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hermes-agent/agent/auxiliary_client.py
kshitijk4poor 43de1ca8c2 refactor: remove _nr_to_assistant_message shim + fix flush_memories guard 
NormalizedResponse and ToolCall now have backward-compat properties
so the agent loop can read them directly without the shim:

  ToolCall: .type, .function (returns self), .call_id, .response_item_id
  NormalizedResponse: .reasoning_content, .reasoning_details,
                      .codex_reasoning_items

This eliminates the 35-line shim and its 4 call sites in run_agent.py.

Also changes flush_memories guard from hasattr(response, 'choices')
to self.api_mode in ('chat_completions', 'bedrock_converse') so it
works with raw boto3 dicts too.

WS1 items 3+4 of Cycle 2 (#14418).
2026-04-23 02:30:05 -07:00

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"""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 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__)
# 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",
"minimax-china": "minimax-cn",
"minimax_cn": "minimax-cn",
"claude": "anthropic",
"claude-code": "anthropic",
}
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()
if main_prov and main_prov not in ("auto", "main", ""):
return main_prov
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",
"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-omni",
"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 flush_memories and similar callers
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 _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")
if custom_mode == "codex_responses":
real_client = OpenAI(api_key=custom_key, base_url=custom_base)
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=custom_base), model
return (
AnthropicAuxiliaryClient(real_client, model, custom_key, custom_base, is_oauth=False),
model,
)
return OpenAI(api_key=custom_key, base_url=custom_base), 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 _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):
"""Convert a sync client to its async counterpart, preserving Codex routing."""
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.models import copilot_default_headers
async_kwargs["default_headers"] = copilot_default_headers()
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,
) -> 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) 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 OPENROUTER_API_KEY not set")
return None, None
final_model = _normalize_resolved_model(model or default, provider)
return (_to_async_client(client, final_model) 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) 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) 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 = {}
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.models import copilot_default_headers
extra["default_headers"] = copilot_default_headers()
client = OpenAI(api_key=custom_key, base_url=custom_base, **extra)
client = _wrap_if_needed(client, final_model, custom_base)
return (_to_async_client(client, final_model) 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) 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 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"
if custom_base:
final_model = _normalize_resolved_model(
model or custom_entry.get("model") or _read_main_model() or "gpt-4o-mini",
provider,
)
client = OpenAI(api_key=custom_key, base_url=custom_base)
client = _wrap_if_needed(client, final_model, custom_base)
logger.debug(
"resolve_provider_client: named custom provider %r (%s)",
provider, final_model)
return (_to_async_client(client, final_model) 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) 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) 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.models import copilot_default_headers
headers.update(copilot_default_headers())
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) 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) 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 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) -> Tuple[Optional[Any], Optional[str]]:
provider = _normalize_vision_provider(provider)
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)
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", ""):
if main_provider == "nous":
sync_client, default_model = _resolve_strict_vision_backend(main_provider)
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:
vision_model = _PROVIDER_VISION_MODELS.get(main_provider, main_model)
rpc_client, rpc_model = resolve_provider_client(
main_provider, vision_model,
api_mode=resolved_api_mode)
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)
return _finalize(requested, sync_client, default_model)
client, final_model = _get_cached_client(requested, resolved_model, async_mode,
api_mode=resolved_api_mode)
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,
) -> 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)
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,
) -> 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 "")
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,
)
_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 _compat_model(client: Any, model: Optional[str], cached_default: Optional[str]) -> Optional[str]:
"""Drop OpenRouter-format model slugs (with '/') for non-OpenRouter clients.
Mirrors the guard in resolve_provider_client() which is skipped on cache hits.
"""
if model and "/" in model and not _is_openrouter_client(client):
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,
) -> 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,
)
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,
)
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. 0.3 on
# flush_memories, 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", "flush_memories").
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 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:
err_str = str(first_err)
if "max_tokens" in err_str or "unsupported_parameter" in err_str:
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,
)
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)
# ── 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:
err_str = str(first_err)
if "max_tokens" in err_str or "unsupported_parameter" in err_str:
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,
)
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)
# ── 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 "")
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
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