run_agent.py

#!/usr/bin/env python3
"""
AI Agent Runner with Tool Calling

This module provides a clean, standalone agent that can execute AI models
with tool calling capabilities. It handles the conversation loop, tool execution,
and response management.

Features:
- Automatic tool calling loop until completion
- Configurable model parameters
- Error handling and recovery
- Message history management
- Support for multiple model providers

Usage:
    from run_agent import AIAgent
    
    agent = AIAgent(base_url="http://localhost:30000/v1", model="claude-opus-4-20250514")
    response = agent.run_conversation("Tell me about the latest Python updates")
"""

# IMPORTANT: hermes_bootstrap must be the very first import — UTF-8 stdio
# on Windows.  No-op on POSIX.  See hermes_bootstrap.py for full rationale.
try:
    import hermes_bootstrap  # noqa: F401
except ModuleNotFoundError:
    # Graceful fallback when hermes_bootstrap isn't registered in the venv
    # yet — happens during partial ``hermes update`` where git-reset landed
    # new code but ``uv pip install -e .`` didn't finish.  Missing bootstrap
    # means UTF-8 stdio setup is skipped on Windows; POSIX is unaffected.
    pass

import asyncio
import base64
import concurrent.futures
import contextvars
import copy
import hashlib
import json
import logging
logger = logging.getLogger(__name__)
import os
import random
import re
import ssl
import sys
import tempfile
import time
import threading
from types import SimpleNamespace
import urllib.request
import uuid
from typing import List, Dict, Any, Optional
from urllib.parse import urlparse, parse_qs, urlunparse
# NOTE: `from openai import OpenAI` is deliberately NOT at module top — the
# SDK pulls ~240 ms of imports. We expose `OpenAI` as a thin proxy object
# that imports the SDK on first call/isinstance check. This preserves:
#   (a) the single in-module `OpenAI(**client_kwargs)` call site at
#       _create_openai_client, and
#   (b) `patch("run_agent.OpenAI", ...)` test patterns used by ~28 test files.
#
# NOTE: `fire` is ONLY used in the `__main__` block below (for running
# run_agent.py directly as a CLI) — it is NOT needed for library usage.
# It is imported there, not here, so that importing run_agent from a
# daemon thread (e.g. curator's forked review agent) never fails with
# ModuleNotFoundError on broken/partial installs where `fire` isn't present.
from datetime import datetime
from pathlib import Path

from hermes_constants import get_hermes_home

# OpenAI lazy proxy + safe stdio + proxy URL helpers — see agent/process_bootstrap.py.
# `OpenAI` is re-exported here so `patch("run_agent.OpenAI", ...)` in tests works.
from agent.process_bootstrap import (
    OpenAI,
    _OpenAIProxy,
    _load_openai_cls,
    _SafeWriter,
    _install_safe_stdio,
    _get_proxy_from_env,
    _get_proxy_for_base_url,
)
from agent.iteration_budget import IterationBudget


from hermes_cli.env_loader import load_hermes_dotenv
from hermes_cli.timeouts import (
    get_provider_request_timeout,
    get_provider_stale_timeout,
)

_hermes_home = get_hermes_home()
_project_env = Path(__file__).parent / '.env'
_loaded_env_paths = load_hermes_dotenv(hermes_home=_hermes_home, project_env=_project_env)
if _loaded_env_paths:
    for _env_path in _loaded_env_paths:
        logger.info("Loaded environment variables from %s", _env_path)
else:
    logger.info("No .env file found. Using system environment variables.")


# Import our tool system
from model_tools import (
    get_tool_definitions,
    get_toolset_for_tool,
    handle_function_call,
    check_toolset_requirements,
)
from tools.terminal_tool import cleanup_vm, get_active_env, is_persistent_env
from tools.terminal_tool import (
    set_approval_callback as _set_approval_callback,
    set_sudo_password_callback as _set_sudo_password_callback,
    _get_approval_callback,
    _get_sudo_password_callback,
)
from tools.tool_result_storage import maybe_persist_tool_result, enforce_turn_budget
from tools.interrupt import set_interrupt as _set_interrupt
from tools.browser_tool import cleanup_browser


# Agent internals extracted to agent/ package for modularity
from agent.memory_manager import StreamingContextScrubber, build_memory_context_block, sanitize_context
from agent.think_scrubber import StreamingThinkScrubber
from agent.retry_utils import jittered_backoff
from agent.error_classifier import classify_api_error, FailoverReason
from agent.prompt_builder import (
    DEFAULT_AGENT_IDENTITY, PLATFORM_HINTS,
    MEMORY_GUIDANCE, SESSION_SEARCH_GUIDANCE, SKILLS_GUIDANCE,
    HERMES_AGENT_HELP_GUIDANCE,
    KANBAN_GUIDANCE,
    build_nous_subscription_prompt,
)
from agent.model_metadata import (
    fetch_model_metadata,
    estimate_tokens_rough, estimate_messages_tokens_rough, estimate_request_tokens_rough,
    get_next_probe_tier, parse_context_limit_from_error,
    parse_available_output_tokens_from_error,
    save_context_length, is_local_endpoint,
    query_ollama_num_ctx,
)
from agent.context_compressor import ContextCompressor
from agent.subdirectory_hints import SubdirectoryHintTracker
from agent.prompt_caching import apply_anthropic_cache_control
from agent.prompt_builder import build_skills_system_prompt, build_context_files_prompt, build_environment_hints, load_soul_md, TOOL_USE_ENFORCEMENT_GUIDANCE, TOOL_USE_ENFORCEMENT_MODELS, GOOGLE_MODEL_OPERATIONAL_GUIDANCE, OPENAI_MODEL_EXECUTION_GUIDANCE
from agent.usage_pricing import estimate_usage_cost, normalize_usage
from agent.codex_responses_adapter import (
    _derive_responses_function_call_id as _codex_derive_responses_function_call_id,
    _deterministic_call_id as _codex_deterministic_call_id,
    _split_responses_tool_id as _codex_split_responses_tool_id,
    _summarize_user_message_for_log,
)
from agent.display import (
    KawaiiSpinner, build_tool_preview as _build_tool_preview,
    get_cute_tool_message as _get_cute_tool_message_impl,
    _detect_tool_failure,
    get_tool_emoji as _get_tool_emoji,
)
from agent.tool_guardrails import (
    ToolCallGuardrailConfig,
    ToolCallGuardrailController,
    ToolGuardrailDecision,
    append_toolguard_guidance,
    toolguard_synthetic_result,
)
from agent.tool_result_classification import (
    FILE_MUTATING_TOOL_NAMES as _FILE_MUTATING_TOOLS,
    file_mutation_result_landed,
)
from agent.trajectory import (
    convert_scratchpad_to_think,
    save_trajectory as _save_trajectory_to_file,
)
from agent.message_sanitization import (
    _SURROGATE_RE,
    _sanitize_surrogates,
    _sanitize_structure_surrogates,
    _sanitize_messages_surrogates,
    _escape_invalid_chars_in_json_strings,
    _repair_tool_call_arguments,
    _strip_non_ascii,
    _sanitize_messages_non_ascii,
    _sanitize_tools_non_ascii,
    _strip_images_from_messages,
    _sanitize_structure_non_ascii,
)
from agent.tool_dispatch_helpers import (
    _NEVER_PARALLEL_TOOLS,
    _PARALLEL_SAFE_TOOLS,
    _PATH_SCOPED_TOOLS,
    _DESTRUCTIVE_PATTERNS,
    _REDIRECT_OVERWRITE,
    _is_destructive_command,
    _should_parallelize_tool_batch,
    _extract_parallel_scope_path,
    _paths_overlap,
    _is_multimodal_tool_result,
    _multimodal_text_summary,
    _append_subdir_hint_to_multimodal,
    _extract_file_mutation_targets,
    _extract_error_preview,
    _trajectory_normalize_msg,
)
from utils import atomic_json_write, base_url_host_matches, base_url_hostname, env_var_enabled, normalize_proxy_url
from hermes_cli.config import cfg_get



_MAX_TOOL_WORKERS = 8

# Guard so the OpenRouter metadata pre-warm thread is only spawned once per
# process, not once per AIAgent instantiation.  Without this, long-running
# gateway processes leak one OS thread per incoming message and eventually
# exhaust the system thread limit (RuntimeError: can't start new thread).
_openrouter_prewarm_done = threading.Event()

# =========================================================================
# Large tool result handler — save oversized output to temp file
# =========================================================================


# =========================================================================
# Qwen Portal headers — mimics QwenCode CLI for portal.qwen.ai compatibility.
# Extracted as a module-level helper so both __init__ and
# _apply_client_headers_for_base_url can share it.
# =========================================================================
_QWEN_CODE_VERSION = "0.14.1"


def _routermint_headers() -> dict:
    """Return the User-Agent RouterMint needs to avoid Cloudflare 1010 blocks."""
    from hermes_cli import __version__ as _HERMES_VERSION

    return {
        "User-Agent": f"HermesAgent/{_HERMES_VERSION}",
    }


def _pool_may_recover_from_rate_limit(
    pool, *, provider: str | None = None, base_url: str | None = None
) -> bool:
    """Decide whether to wait for credential-pool rotation instead of falling back.

    The existing pool-rotation path requires the pool to (1) exist and (2) have
    at least one entry not currently in exhaustion cooldown.  But rotation is
    only meaningful when the pool has more than one entry.

    With a single-credential pool (common for Gemini OAuth, Vertex service
    accounts, and any "one personal key" configuration), the primary entry
    just 429'd and there is nothing to rotate to.  Waiting for the pool
    cooldown to expire means retrying against the same exhausted quota — the
    daily-quota 429 will recur immediately, and the retry budget is burned.

    Additionally, Google CloudCode / Gemini CLI rate limits are ACCOUNT-level
    throttles — even a multi-entry pool shares the same quota window, so
    rotation won't recover.  Skip straight to the fallback for those (#13636).

    In those cases we must fall back to the configured ``fallback_model``
    instead.  Returns True only when rotation has somewhere to go.

    See issues #11314 and #13636.
    """
    if pool is None:
        return False
    if not pool.has_available():
        return False
    # CloudCode / Gemini CLI quotas are account-wide — all pool entries share
    # the same throttle window, so rotation can't recover.  Prefer fallback.
    if provider == "google-gemini-cli" or str(base_url or "").startswith("cloudcode-pa://"):
        return False
    return len(pool.entries()) > 1


def _qwen_portal_headers() -> dict:
    """Return default HTTP headers required by Qwen Portal API."""
    import platform as _plat

    _ua = f"QwenCode/{_QWEN_CODE_VERSION} ({_plat.system().lower()}; {_plat.machine()})"
    return {
        "User-Agent": _ua,
        "X-DashScope-CacheControl": "enable",
        "X-DashScope-UserAgent": _ua,
        "X-DashScope-AuthType": "qwen-oauth",
    }


class _StreamErrorEvent(Exception):
    """Synthesized provider error surfaced from a Responses ``error`` SSE frame.

    Some Codex-style Responses backends (xAI for subscription/quota
    failures, custom relays under malformed-tool-call conditions) emit a
    standalone ``type=error`` frame instead of routing the failure
    through ``response.failed`` or returning an HTTP 4xx.  The fallback
    streaming path raises this exception so ``_summarize_api_error`` and
    ``_extract_api_error_context`` see a familiar ``.body`` /
    ``.status_code`` shape and the entitlement detector can match the
    underlying provider message ("do not have an active Grok
    subscription", etc.).
    """

    def __init__(
        self,
        message: str,
        *,
        code: Optional[str] = None,
        param: Optional[str] = None,
        status_code: Optional[int] = None,
    ) -> None:
        super().__init__(message)
        self.message = message
        self.code = code
        self.param = param
        self.status_code = status_code
        # OpenAI SDK-shaped body so _extract_api_error_context /
        # _summarize_api_error / classify_api_error all pick it up.
        self.body: Dict[str, Any] = {
            "error": {
                "message": message,
                "code": code,
                "param": param,
                "type": "error",
            }
        }


class AIAgent:
    """
    AI Agent with tool calling capabilities.

    This class manages the conversation flow, tool execution, and response handling
    for AI models that support function calling.
    """

    _TOOL_CALL_ARGUMENTS_CORRUPTION_MARKER = (
        "[hermes-agent: tool call arguments were corrupted in this session and "
        "have been dropped to keep the conversation alive. See issue #15236.]"
    )

    @property
    def base_url(self) -> str:
        return self._base_url

    @base_url.setter
    def base_url(self, value: str) -> None:
        self._base_url = value
        self._base_url_lower = value.lower() if value else ""
        self._base_url_hostname = base_url_hostname(value)

    def __init__(
        self,
        base_url: str = None,
        api_key: str = None,
        provider: str = None,
        api_mode: str = None,
        acp_command: str = None,
        acp_args: list[str] | None = None,
        command: str = None,
        args: list[str] | None = None,
        model: str = "",
        max_iterations: int = 90,  # Default tool-calling iterations (shared with subagents)
        tool_delay: float = 1.0,
        enabled_toolsets: List[str] = None,
        disabled_toolsets: List[str] = None,
        save_trajectories: bool = False,
        verbose_logging: bool = False,
        quiet_mode: bool = False,
        ephemeral_system_prompt: str = None,
        log_prefix_chars: int = 100,
        log_prefix: str = "",
        providers_allowed: List[str] = None,
        providers_ignored: List[str] = None,
        providers_order: List[str] = None,
        provider_sort: str = None,
        provider_require_parameters: bool = False,
        provider_data_collection: str = None,
        openrouter_min_coding_score: Optional[float] = None,
        session_id: str = None,
        tool_progress_callback: callable = None,
        tool_start_callback: callable = None,
        tool_complete_callback: callable = None,
        thinking_callback: callable = None,
        reasoning_callback: callable = None,
        clarify_callback: callable = None,
        step_callback: callable = None,
        stream_delta_callback: callable = None,
        interim_assistant_callback: callable = None,
        tool_gen_callback: callable = None,
        status_callback: callable = None,
        max_tokens: int = None,
        reasoning_config: Dict[str, Any] = None,
        service_tier: str = None,
        request_overrides: Dict[str, Any] = None,
        prefill_messages: List[Dict[str, Any]] = None,
        platform: str = None,
        user_id: str = None,
        user_name: str = None,
        chat_id: str = None,
        chat_name: str = None,
        chat_type: str = None,
        thread_id: str = None,
        gateway_session_key: str = None,
        skip_context_files: bool = False,
        load_soul_identity: bool = False,
        skip_memory: bool = False,
        session_db=None,
        parent_session_id: str = None,
        iteration_budget: "IterationBudget" = None,
        fallback_model: Dict[str, Any] = None,
        credential_pool=None,
        checkpoints_enabled: bool = False,
        checkpoint_max_snapshots: int = 20,
        checkpoint_max_total_size_mb: int = 500,
        checkpoint_max_file_size_mb: int = 10,
        pass_session_id: bool = False,
    ):
        """Forwarder — see ``agent.agent_init.init_agent``."""
        from agent.agent_init import init_agent
        init_agent(
            self,
            base_url=base_url,
            api_key=api_key,
            provider=provider,
            api_mode=api_mode,
            acp_command=acp_command,
            acp_args=acp_args,
            command=command,
            args=args,
            model=model,
            max_iterations=max_iterations,
            tool_delay=tool_delay,
            enabled_toolsets=enabled_toolsets,
            disabled_toolsets=disabled_toolsets,
            save_trajectories=save_trajectories,
            verbose_logging=verbose_logging,
            quiet_mode=quiet_mode,
            ephemeral_system_prompt=ephemeral_system_prompt,
            log_prefix_chars=log_prefix_chars,
            log_prefix=log_prefix,
            providers_allowed=providers_allowed,
            providers_ignored=providers_ignored,
            providers_order=providers_order,
            provider_sort=provider_sort,
            provider_require_parameters=provider_require_parameters,
            provider_data_collection=provider_data_collection,
            openrouter_min_coding_score=openrouter_min_coding_score,
            session_id=session_id,
            tool_progress_callback=tool_progress_callback,
            tool_start_callback=tool_start_callback,
            tool_complete_callback=tool_complete_callback,
            thinking_callback=thinking_callback,
            reasoning_callback=reasoning_callback,
            clarify_callback=clarify_callback,
            step_callback=step_callback,
            stream_delta_callback=stream_delta_callback,
            interim_assistant_callback=interim_assistant_callback,
            tool_gen_callback=tool_gen_callback,
            status_callback=status_callback,
            max_tokens=max_tokens,
            reasoning_config=reasoning_config,
            service_tier=service_tier,
            request_overrides=request_overrides,
            prefill_messages=prefill_messages,
            platform=platform,
            user_id=user_id,
            user_name=user_name,
            chat_id=chat_id,
            chat_name=chat_name,
            chat_type=chat_type,
            thread_id=thread_id,
            gateway_session_key=gateway_session_key,
            skip_context_files=skip_context_files,
            load_soul_identity=load_soul_identity,
            skip_memory=skip_memory,
            session_db=session_db,
            parent_session_id=parent_session_id,
            iteration_budget=iteration_budget,
            fallback_model=fallback_model,
            credential_pool=credential_pool,
            checkpoints_enabled=checkpoints_enabled,
            checkpoint_max_snapshots=checkpoint_max_snapshots,
            checkpoint_max_total_size_mb=checkpoint_max_total_size_mb,
            checkpoint_max_file_size_mb=checkpoint_max_file_size_mb,
            pass_session_id=pass_session_id,
        )

    def _get_session_db_for_recall(self):
        """Return a SessionDB for recall, lazily creating it if an entrypoint forgot.

        Most frontends pass ``session_db`` into ``AIAgent`` explicitly, but recall
        is important enough that a missing constructor argument should degrade by
        opening the default state DB instead of making the advertised
        ``session_search`` tool unusable.
        """
        if self._session_db is not None:
            return self._session_db
        try:
            from hermes_state import SessionDB

            self._session_db = SessionDB()
            return self._session_db
        except Exception as exc:
            logger.debug("SessionDB unavailable for recall", exc_info=True)
            return None

    def _ensure_db_session(self) -> None:
        """Create session DB row on first use. Disables _session_db on failure."""
        if self._session_db_created or not self._session_db:
            return
        try:
            self._session_db.create_session(
                session_id=self.session_id,
                source=self.platform or os.environ.get("HERMES_SESSION_SOURCE", "cli"),
                model=self.model,
                model_config=self._session_init_model_config,
                system_prompt=self._cached_system_prompt,
                user_id=None,
                parent_session_id=self._parent_session_id,
            )
            self._session_db_created = True
        except Exception as e:
            # Transient failure (e.g. SQLite lock). Keep _session_db alive —
            # _session_db_created stays False so next run_conversation() retries.
            logger.warning(
                "Session DB creation failed (will retry next turn): %s", e
            )

    def reset_session_state(self):
        """Reset all session-scoped token counters to 0 for a fresh session.
        
