feat: verify AI Gateway provider UX and attribution headers (#950)

Closes #950

- promote Vercel AI Gateway near the top of the provider picker
- add dedicated AI Gateway model flow with Vercel API-key deep link and live pricing
- use curated AI Gateway catalog refresh with free Moonshot auto-promotion
- apply AI Gateway attribution headers on runtime clients
- add targeted QA tests for provider UX and attribution headers

agent/anthropic_adapter.py

@@ -1396,8 +1396,6 @@ def normalize_anthropic_response(
         "tool_use": "tool_calls",
         "max_tokens": "length",
         "stop_sequence": "stop",
-        "refusal": "content_filter",
-        "model_context_window_exceeded": "length",
     }
     finish_reason = stop_reason_map.get(response.stop_reason, "stop")
 
@@ -1411,42 +1409,3 @@ def normalize_anthropic_response(
         ),
         finish_reason,
     )
-
-
-def normalize_anthropic_response_v2(
-    response,
-    strip_tool_prefix: bool = False,
-) -> "NormalizedResponse":
-    """Normalize Anthropic response to NormalizedResponse.
-
-    Wraps the existing normalize_anthropic_response() and maps its output
-    to the shared transport types. This allows incremental migration
-    without disturbing the legacy call sites.
-    """
-    from agent.transports.types import NormalizedResponse, build_tool_call
-
-    assistant_msg, finish_reason = normalize_anthropic_response(response, strip_tool_prefix)
-
-    tool_calls = None
-    if assistant_msg.tool_calls:
-        tool_calls = [
-            build_tool_call(
-                id=tc.id,
-                name=tc.function.name,
-                arguments=tc.function.arguments,
-            )
-            for tc in assistant_msg.tool_calls
-        ]
-
-    provider_data = {}
-    if getattr(assistant_msg, "reasoning_details", None):
-        provider_data["reasoning_details"] = assistant_msg.reasoning_details
-
-    return NormalizedResponse(
-        content=assistant_msg.content,
-        tool_calls=tool_calls,
-        finish_reason=finish_reason,
-        reasoning=getattr(assistant_msg, "reasoning", None),
-        usage=None,
-        provider_data=provider_data or None,
-    )

agent/auxiliary_client.py

@@ -1,4 +1,4 @@
-from agent.telemetry_logger import log_token_usage\n"""Shared auxiliary client router for side tasks.
+"""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
@@ -38,6 +38,7 @@ import json
 import logging
 import os
 import threading
+from agent.telemetry_logger import log_token_usage
 import time
 from pathlib import Path  # noqa: F401 — used by test mocks
 from types import SimpleNamespace
@@ -122,6 +123,16 @@ _OR_HEADERS = {
     "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 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.
@@ -396,7 +407,8 @@ class _CodexCompletionsAdapter:
                     prompt_tokens=getattr(resp_usage, "input_tokens", 0),
                     completion_tokens=getattr(resp_usage, "output_tokens", 0),
                     total_tokens=getattr(resp_usage, "total_tokens", 0),
-                )\n        log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
+                )
+                log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
         except Exception as exc:
             logger.debug("Codex auxiliary Responses API call failed: %s", exc)
             raise
@@ -529,7 +541,8 @@ class _AnthropicCompletionsAdapter:
                 prompt_tokens=prompt_tokens,
                 completion_tokens=completion_tokens,
                 total_tokens=total_tokens,
-            )\n    log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
+            )
+            log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
 
         choice = SimpleNamespace(
             index=0,

agent/transports/__init__.py

@@ -1,57 +0,0 @@
-"""Transport layer types and registry for provider response normalization.
-
-Usage:
-    from agent.transports import get_transport
-    transport = get_transport("anthropic_messages")
-    result = transport.normalize_response(raw_response)
-"""
-
-from agent.transports.types import (  # noqa: F401
-    NormalizedResponse,
-    ToolCall,
-    Usage,
-    build_tool_call,
-    map_finish_reason,
-)
-
-_REGISTRY: dict = {}
-
-
-def register_transport(api_mode: str, transport_cls: type) -> None:
-    """Register a transport class for an api_mode string."""
-    _REGISTRY[api_mode] = transport_cls
-
-
-def get_transport(api_mode: str):
-    """Get a transport instance for the given api_mode.
-
-    Returns None if no transport is registered for this api_mode.
-    This allows gradual migration — call sites can check for None
-    and fall back to the legacy code path.
-    """
-    if not _REGISTRY:
-        _discover_transports()
-    cls = _REGISTRY.get(api_mode)
-    if cls is None:
-        return None
-    return cls()
-
-
-def _discover_transports() -> None:
-    """Import all transport modules to trigger auto-registration."""
-    try:
-        import agent.transports.anthropic  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.codex  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.chat_completions  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.bedrock  # noqa: F401
-    except ImportError:
-        pass

