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Pick openrouter/pareto-code as your model and OpenRouter auto-routes each
request to the cheapest model meeting your coding-quality bar (ranked by
Artificial Analysis). The new openrouter.min_coding_score config key (0.0-1.0,
default 0.65) tunes the floor.
- hermes_cli/models.py: add openrouter/pareto-code to OPENROUTER_MODELS so
it shows up in the picker with a description
- hermes_cli/config.py: add openrouter.min_coding_score (default 0.65 — lands
on a mid-tier coder on the current Pareto frontier)
- plugins/model-providers/openrouter: emit extra_body.plugins =
[{id: pareto-router, min_coding_score: X}] when model is openrouter/pareto-code
AND the score is a valid float in [0.0, 1.0]
- agent/transports/chat_completions.py: same emission on the legacy flag
path (when no provider profile is loaded)
- run_agent.py: openrouter_min_coding_score kwarg + storage; plumbed into
both build_kwargs() invocations and the context-summary extra_body path
- cli.py: read openrouter.min_coding_score once at init, validate float in
[0,1], pass to AIAgent constructions (CLI + background-task paths)
- cron/scheduler.py, batch_runner.py, tools/delegate_tool.py,
tui_gateway/server.py: propagate the kwarg (mirrors providers_order
plumbing — subagents inherit, cron/batch read from config)
- tests: profile-level + transport-level coverage of the model gating,
unset/empty/out-of-range handling, and the legacy flag path
- docs: new 'OpenRouter Pareto Code Router' section in providers.md
Verified end-to-end against api.openrouter.ai: at score=0.65 we land on a
mid-tier coder, at omission we get the strongest. Score is silently dropped
on any model other than openrouter/pareto-code, so it's safe to leave set.
115 lines
4 KiB
Python
115 lines
4 KiB
Python
"""OpenRouter provider profile."""
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import logging
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from typing import Any
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from providers import register_provider
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from providers.base import ProviderProfile
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logger = logging.getLogger(__name__)
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_CACHE: list[str] | None = None
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class OpenRouterProfile(ProviderProfile):
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"""OpenRouter aggregator — provider preferences, reasoning config passthrough."""
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def fetch_models(
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self,
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*,
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api_key: str | None = None,
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timeout: float = 8.0,
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) -> list[str] | None:
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"""Fetch from public OpenRouter catalog — no auth required.
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Note: Tool-call capability filtering is applied by hermes_cli/models.py
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via fetch_openrouter_models() → _openrouter_model_supports_tools(), not
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here. The picker early-returns via the dedicated openrouter path before
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reaching this method, so filtering here would be unreachable.
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"""
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global _CACHE # noqa: PLW0603
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if _CACHE is not None:
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return _CACHE
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try:
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result = super().fetch_models(api_key=None, timeout=timeout)
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if result is not None:
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_CACHE = result
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return result
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except Exception as exc:
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logger.debug("fetch_models(openrouter): %s", exc)
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return None
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def build_extra_body(
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self, *, session_id: str | None = None, **context: Any
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) -> dict[str, Any]:
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body: dict[str, Any] = {}
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prefs = context.get("provider_preferences")
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if prefs:
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body["provider"] = prefs
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# Pareto Code router — model-gated. The plugins block is only
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# meaningful for openrouter/pareto-code; sending it on any other
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# model has no documented effect and would be confusing in logs.
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# See: https://openrouter.ai/docs/guides/routing/routers/pareto-router
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model = (context.get("model") or "")
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if model == "openrouter/pareto-code":
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score = context.get("openrouter_min_coding_score")
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if score is not None and score != "":
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try:
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score_f = float(score)
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except (TypeError, ValueError):
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score_f = None
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if score_f is not None and 0.0 <= score_f <= 1.0:
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body["plugins"] = [
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{"id": "pareto-router", "min_coding_score": score_f}
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]
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return body
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def build_api_kwargs_extras(
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self,
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*,
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reasoning_config: dict | None = None,
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supports_reasoning: bool = False,
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model: str | None = None,
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session_id: str | None = None,
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**context: Any,
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) -> tuple[dict[str, Any], dict[str, Any]]:
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"""OpenRouter passes the full reasoning_config dict as extra_body.reasoning.
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For xAI Grok models routed through OpenRouter, attach the
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``x-grok-conv-id`` header so that xAI's prompt cache stays pinned to
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the same backend server across turns.
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"""
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extra_body: dict[str, Any] = {}
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if supports_reasoning:
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if reasoning_config is not None:
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extra_body["reasoning"] = dict(reasoning_config)
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else:
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extra_body["reasoning"] = {"enabled": True, "effort": "medium"}
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extra_headers: dict[str, Any] = {}
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if session_id and model and model.startswith(("x-ai/grok-", "xai/grok-")):
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extra_headers["x-grok-conv-id"] = session_id
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return extra_body, {"extra_headers": extra_headers} if extra_headers else {}
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openrouter = OpenRouterProfile(
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name="openrouter",
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aliases=("or",),
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env_vars=("OPENROUTER_API_KEY",),
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display_name="OpenRouter",
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description="OpenRouter — unified API for 200+ models",
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signup_url="https://openrouter.ai/keys",
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base_url="https://openrouter.ai/api/v1",
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models_url="https://openrouter.ai/api/v1/models",
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fallback_models=(
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"anthropic/claude-sonnet-4.6",
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"openai/gpt-5.4",
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"deepseek/deepseek-chat",
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"google/gemini-3-flash-preview",
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"qwen/qwen3-plus",
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),
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)
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register_provider(openrouter)
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