        This method encapsulates the reset logic for all session-level metrics
        including:
        - Token usage counters (input, output, total, prompt, completion)
        - Cache read/write tokens
        - API call count
        - Reasoning tokens
        - Estimated cost tracking
        - Context compressor internal counters
        
        The method safely handles optional attributes (e.g., context compressor)
        using ``hasattr`` checks.
        
        This keeps the counter reset logic DRY and maintainable in one place
        rather than scattering it across multiple methods.
        """
        # Token usage counters
        self.session_total_tokens = 0
        self.session_input_tokens = 0
        self.session_output_tokens = 0
        self.session_prompt_tokens = 0
        self.session_completion_tokens = 0
        self.session_cache_read_tokens = 0
        self.session_cache_write_tokens = 0
        self.session_reasoning_tokens = 0
        self.session_api_calls = 0
        self.session_estimated_cost_usd = 0.0
        self.session_cost_status = "unknown"
        self.session_cost_source = "none"
        
        # Turn counter (added after reset_session_state was first written — #2635)
        self._user_turn_count = 0

        # Context engine reset (works for both built-in compressor and plugins)
        if hasattr(self, "context_compressor") and self.context_compressor:
            self.context_compressor.on_session_reset()

    def _ensure_lmstudio_runtime_loaded(self, config_context_length: Optional[int] = None) -> None:
        """
        Preload the LM Studio model with at least Hermes' minimum context.
        """
        if (self.provider or "").strip().lower() != "lmstudio":
            return
        try:
            from agent.model_metadata import MINIMUM_CONTEXT_LENGTH
            from hermes_cli.models import ensure_lmstudio_model_loaded
            if config_context_length is None:
                config_context_length = getattr(self, "_config_context_length", None)
            target_ctx = max(config_context_length or 0, MINIMUM_CONTEXT_LENGTH)
            loaded_ctx = ensure_lmstudio_model_loaded(
                self.model, self.base_url, getattr(self, "api_key", ""), target_ctx,
            )
            if loaded_ctx:
                # Push into the live compressor so the status bar reflects the
                # real loaded ctx the moment the load resolves, instead of
                # holding the previous model's value (or "ctx --") through the
                # next render tick.
                cc = getattr(self, "context_compressor", None)
                if cc is not None:
                    cc.update_model(
                        model=self.model,
                        context_length=loaded_ctx,
                        base_url=self.base_url,
                        api_key=getattr(self, "api_key", ""),
                        provider=self.provider,
                        api_mode=self.api_mode,
                    )
        except Exception as err:
            logger.debug("LM Studio preload skipped: %s", err)

    def switch_model(self, new_model, new_provider, api_key='', base_url='', api_mode=''):
        """Forwarder — see ``agent.agent_runtime_helpers.switch_model``."""
        from agent.agent_runtime_helpers import switch_model
        return switch_model(self, new_model, new_provider, api_key, base_url, api_mode)

    def _safe_print(self, *args, **kwargs):
        """Print that silently handles broken pipes / closed stdout.

        In headless environments (systemd, Docker, nohup) stdout may become
        unavailable mid-session.  A raw ``print()`` raises ``OSError`` which
        can crash cron jobs and lose completed work.

        Internally routes through ``self._print_fn`` (default: builtin
        ``print``) so callers such as the CLI can inject a renderer that
        handles ANSI escape sequences properly (e.g. prompt_toolkit's
        ``print_formatted_text(ANSI(...))``) without touching this method.
        """
        try:
            fn = self._print_fn or print
            fn(*args, **kwargs)
        except (OSError, ValueError):
            pass

    def _vprint(self, *args, force: bool = False, **kwargs):
        """Verbose print — suppressed when actively streaming tokens.

        Pass ``force=True`` for error/warning messages that should always be
        shown even during streaming playback (TTS or display).

        During tool execution (``_executing_tools`` is True), printing is
        allowed even with stream consumers registered because no tokens
        are being streamed at that point.

        After the main response has been delivered and the remaining tool
        calls are post-response housekeeping (``_mute_post_response``),
        all non-forced output is suppressed.

        ``suppress_status_output`` is a stricter CLI automation mode used by
        parseable single-query flows such as ``hermes chat -q``. In that mode,
        all status/diagnostic prints routed through ``_vprint`` are suppressed
        so stdout stays machine-readable.
        """
        if getattr(self, "suppress_status_output", False):
            return
        if not force and getattr(self, "_mute_post_response", False):
            return
        if not force and self._has_stream_consumers() and not self._executing_tools:
            return
        self._safe_print(*args, **kwargs)

    def _should_start_quiet_spinner(self) -> bool:
        """Return True when quiet-mode spinner output has a safe sink.

        In headless/stdio-protocol environments, a raw spinner with no custom
        ``_print_fn`` falls back to ``sys.stdout`` and can corrupt protocol
        streams such as ACP JSON-RPC. Allow quiet spinners only when either:
        - output is explicitly rerouted via ``_print_fn``; or
        - stdout is a real TTY.
        """
        if self._print_fn is not None:
            return True
        stream = getattr(sys, "stdout", None)
        if stream is None:
            return False
        try:
            return bool(stream.isatty())
        except (AttributeError, ValueError, OSError):
            return False

    def _should_emit_quiet_tool_messages(self) -> bool:
        """Return True when quiet-mode tool summaries should print directly.

        Quiet mode is used by both the interactive CLI and embedded/library
        callers. The CLI may still want compact progress hints when no callback
        owns rendering. Embedded/library callers, on the other hand, expect
        quiet mode to be truly silent.
        """
        return (
            self.quiet_mode
            and not self.tool_progress_callback
            and getattr(self, "platform", "") == "cli"
        )

    def _emit_status(self, message: str) -> None:
        """Emit a lifecycle status message to both CLI and gateway channels.

        CLI users see the message via ``_vprint(force=True)`` so it is always
        visible regardless of verbose/quiet mode.  Gateway consumers receive
        it through ``status_callback("lifecycle", ...)``.

        This helper never raises — exceptions are swallowed so it cannot
        interrupt the retry/fallback logic.
        """
        try:
            self._vprint(f"{self.log_prefix}{message}", force=True)
        except Exception:
            pass
        if self.status_callback:
            try:
                self.status_callback("lifecycle", message)
            except Exception:
                logger.debug("status_callback error in _emit_status", exc_info=True)

    def _emit_warning(self, message: str) -> None:
        """Emit a user-visible warning through the same status plumbing.

        Unlike debug logs, these warnings are meant for degraded side paths
        such as auxiliary compression or memory flushes where the main turn can
        continue but the user needs to know something important failed.
        """
        try:
            self._vprint(f"{self.log_prefix}{message}", force=True)
        except Exception:
            pass
        if self.status_callback:
            try:
                self.status_callback("warn", message)
            except Exception:
                logger.debug("status_callback error in _emit_warning", exc_info=True)

    # Stream-diagnostic class header preserved for backward compat —
    # actual list lives in ``agent.stream_diag.STREAM_DIAG_HEADERS``.
    from agent.stream_diag import STREAM_DIAG_HEADERS as _STREAM_DIAG_HEADERS  # noqa: E402

    @staticmethod
    def _stream_diag_init() -> Dict[str, Any]:
        """Forwarder — see ``agent.stream_diag.stream_diag_init``."""
        from agent.stream_diag import stream_diag_init
        return stream_diag_init()

    def _stream_diag_capture_response(
        self, diag: Dict[str, Any], http_response: Any
    ) -> None:
        """Forwarder — see ``agent.stream_diag.stream_diag_capture_response``."""
        from agent.stream_diag import stream_diag_capture_response
        stream_diag_capture_response(self, diag, http_response)

    @staticmethod
    def _flatten_exception_chain(error: BaseException) -> str:
        """Forwarder — see ``agent.stream_diag.flatten_exception_chain``."""
        from agent.stream_diag import flatten_exception_chain
        return flatten_exception_chain(error)

    def _is_provider_stream_parse_error(self, error: BaseException) -> bool:
        """Return True for malformed provider streaming data from SDK parsers.

        Some Anthropic-compatible streaming providers can send a malformed
        event-stream frame.  The Anthropic SDK surfaces that as a plain
        ``ValueError`` such as ``expected ident at line 1 column 149``.  That
        is provider wire-format trouble, not local request validation, so it
        should follow the same retry path as a truncated JSON body.
        """
        if getattr(self, "api_mode", None) != "anthropic_messages":
            return False
        if not isinstance(error, ValueError):
            return False
        if isinstance(error, (UnicodeEncodeError, json.JSONDecodeError)):
            return False
        message = str(error).strip().lower()
        return "expected ident at line" in message

    def _log_stream_retry(
        self,
        *,
        kind: str,
        error: BaseException,
        attempt: int,
        max_attempts: int,
        mid_tool_call: bool,
        diag: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Forwarder — see ``agent.stream_diag.log_stream_retry``."""
        from agent.stream_diag import log_stream_retry
        log_stream_retry(
            self, kind=kind, error=error, attempt=attempt,
            max_attempts=max_attempts, mid_tool_call=mid_tool_call, diag=diag,
        )

    def _emit_stream_drop(
        self,
        *,
        error: BaseException,
        attempt: int,
        max_attempts: int,
        mid_tool_call: bool,
        diag: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Forwarder — see ``agent.stream_diag.emit_stream_drop``."""
        from agent.stream_diag import emit_stream_drop
        emit_stream_drop(
            self, error=error, attempt=attempt, max_attempts=max_attempts,
            mid_tool_call=mid_tool_call, diag=diag,
        )

    def _emit_auxiliary_failure(self, task: str, exc: BaseException) -> None:
        """Surface a compact warning for failed auxiliary work."""
        try:
            detail = self._summarize_api_error(exc)
        except Exception:
            detail = str(exc)
        detail = (detail or exc.__class__.__name__).strip()
        if len(detail) > 220:
            detail = detail[:217].rstrip() + "..."
        self._emit_warning(f"⚠ Auxiliary {task} failed: {detail}")

    def _current_main_runtime(self) -> Dict[str, str]:
        """Return the live main runtime for session-scoped auxiliary routing."""
        return {
            "model": getattr(self, "model", "") or "",
            "provider": getattr(self, "provider", "") or "",
            "base_url": getattr(self, "base_url", "") or "",
            "api_key": getattr(self, "api_key", "") or "",
            "api_mode": getattr(self, "api_mode", "") or "",
        }

    def _check_compression_model_feasibility(self) -> None:
        """Forwarder — see ``agent.conversation_compression.check_compression_model_feasibility``."""
        from agent.conversation_compression import check_compression_model_feasibility
        check_compression_model_feasibility(self)

    def _replay_compression_warning(self) -> None:
        """Forwarder — see ``agent.conversation_compression.replay_compression_warning``."""
        from agent.conversation_compression import replay_compression_warning
        replay_compression_warning(self)

    def _is_direct_openai_url(self, base_url: str = None) -> bool:
        """Return True when a base URL targets OpenAI's native API."""
        if base_url is not None:
            hostname = base_url_hostname(base_url)
        else:
            hostname = getattr(self, "_base_url_hostname", "") or base_url_hostname(
                getattr(self, "_base_url_lower", "")
            )
        return hostname == "api.openai.com"

    def _is_azure_openai_url(self, base_url: str = None) -> bool:
        """Return True when a base URL targets Azure OpenAI.

        Azure OpenAI exposes an OpenAI-compatible endpoint at
        ``{resource}.openai.azure.com/openai/v1`` that accepts the
        standard ``openai`` Python client.  Unlike api.openai.com it
        does NOT support the Responses API — gpt-5.x models are served
        on the regular ``/chat/completions`` path — so routing decisions
        must treat Azure separately from direct OpenAI.
        """
        if base_url is not None:
            url = str(base_url).lower()
        else:
            url = getattr(self, "_base_url_lower", "") or ""
        return "openai.azure.com" in url

    def _is_github_copilot_url(self, base_url: str = None) -> bool:
        """Return True when a base URL targets GitHub Copilot's OpenAI-compatible API."""
        if base_url is not None:
            hostname = base_url_hostname(base_url)
        else:
            hostname = getattr(self, "_base_url_hostname", "") or base_url_hostname(
                getattr(self, "_base_url_lower", "")
            )
        return hostname == "api.githubcopilot.com"

    def _resolved_api_call_timeout(self) -> float:
        """Resolve the effective per-call request timeout in seconds.

        Priority:
          1. ``providers.<id>.models.<model>.timeout_seconds`` (per-model override)
          2. ``providers.<id>.request_timeout_seconds`` (provider-wide)
          3. ``HERMES_API_TIMEOUT`` env var (legacy escape hatch)
          4. 1800.0s default

        Used by OpenAI-wire chat completions (streaming and non-streaming) so
        the per-provider config knob wins over the 1800s default.  Without this
        helper, the hardcoded ``HERMES_API_TIMEOUT`` fallback would always be
        passed as a per-call ``timeout=`` kwarg, overriding the client-level
        timeout the AIAgent.__init__ path configured.
        """
        cfg = get_provider_request_timeout(self.provider, self.model)
        if cfg is not None:
            return cfg
        return float(os.getenv("HERMES_API_TIMEOUT", 1800.0))

    def _resolved_api_call_stale_timeout_base(self) -> tuple[float, bool]:
        """Resolve the base non-stream stale timeout and whether it is implicit.

        Priority:
          1. ``providers.<id>.models.<model>.stale_timeout_seconds``
          2. ``providers.<id>.stale_timeout_seconds``
          3. ``HERMES_API_CALL_STALE_TIMEOUT`` env var
          4. 300.0s default

        Returns ``(timeout_seconds, uses_implicit_default)`` so the caller can
        preserve legacy behaviors that only apply when the user has *not*
        explicitly configured a stale timeout, such as auto-disabling the
        detector for local endpoints.
        """
        cfg = get_provider_stale_timeout(self.provider, self.model)
        if cfg is not None:
            return cfg, False

        env_timeout = os.getenv("HERMES_API_CALL_STALE_TIMEOUT")
        if env_timeout is not None:
            return float(env_timeout), False

        return 300.0, True

    def _compute_non_stream_stale_timeout(self, messages: list[dict[str, Any]]) -> float:
        """Compute the effective non-stream stale timeout for this request."""
        stale_base, uses_implicit_default = self._resolved_api_call_stale_timeout_base()
        base_url = getattr(self, "_base_url", None) or self.base_url or ""
        if uses_implicit_default and base_url and is_local_endpoint(base_url):
            return float("inf")

        est_tokens = sum(len(str(v)) for v in messages) // 4
        if est_tokens > 100_000:
            return max(stale_base, 600.0)
        if est_tokens > 50_000:
            return max(stale_base, 450.0)
        return stale_base

    def _is_openrouter_url(self) -> bool:
        """Return True when the base URL targets OpenRouter."""
        return base_url_host_matches(self._base_url_lower, "openrouter.ai")

    def _anthropic_prompt_cache_policy(
        self,
        *,
        provider: Optional[str] = None,
        base_url: Optional[str] = None,
        api_mode: Optional[str] = None,
        model: Optional[str] = None,
    ) -> tuple[bool, bool]:
        """Forwarder — see ``agent.agent_runtime_helpers.anthropic_prompt_cache_policy``."""
        from agent.agent_runtime_helpers import anthropic_prompt_cache_policy
        return anthropic_prompt_cache_policy(self, provider=provider, base_url=base_url, api_mode=api_mode, model=model)

    @staticmethod
    def _model_requires_responses_api(model: str) -> bool:
        """Return True for models that require the Responses API path.

        GPT-5.x models are rejected on /v1/chat/completions by both
        OpenAI and OpenRouter (error: ``unsupported_api_for_model``).
        Detect these so the correct api_mode is set regardless of
        which provider is serving the model.
        """
        m = model.lower()
        # Strip vendor prefix (e.g. "openai/gpt-5.4" → "gpt-5.4")
        if "/" in m:
            m = m.rsplit("/", 1)[-1]
        return m.startswith("gpt-5")

    @staticmethod
    def _provider_model_requires_responses_api(
        model: str,
        *,
        provider: Optional[str] = None,
    ) -> bool:
        """Return True when this provider/model pair should use Responses API."""
        normalized_provider = (provider or "").strip().lower()
        # Nous serves GPT-5.x models via its OpenAI-compatible chat
        # completions endpoint; its /v1/responses endpoint returns 404.
        if normalized_provider == "nous":
            return False
        if normalized_provider == "copilot":
            try:
                from hermes_cli.models import _should_use_copilot_responses_api
                return _should_use_copilot_responses_api(model)
            except Exception:
                # Fall back to the generic GPT-5 rule if Copilot-specific
                # logic is unavailable for any reason.
                pass
        return AIAgent._model_requires_responses_api(model)

    def _max_tokens_param(self, value: int) -> dict:
        """Return the correct max tokens kwarg for the current provider.

        OpenAI's newer models (gpt-4o, o-series, gpt-5+) require
        'max_completion_tokens'. Azure OpenAI also requires
        'max_completion_tokens' for gpt-5.x models served via the
        OpenAI-compatible endpoint. OpenRouter, local models, and older
        OpenAI models use 'max_tokens'.
        """
        if self._is_direct_openai_url() or self._is_azure_openai_url() or self._is_github_copilot_url():
            return {"max_completion_tokens": value}
        return {"max_tokens": value}

    def _has_content_after_think_block(self, content: str) -> bool:
        """
        Check if content has actual text after any reasoning/thinking blocks.

        This detects cases where the model only outputs reasoning but no actual
        response, which indicates an incomplete generation that should be retried.
        Must stay in sync with _strip_think_blocks() tag variants.