agent/transports/anthropic.py

@@ -1,95 +0,0 @@
-"""Anthropic Messages API transport.
-
-Delegates to the existing adapter functions in agent/anthropic_adapter.py.
-This transport owns format conversion and normalization — NOT client lifecycle.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from agent.transports.base import ProviderTransport
-from agent.transports.types import NormalizedResponse
-
-
-class AnthropicTransport(ProviderTransport):
-    """Transport for api_mode='anthropic_messages'."""
-
-    @property
-    def api_mode(self) -> str:
-        return "anthropic_messages"
-
-    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
-        from agent.anthropic_adapter import convert_messages_to_anthropic
-
-        base_url = kwargs.get("base_url")
-        return convert_messages_to_anthropic(messages, base_url=base_url)
-
-    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
-        from agent.anthropic_adapter import convert_tools_to_anthropic
-
-        return convert_tools_to_anthropic(tools)
-
-    def build_kwargs(
-        self,
-        model: str,
-        messages: List[Dict[str, Any]],
-        tools: Optional[List[Dict[str, Any]]] = None,
-        **params,
-    ) -> Dict[str, Any]:
-        from agent.anthropic_adapter import build_anthropic_kwargs
-
-        return build_anthropic_kwargs(
-            model=model,
-            messages=messages,
-            tools=tools,
-            max_tokens=params.get("max_tokens", 16384),
-            reasoning_config=params.get("reasoning_config"),
-            tool_choice=params.get("tool_choice"),
-            is_oauth=params.get("is_oauth", False),
-            preserve_dots=params.get("preserve_dots", False),
-            context_length=params.get("context_length"),
-            base_url=params.get("base_url"),
-            fast_mode=params.get("fast_mode", False),
-        )
-
-    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
-        from agent.anthropic_adapter import normalize_anthropic_response_v2
-
-        strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
-        return normalize_anthropic_response_v2(response, strip_tool_prefix=strip_tool_prefix)
-
-    def validate_response(self, response: Any) -> bool:
-        if response is None:
-            return False
-        content_blocks = getattr(response, "content", None)
-        if not isinstance(content_blocks, list):
-            return False
-        if not content_blocks:
-            return False
-        return True
-
-    def extract_cache_stats(self, response: Any):
-        usage = getattr(response, "usage", None)
-        if usage is None:
-            return None
-        cached = getattr(usage, "cache_read_input_tokens", 0) or 0
-        written = getattr(usage, "cache_creation_input_tokens", 0) or 0
-        if cached or written:
-            return {"cached_tokens": cached, "creation_tokens": written}
-        return None
-
-    _STOP_REASON_MAP = {
-        "end_turn": "stop",
-        "tool_use": "tool_calls",
-        "max_tokens": "length",
-        "stop_sequence": "stop",
-        "refusal": "content_filter",
-        "model_context_window_exceeded": "length",
-    }
-
-    def map_finish_reason(self, raw_reason: str) -> str:
-        return self._STOP_REASON_MAP.get(raw_reason, "stop")
-
-
-from agent.transports import register_transport  # noqa: E402
-
-register_transport("anthropic_messages", AnthropicTransport)

agent/transports/base.py

@@ -1,61 +0,0 @@
-"""Abstract base for provider transports.
-
-A transport owns the data path for one api_mode:
-  convert_messages → convert_tools → build_kwargs → normalize_response
-
-It does NOT own: client construction, streaming, credential refresh,
-prompt caching, interrupt handling, or retry logic. Those stay on AIAgent.
-"""
-
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
-
-from agent.transports.types import NormalizedResponse
-
-
-class ProviderTransport(ABC):
-    """Base class for provider-specific format conversion and normalization."""
-
-    @property
-    @abstractmethod
-    def api_mode(self) -> str:
-        """The api_mode string this transport handles."""
-        ...
-
-    @abstractmethod
-    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
-        """Convert OpenAI-format messages to provider-native format."""
-        ...
-
-    @abstractmethod
-    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
-        """Convert OpenAI-format tool definitions to provider-native format."""
-        ...
-
-    @abstractmethod
-    def build_kwargs(
-        self,
-        model: str,
-        messages: List[Dict[str, Any]],
-        tools: Optional[List[Dict[str, Any]]] = None,
-        **params,
-    ) -> Dict[str, Any]:
-        """Build the complete provider kwargs dict."""
-        ...
-
-    @abstractmethod
-    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
-        """Normalize a raw provider response to the shared NormalizedResponse type."""
-        ...
-
-    def validate_response(self, response: Any) -> bool:
-        """Optional structural validation for raw responses."""
-        return True
-
-    def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
-        """Optional cache stats extraction."""
-        return None
-
-    def map_finish_reason(self, raw_reason: str) -> str:
-        """Optional stop-reason mapping. Defaults to passthrough."""
-        return raw_reason

