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0xallamandClaude Opus 4.7
0518599f29
``MultiProvider`` was constructed with no openai kwargs, so the inner ``OpenAIProvider`` defaulted to reading ``OPENAI_API_KEY`` from the environment. Strix's contract is that ``LLM_API_KEY`` works for every provider, so users with ``STRIX_LLM=openai/<model>`` + ``LLM_API_KEY`` hit ``openai.OpenAIError`` at the first turn — the warm-up call worked because that path goes through ``litellm.completion`` directly with explicit creds, but the actual scan went through the SDK's MultiProvider where the key was never plumbed. Pass ``Settings.llm.api_key`` and ``Settings.llm.api_base`` through to the underlying ``OpenAIProvider`` via the ``openai_api_key`` / ``openai_base_url`` ctor kwargs. ``openai_use_responses`` flips to ``False`` when ``LLM_API_BASE`` is set — non-default base URLs are the reliable signal that the user is on an OpenAI-compatible endpoint that doesn't speak the Responses API. Genuine OpenAI usage keeps the Responses API as the default transport. The ``anthropic/`` prefix continues to route through ``AnthropicCachingLitellmModel`` for prompt caching; ``litellm/`` and other prefixes still fall through to the SDK's stock routing. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Multi-provider routing setup.
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Wraps the SDK's :class:`MultiProvider` and threads Strix's
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``LLM_API_KEY`` / ``LLM_API_BASE`` into the underlying provider chain.
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Routing:
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- ``anthropic/<model>`` → :class:`AnthropicCachingLitellmModel` so
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prompt caching kicks in (we inject ``cache_control`` on the system
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message before the litellm call).
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- ``openai/<model>`` (and bare model names) → SDK-native
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:class:`OpenAIProvider`, instantiated with our settings credentials so
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``LLM_API_KEY`` works without forcing the user to also export
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``OPENAI_API_KEY``. Keeps the Responses API as the default transport
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for genuine OpenAI usage.
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- Every other prefix (``litellm/...``, ``any-llm/...``, …) falls through
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to whatever the SDK does natively.
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Real-OpenAI vs OpenAI-compatible differentiation is by
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``Settings.llm.api_base`` presence. If the user pointed at a non-default
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base URL they're almost certainly on an OpenAI-compatible endpoint that
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doesn't speak the Responses API, so we flip ``openai_use_responses=False``
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to make the inner provider use chat-completions transport instead.
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"""
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from __future__ import annotations
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import logging
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from agents.exceptions import UserError
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from agents.models.interface import Model, ModelProvider
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from agents.models.multi_provider import MultiProvider, MultiProviderMap
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from strix.config import load_settings
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from strix.llm.anthropic_cache_wrapper import AnthropicCachingLitellmModel
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logger = logging.getLogger(__name__)
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class _AnthropicCachingProvider(ModelProvider):
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"""Routes ``anthropic/<model>`` aliases through
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:class:`AnthropicCachingLitellmModel`.
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The SDK's ``MultiProvider`` strips the matched prefix before calling
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``get_model``, so we receive bare ``"<model>"`` (e.g.
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``"claude-sonnet-4-6"``) and re-prefix with ``anthropic/`` so litellm
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routes to the Anthropic API.
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"""
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def get_model(self, model_name: str | None) -> Model:
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if not model_name:
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raise UserError(
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"Anthropic provider requires a non-empty model name (e.g. 'claude-sonnet-4-6').",
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)
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full = model_name if model_name.startswith("anthropic/") else f"anthropic/{model_name}"
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logger.debug("Anthropic provider: building cached model for %s", full)
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return AnthropicCachingLitellmModel(model=full)
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def build_multi_provider() -> MultiProvider:
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"""Build the configured MultiProvider.
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Registers the ``anthropic/`` route through our caching wrapper and
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threads ``Settings.llm`` credentials into the SDK-native
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:class:`OpenAIProvider` so ``openai/<model>`` works with our single
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``LLM_API_KEY`` env var. ``Settings.llm.api_base`` (when set) flips
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the OpenAI provider to chat-completions transport — the de-facto
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signal that the user is hitting an OpenAI-compatible endpoint that
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doesn't implement the Responses API.
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"""
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pmap = MultiProviderMap() # type: ignore[no-untyped-call]
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pmap.add_provider("anthropic", _AnthropicCachingProvider())
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llm = load_settings().llm
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use_responses = llm.api_base is None # default endpoint → real OpenAI
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logger.debug(
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"MultiProvider built with anthropic/ cached + openai/ native "
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"(api_key=%s, base_url=%s, use_responses=%s)",
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"set" if llm.api_key else "unset",
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llm.api_base or "default",
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use_responses,
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)
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return MultiProvider(
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provider_map=pmap,
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openai_api_key=llm.api_key,
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openai_base_url=llm.api_base,
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openai_use_responses=use_responses,
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)
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