mirror of
https://github.com/usestrix/strix.git
synced 2026-08-16 01:16:40 +02:00
Ahmed AllamandAhmed Allam
88ad3e4472
An httpx.Timeout in ModelSettings.extra_args crashes ModelSettings.to_json_dict() (PydanticSerializationError) on the Chat Completions and LiteLLM model paths, which serialize settings for their tracing generation span — failing every model turn on those paths. Pass the timeout as a plain float, which httpx-based clients apply as the read (inactivity) timeout.
328 lines
11 KiB
Python
328 lines
11 KiB
Python
"""SDK model configuration helpers."""
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from __future__ import annotations
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import os
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from typing import TYPE_CHECKING
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from agents import set_default_openai_api, set_default_openai_key, set_tracing_disabled
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from agents.models.multi_provider import MultiProvider
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from agents.retry import (
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ModelRetryBackoffSettings,
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ModelRetrySettings,
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RetryPolicyContext,
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retry_policies,
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)
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if TYPE_CHECKING:
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from agents.models.interface import ModelProvider
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from strix.config.settings import Settings
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def request_timeout_extra_args(timeout_s: float | None) -> dict[str, float] | None:
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"""Per-request model timeout as ``extra_args``, forwarded to the provider call.
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A stalled stream trips the timeout and is retried by ``DEFAULT_MODEL_RETRY``,
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restoring pre-v1's per-turn inactivity guard.
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The value MUST be a plain ``float``, not an ``httpx.Timeout``. The Chat
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Completions and LiteLLM model paths build their tracing generation span from
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``ModelSettings.to_json_dict()``, which pydantic-serializes ``extra_args`` in
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JSON mode; an ``httpx.Timeout`` is not JSON-serializable and raises
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``PydanticSerializationError`` there, failing every turn on those paths. A
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scalar serializes cleanly and, on httpx-based clients, is applied as the read
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(inactivity) timeout — not a total-duration deadline.
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"""
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if not timeout_s or timeout_s <= 0:
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return None
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return {"timeout": timeout_s}
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def _retry_statusless_provider_errors(context: RetryPolicyContext) -> bool:
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"""Retry statusless provider errors (e.g. mid-stream quota/billing), but not aborts."""
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normalized = context.normalized
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if normalized.is_abort:
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return False
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return normalized.status_code is None
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class StrixProvider(MultiProvider):
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"""Route any non-OpenAI prefix through LiteLLM with the prefix preserved,
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so users type ``deepseek/deepseek-chat`` rather than
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``litellm/deepseek/deepseek-chat``.
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"""
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def _resolve_prefixed_model(
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self,
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*,
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original_model_name: str,
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prefix: str,
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stripped_model_name: str | None,
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) -> tuple[ModelProvider, str | None]:
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if prefix in {"openai", "litellm", "any-llm"}:
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return super()._resolve_prefixed_model(
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original_model_name=original_model_name,
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prefix=prefix,
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stripped_model_name=stripped_model_name,
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)
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if prefix == "ollama" and stripped_model_name:
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return self._get_fallback_provider("litellm"), f"ollama_chat/{stripped_model_name}"
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return self._get_fallback_provider("litellm"), original_model_name
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DEFAULT_MODEL_RETRY = ModelRetrySettings(
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max_retries=5,
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backoff=ModelRetryBackoffSettings(
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initial_delay=2.0,
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max_delay=90.0,
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multiplier=2.0,
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jitter=False,
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),
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policy=retry_policies.any(
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retry_policies.provider_suggested(),
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retry_policies.network_error(),
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retry_policies.http_status((429, 500, 502, 503, 504)),
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_retry_statusless_provider_errors,
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),
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)
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RECOMMENDED_MODEL_NAMES = (
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"openai/gpt-5.6",
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"openai/gpt-5.6-sol",
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"openai/gpt-5.6-terra",
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"openai/gpt-5.5",
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"openai/gpt-5.5-pro",
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"openai/gpt-5.4",
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"openai/gpt-5.3-codex",
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"anthropic/claude-fable-5",
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"anthropic/claude-opus-4-8",
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"anthropic/claude-opus-4-7",
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"anthropic/claude-sonnet-5",
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"anthropic/claude-sonnet-4-6",
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"vertex_ai/gemini-3.1-pro-preview",
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"gemini/gemini-3.1-pro-preview",
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"deepseek/deepseek-v4-pro",
