hermes-agent/agent/auxiliary_client.py

"""Shared auxiliary client router for side tasks.

Provides a single resolution chain so every consumer (context compression,
session search, web extraction, vision analysis, browser vision) picks up
the best available backend without duplicating fallback logic.

Resolution order for text tasks (auto mode):
  1. OpenRouter  (OPENROUTER_API_KEY)
  2. Nous Portal (~/.hermes/auth.json active provider)
  3. Custom endpoint (config.yaml model.base_url + OPENAI_API_KEY)
  4. Codex OAuth (Responses API via chatgpt.com with gpt-5.3-codex,
     wrapped to look like a chat.completions client)
  5. Native Anthropic
  6. Direct API-key providers (z.ai/GLM, Kimi/Moonshot, MiniMax, MiniMax-CN)
  7. None

Resolution order for vision/multimodal tasks (auto mode):
  1. Selected main provider, if it is one of the supported vision backends below
  2. OpenRouter
  3. Nous Portal
  4. Codex OAuth (gpt-5.3-codex supports vision via Responses API)
  5. Native Anthropic
  6. Custom endpoint (for local vision models: Qwen-VL, LLaVA, Pixtral, etc.)
  7. None

Per-task overrides are configured in config.yaml under the ``auxiliary:`` section
(e.g. ``auxiliary.vision.provider``, ``auxiliary.compression.model``).
Default "auto" follows the chains above.

Payment / credit exhaustion fallback:
  When a resolved provider returns HTTP 402 or a credit-related error,
  call_llm() automatically retries with the next available provider in the
  auto-detection chain.  This handles the common case where a user depletes
  their OpenRouter balance but has Codex OAuth or another provider available.
"""

import json
import logging
import os
import threading
import time
from pathlib import Path  # noqa: F401 — used by test mocks
from types import SimpleNamespace
from typing import Any, Dict, List, Optional, Tuple
from urllib.parse import urlparse, parse_qs, urlunparse

from openai import OpenAI

from agent.credential_pool import load_pool
from hermes_cli.config import get_hermes_home
from hermes_constants import OPENROUTER_BASE_URL
from utils import base_url_host_matches, base_url_hostname, normalize_proxy_env_vars

logger = logging.getLogger(__name__)


def _extract_url_query_params(url: str):
    """Extract query params from URL, return (clean_url, default_query dict or None)."""
    parsed = urlparse(url)
    if parsed.query:
        clean = urlunparse(parsed._replace(query=""))
        params = {k: v[0] for k, v in parse_qs(parsed.query).items()}
        return clean, params
    return url, None


# Module-level flag: only warn once per process about stale OPENAI_BASE_URL.
_stale_base_url_warned = False

_PROVIDER_ALIASES = {
    "google": "gemini",
    "google-gemini": "gemini",
    "google-ai-studio": "gemini",
    "x-ai": "xai",
    "x.ai": "xai",
    "grok": "xai",
    "glm": "zai",
    "z-ai": "zai",
    "z.ai": "zai",
    "zhipu": "zai",
    "kimi": "kimi-coding",
    "moonshot": "kimi-coding",
    "kimi-cn": "kimi-coding-cn",
    "moonshot-cn": "kimi-coding-cn",
    "gmi-cloud": "gmi",
    "gmicloud": "gmi",
    "minimax-china": "minimax-cn",
    "minimax_cn": "minimax-cn",
    "claude": "anthropic",
    "claude-code": "anthropic",
    "github": "copilot",
    "github-copilot": "copilot",
    "github-model": "copilot",
    "github-models": "copilot",
    "github-copilot-acp": "copilot-acp",
    "copilot-acp-agent": "copilot-acp",
}


def _normalize_aux_provider(provider: Optional[str]) -> str:
    normalized = (provider or "auto").strip().lower()
    if normalized.startswith("custom:"):
        suffix = normalized.split(":", 1)[1].strip()
        if not suffix:
            return "custom"
        normalized = suffix
    if normalized == "codex":
        return "openai-codex"
    if normalized == "main":
        # Resolve to the user's actual main provider so named custom providers
        # and non-aggregator providers (DeepSeek, Alibaba, etc.) work correctly.
        main_prov = (_read_main_provider() or "").strip().lower()
        if main_prov and main_prov not in ("auto", "main", ""):
            normalized = main_prov
        else:
            return "custom"
    return _PROVIDER_ALIASES.get(normalized, normalized)


# Sentinel: when returned by _fixed_temperature_for_model(), callers must
# strip the ``temperature`` key from API kwargs entirely so the provider's
# server-side default applies.  Kimi/Moonshot models manage temperature
# internally — sending *any* value (even the "correct" one) can conflict
# with gateway-side mode selection (thinking → 1.0, non-thinking → 0.6).
OMIT_TEMPERATURE: object = object()


def _is_kimi_model(model: Optional[str]) -> bool:
    """True for any Kimi / Moonshot model that manages temperature server-side."""
    bare = (model or "").strip().lower().rsplit("/", 1)[-1]
    return bare.startswith("kimi-") or bare == "kimi"


def _fixed_temperature_for_model(
    model: Optional[str],
    base_url: Optional[str] = None,
) -> "Optional[float] | object":
    """Return a temperature directive for models with strict contracts.

    Returns:
        ``OMIT_TEMPERATURE`` — caller must remove the ``temperature`` key so the
            provider chooses its own default.  Used for all Kimi / Moonshot
            models whose gateway selects temperature server-side.
        ``float`` — a specific value the caller must use (reserved for future
            models with fixed-temperature contracts).
        ``None`` — no override; caller should use its own default.
    """
    if _is_kimi_model(model):
        logger.debug("Omitting temperature for Kimi model %r (server-managed)", model)
        return OMIT_TEMPERATURE
    return None

# Default auxiliary models for direct API-key providers (cheap/fast for side tasks)
_API_KEY_PROVIDER_AUX_MODELS: Dict[str, str] = {
    "gemini": "gemini-3-flash-preview",
    "zai": "glm-4.5-flash",
    "kimi-coding": "kimi-k2-turbo-preview",
    "stepfun": "step-3.5-flash",
    "kimi-coding-cn": "kimi-k2-turbo-preview",
    "gmi": "google/gemini-3.1-flash-lite-preview",
    "minimax": "MiniMax-M2.7",
    "minimax-cn": "MiniMax-M2.7",
    "anthropic": "claude-haiku-4-5-20251001",
    "ai-gateway": "google/gemini-3-flash",
    "opencode-zen": "gemini-3-flash",
    "opencode-go": "glm-5",
    "kilocode": "google/gemini-3-flash-preview",
    "ollama-cloud": "nemotron-3-nano:30b",
}

# Vision-specific model overrides for direct providers.
# When the user's main provider has a dedicated vision/multimodal model that
# differs from their main chat model, map it here.  The vision auto-detect
# "exotic provider" branch checks this before falling back to the main model.
_PROVIDER_VISION_MODELS: Dict[str, str] = {
    "xiaomi": "mimo-v2.5",
    "zai": "glm-5v-turbo",
}

# OpenRouter app attribution headers
_OR_HEADERS = {
    "HTTP-Referer": "https://hermes-agent.nousresearch.com",
    "X-OpenRouter-Title": "Hermes Agent",
    "X-OpenRouter-Categories": "productivity,cli-agent",
}

# Vercel AI Gateway app attribution headers. HTTP-Referer maps to
# referrerUrl and X-Title maps to appName in the gateway's analytics.
from hermes_cli import __version__ as _HERMES_VERSION

_AI_GATEWAY_HEADERS = {
    "HTTP-Referer": "https://hermes-agent.nousresearch.com",
    "X-Title": "Hermes Agent",
    "User-Agent": f"HermesAgent/{_HERMES_VERSION}",
}

# Nous Portal extra_body for product attribution.
# Callers should pass this as extra_body in chat.completions.create()
# when the auxiliary client is backed by Nous Portal.
NOUS_EXTRA_BODY = {"tags": ["product=hermes-agent"]}

# Set at resolve time — True if the auxiliary client points to Nous Portal
auxiliary_is_nous: bool = False

# Default auxiliary models per provider
_OPENROUTER_MODEL = "google/gemini-3-flash-preview"
_NOUS_MODEL = "google/gemini-3-flash-preview"
_NOUS_DEFAULT_BASE_URL = "https://inference-api.nousresearch.com/v1"
_ANTHROPIC_DEFAULT_BASE_URL = "https://api.anthropic.com"
_AUTH_JSON_PATH = get_hermes_home() / "auth.json"

# Codex fallback: uses the Responses API (the only endpoint the Codex
# OAuth token can access) with a fast model for auxiliary tasks.
# ChatGPT-backed Codex accounts currently reject gpt-5.3-codex for these
# auxiliary flows, while gpt-5.2-codex remains broadly available and supports
# vision via Responses.
_CODEX_AUX_MODEL = "gpt-5.2-codex"
_CODEX_AUX_BASE_URL = "https://chatgpt.com/backend-api/codex"


def _codex_cloudflare_headers(access_token: str) -> Dict[str, str]:
    """Headers required to avoid Cloudflare 403s on chatgpt.com/backend-api/codex.

    The Cloudflare layer in front of the Codex endpoint whitelists a small set of
    first-party originators (``codex_cli_rs``, ``codex_vscode``, ``codex_sdk_ts``,
    anything starting with ``Codex``). Requests from non-residential IPs (VPS,
    server-hosted agents) that don't advertise an allowed originator are served
    a 403 with ``cf-mitigated: challenge`` regardless of auth correctness.

    We pin ``originator: codex_cli_rs`` to match the upstream codex-rs CLI, set
    ``User-Agent`` to a codex_cli_rs-shaped string (beats SDK fingerprinting),
    and extract ``ChatGPT-Account-ID`` (canonical casing, from codex-rs
    ``auth.rs``) out of the OAuth JWT's ``chatgpt_account_id`` claim.

    Malformed tokens are tolerated — we drop the account-ID header rather than
    raise, so a bad token still surfaces as an auth error (401) instead of a
    crash at client construction.
    """
    headers = {
        "User-Agent": "codex_cli_rs/0.0.0 (Hermes Agent)",
        "originator": "codex_cli_rs",
    }
    if not isinstance(access_token, str) or not access_token.strip():
        return headers
    try:
        import base64
        parts = access_token.split(".")
        if len(parts) < 2:
            return headers
        payload_b64 = parts[1] + "=" * (-len(parts[1]) % 4)
        claims = json.loads(base64.urlsafe_b64decode(payload_b64))
        acct_id = claims.get("https://api.openai.com/auth", {}).get("chatgpt_account_id")
        if isinstance(acct_id, str) and acct_id:
            headers["ChatGPT-Account-ID"] = acct_id
    except Exception:
        pass
    return headers


def _to_openai_base_url(base_url: str) -> str:
    """Normalize an Anthropic-style base URL to OpenAI-compatible format.

    Some providers (MiniMax, MiniMax-CN) expose an ``/anthropic`` endpoint for
    the Anthropic Messages API and a separate ``/v1`` endpoint for OpenAI chat
    completions.  The auxiliary client uses the OpenAI SDK, so it must hit the
    ``/v1`` surface.  Passing the raw ``inference_base_url`` causes requests to
    land on ``/anthropic/chat/completions`` — a 404.
    """
    url = str(base_url or "").strip().rstrip("/")
    if url.endswith("/anthropic"):
        rewritten = url[: -len("/anthropic")] + "/v1"
        logger.debug("Auxiliary client: rewrote base URL %s → %s", url, rewritten)
        return rewritten
    return url


def _select_pool_entry(provider: str) -> Tuple[bool, Optional[Any]]:
    """Return (pool_exists_for_provider, selected_entry)."""
    try:
        pool = load_pool(provider)
    except Exception as exc:
        logger.debug("Auxiliary client: could not load pool for %s: %s", provider, exc)
        return False, None
    if not pool or not pool.has_credentials():
        return False, None
    try:
        return True, pool.select()
    except Exception as exc:
        logger.debug("Auxiliary client: could not select pool entry for %s: %s", provider, exc)
        return True, None


def _pool_runtime_api_key(entry: Any) -> str:
    if entry is None:
        return ""
    # Use the PooledCredential.runtime_api_key property which handles
    # provider-specific fallback (e.g. agent_key for nous).
    key = getattr(entry, "runtime_api_key", None) or getattr(entry, "access_token", "")
    return str(key or "").strip()


def _pool_runtime_base_url(entry: Any, fallback: str = "") -> str:
    if entry is None:
        return str(fallback or "").strip().rstrip("/")
    # runtime_base_url handles provider-specific logic (e.g. nous prefers inference_base_url).
    # Fall back through inference_base_url and base_url for non-PooledCredential entries.
    url = (
        getattr(entry, "runtime_base_url", None)
        or getattr(entry, "inference_base_url", None)
        or getattr(entry, "base_url", None)
        or fallback
    )
    return str(url or "").strip().rstrip("/")


# ── Codex Responses → chat.completions adapter ─────────────────────────────
# All auxiliary consumers call client.chat.completions.create(**kwargs) and
# read response.choices[0].message.content. This adapter translates those
# calls to the Codex Responses API so callers don't need any changes.


def _convert_content_for_responses(content: Any) -> Any:
    """Convert chat.completions content to Responses API format.

    chat.completions uses:
      {"type": "text", "text": "..."}
      {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}

    Responses API uses:
      {"type": "input_text", "text": "..."}
      {"type": "input_image", "image_url": "data:image/png;base64,..."}

    If content is a plain string, it's returned as-is (the Responses API
    accepts strings directly for text-only messages).
    """
    if isinstance(content, str):
        return content
    if not isinstance(content, list):
        return str(content) if content else ""

    converted: List[Dict[str, Any]] = []
    for part in content:
        if not isinstance(part, dict):
            continue
        ptype = part.get("type", "")
        if ptype == "text":
            converted.append({"type": "input_text", "text": part.get("text", "")})
        elif ptype == "image_url":
            # chat.completions nests the URL: {"image_url": {"url": "..."}}
            image_data = part.get("image_url", {})
            url = image_data.get("url", "") if isinstance(image_data, dict) else str(image_data)
            entry: Dict[str, Any] = {"type": "input_image", "image_url": url}
            # Preserve detail if specified
            detail = image_data.get("detail") if isinstance(image_data, dict) else None
            if detail:
                entry["detail"] = detail
            converted.append(entry)
        elif ptype in ("input_text", "input_image"):
            # Already in Responses format — pass through
            converted.append(part)
        else:
            # Unknown content type — try to preserve as text
            text = part.get("text", "")
            if text:
                converted.append({"type": "input_text", "text": text})

    return converted or ""


class _CodexCompletionsAdapter:
    """Drop-in shim that accepts chat.completions.create() kwargs and
    routes them through the Codex Responses streaming API."""

    def __init__(self, real_client: OpenAI, model: str):
        self._client = real_client
        self._model = model

    def create(self, **kwargs) -> Any:
        messages = kwargs.get("messages", [])
        model = kwargs.get("model", self._model)

        # Separate system/instructions from conversation messages.
        # Convert chat.completions multimodal content blocks to Responses
        # API format (input_text / input_image instead of text / image_url).
        instructions = "You are a helpful assistant."
        input_msgs: List[Dict[str, Any]] = []
        for msg in messages:
            role = msg.get("role", "user")
            content = msg.get("content") or ""
            if role == "system":
                instructions = content if isinstance(content, str) else str(content)
            else:
                input_msgs.append({
                    "role": role,
                    "content": _convert_content_for_responses(content),
                })

        resp_kwargs: Dict[str, Any] = {
            "model": model,
            "instructions": instructions,
            "input": input_msgs or [{"role": "user", "content": ""}],
            "store": False,
        }

        # Note: the Codex endpoint (chatgpt.com/backend-api/codex) does NOT
        # support max_output_tokens or temperature — omit to avoid 400 errors.