agent/transports/types.py

@@ -1,58 +0,0 @@
-"""Shared types for normalized provider responses."""
-
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional
-
-
-@dataclass
-class ToolCall:
-    """A normalized tool call from any provider."""
-
-    id: Optional[str]
-    name: str
-    arguments: str
-    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
-
-
-@dataclass
-class Usage:
-    """Token usage from an API response."""
-
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    total_tokens: int = 0
-    cached_tokens: int = 0
-
-
-@dataclass
-class NormalizedResponse:
-    """Normalized API response from any provider."""
-
-    content: Optional[str]
-    tool_calls: Optional[List[ToolCall]]
-    finish_reason: str
-    reasoning: Optional[str] = None
-    usage: Optional[Usage] = None
-    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
-
-
-def build_tool_call(
-    id: Optional[str],
-    name: str,
-    arguments: Any,
-    **provider_fields: Any,
-) -> ToolCall:
-    """Build a ToolCall, auto-serialising dict arguments."""
-    args_str = json.dumps(arguments) if isinstance(arguments, dict) else str(arguments)
-    provider_data = dict(provider_fields) if provider_fields else None
-    return ToolCall(id=id, name=name, arguments=args_str, provider_data=provider_data)
-
-
-def map_finish_reason(reason: Optional[str], mapping: Dict[str, str]) -> str:
-    """Translate a provider-specific stop reason to the normalized set."""
-    if reason is None:
-        return "stop"
-    return mapping.get(reason, "stop")

hermes_cli/main.py

@@ -168,7 +168,7 @@ import time as _time
 from datetime import datetime
 
 from hermes_cli import __version__, __release_date__
-from hermes_constants import OPENROUTER_BASE_URL
+from hermes_constants import AI_GATEWAY_BASE_URL, OPENROUTER_BASE_URL
 
 logger = logging.getLogger(__name__)
 
@@ -1112,6 +1112,8 @@ def select_provider_and_model(args=None):
     # Step 2: Provider-specific setup + model selection
     if selected_provider == "openrouter":
         _model_flow_openrouter(config, current_model)
+    elif selected_provider == "ai-gateway":
+        _model_flow_ai_gateway(config, current_model)
     elif selected_provider == "nous":
         _model_flow_nous(config, current_model, args=args)
     elif selected_provider == "openai-codex":
@@ -1267,6 +1269,55 @@ def _model_flow_openrouter(config, current_model=""):
         print("No change.")
 
 
+def _model_flow_ai_gateway(config, current_model=""):
+    """Vercel AI Gateway provider: ensure API key, then pick model with pricing."""
+    from hermes_cli.auth import _prompt_model_selection, _save_model_choice, deactivate_provider
+    from hermes_cli.config import get_env_value, save_env_value
+    from hermes_cli.models import ai_gateway_model_ids, get_pricing_for_provider
+
+    api_key = get_env_value("AI_GATEWAY_API_KEY")
+    if not api_key:
+        print("No Vercel AI Gateway API key configured.")
+        print("Create API key here: https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai-gateway&title=AI+Gateway")
+        print("Add a payment method to get $5 in free credits.")
+        print()
+        try:
+            import getpass
+            key = getpass.getpass("AI Gateway API key (or Enter to cancel): ").strip()
+        except (KeyboardInterrupt, EOFError):
+            print()
+            return
+        if not key:
+            print("Cancelled.")
+            return
+        save_env_value("AI_GATEWAY_API_KEY", key)
+        print("API key saved.")
+        print()
+
+    models_list = ai_gateway_model_ids(force_refresh=True)
+    pricing = get_pricing_for_provider("ai-gateway", force_refresh=True)
+
+    selected = _prompt_model_selection(models_list, current_model=current_model, pricing=pricing)
+    if selected:
+        _save_model_choice(selected)
+
+        from hermes_cli.config import load_config, save_config
+
+        cfg = load_config()
+        model = cfg.get("model")
+        if not isinstance(model, dict):
+            model = {"default": model} if model else {}
+            cfg["model"] = model
+        model["provider"] = "ai-gateway"
+        model["base_url"] = AI_GATEWAY_BASE_URL
+        model["api_mode"] = "chat_completions"
+        save_config(cfg)
+        deactivate_provider()
+        print(f"Default model set to: {selected} (via Vercel AI Gateway)")
+    else:
+        print("No change.")
+
+
 def _model_flow_nous(config, current_model="", args=None):
     """Nous Portal provider: ensure logged in, then pick model."""
     from hermes_cli.auth import (