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"deepseek/deepseek-v4-flash",
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"dashscope/qwen3.7-max-2026-06-08",
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"moonshot/kimi-k2.7-code",
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"moonshot/kimi-k2.6",
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)
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_RECOMMENDED_MODEL_NAME_SET = frozenset(name.lower() for name in RECOMMENDED_MODEL_NAMES)
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FRONTIER_MODEL_FAMILIES = (
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(("azure", "azure_ai", "bedrock_mantle", "openai"), ("gpt-5",)),
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(
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("anthropic", "azure_ai", "bedrock", "claude", "databricks", "snowflake", "vertex_ai"),
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("claude-fable-5", "claude-opus-4", "claude-sonnet-5", "claude-sonnet-4"),
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),
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(("google", "gemini", "vertex_ai"), ("gemini-3",)),
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(("deepseek",), ("deepseek-v4", "deepseek-r1", "deepseek-reasoner")),
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(("alibaba", "dashscope", "qwen"), ("qwen3.7", "qwen3.5", "qwen3-max")),
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(("moonshot", "moonshotai", "kimi"), ("kimi-k2.7", "kimi-k2.6", "kimi-k2.5")),
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)
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def configure_sdk_model_defaults(settings: Settings) -> None:
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"""Apply Strix config to SDK-native defaults."""
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llm = settings.llm
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set_tracing_disabled(True)
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_configure_litellm_compatibility()
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_configure_openrouter_attribution(llm.model)
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if llm.api_key:
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set_default_openai_key(llm.api_key, use_for_tracing=False)
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_configure_litellm_default("api_key", llm.api_key)
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_mirror_api_key_to_provider_env(llm.model, llm.api_key)
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if llm.api_base:
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os.environ["OPENAI_BASE_URL"] = llm.api_base
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_configure_litellm_default("api_base", llm.api_base)
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set_default_openai_api("chat_completions")
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else:
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set_default_openai_api("responses")
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def _mirror_api_key_to_provider_env(model_name: str | None, api_key: str) -> None:
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if not model_name:
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return
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import litellm
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name = model_name.strip()
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for prefix in ("litellm/", "any-llm/"):
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if name.lower().startswith(prefix):
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name = name[len(prefix) :]
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break
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try:
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report = litellm.validate_environment(model=name.lower())
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except Exception: # noqa: BLE001
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return
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for env_key in report.get("missing_keys") or []:
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if env_key.endswith("_API_KEY"):
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os.environ.setdefault(env_key, api_key)
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def _configure_litellm_compatibility() -> None:
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"""Apply LiteLLM compatibility, privacy, and callback settings."""
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import litellm
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litellm.drop_params = True
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litellm.modify_params = True
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litellm.turn_off_message_logging = True
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# Strix uses LiteLLM's success callback to capture provider-reported cost.
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# Disabling streaming logging also disables that callback for streamed calls.
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litellm.disable_streaming_logging = False
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litellm.suppress_debug_info = True
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_register_litellm_cost_callback()
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_OPENROUTER_ATTRIBUTION_HEADERS = {
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"HTTP-Referer": "https://strix.ai",
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"X-Title": "Strix",
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"X-OpenRouter-Categories": "cli-agent",
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}
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def _configure_openrouter_attribution(model_name: str | None) -> None:
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import litellm
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current: object = litellm.headers
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existing: dict[str, str] = current if isinstance(current, dict) else {}
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if not model_name or "openrouter/" not in model_name.strip().lower():
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if any(key in existing for key in _OPENROUTER_ATTRIBUTION_HEADERS):
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remaining = {
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k: v for k, v in existing.items() if k not in _OPENROUTER_ATTRIBUTION_HEADERS
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}
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litellm.headers = remaining or None # type: ignore[assignment]
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return
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litellm.headers = {**existing, **_OPENROUTER_ATTRIBUTION_HEADERS} # type: ignore[assignment]
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def _register_litellm_cost_callback() -> None:
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import litellm
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from strix.report.state import litellm_cost_callback
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for bucket_name in ("success_callback", "_async_success_callback"):
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bucket = getattr(litellm, bucket_name, None)
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if not isinstance(bucket, list):
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continue
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if litellm_cost_callback in bucket:
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continue
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bucket.append(litellm_cost_callback)
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def _configure_litellm_default(name: str, value: str) -> None:
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"""Set LiteLLM's module-level defaults without adding a provider wrapper."""