        # Tools support for auxiliary callers (e.g. skills_hub) that pass function schemas
        tools = kwargs.get("tools")
        if tools:
            converted = []
            for t in tools:
                fn = t.get("function", {}) if isinstance(t, dict) else {}
                name = fn.get("name")
                if not name:
                    continue
                converted.append({
                    "type": "function",
                    "name": name,
                    "description": fn.get("description", ""),
                    "parameters": fn.get("parameters", {}),
                })
            if converted:
                resp_kwargs["tools"] = converted

        # Stream and collect the response
        text_parts: List[str] = []
        tool_calls_raw: List[Any] = []
        usage = None

        try:
            # Collect output items and text deltas during streaming —
            # the Codex backend can return empty response.output from
            # get_final_response() even when items were streamed.
            collected_output_items: List[Any] = []
            collected_text_deltas: List[str] = []
            has_function_calls = False
            with self._client.responses.stream(**resp_kwargs) as stream:
                for _event in stream:
                    _etype = getattr(_event, "type", "")
                    if _etype == "response.output_item.done":
                        _done = getattr(_event, "item", None)
                        if _done is not None:
                            collected_output_items.append(_done)
                    elif "output_text.delta" in _etype:
                        _delta = getattr(_event, "delta", "")
                        if _delta:
                            collected_text_deltas.append(_delta)
                    elif "function_call" in _etype:
                        has_function_calls = True
                final = stream.get_final_response()

            # Backfill empty output from collected stream events
            _output = getattr(final, "output", None)
            if isinstance(_output, list) and not _output:
                if collected_output_items:
                    final.output = list(collected_output_items)
                    logger.debug(
                        "Codex auxiliary: backfilled %d output items from stream events",
                        len(collected_output_items),
                    )
                elif collected_text_deltas and not has_function_calls:
                    # Only synthesize text when no tool calls were streamed —
                    # a function_call response with incidental text should not
                    # be collapsed into a plain-text message.
                    assembled = "".join(collected_text_deltas)
                    final.output = [SimpleNamespace(
                        type="message", role="assistant", status="completed",
                        content=[SimpleNamespace(type="output_text", text=assembled)],
                    )]
                    logger.debug(
                        "Codex auxiliary: synthesized from %d deltas (%d chars)",
                        len(collected_text_deltas), len(assembled),
                    )

            # Extract text and tool calls from the Responses output.
            # Items may be SDK objects (attrs) or dicts (raw/fallback paths),
            # so use a helper that handles both shapes.
            def _item_get(obj: Any, key: str, default: Any = None) -> Any:
                val = getattr(obj, key, None)
                if val is None and isinstance(obj, dict):
                    val = obj.get(key, default)
                return val if val is not None else default

            for item in getattr(final, "output", []):
                item_type = _item_get(item, "type")
                if item_type == "message":
                    for part in (_item_get(item, "content") or []):
                        ptype = _item_get(part, "type")
                        if ptype in ("output_text", "text"):
                            text_parts.append(_item_get(part, "text", ""))
                elif item_type == "function_call":
                    tool_calls_raw.append(SimpleNamespace(
                        id=_item_get(item, "call_id", ""),
                        type="function",
                        function=SimpleNamespace(
                            name=_item_get(item, "name", ""),
                            arguments=_item_get(item, "arguments", "{}"),
                        ),
                    ))

            resp_usage = getattr(final, "usage", None)
            if resp_usage:
                usage = SimpleNamespace(
                    prompt_tokens=getattr(resp_usage, "input_tokens", 0),
                    completion_tokens=getattr(resp_usage, "output_tokens", 0),
                    total_tokens=getattr(resp_usage, "total_tokens", 0),
                )
        except Exception as exc:
            logger.debug("Codex auxiliary Responses API call failed: %s", exc)
            raise

        content = "".join(text_parts).strip() or None

        # Build a response that looks like chat.completions
        message = SimpleNamespace(
            role="assistant",
            content=content,
            tool_calls=tool_calls_raw or None,
        )
        choice = SimpleNamespace(
            index=0,
            message=message,
            finish_reason="stop" if not tool_calls_raw else "tool_calls",
        )
        return SimpleNamespace(
            choices=[choice],
            model=model,
            usage=usage,
        )


class _CodexChatShim:
    """Wraps the adapter to provide client.chat.completions.create()."""

    def __init__(self, adapter: _CodexCompletionsAdapter):
        self.completions = adapter


class CodexAuxiliaryClient:
    """OpenAI-client-compatible wrapper that routes through Codex Responses API.

    Consumers can call client.chat.completions.create(**kwargs) as normal.
    Also exposes .api_key and .base_url for introspection by async wrappers.
    """

    def __init__(self, real_client: OpenAI, model: str):
        self._real_client = real_client
        adapter = _CodexCompletionsAdapter(real_client, model)
        self.chat = _CodexChatShim(adapter)
        self.api_key = real_client.api_key
        self.base_url = real_client.base_url

    def close(self):
        self._real_client.close()


class _AsyncCodexCompletionsAdapter:
    """Async version of the Codex Responses adapter.

    Wraps the sync adapter via asyncio.to_thread() so async consumers
    (web_tools, session_search) can await it as normal.
    """

    def __init__(self, sync_adapter: _CodexCompletionsAdapter):
        self._sync = sync_adapter

    async def create(self, **kwargs) -> Any:
        import asyncio
        return await asyncio.to_thread(self._sync.create, **kwargs)


class _AsyncCodexChatShim:
    def __init__(self, adapter: _AsyncCodexCompletionsAdapter):
        self.completions = adapter


class AsyncCodexAuxiliaryClient:
    """Async-compatible wrapper matching AsyncOpenAI.chat.completions.create()."""

    def __init__(self, sync_wrapper: "CodexAuxiliaryClient"):
        sync_adapter = sync_wrapper.chat.completions
        async_adapter = _AsyncCodexCompletionsAdapter(sync_adapter)
        self.chat = _AsyncCodexChatShim(async_adapter)
        self.api_key = sync_wrapper.api_key
        self.base_url = sync_wrapper.base_url


class _AnthropicCompletionsAdapter:
    """OpenAI-client-compatible adapter for Anthropic Messages API."""

    def __init__(self, real_client: Any, model: str, is_oauth: bool = False):
        self._client = real_client
        self._model = model
        self._is_oauth = is_oauth

    def create(self, **kwargs) -> Any:
        from agent.anthropic_adapter import build_anthropic_kwargs
        from agent.transports import get_transport

        messages = kwargs.get("messages", [])
        model = kwargs.get("model", self._model)
        tools = kwargs.get("tools")
        tool_choice = kwargs.get("tool_choice")
        max_tokens = kwargs.get("max_tokens") or kwargs.get("max_completion_tokens") or 2000
        temperature = kwargs.get("temperature")

        normalized_tool_choice = None
        if isinstance(tool_choice, str):
            normalized_tool_choice = tool_choice
        elif isinstance(tool_choice, dict):
            choice_type = str(tool_choice.get("type", "")).lower()
            if choice_type == "function":
                normalized_tool_choice = tool_choice.get("function", {}).get("name")
            elif choice_type in {"auto", "required", "none"}:
                normalized_tool_choice = choice_type

        anthropic_kwargs = build_anthropic_kwargs(
            model=model,
            messages=messages,
            tools=tools,
            max_tokens=max_tokens,
            reasoning_config=None,
            tool_choice=normalized_tool_choice,
            is_oauth=self._is_oauth,
        )
        # Opus 4.7+ rejects any non-default temperature/top_p/top_k; only set
        # temperature for models that still accept it. build_anthropic_kwargs
        # additionally strips these keys as a safety net — keep both layers.
        if temperature is not None:
            from agent.anthropic_adapter import _forbids_sampling_params
            if not _forbids_sampling_params(model):
                anthropic_kwargs["temperature"] = temperature

        response = self._client.messages.create(**anthropic_kwargs)
        _transport = get_transport("anthropic_messages")
        _nr = _transport.normalize_response(
            response, strip_tool_prefix=self._is_oauth
        )

        # ToolCall already duck-types as OpenAI shape (.type, .function.name,
        # .function.arguments) via properties, so no wrapping needed.
        assistant_message = SimpleNamespace(
            content=_nr.content,
            tool_calls=_nr.tool_calls,
            reasoning=_nr.reasoning,
        )
        finish_reason = _nr.finish_reason

        usage = None
        if hasattr(response, "usage") and response.usage:
            prompt_tokens = getattr(response.usage, "input_tokens", 0) or 0
            completion_tokens = getattr(response.usage, "output_tokens", 0) or 0
            total_tokens = getattr(response.usage, "total_tokens", 0) or (prompt_tokens + completion_tokens)
            usage = SimpleNamespace(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=total_tokens,
            )

        choice = SimpleNamespace(
            index=0,
            message=assistant_message,
            finish_reason=finish_reason,
        )
        return SimpleNamespace(
            choices=[choice],
            model=model,
            usage=usage,
        )


class _AnthropicChatShim:
    def __init__(self, adapter: _AnthropicCompletionsAdapter):
        self.completions = adapter


class AnthropicAuxiliaryClient:
    """OpenAI-client-compatible wrapper over a native Anthropic client."""

    def __init__(self, real_client: Any, model: str, api_key: str, base_url: str, is_oauth: bool = False):
        self._real_client = real_client
        adapter = _AnthropicCompletionsAdapter(real_client, model, is_oauth=is_oauth)
        self.chat = _AnthropicChatShim(adapter)
        self.api_key = api_key
        self.base_url = base_url

    def close(self):
        close_fn = getattr(self._real_client, "close", None)
        if callable(close_fn):
            close_fn()


class _AsyncAnthropicCompletionsAdapter:
    def __init__(self, sync_adapter: _AnthropicCompletionsAdapter):
        self._sync = sync_adapter

    async def create(self, **kwargs) -> Any:
        import asyncio
        return await asyncio.to_thread(self._sync.create, **kwargs)


class _AsyncAnthropicChatShim:
    def __init__(self, adapter: _AsyncAnthropicCompletionsAdapter):
        self.completions = adapter


class AsyncAnthropicAuxiliaryClient:
    def __init__(self, sync_wrapper: "AnthropicAuxiliaryClient"):
        sync_adapter = sync_wrapper.chat.completions
        async_adapter = _AsyncAnthropicCompletionsAdapter(sync_adapter)
        self.chat = _AsyncAnthropicChatShim(async_adapter)
        self.api_key = sync_wrapper.api_key
        self.base_url = sync_wrapper.base_url


def _read_nous_auth() -> Optional[dict]:
    """Read and validate ~/.hermes/auth.json for an active Nous provider.

    Returns the provider state dict if Nous is active with tokens,
    otherwise None.
    """
    pool_present, entry = _select_pool_entry("nous")
    if pool_present:
        if entry is None:
            return None
        return {
            "access_token": getattr(entry, "access_token", ""),
            "refresh_token": getattr(entry, "refresh_token", None),
            "agent_key": getattr(entry, "agent_key", None),
            "inference_base_url": _pool_runtime_base_url(entry, _NOUS_DEFAULT_BASE_URL),
            "portal_base_url": getattr(entry, "portal_base_url", None),
            "client_id": getattr(entry, "client_id", None),
            "scope": getattr(entry, "scope", None),
            "token_type": getattr(entry, "token_type", "Bearer"),
            "source": "pool",
        }

    try:
        if not _AUTH_JSON_PATH.is_file():
            return None
        data = json.loads(_AUTH_JSON_PATH.read_text())
        if data.get("active_provider") != "nous":
            return None
        provider = data.get("providers", {}).get("nous", {})
        # Must have at least an access_token or agent_key
        if not provider.get("agent_key") and not provider.get("access_token"):
            return None
        return provider
    except Exception as exc:
        logger.debug("Could not read Nous auth: %s", exc)
        return None


def _nous_api_key(provider: dict) -> str:
    """Extract the best API key from a Nous provider state dict."""
    return provider.get("agent_key") or provider.get("access_token", "")


def _nous_base_url() -> str:
    """Resolve the Nous inference base URL from env or default."""
    return os.getenv("NOUS_INFERENCE_BASE_URL", _NOUS_DEFAULT_BASE_URL)


def _resolve_nous_runtime_api(*, force_refresh: bool = False) -> Optional[tuple[str, str]]:
    """Return fresh Nous runtime credentials when available.

    This mirrors the main agent's 401 recovery path and keeps auxiliary
    clients aligned with the singleton auth store + mint flow instead of
    relying only on whatever raw tokens happen to be sitting in auth.json
    or the credential pool.
    """
    try:
        from hermes_cli.auth import 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")),
            force_mint=force_refresh,
        )
    except Exception as exc:
        logger.debug("Auxiliary Nous runtime credential resolution failed: %s", exc)
        return None

    api_key = str(creds.get("api_key") or "").strip()
    base_url = str(creds.get("base_url") or "").strip().rstrip("/")
    if not api_key or not base_url:
        return None
    return api_key, base_url


def _read_codex_access_token() -> Optional[str]:
    """Read a valid, non-expired Codex OAuth access token from Hermes auth store.

    If a credential pool exists but currently has no selectable runtime entry
    (for example all pool slots are marked exhausted), fall back to the
    profile's auth.json token instead of hard-failing. This keeps explicit
    fallback-to-Codex working when the pool state is stale but the stored OAuth
    token is still valid.
    """
    pool_present, entry = _select_pool_entry("openai-codex")
    if pool_present:
        token = _pool_runtime_api_key(entry)
        if token:
            return token

    try:
        from hermes_cli.auth import _read_codex_tokens
        data = _read_codex_tokens()
        tokens = data.get("tokens", {})
        access_token = tokens.get("access_token")
        if not isinstance(access_token, str) or not access_token.strip():
            return None

        # Check JWT expiry — expired tokens block the auto chain and
        # prevent fallback to working providers (e.g. Anthropic).
        try:
            import base64
            payload = access_token.split(".")[1]
            payload += "=" * (-len(payload) % 4)
            claims = json.loads(base64.urlsafe_b64decode(payload))
            exp = claims.get("exp", 0)
            if exp and time.time() > exp:
                logger.debug("Codex access token expired (exp=%s), skipping", exp)
                return None
        except Exception:
            pass  # Non-JWT token or decode error — use as-is

        return access_token.strip()
    except Exception as exc:
        logger.debug("Could not read Codex auth for auxiliary client: %s", exc)
        return None


def _resolve_api_key_provider() -> Tuple[Optional[OpenAI], Optional[str]]:
    """Try each API-key provider in PROVIDER_REGISTRY order.

