. try: from agent.billing_links import build_billing_block _link = build_billing_block(provider=provider, base_url=base_url, model=model) if _link.provider_label: provider_label = _link.provider_label billing_url = _link.billing_url except Exception: billing_url = None lines = [ ( f"{provider_label} reported that billing, credits, or account " f"entitlement is exhausted for {model_label}." ), "Add credits or update billing with that provider, then retry.", ] if billing_url: lines.append(f"{provider_label} billing: {billing_url}") lines.append("You can switch providers temporarily with /model --provider .") return "\n".join(lines) def _billing_block_dict( provider, base_url, model, message="", *, unverified: bool = False ) -> Optional[dict]: """Best-effort structured billing descriptor (None if billing_links is unavailable).""" try: from agent.billing_links import build_billing_block block = build_billing_block( provider=provider, base_url=str(base_url), model=model, message=message ).to_dict() except Exception: return None if block is not None and unverified: # Carry the classifier's ambiguity into the structured descriptor so # every surface rendering the block can hedge too (#82154). block["unverified"] = True return block def _billing_terminal_label(summary: str, unverified: bool) -> str: """Terminal-failure prefix for a billing-classified error. ``unverified`` (#82154): the Anthropic "out of extra usage" 400 can be a content-filter rejection, so the line must not assert exhaustion as fact.""" if unverified: return ( "Provider reported usage/credit exhaustion (unverified — the same " f"error can be a content-filter rejection, not billing): {summary}" ) return f"Billing or credits exhausted: {summary}" def _billing_failure_result( *, classified, summary: str, messages, api_call_count: int, provider: str, base_url, model: str, guidance: Optional[str] = None, ) -> dict: """Structured terminal result for a billing-classified failure. Single construction point so label, guidance, structured block and ambiguity flag stay consistent across the non-retryable abort and max-retries paths (#82154).""" unverified = bool(getattr(classified, "billing_unverified", False)) if guidance is None: guidance = _billing_or_entitlement_message( capability="model access", provider=provider, base_url=str(base_url), model=model, unverified=unverified, ) final = _billing_terminal_label(summary, unverified) if guidance: final += f"\n\n{guidance}" return { "final_response": final, "messages": messages, "api_calls": api_call_count, "completed": False, "failed": True, "error": summary, "failure_reason": classified.reason.value, # Classifier's own retry verdict so UI (agent/error_surface.py) shows Retry # only when a re-run can differ, not re-derived from a second taxonomy. "failure_retryable": bool(classified.retryable), # The billing verdict may rest on an ambiguous body (#82154) — carry # that through the structured result, not just the prose. "billing_unverified": unverified, "billing_block": _billing_block_dict( provider, base_url, model, guidance, unverified=unverified ), } def _print_billing_or_entitlement_guidance( agent, *, capability: str, provider: str, base_url: str, model: str, unverified: bool = False, ) -> bool: return _print_guidance(agent, _billing_or_entitlement_message( capability=capability, provider=provider, base_url=base_url, model=model, unverified=unverified, )) def _bot_chat_prompt_stale(agent, stored_prompt: str) -> bool: """Bot Chat capability epoch check for a stored prompt. The stored prompt embeds a capability fingerprint; a mismatch is a deliberate once-per-change rebuild. Unstamped prompts never match; probe failures fail closed to "reuse" so the cache is kept. Legacy upgrade: a Bot Chat prompt predating the epoch mechanism gets ONE title-gated migration rebuild; the stamped result cannot re-fire.""" try: from tools.bot_mode_probe import ( BOT_CHAT_TITLE, stored_bot_chat_prompt_needs_upgrade, stored_prompt_capability_stale, ) home = None try: from agent.system_prompt import _agent_home home = _agent_home(agent) except Exception: pass if stored_prompt_capability_stale(stored_prompt, home): return True if not getattr(agent, "_bot_mode_protocol", True): return False title = str(getattr(agent, "_session_title_hint", "") or "").strip() if not title and agent._session_db and agent.session_id: try: title = str(agent._session_db.get_session_title(agent.session_id) or "").strip() except Exception: title = "" return title == BOT_CHAT_TITLE and bool( stored_bot_chat_prompt_needs_upgrade(stored_prompt, home) ) except Exception: return False def _persist_system_prompt(agent, failure_message: str, *, persist_tools: bool = False) -> None: """Persist ``agent._cached_system_prompt`` to the session row; failures log at WARNING (with ``failure_message``) because the gateway path (fresh AIAgent per turn) reads this row every turn, so a silent failure breaks prefix-cache reuse.""" if not agent._session_db: return try: agent._session_db.update_system_prompt(agent.session_id, agent._cached_system_prompt) if persist_tools: from tools.mcp_tool import persist_agent_tool_names persist_agent_tool_names(agent) except Exception as exc: logger.warning(failure_message, agent.session_id, exc) def _restore_or_build_system_prompt(agent, system_message, conversation_history): """Restore the cached system prompt from the session DB or build it fresh. Mutates ``agent._cached_system_prompt`` and persists a freshly-built prompt on first build. Row states ``missing``/``null``/``empty``/``present`` are logged and DB failures log at WARNING so silent prefix-cache misses show in ``agent.log``.""" stored_prompt = None stored_state = "missing" session_row = None if conversation_history and agent._session_db: try: session_row = agent._session_db.get_session(agent.session_id) if session_row is not None: raw_prompt = session_row.get("system_prompt") if raw_prompt