        Args:
            content: The assistant message content to check

        Returns:
            True if there's meaningful content after think blocks, False otherwise
        """
        if not content:
            return False

        # Remove all reasoning tag variants (must match _strip_think_blocks)
        cleaned = self._strip_think_blocks(content)

        # Check if there's any non-whitespace content remaining
        return bool(cleaned.strip())

    def _strip_think_blocks(self, content: str) -> str:
        """Forwarder — see ``agent.agent_runtime_helpers.strip_think_blocks``."""
        from agent.agent_runtime_helpers import strip_think_blocks
        return strip_think_blocks(self, content)

    @staticmethod
    def _has_natural_response_ending(content: str) -> bool:
        """Heuristic: does visible assistant text look intentionally finished?"""
        if not content:
            return False
        stripped = content.rstrip()
        if not stripped:
            return False
        if stripped.endswith("```"):
            return True
        if stripped.endswith('^'):
            return True
        last = stripped[-1]
        if last in '.!?:)"\']}。！？：）】」』》^':
            return True
        # Emoji ranges (Misc Symbols, Dingbats, Emoticons, Supplemental, etc.)
        if ord(last) >= 0x1F300:
            return True
        return False

    def _is_ollama_glm_backend(self) -> bool:
        """Detect the narrow backend family affected by Ollama/GLM stop misreports."""
        model_lower = (self.model or "").lower()
        provider_lower = (self.provider or "").lower()
        if "glm" not in model_lower and provider_lower != "zai":
            return False
        if "ollama" in self._base_url_lower or ":11434" in self._base_url_lower:
            return True
        return bool(self.base_url and is_local_endpoint(self.base_url))

    def _should_treat_stop_as_truncated(
        self,
        finish_reason: str,
        assistant_message,
        messages: Optional[list] = None,
    ) -> bool:
        """Detect conservative stop->length misreports for Ollama-hosted GLM models."""
        if finish_reason != "stop" or self.api_mode != "chat_completions":
            return False
        if not self._is_ollama_glm_backend():
            return False
        if not any(
            isinstance(msg, dict) and msg.get("role") == "tool"
            for msg in (messages or [])
        ):
            return False
        if assistant_message is None or getattr(assistant_message, "tool_calls", None):
            return False

        content = getattr(assistant_message, "content", None)
        if not isinstance(content, str):
            return False

        visible_text = self._strip_think_blocks(content).strip()
        if not visible_text:
            return False
        if len(visible_text) < 20 or not re.search(r"\s", visible_text):
            return False

        return not self._has_natural_response_ending(visible_text)

    def _looks_like_codex_intermediate_ack(
        self,
        user_message: str,
        assistant_content: str,
        messages: List[Dict[str, Any]],
    ) -> bool:
        """Forwarder — see ``agent.agent_runtime_helpers.looks_like_codex_intermediate_ack``."""
        from agent.agent_runtime_helpers import looks_like_codex_intermediate_ack
        return looks_like_codex_intermediate_ack(self, user_message, assistant_content, messages)

    def _extract_reasoning(self, assistant_message) -> Optional[str]:
        """Forwarder — see ``agent.agent_runtime_helpers.extract_reasoning``."""
        from agent.agent_runtime_helpers import extract_reasoning
        return extract_reasoning(self, assistant_message)

    def _cleanup_task_resources(self, task_id: str) -> None:
        """Forwarder — see ``agent.chat_completion_helpers.cleanup_task_resources``."""
        from agent.chat_completion_helpers import cleanup_task_resources
        return cleanup_task_resources(self, task_id)

    # ------------------------------------------------------------------
    # Background memory/skill review — prompts live in agent.background_review
    # ------------------------------------------------------------------
    from agent.background_review import (
        _MEMORY_REVIEW_PROMPT,
        _SKILL_REVIEW_PROMPT,
        _COMBINED_REVIEW_PROMPT,
    )

    @staticmethod
    def _summarize_background_review_actions(
        review_messages: List[Dict],
        prior_snapshot: List[Dict],
    ) -> List[str]:
        """Forwarder — see ``agent.background_review.summarize_background_review_actions``."""
        from agent.background_review import summarize_background_review_actions
        return summarize_background_review_actions(review_messages, prior_snapshot)

    def _spawn_background_review(
        self,
        messages_snapshot: List[Dict],
        review_memory: bool = False,
        review_skills: bool = False,
    ) -> None:
        """Spawn the background memory/skill review thread.

        Thin wrapper — the heavy lifting lives in
        ``agent.background_review.spawn_background_review_thread`` which
        returns the thread target.  ``threading.Thread`` is constructed
        here so existing tests that patch ``run_agent.threading.Thread``
        keep working.
        """
        from agent.background_review import spawn_background_review_thread
        target, _prompt = spawn_background_review_thread(
            self,
            messages_snapshot,
            review_memory=review_memory,
            review_skills=review_skills,
        )
        t = threading.Thread(target=target, daemon=True, name="bg-review")
        t.start()

    def _build_memory_write_metadata(
        self,
        *,
        write_origin: Optional[str] = None,
        execution_context: Optional[str] = None,
        task_id: Optional[str] = None,
        tool_call_id: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Forwarder — see ``agent.background_review.build_memory_write_metadata``."""
        from agent.background_review import build_memory_write_metadata
        return build_memory_write_metadata(
            self,
            write_origin=write_origin,
            execution_context=execution_context,
            task_id=task_id,
            tool_call_id=tool_call_id,
        )

    def _apply_persist_user_message_override(self, messages: List[Dict]) -> None:
        """Rewrite the current-turn user message before persistence/return.

        Some call paths need an API-only user-message variant without letting
        that synthetic text leak into persisted transcripts or resumed session
        history. When an override is configured for the active turn, mutate the
        in-memory messages list in place so both persistence and returned
        history stay clean.
        """
        idx = getattr(self, "_persist_user_message_idx", None)
        override = getattr(self, "_persist_user_message_override", None)
        if override is None or idx is None:
            return
        if 0 <= idx < len(messages):
            msg = messages[idx]
            if isinstance(msg, dict) and msg.get("role") == "user":
                msg["content"] = override

    def _persist_session(self, messages: List[Dict], conversation_history: List[Dict] = None):
        """Save session state to both JSON log and SQLite on any exit path.

        Ensures conversations are never lost, even on errors or early returns.
        """
        self._drop_trailing_empty_response_scaffolding(messages)
        self._apply_persist_user_message_override(messages)
        self._session_messages = messages
        self._save_session_log(messages)
        self._flush_messages_to_session_db(messages, conversation_history)

    def _drop_trailing_empty_response_scaffolding(self, messages: List[Dict]) -> None:
        """Remove private empty-response retry/failure scaffolding from transcript tails.

        Also rewinds past any trailing tool-result / assistant(tool_calls) pair
        that the failed iteration left hanging. Without this, the tail ends at
        a raw ``tool`` message and the next user turn lands as
        ``...tool, user, user`` — a protocol-invalid sequence that most
        providers silently reject (returns empty content), causing the
        empty-retry loop to fire forever. See #<TBD>.
        """
        # Pass 1: strip the flagged scaffolding messages themselves.
        dropped_scaffolding = False
        while (
            messages
            and isinstance(messages[-1], dict)
            and (
                messages[-1].get("_empty_recovery_synthetic")
                or messages[-1].get("_empty_terminal_sentinel")
            )
        ):
            messages.pop()
            dropped_scaffolding = True

        # Pass 2: if we stripped scaffolding, rewind through any trailing
        # tool-result messages plus the assistant(tool_calls) message that
        # produced them. This preserves role alternation so the next user
        # message follows a user or assistant message, not an orphan tool
        # result. Only runs when scaffolding was actually present — normal
        # conversation tails (real tool loops mid-progress) are untouched.
        if not dropped_scaffolding:
            return

        # Drop any trailing tool-result messages
        while (
            messages
            and isinstance(messages[-1], dict)
            and messages[-1].get("role") == "tool"
        ):
            messages.pop()

        # Drop the assistant message that issued the tool calls, if the tail
        # now ends in an assistant-with-tool_calls (the pair that owned the
        # just-popped tool results). Without this, the tail is
        # ``assistant(tool_calls=...)`` with no tool answers, which some
        # providers also reject.
        if (
            messages
            and isinstance(messages[-1], dict)
            and messages[-1].get("role") == "assistant"
            and messages[-1].get("tool_calls")
        ):
            messages.pop()

    def _repair_message_sequence(self, messages: List[Dict]) -> int:
        """Forwarder — see ``agent.agent_runtime_helpers.repair_message_sequence``."""
        from agent.agent_runtime_helpers import repair_message_sequence
        return repair_message_sequence(self, messages)

    def _flush_messages_to_session_db(self, messages: List[Dict], conversation_history: List[Dict] = None):
        """Persist any un-flushed messages to the SQLite session store.

        Uses _last_flushed_db_idx to track which messages have already been
        written, so repeated calls (from multiple exit paths) only write
        truly new messages — preventing the duplicate-write bug (#860).
        """
        if not self._session_db:
            return
        self._apply_persist_user_message_override(messages)
        try:
            # Retry row creation if the earlier attempt failed transiently.
            if not self._session_db_created:
                self._ensure_db_session()
            start_idx = len(conversation_history) if conversation_history else 0
            flush_from = max(start_idx, self._last_flushed_db_idx)
            for msg in messages[flush_from:]:
                role = msg.get("role", "unknown")
                content = msg.get("content")
                # Persist multimodal tool results as their text summary only —
                # base64 images would bloat the session DB and aren't useful
                # for cross-session replay.
                if _is_multimodal_tool_result(content):
                    content = _multimodal_text_summary(content)
                elif isinstance(content, list):
                    # List of OpenAI-style content parts: strip images, keep text.
                    _txt = []
                    for p in content:
                        if isinstance(p, dict) and p.get("type") == "text":
                            _txt.append(str(p.get("text", "")))
                        elif isinstance(p, dict) and p.get("type") in {"image", "image_url", "input_image"}:
                            _txt.append("[screenshot]")
                    content = "\n".join(_txt) if _txt else None
                tool_calls_data = None
                if hasattr(msg, "tool_calls") and isinstance(msg.tool_calls, list) and msg.tool_calls:
                    tool_calls_data = [
                        {"name": tc.function.name, "arguments": tc.function.arguments}
                        for tc in msg.tool_calls
                    ]
                elif isinstance(msg.get("tool_calls"), list):
                    tool_calls_data = msg["tool_calls"]
                self._session_db.append_message(
                    session_id=self.session_id,
                    role=role,
                    content=content,
                    tool_name=msg.get("tool_name"),
                    tool_calls=tool_calls_data,
                    tool_call_id=msg.get("tool_call_id"),
                    finish_reason=msg.get("finish_reason"),
                    reasoning=msg.get("reasoning") if role == "assistant" else None,
                    reasoning_content=msg.get("reasoning_content") if role == "assistant" else None,
                    reasoning_details=msg.get("reasoning_details") if role == "assistant" else None,
                    codex_reasoning_items=msg.get("codex_reasoning_items") if role == "assistant" else None,
                    codex_message_items=msg.get("codex_message_items") if role == "assistant" else None,
                )
            self._last_flushed_db_idx = len(messages)
        except Exception as e:
            logger.warning("Session DB append_message failed: %s", e)

    def _get_messages_up_to_last_assistant(self, messages: List[Dict]) -> List[Dict]:
        """
        Get messages up to (but not including) the last assistant turn.
        
        This is used when we need to "roll back" to the last successful point
        in the conversation, typically when the final assistant message is
        incomplete or malformed.
        
        Args:
            messages: Full message list
            
        Returns:
            Messages up to the last complete assistant turn (ending with user/tool message)
        """
        if not messages:
            return []
        
        # Find the index of the last assistant message
        last_assistant_idx = None
        for i in range(len(messages) - 1, -1, -1):
            if messages[i].get("role") == "assistant":
                last_assistant_idx = i
                break
        
        if last_assistant_idx is None:
            # No assistant message found, return all messages
            return messages.copy()
        
        # Return everything up to (not including) the last assistant message
        return messages[:last_assistant_idx]

    def _format_tools_for_system_message(self) -> str:
        """Forwarder — see ``agent.system_prompt.format_tools_for_system_message``."""
        from agent.system_prompt import format_tools_for_system_message
        return format_tools_for_system_message(self)

    def _convert_to_trajectory_format(self, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]:
        """Forwarder — see ``agent.agent_runtime_helpers.convert_to_trajectory_format``."""
        from agent.agent_runtime_helpers import convert_to_trajectory_format
        return convert_to_trajectory_format(self, messages, user_query, completed)

    def _save_trajectory(self, messages: List[Dict[str, Any]], user_query: str, completed: bool):
        """
        Save conversation trajectory to JSONL file.
        
        Args:
            messages (List[Dict]): Complete message history
            user_query (str): Original user query
            completed (bool): Whether the conversation completed successfully
        """
        if not self.save_trajectories:
            return
        
        trajectory = self._convert_to_trajectory_format(messages, user_query, completed)
        _save_trajectory_to_file(trajectory, self.model, completed)

    @staticmethod
    def _is_entitlement_failure(
        error_context: Optional[Dict[str, Any]],
        status_code: Optional[int],
    ) -> bool:
        """Detect subscription/entitlement 403s that masquerade as auth failures.

        Returned True only when the body text matches a known entitlement
        shape AND the status is 401/403.  Refreshing an OAuth token cannot
        fix an unsubscribed account, so callers should surface the error
        instead of looping the credential pool.

        Current matches:
          * xAI OAuth: "do not have an active Grok subscription" /
            "out of available resources" / "does not have permission" + "grok"

        Extend here for new providers as we discover them (Anthropic's
        Claude Max OAuth entitlement errors look distinct enough today that
        the existing 1M-context-beta branch handles them; revisit if other
        subscription tiers start producing the same loop signature).
        """
        if status_code not in {401, 403, None}:
            return False
        if not isinstance(error_context, dict):
            return False
        message = str(error_context.get("message") or "").lower()
        reason = str(error_context.get("reason") or "").lower()
        haystack = f"{message} {reason}"
        if not haystack.strip():
            return False
        if "do not have an active grok subscription" in haystack:
            return True
        if "out of available resources" in haystack and "grok" in haystack:
            return True
        if "does not have permission" in haystack and "grok" in haystack:
            return True
        return False

    @staticmethod
    def _summarize_api_error(error: Exception) -> str:
        """Extract a human-readable one-liner from an API error.

        Handles Cloudflare HTML error pages (502, 503, etc.) by pulling the
        <title> tag instead of dumping raw HTML.  Falls back to a truncated
        str(error) for everything else.
        """
        raw = str(error)

        if (
            isinstance(error, ValueError)
            and "expected ident at line" in raw.lower()
        ):
            return f"Malformed provider streaming response: {raw[:300]}"

        # Cloudflare / proxy HTML pages: grab the <title> for a clean summary
        if "<!DOCTYPE" in raw or "<html" in raw:
            m = re.search(r"<title[^>]*>([^<]+)</title>", raw, re.IGNORECASE)
            title = m.group(1).strip() if m else "HTML error page (title not found)"
            # Also grab Cloudflare Ray ID if present
            ray = re.search(r"Cloudflare Ray ID:\s*<strong[^>]*>([^<]+)</strong>", raw)
            ray_id = ray.group(1).strip() if ray else None
            status_code = getattr(error, "status_code", None)
            parts = []
            if status_code:
                parts.append(f"HTTP {status_code}")
            parts.append(title)
            if ray_id:
                parts.append(f"Ray {ray_id}")
            return " — ".join(parts)

        # JSON body errors from OpenAI/Anthropic SDKs
        body = getattr(error, "body", None)
        if isinstance(body, dict):
            msg = body.get("error", {}).get("message") if isinstance(body.get("error"), dict) else body.get("message")
            if msg:
                status_code = getattr(error, "status_code", None)
                prefix = f"HTTP {status_code}: " if status_code else ""
                return f"{prefix}{msg[:300]}"

        # Fallback: truncate the raw string but give more room than 200 chars
        status_code = getattr(error, "status_code", None)
        prefix = f"HTTP {status_code}: " if status_code else ""
        return f"{prefix}{raw[:500]}"

    def _mask_api_key_for_logs(self, key: Any) -> Optional[str]:
        # Azure Foundry Entra ID bearer providers are callables — never
        # invoke them in log paths; identify the auth surface instead.
        if callable(key) and not isinstance(key, str):
            return "<entra-id-bearer>"
        if not key:
            return None
        if len(key) <= 12:
            return "***"
        return f"{key[:8]}...{key[-4:]}"

    def _clean_error_message(self, error_msg: str) -> str:
        """
        Clean up error messages for user display, removing HTML content and truncating.
        
        Args:
            error_msg: Raw error message from API or exception
            
        Returns:
            Clean, user-friendly error message
        """
        if not error_msg:
            return "Unknown error"
            
        # Remove HTML content (common with CloudFlare and gateway error pages)
        if error_msg.strip().startswith('<!DOCTYPE html') or '<html' in error_msg:
            return "Service temporarily unavailable (HTML error page returned)"
            
        # Remove newlines and excessive whitespace
        cleaned = ' '.join(error_msg.split())
        
        # Truncate if too long
        if len(cleaned) > 150:
            cleaned = cleaned[:150] + "..."
            
        return cleaned

    @staticmethod
    def _extract_api_error_context(error: Exception) -> Dict[str, Any]:
        """Forwarder — see ``agent.agent_runtime_helpers.extract_api_error_context``."""
        from agent.agent_runtime_helpers import extract_api_error_context
        return extract_api_error_context(error)

    def _usage_summary_for_api_request_hook(self, response: Any) -> Optional[Dict[str, Any]]:
        """Token buckets for ``post_api_request`` plugins (no raw ``response`` object)."""
        if response is None:
            return None
        raw_usage = getattr(response, "usage", None)
        if not raw_usage:
            return None
        from dataclasses import asdict

        cu = normalize_usage(raw_usage, provider=self.provider, api_mode=self.api_mode)
        summary = asdict(cu)
        summary.pop("raw_usage", None)
        summary["prompt_tokens"] = cu.prompt_tokens
        summary["total_tokens"] = cu.total_tokens
        return summary

    def _dump_api_request_debug(
        self,
        api_kwargs: Dict[str, Any],
        *,
        reason: str,
        error: Optional[Exception] = None,
    ) -> Optional[Path]:
        """Forwarder — see ``agent.agent_runtime_helpers.dump_api_request_debug``."""
        from agent.agent_runtime_helpers import dump_api_request_debug
        return dump_api_request_debug(self, api_kwargs, reason=reason, error=error)

    @staticmethod
    def _clean_session_content(content: str) -> str:
        """Convert REASONING_SCRATCHPAD to think tags and clean up whitespace."""
        if not content:
            return content
        content = convert_scratchpad_to_think(content)
        content = re.sub(r'\n+(<think>)', r'\n\1', content)
        content = re.sub(r'(</think>)\n+', r'\1\n', content)
        return content.strip()

    def _save_session_log(self, messages: List[Dict[str, Any]] = None):
        """Optional per-session JSON snapshot writer.

        Gated by ``sessions.write_json_snapshots`` (default False).  state.db
        is the canonical message store; this writer exists only for users
        whose external tooling consumes ``~/.hermes/sessions/session_{sid}.json``
        directly.  When the flag is off this is a fast no-op.