    Returns (client, model) for the first provider with usable runtime
    credentials, or (None, None) if none are configured.
    """
    try:
        from hermes_cli.auth import PROVIDER_REGISTRY, resolve_api_key_provider_credentials
    except ImportError:
        logger.debug("Could not import PROVIDER_REGISTRY for API-key fallback")
        return None, None

    for provider_id, pconfig in PROVIDER_REGISTRY.items():
        if pconfig.auth_type != "api_key":
            continue
        if provider_id == "anthropic":
            # Only try anthropic when the user has explicitly configured it.
            # Without this gate, Claude Code credentials get silently used
            # as auxiliary fallback when the user's primary provider fails.
            try:
                from hermes_cli.auth import is_provider_explicitly_configured
                if not is_provider_explicitly_configured("anthropic"):
                    continue
            except ImportError:
                pass
            return _try_anthropic()

        pool_present, entry = _select_pool_entry(provider_id)
        if pool_present:
            api_key = _pool_runtime_api_key(entry)
            if not api_key:
                continue

            base_url = _to_openai_base_url(
                _pool_runtime_base_url(entry, pconfig.inference_base_url) or pconfig.inference_base_url
            )
            model = _API_KEY_PROVIDER_AUX_MODELS.get(provider_id)
            if model is None:
                continue  # skip provider if we don't know a valid aux model
            logger.debug("Auxiliary text client: %s (%s) via pool", pconfig.name, model)
            if provider_id == "gemini":
                from agent.gemini_native_adapter import GeminiNativeClient, is_native_gemini_base_url

                if is_native_gemini_base_url(base_url):
                    return GeminiNativeClient(api_key=api_key, base_url=base_url), model
            extra = {}
            if base_url_host_matches(base_url, "api.kimi.com"):
                extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
            elif base_url_host_matches(base_url, "api.githubcopilot.com"):
                from hermes_cli.models import copilot_default_headers

                extra["default_headers"] = copilot_default_headers()
            return OpenAI(api_key=api_key, base_url=base_url, **extra), model

        creds = resolve_api_key_provider_credentials(provider_id)
        api_key = str(creds.get("api_key", "")).strip()
        if not api_key:
            continue

        base_url = _to_openai_base_url(
            str(creds.get("base_url", "")).strip().rstrip("/") or pconfig.inference_base_url
        )
        model = _API_KEY_PROVIDER_AUX_MODELS.get(provider_id)
        if model is None:
            continue  # skip provider if we don't know a valid aux model
        logger.debug("Auxiliary text client: %s (%s)", pconfig.name, model)
        if provider_id == "gemini":
            from agent.gemini_native_adapter import GeminiNativeClient, is_native_gemini_base_url

            if is_native_gemini_base_url(base_url):
                return GeminiNativeClient(api_key=api_key, base_url=base_url), model
        extra = {}
        if base_url_host_matches(base_url, "api.kimi.com"):
            extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
        elif base_url_host_matches(base_url, "api.githubcopilot.com"):
            from hermes_cli.models import copilot_default_headers

            extra["default_headers"] = copilot_default_headers()
        return OpenAI(api_key=api_key, base_url=base_url, **extra), model

    return None, None


# ── Provider resolution helpers ─────────────────────────────────────────────



def _try_openrouter() -> Tuple[Optional[OpenAI], Optional[str]]:
    pool_present, entry = _select_pool_entry("openrouter")
    if pool_present:
        or_key = _pool_runtime_api_key(entry)
        if not or_key:
            return None, None
        base_url = _pool_runtime_base_url(entry, OPENROUTER_BASE_URL) or OPENROUTER_BASE_URL
        logger.debug("Auxiliary client: OpenRouter via pool")
        return OpenAI(api_key=or_key, base_url=base_url,
                       default_headers=_OR_HEADERS), _OPENROUTER_MODEL

    or_key = os.getenv("OPENROUTER_API_KEY")
    if not or_key:
        return None, None
    logger.debug("Auxiliary client: OpenRouter")
    return OpenAI(api_key=or_key, base_url=OPENROUTER_BASE_URL,
                   default_headers=_OR_HEADERS), _OPENROUTER_MODEL


def _describe_openrouter_unavailable() -> str:
    """Return a more precise OpenRouter auth failure reason for logs."""
    pool_present, entry = _select_pool_entry("openrouter")
    if pool_present:
        if entry is None:
            return "OpenRouter credential pool has no usable entries (credentials may be exhausted)"
        if not _pool_runtime_api_key(entry):
            return "OpenRouter credential pool entry is missing a runtime API key"
    if not str(os.getenv("OPENROUTER_API_KEY") or "").strip():
        return "OPENROUTER_API_KEY not set"
    return "no usable OpenRouter credentials found"


def _try_nous(vision: bool = False) -> Tuple[Optional[OpenAI], Optional[str]]:
    # Check cross-session rate limit guard before attempting Nous —
    # if another session already recorded a 429, skip Nous entirely
    # to avoid piling more requests onto the tapped RPH bucket.
    try:
        from agent.nous_rate_guard import nous_rate_limit_remaining
        _remaining = nous_rate_limit_remaining()
        if _remaining is not None and _remaining > 0:
            logger.debug(
                "Auxiliary: skipping Nous Portal (rate-limited, resets in %.0fs)",
                _remaining,
            )
            return None, None
    except Exception:
        pass

    nous = _read_nous_auth()
    runtime = _resolve_nous_runtime_api(force_refresh=False)
    if runtime is None and not nous:
        return None, None
    global auxiliary_is_nous
    auxiliary_is_nous = True
    logger.debug("Auxiliary client: Nous Portal")

    # Ask the Portal which model it currently recommends for this task type.
    # The /api/nous/recommended-models endpoint is the authoritative source:
    # it distinguishes paid vs free tier recommendations, and get_nous_recommended_aux_model
    # auto-detects the caller's tier via check_nous_free_tier().  Fall back to
    # _NOUS_MODEL (google/gemini-3-flash-preview) when the Portal is unreachable
    # or returns a null recommendation for this task type.
    model = _NOUS_MODEL
    try:
        from hermes_cli.models import get_nous_recommended_aux_model
        recommended = get_nous_recommended_aux_model(vision=vision)
        if recommended:
            model = recommended
            logger.debug(
                "Auxiliary/%s: using Portal-recommended model %s",
                "vision" if vision else "text", model,
            )
        else:
            logger.debug(
                "Auxiliary/%s: no Portal recommendation, falling back to %s",
                "vision" if vision else "text", model,
            )
    except Exception as exc:
        logger.debug(
            "Auxiliary/%s: recommended-models lookup failed (%s); "
            "falling back to %s",
            "vision" if vision else "text", exc, model,
        )

    if runtime is not None:
        api_key, base_url = runtime
    else:
        api_key = _nous_api_key(nous or {})
        base_url = str((nous or {}).get("inference_base_url") or _nous_base_url()).rstrip("/")
    return (
        OpenAI(
            api_key=api_key,
            base_url=base_url,
        ),
        model,
    )


def _read_main_model() -> str:
    """Read the user's configured main model from config.yaml.

    config.yaml model.default is the single source of truth for the active
    model. Environment variables are no longer consulted.
    """
    try:
        from hermes_cli.config import load_config
        cfg = load_config()
        model_cfg = cfg.get("model", {})
        if isinstance(model_cfg, str) and model_cfg.strip():
            return model_cfg.strip()
        if isinstance(model_cfg, dict):
            default = model_cfg.get("default", "")
            if isinstance(default, str) and default.strip():
                return default.strip()
    except Exception:
        pass
    return ""


def _read_main_provider() -> str:
    """Read the user's configured main provider from config.yaml.

    Returns the lowercase provider id (e.g. "alibaba", "openrouter") or ""
    if not configured.
    """
    try:
        from hermes_cli.config import load_config
        cfg = load_config()
        model_cfg = cfg.get("model", {})
        if isinstance(model_cfg, dict):
            provider = model_cfg.get("provider", "")
            if isinstance(provider, str) and provider.strip():
                return provider.strip().lower()
    except Exception:
        pass
    return ""


def _resolve_custom_runtime() -> Tuple[Optional[str], Optional[str], Optional[str]]:
    """Resolve the active custom/main endpoint the same way the main CLI does.

    This covers both env-driven OPENAI_BASE_URL setups and config-saved custom
    endpoints where the base URL lives in config.yaml instead of the live
    environment.
    """
    try:
        from hermes_cli.runtime_provider import resolve_runtime_provider

        runtime = resolve_runtime_provider(requested="custom")
    except Exception as exc:
        logger.debug("Auxiliary client: custom runtime resolution failed: %s", exc)
        runtime = None

    if not isinstance(runtime, dict):
        openai_base = os.getenv("OPENAI_BASE_URL", "").strip().rstrip("/")
        openai_key = os.getenv("OPENAI_API_KEY", "").strip()
        if not openai_base:
            return None, None, None
        runtime = {
            "base_url": openai_base,
            "api_key": openai_key,
        }

    custom_base = runtime.get("base_url")
    custom_key = runtime.get("api_key")
    custom_mode = runtime.get("api_mode")
    if not isinstance(custom_base, str) or not custom_base.strip():
        return None, None, None

    custom_base = custom_base.strip().rstrip("/")
    if base_url_host_matches(custom_base, "openrouter.ai"):
        # requested='custom' falls back to OpenRouter when no custom endpoint is
        # configured. Treat that as "no custom endpoint" for auxiliary routing.
        return None, None, None

    # Local servers (Ollama, llama.cpp, vLLM, LM Studio) don't require auth.
    # Use a placeholder key — the OpenAI SDK requires a non-empty string but
    # local servers ignore the Authorization header.  Same fix as cli.py
    # _ensure_runtime_credentials() (PR #2556).
    if not isinstance(custom_key, str) or not custom_key.strip():
        custom_key = "no-key-required"

    if not isinstance(custom_mode, str) or not custom_mode.strip():
        custom_mode = None

    return custom_base, custom_key.strip(), custom_mode


def _current_custom_base_url() -> str:
    custom_base, _, _ = _resolve_custom_runtime()
    return custom_base or ""


def _validate_proxy_env_urls() -> None:
    """Fail fast with a clear error when proxy env vars have malformed URLs.

    Common cause: shell config (e.g. .zshrc) with a typo like
    ``export HTTP_PROXY=http://127.0.0.1:6153export NEXT_VAR=...``
    which concatenates 'export' into the port number.  Without this
    check the OpenAI/httpx client raises a cryptic ``Invalid port``
    error that doesn't name the offending env var.
    """
    from urllib.parse import urlparse

    normalize_proxy_env_vars()

    for key in ("HTTPS_PROXY", "HTTP_PROXY", "ALL_PROXY",
                "https_proxy", "http_proxy", "all_proxy"):
        value = str(os.environ.get(key) or "").strip()
        if not value:
            continue
        try:
            parsed = urlparse(value)
            if parsed.scheme:
                _ = parsed.port          # raises ValueError for e.g. '6153export'
        except ValueError as exc:
            raise RuntimeError(
                f"Malformed proxy environment variable {key}={value!r}. "
                "Fix or unset your proxy settings and try again."
            ) from exc


def _validate_base_url(base_url: str) -> None:
    """Reject obviously broken custom endpoint URLs before they reach httpx."""
    from urllib.parse import urlparse

    candidate = str(base_url or "").strip()
    if not candidate or candidate.startswith("acp://"):
        return
    try:
        parsed = urlparse(candidate)
        if parsed.scheme in {"http", "https"}:
            _ = parsed.port              # raises ValueError for malformed ports
    except ValueError as exc:
        raise RuntimeError(
            f"Malformed custom endpoint URL: {candidate!r}. "
            "Run `hermes setup` or `hermes model` and enter a valid http(s) base URL."
        ) from exc


def _try_custom_endpoint() -> Tuple[Optional[Any], Optional[str]]:
    runtime = _resolve_custom_runtime()
    if len(runtime) == 2:
        custom_base, custom_key = runtime
        custom_mode = None
    else:
        custom_base, custom_key, custom_mode = runtime
    if not custom_base or not custom_key:
        return None, None
    if custom_base.lower().startswith(_CODEX_AUX_BASE_URL.lower()):
        return None, None
    model = _read_main_model() or "gpt-4o-mini"
    logger.debug("Auxiliary client: custom endpoint (%s, api_mode=%s)", model, custom_mode or "chat_completions")
    _clean_base, _dq = _extract_url_query_params(custom_base)
    _extra = {"default_query": _dq} if _dq else {}
    if custom_mode == "codex_responses":
        real_client = OpenAI(api_key=custom_key, base_url=_clean_base, **_extra)
        return CodexAuxiliaryClient(real_client, model), model
    if custom_mode == "anthropic_messages":
        # Third-party Anthropic-compatible gateway (MiniMax, Zhipu GLM,
        # LiteLLM proxies, etc.).  Must NEVER be treated as OAuth —
        # Anthropic OAuth claims only apply to api.anthropic.com.
        try:
            from agent.anthropic_adapter import build_anthropic_client
            real_client = build_anthropic_client(custom_key, custom_base)
        except ImportError:
            logger.warning(
                "Custom endpoint declares api_mode=anthropic_messages but the "
                "anthropic SDK is not installed — falling back to OpenAI-wire."
            )
            return OpenAI(api_key=custom_key, base_url=_clean_base, **_extra), model
        return (
            AnthropicAuxiliaryClient(real_client, model, custom_key, custom_base, is_oauth=False),
            model,
        )
    return OpenAI(api_key=custom_key, base_url=_clean_base, **_extra), model


def _try_codex() -> Tuple[Optional[Any], Optional[str]]:
    pool_present, entry = _select_pool_entry("openai-codex")
    if pool_present:
        codex_token = _pool_runtime_api_key(entry)
        if codex_token:
            base_url = _pool_runtime_base_url(entry, _CODEX_AUX_BASE_URL) or _CODEX_AUX_BASE_URL
        else:
            codex_token = _read_codex_access_token()
            if not codex_token:
                return None, None
            base_url = _CODEX_AUX_BASE_URL
    else:
        codex_token = _read_codex_access_token()
        if not codex_token:
            return None, None
        base_url = _CODEX_AUX_BASE_URL
    logger.debug("Auxiliary client: Codex OAuth (%s via Responses API)", _CODEX_AUX_MODEL)
    real_client = OpenAI(
        api_key=codex_token,
        base_url=base_url,
        default_headers=_codex_cloudflare_headers(codex_token),
    )
    return CodexAuxiliaryClient(real_client, _CODEX_AUX_MODEL), _CODEX_AUX_MODEL


def _try_anthropic() -> Tuple[Optional[Any], Optional[str]]:
    try:
        from agent.anthropic_adapter import build_anthropic_client, resolve_anthropic_token
    except ImportError:
        return None, None

    pool_present, entry = _select_pool_entry("anthropic")
    if pool_present:
        if entry is None:
            return None, None
        token = _pool_runtime_api_key(entry)
    else:
        entry = None
        token = resolve_anthropic_token()
    if not token:
        return None, None

    # Allow base URL override from config.yaml model.base_url, but only
    # when the configured provider is anthropic — otherwise a non-Anthropic
    # base_url (e.g. Codex endpoint) would leak into Anthropic requests.
    base_url = _pool_runtime_base_url(entry, _ANTHROPIC_DEFAULT_BASE_URL) if pool_present else _ANTHROPIC_DEFAULT_BASE_URL
    try:
        from hermes_cli.config import load_config
        cfg = load_config()
        model_cfg = cfg.get("model")
        if isinstance(model_cfg, dict):
            cfg_provider = str(model_cfg.get("provider") or "").strip().lower()
            if cfg_provider == "anthropic":
                cfg_base_url = (model_cfg.get("base_url") or "").strip().rstrip("/")
                if cfg_base_url:
                    base_url = cfg_base_url
    except Exception:
        pass

    from agent.anthropic_adapter import _is_oauth_token
    is_oauth = _is_oauth_token(token)
    model = _API_KEY_PROVIDER_AUX_MODELS.get("anthropic", "claude-haiku-4-5-20251001")
    logger.debug("Auxiliary client: Anthropic native (%s) at %s (oauth=%s)", model, base_url, is_oauth)
    try:
        real_client = build_anthropic_client(token, base_url)
    except ImportError:
        # The anthropic_adapter module imports fine but the SDK itself is
        # missing — build_anthropic_client raises ImportError at call time
        # when _anthropic_sdk is None.  Treat as unavailable.
        return None, None
    return AnthropicAuxiliaryClient(real_client, model, token, base_url, is_oauth=is_oauth), model


_AUTO_PROVIDER_LABELS = {
    "_try_openrouter": "openrouter",
    "_try_nous": "nous",
    "_try_custom_endpoint": "local/custom",
    "_try_codex": "openai-codex",
    "_resolve_api_key_provider": "api-key",
}

_MAIN_RUNTIME_FIELDS = ("provider", "model", "base_url", "api_key", "api_mode")


def _normalize_main_runtime(main_runtime: Optional[Dict[str, Any]]) -> Dict[str, str]:
    """Return a sanitized copy of a live main-runtime override."""
    if not isinstance(main_runtime, dict):
        return {}
    normalized: Dict[str, str] = {}
    for field in _MAIN_RUNTIME_FIELDS:
        value = main_runtime.get(field)
        if isinstance(value, str) and value.strip():
            normalized[field] = value.strip()
    provider = normalized.get("provider")
    if provider:
        normalized["provider"] = provider.lower()
    return normalized


def _get_provider_chain() -> List[tuple]:
    """Return the ordered provider detection chain.

    Built at call time (not module level) so that test patches
    on the ``_try_*`` functions are picked up correctly.
    """
    return [
        ("openrouter", _try_openrouter),
        ("nous", _try_nous),
        ("local/custom", _try_custom_endpoint),
        ("openai-codex", _try_codex),
        ("api-key", _resolve_api_key_provider),
    ]


def _is_payment_error(exc: Exception) -> bool:
    """Detect payment/credit/quota exhaustion errors.