is None: stored_state = "null" elif raw_prompt == "": stored_state = "empty" else: stored_prompt = raw_prompt stored_state = "present" except Exception as exc: logger.warning( "Session DB get_session failed for system-prompt restore " "(session=%s): %s. Falling back to fresh build — prefix " "cache will miss for this turn.", agent.session_id, exc, ) if stored_prompt and _stored_prompt_matches_runtime(agent, stored_prompt): if _bot_chat_prompt_stale(agent, stored_prompt): logger.info( "Bot Chat capability epoch changed for session %s; rebuilding " "system prompt to adopt the new capability surface (one-time " "prefix-cache break).", agent.session_id, ) agent._session_title_hint = "Bot Chat" # The skills index cache (LRU + disk snapshot) does not watch the skills # dir; a capability refresh must rebuild THROUGH it or new skills are lost. try: from agent.prompt_builder import clear_skills_system_prompt_cache clear_skills_system_prompt_cache(clear_snapshot=True) except Exception: pass agent._cached_system_prompt = agent._build_system_prompt(system_message) # Persist so the NEXT turn restores the new bytes verbatim (cache break is # once per capability change). on_session_start not re-fired: continuation. _persist_system_prompt( agent, "Session DB update_system_prompt failed after Bot Chat " "capability refresh (session=%s): %s. The refresh will " "re-fire next turn.", ) return # Continuing session — reuse the exact system prompt from the # previous turn so the Anthropic cache prefix matches. agent._cached_system_prompt = stored_prompt # Same contract for tools[]: pin the array to the order this session already # sent (tools freeze) instead of re-probing every check_fn on a fresh AIAgent. try: saved_tools = session_row.get("tool_names") if session_row else None if saved_tools: from tools.mcp_tool import restore_agent_tool_prefix restore_agent_tool_prefix(agent, json.loads(saved_tools)) except Exception: logger.debug("tool prefix restore skipped", exc_info=True) # Prompt-section callbacks are new-session-only; recover their frozen bytes # from the persisted prompt so a compression rebuild keeps them. The static # prefix is not persisted either; rebuild it for the early cache breakpoint or # fresh-per-turn gateway agents fall back to the single-breakpoint layout # (reconstruct_static_prefix gates on _use_prompt_caching, fails open to legacy). from agent.system_prompt import reconstruct_static_prefix, restore_plugin_prompt_sections restore_plugin_prompt_sections(agent, stored_prompt) reconstruct_static_prefix(agent, system_message=system_message) return if stored_prompt: stored_state = "stale_runtime" logger.info( "Stored system prompt for session %s has stale runtime identity; " "rebuilding for model=%s provider=%s.", agent.session_id, getattr(agent, "model", "") or "", getattr(agent, "provider", "") or "", ) if conversation_history and stored_state in ("null", "empty"): # Continuing session with an unusable stored prompt: every turn now rebuilds # and the prefix cache misses every time. logger.warning( "Stored system prompt for session %s is %s; rebuilding " "from scratch this turn. Prefix cache will miss until " "the rebuild persists. Investigate the previous turn's " "update_system_prompt write path.", agent.session_id, stored_state, ) # First turn of a new session (or recovering from a broken stored prompt). agent._cached_system_prompt = agent._build_system_prompt(system_message) # Plugin hook: on_session_start — fired once for a brand-new session, not on continuation. try: from hermes_cli.lifecycle import invoke_hook as _invoke_hook _invoke_hook( "on_session_start", session_id=agent.session_id, model=agent.model, platform=getattr(agent, "platform", None) or "", ) except Exception as exc: logger.warning("on_session_start hook failed: %s", exc) # Cold-start credits seed (L3) fallback for the first-turn path; TUI/desktop seed at # session open, so this is idempotent (skips when _credits_state exists). Fail-open. try: from agent.credits_tracker import seed_credits_at_session_start seed_credits_at_session_start(agent) except Exception: logger.debug("cold-start credits seed failed (fail-open)", exc_info=True) _persist_system_prompt( agent, "Session DB update_system_prompt failed for session %s: " "%s. Subsequent turns will rebuild the system prompt and " "miss the prefix cache.", persist_tools=True, ) def _stored_prompt_matches_runtime(agent, prompt: str) -> bool: """Return False when the persisted runtime-identity lines are stale.""" def line_value(label: str) -> str: """Last matching line wins. Safe ONLY for fields in the volatile tier at the END of the prompt; embedded project context could shadow earlier fields — see ``host_info_value``.""" prefix = f"{label}:" value = "" for line in prompt.splitlines(): if line.startswith(prefix): value = line[len(prefix):].strip() return value def host_info_value(label: str) -> str: """Read a field from the prompt's own host-info block. Anchors on the FIRST ``User home directory:`` line so a user's ``AGENTS.md`` row cannot match; a false mismatch would rebuild the prompt every turn.""" prefix = f"{label}:" lines = prompt.splitlines() for idx, line in enumerate(lines): if not line.startswith("User home directory:"): continue for candidate in lines[idx + 1: idx + 4]: if candidate.startswith(prefix): return candidate[len(prefix):].strip() return "" stored_model = line_value("Model") current_model = str(getattr(agent, "model", "") or "").strip() if stored_model and current_model and stored_model != current_model: return False stored_provider = line_value("Provider") current_provider = str(getattr(agent, "provider", "") or "").strip() if stored_provider and current_provider and stored_provider != current_provider: return False # cwd drift check. Compare against resolve_agent_cwd() — the SAME resolver used to # build the prompt — so TERMINAL_CWD sessions are not falsely rejected. stored_cwd = host_info_value("Current working directory") if stored_cwd: if stored_cwd != str(resolve_agent_cwd()): return False # Runtime-surface drift: reusing a desktop-built prompt on a terminal session (or # vice versa) would inject the wrong runtime hints. stored_platform = line_value("Platform") current_platform = str(getattr(agent, "platform", "") or "").strip() if stored_platform and current_platform and stored_platform != current_platform: return False return True # Named constants for the _get_continuation_prompt variants so # _is_synthetic_compression_user_turn can recognize them by content after a crash # persists one; SessionDB projection strips the _length_continuation_nudge tag. _LENGTH_CONTINUATION_NETWORK_STUB = ( "[System: The previous response was cut off by a " "network error mid-stream. Continue exactly where " "you left off. Do not restart or repeat prior text. " "Finish the answer directly.]" ) _LENGTH_CONTINUATION_OUTPUT_LIMIT = ( "[System: Your previous response was truncated by the output " "length limit. Continue exactly where you left off. Do not " "restart or repeat prior text. Finish the answer directly.]" ) # The dropped-tools variant interpolates tool names, so # _is_synthetic_compression_user_turn matches this prefix with str.startswith. _LENGTH_CONTINUATION_DROPPED_TOOLS_PREFIX = "[System: Your previous tool call " def _get_continuation_prompt(is_partial_stub: bool, dropped_tools: Optional[List[str]] = None) -> str: if is_partial_stub and dropped_tools: tool_list = ", ".join(dropped_tools[:3]) return ( f"{_LENGTH_CONTINUATION_DROPPED_TOOLS_PREFIX}" f"({tool_list}) was too large and " "the stream timed out before it " "could be delivered. Do NOT retry " "the same tool call with the same " "large content. Instead, break the " "content into multiple smaller tool " "calls (e.g. use multiple patch calls " "or write smaller files). Each tool " "call's arguments must be under ~8K " "tokens to avoid stream timeouts.]" ) elif is_partial_stub: return _LENGTH_CONTINUATION_NETWORK_STUB else: return _LENGTH_CONTINUATION_OUTPUT_LIMIT # Nudge for Codex/Responses turns that returned only internal reasoning: a bare retry # would be byte-identical (nothing replayable emitted), so the model repeats it. _CODEX_INCOMPLETE_NUDGE = ( "[System: Your previous response contained only internal reasoning and " "never produced a visible answer or tool call. Do not keep thinking. " "Produce your final answer as plain text now (or make the tool call " "you were planning).]" ) # Re-prompt after an acknowledgment-only Codex/Responses reply; named so # _is_synthetic_compression_user_turn can recognize it like _CODEX_INCOMPLETE_NUDGE. _CODEX_ACK_CONTINUATION_NUDGE = ( "[System: Continue now. Execute the required tool calls and only " "send your final answer after completing the task.]" ) # Re-prompt for finish_reason="tool_calls" with empty tool_calls. Named like # _CODEX_ACK_CONTINUATION_NUDGE: an interrupt mid-retry can persist it. _DROPPED_TOOLCALL_NUDGE_CONTENT = ( "Your previous turn indicated a tool call but none was " "included. Do not narrate a plan or restate intent — issue " "the actual tool call now to continue the task." ) # Re-prompt for an empty response after tool calls (#9400). Named because its # _empty_recovery_synthetic metadata flag does not survive SessionDB projection. _EMPTY_TOOL_RESPONSE_NUDGE = ( "You just executed tool calls but returned an " "empty response. Please process the tool " "results above and continue with the task." ) # Shared recovery trailer for both content-policy refusal paths (HTTP-200 # content_filter and the content_policy_blocked exception) so guidance cannot drift. _CONTENT_POLICY_RECOVERY_HINT = ( "Try rephrasing the request, narrowing the context, or " "adding a fallback provider with `hermes fallback add`." ) # Memo for send-path tool-call argument canonicalization, which re-runs on every # historical call each iteration. Sound: canonicalization is pure and deterministic; # malformed strings raise before being stored, so the repair fallback is never memoized. _CANON_ARGS_CACHE: Dict[str, str] = {} _CANON_ARGS_CACHE_MAX = 4096 # Count bound alone does not bound MEMORY: argument strings can run 100KB+, so a byte # budget bounds the worst case while keeping the memo effective for ~0.5-2KB args. _CANON_ARGS_CACHE_MAX_BYTES = 32 * 1024 * 1024 _canon_args_cache_bytes = 0 def _canonicalize_tool_call_arguments(arg_str: str) -> str: """Return the canonical wire form of a tool-call arguments JSON string. Raises whatever ``json.loads`` raises on malformed input; the caller falls back to ``_repair_tool_call_arguments``.""" global _canon_args_cache_bytes cached = _CANON_ARGS_CACHE.get(arg_str) if cached is not None: return cached canonical = json.dumps( json.loads(arg_str), separators=(",", ":"), sort_keys=True, ) _CANON_ARGS_CACHE[arg_str] = canonical _canon_args_cache_bytes += len(arg_str) + len(canonical) while len(_CANON_ARGS_CACHE) > _CANON_ARGS_CACHE_MAX or ( _canon_args_cache_bytes > _CANON_ARGS_CACHE_MAX_BYTES and len(_CANON_ARGS_CACHE) > 1 ): try: evicted_key = next(iter(_CANON_ARGS_CACHE)) evicted_val = _CANON_ARGS_CACHE.pop(evicted_key) _canon_args_cache_bytes -= len(evicted_key) + len(evicted_val) except (StopIteration, KeyError, RuntimeError): break return canonical def _clone_message_for_send(msg): """Structural clone of a history message for the per-call API copy. Clones every dict/list recursively while sharing immutable leaves, so in-place send-path rewrites can never reach the persisted transcript (#80498). Cheaper than copy.deepcopy; messages are JSON-shaped and acyclic, tuples are shared as leaves.""" if isinstance(msg, dict): return { k: _clone_message_for_send(v) if isinstance(v, (dict, list)) else v for k, v in msg.items() } if isinstance(msg, list): return [ _clone_message_for_send(v) if isinstance(v, (dict, list)) else v for v in msg ] return msg def _canonicalize_api_tool_calls(api_messages) -> None: """Canonicalize tool-call argument JSON on the send-path message copy. Rewrites ``tool_calls`` in place (copy-on-write for the dicts it touches; persisted history untouched). The memo bounds parse/serialize to one per UNIQUE string.""" for am in api_messages: tcs = am.get("tool_calls") if not tcs: continue new_tcs = [] for tc in tcs: if isinstance(tc, dict) and "function" in tc: try: tc = {**tc, "function": { **tc["function"], "arguments": _canonicalize_tool_call_arguments( tc["function"]["arguments"] ), }} except Exception: # Copy-on-write as defense in depth: callers may pass shallow # copies, and writing into a shared tc["function"] rewrote the # stored turn with "{}" on the unrepairable path (#80498). tc = {**tc, "function": { **tc["function"], "arguments": _repair_tool_call_arguments( tc["function"]["arguments"], tc["function"].get("name", "?"), ), }} new_tcs.append(tc) am["tool_calls"] = new_tcs def _invalid_tool_name_error_content(name: str, valid_tool_names) -> str: """Error-result content for a tool call whose name isn't a real tool. A blank name is a model echoing tool-call syntax seen in data, not a typo (#47967); dumping the catalog feeds that loop, so send a terse error instead. A nonempty wrong name still gets the catalog so the model can self-correct.""" if not (name or "").strip(): return ( "Tool call rejected: the tool name was empty. " "If tool-call XML or JSON appeared in file " "contents or tool output, that is data — do " "not re-emit it as a tool call. To call a " "tool, use a valid name from your tool list; " "otherwise reply in plain text." ) available = ", ".join(sorted(valid_tool_names)) return f"Tool '{name}' does not exist. Available tools: {available}" def _content_policy_blocked_result( messages: List[Dict], api_call_count: int, *, final_response: str, error_detail: str, ) -> Dict[str, Any]: """Build the terminal turn result for a content-policy block. Refusals are deterministic for the unchanged prompt, so no retry; both the HTTP-200 and exception paths return this shape with a ``content_policy_blocked:`` error.""" return { "final_response": final_response, "messages": messages, "api_calls": api_call_count, "completed": False, "failed": True, "error": f"content_policy_blocked: {error_detail}", } def _compression_deferred_result( agent, messages: List[Dict], api_call_count: int, reason: str = "lock", ) -> Dict[str, Any]: """Build the soft turn result for a transiently-deferred compression. Both ``reason="lock"`` and ``reason="transient_block"`` must end as ``compression_deferred``, never ``compression_exhausted`` — the gateway wipes the session on exhaustion (#9893/#35809). ``failed`` stays False; the turn persists.""" if reason == "transient_block": block = getattr(agent, "_compression_blocked_transient", None) logger.info( "turn deferred: compression transiently blocked (%s) " "(session=%s) — not counting as compression exhaustion", block if isinstance(block, str) else "unknown guard", agent.session_id or "none", ) _final = ( "Context compression is temporarily paused after a recent " "failed attempt. Please retry in a moment — compression will " "resume automatically (or run /compress to force a retry now)." ) else: holder = getattr(agent, "_compression_skipped_due_to_lock", None) logger.info( "turn deferred: compression lock held by another path " "(session=%s holder=%s) — not counting as compression exhaustion", agent.session_id or "none", holder if isinstance(holder, str) else "unconfirmed", ) _final = ( "Context compression is already running for this session. " "Please retry in a moment — your next message will be processed " "once the concurrent compression finishes." ) try: agent._flush_status_buffer() except Exception: pass return { "final_response": _final, "messages": messages, "completed": False, "api_calls": api_call_count, "error": _final, "partial": True, "failed": False, "compression_deferred": True, "session_id": agent.session_id, } def _provider_overflow_exhausted_result( agent, messages: List[Dict], conversation_history, api_call_count: int, request_pressure_tokens: int, max_compression_attempts: int, ) -> Dict[str, Any]: """Fail closed when a rebuilt request is still too large after recovery.""" agent._flush_status_buffer() logger.error( "%sContext compression failed after %d attempts; rebuilt request " "remains over threshold at ~%s tokens.", agent.log_prefix, max_compression_attempts, f"{request_pressure_tokens:,}", ) agent._persist_session(messages, conversation_history) final_response = ( "Context length exceeded: compression could not reduce the rebuilt " "request below the safe threshold." ) return { "final_response": final_response, "messages": messages, "completed": False, "api_calls": api_call_count, "error": final_response, "partial": True, "failed": True, "compression_exhausted": True, "turn_exit_reason": "context_compression_exhausted", } def _rewrite_system_content_blocks(system_message: dict, effective: str) -> bool: """Rewrite a cache-decorated system message in place, keeping its blocks. Assigning a bare string over the ``[static prefix, volatile tail]`` block list drops both cache_control breakpoints. Only the LAST ``Model:``/``Provider:`` lines change. Returns False when the shape cannot be safely patched.""" content = system_message.get("content") if not isinstance(content, list) or not content: return False if not all( isinstance(part, dict) and part.get("type") == "text" for part in content ): return False if len(content) == 1: content[0]["text"] = effective return True if len(content) == 2: head = content[0].get("text") or "" if head and effective.startswith(head): tail = effective[len(head):] if tail: content[1]["text"] = tail return True return False def _sync_failover_system_message(agent, api_messages, active_system_prompt): """Refresh the in-flight system message after a provider failover. ``try_activate_fallback`` rewrites