hermes_cli/models.py

@@ -58,6 +58,28 @@ OPENROUTER_MODELS: list[tuple[str, str]] = [
 
 _openrouter_catalog_cache: list[tuple[str, str]] | None = None
 
+# Fallback Vercel AI Gateway snapshot used when the live catalog is unavailable.
+# OSS / open-weight models prioritized first, then closed-source by family.
+VERCEL_AI_GATEWAY_MODELS: list[tuple[str, str]] = [
+    ("moonshotai/kimi-k2.6", "recommended"),
+    ("alibaba/qwen3.6-plus", ""),
+    ("zai/glm-5.1", ""),
+    ("minimax/minimax-m2.7", ""),
+    ("anthropic/claude-sonnet-4.6", ""),
+    ("anthropic/claude-opus-4.7", ""),
+    ("anthropic/claude-opus-4.6", ""),
+    ("anthropic/claude-haiku-4.5", ""),
+    ("openai/gpt-5.4", ""),
+    ("openai/gpt-5.4-mini", ""),
+    ("openai/gpt-5.3-codex", ""),
+    ("google/gemini-3.1-pro-preview", ""),
+    ("google/gemini-3-flash", ""),
+    ("google/gemini-3.1-flash-lite-preview", ""),
+    ("xai/grok-4.20-reasoning", ""),
+]
+
+_ai_gateway_catalog_cache: list[tuple[str, str]] | None = None
+
 
 def _codex_curated_models() -> list[str]:
     """Derive the openai-codex curated list from codex_models.py.
@@ -258,18 +280,21 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
         "minimax-m2.5",
     ],
     "ai-gateway": [
-        "anthropic/claude-opus-4.6",
+        "moonshotai/kimi-k2.6",
+        "alibaba/qwen3.6-plus",
+        "zai/glm-5.1",
+        "minimax/minimax-m2.7",
         "anthropic/claude-sonnet-4.6",
-        "anthropic/claude-sonnet-4.5",
+        "anthropic/claude-opus-4.7",
+        "anthropic/claude-opus-4.6",
         "anthropic/claude-haiku-4.5",
-        "openai/gpt-5",
-        "openai/gpt-4.1",
-        "openai/gpt-4.1-mini",
-        "google/gemini-3-pro-preview",
+        "openai/gpt-5.4",
+        "openai/gpt-5.4-mini",
+        "openai/gpt-5.3-codex",
+        "google/gemini-3.1-pro-preview",
         "google/gemini-3-flash",
-        "google/gemini-2.5-pro",
-        "google/gemini-2.5-flash",
-        "deepseek/deepseek-v3.2",
+        "google/gemini-3.1-flash-lite-preview",
+        "xai/grok-4.20-reasoning",
     ],
     "kilocode": [
         "anthropic/claude-opus-4.6",
@@ -516,6 +541,7 @@ class ProviderEntry(NamedTuple):
 CANONICAL_PROVIDERS: list[ProviderEntry] = [
     ProviderEntry("nous",           "Nous Portal",              "Nous Portal (Nous Research subscription)"),
     ProviderEntry("openrouter",     "OpenRouter",               "OpenRouter (100+ models, pay-per-use)"),
+    ProviderEntry("ai-gateway",     "Vercel AI Gateway",        "Vercel AI Gateway (200+ models, $5 free credit, no markup)"),
     ProviderEntry("anthropic",      "Anthropic",                "Anthropic (Claude models — API key or Claude Code)"),
     ProviderEntry("openai-codex",   "OpenAI Codex",             "OpenAI Codex"),
     ProviderEntry("xiaomi",         "Xiaomi MiMo",              "Xiaomi MiMo (MiMo-V2 models — pro, omni, flash)"),
@@ -536,7 +562,6 @@ CANONICAL_PROVIDERS: list[ProviderEntry] = [
     ProviderEntry("kilocode",       "Kilo Code",                "Kilo Code (Kilo Gateway API)"),
     ProviderEntry("opencode-zen",   "OpenCode Zen",             "OpenCode Zen (35+ curated models, pay-as-you-go)"),
     ProviderEntry("opencode-go",    "OpenCode Go",              "OpenCode Go (open models, $10/month subscription)"),
-    ProviderEntry("ai-gateway",     "Vercel AI Gateway",        "Vercel AI Gateway (200+ models, pay-per-use)"),
 ]
 