        When enabled, rewrites the snapshot after every persistence point with
        the full message list (assistant content normalized via
        ``_clean_session_content`` to convert REASONING_SCRATCHPAD to think
        tags).  The truncation guard ("don't overwrite a larger log with
        fewer messages") is preserved so resume + branch don't clobber a
        fuller existing snapshot.
        """
        if not getattr(self, "_session_json_enabled", False):
            return
        messages = messages or self._session_messages
        if not messages:
            return

        # Re-derive the target path each call so /branch and /compress
        # session-id changes land in the right file without any re-point
        # bookkeeping at the call sites.
        try:
            log_file = self.logs_dir / f"session_{self.session_id}.json"
        except Exception:
            return

        try:
            cleaned = []
            for msg in messages:
                if msg.get("role") == "assistant" and msg.get("content"):
                    msg = dict(msg)
                    msg["content"] = self._clean_session_content(msg["content"])
                cleaned.append(msg)

            # Guard: never overwrite a larger session log with fewer messages.
            # Protects against data loss when a resumed agent starts with
            # partial history and would otherwise clobber the full JSON log.
            if log_file.exists():
                try:
                    existing = json.loads(log_file.read_text(encoding="utf-8"))
                    existing_count = existing.get("message_count", len(existing.get("messages", [])))
                    if existing_count > len(cleaned):
                        logging.debug(
                            "Skipping session log overwrite: existing has %d messages, current has %d",
                            existing_count, len(cleaned),
                        )
                        return
                except Exception:
                    pass  # corrupted existing file — allow the overwrite

            entry = {
                "session_id": self.session_id,
                "model": self.model,
                "base_url": self.base_url,
                "platform": self.platform,
                "session_start": self.session_start.isoformat(),
                "last_updated": datetime.now().isoformat(),
                "system_prompt": self._cached_system_prompt or "",
                "tools": self.tools or [],
                "message_count": len(cleaned),
                "messages": cleaned,
            }

            atomic_json_write(
                log_file,
                entry,
                indent=2,
                default=str,
            )

        except Exception as e:
            if self.verbose_logging:
                logging.warning(f"Failed to save session log: {e}")


    def interrupt(self, message: str = None) -> None:
        """
        Request the agent to interrupt its current tool-calling loop.
        
        Call this from another thread (e.g., input handler, message receiver)
        to gracefully stop the agent and process a new message.
        
        Also signals long-running tool executions (e.g. terminal commands)
        to terminate early, so the agent can respond immediately.
        
        Args:
            message: Optional new message that triggered the interrupt.
                     If provided, the agent will include this in its response context.
        
        Example (CLI):
            # In a separate input thread:
            if user_typed_something:
                agent.interrupt(user_input)
        
        Example (Messaging):
            # When new message arrives for active session:
            if session_has_running_agent:
                running_agent.interrupt(new_message.text)
        """
        self._interrupt_requested = True
        self._interrupt_message = message
        # Signal all tools to abort any in-flight operations immediately.
        # Scope the interrupt to this agent's execution thread so other
        # agents running in the same process (gateway) are not affected.
        if self._execution_thread_id is not None:
            _set_interrupt(True, self._execution_thread_id)
            self._interrupt_thread_signal_pending = False
        else:
            # The interrupt arrived before run_conversation() finished
            # binding the agent to its execution thread. Defer the tool-level
            # interrupt signal until startup completes instead of targeting
            # the caller thread by mistake.
            self._interrupt_thread_signal_pending = True
        # Fan out to concurrent-tool worker threads.  Those workers run tools
        # on their own tids (ThreadPoolExecutor workers), so `is_interrupted()`
        # inside a tool only sees an interrupt when their specific tid is in
        # the `_interrupted_threads` set.  Without this propagation, an
        # already-running concurrent tool (e.g. a terminal command hung on
        # network I/O) never notices the interrupt and has to run to its own
        # timeout.  See `_run_tool` for the matching entry/exit bookkeeping.
        # `getattr` fallback covers test stubs that build AIAgent via
        # object.__new__ and skip __init__.
        _tracker = getattr(self, "_tool_worker_threads", None)
        _tracker_lock = getattr(self, "_tool_worker_threads_lock", None)
        if _tracker is not None and _tracker_lock is not None:
            with _tracker_lock:
                _worker_tids = list(_tracker)
            for _wtid in _worker_tids:
                try:
                    _set_interrupt(True, _wtid)
                except Exception:
                    pass
        # Propagate interrupt to any running child agents (subagent delegation)
        with self._active_children_lock:
            children_copy = list(self._active_children)
        for child in children_copy:
            try:
                child.interrupt(message)
            except Exception as e:
                logger.debug("Failed to propagate interrupt to child agent: %s", e)
        if not self.quiet_mode:
            print("\n⚡ Interrupt requested" + (f": '{message[:40]}...'" if message and len(message) > 40 else f": '{message}'" if message else ""))

    def clear_interrupt(self) -> None:
        """Clear any pending interrupt request and the per-thread tool interrupt signal."""
        self._interrupt_requested = False
        self._interrupt_message = None
        self._interrupt_thread_signal_pending = False
        if self._execution_thread_id is not None:
            _set_interrupt(False, self._execution_thread_id)
        # Also clear any concurrent-tool worker thread bits.  Tracked
        # workers normally clear their own bit on exit, but an explicit
        # clear here guarantees no stale interrupt can survive a turn
        # boundary and fire on a subsequent, unrelated tool call that
        # happens to get scheduled onto the same recycled worker tid.
        # `getattr` fallback covers test stubs that build AIAgent via
        # object.__new__ and skip __init__.
        _tracker = getattr(self, "_tool_worker_threads", None)
        _tracker_lock = getattr(self, "_tool_worker_threads_lock", None)
        if _tracker is not None and _tracker_lock is not None:
            with _tracker_lock:
                _worker_tids = list(_tracker)
            for _wtid in _worker_tids:
                try:
                    _set_interrupt(False, _wtid)
                except Exception:
                    pass
        # A hard interrupt supersedes any pending /steer — the steer was
        # meant for the agent's next tool-call iteration, which will no
        # longer happen. Drop it instead of surprising the user with a
        # late injection on the post-interrupt turn.
        _steer_lock = getattr(self, "_pending_steer_lock", None)
        if _steer_lock is not None:
            with _steer_lock:
                self._pending_steer = None

    def steer(self, text: str) -> bool:
        """
        Inject a user message into the next tool result without interrupting.

        Unlike interrupt(), this does NOT stop the current tool call. The
        text is stashed and the agent loop appends it to the LAST tool
        result's content once the current tool batch finishes. The model
        sees the steer as part of the tool output on its next iteration.

        Thread-safe: callable from gateway/CLI/TUI threads. Multiple calls
        before the drain point concatenate with newlines.

        Args:
            text: The user text to inject. Empty strings are ignored.

        Returns:
            True if the steer was accepted, False if the text was empty.
        """
        if not text or not text.strip():
            return False
        cleaned = text.strip()
        _lock = getattr(self, "_pending_steer_lock", None)
        if _lock is None:
            # Test stubs that built AIAgent via object.__new__ skip __init__.
            # Fall back to direct attribute set; no concurrent callers expected
            # in those stubs.
            existing = getattr(self, "_pending_steer", None)
            self._pending_steer = (existing + "\n" + cleaned) if existing else cleaned
            return True
        with _lock:
            if self._pending_steer:
                self._pending_steer = self._pending_steer + "\n" + cleaned
            else:
                self._pending_steer = cleaned
        return True

    def _drain_pending_steer(self) -> Optional[str]:
        """Return the pending steer text (if any) and clear the slot.

        Safe to call from the agent execution thread after appending tool
        results. Returns None when no steer is pending.
        """
        _lock = getattr(self, "_pending_steer_lock", None)
        if _lock is None:
            text = getattr(self, "_pending_steer", None)
            self._pending_steer = None
            return text
        with _lock:
            text = self._pending_steer
            self._pending_steer = None
        return text

    def _record_file_mutation_result(
        self,
        tool_name: str,
        args: Dict[str, Any],
        result: Any,
        is_error: bool,
    ) -> None:
        """Record a ``write_file`` / ``patch`` outcome for the turn-end verifier.

        On failure, store ``{path: {error_preview, tool}}`` entries.  On
        success, remove any prior failure entries for the same paths (the
        model recovered within the turn).  Silently no-ops if the per-turn
        state dict hasn't been initialised yet (e.g. a tool dispatched
        outside ``run_conversation``).
        """
        if tool_name not in _FILE_MUTATING_TOOLS:
            return
        state = getattr(self, "_turn_failed_file_mutations", None)
        if state is None:
            return
        targets = _extract_file_mutation_targets(tool_name, args)
        if not targets:
            return
        landed = file_mutation_result_landed(tool_name, result)
        if is_error and not landed:
            preview = _extract_error_preview(result)
            for path in targets:
                # Keep the FIRST error we saw for a given path unless we
                # later see success.  A repeated failure with a different
                # message shouldn't silently overwrite the original.
                if path not in state:
                    state[path] = {
                        "tool": tool_name,
                        "error_preview": preview,
                    }
        else:
            for path in targets:
                state.pop(path, None)

    def _file_mutation_verifier_enabled(self) -> bool:
        """Check whether the per-turn file-mutation verifier footer is on.

        Config path: ``display.file_mutation_verifier`` (bool, default True).
        ``HERMES_FILE_MUTATION_VERIFIER`` env var overrides config.  Exposed
        as a method so tests can patch a single seam without reaching into
        the private ``_turn_failed_file_mutations`` state dict.
        """
        try:
            import os as _os
            env = _os.environ.get("HERMES_FILE_MUTATION_VERIFIER")
            if env is not None:
                return env.strip().lower() not in {"0", "false", "no", "off"}
            # Read from the persisted config.yaml so gateway and CLI share
            # the same setting.  Import lazily to avoid a startup-time cycle.
            try:
                from hermes_cli.config import load_config as _load_config
                _cfg = _load_config() or {}
            except Exception:
                _cfg = {}
            _display = _cfg.get("display") if isinstance(_cfg, dict) else None
            if isinstance(_display, dict) and "file_mutation_verifier" in _display:
                return bool(_display.get("file_mutation_verifier"))
        except Exception:
            pass
        return True  # safe default: verifier on

    @staticmethod
    def _format_file_mutation_failure_footer(failed: Dict[str, Dict[str, Any]]) -> str:
        """Render the per-turn failed-mutation dict as a user-facing footer.

        Displays up to 10 paths with their first error preview, then a
        count of any additional failures.  Returns an empty string when
        the dict is empty so callers can concatenate unconditionally.
        """
        if not failed:
            return ""
        lines = [
            "⚠️ File-mutation verifier: "
            f"{len(failed)} file(s) were NOT modified this turn despite any "
            "wording above that may suggest otherwise. Run `git status` or "
            "`read_file` to confirm."
        ]
        shown = 0
        for path, info in failed.items():
            if shown >= 10:
                break
            preview = (info.get("error_preview") or "").strip()
            tool = info.get("tool") or "patch"
            if preview:
                lines.append(f"  • {path} — [{tool}] {preview}")
            else:
                lines.append(f"  • {path} — [{tool}] failed")
            shown += 1
        remaining = len(failed) - shown
        if remaining > 0:
            lines.append(f"  • … and {remaining} more")
        return "\n".join(lines)

    def _apply_pending_steer_to_tool_results(self, messages: list, num_tool_msgs: int) -> None:
        """Forwarder — see ``agent.agent_runtime_helpers.apply_pending_steer_to_tool_results``."""
        from agent.agent_runtime_helpers import apply_pending_steer_to_tool_results
        return apply_pending_steer_to_tool_results(self, messages, num_tool_msgs)

    def _touch_activity(self, desc: str) -> None:
        """Update the last-activity timestamp and description (thread-safe)."""
        self._last_activity_ts = time.time()
        self._last_activity_desc = desc

    def _capture_rate_limits(self, http_response: Any) -> None:
        """Parse x-ratelimit-* headers from an HTTP response and cache the state.

        Called after each streaming API call.  The httpx Response object is
        available on the OpenAI SDK Stream via ``stream.response``.
        """
        if http_response is None:
            return
        headers = getattr(http_response, "headers", None)
        if not headers:
            return
        try:
            from agent.rate_limit_tracker import parse_rate_limit_headers
            state = parse_rate_limit_headers(headers, provider=self.provider)
            if state is not None:
                self._rate_limit_state = state
        except Exception:
            pass  # Never let header parsing break the agent loop

    def get_rate_limit_state(self):
        """Return the last captured RateLimitState, or None."""
        return self._rate_limit_state

    def _check_openrouter_cache_status(self, http_response: Any) -> None:
        """Read X-OpenRouter-Cache-Status from response headers and log it.

        Increments ``_or_cache_hits`` on HIT so callers can report savings.
        """
        if http_response is None:
            return
        headers = getattr(http_response, "headers", None)
        if not headers:
            return
        try:
            status = headers.get("x-openrouter-cache-status")
            if not status:
                return
            if status.upper() == "HIT":
                self._or_cache_hits += 1
                logger.info("OpenRouter response cache HIT (total: %d)", self._or_cache_hits)
            else:
                logger.debug("OpenRouter response cache %s", status.upper())
        except Exception:
            pass  # Never let header parsing break the agent loop

    def get_activity_summary(self) -> dict:
        """Return a snapshot of the agent's current activity for diagnostics.

        Called by the gateway timeout handler to report what the agent was doing
        when it was killed, and by the periodic "still working" notifications.
        """
        elapsed = time.time() - self._last_activity_ts
        return {
            "last_activity_ts": self._last_activity_ts,
            "last_activity_desc": self._last_activity_desc,
            "seconds_since_activity": round(elapsed, 1),
            "current_tool": self._current_tool,
            "api_call_count": self._api_call_count,
            "max_iterations": self.max_iterations,
            "budget_used": self.iteration_budget.used,
            "budget_max": self.iteration_budget.max_total,
        }

    def shutdown_memory_provider(self, messages: list = None) -> None:
        """Shut down the memory provider and context engine — call at actual session boundaries.

        This calls on_session_end() then shutdown_all() on the memory
        manager, and on_session_end() on the context engine.
        NOT called per-turn — only at CLI exit, /reset, gateway
        session expiry, etc.
        """
        if self._memory_manager:
            try:
                self._memory_manager.on_session_end(messages or [])
            except Exception:
                pass
            try:
                self._memory_manager.shutdown_all()
            except Exception:
                pass
        # Notify context engine of session end (flush DAG, close DBs, etc.)
        if hasattr(self, "context_compressor") and self.context_compressor:
            try:
                self.context_compressor.on_session_end(
                    self.session_id or "",
                    messages or [],
                )
            except Exception:
                pass

    def commit_memory_session(self, messages: list = None) -> None:
        """Trigger end-of-session extraction without tearing providers down.
        Called when session_id rotates (e.g. /new, context compression);
        providers keep their state and continue running under the old
        session_id — they just flush pending extraction now."""
        if self._memory_manager:
            try:
                self._memory_manager.on_session_end(messages or [])
            except Exception:
                pass
        # Notify context engine of session end too — same lifecycle moment as
        # the memory manager's on_session_end. Without this, engines that
        # accumulate per-session state (DAGs, summaries) leak that state from
        # the rotated-out session into whatever comes next under the same
        # compressor instance. Mirrors the call in shutdown_memory_provider().
        # See issue #22394.
        if hasattr(self, "context_compressor") and self.context_compressor:
            try:
                self.context_compressor.on_session_end(
                    self.session_id or "",
                    messages or [],
                )
            except Exception:
                pass

    def _sync_external_memory_for_turn(
        self,
        *,
        original_user_message: Any,
        final_response: Any,
        interrupted: bool,
    ) -> None:
        """Mirror a completed turn into external memory providers.

        Called at the end of ``run_conversation`` with the cleaned user
        message (``original_user_message``) and the finalised assistant
        response.  The external memory backend gets both ``sync_all`` (to
        persist the exchange) and ``queue_prefetch_all`` (to start
        warming context for the next turn) in one shot.

        Uses ``original_user_message`` rather than ``user_message``
        because the latter may carry injected skill content that bloats
        or breaks provider queries.

        Interrupted turns are skipped entirely (#15218).  A partial
        assistant output, an aborted tool chain, or a mid-stream reset
        is not durable conversational truth — mirroring it into an
        external memory backend pollutes future recall with state the
        user never saw completed.  The prefetch is gated on the same
        flag: the user's next message is almost certainly a retry of
        the same intent, and a prefetch keyed on the interrupted turn
        would fire against stale context.

        Normal completed turns still sync as before.  The whole body is
        wrapped in ``try/except Exception`` because external memory
        providers are strictly best-effort — a misconfigured or offline
        backend must not block the user from seeing their response.
        """
        if interrupted:
            return
        if not (self._memory_manager and final_response and original_user_message):
            return
        try:
            self._memory_manager.sync_all(
                original_user_message, final_response,
                session_id=self.session_id or "",
            )
            self._memory_manager.queue_prefetch_all(
                original_user_message,
                session_id=self.session_id or "",
            )
        except Exception:
            pass

    def release_clients(self) -> None:
        """Release LLM client resources WITHOUT tearing down session tool state.

        Used by the gateway when evicting this agent from _agent_cache for
        memory-management reasons (LRU cap or idle TTL) — the session may
        resume at any time with a freshly-built AIAgent that reuses the
        same task_id / session_id, so we must NOT kill:
          - process_registry entries for task_id (user's bg shells)
          - terminal sandbox for task_id (cwd, env, shell state)
          - browser daemon for task_id (open tabs, cookies)
          - memory provider (has its own lifecycle; keeps running)

        We DO close:
          - OpenAI/httpx client pool (big chunk of held memory + sockets;
            the rebuilt agent gets a fresh client anyway)
          - Active child subagents (per-turn artefacts; safe to drop)

        Safe to call multiple times.  Distinct from close() — which is the
        hard teardown for actual session boundaries (/new, /reset, session
        expiry).
        """
        # Close active child agents (per-turn; no cross-turn persistence).
        try:
            with self._active_children_lock:
                children = list(self._active_children)
                self._active_children.clear()
            for child in children:
                try:
                    child.release_clients()
                except Exception:
                    # Fall back to full close on children; they're per-turn.
                    try:
                        child.close()
                    except Exception:
                        pass
        except Exception:
            pass

        # Close the OpenAI/httpx client to release sockets immediately.
        try:
            client = getattr(self, "client", None)
            if client is not None:
                self._close_openai_client(client, reason="cache_evict", shared=True)
                self.client = None
        except Exception:
            pass

    def close(self) -> None:
        """Release all resources held by this agent instance.

        Cleans up subprocess resources that would otherwise become orphans:
        - Background processes tracked in ProcessRegistry
        - Terminal sandbox environments
        - Browser daemon sessions
        - Active child agents (subagent delegation)
        - OpenAI/httpx client connections

        Safe to call multiple times (idempotent).  Each cleanup step is
        independently guarded so a failure in one does not prevent the rest.
        """
        task_id = getattr(self, "session_id", None) or ""

        # 1. Kill background processes for this task
        try:
            from tools.process_registry import process_registry
            process_registry.kill_all(task_id=task_id)
        except Exception:
            pass

        # 2. Clean terminal sandbox environments
        try:
            cleanup_vm(task_id)
        except Exception:
            pass

        # 3. Clean browser daemon sessions
        try:
            cleanup_browser(task_id)
        except Exception:
            pass

        # 4. Close active child agents
        try:
            with self._active_children_lock:
                children = list(self._active_children)
                self._active_children.clear()
            for child in children:
                try:
                    child.close()
                except Exception:
                    pass
        except Exception:
            pass

        # 5. Close the OpenAI/httpx client
        try:
            client = getattr(self, "client", None)
            if client is not None:
                self._close_openai_client(client, reason="agent_close", shared=True)
                self.client = None
        except Exception:
            pass

    def _hydrate_todo_store(self, history: List[Dict[str, Any]]) -> None:
        """
        Recover todo state from conversation history.
        