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import litellm
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setattr(litellm, name, value)
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def uses_chat_completions_tool_schema(model_name: str, settings: Settings) -> bool:
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"""Return whether the resolved SDK route can only receive JSON function tools."""
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model = model_name.strip().lower()
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if "/" in model and not model.startswith("openai/"):
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return True
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if settings.llm.api_base:
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return True
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return not model_supports_reasoning(model_name)
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def model_supports_reasoning(model_name: str) -> bool:
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import litellm
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name = model_name.strip().lower()
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for prefix in ("litellm/", "any-llm/", "openai/"):
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if name.startswith(prefix):
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name = name[len(prefix) :]
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break
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entry = litellm.model_cost.get(name)
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if entry is None and "/" in name:
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entry = litellm.model_cost.get(name.rsplit("/", 1)[1])
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return bool(entry and entry.get("supports_reasoning"))
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def is_recommended_or_frontier_model(model_name: str) -> bool:
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"""Return whether a model is recommended or in a frontier model family."""
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name = _normalized_model_name(model_name)
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if not name:
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return False
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if name in _RECOMMENDED_MODEL_NAME_SET:
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return True
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provider_name, bare_model_name = _split_model_provider(name)
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return any(
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_matches_frontier_family(provider_name, bare_model_name, provider_markers, prefixes)
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for provider_markers, prefixes in FRONTIER_MODEL_FAMILIES
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)
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def _normalized_model_name(model_name: str) -> str:
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name = model_name.strip().lower()
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for prefix in ("litellm/", "any-llm/"):
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if name.startswith(prefix):
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name = name[len(prefix) :]
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break
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return name
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def _split_model_provider(model_name: str) -> tuple[str | None, str]:
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if "/" not in model_name:
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return None, model_name
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provider_name, bare_model_name = model_name.rsplit("/", 1)
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return provider_name, bare_model_name
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def _matches_frontier_family(
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provider_name: str | None,
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model_name: str,
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provider_markers: tuple[str, ...],
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model_prefixes: tuple[str, ...],
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) -> bool:
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if not _matches_model_prefix(model_name, model_prefixes):
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return False
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if provider_name is None:
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return True
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return _contains_provider_marker(
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provider_name, provider_markers, split_compound_names=True
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) or _contains_provider_marker(model_name, provider_markers)
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def _matches_model_prefix(model_name: str, model_prefixes: tuple[str, ...]) -> bool:
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return any(
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candidate.startswith(prefix)
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for candidate in _model_name_candidates(model_name)
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for prefix in model_prefixes
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)
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def _model_name_candidates(model_name: str) -> tuple[str, ...]:
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if "." not in model_name:
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return (model_name,)
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suffixes = tuple(
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model_name.split(".", index)[-1] for index in range(1, model_name.count(".") + 1)
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)
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return (model_name, *suffixes)
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def _contains_provider_marker(
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value: str, provider_markers: tuple[str, ...], *, split_compound_names: bool = False
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) -> bool:
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parts = set(value.replace(".", "/").split("/"))
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if split_compound_names:
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for separator in ("_", "-"):
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parts.update(piece for part in tuple(parts) for piece in part.split(separator))
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return any(marker in parts for marker in provider_markers)
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def is_known_openai_bare_model(model_name: str) -> bool:
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import litellm
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name = model_name.strip().lower()
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if not name or "/" in name:
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return False
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entry = litellm.model_cost.get(name)
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return bool(entry and entry.get("litellm_provider") == "openai")
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