the identity lines on ``_cached_system_prompt``, but this call block's ``api_messages`` were built pre-failover and are reused each retry. Mutates ``api_messages[0]`` in place; returns the new ``active_system_prompt``.""" sp = getattr(agent, "_cached_system_prompt", None) if not isinstance(sp, str) or not sp: return active_system_prompt if api_messages and api_messages[0].get("role") == "system": effective = sp if agent.ephemeral_system_prompt: effective = (effective + "\n\n" + agent.ephemeral_system_prompt).strip() if not _rewrite_system_content_blocks(api_messages[0], effective): api_messages[0]["content"] = effective return sp def _arm_fallback_restart(agent, api_messages, active_system_prompt, _retry): """After ``_try_activate_fallback`` succeeded: sync the system message to the new provider and arm ``restart_with_rebuilt_messages`` (re-issue against the fallback, refunding the stalled attempt). Callers also reset ``retry_count`` / ``compression_attempts`` to 0 and ``break`` the retry loop.""" active_system_prompt = _sync_failover_system_message( agent, api_messages, active_system_prompt) _retry.primary_recovery_attempted = False _retry.restart_with_rebuilt_messages = True return active_system_prompt def _ensure_cached_system_prompt_static(agent, system_message=None) -> None: """Rebuild ``_cached_system_prompt_static`` when caching becomes active (#72626). Sessions restored under a cache-off primary skip the static-prefix rebuild; a later failover to a cache-on provider would otherwise silently fall back to the legacy system-plus-3 layout. Wraps ``reconstruct_static_prefix`` (memoizes failures).""" from agent.system_prompt import reconstruct_static_prefix reconstruct_static_prefix( agent, system_message=system_message, log_label="failover redecoration" ) def _peel_moa_guidance( messages: List[Dict[str, Any]], guidance: Any, ) -> List[Dict[str, Any]]: """Remove MoA reference guidance attached by ``_attach_reference_guidance``. Kept adjacent to the attach so the forward/inverse shapes evolve together.""" from agent.moa_loop import peel_reference_guidance return peel_reference_guidance(messages, guidance) def _redecorate_prompt_cache_for_provider( agent, api_messages: List[Dict[str, Any]], *, system_message=None, moa_prepared: Optional[Dict[str, Any]] = None, tools_for_api: Optional[List[Dict[str, Any]]] = None, ) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]] | tuple[List[Dict[str, Any]], Optional[Dict[str, Any]], List[Dict[str, Any]]]: """Strip and re-apply cache_control for the *current* provider policy. Decoration runs once per call block for the primary provider, but failover ``continue`` paths reuse ``api_messages`` (#72626), so reshape at the top of each retry from the mutated in-flight request. MoA guidance is peeled and rebased.""" messages: List[Dict[str, Any]] = [ dict(m) if isinstance(m, dict) else m for m in (api_messages or []) ] prepared = moa_prepared guidance = prepared.get("guidance") if isinstance(prepared, dict) else None if guidance: messages = _peel_moa_guidance(messages, guidance) strip_anthropic_cache_control(messages) planned_tools = strip_anthropic_tool_cache_control( tools_for_api if tools_for_api is not None else getattr(agent, "tools", []) ) if prepared is not None and getattr(agent, "provider", None) == "moa": # Prepared MoA state is canonical: the synchronous acting-aggregator # sender owns its destination-local cache plan after it resolves the slot. completions = getattr(getattr(agent.client, "chat", None), "completions", None) rebase = getattr(completions, "rebase_prepared_request", None) if callable(rebase): prepared = rebase(prepared, messages) messages = prepared["messages"] if tools_for_api is None: return messages, prepared return messages, prepared, planned_tools # Direct attribute access, not getattr: the flags are always initialized on # AIAgent, and a default would mask a real init bug as silent cache-off. if agent._use_prompt_caching: _ensure_cached_system_prompt_static(agent, system_message=system_message) static = getattr(agent, "_cached_system_prompt_static", None) direct_tool_cache = getattr( agent, "_direct_native_anthropic_tool_cache_capability", lambda: False, )() from agent.prompt_caching import envelope_tool_part_cache_markers_supported plan = build_prompt_cache_plan( messages, planned_tools, # Clamp per-destination: a configured 1h regresses to 5m on # Qwen/Alibaba routes, whose context cache is 5m-only (#84733). cache_ttl=effective_cache_ttl( agent._cache_ttl, provider=agent.provider, model=agent.model, ), native_anthropic=agent._use_native_cache_layout, static_system_prefix=static if isinstance(static, str) else None, direct_native_tool_cache=direct_tool_cache, # LiteLLM-style envelope routes forward part-level markers into # tool_result.content[] → non-retryable 400 (#89886). tool_part_markers=envelope_tool_part_cache_markers_supported( getattr(agent, "provider", ""), getattr(agent, "base_url", "") ), ) messages = plan.messages planned_tools = plan.tools if tools_for_api is None: return messages, prepared return messages, prepared, planned_tools def _apply_context_engine_selection( agent: Any, api_messages: List[Dict[str, Any]], conversation_messages: List[Dict[str, Any]], incoming_message: Optional[Dict[str, Any]], *, logger: Any, ) -> List[Dict[str, Any]]: """Run the optional per-turn ``ContextEngine.select_context()`` hook. Returns the (possibly replaced) request list. Fail-open: a missing hook, exception, or invalid return yields ``api_messages`` unchanged; history is never mutated.""" engine = getattr(agent, "context_compressor", None) if engine is None or not hasattr(engine, "select_context"): return api_messages # Skip the no-op base ``select_context`` so non-implementing engines pay nothing; # ``hasattr`` is not enough: the ABC defines a default. Lazy import avoids a cycle. try: from agent.context_engine import ContextEngine as _CE if