        The gateway creates a fresh AIAgent per message, so the in-memory
        TodoStore is empty. We scan the history for the most recent todo
        tool response and replay it to reconstruct the state.
        """
        # Walk history backwards to find the most recent todo tool response
        last_todo_response = None
        for msg in reversed(history):
            if msg.get("role") != "tool":
                continue
            content = msg.get("content", "")
            # Quick check: todo responses contain "todos" key
            if '"todos"' not in content:
                continue
            try:
                data = json.loads(content)
                if "todos" in data and isinstance(data["todos"], list):
                    last_todo_response = data["todos"]
                    break
            except (json.JSONDecodeError, TypeError):
                continue
        
        if last_todo_response:
            # Replay the items into the store (replace mode)
            self._todo_store.write(last_todo_response, merge=False)
            if not self.quiet_mode:
                self._vprint(f"{self.log_prefix}📋 Restored {len(last_todo_response)} todo item(s) from history")
        _set_interrupt(False)

    @property
    def is_interrupted(self) -> bool:
        """Check if an interrupt has been requested."""
        return self._interrupt_requested










    def _build_system_prompt_parts(self, system_message: str = None) -> Dict[str, str]:
        """Forwarder — see ``agent.system_prompt.build_system_prompt_parts``."""
        from agent.system_prompt import build_system_prompt_parts
        return build_system_prompt_parts(self, system_message=system_message)

    def _build_system_prompt(self, system_message: str = None) -> str:
        """Forwarder — see ``agent.system_prompt.build_system_prompt``."""
        from agent.system_prompt import build_system_prompt
        return build_system_prompt(self, system_message=system_message)

    @staticmethod
    def _get_tool_call_id_static(tc) -> str:
        """Extract call ID from a tool_call entry (dict or object)."""
        if isinstance(tc, dict):
            return tc.get("call_id", "") or tc.get("id", "") or ""
        return getattr(tc, "call_id", "") or getattr(tc, "id", "") or ""

    @staticmethod
    def _get_tool_call_name_static(tc) -> str:
        """Extract function name from a tool_call entry (dict or object).

        Gemini's OpenAI-compatibility endpoint requires every `role: tool`
        message to carry the matching function name. OpenAI/Anthropic/ollama
        tolerate its absence, so the field is best-effort: callers fall back
        to "" and the message still works elsewhere.
        """
        if isinstance(tc, dict):
            fn = tc.get("function")
            if isinstance(fn, dict):
                return fn.get("name", "") or ""
            return ""
        fn = getattr(tc, "function", None)
        return getattr(fn, "name", "") or ""

    _VALID_API_ROLES = frozenset({"system", "user", "assistant", "tool", "function", "developer"})

    @staticmethod
    def _sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Forwarder — see ``agent.agent_runtime_helpers.sanitize_api_messages``."""
        from agent.agent_runtime_helpers import sanitize_api_messages
        return sanitize_api_messages(messages)

    @staticmethod
    def _is_thinking_only_assistant(msg: Dict[str, Any]) -> bool:
        """Return True if ``msg`` is an assistant turn whose only payload is reasoning.

        "Thinking-only" means the model emitted reasoning (``reasoning`` or
        ``reasoning_content``) but no visible text and no tool_calls. When sent
        back to providers that convert reasoning into thinking blocks (native
        Anthropic, OpenRouter Anthropic, third-party Anthropic-compatible
        gateways), the resulting message has only thinking blocks — which
        Anthropic rejects with HTTP 400 "The final block in an assistant
        message cannot be `thinking`."

        Symmetric with Claude Code's ``filterOrphanedThinkingOnlyMessages``
        (src/utils/messages.ts). We drop the whole turn from the API copy
        rather than fabricating stub text — the message log (UI transcript)
        keeps the reasoning block; only the wire copy is cleaned.
        """
        if not isinstance(msg, dict) or msg.get("role") != "assistant":
            return False
        if msg.get("tool_calls"):
            return False
        # Does it have any actual output?
        content = msg.get("content")
        if isinstance(content, str):
            if content.strip():
                return False
        elif isinstance(content, list):
            for block in content:
                if not isinstance(block, dict):
                    if block:  # non-empty non-dict string etc.
                        return False
                    continue
                btype = block.get("type")
                if btype in {"thinking", "redacted_thinking"}:
                    continue
                if btype == "text":
                    text = block.get("text", "")
                    if isinstance(text, str) and text.strip():
                        return False
                    continue
                # tool_use, image, document, etc. — real payload
                return False
        elif content is not None and content != "":
            return False
        # Content is empty-ish. Is there reasoning to make it thinking-only?
        reasoning = msg.get("reasoning_content") or msg.get("reasoning")
        if isinstance(reasoning, str) and reasoning.strip():
            return True
        # reasoning_details list form
        rd = msg.get("reasoning_details")
        if isinstance(rd, list) and rd:
            return True
        return False

    @staticmethod
    def _drop_thinking_only_and_merge_users(
        messages: List[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        """Forwarder — see ``agent.agent_runtime_helpers.drop_thinking_only_and_merge_users``."""
        from agent.agent_runtime_helpers import drop_thinking_only_and_merge_users
        return drop_thinking_only_and_merge_users(messages)

    @staticmethod
    def _cap_delegate_task_calls(tool_calls: list) -> list:
        """Truncate excess delegate_task calls to max_concurrent_children.

        The delegate_tool caps the task list inside a single call, but the
        model can emit multiple separate delegate_task tool_calls in one
        turn.  This truncates the excess, preserving all non-delegate calls.

        Returns the original list if no truncation was needed.
        """
        from tools.delegate_tool import _get_max_concurrent_children
        max_children = _get_max_concurrent_children()
        delegate_count = sum(1 for tc in tool_calls if tc.function.name == "delegate_task")
        if delegate_count <= max_children:
            return tool_calls
        kept_delegates = 0
        truncated = []
        for tc in tool_calls:
            if tc.function.name == "delegate_task":
                if kept_delegates < max_children:
                    truncated.append(tc)
                    kept_delegates += 1
            else:
                truncated.append(tc)
        logger.warning(
            "Truncated %d excess delegate_task call(s) to enforce "
            "max_concurrent_children=%d limit",
            delegate_count - max_children, max_children,
        )
        return truncated

    @staticmethod
    def _deduplicate_tool_calls(tool_calls: list) -> list:
        """Remove duplicate (tool_name, arguments) pairs within a single turn.

        Only the first occurrence of each unique pair is kept.
        Returns the original list if no duplicates were found.
        """
        seen: set = set()
        unique: list = []
        for tc in tool_calls:
            key = (tc.function.name, tc.function.arguments)
            if key not in seen:
                seen.add(key)
                unique.append(tc)
            else:
                logger.warning("Removed duplicate tool call: %s", tc.function.name)
        return unique if len(unique) < len(tool_calls) else tool_calls

    def _repair_tool_call(self, tool_name: str) -> str | None:
        """Forwarder — see ``agent.agent_runtime_helpers.repair_tool_call``."""
        from agent.agent_runtime_helpers import repair_tool_call
        return repair_tool_call(self, tool_name)

    def _invalidate_system_prompt(self):
        """Forwarder — see ``agent.system_prompt.invalidate_system_prompt``."""
        from agent.system_prompt import invalidate_system_prompt
        invalidate_system_prompt(self)

    @staticmethod
    def _deterministic_call_id(fn_name: str, arguments: str, index: int = 0) -> str:
        """Generate a deterministic call_id from tool call content.

        Used as a fallback when the API doesn't provide a call_id.
        Deterministic IDs prevent cache invalidation — random UUIDs would
        make every API call's prefix unique, breaking OpenAI's prompt cache.
        """
        return _codex_deterministic_call_id(fn_name, arguments, index)

    @staticmethod
    def _split_responses_tool_id(raw_id: Any) -> tuple[Optional[str], Optional[str]]:
        """Split a stored tool id into (call_id, response_item_id)."""
        return _codex_split_responses_tool_id(raw_id)

    def _derive_responses_function_call_id(
        self,
        call_id: str,
        response_item_id: Optional[str] = None,
    ) -> str:
        """Build a valid Responses `function_call.id` (must start with `fc_`)."""
        return _codex_derive_responses_function_call_id(call_id, response_item_id)

    def _thread_identity(self) -> str:
        thread = threading.current_thread()
        return f"{thread.name}:{thread.ident}"

    def _client_log_context(self) -> str:
        provider = getattr(self, "provider", "unknown")
        base_url = getattr(self, "base_url", "unknown")
        model = getattr(self, "model", "unknown")
        return (
            f"thread={self._thread_identity()} provider={provider} "
            f"base_url={base_url} model={model}"
        )

    def _openai_client_lock(self) -> threading.RLock:
        lock = getattr(self, "_client_lock", None)
        if lock is None:
            lock = threading.RLock()
            self._client_lock = lock
        return lock

    @staticmethod
    def _is_openai_client_closed(client: Any) -> bool:
        """Check if an OpenAI client is closed.

        Handles both property and method forms of is_closed:
        - httpx.Client.is_closed is a bool property
        - openai.OpenAI.is_closed is a method returning bool

        Prior bug: getattr(client, "is_closed", False) returned the bound method,
        which is always truthy, causing unnecessary client recreation on every call.
        """
        from unittest.mock import Mock

        if isinstance(client, Mock):
            return False

        is_closed_attr = getattr(client, "is_closed", None)
        if is_closed_attr is not None:
            # Handle method (openai SDK) vs property (httpx)
            if callable(is_closed_attr):
                if is_closed_attr():
                    return True
            elif bool(is_closed_attr):
                return True

        http_client = getattr(client, "_client", None)
        if http_client is not None:
            return bool(getattr(http_client, "is_closed", False))
        return False

    @staticmethod
    def _build_keepalive_http_client(base_url: str = "") -> Any:
        try:
            import httpx as _httpx
            import socket as _socket

            _sock_opts = [(_socket.SOL_SOCKET, _socket.SO_KEEPALIVE, 1)]
            if hasattr(_socket, "TCP_KEEPIDLE"):
                _sock_opts.append((_socket.IPPROTO_TCP, _socket.TCP_KEEPIDLE, 30))
                _sock_opts.append((_socket.IPPROTO_TCP, _socket.TCP_KEEPINTVL, 10))
                _sock_opts.append((_socket.IPPROTO_TCP, _socket.TCP_KEEPCNT, 3))
            elif hasattr(_socket, "TCP_KEEPALIVE"):
                _sock_opts.append((_socket.IPPROTO_TCP, _socket.TCP_KEEPALIVE, 30))
            # When a custom transport is provided, httpx won't auto-read proxy
            # from env vars (allow_env_proxies = trust_env and transport is None).
            # Explicitly read proxy settings while still honoring NO_PROXY for
            # loopback / local endpoints such as a locally hosted sub2api.
            _proxy = _get_proxy_for_base_url(base_url)
            return _httpx.Client(
                transport=_httpx.HTTPTransport(socket_options=_sock_opts),
                proxy=_proxy,
            )
        except Exception:
            return None

    def _create_openai_client(self, client_kwargs: dict, *, reason: str, shared: bool) -> Any:
        """Forwarder — see ``agent.agent_runtime_helpers.create_openai_client``."""
        from agent.agent_runtime_helpers import create_openai_client
        return create_openai_client(self, client_kwargs, reason=reason, shared=shared)

    @staticmethod
    def _force_close_tcp_sockets(client: Any) -> int:
        """Forwarder — see ``agent.agent_runtime_helpers.force_close_tcp_sockets``."""
        from agent.agent_runtime_helpers import force_close_tcp_sockets
        return force_close_tcp_sockets(client)

    def _close_openai_client(self, client: Any, *, reason: str, shared: bool) -> None:
        if client is None:
            return
        # Force-close TCP sockets first to prevent CLOSE-WAIT accumulation,
        # then do the graceful SDK-level close.
        force_closed = self._force_close_tcp_sockets(client)
        try:
            client.close()
            logger.info(
                "OpenAI client closed (%s, shared=%s, tcp_force_closed=%d) %s",
                reason,
                shared,
                force_closed,
                self._client_log_context(),
            )
        except Exception as exc:
            logger.debug(
                "OpenAI client close failed (%s, shared=%s) %s error=%s",
                reason,
                shared,
                self._client_log_context(),
                exc,
            )

    def _replace_primary_openai_client(self, *, reason: str) -> bool:
        with self._openai_client_lock():
            old_client = getattr(self, "client", None)
            try:
                new_client = self._create_openai_client(self._client_kwargs, reason=reason, shared=True)
            except Exception as exc:
                logger.warning(
                    "Failed to rebuild shared OpenAI client (%s) %s error=%s",
                    reason,
                    self._client_log_context(),
                    exc,
                )
                return False
            self.client = new_client
        self._close_openai_client(old_client, reason=f"replace:{reason}", shared=True)
        return True

    def _ensure_primary_openai_client(self, *, reason: str) -> Any:
        with self._openai_client_lock():
            client = getattr(self, "client", None)
            if client is not None and not self._is_openai_client_closed(client):
                return client

        logger.warning(
            "Detected closed shared OpenAI client; recreating before use (%s) %s",
            reason,
            self._client_log_context(),
        )
        if not self._replace_primary_openai_client(reason=f"recreate_closed:{reason}"):
            raise RuntimeError("Failed to recreate closed OpenAI client")
        with self._openai_client_lock():
            return self.client

    def _cleanup_dead_connections(self) -> bool:
        """Forwarder — see ``agent.agent_runtime_helpers.cleanup_dead_connections``."""
        from agent.agent_runtime_helpers import cleanup_dead_connections
        return cleanup_dead_connections(self)

    @staticmethod
    def _api_kwargs_have_image_parts(api_kwargs: dict) -> bool:
        """Return True when the outbound request still contains native image parts."""
        if not isinstance(api_kwargs, dict):
            return False
        candidates = []
        messages = api_kwargs.get("messages")
        if isinstance(messages, list):
            candidates.extend(messages)
        # Responses API payloads use `input`; after conversion, image parts can
        # still be present there instead of in `messages`.
        response_input = api_kwargs.get("input")
        if isinstance(response_input, list):
            candidates.extend(response_input)

        def _contains_image(value: Any) -> bool:
            if isinstance(value, dict):
                ptype = value.get("type")
                if ptype in {"image_url", "input_image"}:
                    return True
                return any(_contains_image(v) for v in value.values())
            if isinstance(value, list):
                return any(_contains_image(v) for v in value)
            return False

        return any(_contains_image(item) for item in candidates)

    def _copilot_headers_for_request(self, *, is_vision: bool) -> dict:
        from hermes_cli.copilot_auth import copilot_request_headers

        return copilot_request_headers(is_agent_turn=True, is_vision=is_vision)

    def _create_request_openai_client(self, *, reason: str, api_kwargs: Optional[dict] = None) -> Any:
        from unittest.mock import Mock

        primary_client = self._ensure_primary_openai_client(reason=reason)
        if isinstance(primary_client, Mock):
            return primary_client
        with self._openai_client_lock():
            request_kwargs = dict(self._client_kwargs)
        # Per-request OpenAI-wire clients (used by both the non-streaming
        # chat-completions path and the streaming chat-completions path
        # in `_interruptible_api_call`) should not run the SDK's built-in
        # retry loop: the agent's outer loop owns retries with credential
        # rotation, provider fallback, and backoff that the SDK can't
        # see. Leaving SDK retries on (default 2) compounds with our outer
        # retries and lets a single hung provider request stretch to ~3x
        # the per-call timeout before our stale detector reports it.
        # Shared/primary clients and Anthropic / Bedrock paths are
        # unaffected (they don't go through here).
        request_kwargs["max_retries"] = 0
        if (
            base_url_host_matches(str(request_kwargs.get("base_url", "")), "api.githubcopilot.com")
            and self._api_kwargs_have_image_parts(api_kwargs or {})
        ):
            request_kwargs["default_headers"] = self._copilot_headers_for_request(is_vision=True)
        return self._create_openai_client(request_kwargs, reason=reason, shared=False)

    def _close_request_openai_client(self, client: Any, *, reason: str) -> None:
        self._close_openai_client(client, reason=reason, shared=False)

    def _run_codex_stream(self, api_kwargs: dict, client: Any = None, on_first_delta: callable = None):
        """Forwarder — see ``agent.codex_runtime.run_codex_stream``."""
        from agent.codex_runtime import run_codex_stream
        return run_codex_stream(self, api_kwargs, client, on_first_delta)

    def _run_codex_create_stream_fallback(self, api_kwargs: dict, client: Any = None):
        """Forwarder — see ``agent.codex_runtime.run_codex_create_stream_fallback``."""
        from agent.codex_runtime import run_codex_create_stream_fallback
        return run_codex_create_stream_fallback(self, api_kwargs, client)

    def _try_refresh_codex_client_credentials(self, *, force: bool = True) -> bool:
        if self.api_mode != "codex_responses" or self.provider not in {"openai-codex", "xai-oauth"}:
            return False

        # Guard against silent account swap.
        #
        # When an agent is using a non-singleton credential — e.g. a manual
        # pool entry (``hermes auth add xai-oauth``) whose tokens belong to
        # a different account than the loopback_pkce singleton, or an agent
        # constructed with an explicit ``api_key=`` arg — force-refreshing
        # the singleton here and adopting its tokens silently re-routes the
        # rest of the conversation onto the singleton's account.  The
        # credential pool's reactive recovery (``_recover_with_credential_pool``)
        # is the right channel for that case; this path is the
        # singleton-only fallback used when the pool can't recover, and
        # MUST only fire when the agent really is on singleton tokens.
        try:
            if self.provider == "openai-codex":
                from hermes_cli.auth import resolve_codex_runtime_credentials

                singleton_now = resolve_codex_runtime_credentials(
                    refresh_if_expiring=False,
                )
            else:
                from hermes_cli.auth import resolve_xai_oauth_runtime_credentials

                singleton_now = resolve_xai_oauth_runtime_credentials(
                    refresh_if_expiring=False,
                )
        except Exception as exc:
            logger.debug("%s singleton read failed: %s", self.provider, exc)
            return False

        singleton_key = str(singleton_now.get("api_key") or "").strip()
        active_key = str(self.api_key or "").strip()
        if singleton_key and active_key and singleton_key != active_key:
            logger.debug(
                "%s singleton tokens differ from the active api_key; "
                "skipping singleton force-refresh to avoid silent account swap. "
                "Reactive credential rotation should go through the pool.",
                self.provider,
            )
            return False

        try:
            if self.provider == "openai-codex":
                from hermes_cli.auth import resolve_codex_runtime_credentials

                creds = resolve_codex_runtime_credentials(force_refresh=force)
            else:
                from hermes_cli.auth import resolve_xai_oauth_runtime_credentials

                creds = resolve_xai_oauth_runtime_credentials(force_refresh=force)
        except Exception as exc:
            logger.debug("%s credential refresh failed: %s", self.provider, exc)
            return False

        api_key = creds.get("api_key")
        base_url = creds.get("base_url")
        if not isinstance(api_key, str) or not api_key.strip():
            return False
        if not isinstance(base_url, str) or not base_url.strip():
            return False

        self.api_key = api_key.strip()
        self.base_url = base_url.strip().rstrip("/")
        self._client_kwargs["api_key"] = self.api_key
        self._client_kwargs["base_url"] = self.base_url

        if not self._replace_primary_openai_client(reason=f"{self.provider}_credential_refresh"):
            return False

        return True

    def _try_refresh_nous_client_credentials(self, *, force: bool = True) -> bool:
        if self.api_mode != "chat_completions" or self.provider != "nous":
            return False

        try:
            from hermes_cli.auth import (
                NOUS_INFERENCE_AUTH_MODE_AUTO,
                NOUS_INFERENCE_AUTH_MODE_LEGACY,
                resolve_nous_runtime_credentials,
            )

            creds = resolve_nous_runtime_credentials(
                min_key_ttl_seconds=max(60, int(os.getenv("HERMES_NOUS_MIN_KEY_TTL_SECONDS", "1800"))),
                timeout_seconds=float(os.getenv("HERMES_NOUS_TIMEOUT_SECONDS", "15")),
                inference_auth_mode=(
                    NOUS_INFERENCE_AUTH_MODE_LEGACY
                    if force
                    else NOUS_INFERENCE_AUTH_MODE_AUTO
                ),
            )
        except Exception as exc:
            logger.debug("Nous credential refresh failed: %s", exc)
            return False

        api_key = creds.get("api_key")
        base_url = creds.get("base_url")
        if not isinstance(api_key, str) or not api_key.strip():
            return False
        if not isinstance(base_url, str) or not base_url.strip():
            return False

        self.api_key = api_key.strip()
        self.base_url = base_url.strip().rstrip("/")
        self._client_kwargs["api_key"] = self.api_key
        self._client_kwargs["base_url"] = self.base_url
        # Nous requests should not inherit OpenRouter-only attribution headers.
        self._client_kwargs.pop("default_headers", None)

        if not self._replace_primary_openai_client(reason="nous_credential_refresh"):
            return False

        return True

    def _try_refresh_copilot_client_credentials(self) -> bool:
        """Refresh Copilot credentials and rebuild the shared OpenAI client.