    Returns True for HTTP 402 (Payment Required) and for 429/other errors
    whose message indicates billing exhaustion rather than rate limiting.
    """
    status = getattr(exc, "status_code", None)
    if status == 402:
        return True
    err_lower = str(exc).lower()
    # OpenRouter and other providers include "credits" or "afford" in 402 bodies,
    # but sometimes wrap them in 429 or other codes.
    if status in (402, 429, None):
        if any(kw in err_lower for kw in ("credits", "insufficient funds",
                                           "can only afford", "billing",
                                           "payment required")):
            return True
    return False


def _is_connection_error(exc: Exception) -> bool:
    """Detect connection/network errors that warrant provider fallback.

    Returns True for errors indicating the provider endpoint is unreachable
    (DNS failure, connection refused, TLS errors, timeouts).  These are
    distinct from API errors (4xx/5xx) which indicate the provider IS
    reachable but returned an error.
    """
    from openai import APIConnectionError, APITimeoutError

    if isinstance(exc, (APIConnectionError, APITimeoutError)):
        return True
    # urllib3 / httpx / httpcore connection errors
    err_type = type(exc).__name__
    if any(kw in err_type for kw in ("Connection", "Timeout", "DNS", "SSL")):
        return True
    err_lower = str(exc).lower()
    if any(kw in err_lower for kw in (
        "connection refused", "name or service not known",
        "no route to host", "network is unreachable",
        "timed out", "connection reset",
    )):
        return True
    return False


def _is_auth_error(exc: Exception) -> bool:
    """Detect auth failures that should trigger provider-specific refresh."""
    status = getattr(exc, "status_code", None)
    if status == 401:
        return True
    err_lower = str(exc).lower()
    return "error code: 401" in err_lower or "authenticationerror" in type(exc).__name__.lower()


def _is_unsupported_parameter_error(exc: Exception, param: str) -> bool:
    """Detect provider 400s for an unsupported request parameter.

    Different OpenAI-compatible endpoints phrase the same class of error a few
    ways: ``Unsupported parameter: X``, ``unsupported_parameter`` with a
    ``param`` field, ``X is not supported``, ``unknown parameter: X``,
    ``unrecognized request argument: X``.  We match on both the parameter
    name and a generic "unsupported/unknown/unrecognized parameter" marker so
    call sites can reactively retry without the offending key instead of
    surfacing a noisy auxiliary failure.

    Generalizes the temperature-specific detector that originally shipped
    with PR #15621 so the same retry strategy can cover ``max_tokens``,
    ``seed``, ``top_p``, and any future quirk. Credit @nicholasrae (PR #15416)
    for the generalization pattern.
    """
    param_lower = (param or "").lower()
    if not param_lower:
        return False
    err_lower = str(exc).lower()
    if param_lower not in err_lower:
        return False
    return any(marker in err_lower for marker in (
        "unsupported parameter",
        "unsupported_parameter",
        "not supported",
        "does not support",
        "unknown parameter",
        "unrecognized request argument",
        "unrecognized parameter",
        "invalid parameter",
    ))


def _is_unsupported_temperature_error(exc: Exception) -> bool:
    """Back-compat wrapper: detect API errors where the model rejects ``temperature``.

    Delegates to :func:`_is_unsupported_parameter_error`; kept as a separate
    public symbol because existing tests and call sites import it by name.
    """
    return _is_unsupported_parameter_error(exc, "temperature")


def _evict_cached_clients(provider: str) -> None:
    """Drop cached auxiliary clients for a provider so fresh creds are used."""
    normalized = _normalize_aux_provider(provider)
    with _client_cache_lock:
        stale_keys = [
            key for key in _client_cache
            if _normalize_aux_provider(str(key[0])) == normalized
        ]
        for key in stale_keys:
            client = _client_cache.get(key, (None, None, None))[0]
            if client is not None:
                _force_close_async_httpx(client)
                try:
                    close_fn = getattr(client, "close", None)
                    if callable(close_fn):
                        close_fn()
                except Exception:
                    pass
            _client_cache.pop(key, None)


def _refresh_provider_credentials(provider: str) -> bool:
    """Refresh short-lived credentials for OAuth-backed auxiliary providers."""
    normalized = _normalize_aux_provider(provider)
    try:
        if normalized == "openai-codex":
            from hermes_cli.auth import resolve_codex_runtime_credentials

            creds = resolve_codex_runtime_credentials(force_refresh=True)
            if not str(creds.get("api_key", "") or "").strip():
                return False
            _evict_cached_clients(normalized)
            return True
        if normalized == "nous":
            from hermes_cli.auth import 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")),
                force_mint=True,
            )
            if not str(creds.get("api_key", "") or "").strip():
                return False
            _evict_cached_clients(normalized)
            return True
        if normalized == "anthropic":
            from agent.anthropic_adapter import read_claude_code_credentials, _refresh_oauth_token, resolve_anthropic_token

            creds = read_claude_code_credentials()
            token = _refresh_oauth_token(creds) if isinstance(creds, dict) and creds.get("refreshToken") else None
            if not str(token or "").strip():
                token = resolve_anthropic_token()
            if not str(token or "").strip():
                return False
            _evict_cached_clients(normalized)
            return True
    except Exception as exc:
        logger.debug("Auxiliary provider credential refresh failed for %s: %s", normalized, exc)
        return False
    return False


def _try_payment_fallback(
    failed_provider: str,
    task: str = None,
    reason: str = "payment error",
) -> Tuple[Optional[Any], Optional[str], str]:
    """Try alternative providers after a payment/credit or connection error.

    Iterates the standard auto-detection chain, skipping the provider that
    failed.

    Returns:
        (client, model, provider_label) or (None, None, "") if no fallback.
    """
    # Normalise the failed provider label for matching.
    skip = failed_provider.lower().strip()
    # Also skip Step-1 main-provider path if it maps to the same backend.
    # (e.g. main_provider="openrouter" → skip "openrouter" in chain)
    main_provider = _read_main_provider()
    skip_labels = {skip}
    if main_provider and main_provider.lower() in skip:
        skip_labels.add(main_provider.lower())
    # Map common resolved_provider values back to chain labels.
    _alias_to_label = {"openrouter": "openrouter", "nous": "nous",
                       "openai-codex": "openai-codex", "codex": "openai-codex",
                       "custom": "local/custom", "local/custom": "local/custom"}
    skip_chain_labels = {_alias_to_label.get(s, s) for s in skip_labels}

    tried = []
    for label, try_fn in _get_provider_chain():
        if label in skip_chain_labels:
            continue
        client, model = try_fn()
        if client is not None:
            logger.info(
                "Auxiliary %s: %s on %s — falling back to %s (%s)",
                task or "call", reason, failed_provider, label, model or "default",
            )
            return client, model, label
        tried.append(label)

    logger.warning(
        "Auxiliary %s: %s on %s and no fallback available (tried: %s)",
        task or "call", reason, failed_provider, ", ".join(tried),
    )
    return None, None, ""


def _resolve_auto(main_runtime: Optional[Dict[str, Any]] = None) -> Tuple[Optional[OpenAI], Optional[str]]:
    """Full auto-detection chain.

    Priority:
      1. User's main provider + main model, regardless of provider type.
         This means auxiliary tasks (compression, vision, web extraction,
         session search, etc.) use the same model the user configured for
         chat.  Users on OpenRouter/Nous get their chosen chat model; users
         on DeepSeek/ZAI/Alibaba get theirs; etc.  Running aux tasks on the
         user's picked model keeps behavior predictable — no surprise
         switches to a cheap fallback model for side tasks.
      2. OpenRouter → Nous → custom → Codex → API-key providers (fallback
         chain, only used when the main provider has no working client).
    """
    global auxiliary_is_nous, _stale_base_url_warned
    auxiliary_is_nous = False  # Reset — _try_nous() will set True if it wins
    runtime = _normalize_main_runtime(main_runtime)
    runtime_provider = runtime.get("provider", "")
    runtime_model = runtime.get("model", "")
    runtime_base_url = runtime.get("base_url", "")
    runtime_api_key = runtime.get("api_key", "")
    runtime_api_mode = runtime.get("api_mode", "")

    # ── Warn once if OPENAI_BASE_URL is set but config.yaml uses a named
    #    provider (not 'custom').  This catches the common "env poisoning"
    #    scenario where a user switches providers via `hermes model` but the
    #    old OPENAI_BASE_URL lingers in ~/.hermes/.env. ──
    if not _stale_base_url_warned:
        _env_base = os.getenv("OPENAI_BASE_URL", "").strip()
        _cfg_provider = runtime_provider or _read_main_provider()
        if (_env_base and _cfg_provider
                and _cfg_provider != "custom"
                and not _cfg_provider.startswith("custom:")):
            logger.warning(
                "OPENAI_BASE_URL is set (%s) but model.provider is '%s'. "
                "Auxiliary clients may route to the wrong endpoint. "
                "Run: hermes model to reconfigure, or remove "
                "OPENAI_BASE_URL from ~/.hermes/.env",
                _env_base, _cfg_provider,
            )
            _stale_base_url_warned = True

    # ── Step 1: main provider + main model → use them directly ──
    #
    # This is the primary aux backend for every user.  "auto" means
    # "use my main chat model for side tasks as well" — including users
    # on aggregators (OpenRouter, Nous) who previously got routed to a
    # cheap provider-side default.  Explicit per-task overrides set via
    # config.yaml (auxiliary.<task>.provider) still win over this.
    main_provider = runtime_provider or _read_main_provider()
    main_model = runtime_model or _read_main_model()
    if (main_provider and main_model
            and main_provider not in ("auto", "")):
        resolved_provider = main_provider
        explicit_base_url = None
        explicit_api_key = None
        if runtime_base_url and (main_provider == "custom" or main_provider.startswith("custom:")):
            resolved_provider = "custom"
            explicit_base_url = runtime_base_url
            explicit_api_key = runtime_api_key or None
        client, resolved = resolve_provider_client(
            resolved_provider,
            main_model,
            explicit_base_url=explicit_base_url,
            explicit_api_key=explicit_api_key,
            api_mode=runtime_api_mode or None,
        )
        if client is not None:
            logger.info("Auxiliary auto-detect: using main provider %s (%s)",
                        main_provider, resolved or main_model)
            return client, resolved or main_model

    # ── Step 2: aggregator / fallback chain ──────────────────────────────
    tried = []
    for label, try_fn in _get_provider_chain():
        client, model = try_fn()
        if client is not None:
            if tried:
                logger.info("Auxiliary auto-detect: using %s (%s) — skipped: %s",
                            label, model or "default", ", ".join(tried))
            else:
                logger.info("Auxiliary auto-detect: using %s (%s)", label, model or "default")
            return client, model
        tried.append(label)
    logger.warning("Auxiliary auto-detect: no provider available (tried: %s). "
                   "Compression, summarization, and memory flush will not work. "
                   "Set OPENROUTER_API_KEY or configure a local model in config.yaml.",
                   ", ".join(tried))
    return None, None


# ── Centralized Provider Router ─────────────────────────────────────────────
#
# resolve_provider_client() is the single entry point for creating a properly
# configured client given a (provider, model) pair.  It handles auth lookup,
# base URL resolution, provider-specific headers, and API format differences
# (Chat Completions vs Responses API for Codex).
#
# All auxiliary consumer code should go through this or the public helpers
# below — never look up auth env vars ad-hoc.


def _to_async_client(sync_client, model: str, is_vision: bool = False):
    """Convert a sync client to its async counterpart, preserving Codex routing.

    When ``is_vision=True`` and the underlying base URL is Copilot, the
    resulting async client carries the ``Copilot-Vision-Request: true``
    header so the request is routed to Copilot's vision-capable
    infrastructure (otherwise vision payloads silently time out).
    """
    from openai import AsyncOpenAI

    if isinstance(sync_client, CodexAuxiliaryClient):
        return AsyncCodexAuxiliaryClient(sync_client), model
    if isinstance(sync_client, AnthropicAuxiliaryClient):
        return AsyncAnthropicAuxiliaryClient(sync_client), model
    try:
        from agent.gemini_native_adapter import GeminiNativeClient, AsyncGeminiNativeClient

        if isinstance(sync_client, GeminiNativeClient):
            return AsyncGeminiNativeClient(sync_client), model
    except ImportError:
        pass
    try:
        from agent.copilot_acp_client import CopilotACPClient
        if isinstance(sync_client, CopilotACPClient):
            return sync_client, model
    except ImportError:
        pass

    async_kwargs = {
        "api_key": sync_client.api_key,
        "base_url": str(sync_client.base_url),
    }
    sync_base_url = str(sync_client.base_url)
    if base_url_host_matches(sync_base_url, "openrouter.ai"):
        async_kwargs["default_headers"] = dict(_OR_HEADERS)
    elif base_url_host_matches(sync_base_url, "api.githubcopilot.com"):
        from hermes_cli.copilot_auth import copilot_request_headers

        async_kwargs["default_headers"] = copilot_request_headers(
            is_agent_turn=True, is_vision=is_vision
        )
    elif base_url_host_matches(sync_base_url, "api.kimi.com"):
        async_kwargs["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
    return AsyncOpenAI(**async_kwargs), model


def _normalize_resolved_model(model_name: Optional[str], provider: str) -> Optional[str]:
    """Normalize a resolved model for the provider that will receive it."""
    if not model_name:
        return model_name
    try:
        from hermes_cli.model_normalize import normalize_model_for_provider

        return normalize_model_for_provider(model_name, provider)
    except Exception:
        return model_name


def resolve_provider_client(
    provider: str,
    model: str = None,
    async_mode: bool = False,
    raw_codex: bool = False,
    explicit_base_url: str = None,
    explicit_api_key: str = None,
    api_mode: str = None,
    main_runtime: Optional[Dict[str, Any]] = None,
    is_vision: bool = False,
) -> Tuple[Optional[Any], Optional[str]]:
    """Central router: given a provider name and optional model, return a
    configured client with the correct auth, base URL, and API format.

    The returned client always exposes ``.chat.completions.create()`` — for
    Codex/Responses API providers, an adapter handles the translation
    transparently.

    Args:
        provider: Provider identifier.  One of:
            "openrouter", "nous", "openai-codex" (or "codex"),
            "zai", "kimi-coding", "minimax", "minimax-cn",
            "custom" (OPENAI_BASE_URL + OPENAI_API_KEY),
            "auto" (full auto-detection chain).
        model: Model slug override.  If None, uses the provider's default
               auxiliary model.
        async_mode: If True, return an async-compatible client.
        raw_codex: If True, return a raw OpenAI client for Codex providers
            instead of wrapping in CodexAuxiliaryClient.  Use this when
            the caller needs direct access to responses.stream() (e.g.,
            the main agent loop).
        explicit_base_url: Optional direct OpenAI-compatible endpoint.
        explicit_api_key: Optional API key paired with explicit_base_url.
        api_mode: API mode override.  One of "chat_completions",
            "codex_responses", or None (auto-detect).  When set to
            "codex_responses", the client is wrapped in
            CodexAuxiliaryClient to route through the Responses API.

    Returns:
        (client, resolved_model) or (None, None) if auth is unavailable.
    """
    _validate_proxy_env_urls()
    # Normalise aliases
    provider = _normalize_aux_provider(provider)

    def _needs_codex_wrap(client_obj, base_url_str: str, model_str: str) -> bool:
        """Decide if a plain OpenAI client should be wrapped for Responses API.