getattr(engine.select_context, "__func__", None) is _CE.select_context: return api_messages except Exception: pass session_label = getattr(agent, "session_id", None) or "-" # Structural clones: the engine must not be able to write through nested # containers into persisted history; only the request list is acted on (#80498). _conv_copy = [_clone_message_for_send(m) for m in conversation_messages] \ if conversation_messages is not None else None _incoming_copy = _clone_message_for_send(incoming_message) if isinstance(incoming_message, dict) else incoming_message try: selected = engine.select_context( api_messages, conversation_messages=_conv_copy, incoming_message=_incoming_copy, budget_tokens=getattr(engine, "context_length", 0) or 0, ) except Exception: logger.warning( "Context engine select_context hook failed; using unmodified " "request messages (session=%s)", session_label, exc_info=True, ) return api_messages if selected is None: return api_messages # Require a NON-EMPTY list of dicts: ``all([])`` is ``True``, so a ``[]`` from a # buggy engine would otherwise replace the request instead of failing open. if isinstance(selected, list) and selected and all(isinstance(m, dict) for m in selected): return selected logger.warning( "Context engine select_context returned an invalid value " "(not a non-empty list of dicts); ignoring (session=%s)", session_label, ) return api_messages def _notify_context_engine_turn_complete( agent: Any, messages: List[Dict[str, Any]], *, usage: Optional[Dict[str, Any]] = None, logger: Any, **meta: Any, ) -> None: """Notify the active context engine that a user turn has finished. Fail-open: a missing/no-op hook or any exception is swallowed. ``messages`` is passed as a copy so the engine cannot mutate the persisted transcript.""" engine = getattr(agent, "context_compressor", None) hook = getattr(engine, "on_turn_complete", None) if engine is None or not callable(hook): return # Skip the no-op base ``on_turn_complete`` so non-implementing engines pay nothing # per turn. Lazy import avoids an import cycle with agent.context_engine. try: from agent.context_engine import ContextEngine as _CE if getattr(hook, "__func__", None) is _CE.on_turn_complete: return except Exception: pass try: hook( # Structural clones: dict(m) would let a hook write into nested containers # of the persisted transcript (#80498). [_clone_message_for_send(m) for m in messages], usage=usage, **meta, ) except Exception: logger.warning( "Context engine on_turn_complete hook failed (session=%s)", getattr(agent, "session_id", None) or "-", exc_info=True, ) def _decode_inline_moa_turn(user_message, persist_user_message): """Decode a MoA preset encoded into ``user_message`` (``hermes_cli.moa_config``). Returns ``(user_message, moa_config, persist_user_message)``; unchanged with ``moa_config=None`` when nothing is encoded or decoding fails.""" try: from hermes_cli.moa_config import decode_moa_turn _decoded_message, _decoded_moa_config = decode_moa_turn(user_message) if _decoded_moa_config is not None: if persist_user_message is None: persist_user_message = _decoded_message return _decoded_message, _decoded_moa_config, persist_user_message except Exception: pass return user_message, None, persist_user_message def _preflight_timeout_result(agent, exc, conversation_history) -> Dict[str, Any]: """Typed recovery result when turn-start preflight compression timed out (#98424): no provider call was sent. Surfaces hide raw exception text, which would bury the actionable guidance and skip the compression_exhausted recovery contract.""" logger.warning( "Turn-start preflight compression timed out — ending turn with " "typed recovery result: %s", exc, ) # Clear the tripwire slot note_turn_start registered; the early return skips the # persist funnel that clears it. The user row is deliberately NOT persisted: # the gateway skips persistence for compression_exhausted results (#7100). from agent.agent_runtime_helpers import note_turn_persisted note_turn_persisted(agent) # Not _COMPRESSION_TIMEOUT_FINAL_RESPONSE — that describes a different state # (compression ran, could not reduce); the exception text carries the guidance. _final_response = str(exc) return { "final_response": _final_response, "messages": list(conversation_history or []), "completed": False, "api_calls": 0, "error": _final_response, "partial": True, "failed": True, "compression_exhausted": True, "turn_exit_reason": "context_compression_timeout", } @dataclass class _LoopState: """Every local the turn loop threads through the phase helpers in ``agent/turn_*.py``. Each helper takes the loop locals it needs as keyword arguments named exactly like these fields and returns a verdict dataclass whose non-``action``/``result`` fields carry the same names; :func:`_run_phase` passes and copies them back by name, so a field added to a helper's signature or verdict needs a field here and nothing else. Per-iteration slots (``response`` … ``assistant_message``) are rebound by the phases before any later phase reads them, exactly as the former inline locals were.""" # Fixed for the turn. user_message: Any system_message: Any moa_config: Any original_user_message: Any conversation_history: Any effective_task_id: Any turn_id: Any _should_review_memory: Any _plugin_user_context: Any _ext_prefetch_cache: Any # Turn-scoped state (rebound by the phases). messages: Any active_system_prompt: Any current_turn_user_idx: Any _preflight_compression_blocked: Any # Per-turn compression attempt cap shared by the pre-API gate, 413 handlers and # post-tool compaction; a consecutive-ineffective-attempt backstop, rearmed only # after a provider response reports a prompt below threshold. Default 3 if unset. max_compression_attempts: Any api_call_count: int = 0 final_response: Any = None interrupted: bool = False failed: bool = False codex_ack_continuations: int = 0 length_continue_retries: int = 0 # Total