        Copilot tokens may remain the same string across refreshes (`gh auth token`
        returns a stable OAuth token in many setups). We still rebuild the client
        on 401 so retries recover from stale auth/client state without requiring
        a session restart.
        """
        if self.provider != "copilot":
            return False

        try:
            from hermes_cli.copilot_auth import resolve_copilot_token

            new_token, token_source = resolve_copilot_token()
        except Exception as exc:
            logger.debug("Copilot credential refresh failed: %s", exc)
            return False

        if not isinstance(new_token, str) or not new_token.strip():
            return False

        new_token = new_token.strip()

        self.api_key = new_token
        self._client_kwargs["api_key"] = self.api_key
        self._client_kwargs["base_url"] = self.base_url
        self._apply_client_headers_for_base_url(str(self.base_url or ""))

        if not self._replace_primary_openai_client(reason="copilot_credential_refresh"):
            return False

        logger.info("Copilot credentials refreshed from %s", token_source)
        return True

    def _try_refresh_anthropic_client_credentials(self) -> bool:
        if self.api_mode != "anthropic_messages" or not hasattr(self, "_anthropic_api_key"):
            return False
        # Only refresh credentials for the native Anthropic provider.
        # Other anthropic_messages providers (MiniMax, Alibaba, etc.) use their own keys.
        if self.provider != "anthropic":
            return False
        # Azure endpoints use static API keys — OAuth token rotation doesn't apply.
        # Refreshing would pick up ~/.claude/.credentials.json OAuth token and break auth.
        _base = getattr(self, "_anthropic_base_url", "") or ""
        if "azure.com" in _base:
            return False

        try:
            from agent.anthropic_adapter import resolve_anthropic_token, build_anthropic_client

            new_token = resolve_anthropic_token()
        except Exception as exc:
            logger.debug("Anthropic credential refresh failed: %s", exc)
            return False

        if not isinstance(new_token, str) or not new_token.strip():
            return False
        new_token = new_token.strip()
        if new_token == self._anthropic_api_key:
            return False

        try:
            self._anthropic_client.close()
        except Exception:
            pass

        try:
            self._anthropic_client = build_anthropic_client(
                new_token,
                getattr(self, "_anthropic_base_url", None),
                timeout=get_provider_request_timeout(self.provider, self.model),
            )
        except Exception as exc:
            logger.warning("Failed to rebuild Anthropic client after credential refresh: %s", exc)
            return False

        self._anthropic_api_key = new_token
        # Update OAuth flag — token type may have changed (API key ↔ OAuth).
        # Only treat as OAuth on native Anthropic; third-party endpoints using
        # the Anthropic protocol must not trip OAuth paths (#1739 & third-party
        # identity-injection guard).
        from agent.anthropic_adapter import _is_oauth_token
        self._is_anthropic_oauth = _is_oauth_token(new_token) if self.provider == "anthropic" else False
        return True

    def _apply_client_headers_for_base_url(self, base_url: str) -> None:
        from agent.auxiliary_client import (
            _AI_GATEWAY_HEADERS,
            build_nvidia_nim_headers,
            build_or_headers,
        )

        if base_url_host_matches(base_url, "openrouter.ai"):
            self._client_kwargs["default_headers"] = build_or_headers()
        elif base_url_host_matches(base_url, "ai-gateway.vercel.sh"):
            self._client_kwargs["default_headers"] = dict(_AI_GATEWAY_HEADERS)
        elif base_url_host_matches(base_url, "integrate.api.nvidia.com"):
            self._client_kwargs["default_headers"] = build_nvidia_nim_headers(base_url)
        elif base_url_host_matches(base_url, "api.routermint.com"):
            self._client_kwargs["default_headers"] = _routermint_headers()
        elif base_url_host_matches(base_url, "api.githubcopilot.com"):
            from hermes_cli.models import copilot_default_headers

            self._client_kwargs["default_headers"] = copilot_default_headers()
        elif base_url_host_matches(base_url, "api.kimi.com"):
            self._client_kwargs["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
        elif base_url_host_matches(base_url, "portal.qwen.ai"):
            self._client_kwargs["default_headers"] = _qwen_portal_headers()
        elif base_url_host_matches(base_url, "chatgpt.com"):
            from agent.auxiliary_client import _codex_cloudflare_headers
            self._client_kwargs["default_headers"] = _codex_cloudflare_headers(
                self._client_kwargs.get("api_key", "")
            )
        else:
            # No URL-specific headers — check profile.default_headers before clearing.
            _ph_headers = None
            try:
                from providers import get_provider_profile as _gpf2
                _ph2 = _gpf2(self.provider)
                if _ph2 and _ph2.default_headers:
                    _ph_headers = dict(_ph2.default_headers)
            except Exception:
                pass
            if _ph_headers:
                self._client_kwargs["default_headers"] = _ph_headers
            else:
                self._client_kwargs.pop("default_headers", None)

    def _swap_credential(self, entry) -> None:
        runtime_key = getattr(entry, "runtime_api_key", None) or getattr(entry, "access_token", "")
        runtime_base = getattr(entry, "runtime_base_url", None) or getattr(entry, "base_url", None) or self.base_url

        if self.api_mode == "anthropic_messages":
            from agent.anthropic_adapter import build_anthropic_client, _is_oauth_token

            try:
                self._anthropic_client.close()
            except Exception:
                pass

            self._anthropic_api_key = runtime_key
            self._anthropic_base_url = runtime_base
            self._anthropic_client = build_anthropic_client(
                runtime_key, runtime_base,
                timeout=get_provider_request_timeout(self.provider, self.model),
            )
            self._is_anthropic_oauth = _is_oauth_token(runtime_key) if self.provider == "anthropic" else False
            self.api_key = runtime_key
            self.base_url = runtime_base
            return

        self.api_key = runtime_key
        self.base_url = runtime_base.rstrip("/") if isinstance(runtime_base, str) else runtime_base
        self._client_kwargs["api_key"] = self.api_key
        self._client_kwargs["base_url"] = self.base_url
        self._apply_client_headers_for_base_url(self.base_url)
        self._replace_primary_openai_client(reason="credential_rotation")

    def _recover_with_credential_pool(
        self,
        *,
        status_code: Optional[int],
        has_retried_429: bool,
        classified_reason: Optional[FailoverReason] = None,
        error_context: Optional[Dict[str, Any]] = None,
    ) -> tuple[bool, bool]:
        """Forwarder — see ``agent.agent_runtime_helpers.recover_with_credential_pool``."""
        from agent.agent_runtime_helpers import recover_with_credential_pool
        return recover_with_credential_pool(self, status_code=status_code, has_retried_429=has_retried_429, classified_reason=classified_reason, error_context=error_context)

    def _credential_pool_may_recover_rate_limit(self) -> bool:
        """Whether a rate-limit retry should wait for same-provider credentials."""
        pool = self._credential_pool
        if pool is None:
            return False
        if (
            self.provider == "google-gemini-cli"
            or str(getattr(self, "base_url", "")).startswith("cloudcode-pa://")
        ):
            # CloudCode/Gemini quota windows are usually account-level throttles.
            # Prefer the configured fallback immediately instead of waiting out
            # Retry-After while a pooled OAuth credential may still appear usable.
            return False
        return pool.has_available()

    def _anthropic_messages_create(self, api_kwargs: dict):
        if self.api_mode == "anthropic_messages":
            self._try_refresh_anthropic_client_credentials()
        return self._anthropic_client.messages.create(**api_kwargs)

    def _rebuild_anthropic_client(self) -> None:
        """Rebuild the Anthropic client after an interrupt or stale call.

        Handles both direct Anthropic and Bedrock-hosted Anthropic models
        correctly — rebuilding with the Bedrock SDK when provider is bedrock,
        rather than always falling back to build_anthropic_client() which
        requires a direct Anthropic API key.

        Honors ``self._oauth_1m_beta_disabled`` (set by the reactive recovery
        path when an OAuth subscription rejects the 1M-context beta) so the
        rebuilt client carries the reduced beta set.
        """
        _drop_1m = bool(getattr(self, "_oauth_1m_beta_disabled", False))
        if getattr(self, "provider", None) == "bedrock":
            from agent.anthropic_adapter import build_anthropic_bedrock_client
            region = getattr(self, "_bedrock_region", "us-east-1") or "us-east-1"
            self._anthropic_client = build_anthropic_bedrock_client(region)
        else:
            from agent.anthropic_adapter import build_anthropic_client
            self._anthropic_client = build_anthropic_client(
                self._anthropic_api_key,
                getattr(self, "_anthropic_base_url", None),
                timeout=get_provider_request_timeout(self.provider, self.model),
                drop_context_1m_beta=_drop_1m,
            )

    def _interruptible_api_call(self, api_kwargs: dict):
        """Forwarder — see ``agent.chat_completion_helpers.interruptible_api_call``."""
        from agent.chat_completion_helpers import interruptible_api_call
        return interruptible_api_call(self, api_kwargs)

    # ── Unified streaming API call ─────────────────────────────────────────

    def _reset_stream_delivery_tracking(self) -> None:
        """Reset tracking for text delivered during the current model response."""
        # Flush any benign partial-tag tail held by the think scrubber
        # first (#17924): an innocent '<' at the end of the stream that
        # turned out not to be a tag prefix should reach the UI.  Then
        # flush the context scrubber.  Order matters — the think
        # scrubber's output feeds into the context scrubber's state.
        think_scrubber = getattr(self, "_stream_think_scrubber", None)
        if think_scrubber is not None:
            think_tail = think_scrubber.flush()
            if think_tail:
                # Route the tail through the context scrubber too so a
                # memory-context span straddling the final boundary is
                # still caught.
                ctx_scrubber = getattr(self, "_stream_context_scrubber", None)
                if ctx_scrubber is not None:
                    think_tail = ctx_scrubber.feed(think_tail)
                if think_tail:
                    callbacks = [cb for cb in (self.stream_delta_callback, self._stream_callback) if cb is not None]
                    for cb in callbacks:
                        try:
                            cb(think_tail)
                        except Exception:
                            pass
                    self._record_streamed_assistant_text(think_tail)
        # Flush any benign partial-tag tail held by the context scrubber so it
        # reaches the UI before we clear state for the next model call.  If
        # the scrubber is mid-span, flush() drops the orphaned content.
        scrubber = getattr(self, "_stream_context_scrubber", None)
        if scrubber is not None:
            tail = scrubber.flush()
            if tail:
                callbacks = [cb for cb in (self.stream_delta_callback, self._stream_callback) if cb is not None]
                for cb in callbacks:
                    try:
                        cb(tail)
                    except Exception:
                        pass
                self._record_streamed_assistant_text(tail)
        self._current_streamed_assistant_text = ""

    def _record_streamed_assistant_text(self, text: str) -> None:
        """Accumulate visible assistant text emitted through stream callbacks."""
        if isinstance(text, str) and text:
            self._current_streamed_assistant_text = (
                getattr(self, "_current_streamed_assistant_text", "") + text
            )

    @staticmethod
    def _normalize_interim_visible_text(text: str) -> str:
        if not isinstance(text, str):
            return ""
        return re.sub(r"\s+", " ", text).strip()

    def _interim_content_was_streamed(self, content: str) -> bool:
        visible_content = self._normalize_interim_visible_text(
            self._strip_think_blocks(content or "")
        )
        if not visible_content:
            return False
        streamed = self._normalize_interim_visible_text(
            self._strip_think_blocks(getattr(self, "_current_streamed_assistant_text", "") or "")
        )
        return bool(streamed) and streamed == visible_content

    def _emit_interim_assistant_message(self, assistant_msg: Dict[str, Any]) -> None:
        """Surface a real mid-turn assistant commentary message to the UI layer."""
        cb = getattr(self, "interim_assistant_callback", None)
        if cb is None or not isinstance(assistant_msg, dict):
            return
        content = assistant_msg.get("content")
        visible = self._strip_think_blocks(content or "").strip()
        if not visible or visible == "(empty)":
            return
        already_streamed = self._interim_content_was_streamed(visible)
        try:
            cb(visible, already_streamed=already_streamed)
        except Exception:
            logger.debug("interim_assistant_callback error", exc_info=True)

    def _fire_stream_delta(self, text: str) -> None:
        """Fire all registered stream delta callbacks (display + TTS)."""
        # If a tool iteration set the break flag, prepend a single paragraph
        # break before the first real text delta.  This prevents the original
        # problem (text concatenation across tool boundaries) without stacking
        # blank lines when multiple tool iterations run back-to-back.
        if getattr(self, "_stream_needs_break", False) and text and text.strip():
            self._stream_needs_break = False
            text = "\n\n" + text
            prepended_break = True
        else:
            prepended_break = False
        if isinstance(text, str):
            # Suppress reasoning/thinking blocks via the stateful
            # scrubber (#17924).  Earlier versions ran _strip_think_blocks
            # per-delta here, which destroyed downstream state machines
            # when a tag was split across deltas (e.g. MiniMax-M2.7
            # sends '<think>' and its content as separate deltas —
            # regex case 2 erased the first delta, so the CLI/gateway
            # state machine never saw the open tag and leaked the
            # reasoning content as regular response text).
            think_scrubber = getattr(self, "_stream_think_scrubber", None)
            if think_scrubber is not None:
                text = think_scrubber.feed(text or "")
            else:
                # Defensive: legacy callers without the scrubber attribute.
                text = self._strip_think_blocks(text or "")
            # Then feed through the stateful context scrubber so memory-context
            # spans split across chunks cannot leak to the UI (#5719).
            scrubber = getattr(self, "_stream_context_scrubber", None)
            if scrubber is not None:
                text = scrubber.feed(text)
            else:
                # Defensive: legacy callers without the scrubber attribute.
                text = sanitize_context(text)
            # Only strip leading newlines on the first delta — mid-stream "\n" is legitimate markdown.
            if not prepended_break and not getattr(
                self, "_current_streamed_assistant_text", ""
            ):
                text = text.lstrip("\n")
        if not text:
            return
        callbacks = [cb for cb in (self.stream_delta_callback, self._stream_callback) if cb is not None]
        delivered = False
        for cb in callbacks:
            try:
                cb(text)
                delivered = True
            except Exception:
                pass
        if delivered:
            self._record_streamed_assistant_text(text)

    def _fire_reasoning_delta(self, text: str) -> None:
        """Fire reasoning callback if registered."""
        cb = self.reasoning_callback
        if cb is not None:
            try:
                cb(text)
            except Exception:
                pass

    def _fire_tool_gen_started(self, tool_name: str) -> None:
        """Notify display layer that the model is generating tool call arguments.