        Returns True when api_mode is explicitly "codex_responses", or when
        auto-detection (api.openai.com + codex-family model) suggests it.
        Already-wrapped clients (CodexAuxiliaryClient) are skipped.
        """
        if isinstance(client_obj, CodexAuxiliaryClient):
            return False
        if raw_codex:
            return False
        if api_mode == "codex_responses":
            return True
        # Auto-detect: api.openai.com + codex model name pattern
        if api_mode and api_mode != "codex_responses":
            return False  # explicit non-codex mode
        if base_url_hostname(base_url_str) == "api.openai.com":
            model_lower = (model_str or "").lower()
            if "codex" in model_lower:
                return True
        return False

    def _wrap_if_needed(client_obj, final_model_str: str, base_url_str: str = ""):
        """Wrap a plain OpenAI client in CodexAuxiliaryClient if Responses API is needed."""
        if _needs_codex_wrap(client_obj, base_url_str, final_model_str):
            logger.debug(
                "resolve_provider_client: wrapping client in CodexAuxiliaryClient "
                "(api_mode=%s, model=%s, base_url=%s)",
                api_mode or "auto-detected", final_model_str,
                base_url_str[:60] if base_url_str else "")
            return CodexAuxiliaryClient(client_obj, final_model_str)
        return client_obj

    # ── Auto: try all providers in priority order ────────────────────
    if provider == "auto":
        client, resolved = _resolve_auto(main_runtime=main_runtime)
        if client is None:
            return None, None
        # When auto-detection lands on a non-OpenRouter provider (e.g. a
        # local server), an OpenRouter-formatted model override like
        # "google/gemini-3-flash-preview" won't work.  Drop it and use
        # the provider's own default model instead.
        if model and "/" in model and resolved and "/" not in resolved:
            logger.debug(
                "Dropping OpenRouter-format model %r for non-OpenRouter "
                "auxiliary provider (using %r instead)", model, resolved)
            model = None
        final_model = model or resolved
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    # ── OpenRouter ───────────────────────────────────────────────────
    if provider == "openrouter":
        client, default = _try_openrouter()
        if client is None:
            logger.warning(
                "resolve_provider_client: openrouter requested but %s",
                _describe_openrouter_unavailable(),
            )
            return None, None
        final_model = _normalize_resolved_model(model or default, provider)
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    # ── Nous Portal (OAuth) ──────────────────────────────────────────
    if provider == "nous":
        # Detect vision tasks: either explicit model override from
        # _PROVIDER_VISION_MODELS, or caller passed a known vision model.
        _is_vision = (
            model in _PROVIDER_VISION_MODELS.values()
            or (model or "").strip().lower() == "mimo-v2-omni"
        )
        client, default = _try_nous(vision=_is_vision)
        if client is None:
            logger.warning("resolve_provider_client: nous requested "
                           "but Nous Portal not configured (run: hermes auth)")
            return None, None
        final_model = _normalize_resolved_model(model or default, provider)
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    # ── OpenAI Codex (OAuth → Responses API) ─────────────────────────
    if provider == "openai-codex":
        if raw_codex:
            # Return the raw OpenAI client for callers that need direct
            # access to responses.stream() (e.g., the main agent loop).
            codex_token = _read_codex_access_token()
            if not codex_token:
                logger.warning("resolve_provider_client: openai-codex requested "
                               "but no Codex OAuth token found (run: hermes model)")
                return None, None
            final_model = _normalize_resolved_model(model or _CODEX_AUX_MODEL, provider)
            raw_client = OpenAI(
                api_key=codex_token,
                base_url=_CODEX_AUX_BASE_URL,
                default_headers=_codex_cloudflare_headers(codex_token),
            )
            return (raw_client, final_model)
        # Standard path: wrap in CodexAuxiliaryClient adapter
        client, default = _try_codex()
        if client is None:
            logger.warning("resolve_provider_client: openai-codex requested "
                           "but no Codex OAuth token found (run: hermes model)")
            return None, None
        final_model = _normalize_resolved_model(model or default, provider)
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    # ── Custom endpoint (OPENAI_BASE_URL + OPENAI_API_KEY) ───────────
    if provider == "custom":
        if explicit_base_url:
            custom_base = explicit_base_url.strip()
            custom_key = (
                (explicit_api_key or "").strip()
                or os.getenv("OPENAI_API_KEY", "").strip()
                or "no-key-required"  # local servers don't need auth
            )
            if not custom_base:
                logger.warning(
                    "resolve_provider_client: explicit custom endpoint requested "
                    "but base_url is empty"
                )
                return None, None
            final_model = _normalize_resolved_model(
                model or _read_main_model() or "gpt-4o-mini",
                provider,
            )
            extra = {}
            _clean_base, _dq = _extract_url_query_params(custom_base)
            if _dq:
                extra["default_query"] = _dq
            if base_url_host_matches(custom_base, "api.kimi.com"):
                extra["default_headers"] = {"User-Agent": "claude-code/0.1.0"}
            elif base_url_host_matches(custom_base, "api.githubcopilot.com"):
                from hermes_cli.copilot_auth import copilot_request_headers
                extra["default_headers"] = copilot_request_headers(
                    is_agent_turn=True, is_vision=is_vision
                )
            client = OpenAI(api_key=custom_key, base_url=_clean_base, **extra)
            client = _wrap_if_needed(client, final_model, custom_base)
            return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                    else (client, final_model))
        # Try custom first, then codex, then API-key providers
        for try_fn in (_try_custom_endpoint, _try_codex,
                       _resolve_api_key_provider):
            client, default = try_fn()
            if client is not None:
                final_model = _normalize_resolved_model(model or default, provider)
                _cbase = str(getattr(client, "base_url", "") or "")
                client = _wrap_if_needed(client, final_model, _cbase)
                return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                        else (client, final_model))
        logger.warning("resolve_provider_client: custom/main requested "
                       "but no endpoint credentials found")
        return None, None

    # ── Named custom providers (config.yaml providers dict / custom_providers list) ───
    try:
        from hermes_cli.runtime_provider import _get_named_custom_provider
        custom_entry = _get_named_custom_provider(provider)
        if custom_entry:
            custom_base = custom_entry.get("base_url", "").strip()
            custom_key = custom_entry.get("api_key", "").strip()
            custom_key_env = custom_entry.get("key_env", "").strip()
            if not custom_key and custom_key_env:
                custom_key = os.getenv(custom_key_env, "").strip()
            custom_key = custom_key or "no-key-required"
            # An explicit per-task api_mode override (from _resolve_task_provider_model)
            # wins; otherwise fall back to what the provider entry declared.
            entry_api_mode = (api_mode or custom_entry.get("api_mode") or "").strip()
            if custom_base:
                final_model = _normalize_resolved_model(
                    model or custom_entry.get("model") or _read_main_model() or "gpt-4o-mini",
                    provider,
                )
                _clean_base2, _dq2 = _extract_url_query_params(custom_base)
                _extra2 = {"default_query": _dq2} if _dq2 else {}
                logger.debug(
                    "resolve_provider_client: named custom provider %r (%s, api_mode=%s)",
                    provider, final_model, entry_api_mode or "chat_completions")
                # anthropic_messages: route through the Anthropic Messages API
                # via AnthropicAuxiliaryClient. Mirrors the anonymous-custom
                # branch in _try_custom_endpoint(). See #15033.
                if entry_api_mode == "anthropic_messages":
                    try:
                        from agent.anthropic_adapter import build_anthropic_client
                        real_client = build_anthropic_client(custom_key, custom_base)
                    except ImportError:
                        logger.warning(
                            "Named custom provider %r declares api_mode="
                            "anthropic_messages but the anthropic SDK is not "
                            "installed — falling back to OpenAI-wire.",
                            provider,
                        )
                        client = OpenAI(api_key=custom_key, base_url=_clean_base2, **_extra2)
                        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                                else (client, final_model))
                    sync_anthropic = AnthropicAuxiliaryClient(
                        real_client, final_model, custom_key, custom_base, is_oauth=False,
                    )
                    if async_mode:
                        return AsyncAnthropicAuxiliaryClient(sync_anthropic), final_model
                    return sync_anthropic, final_model
                client = OpenAI(api_key=custom_key, base_url=_clean_base2, **_extra2)
                # codex_responses or inherited auto-detect (via _wrap_if_needed).
                # _wrap_if_needed reads the closed-over `api_mode` (the task-level
                # override). Named-provider entry api_mode=codex_responses also
                # flows through here.
                if entry_api_mode == "codex_responses" and not isinstance(
                    client, CodexAuxiliaryClient
                ):
                    client = CodexAuxiliaryClient(client, final_model)
                else:
                    client = _wrap_if_needed(client, final_model, custom_base)
                return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                        else (client, final_model))
            logger.warning(
                "resolve_provider_client: named custom provider %r has no base_url",
                provider)
            return None, None
    except ImportError:
        pass

    # ── API-key providers from PROVIDER_REGISTRY ─────────────────────
    try:
        from hermes_cli.auth import (
            PROVIDER_REGISTRY,
            resolve_api_key_provider_credentials,
            resolve_external_process_provider_credentials,
        )
    except ImportError:
        logger.debug("hermes_cli.auth not available for provider %s", provider)
        return None, None

    pconfig = PROVIDER_REGISTRY.get(provider)
    if pconfig is None:
        logger.warning("resolve_provider_client: unknown provider %r", provider)
        return None, None

    if pconfig.auth_type == "api_key":
        if provider == "anthropic":
            client, default_model = _try_anthropic()
            if client is None:
                logger.warning("resolve_provider_client: anthropic requested but no Anthropic credentials found")
                return None, None
            final_model = _normalize_resolved_model(model or default_model, provider)
            return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode else (client, final_model))

        creds = resolve_api_key_provider_credentials(provider)
        api_key = str(creds.get("api_key", "")).strip()
        if not api_key:
            tried_sources = list(pconfig.api_key_env_vars)
            if provider == "copilot":
                tried_sources.append("gh auth token")
            logger.debug("resolve_provider_client: provider %s has no API "
                         "key configured (tried: %s)",
                         provider, ", ".join(tried_sources))
            return None, None

        base_url = _to_openai_base_url(
            str(creds.get("base_url", "")).strip().rstrip("/") or pconfig.inference_base_url
        )

        default_model = _API_KEY_PROVIDER_AUX_MODELS.get(provider, "")
        final_model = _normalize_resolved_model(model or default_model, provider)

        if provider == "gemini":
            from agent.gemini_native_adapter import GeminiNativeClient, is_native_gemini_base_url

            if is_native_gemini_base_url(base_url):
                client = GeminiNativeClient(api_key=api_key, base_url=base_url)
                logger.debug("resolve_provider_client: %s (%s)", provider, final_model)
                return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                        else (client, final_model))

        # Provider-specific headers
        headers = {}
        if base_url_host_matches(base_url, "api.kimi.com"):
            headers["User-Agent"] = "claude-code/0.1.0"
        elif base_url_host_matches(base_url, "api.githubcopilot.com"):
            from hermes_cli.copilot_auth import copilot_request_headers

            headers.update(copilot_request_headers(
                is_agent_turn=True, is_vision=is_vision
            ))
        client = OpenAI(api_key=api_key, base_url=base_url,
                        **({"default_headers": headers} if headers else {}))

        # Copilot GPT-5+ models (except gpt-5-mini) require the Responses
        # API — they are not accessible via /chat/completions.  Wrap the
        # plain client in CodexAuxiliaryClient so call_llm() transparently
        # routes through responses.stream().
        if provider == "copilot" and final_model and not raw_codex:
            try:
                from hermes_cli.models import _should_use_copilot_responses_api
                if _should_use_copilot_responses_api(final_model):
                    logger.debug(
                        "resolve_provider_client: copilot model %s needs "
                        "Responses API — wrapping with CodexAuxiliaryClient",
                        final_model)
                    client = CodexAuxiliaryClient(client, final_model)
            except ImportError:
                pass

        # Honor api_mode for any API-key provider (e.g. direct OpenAI with
        # codex-family models).  The copilot-specific wrapping above handles
        # copilot; this covers the general case (#6800).
        client = _wrap_if_needed(client, final_model, base_url)

        logger.debug("resolve_provider_client: %s (%s)", provider, final_model)
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    if pconfig.auth_type == "external_process":
        creds = resolve_external_process_provider_credentials(provider)
        final_model = _normalize_resolved_model(model or _read_main_model(), provider)
        if provider == "copilot-acp":
            api_key = str(creds.get("api_key", "")).strip()
            base_url = str(creds.get("base_url", "")).strip()
            command = str(creds.get("command", "")).strip() or None
            args = list(creds.get("args") or [])
            if not final_model:
                logger.warning(
                    "resolve_provider_client: copilot-acp requested but no model "
                    "was provided or configured"
                )
                return None, None
            if not api_key or not base_url:
                logger.warning(
                    "resolve_provider_client: copilot-acp requested but external "
                    "process credentials are incomplete"
                )
                return None, None
            from agent.copilot_acp_client import CopilotACPClient

            client = CopilotACPClient(
                api_key=api_key,
                base_url=base_url,
                command=command,
                args=args,
            )
            logger.debug("resolve_provider_client: %s (%s)", provider, final_model)
            return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                    else (client, final_model))
        logger.warning("resolve_provider_client: external-process provider %s not "
                       "directly supported", provider)
        return None, None

    elif pconfig.auth_type == "aws_sdk":
        # AWS SDK providers (Bedrock) — use the Anthropic Bedrock client via
        # boto3's credential chain (IAM roles, SSO, env vars, instance metadata).
        try:
            from agent.bedrock_adapter import has_aws_credentials, resolve_bedrock_region
            from agent.anthropic_adapter import build_anthropic_bedrock_client
        except ImportError:
            logger.warning("resolve_provider_client: bedrock requested but "
                           "boto3 or anthropic SDK not installed")
            return None, None

        if not has_aws_credentials():
            logger.debug("resolve_provider_client: bedrock requested but "
                         "no AWS credentials found")
            return None, None

        region = resolve_bedrock_region()
        default_model = "anthropic.claude-haiku-4-5-20251001-v1:0"
        final_model = _normalize_resolved_model(model or default_model, provider)
        try:
            real_client = build_anthropic_bedrock_client(region)
        except ImportError as exc:
            logger.warning("resolve_provider_client: cannot create Bedrock "
                           "client: %s", exc)
            return None, None
        client = AnthropicAuxiliaryClient(
            real_client, final_model, api_key="aws-sdk",
            base_url=f"https://bedrock-runtime.{region}.amazonaws.com",
        )
        logger.debug("resolve_provider_client: bedrock (%s, %s)", final_model, region)
        return (_to_async_client(client, final_model, is_vision=is_vision) if async_mode
                else (client, final_model))

    elif pconfig.auth_type in ("oauth_device_code", "oauth_external"):
        # OAuth providers — route through their specific try functions
        if provider == "nous":
            return resolve_provider_client("nous", model, async_mode)
        if provider == "openai-codex":
            return resolve_provider_client("openai-codex", model, async_mode)
        # Other OAuth providers not directly supported
        logger.warning("resolve_provider_client: OAuth provider %s not "
                       "directly supported, try 'auto'", provider)
        return None, None

    logger.warning("resolve_provider_client: unhandled auth_type %s for %s",
                   pconfig.auth_type, provider)
    return None, None


# ── Public API ──────────────────────────────────────────────────────────────

def get_text_auxiliary_client(
    task: str = "",
    *,
    main_runtime: Optional[Dict[str, Any]] = None,
) -> Tuple[Optional[OpenAI], Optional[str]]:
    """Return (client, default_model_slug) for text-only auxiliary tasks.

    Args:
        task: Optional task name ("compression", "web_extract") to check
              for a task-specific provider override.

    Callers may override the returned model via config.yaml
    (e.g. auxiliary.compression.model, auxiliary.web_extract.model).
    """
    provider, model, base_url, api_key, api_mode = _resolve_task_provider_model(task or None)
    return resolve_provider_client(
        provider,
        model=model,
        explicit_base_url=base_url,
        explicit_api_key=api_key,
        api_mode=api_mode,
        main_runtime=main_runtime,
    )


def get_async_text_auxiliary_client(task: str = "", *, main_runtime: Optional[Dict[str, Any]] = None):
    """Return (async_client, model_slug) for async consumers.