outer-loop exceptions this turn (#92450) — see _MAX_OUTER_LOOP_ERRORS. _outer_error_count: int = 0 truncated_tool_call_retries: int = 0 truncated_response_parts: List[str] = field(default_factory=list) compression_attempts: int = 0 _last_preflight_pressure: Optional[int] = None # A provider overflow outweighs the rough-estimate calibration that defers preflight # after compaction: stay armed until the rebuilt request is below the threshold. _provider_overflow_recovery_pending: bool = False # Armed when a compression host-timeout ends the turn; finalize reuses the gateway # context-recovery contract (error/partial/compression_exhausted) (#98722). _compression_timeout_exhausted: bool = False _turn_exit_reason: str = "unknown" # Diagnostic: why the loop ended # Last answer held back by a verification gate: if the continuation exhausts the # budget this is the best user-facing result, distinct from error/recovery text. _pending_verification_response: Any = None # Whether that candidate was already streamed as interim; ``_response_was_previewed`` # is set ONLY if it becomes the final response (#65919). _pending_verification_response_previewed: bool = False # If pre-API compression fires after MoA advisors ran, retain their guidance and # rebase it onto the compacted transcript next iteration — no second fan-out. pending_moa_prepared_request: Any = None # Per-iteration slots. request_logger: Any = None api_messages: Any = None tools_for_api: Any = None _moa_prepared_request: Any = None approx_tokens: Any = None request_pressure_tokens: Any = None total_chars: Any = None thinking_spinner: Any = None api_start_time: Any = None retry_count: int = 0 max_retries: Any = None _retry: Any = None finish_reason: str = "stop" response: Any = None # None when every retry failed api_kwargs: Any = None # None until built; read by the except handlers api_request_id: Any = None _original_api_kwargs: Any = None _llm_middleware_trace: Any = None api_duration: Any = None assistant_message: Any = None # Keyword names each phase helper takes (minus ``agent``), cached per function object. _PHASE_PARAMS: Dict[Any, tuple] = {} # Verdict fields the loop latches (only ever sets True) instead of copying back: # ``handle_api_error`` reports overflow recovery per call and must not clear an earlier arm. _LATCHED_VERDICT_FIELDS = {"handle_api_error": frozenset({"_provider_overflow_recovery_pending"})} def _run_phase(fn, agent, state: _LoopState, **extra): """Call phase helper ``fn`` with the loop locals it names, copy its verdict fields back. ``extra`` supplies non-state arguments (the caught exception). Returns the verdict so the caller can act on ``.action`` / ``.result``.""" params = _PHASE_PARAMS.get(fn) if params is None: params = _PHASE_PARAMS[fn] = tuple( p for p in inspect.signature(fn).parameters if p != "agent" ) verdict = fn(agent, **{ name: extra[name] if name in extra else getattr(state, name) for name in params }) latched = _LATCHED_VERDICT_FIELDS.get(getattr(fn, "__name__", ""), ()) for f in fields(verdict): if f.name in ("action", "result"): continue value = getattr(verdict, f.name) if f.name not in latched: setattr(state, f.name, value) elif value: setattr(state, f.name, True) return verdict def run_conversation( agent, user_message: Any, system_message: str = None, conversation_history: List[Dict[str, Any]] = None, task_id: str = None, stream_callback: Optional[callable] = None, persist_user_message: Optional[Any] = None, persist_user_timestamp: Optional[float] = None, persist_user_display_kind: Optional[str] = None, persist_user_display_metadata: Optional[Dict[str, Any]] = None, persist_user_platform_id: Optional[str] = None, moa_config: Optional[dict[str, Any]] = None, ) -> Dict[str, Any]: """Run a complete conversation with tool calling until completion. Args: stream_callback: per-text-delta callback (TTS); None uses the non-streaming path. persist_user_message: clean text to store when ``user_message`` carries API-only synthetic prefixes; ``persist_user_timestamp`` / ``persist_user_platform_id`` are stored as metadata (platform id lets restart drain recovery dedup). persist_user_display_kind/metadata: display-only event rendering (``auto_continue``, ``model_switch``); the model still receives the message unchanged. Returns: dict with the final response and message history.""" if moa_config is None: user_message, moa_config, persist_user_message = _decode_inline_moa_turn( user_message, persist_user_message ) # The gateway caches agents across turns; compression state is per-turn, or a stale # in-place boundary would make a later uncompressed result look compacted. agent._last_compaction_in_place = False agent._last_compression_attempt_recorded = False agent._last_compression_attempt_in_place = None begin_fast_mode_turn(agent, conversation_history) # Adopt ~/.hermes/.env credential/base-url edits made since the last turn — a # Settings save updates .env, not this worker's client (#67821). No-op if unchanged. try: agent._try_refresh_env_client_credentials() except Exception: logger.debug("per-turn env credential refresh failed", exc_info=True) # Per-turn setup (the prologue): ``build_turn_context`` (agent/turn_context.py) # mutates ``agent`` as the inline code did and returns the locals the loop reads. try: _ctx = build_turn_context( agent, user_message, system_message, conversation_history, task_id, stream_callback, persist_user_message, persist_user_timestamp, persist_user_display_kind=persist_user_display_kind, persist_user_display_metadata=persist_user_display_metadata, persist_user_platform_id=persist_user_platform_id, restore_or_build_system_prompt=_restore_or_build_system_prompt, install_safe_stdio=_install_safe_stdio, sanitize_surrogates=_sanitize_surrogates, summarize_user_message_for_log=_summarize_user_message_for_log, set_session_context=set_session_context, set_current_write_origin=set_current_write_origin, ra=_ra, # MoA turns