        Fires once per tool name when the streaming response begins producing
        tool_call / tool_use tokens.  Gives the TUI a chance to show a spinner
        or status line so the user isn't staring at a frozen screen while a
        large tool payload (e.g. a 45 KB write_file) is being generated.
        """
        cb = self.tool_gen_callback
        if cb is not None:
            try:
                cb(tool_name)
            except Exception:
                pass

    def _has_stream_consumers(self) -> bool:
        """Return True if any streaming consumer is registered."""
        return (
            self.stream_delta_callback is not None
            or getattr(self, "_stream_callback", None) is not None
        )

    def _interruptible_streaming_api_call(
        self, api_kwargs: dict, *, on_first_delta: callable = None
    ):
        """Forwarder — see ``agent.chat_completion_helpers.interruptible_streaming_api_call``."""
        from agent.chat_completion_helpers import interruptible_streaming_api_call
        return interruptible_streaming_api_call(self, api_kwargs, on_first_delta=on_first_delta)

    def _try_activate_fallback(self, reason: "FailoverReason | None" = None) -> bool:
        """Forwarder — see ``agent.chat_completion_helpers.try_activate_fallback``."""
        from agent.chat_completion_helpers import try_activate_fallback
        return try_activate_fallback(self, reason)

    # ── Per-turn primary restoration ─────────────────────────────────────

    def _restore_primary_runtime(self) -> bool:
        """Forwarder — see ``agent.agent_runtime_helpers.restore_primary_runtime``."""
        from agent.agent_runtime_helpers import restore_primary_runtime
        return restore_primary_runtime(self)

    def _try_recover_primary_transport(
        self, api_error: Exception, *, retry_count: int, max_retries: int,
    ) -> bool:
        """Forwarder — see ``agent.agent_runtime_helpers.try_recover_primary_transport``."""
        from agent.agent_runtime_helpers import try_recover_primary_transport
        return try_recover_primary_transport(self, api_error, retry_count=retry_count, max_retries=max_retries)

    @staticmethod
    def _content_has_image_parts(content: Any) -> bool:
        if not isinstance(content, list):
            return False
        for part in content:
            if isinstance(part, dict) and part.get("type") in {"image_url", "input_image"}:
                return True
        return False

    @staticmethod
    def _materialize_data_url_for_vision(image_url: str) -> tuple[str, Optional[Path]]:
        header, _, data = str(image_url or "").partition(",")
        mime = "image/jpeg"
        if header.startswith("data:"):
            mime_part = header[len("data:"):].split(";", 1)[0].strip()
            if mime_part.startswith("image/"):
                mime = mime_part
        suffix = {
            "image/png": ".png",
            "image/gif": ".gif",
            "image/webp": ".webp",
            "image/jpeg": ".jpg",
            "image/jpg": ".jpg",
        }.get(mime, ".jpg")
        tmp = tempfile.NamedTemporaryFile(prefix="anthropic_image_", suffix=suffix, delete=False)
        try:
            with tmp:
                tmp.write(base64.b64decode(data))
        except Exception:
            # delete=False means a corrupt/unsupported data URL would otherwise
            # leak a zero-byte temp file on every failed materialization.
            try:
                os.unlink(tmp.name)
            except OSError:
                pass
            raise
        path = Path(tmp.name)
        return str(path), path

    def _describe_image_for_anthropic_fallback(self, image_url: str, role: str) -> str:
        cache_key = hashlib.sha256(str(image_url or "").encode("utf-8")).hexdigest()
        cached = self._anthropic_image_fallback_cache.get(cache_key)
        if cached:
            return cached

        role_label = {
            "assistant": "assistant",
            "tool": "tool result",
        }.get(role, "user")
        analysis_prompt = (
            "Describe everything visible in this image in thorough detail. "
            "Include any text, code, UI, data, objects, people, layout, colors, "
            "and any other notable visual information."
        )

        vision_source = str(image_url or "")
        cleanup_path: Optional[Path] = None
        if vision_source.startswith("data:"):
            vision_source, cleanup_path = self._materialize_data_url_for_vision(vision_source)

        description = ""
        try:
            from tools.vision_tools import vision_analyze_tool

            result_json = asyncio.run(
                vision_analyze_tool(image_url=vision_source, user_prompt=analysis_prompt)
            )
            result = json.loads(result_json) if isinstance(result_json, str) else {}
            description = (result.get("analysis") or "").strip()
        except Exception as e:
            description = f"Image analysis failed: {e}"
        finally:
            if cleanup_path and cleanup_path.exists():
                try:
                    cleanup_path.unlink()
                except OSError:
                    pass

        if not description:
            description = "Image analysis failed."

        note = f"[The {role_label} attached an image. Here's what it contains:\n{description}]"
        if vision_source and not str(image_url or "").startswith("data:"):
            note += (
                f"\n[If you need a closer look, use vision_analyze with image_url: {vision_source}]"
            )

        self._anthropic_image_fallback_cache[cache_key] = note
        return note

    def _model_supports_vision(self) -> bool:
        """Return True if the active provider+model reports native vision.

        Used to decide whether to strip image content parts from API-bound
        messages (for non-vision models) or let the provider adapter handle
        them natively (for vision-capable models).
        """
        try:
            from agent.models_dev import get_model_capabilities
            provider = (getattr(self, "provider", "") or "").strip()
            model = (getattr(self, "model", "") or "").strip()
            if not provider or not model:
                return False
            caps = get_model_capabilities(provider, model)
            if caps is None:
                return False
            return bool(caps.supports_vision)
        except Exception:
            return False

    def _preprocess_anthropic_content(self, content: Any, role: str) -> Any:
        if not self._content_has_image_parts(content):
            return content

        text_parts: List[str] = []
        image_notes: List[str] = []
        for part in content:
            if isinstance(part, str):
                if part.strip():
                    text_parts.append(part.strip())
                continue
            if not isinstance(part, dict):
                continue

            ptype = part.get("type")
            if ptype in {"text", "input_text"}:
                text = str(part.get("text", "") or "").strip()
                if text:
                    text_parts.append(text)
                continue

            if ptype in {"image_url", "input_image"}:
                image_data = part.get("image_url", {})
                image_url = image_data.get("url", "") if isinstance(image_data, dict) else str(image_data or "")
                if image_url:
                    image_notes.append(self._describe_image_for_anthropic_fallback(image_url, role))
                else:
                    image_notes.append("[An image was attached but no image source was available.]")
                continue

            text = str(part.get("text", "") or "").strip()
            if text:
                text_parts.append(text)

        prefix = "\n\n".join(note for note in image_notes if note).strip()
        suffix = "\n".join(text for text in text_parts if text).strip()
        if prefix and suffix:
            return f"{prefix}\n\n{suffix}"
        if prefix:
            return prefix
        if suffix:
            return suffix
        return "[A multimodal message was converted to text for Anthropic compatibility.]"

    def _get_transport(self, api_mode: str = None):
        """Return the cached transport for the given (or current) api_mode.

        Lazy-initializes on first call per api_mode. Returns None if no
        transport is registered for the mode.
        """
        mode = api_mode or self.api_mode
        cache = getattr(self, "_transport_cache", None)
        if cache is None:
            cache = {}
            self._transport_cache = cache
        t = cache.get(mode)
        if t is None:
            from agent.transports import get_transport
            t = get_transport(mode)
            cache[mode] = t
        return t

    def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list:
        # Fast exit when no message carries image content at all.
        if not any(
            isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
            for msg in api_messages
        ):
            return api_messages

        # The Anthropic adapter (agent/anthropic_adapter.py:_convert_content_part_to_anthropic)
        # already translates OpenAI-style image_url/input_image parts into
        # native Anthropic ``{"type": "image", "source": ...}`` blocks. When
        # the active model supports vision we let the adapter do its job and
        # skip this legacy text-fallback preprocessor entirely.
        if self._model_supports_vision():
            return api_messages

        # Non-vision Anthropic model (rare today, but keep the fallback for
        # compat): replace each image part with a vision_analyze text note.
        transformed = copy.deepcopy(api_messages)
        for msg in transformed:
            if not isinstance(msg, dict):
                continue
            msg["content"] = self._preprocess_anthropic_content(
                msg.get("content"),
                str(msg.get("role", "user") or "user"),
            )
        return transformed

    def _prepare_messages_for_non_vision_model(self, api_messages: list) -> list:
        """Strip native image parts when the active model lacks vision.

        Runs on the chat.completions / codex_responses paths. Vision-capable
        models pass through unchanged (provider and any downstream translator
        handle the image parts natively). Non-vision models get each image
        replaced by a cached vision_analyze text description so the turn
        doesn't fail with "model does not support image input".
        """
        if not any(
            isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
            for msg in api_messages
        ):
            return api_messages

        if self._model_supports_vision():
            return api_messages

        transformed = copy.deepcopy(api_messages)
        for msg in transformed:
            if not isinstance(msg, dict):
                continue
            # Reuse the Anthropic text-fallback preprocessor — the behaviour is
            # identical (walk content parts, replace images with cached
            # descriptions, merge back into a single text or structured
            # content). Naming is historical.
            msg["content"] = self._preprocess_anthropic_content(
                msg.get("content"),
                str(msg.get("role", "user") or "user"),
            )
        return transformed

    def _tool_result_content_for_active_model(self, tool_name: str, result: Any) -> Any:
        """Return the tool message content that is safe for the active model.

        Multimodal tool results normally unwrap to OpenAI-style content parts so
        vision-capable models can inspect screenshots.  Text-only providers must
        not receive those image parts, because a rejected tool result becomes
        part of the canonical history and can make the next user turn fail before
        the agent has a chance to recover.
        """
        if not _is_multimodal_tool_result(result):
            return result

        content = result.get("content") or []
        if not self._content_has_image_parts(content):
            return content

        if self._model_supports_vision():
            return content

        summary = _multimodal_text_summary(result)
        if tool_name == "computer_use":
            return json.dumps({
                "error": (
                    "computer_use returned screenshot/image content, but the active "
                    "model/provider does not support image input. Switch to a "
                    "vision-capable model for desktop computer use, or use browser "
                    "tools for browser tasks."
                ),
                "text_summary": summary,
            })

        logger.warning(
            "Tool %s returned image content for non-vision model %s/%s; "
            "falling back to text summary",
            tool_name,
            self.provider,
            self.model,
        )
        return summary

    def _try_shrink_image_parts_in_messages(self, api_messages: list) -> bool:
        """Forwarder — see ``agent.conversation_compression.try_shrink_image_parts_in_messages``."""
        from agent.conversation_compression import try_shrink_image_parts_in_messages
        return try_shrink_image_parts_in_messages(api_messages)

    def _anthropic_preserve_dots(self) -> bool:
        """True when using an anthropic-compatible endpoint that preserves dots in model names.
        Alibaba/DashScope keeps dots (e.g. qwen3.5-plus).
        MiniMax keeps dots (e.g. MiniMax-M2.7).
        Xiaomi MiMo keeps dots (e.g. mimo-v2.5, mimo-v2.5-pro).
        OpenCode Go/Zen keeps dots for non-Claude models (e.g. minimax-m2.5-free).
        ZAI/Zhipu keeps dots (e.g. glm-4.7, glm-5.1).
        AWS Bedrock uses dotted inference-profile IDs
        (e.g. ``global.anthropic.claude-opus-4-7``,
        ``us.anthropic.claude-sonnet-4-5-20250929-v1:0``) and rejects
        the hyphenated form with
        ``HTTP 400 The provided model identifier is invalid``.
        Regression for #11976; mirrors the opencode-go fix for #5211
        (commit f77be22c), which extended this same allowlist."""
        if (getattr(self, "provider", "") or "").lower() in {
            "alibaba", "minimax", "minimax-cn",
            "opencode-go", "opencode-zen",
            "zai", "bedrock",
            "xiaomi",
        }:
            return True
        base = (getattr(self, "base_url", "") or "").lower()
        return (
            "dashscope" in base
            or "aliyuncs" in base
            or "minimax" in base
            or "opencode.ai/zen/" in base
            or "bigmodel.cn" in base
            or "xiaomimimo.com" in base
            # AWS Bedrock runtime endpoints — defense-in-depth when
            # ``provider`` is unset but ``base_url`` still names Bedrock.
            or "bedrock-runtime." in base
        )

    def _is_qwen_portal(self) -> bool:
        """Return True when the base URL targets Qwen Portal."""
        return base_url_host_matches(self._base_url_lower, "portal.qwen.ai")

    def _qwen_prepare_chat_messages(self, api_messages: list) -> list:
        prepared = copy.deepcopy(api_messages)
        if not prepared:
            return prepared

        for msg in prepared:
            if not isinstance(msg, dict):
                continue
            content = msg.get("content")
            if isinstance(content, str):
                msg["content"] = [{"type": "text", "text": content}]
            elif isinstance(content, list):
                # Normalize: convert bare strings to text dicts, keep dicts as-is.
                # deepcopy already created independent copies, no need for dict().
                normalized_parts = []
                for part in content:
                    if isinstance(part, str):
                        normalized_parts.append({"type": "text", "text": part})
                    elif isinstance(part, dict):
                        normalized_parts.append(part)
                if normalized_parts:
                    msg["content"] = normalized_parts

        # Inject cache_control on the last part of the system message.
        for msg in prepared:
            if isinstance(msg, dict) and msg.get("role") == "system":
                content = msg.get("content")
                if isinstance(content, list) and content and isinstance(content[-1], dict):
                    content[-1]["cache_control"] = {"type": "ephemeral"}
                break

        return prepared

    def _qwen_prepare_chat_messages_inplace(self, messages: list) -> None:
        """In-place variant — mutates an already-copied message list."""
        if not messages:
            return

        for msg in messages:
            if not isinstance(msg, dict):
                continue
            content = msg.get("content")
            if isinstance(content, str):
                msg["content"] = [{"type": "text", "text": content}]
            elif isinstance(content, list):
                normalized_parts = []
                for part in content:
                    if isinstance(part, str):
                        normalized_parts.append({"type": "text", "text": part})
                    elif isinstance(part, dict):
                        normalized_parts.append(part)
                if normalized_parts:
                    msg["content"] = normalized_parts

        for msg in messages:
            if isinstance(msg, dict) and msg.get("role") == "system":
                content = msg.get("content")
                if isinstance(content, list) and content and isinstance(content[-1], dict):
                    content[-1]["cache_control"] = {"type": "ephemeral"}
                break

    def _build_api_kwargs(self, api_messages: list) -> dict:
        """Forwarder — see ``agent.chat_completion_helpers.build_api_kwargs``."""
        from agent.chat_completion_helpers import build_api_kwargs
        return build_api_kwargs(self, api_messages)

    def _supports_reasoning_extra_body(self) -> bool:
        """Return True when reasoning extra_body is safe to send for this route/model.

        OpenRouter forwards unknown extra_body fields to upstream providers.
        Some providers/routes reject `reasoning` with 400s, so gate it to
        known reasoning-capable model families and direct Nous Portal.
        """
        if base_url_host_matches(self._base_url_lower, "nousresearch.com"):
            return True
        if base_url_host_matches(self._base_url_lower, "ai-gateway.vercel.sh"):
            return True
        if (
            base_url_host_matches(self._base_url_lower, "models.github.ai")
            or base_url_host_matches(self._base_url_lower, "api.githubcopilot.com")
        ):
            try:
                from hermes_cli.models import github_model_reasoning_efforts

                return bool(github_model_reasoning_efforts(self.model))
            except Exception:
                return False
        if (self.provider or "").strip().lower() == "lmstudio":
            opts = self._lmstudio_reasoning_options_cached()
            # "off-only" (or absent) means no real reasoning capability.
            return any(opt and opt != "off" for opt in opts)
        if "openrouter" not in self._base_url_lower:
            return False
        if "api.mistral.ai" in self._base_url_lower:
            return False

        model = (self.model or "").lower()
        reasoning_model_prefixes = (
            "deepseek/",
            "anthropic/",
            "openai/",
            "x-ai/",
            "google/gemini-2",
            "google/gemma-4",
            "qwen/qwen3",
            "tencent/hy3-preview",
            "xiaomi/",
        )
        return any(model.startswith(prefix) for prefix in reasoning_model_prefixes)

    def _lmstudio_reasoning_options_cached(self) -> list[str]:
        """Probe LM Studio's published reasoning ``allowed_options`` once per
        (model, base_url). The list (e.g. ``["off","on"]`` or
        ``["off","minimal","low"]``) is needed both for the supports-reasoning
        gate and for clamping the emitted ``reasoning_effort`` so toggle-style
        models don't 400 on ``high``. Cache is keyed on (model, base_url) so
        ``/model`` swaps and base-URL changes don't reuse a stale list.
        Non-empty results are cached permanently (model capabilities don't
        change). Empty results (transient probe failure OR genuinely
        non-reasoning model) are cached with a 60-second TTL to avoid an
        HTTP round-trip on every turn while still retrying reasonably soon.
        """
        import time as _time

        cache = getattr(self, "_lm_reasoning_opts_cache", None)
        if cache is None:
            cache = self._lm_reasoning_opts_cache = {}
        key = (self.model, self.base_url)
        cached = cache.get(key)
        if cached is not None:
            opts, ts = cached
            # Non-empty → permanent. Empty → 60s TTL.
            if opts or (_time.monotonic() - ts) < 60:
                return opts
        try:
            from hermes_cli.models import lmstudio_model_reasoning_options
            opts = lmstudio_model_reasoning_options(
                self.model, self.base_url, getattr(self, "api_key", ""),
            )
        except Exception:
            opts = []
        cache[key] = (opts, _time.monotonic())
        return opts

    def _resolve_lmstudio_summary_reasoning_effort(self) -> Optional[str]:
        """Resolve a safe top-level ``reasoning_effort`` for LM Studio.

        The iteration-limit summary path calls ``chat.completions.create()``
        directly, bypassing the transport. Share the helper so the two paths
        can't drift on effort resolution and clamping.
        """
        from agent.lmstudio_reasoning import resolve_lmstudio_effort
        return resolve_lmstudio_effort(
            self.reasoning_config,
            self._lmstudio_reasoning_options_cached(),
        )

    def _github_models_reasoning_extra_body(self) -> dict | None:
        """Format reasoning payload for GitHub Models/OpenAI-compatible routes."""
        try:
            from hermes_cli.models import github_model_reasoning_efforts
        except Exception:
            return None

        supported_efforts = github_model_reasoning_efforts(self.model)
        if not supported_efforts:
            return None

        if self.reasoning_config and isinstance(self.reasoning_config, dict):
            if self.reasoning_config.get("enabled") is False:
                return None
            requested_effort = str(
                self.reasoning_config.get("effort", "medium")
            ).strip().lower()
        else:
            requested_effort = "medium"

        if requested_effort == "xhigh" and "high" in supported_efforts:
            requested_effort = "high"
        elif requested_effort not in supported_efforts:
            if requested_effort == "minimal" and "low" in supported_efforts:
                requested_effort = "low"
            elif "medium" in supported_efforts:
                requested_effort = "medium"
            else:
                requested_effort = supported_efforts[0]

        return {"effort": requested_effort}

    def _build_assistant_message(self, assistant_message, finish_reason: str) -> dict:
        """Forwarder — see ``agent.chat_completion_helpers.build_assistant_message``."""
        from agent.chat_completion_helpers import build_assistant_message
        return build_assistant_message(self, assistant_message, finish_reason)

    def _needs_thinking_reasoning_pad(self) -> bool:
        """Return True when the active provider enforces reasoning_content echo-back.

        DeepSeek v4 thinking and Kimi / Moonshot thinking both reject replays
        of assistant tool-call messages that omit ``reasoning_content`` (refs
        #15250, #17400). Xiaomi MiMo thinking mode has the same requirement.

        Result cached on the AIAgent instance keyed by (provider, model,
        base_url); invalidated whenever ``switch_model()`` /
        ``_try_activate_fallback()`` mutate any of those. This is hot — the
        agent loop hits ~16 invocations per turn, each of which would
        otherwise re-run ~5 ``base_url_host_matches`` (and therefore
        ``urlparse``) calls under it. Caching drops the per-turn cost from
        ~5us × 16 = ~80us to <1us.
        """
        key = (self.provider, self.model, getattr(self, "_base_url_lower", self.base_url))
        cached = getattr(self, "_thinking_pad_cache", None)
        if cached is not None and cached[0] == key:
            return cached[1]
        result = (
            self._needs_deepseek_tool_reasoning()
            or self._needs_kimi_tool_reasoning()
            or self._needs_mimo_tool_reasoning()
        )
        self._thinking_pad_cache = (key, result)
        return result

    def _needs_kimi_tool_reasoning(self) -> bool:
        """Return True when the current provider is Kimi / Moonshot thinking mode.

        Kimi ``/coding`` and Moonshot thinking mode both require
        ``reasoning_content`` on every assistant tool-call message; omitting
        it causes the next replay to fail with HTTP 400.