    For standard providers returns (AsyncOpenAI, model). For Codex returns
    (AsyncCodexAuxiliaryClient, model) which wraps the Responses API.
    Returns (None, None) when no provider is available.
    """
    provider, model, base_url, api_key, api_mode = _resolve_task_provider_model(task or None)
    return resolve_provider_client(
        provider,
        model=model,
        async_mode=True,
        explicit_base_url=base_url,
        explicit_api_key=api_key,
        api_mode=api_mode,
        main_runtime=main_runtime,
    )


_VISION_AUTO_PROVIDER_ORDER = (
    "openrouter",
    "nous",
)


def _normalize_vision_provider(provider: Optional[str]) -> str:
    return _normalize_aux_provider(provider)


def _resolve_strict_vision_backend(
    provider: str,
    model: Optional[str] = None,
) -> Tuple[Optional[Any], Optional[str]]:
    provider = _normalize_vision_provider(provider)
    if provider == "copilot":
        return resolve_provider_client("copilot", model, is_vision=True)
    if provider == "openrouter":
        return _try_openrouter()
    if provider == "nous":
        return _try_nous(vision=True)
    if provider == "openai-codex":
        return _try_codex()
    if provider == "anthropic":
        return _try_anthropic()
    if provider == "custom":
        return _try_custom_endpoint()
    return None, None


def _strict_vision_backend_available(provider: str) -> bool:
    return _resolve_strict_vision_backend(provider)[0] is not None


def get_available_vision_backends() -> List[str]:
    """Return the currently available vision backends in auto-selection order.

    Order: active provider → OpenRouter → Nous → stop.  This is the single
    source of truth for setup, tool gating, and runtime auto-routing of
    vision tasks.
    """
    available: List[str] = []
    # 1. Active provider — if the user configured a provider, try it first.
    main_provider = _read_main_provider()
    if main_provider and main_provider not in ("auto", ""):
        if main_provider in _VISION_AUTO_PROVIDER_ORDER:
            if _strict_vision_backend_available(main_provider):
                available.append(main_provider)
        else:
            client, _ = resolve_provider_client(main_provider, _read_main_model())
            if client is not None:
                available.append(main_provider)
    # 2. OpenRouter, 3. Nous — skip if already covered by main provider.
    for p in _VISION_AUTO_PROVIDER_ORDER:
        if p not in available and _strict_vision_backend_available(p):
            available.append(p)
    return available


def resolve_vision_provider_client(
    provider: Optional[str] = None,
    model: Optional[str] = None,
    *,
    base_url: Optional[str] = None,
    api_key: Optional[str] = None,
    async_mode: bool = False,
) -> Tuple[Optional[str], Optional[Any], Optional[str]]:
    """Resolve the client actually used for vision tasks.

    Direct endpoint overrides take precedence over provider selection. Explicit
    provider overrides still use the generic provider router for non-standard
    backends, so users can intentionally force experimental providers. Auto mode
    stays conservative and only tries vision backends known to work today.
    """
    requested, resolved_model, resolved_base_url, resolved_api_key, resolved_api_mode = _resolve_task_provider_model(
        "vision", provider, model, base_url, api_key
    )
    requested = _normalize_vision_provider(requested)

    def _finalize(resolved_provider: str, sync_client: Any, default_model: Optional[str]):
        if sync_client is None:
            return resolved_provider, None, None
        final_model = resolved_model or default_model
        if async_mode:
            async_client, async_model = _to_async_client(sync_client, final_model, is_vision=True)
            return resolved_provider, async_client, async_model
        return resolved_provider, sync_client, final_model

    if resolved_base_url:
        client, final_model = resolve_provider_client(
            "custom",
            model=resolved_model,
            async_mode=async_mode,
            explicit_base_url=resolved_base_url,
            explicit_api_key=resolved_api_key,
            api_mode=resolved_api_mode,
        )
        if client is None:
            return "custom", None, None
        return "custom", client, final_model

    if requested == "auto":
        # Vision auto-detection order:
        #   1. User's main provider + main model (including aggregators).
        #      _PROVIDER_VISION_MODELS provides per-provider vision model
        #      overrides when the provider has a dedicated multimodal model
        #      that differs from the chat model (e.g. xiaomi → mimo-v2-omni,
        #      zai → glm-5v-turbo). Nous is the exception: it has a dedicated
        #      strict vision backend with tier-aware defaults, so it must not
        #      fall through to the user's text chat model here.
        #   2. OpenRouter  (vision-capable aggregator fallback)
        #   3. Nous Portal (vision-capable aggregator fallback)
        #   4. Stop
        main_provider = _read_main_provider()
        main_model = _read_main_model()
        if main_provider and main_provider not in ("auto", ""):
            vision_model = _PROVIDER_VISION_MODELS.get(main_provider, main_model)
            if main_provider == "nous":
                sync_client, default_model = _resolve_strict_vision_backend(
                    main_provider, vision_model
                )
                if sync_client is not None:
                    logger.info(
                        "Vision auto-detect: using main provider %s (%s)",
                        main_provider, default_model or resolved_model or main_model,
                    )
                    return _finalize(main_provider, sync_client, default_model)
            else:
                rpc_client, rpc_model = resolve_provider_client(
                    main_provider, vision_model,
                    api_mode=resolved_api_mode,
                    is_vision=True)
                if rpc_client is not None:
                    logger.info(
                        "Vision auto-detect: using main provider %s (%s)",
                        main_provider, rpc_model or vision_model,
                    )
                    return _finalize(
                        main_provider, rpc_client, rpc_model or vision_model)

        # Fall back through aggregators (uses their dedicated vision model,
        # not the user's main model) when main provider has no client.
        for candidate in _VISION_AUTO_PROVIDER_ORDER:
            if candidate == main_provider:
                continue  # already tried above
            sync_client, default_model = _resolve_strict_vision_backend(candidate)
            if sync_client is not None:
                return _finalize(candidate, sync_client, default_model)

        logger.debug("Auxiliary vision client: none available")
        return None, None, None

    if requested in _VISION_AUTO_PROVIDER_ORDER:
        sync_client, default_model = _resolve_strict_vision_backend(
            requested, resolved_model
        )
        return _finalize(requested, sync_client, default_model)

    client, final_model = _get_cached_client(requested, resolved_model, async_mode,
                                             api_mode=resolved_api_mode,
                                             is_vision=True)
    if client is None:
        return requested, None, None
    return requested, client, final_model


def get_auxiliary_extra_body() -> dict:
    """Return extra_body kwargs for auxiliary API calls.
    
    Includes Nous Portal product tags when the auxiliary client is backed
    by Nous Portal. Returns empty dict otherwise.
    """
    return dict(NOUS_EXTRA_BODY) if auxiliary_is_nous else {}


def auxiliary_max_tokens_param(value: int) -> dict:
    """Return the correct max tokens kwarg for the auxiliary client's provider.
    
    OpenRouter and local models use 'max_tokens'. Direct OpenAI with newer
    models (gpt-4o, o-series, gpt-5+) requires 'max_completion_tokens'.
    The Codex adapter translates max_tokens internally, so we use max_tokens
    for it as well.
    """
    custom_base = _current_custom_base_url()
    or_key = os.getenv("OPENROUTER_API_KEY")
    # Only use max_completion_tokens for direct OpenAI custom endpoints
    if (not or_key
            and _read_nous_auth() is None
            and base_url_hostname(custom_base) == "api.openai.com"):
        return {"max_completion_tokens": value}
    return {"max_tokens": value}


# ── Centralized LLM Call API ────────────────────────────────────────────────
#
# call_llm() and async_call_llm() own the full request lifecycle:
#   1. Resolve provider + model from task config (or explicit args)
#   2. Get or create a cached client for that provider
#   3. Format request args for the provider + model (max_tokens handling, etc.)
#   4. Make the API call
#   5. Return the response
#
# Every auxiliary LLM consumer should use these instead of manually
# constructing clients and calling .chat.completions.create().

# Client cache: (provider, async_mode, base_url, api_key, api_mode, runtime_key) -> (client, default_model, loop)
# NOTE: loop identity is NOT part of the key.  On async cache hits we check
# whether the cached loop is the *current* loop; if not, the stale entry is
# replaced in-place.  This bounds cache growth to one entry per unique
# provider config rather than one per (config × event-loop), which previously
# caused unbounded fd accumulation in long-running gateway processes (#10200).
_client_cache: Dict[tuple, tuple] = {}
_client_cache_lock = threading.Lock()
_CLIENT_CACHE_MAX_SIZE = 64  # safety belt — evict oldest when exceeded


def _client_cache_key(
    provider: str,
    *,
    async_mode: bool,
    base_url: Optional[str] = None,
    api_key: Optional[str] = None,
    api_mode: Optional[str] = None,
    main_runtime: Optional[Dict[str, Any]] = None,
    is_vision: bool = False,
) -> tuple:
    runtime = _normalize_main_runtime(main_runtime)
    runtime_key = tuple(runtime.get(field, "") for field in _MAIN_RUNTIME_FIELDS) if provider == "auto" else ()
    return (provider, async_mode, base_url or "", api_key or "", api_mode or "", runtime_key, is_vision)


def _store_cached_client(cache_key: tuple, client: Any, default_model: Optional[str], *, bound_loop: Any = None) -> None:
    with _client_cache_lock:
        old_entry = _client_cache.get(cache_key)
        if old_entry is not None and old_entry[0] is not client:
            _force_close_async_httpx(old_entry[0])
            try:
                close_fn = getattr(old_entry[0], "close", None)
                if callable(close_fn):
                    close_fn()
            except Exception:
                pass
        _client_cache[cache_key] = (client, default_model, bound_loop)


def _refresh_nous_auxiliary_client(
    *,
    cache_provider: str,
    model: Optional[str],
    async_mode: bool,
    base_url: Optional[str] = None,
    api_key: Optional[str] = None,
    api_mode: Optional[str] = None,
    main_runtime: Optional[Dict[str, Any]] = None,
    is_vision: bool = False,
) -> Tuple[Optional[Any], Optional[str]]:
    """Refresh Nous runtime creds, rebuild the client, and replace the cache entry."""
    runtime = _resolve_nous_runtime_api(force_refresh=True)
    if runtime is None:
        return None, model

    fresh_key, fresh_base_url = runtime
    sync_client = OpenAI(api_key=fresh_key, base_url=fresh_base_url)
    final_model = model

    current_loop = None
    if async_mode:
        try:
            import asyncio as _aio
            current_loop = _aio.get_event_loop()
        except RuntimeError:
            pass
        client, final_model = _to_async_client(sync_client, final_model or "", is_vision=is_vision)
    else:
        client = sync_client

    cache_key = _client_cache_key(
        cache_provider,
        async_mode=async_mode,
        base_url=base_url,
        api_key=api_key,
        api_mode=api_mode,
        main_runtime=main_runtime,
        is_vision=is_vision,
    )
    _store_cached_client(cache_key, client, final_model, bound_loop=current_loop)
    return client, final_model


def neuter_async_httpx_del() -> None:
    """Monkey-patch ``AsyncHttpxClientWrapper.__del__`` to be a no-op.

    The OpenAI SDK's ``AsyncHttpxClientWrapper.__del__`` schedules
    ``self.aclose()`` via ``asyncio.get_running_loop().create_task()``.
    When an ``AsyncOpenAI`` client is garbage-collected while
    prompt_toolkit's event loop is running (the common CLI idle state),
    the ``aclose()`` task runs on prompt_toolkit's loop but the
    underlying TCP transport is bound to a *different* loop (the worker
    thread's loop that the client was originally created on).  If that
    loop is closed or its thread is dead, the transport's
    ``self._loop.call_soon()`` raises ``RuntimeError("Event loop is
    closed")``, which prompt_toolkit surfaces as "Unhandled exception
    in event loop ... Press ENTER to continue...".

    Neutering ``__del__`` is safe because:
    - Cached clients are explicitly cleaned via ``_force_close_async_httpx``
      on stale-loop detection and ``shutdown_cached_clients`` on exit.
    - Uncached clients' TCP connections are cleaned up by the OS when the
      process exits.
    - The OpenAI SDK itself marks this as a TODO (``# TODO(someday):
      support non asyncio runtimes here``).

    Call this once at CLI startup, before any ``AsyncOpenAI`` clients are
    created.
    """
    try:
        from openai._base_client import AsyncHttpxClientWrapper
        AsyncHttpxClientWrapper.__del__ = lambda self: None  # type: ignore[assignment]
    except (ImportError, AttributeError):
        pass  # Graceful degradation if the SDK changes its internals


def _force_close_async_httpx(client: Any) -> None:
    """Mark the httpx AsyncClient inside an AsyncOpenAI client as closed.

    This prevents ``AsyncHttpxClientWrapper.__del__`` from scheduling
    ``aclose()`` on a (potentially closed) event loop, which causes
    ``RuntimeError: Event loop is closed`` → prompt_toolkit's
    "Press ENTER to continue..." handler.

    We intentionally do NOT run the full async close path — the
    connections will be dropped by the OS when the process exits.
    """
    try:
        from httpx._client import ClientState
        inner = getattr(client, "_client", None)
        if inner is not None and not getattr(inner, "is_closed", True):
            inner._state = ClientState.CLOSED
    except Exception:
        pass


def shutdown_cached_clients() -> None:
    """Close all cached clients (sync and async) to prevent event-loop errors.

    Call this during CLI shutdown, *before* the event loop is closed, to
    avoid ``AsyncHttpxClientWrapper.__del__`` raising on a dead loop.
    """
    import inspect

    with _client_cache_lock:
        for key, entry in list(_client_cache.items()):
            client = entry[0]
            if client is None:
                continue
            # Mark any async httpx transport as closed first (prevents __del__
            # from scheduling aclose() on a dead event loop).
            _force_close_async_httpx(client)
            # Sync clients: close the httpx connection pool cleanly.
            # Async clients: skip — we already neutered __del__ above.
            try:
                close_fn = getattr(client, "close", None)
                if close_fn and not inspect.iscoroutinefunction(close_fn):
                    close_fn()
            except Exception:
                pass
        _client_cache.clear()


def cleanup_stale_async_clients() -> None:
    """Force-close cached async clients whose event loop is closed.

    Call this after each agent turn to proactively clean up stale clients
    before GC can trigger ``AsyncHttpxClientWrapper.__del__`` on them.
    This is defense-in-depth — the primary fix is ``neuter_async_httpx_del``
    which disables ``__del__`` entirely.
    """
    with _client_cache_lock:
        stale_keys = []
        for key, entry in _client_cache.items():
            client, _default, cached_loop = entry
            if cached_loop is not None and cached_loop.is_closed():
                _force_close_async_httpx(client)
                stale_keys.append(key)
        for key in stale_keys:
            del _client_cache[key]


def _is_openrouter_client(client: Any) -> bool:
    for obj in (client, getattr(client, "_client", None), getattr(client, "client", None)):
        if obj and base_url_host_matches(str(getattr(obj, "base_url", "") or ""), "openrouter.ai"):
            return True
    return False


def _cached_client_accepts_slash_models(client: Any, cached_default: Optional[str]) -> bool:
    """Best-effort check for cached clients that accept ``vendor/model`` IDs."""
    if _is_openrouter_client(client):
        return True
    return bool(cached_default and "/" in cached_default)


def _compat_model(client: Any, model: Optional[str], cached_default: Optional[str]) -> Optional[str]:
    """Keep slash-bearing model IDs only for cached clients that support them.

    Mirrors the guard in resolve_provider_client() which is skipped on cache hits.
    """
    if model and "/" in model and not _cached_client_accepts_slash_models(client, cached_default):
        return cached_default
    return model or cached_default


def _get_cached_client(
    provider: str,
    model: str = None,
    async_mode: bool = False,
    base_url: str = None,
    api_key: str = None,
    api_mode: str = None,
    main_runtime: Optional[Dict[str, Any]] = None,
    is_vision: bool = False,
) -> Tuple[Optional[Any], Optional[str]]:
    """Get or create a cached client for the given provider.

    Async clients (AsyncOpenAI) use httpx.AsyncClient internally, which
    binds to the event loop that was current when the client was created.
    Using such a client on a *different* loop causes deadlocks or
    RuntimeError.  To prevent cross-loop issues, the cache validates on
    every async hit that the cached loop is the *current, open* loop.
    If the loop changed (e.g. a new gateway worker-thread loop), the stale
    entry is replaced in-place rather than creating an additional entry.