append per-call aggregated context to the API copy of the # user message, so no byte-stable api_content sidecar can be stamped. moa_active=bool(moa_config), ) except PreflightCompressionTimedOut as _preflight_timeout_exc: return _preflight_timeout_result(agent, _preflight_timeout_exc, conversation_history) # Commentary deduplication spans all provider continuations and tool calls # within one user turn, but must not suppress the same phrase next turn. agent._delivered_interim_texts = set() # A configured SessionDB append failure halts only the affected turn. A # cached gateway agent must recover on the next message if storage did. agent._incremental_persistence_failed = False # Cause of the last persistence failure this turn ('locked'/'disk'/'unknown', see # hermes_state.classify_persistence_error). Reset so a prior diagnosis cannot leak. agent._last_persistence_error_cause = None # Per-turn diagnostic: a failed compression-tip adoption in a previous # turn's flush must not be reported against this turn. agent._compression_adoption_failed = False # Turn-scoped one-shot: armed by a thinking-only truncation, consumed by # build_api_kwargs; must not survive an interrupted turn into the next one. agent._ephemeral_reasoning_off = False # Per-turn tally of credential-pool refreshes by (provider, pool-entry-id): caps # same-entry refreshes on a persistent 401 so fallback takes over (#26080). agent._auth_pool_refresh_counts = {} # Per-turn usage forwarded to the context engine's on_turn_complete() hook; left # None on turns that never reach a response so the hook never sees stale usage. agent._last_turn_usage = None s = _LoopState( user_message=_ctx.user_message, system_message=system_message, moa_config=moa_config, original_user_message=_ctx.original_user_message, conversation_history=_ctx.conversation_history, effective_task_id=_ctx.effective_task_id, turn_id=_ctx.turn_id, _should_review_memory=_ctx.should_review_memory, _plugin_user_context=_ctx.plugin_user_context, _ext_prefetch_cache=_ctx.ext_prefetch_cache, messages=_ctx.messages, active_system_prompt=_ctx.active_system_prompt, current_turn_user_idx=_ctx.current_turn_user_idx, _preflight_compression_blocked=_ctx.preflight_compression_blocked, max_compression_attempts=getattr(agent, "max_compression_attempts", 3), ) # Opt-in runtime: api_mode == codex_app_server hands the whole turn to the codex # app-server subprocess (see agent/transports/codex_app_server_session.py). if agent.api_mode == "codex_app_server": return agent._run_codex_app_server_turn( user_message=s.user_message, original_user_message=s.original_user_message, messages=s.messages, effective_task_id=s.effective_task_id, should_review_memory=s._should_review_memory, ) while (s.api_call_count < agent.max_iterations and agent.iteration_budget.remaining > 0) or agent._budget_grace_call: if _run_phase(begin_iteration, agent, s).action == "break": break _run_phase(prepare_iteration, agent, s) _run_phase(assemble_api_request, agent, s) _pg = _run_phase(run_preflight_gate, agent, s) if _pg.action == "return": return _pg.result if _pg.action == "break": break if _pg.action == "continue": continue _run_phase(announce_api_call, agent, s) s.api_start_time = time.time() s.retry_count = 0 s.max_retries = agent._api_max_retries s._retry = TurnRetryState() s.finish_reason = "stop" s.response = None s.api_kwargs = None s.api_request_id = f"{s.turn_id}:api:{s.api_call_count}" agent._current_api_request_id = s.api_request_id while s.retry_count < s.max_retries: _ng = _run_phase(nous_rate_limit_guard, agent, s) if _ng.action == "return": return _ng.result if _ng.action == "break": break try: _run_phase(build_api_request, agent, s) if _run_phase(perform_api_call, agent, s).action == "break": break _rc = _run_phase(check_api_response, agent, s) if _rc.action == "return": return _rc.result if _rc.action == "break": break if _rc.action == "continue": continue except InterruptedError: if _run_phase(handle_api_interrupt, agent, s).action == "break": break except Exception as api_error: _ae = _run_phase(handle_api_error, agent, s, api_error=api_error) if _ae.action == "return": return _ae.result if _ae.action == "break": break if _ae.action == "continue": continue _rs = _run_phase(apply_retry_restarts, agent, s) if _rs.action == "break": break if _rs.action == "continue": continue try: _ri = _run_phase(normalize_model_response, agent, s) if _ri.action == "return": return _ri.result if _ri.action == "continue": continue if s.assistant_message.tool_calls: _tr = _run_phase(run_tool_round, agent, s) if _tr.action == "return": return _tr.result if _tr.action == "break": break if _tr.action == "continue": continue else: _fr = _run_phase(finish_text_response, agent, s) if _fr.action == "return": return _fr.result if _fr.action == "break": break if _fr.action == "continue": continue except Exception as e: if _run_phase(handle_outer_loop_error, agent, s, e=e).action == "break": break # Post-loop finalization lives in agent/turn_finalizer.finalize_turn. result = finalize_turn( agent, final_response=s.final_response, api_call_count=s.api_call_count, interrupted=s.interrupted, failed=s.failed, messages=s.messages, conversation_history=s.conversation_history, effective_task_id=s.effective_task_id, turn_id=s.turn_id, user_message=s.user_message, original_user_message=s.original_user_message, _should_review_memory=s._should_review_memory, _turn_exit_reason=s._turn_exit_reason, _pending_verification_response=s._pending_verification_response, _pending_verification_response_previewed=s._pending_verification_response_previewed, ) if s._compression_timeout_exhausted: # Reuse the gateway's context-recovery contract: transcript stays intact while # future input can move to a clean session (#98722). result["error"] = _COMPRESSION_TIMEOUT_FINAL_RESPONSE result["partial"] = True result["compression_exhausted"] = True return result __all__ = ["run_conversation"]