        Detection is host-driven, not model-name-driven: aggregators like
        OpenRouter that re-export Kimi/Moonshot models speak their own
        protocol and reject ``reasoning_content`` echoes. We only enable the
        kimi-reasoning replay when the request actually targets a
        kimi/moonshot endpoint or the dedicated kimi-coding provider.
        """
        return (
            self.provider in {"kimi-coding", "kimi-coding-cn"}
            or base_url_host_matches(self.base_url, "api.kimi.com")
            or base_url_host_matches(self.base_url, "moonshot.ai")
            or base_url_host_matches(self.base_url, "moonshot.cn")
        )

    def _needs_deepseek_tool_reasoning(self) -> bool:
        """Return True when the current provider is DeepSeek thinking mode.

        DeepSeek V4 thinking mode requires ``reasoning_content`` on every
        assistant tool-call turn; omitting it causes HTTP 400 when the
        message is replayed in a subsequent API request (#15250).
        """
        provider = (self.provider or "").lower()
        model = (self.model or "").lower()
        return (
            provider == "deepseek"
            or "deepseek" in model
            or base_url_host_matches(self.base_url, "api.deepseek.com")
        )

    def _needs_mimo_tool_reasoning(self) -> bool:
        """Return True when the current provider is Xiaomi MiMo thinking mode.

        MiMo thinking mode requires ``reasoning_content`` on every assistant
        tool-call message when replaying history; omitting it causes HTTP 400.
        Refs: https://platform.xiaomimimo.com/docs/zh-CN/usage-guide/passing-back-reasoning_content
        """
        provider = (self.provider or "").lower()
        model = (self.model or "").lower()
        return (
            provider == "xiaomi"
            or "mimo" in model
            or base_url_host_matches(self.base_url, "api.xiaomimimo.com")
            or base_url_host_matches(self.base_url, "xiaomimimo.com")
        )

    def _copy_reasoning_content_for_api(self, source_msg: dict, api_msg: dict) -> None:
        """Forwarder — see ``agent.agent_runtime_helpers.copy_reasoning_content_for_api``."""
        from agent.agent_runtime_helpers import copy_reasoning_content_for_api
        return copy_reasoning_content_for_api(self, source_msg, api_msg)

    @staticmethod
    def _sanitize_tool_calls_for_strict_api(api_msg: dict) -> dict:
        """Strip Codex Responses API fields from tool_calls for strict providers.

        Providers like Mistral, Fireworks, and other strict OpenAI-compatible APIs
        validate the Chat Completions schema and reject unknown fields (call_id,
        response_item_id) with 400 or 422 errors. These fields are preserved in
        the internal message history — this method only modifies the outgoing
        API copy.

        Creates new tool_call dicts rather than mutating in-place, so the
        original messages list retains call_id/response_item_id for Codex
        Responses API compatibility (e.g. if the session falls back to a
        Codex provider later).

        Fields stripped: call_id, response_item_id
        """
        tool_calls = api_msg.get("tool_calls")
        if not isinstance(tool_calls, list):
            return api_msg
        _STRIP_KEYS = {"call_id", "response_item_id"}
        api_msg["tool_calls"] = [
            {k: v for k, v in tc.items() if k not in _STRIP_KEYS}
            if isinstance(tc, dict) else tc
            for tc in tool_calls
        ]
        return api_msg

    @staticmethod
    def _sanitize_tool_call_arguments(
        messages: list,
        *,
        logger=None,
        session_id: str = None,
    ) -> int:
        """Forwarder — see ``agent.agent_runtime_helpers.sanitize_tool_call_arguments``."""
        from agent.agent_runtime_helpers import sanitize_tool_call_arguments
        return sanitize_tool_call_arguments(messages, logger=logger, session_id=session_id)

    def _should_sanitize_tool_calls(self) -> bool:
        """Determine if tool_calls need sanitization for strict APIs.

        Codex Responses API uses fields like call_id and response_item_id
        that are not part of the standard Chat Completions schema. These
        fields must be stripped when calling any other API to avoid
        validation errors (400 Bad Request).

        Returns:
            bool: True if sanitization is needed (non-Codex API), False otherwise.
        """
        return self.api_mode != "codex_responses"

    def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None, task_id: str = "default", focus_topic: str = None, force: bool = False) -> tuple:
        """Forwarder — see ``agent.conversation_compression.compress_context``.

        ``force=True`` is passed by the manual ``/compress`` slash command
        so users can bypass the summary-failure cooldown after an
        auto-compress abort.  Auto-compress callers use the default
        ``force=False``.
        """
        from agent.conversation_compression import compress_context
        return compress_context(
            self, messages, system_message,
            approx_tokens=approx_tokens, task_id=task_id, focus_topic=focus_topic,
            force=force,
        )

    def _set_tool_guardrail_halt(self, decision: ToolGuardrailDecision) -> None:
        """Record the first guardrail decision that should stop this turn."""
        if decision.should_halt and self._tool_guardrail_halt_decision is None:
            self._tool_guardrail_halt_decision = decision

    def _toolguard_controlled_halt_response(self, decision: ToolGuardrailDecision) -> str:
        tool = decision.tool_name or "a tool"
        return (
            f"I stopped retrying {tool} because it hit the tool-call guardrail "
            f"({decision.code}) after {decision.count} repeated non-progressing "
            "attempts. The last tool result explains the blocker; the next step is "
            "to change strategy instead of repeating the same call."
        )

    def _append_guardrail_observation(
        self,
        tool_name: str,
        function_args: dict,
        function_result: str,
        *,
        failed: bool,
    ) -> str:
        decision = self._tool_guardrails.after_call(
            tool_name,
            function_args,
            function_result,
            failed=failed,
        )
        if decision.action in {"warn", "halt"}:
            function_result = append_toolguard_guidance(function_result, decision)
        if decision.should_halt:
            self._set_tool_guardrail_halt(decision)
        return function_result

    def _guardrail_block_result(self, decision: ToolGuardrailDecision) -> str:
        self._set_tool_guardrail_halt(decision)
        return toolguard_synthetic_result(decision)

    def _execute_tool_calls(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
        """Execute tool calls from the assistant message and append results to messages.

        Dispatches to concurrent execution only for batches that look
        independent: read-only tools may always share the parallel path, while
        file reads/writes may do so only when their target paths do not overlap.
        """
        tool_calls = assistant_message.tool_calls

        # Allow _vprint during tool execution even with stream consumers
        self._executing_tools = True
        try:
            if not _should_parallelize_tool_batch(tool_calls):
                return self._execute_tool_calls_sequential(
                    assistant_message, messages, effective_task_id, api_call_count
                )

            return self._execute_tool_calls_concurrent(
                assistant_message, messages, effective_task_id, api_call_count
            )
        finally:
            self._executing_tools = False

    def _dispatch_delegate_task(self, function_args: dict) -> str:
        """Single call site for delegate_task dispatch.

        New DELEGATE_TASK_SCHEMA fields only need to be added here to reach all
        invocation paths (concurrent, sequential, inline).
        """
        from tools.delegate_tool import delegate_task as _delegate_task
        return _delegate_task(
            goal=function_args.get("goal"),
            context=function_args.get("context"),
            toolsets=function_args.get("toolsets"),
            tasks=function_args.get("tasks"),
            max_iterations=function_args.get("max_iterations"),
            acp_command=function_args.get("acp_command"),
            acp_args=function_args.get("acp_args"),
            role=function_args.get("role"),
            parent_agent=self,
        )

    def _invoke_tool(self, function_name: str, function_args: dict, effective_task_id: str,
                     tool_call_id: Optional[str] = None, messages: list = None,
                     pre_tool_block_checked: bool = False) -> str:
        """Forwarder — see ``agent.agent_runtime_helpers.invoke_tool``."""
        from agent.agent_runtime_helpers import invoke_tool
        return invoke_tool(self, function_name, function_args, effective_task_id, tool_call_id, messages, pre_tool_block_checked)

    @staticmethod
    def _wrap_verbose(label: str, text: str, indent: str = "     ") -> str:
        """Word-wrap verbose tool output to fit the terminal width.

        Splits *text* on existing newlines and wraps each line individually,
        preserving intentional line breaks (e.g. pretty-printed JSON).
        Returns a ready-to-print string with *label* on the first line and
        continuation lines indented.
        """
        import shutil as _shutil
        import textwrap as _tw
        cols = _shutil.get_terminal_size((120, 24)).columns
        wrap_width = max(40, cols - len(indent))
        out_lines: list[str] = []
        for raw_line in text.split("\n"):
            if len(raw_line) <= wrap_width:
                out_lines.append(raw_line)
            else:
                wrapped = _tw.wrap(raw_line, width=wrap_width,
                                   break_long_words=True,
                                   break_on_hyphens=False)
                out_lines.extend(wrapped or [raw_line])
        body = ("\n" + indent).join(out_lines)
        return f"{indent}{label}{body}"

    def _execute_tool_calls_concurrent(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
        """Forwarder — see ``agent.tool_executor.execute_tool_calls_concurrent``."""
        from agent.tool_executor import execute_tool_calls_concurrent
        return execute_tool_calls_concurrent(self, assistant_message, messages, effective_task_id, api_call_count)

    def _execute_tool_calls_sequential(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
        """Forwarder — see ``agent.tool_executor.execute_tool_calls_sequential``."""
        from agent.tool_executor import execute_tool_calls_sequential
        return execute_tool_calls_sequential(self, assistant_message, messages, effective_task_id, api_call_count)

    def _handle_max_iterations(self, messages: list, api_call_count: int) -> str:
        """Forwarder — see ``agent.chat_completion_helpers.handle_max_iterations``."""
        from agent.chat_completion_helpers import handle_max_iterations
        return handle_max_iterations(self, messages, api_call_count)

    def run_conversation(
        self,
        user_message: str,
        system_message: str = None,
        conversation_history: List[Dict[str, Any]] = None,
        task_id: str = None,
        stream_callback: Optional[callable] = None,
        persist_user_message: Optional[str] = None,
    ) -> Dict[str, Any]:
        """Forwarder — see ``agent.conversation_loop.run_conversation``."""
        from agent.conversation_loop import run_conversation
        return run_conversation(self, user_message, system_message, conversation_history, task_id, stream_callback, persist_user_message)

    def chat(self, message: str, stream_callback: Optional[callable] = None) -> str:
        """
        Simple chat interface that returns just the final response.

        Args:
            message (str): User message
            stream_callback: Optional callback invoked with each text delta during streaming.

        Returns:
            str: Final assistant response
        """
        result = self.run_conversation(message, stream_callback=stream_callback)
        return result["final_response"]

    def _run_codex_app_server_turn(
        self,
        *,
        user_message: str,
        original_user_message: Any,
        messages: List[Dict[str, Any]],
        effective_task_id: str,
        should_review_memory: bool = False,
    ) -> Dict[str, Any]:
        """Forwarder — see ``agent.codex_runtime.run_codex_app_server_turn``."""
        from agent.codex_runtime import run_codex_app_server_turn
        return run_codex_app_server_turn(self, user_message=user_message, original_user_message=original_user_message, messages=messages, effective_task_id=effective_task_id, should_review_memory=should_review_memory)

def main(
    query: str = None,
    model: str = "",
    api_key: str = None,
    base_url: str = "",
    max_turns: int = 10,
    enabled_toolsets: str = None,
    disabled_toolsets: str = None,
    list_tools: bool = False,
    save_trajectories: bool = False,
    save_sample: bool = False,
    verbose: bool = False,
    log_prefix_chars: int = 20
):
    """
    Main function for running the agent directly.

    Args:
        query (str): Natural language query for the agent. Defaults to Python 3.13 example.
        model (str): Model name to use (OpenRouter format: provider/model). Defaults to anthropic/claude-sonnet-4.6.
        api_key (str): API key for authentication. Uses OPENROUTER_API_KEY env var if not provided.
        base_url (str): Base URL for the model API. Defaults to https://openrouter.ai/api/v1
        max_turns (int): Maximum number of API call iterations. Defaults to 10.
        enabled_toolsets (str): Comma-separated list of toolsets to enable. Supports predefined
                              toolsets (e.g., "research", "development", "safe").
                              Multiple toolsets can be combined: "web,vision"
        disabled_toolsets (str): Comma-separated list of toolsets to disable (e.g., "terminal")
        list_tools (bool): Just list available tools and exit
        save_trajectories (bool): Save conversation trajectories to JSONL files (appends to trajectory_samples.jsonl). Defaults to False.
        save_sample (bool): Save a single trajectory sample to a UUID-named JSONL file for inspection. Defaults to False.
        verbose (bool): Enable verbose logging for debugging. Defaults to False.
        log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses. Defaults to 20.

    Toolset Examples:
        - "research": Web search, extract, crawl + vision tools
    """
    print("🤖 AI Agent with Tool Calling")
    print("=" * 50)
    
    # Handle tool listing
    if list_tools:
        from model_tools import get_all_tool_names, get_available_toolsets
        from toolsets import get_all_toolsets, get_toolset_info
        
        print("📋 Available Tools & Toolsets:")
        print("-" * 50)
        
        # Show new toolsets system
        print("\n🎯 Predefined Toolsets (New System):")
        print("-" * 40)
        all_toolsets = get_all_toolsets()
        
        # Group by category
        basic_toolsets = []
        composite_toolsets = []
        scenario_toolsets = []
        
        for name, toolset in all_toolsets.items():
            info = get_toolset_info(name)
            if info:
                entry = (name, info)
                if name in {"web", "terminal", "vision", "creative", "reasoning"}:
                    basic_toolsets.append(entry)
                elif name in {"research", "development", "analysis", "content_creation", "full_stack"}:
                    composite_toolsets.append(entry)
                else:
                    scenario_toolsets.append(entry)
        
        # Print basic toolsets
        print("\n📌 Basic Toolsets:")
        for name, info in basic_toolsets:
            tools_str = ', '.join(info['resolved_tools']) if info['resolved_tools'] else 'none'
            print(f"  • {name:15} - {info['description']}")
            print(f"    Tools: {tools_str}")
        
        # Print composite toolsets
        print("\n📂 Composite Toolsets (built from other toolsets):")
        for name, info in composite_toolsets:
            includes_str = ', '.join(info['includes']) if info['includes'] else 'none'
            print(f"  • {name:15} - {info['description']}")
            print(f"    Includes: {includes_str}")
            print(f"    Total tools: {info['tool_count']}")
        
        # Print scenario-specific toolsets
        print("\n🎭 Scenario-Specific Toolsets:")
        for name, info in scenario_toolsets:
            print(f"  • {name:20} - {info['description']}")
            print(f"    Total tools: {info['tool_count']}")
        
        
        # Show legacy toolset compatibility
        print("\n📦 Legacy Toolsets (for backward compatibility):")
        legacy_toolsets = get_available_toolsets()
        for name, info in legacy_toolsets.items():
            status = "✅" if info["available"] else "❌"
            print(f"  {status} {name}: {info['description']}")
            if not info["available"]:
                print(f"    Requirements: {', '.join(info['requirements'])}")
        
        # Show individual tools
        all_tools = get_all_tool_names()
        print(f"\n🔧 Individual Tools ({len(all_tools)} available):")
        for tool_name in sorted(all_tools):
            toolset = get_toolset_for_tool(tool_name)
            print(f"  📌 {tool_name} (from {toolset})")
        
        print("\n💡 Usage Examples:")
        print("  # Use predefined toolsets")
        print("  python run_agent.py --enabled_toolsets=research --query='search for Python news'")
        print("  python run_agent.py --enabled_toolsets=development --query='debug this code'")
        print("  python run_agent.py --enabled_toolsets=safe --query='analyze without terminal'")
        print("  ")
        print("  # Combine multiple toolsets")
        print("  python run_agent.py --enabled_toolsets=web,vision --query='analyze website'")
        print("  ")
        print("  # Disable toolsets")
        print("  python run_agent.py --disabled_toolsets=terminal --query='no command execution'")
        print("  ")
        print("  # Run with trajectory saving enabled")
        print("  python run_agent.py --save_trajectories --query='your question here'")
        return
    
    # Parse toolset selection arguments
    enabled_toolsets_list = None
    disabled_toolsets_list = None
    
    if enabled_toolsets:
        enabled_toolsets_list = [t.strip() for t in enabled_toolsets.split(",")]
        print(f"🎯 Enabled toolsets: {enabled_toolsets_list}")
    
    if disabled_toolsets:
        disabled_toolsets_list = [t.strip() for t in disabled_toolsets.split(",")]
        print(f"🚫 Disabled toolsets: {disabled_toolsets_list}")
    
    if save_trajectories:
        print("💾 Trajectory saving: ENABLED")
        print("   - Successful conversations → trajectory_samples.jsonl")
        print("   - Failed conversations → failed_trajectories.jsonl")
    
    # Initialize agent with provided parameters
    try:
        agent = AIAgent(
            base_url=base_url,
            model=model,
            api_key=api_key,
            max_iterations=max_turns,
            enabled_toolsets=enabled_toolsets_list,
            disabled_toolsets=disabled_toolsets_list,
            save_trajectories=save_trajectories,
            verbose_logging=verbose,
            log_prefix_chars=log_prefix_chars
        )
    except RuntimeError as e:
        print(f"❌ Failed to initialize agent: {e}")
        return
    
    # Use provided query or default to Python 3.13 example
    if query is None:
        user_query = (
            "Tell me about the latest developments in Python 3.13 and what new features "
            "developers should know about. Please search for current information and try it out."
        )
    else:
        user_query = query
    
    print(f"\n📝 User Query: {user_query}")
    print("\n" + "=" * 50)
    
    # Run conversation
    result = agent.run_conversation(user_query)
    
    print("\n" + "=" * 50)
    print("📋 CONVERSATION SUMMARY")
    print("=" * 50)
    print(f"✅ Completed: {result['completed']}")
    print(f"📞 API Calls: {result['api_calls']}")
    print(f"💬 Messages: {len(result['messages'])}")
    
    if result['final_response']:
        print("\n🎯 FINAL RESPONSE:")
        print("-" * 30)
        print(result['final_response'])
    
    # Save sample trajectory to UUID-named file if requested
    if save_sample:
        sample_id = str(uuid.uuid4())[:8]
        sample_filename = f"sample_{sample_id}.json"
        
        # Convert messages to trajectory format (same as batch_runner)
        trajectory = agent._convert_to_trajectory_format(
            result['messages'], 
            user_query, 
            result['completed']
        )
        
        entry = {
            "conversations": trajectory,
            "timestamp": datetime.now().isoformat(),
            "model": model,
            "completed": result['completed'],
            "query": user_query
        }
        
        try:
            with open(sample_filename, "w", encoding="utf-8") as f:
                # Pretty-print JSON with indent for readability
                f.write(json.dumps(entry, ensure_ascii=False, indent=2))
            print(f"\n💾 Sample trajectory saved to: {sample_filename}")
        except Exception as e:
            print(f"\n⚠️ Failed to save sample: {e}")
    
    print("\n👋 Agent execution completed!")


if __name__ == "__main__":
    import fire
    fire.Fire(main)