    This keeps cache size bounded to one entry per unique provider config,
    preventing the fd-exhaustion that previously occurred in long-running
    gateways where recycled worker threads created unbounded entries (#10200).
    """
    # Resolve the current event loop for async clients so we can validate
    # cached entries.  Loop identity is NOT in the cache key — instead we
    # check at hit time whether the cached loop is still current and open.
    # This prevents unbounded cache growth from recycled worker-thread loops
    # while still guaranteeing we never reuse a client on the wrong loop
    # (which causes deadlocks, see #2681).
    current_loop = None
    if async_mode:
        try:
            import asyncio as _aio
            current_loop = _aio.get_event_loop()
        except RuntimeError:
            pass
    runtime = _normalize_main_runtime(main_runtime)
    cache_key = _client_cache_key(
        provider,
        async_mode=async_mode,
        base_url=base_url,
        api_key=api_key,
        api_mode=api_mode,
        main_runtime=main_runtime,
        is_vision=is_vision,
    )
    with _client_cache_lock:
        if cache_key in _client_cache:
            cached_client, cached_default, cached_loop = _client_cache[cache_key]
            if async_mode:
                # Validate: the cached client must be bound to the CURRENT,
                # OPEN loop.  If the loop changed or was closed, the httpx
                # transport inside is dead — force-close and replace.
                loop_ok = (
                    cached_loop is not None
                    and cached_loop is current_loop
                    and not cached_loop.is_closed()
                )
                if loop_ok:
                    effective = _compat_model(cached_client, model, cached_default)
                    return cached_client, effective
                # Stale — evict and fall through to create a new client.
                _force_close_async_httpx(cached_client)
                del _client_cache[cache_key]
            else:
                effective = _compat_model(cached_client, model, cached_default)
                return cached_client, effective
    # Build outside the lock
    client, default_model = resolve_provider_client(
        provider,
        model,
        async_mode,
        explicit_base_url=base_url,
        explicit_api_key=api_key,
        api_mode=api_mode,
        main_runtime=runtime,
        is_vision=is_vision,
    )
    if client is not None:
        # For async clients, remember which loop they were created on so we
        # can detect stale entries later.
        bound_loop = current_loop
        with _client_cache_lock:
            if cache_key not in _client_cache:
                # Safety belt: if the cache has grown beyond the max, evict
                # the oldest entries (FIFO — dict preserves insertion order).
                while len(_client_cache) >= _CLIENT_CACHE_MAX_SIZE:
                    evict_key, evict_entry = next(iter(_client_cache.items()))
                    _force_close_async_httpx(evict_entry[0])
                    del _client_cache[evict_key]
                _client_cache[cache_key] = (client, default_model, bound_loop)
            else:
                client, default_model, _ = _client_cache[cache_key]
    return client, model or default_model


def _resolve_task_provider_model(
    task: str = None,
    provider: str = None,
    model: str = None,
    base_url: str = None,
    api_key: str = None,
) -> Tuple[str, Optional[str], Optional[str], Optional[str], Optional[str]]:
    """Determine provider + model for a call.

    Priority:
      1. Explicit provider/model/base_url/api_key args (always win)
      2. Config file (auxiliary.{task}.provider/model/base_url)
      3. "auto" (full auto-detection chain)

    Returns (provider, model, base_url, api_key, api_mode) where model may
    be None (use provider default). When base_url is set, provider is forced
    to "custom" and the task uses that direct endpoint. api_mode is one of
    "chat_completions", "codex_responses", or None (auto-detect).
    """
    cfg_provider = None
    cfg_model = None
    cfg_base_url = None
    cfg_api_key = None
    cfg_api_mode = None

    if task:
        task_config = _get_auxiliary_task_config(task)
        cfg_provider = str(task_config.get("provider", "")).strip() or None
        cfg_model = str(task_config.get("model", "")).strip() or None
        cfg_base_url = str(task_config.get("base_url", "")).strip() or None
        cfg_api_key = str(task_config.get("api_key", "")).strip() or None
        cfg_api_mode = str(task_config.get("api_mode", "")).strip() or None

    resolved_model = model or cfg_model
    resolved_api_mode = cfg_api_mode

    if base_url:
        return "custom", resolved_model, base_url, api_key, resolved_api_mode
    if provider:
        return provider, resolved_model, base_url, api_key, resolved_api_mode

    if task:
        # Config.yaml is the primary source for per-task overrides.
        if cfg_base_url:
            return "custom", resolved_model, cfg_base_url, cfg_api_key, resolved_api_mode
        if cfg_provider and cfg_provider != "auto":
            return cfg_provider, resolved_model, None, None, resolved_api_mode

        return "auto", resolved_model, None, None, resolved_api_mode

    return "auto", resolved_model, None, None, resolved_api_mode


_DEFAULT_AUX_TIMEOUT = 30.0


def _get_auxiliary_task_config(task: str) -> Dict[str, Any]:
    """Return the config dict for auxiliary.<task>, or {} when unavailable."""
    if not task:
        return {}
    try:
        from hermes_cli.config import load_config
        config = load_config()
    except ImportError:
        return {}
    aux = config.get("auxiliary", {}) if isinstance(config, dict) else {}
    task_config = aux.get(task, {}) if isinstance(aux, dict) else {}
    return task_config if isinstance(task_config, dict) else {}


def _get_task_timeout(task: str, default: float = _DEFAULT_AUX_TIMEOUT) -> float:
    """Read timeout from auxiliary.{task}.timeout in config, falling back to *default*."""
    if not task:
        return default
    task_config = _get_auxiliary_task_config(task)
    raw = task_config.get("timeout")
    if raw is not None:
        try:
            return float(raw)
        except (ValueError, TypeError):
            pass
    return default


def _get_task_extra_body(task: str) -> Dict[str, Any]:
    """Read auxiliary.<task>.extra_body and return a shallow copy when valid."""
    task_config = _get_auxiliary_task_config(task)
    raw = task_config.get("extra_body")
    if isinstance(raw, dict):
        return dict(raw)
    return {}


# ---------------------------------------------------------------------------
# Anthropic-compatible endpoint detection + image block conversion
# ---------------------------------------------------------------------------

# Providers that use Anthropic-compatible endpoints (via OpenAI SDK wrapper).
# Their image content blocks must use Anthropic format, not OpenAI format.
_ANTHROPIC_COMPAT_PROVIDERS = frozenset({"minimax", "minimax-cn"})


def _is_anthropic_compat_endpoint(provider: str, base_url: str) -> bool:
    """Detect if an endpoint expects Anthropic-format content blocks.

    Returns True for known Anthropic-compatible providers (MiniMax) and
    any endpoint whose URL contains ``/anthropic`` in the path.
    """
    if provider in _ANTHROPIC_COMPAT_PROVIDERS:
        return True
    url_lower = (base_url or "").lower()
    return "/anthropic" in url_lower


def _convert_openai_images_to_anthropic(messages: list) -> list:
    """Convert OpenAI ``image_url`` content blocks to Anthropic ``image`` blocks.

    Only touches messages that have list-type content with ``image_url`` blocks;
    plain text messages pass through unchanged.
    """
    converted = []
    for msg in messages:
        content = msg.get("content")
        if not isinstance(content, list):
            converted.append(msg)
            continue
        new_content = []
        changed = False
        for block in content:
            if block.get("type") == "image_url":
                image_url_val = (block.get("image_url") or {}).get("url", "")
                if image_url_val.startswith("data:"):
                    # Parse data URI: data:<media_type>;base64,<data>
                    header, _, b64data = image_url_val.partition(",")
                    media_type = "image/png"
                    if ":" in header and ";" in header:
                        media_type = header.split(":", 1)[1].split(";", 1)[0]
                    new_content.append({
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": media_type,
                            "data": b64data,
                        },
                    })
                else:
                    # URL-based image
                    new_content.append({
                        "type": "image",
                        "source": {
                            "type": "url",
                            "url": image_url_val,
                        },
                    })
                changed = True
            else:
                new_content.append(block)
        converted.append({**msg, "content": new_content} if changed else msg)
    return converted



def _build_call_kwargs(
    provider: str,
    model: str,
    messages: list,
    temperature: Optional[float] = None,
    max_tokens: Optional[int] = None,
    tools: Optional[list] = None,
    timeout: float = 30.0,
    extra_body: Optional[dict] = None,
    base_url: Optional[str] = None,
) -> dict:
    """Build kwargs for .chat.completions.create() with model/provider adjustments."""
    kwargs: Dict[str, Any] = {
        "model": model,
        "messages": messages,
        "timeout": timeout,
    }

    fixed_temperature = _fixed_temperature_for_model(model, base_url)
    if fixed_temperature is OMIT_TEMPERATURE:
        temperature = None  # strip — let server choose
    elif fixed_temperature is not None:
        temperature = fixed_temperature

    # Opus 4.7+ rejects any non-default temperature/top_p/top_k — silently
    # drop here so auxiliary callers that hardcode temperature (e.g. 0 on
    # structured-JSON extraction) don't 400 the moment
    # the aux model is flipped to 4.7.
    if temperature is not None:
        from agent.anthropic_adapter import _forbids_sampling_params
        if _forbids_sampling_params(model):
            temperature = None

    if temperature is not None:
        kwargs["temperature"] = temperature

    if max_tokens is not None:
        # Codex adapter handles max_tokens internally; OpenRouter/Nous use max_tokens.
        # Direct OpenAI api.openai.com with newer models needs max_completion_tokens.
        if provider == "custom":
            custom_base = base_url or _current_custom_base_url()
            if base_url_hostname(custom_base) == "api.openai.com":
                kwargs["max_completion_tokens"] = max_tokens
            else:
                kwargs["max_tokens"] = max_tokens
        else:
            kwargs["max_tokens"] = max_tokens

    if tools:
        kwargs["tools"] = tools

    # Provider-specific extra_body
    merged_extra = dict(extra_body or {})
    if provider == "nous" or auxiliary_is_nous:
        merged_extra.setdefault("tags", []).extend(["product=hermes-agent"])
    if merged_extra:
        kwargs["extra_body"] = merged_extra

    return kwargs


def _validate_llm_response(response: Any, task: str = None) -> Any:
    """Validate that an LLM response has the expected .choices[0].message shape.

    Fails fast with a clear error instead of letting malformed payloads
    propagate to downstream consumers where they crash with misleading
    AttributeError (e.g. "'str' object has no attribute 'choices'").

    See #7264.
    """
    if response is None:
        raise RuntimeError(
            f"Auxiliary {task or 'call'}: LLM returned None response"
        )
    # Allow SimpleNamespace responses from adapters (CodexAuxiliaryClient,
    # AnthropicAuxiliaryClient) — they have .choices[0].message.
    try:
        choices = response.choices
        if not choices or not hasattr(choices[0], "message"):
            raise AttributeError("missing choices[0].message")
    except (AttributeError, TypeError, IndexError) as exc:
        response_type = type(response).__name__
        response_preview = str(response)[:120]
        raise RuntimeError(
            f"Auxiliary {task or 'call'}: LLM returned invalid response "
            f"(type={response_type}): {response_preview!r}. "
            f"Expected object with .choices[0].message — check provider "
            f"adapter or custom endpoint compatibility."
        ) from exc
    return response


def call_llm(
    task: str = None,
    *,
    provider: str = None,
    model: str = None,
    base_url: str = None,
    api_key: str = None,
    main_runtime: Optional[Dict[str, Any]] = None,
    messages: list,
    temperature: float = None,
    max_tokens: int = None,
    tools: list = None,
    timeout: float = None,
    extra_body: dict = None,
) -> Any:
    """Centralized synchronous LLM call.

    Resolves provider + model (from task config, explicit args, or auto-detect),
    handles auth, request formatting, and model-specific arg adjustments.

    Args:
        task: Auxiliary task name ("compression", "vision", "web_extract",
              "session_search", "skills_hub", "mcp", "title_generation").
              Reads provider:model from config/env. Ignored if provider is set.
        provider: Explicit provider override.
        model: Explicit model override.
        messages: Chat messages list.
        temperature: Sampling temperature (None = provider default).
        max_tokens: Max output tokens (handles max_tokens vs max_completion_tokens).
        tools: Tool definitions (for function calling).
        timeout: Request timeout in seconds (None = read from auxiliary.{task}.timeout config).
        extra_body: Additional request body fields.

    Returns:
        Response object with .choices[0].message.content

    Raises:
        RuntimeError: If no provider is configured.
    """
    resolved_provider, resolved_model, resolved_base_url, resolved_api_key, resolved_api_mode = _resolve_task_provider_model(
        task, provider, model, base_url, api_key)
    effective_extra_body = _get_task_extra_body(task)
    effective_extra_body.update(extra_body or {})

    if task == "vision":
        effective_provider, client, final_model = resolve_vision_provider_client(
            provider=resolved_provider if resolved_provider != "auto" else provider,
            model=resolved_model or model,
            base_url=resolved_base_url or base_url,
            api_key=resolved_api_key or api_key,
            async_mode=False,
        )
        if client is None and resolved_provider != "auto" and not resolved_base_url:
            logger.warning(
                "Vision provider %s unavailable, falling back to auto vision backends",
                resolved_provider,
            )
            effective_provider, client, final_model = resolve_vision_provider_client(
                provider="auto",
                model=resolved_model,
                async_mode=False,
            )
        if client is None:
            raise RuntimeError(
                f"No LLM provider configured for task={task} provider={resolved_provider}. "
                f"Run: hermes setup"
            )
        resolved_provider = effective_provider or resolved_provider
    else:
        client, final_model = _get_cached_client(
            resolved_provider,
            resolved_model,
            base_url=resolved_base_url,
            api_key=resolved_api_key,
            api_mode=resolved_api_mode,
            main_runtime=main_runtime,
        )
        if client is None:
            # When the user explicitly chose a non-OpenRouter provider but no
            # credentials were found, fail fast instead of silently routing
            # through OpenRouter (which causes confusing 404s).
            _explicit = (resolved_provider or "").strip().lower()
            if _explicit and _explicit not in ("auto", "openrouter", "custom"):
                raise RuntimeError(
                    f"Provider '{_explicit}' is set in config.yaml but no API key "
                    f"was found. Set the {_explicit.upper()}_API_KEY environment "
                    f"variable, or switch to a different provider with `hermes model`."
                )
            # For auto/custom with no credentials, try the full auto chain
            # rather than hardcoding OpenRouter (which may be depleted).
            # Pass model=None so each provider uses its own default —
            # resolved_model may be an OpenRouter-format slug that doesn't
            # work on other providers.
            if not resolved_base_url:
                logger.info("Auxiliary %s: provider %s unavailable, trying auto-detection chain",
                            task or "call", resolved_provider)
                client, final_model = _get_cached_client("auto", main_runtime=main_runtime)
        if client is None:
            raise RuntimeError(
                f"No LLM provider configured for task={task} provider={resolved_provider}. "
                f"Run: hermes setup")

    effective_timeout = timeout if timeout is not None else _get_task_timeout(task)

    # Log what we're about to do — makes auxiliary operations visible
    _base_info = str(getattr(client, "base_url", resolved_base_url) or "")
    if task:
        logger.info("Auxiliary %s: using %s (%s)%s",
                     task, resolved_provider or "auto", final_model or "default",
                     f" at {_base_info}" if _base_info and "openrouter" not in _base_info else "")

    # Pass the client's actual base_url (not just resolved_base_url) so
    # endpoint-specific temperature overrides can distinguish
    # api.moonshot.ai vs api.kimi.com/coding even on auto-detected routes.
    kwargs = _build_call_kwargs(
        resolved_provider, final_model, messages,
        temperature=temperature, max_tokens=max_tokens,
        tools=tools, timeout=effective_timeout, extra_body=effective_extra_body,
        base_url=_base_info or resolved_base_url)

    # Convert image blocks for Anthropic-compatible endpoints (e.g. MiniMax)
    _client_base = str(getattr(client, "base_url", "") or "")
    if _is_anthropic_compat_endpoint(resolved_provider, _client_base):
        kwargs["messages"] = _convert_openai_images_to_anthropic(kwargs["messages"])

    # Handle unsupported temperature, max_tokens vs max_completion_tokens retry,
    # then payment fallback.
    try:
        return _validate_llm_response(
            client.chat.completions.create(**kwargs), task)
    except Exception as first_err:
        if "temperature" in kwargs and _is_unsupported_temperature_error(first_err):
            retry_kwargs = dict(kwargs)
            retry_kwargs.pop("temperature", None)
            logger.info(
                "Auxiliary %s: provider rejected temperature; retrying once without it",
                task or "call",
            )
            try:
                return _validate_llm_response(
                    client.chat.completions.create(**retry_kwargs), task)
            except Exception as retry_err:
                retry_err_str = str(retry_err)
                # If retry still fails, fall through to the max_tokens /
                # payment / auth chains below using the temperature-stripped
                # kwargs.  Re-raise only if the retry hit something those
                # chains won't handle.
                if not (
                    _is_payment_error(retry_err)
                    or _is_connection_error(retry_err)
                    or _is_auth_error(retry_err)
                    or "max_tokens" in retry_err_str
                    or "unsupported_parameter" in retry_err_str
                ):
                    raise
                first_err = retry_err
                kwargs = retry_kwargs

        err_str = str(first_err)
        if max_tokens is not None and (
            "max_tokens" in err_str
            or "unsupported_parameter" in err_str
            or _is_unsupported_parameter_error(first_err, "max_tokens")
        ):
            kwargs.pop("max_tokens", None)
            kwargs["max_completion_tokens"] = max_tokens
            try:
                return _validate_llm_response(
                    client.chat.completions.create(**kwargs), task)
            except Exception as retry_err:
                # If the max_tokens retry also hits a payment or connection
                # error, fall through to the fallback chain below.
                if not (_is_payment_error(retry_err) or _is_connection_error(retry_err)):
                    raise
                first_err = retry_err

        # ── Nous auth refresh parity with main agent ──────────────────
        client_is_nous = (
            resolved_provider == "nous"
            or base_url_host_matches(_base_info, "inference-api.nousresearch.com")
        )
        if _is_auth_error(first_err) and client_is_nous:
            refreshed_client, refreshed_model = _refresh_nous_auxiliary_client(
                cache_provider=resolved_provider or "nous",
                model=final_model,
                async_mode=False,
                base_url=resolved_base_url,
                api_key=resolved_api_key,
                api_mode=resolved_api_mode,
                main_runtime=main_runtime,
                is_vision=(task == "vision"),
            )
            if refreshed_client is not None:
                logger.info("Auxiliary %s: refreshed Nous runtime credentials after 401, retrying",
                            task or "call")
                if refreshed_model and refreshed_model != kwargs.get("model"):
                    kwargs["model"] = refreshed_model
                return _validate_llm_response(
                    refreshed_client.chat.completions.create(**kwargs), task)

        # ── Auth refresh retry ───────────────────────────────────────
        if (_is_auth_error(first_err)
                and resolved_provider not in ("auto", "", None)
                and not client_is_nous):
            if _refresh_provider_credentials(resolved_provider):
                logger.info(
                    "Auxiliary %s: refreshed %s credentials after auth error, retrying",
                    task or "call", resolved_provider,
                )
                retry_client, retry_model = (
                    resolve_vision_provider_client(
                        provider=resolved_provider,
                        model=final_model,
                        async_mode=False,
                    )[1:]
                    if task == "vision"
                    else _get_cached_client(
                        resolved_provider,
                        resolved_model,
                        base_url=resolved_base_url,
                        api_key=resolved_api_key,
                        api_mode=resolved_api_mode,
                        main_runtime=main_runtime,
                    )
                )
                if retry_client is not None:
                    retry_kwargs = _build_call_kwargs(
                        resolved_provider,
                        retry_model or final_model,
                        messages,
                        temperature=temperature,
                        max_tokens=max_tokens,
                        tools=tools,
                        timeout=effective_timeout,
                        extra_body=effective_extra_body,
                        base_url=resolved_base_url,
                    )
                    _retry_base = str(getattr(retry_client, "base_url", "") or "")
                    if _is_anthropic_compat_endpoint(resolved_provider, _retry_base):
                        retry_kwargs["messages"] = _convert_openai_images_to_anthropic(retry_kwargs["messages"])
                    return _validate_llm_response(
                        retry_client.chat.completions.create(**retry_kwargs), task)

        # ── Payment / credit exhaustion fallback ──────────────────────
        # When the resolved provider returns 402 or a credit-related error,
        # try alternative providers instead of giving up.  This handles the
        # common case where a user runs out of OpenRouter credits but has
        # Codex OAuth or another provider available.
        #
        # ── Connection error fallback ────────────────────────────────
        # When a provider endpoint is unreachable (DNS failure, connection
        # refused, timeout), try alternative providers.  This handles stale
        # Codex/OAuth tokens that authenticate but whose endpoint is down,
        # and providers the user never configured that got picked up by
        # the auto-detection chain.
        should_fallback = _is_payment_error(first_err) or _is_connection_error(first_err)
        # Only try alternative providers when the user didn't explicitly
        # configure this task's provider.  Explicit provider = hard constraint;
        # auto (the default) = best-effort fallback chain.  (#7559)
        is_auto = resolved_provider in ("auto", "", None)
        if should_fallback and is_auto:
            reason = "payment error" if _is_payment_error(first_err) else "connection error"
            logger.info("Auxiliary %s: %s on %s (%s), trying fallback",
                        task or "call", reason, resolved_provider, first_err)
            fb_client, fb_model, fb_label = _try_payment_fallback(
                resolved_provider, task, reason=reason)
            if fb_client is not None:
                fb_kwargs = _build_call_kwargs(
                    fb_label, fb_model, messages,
                    temperature=temperature, max_tokens=max_tokens,
                    tools=tools, timeout=effective_timeout,
                    extra_body=effective_extra_body,
                    base_url=str(getattr(fb_client, "base_url", "") or ""))
                return _validate_llm_response(
                    fb_client.chat.completions.create(**fb_kwargs), task)
        raise


def extract_content_or_reasoning(response) -> str:
    """Extract content from an LLM response, falling back to reasoning fields.

    Mirrors the main agent loop's behavior when a reasoning model (DeepSeek-R1,
    Qwen-QwQ, etc.) returns ``content=None`` with reasoning in structured fields.

    Resolution order:
      1. ``message.content`` — strip inline think/reasoning blocks, check for
         remaining non-whitespace text.
      2. ``message.reasoning`` / ``message.reasoning_content`` — direct
         structured reasoning fields (DeepSeek, Moonshot, Novita, etc.).
      3. ``message.reasoning_details`` — OpenRouter unified array format.

    Returns the best available text, or ``""`` if nothing found.
    """
    import re

    msg = response.choices[0].message
    content = (msg.content or "").strip()

    if content:
        # Strip inline think/reasoning blocks (mirrors _strip_think_blocks)
        cleaned = re.sub(
            r"<(?:think|thinking|reasoning|thought|REASONING_SCRATCHPAD)>"
            r".*?"
            r"</(?:think|thinking|reasoning|thought|REASONING_SCRATCHPAD)>",
            "", content, flags=re.DOTALL | re.IGNORECASE,
        ).strip()
        if cleaned:
            return cleaned

    # Content is empty or reasoning-only — try structured reasoning fields
    reasoning_parts: list[str] = []
    for field in ("reasoning", "reasoning_content"):
        val = getattr(msg, field, None)
        if val and isinstance(val, str) and val.strip() and val not in reasoning_parts:
            reasoning_parts.append(val.strip())

    details = getattr(msg, "reasoning_details", None)
    if details and isinstance(details, list):
        for detail in details:
            if isinstance(detail, dict):
                summary = (
                    detail.get("summary")
                    or detail.get("content")
                    or detail.get("text")
                )
                if summary and summary not in reasoning_parts:
                    reasoning_parts.append(summary.strip() if isinstance(summary, str) else str(summary))

    if reasoning_parts:
        return "\n\n".join(reasoning_parts)

    return ""


async def async_call_llm(
    task: str = None,
    *,
    provider: str = None,
    model: str = None,
    base_url: str = None,
    api_key: str = None,
    messages: list,
    temperature: float = None,
    max_tokens: int = None,
    tools: list = None,
    timeout: float = None,
    extra_body: dict = None,
) -> Any:
    """Centralized asynchronous LLM call.

    Same as call_llm() but async. See call_llm() for full documentation.
    """
    resolved_provider, resolved_model, resolved_base_url, resolved_api_key, resolved_api_mode = _resolve_task_provider_model(
        task, provider, model, base_url, api_key)
    effective_extra_body = _get_task_extra_body(task)
    effective_extra_body.update(extra_body or {})

    if task == "vision":
        effective_provider, client, final_model = resolve_vision_provider_client(
            provider=resolved_provider if resolved_provider != "auto" else provider,
            model=resolved_model or model,
            base_url=resolved_base_url or base_url,
            api_key=resolved_api_key or api_key,
            async_mode=True,
        )
        if client is None and resolved_provider != "auto" and not resolved_base_url:
            logger.warning(
                "Vision provider %s unavailable, falling back to auto vision backends",
                resolved_provider,
            )
            effective_provider, client, final_model = resolve_vision_provider_client(
                provider="auto",
                model=resolved_model,
                async_mode=True,
            )
        if client is None:
            raise RuntimeError(
                f"No LLM provider configured for task={task} provider={resolved_provider}. "
                f"Run: hermes setup"
            )
        resolved_provider = effective_provider or resolved_provider
    else:
        client, final_model = _get_cached_client(
            resolved_provider,
            resolved_model,
            async_mode=True,
            base_url=resolved_base_url,
            api_key=resolved_api_key,
            api_mode=resolved_api_mode,
        )
        if client is None:
            _explicit = (resolved_provider or "").strip().lower()
            if _explicit and _explicit not in ("auto", "openrouter", "custom"):
                raise RuntimeError(
                    f"Provider '{_explicit}' is set in config.yaml but no API key "
                    f"was found. Set the {_explicit.upper()}_API_KEY environment "
                    f"variable, or switch to a different provider with `hermes model`."
                )
            if not resolved_base_url:
                logger.info("Auxiliary %s: provider %s unavailable, trying auto-detection chain",
                            task or "call", resolved_provider)
                client, final_model = _get_cached_client("auto", async_mode=True)
        if client is None:
            raise RuntimeError(
                f"No LLM provider configured for task={task} provider={resolved_provider}. "
                f"Run: hermes setup")

    effective_timeout = timeout if timeout is not None else _get_task_timeout(task)

    # Pass the client's actual base_url (not just resolved_base_url) so
    # endpoint-specific temperature overrides can distinguish
    # api.moonshot.ai vs api.kimi.com/coding even on auto-detected routes.
    _client_base = str(getattr(client, "base_url", "") or "")
    kwargs = _build_call_kwargs(
        resolved_provider, final_model, messages,
        temperature=temperature, max_tokens=max_tokens,
        tools=tools, timeout=effective_timeout, extra_body=effective_extra_body,
        base_url=_client_base or resolved_base_url)

    # Convert image blocks for Anthropic-compatible endpoints (e.g. MiniMax)
    if _is_anthropic_compat_endpoint(resolved_provider, _client_base):
        kwargs["messages"] = _convert_openai_images_to_anthropic(kwargs["messages"])

    try:
        return _validate_llm_response(
            await client.chat.completions.create(**kwargs), task)
    except Exception as first_err:
        if "temperature" in kwargs and _is_unsupported_temperature_error(first_err):
            retry_kwargs = dict(kwargs)
            retry_kwargs.pop("temperature", None)
            logger.info(
                "Auxiliary %s (async): provider rejected temperature; retrying once without it",
                task or "call",
            )
            try:
                return _validate_llm_response(
                    await client.chat.completions.create(**retry_kwargs), task)
            except Exception as retry_err:
                retry_err_str = str(retry_err)
                if not (
                    _is_payment_error(retry_err)
                    or _is_connection_error(retry_err)
                    or _is_auth_error(retry_err)
                    or "max_tokens" in retry_err_str
                    or "unsupported_parameter" in retry_err_str
                ):
                    raise
                first_err = retry_err
                kwargs = retry_kwargs

        err_str = str(first_err)
        if max_tokens is not None and (
            "max_tokens" in err_str
            or "unsupported_parameter" in err_str
            or _is_unsupported_parameter_error(first_err, "max_tokens")
        ):
            kwargs.pop("max_tokens", None)
            kwargs["max_completion_tokens"] = max_tokens
            try:
                return _validate_llm_response(
                    await client.chat.completions.create(**kwargs), task)
            except Exception as retry_err:
                # If the max_tokens retry also hits a payment or connection
                # error, fall through to the fallback chain below.
                if not (_is_payment_error(retry_err) or _is_connection_error(retry_err)):
                    raise
                first_err = retry_err

        # ── Nous auth refresh parity with main agent ──────────────────
        client_is_nous = (
            resolved_provider == "nous"
            or base_url_host_matches(_client_base, "inference-api.nousresearch.com")
        )
        if _is_auth_error(first_err) and client_is_nous:
            refreshed_client, refreshed_model = _refresh_nous_auxiliary_client(
                cache_provider=resolved_provider or "nous",
                model=final_model,
                async_mode=True,
                base_url=resolved_base_url,
                api_key=resolved_api_key,
                api_mode=resolved_api_mode,
                is_vision=(task == "vision"),
            )
            if refreshed_client is not None:
                logger.info("Auxiliary %s (async): refreshed Nous runtime credentials after 401, retrying",
                            task or "call")
                if refreshed_model and refreshed_model != kwargs.get("model"):
                    kwargs["model"] = refreshed_model
                return _validate_llm_response(
                    await refreshed_client.chat.completions.create(**kwargs), task)

        # ── Auth refresh retry (mirrors sync call_llm) ───────────────
        if (_is_auth_error(first_err)
                and resolved_provider not in ("auto", "", None)
                and not client_is_nous):
            if _refresh_provider_credentials(resolved_provider):
                logger.info(
                    "Auxiliary %s (async): refreshed %s credentials after auth error, retrying",
                    task or "call", resolved_provider,
                )
                if task == "vision":
                    _, retry_client, retry_model = resolve_vision_provider_client(
                        provider=resolved_provider,
                        model=final_model,
                        async_mode=True,
                    )
                else:
                    retry_client, retry_model = _get_cached_client(
                        resolved_provider,
                        resolved_model,
                        async_mode=True,
                        base_url=resolved_base_url,
                        api_key=resolved_api_key,
                        api_mode=resolved_api_mode,
                    )
                if retry_client is not None:
                    retry_kwargs = _build_call_kwargs(
                        resolved_provider,
                        retry_model or final_model,
                        messages,
                        temperature=temperature,
                        max_tokens=max_tokens,
                        tools=tools,
                        timeout=effective_timeout,
                        extra_body=effective_extra_body,
                        base_url=resolved_base_url,
                    )
                    _retry_base = str(getattr(retry_client, "base_url", "") or "")
                    if _is_anthropic_compat_endpoint(resolved_provider, _retry_base):
                        retry_kwargs["messages"] = _convert_openai_images_to_anthropic(retry_kwargs["messages"])
                    return _validate_llm_response(
                        await retry_client.chat.completions.create(**retry_kwargs), task)

        # ── Payment / connection fallback (mirrors sync call_llm) ─────
        should_fallback = _is_payment_error(first_err) or _is_connection_error(first_err)
        is_auto = resolved_provider in ("auto", "", None)
        if should_fallback and is_auto:
            reason = "payment error" if _is_payment_error(first_err) else "connection error"
            logger.info("Auxiliary %s (async): %s on %s (%s), trying fallback",
                        task or "call", reason, resolved_provider, first_err)
            fb_client, fb_model, fb_label = _try_payment_fallback(
                resolved_provider, task, reason=reason)
            if fb_client is not None:
                fb_kwargs = _build_call_kwargs(
                    fb_label, fb_model, messages,
                    temperature=temperature, max_tokens=max_tokens,
                    tools=tools, timeout=effective_timeout,
                    extra_body=effective_extra_body,
                    base_url=str(getattr(fb_client, "base_url", "") or ""))
                # Convert sync fallback client to async
                async_fb, async_fb_model = _to_async_client(
                    fb_client, fb_model or "", is_vision=(task == "vision")
                )
                if async_fb_model and async_fb_model != fb_kwargs.get("model"):
                    fb_kwargs["model"] = async_fb_model
                return _validate_llm_response(
                    await async_fb.chat.completions.create(**fb_kwargs), task)
        raise