 # Derived dicts — used throughout the codebase
@@ -679,6 +704,90 @@ def model_ids(*, force_refresh: bool = False) -> list[str]:
 
 
 
+def _ai_gateway_model_is_free(pricing: Any) -> bool:
+    """Return True if an AI Gateway model has $0 input AND output pricing."""
+    if not isinstance(pricing, dict):
+        return False
+    try:
+        return float(pricing.get("input", "0")) == 0 and float(pricing.get("output", "0")) == 0
+    except (TypeError, ValueError):
+        return False
+
+
+def fetch_ai_gateway_models(
+    timeout: float = 8.0,
+    *,
+    force_refresh: bool = False,
+) -> list[tuple[str, str]]:
+    """Return the curated AI Gateway picker list, refreshed from the live catalog when possible."""
+    global _ai_gateway_catalog_cache
+
+    if _ai_gateway_catalog_cache is not None and not force_refresh:
+        return list(_ai_gateway_catalog_cache)
+
+    from hermes_constants import AI_GATEWAY_BASE_URL
+
+    fallback = list(VERCEL_AI_GATEWAY_MODELS)
+    preferred_ids = [mid for mid, _ in fallback]
+
+    try:
+        req = urllib.request.Request(
+            f"{AI_GATEWAY_BASE_URL.rstrip('/')}/models",
+            headers={"Accept": "application/json"},
+        )
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
+            payload = json.loads(resp.read().decode())
+    except Exception:
+        return list(_ai_gateway_catalog_cache or fallback)
+
+    live_items = payload.get("data", [])
+    if not isinstance(live_items, list):
+        return list(_ai_gateway_catalog_cache or fallback)
+
+    live_by_id: dict[str, dict[str, Any]] = {}
+    for item in live_items:
+        if not isinstance(item, dict):
+            continue
+        mid = str(item.get("id") or "").strip()
+        if not mid:
+            continue
+        live_by_id[mid] = item
+
+    curated: list[tuple[str, str]] = []
+    for preferred_id in preferred_ids:
+        live_item = live_by_id.get(preferred_id)
+        if live_item is None:
+            continue
+        desc = "free" if _ai_gateway_model_is_free(live_item.get("pricing")) else ""
+        curated.append((preferred_id, desc))
+
+    if not curated:
+        return list(_ai_gateway_catalog_cache or fallback)
+
+    free_moonshot = next(
+        (
+            mid
+            for mid, item in live_by_id.items()
+            if mid.startswith("moonshotai/") and _ai_gateway_model_is_free(item.get("pricing"))
+        ),
+        None,
+    )
+    if free_moonshot:
+        curated = [(mid, desc) for mid, desc in curated if mid != free_moonshot]
+        curated.insert(0, (free_moonshot, "recommended"))
+    else:
+        first_id, _ = curated[0]
+        curated[0] = (first_id, "recommended")
+
+    _ai_gateway_catalog_cache = curated
+    return list(curated)
+
+
+def ai_gateway_model_ids(*, force_refresh: bool = False) -> list[str]:
+    """Return just the AI Gateway model-id strings."""
+    return [mid for mid, _ in fetch_ai_gateway_models(force_refresh=force_refresh)]
+
+
 # ---------------------------------------------------------------------------
 # Pricing helpers — fetch live pricing from OpenRouter-compatible /v1/models
 # ---------------------------------------------------------------------------
@@ -821,6 +930,51 @@ def fetch_models_with_pricing(
     return result
 
 
+def fetch_ai_gateway_pricing(
+    timeout: float = 8.0,
+    *,
+    force_refresh: bool = False,
+) -> dict[str, dict[str, str]]:
+    """Fetch Vercel AI Gateway /v1/models and return Hermes-shaped pricing."""
+    from hermes_constants import AI_GATEWAY_BASE_URL
+
+    cache_key = AI_GATEWAY_BASE_URL.rstrip("/")
+    if not force_refresh and cache_key in _pricing_cache:
+        return _pricing_cache[cache_key]
+
+    try:
+        req = urllib.request.Request(
+            f"{cache_key}/models",
+            headers={"Accept": "application/json"},
+        )
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
+            payload = json.loads(resp.read().decode())
+    except Exception:
+        _pricing_cache[cache_key] = {}
+        return {}
+
+    result: dict[str, dict[str, str]] = {}
+    for item in payload.get("data", []):
+        if not isinstance(item, dict):
+            continue
+        mid = item.get("id")
+        pricing = item.get("pricing")
+        if not (mid and isinstance(pricing, dict)):
+            continue
+        entry: dict[str, str] = {
+            "prompt": str(pricing.get("input", "")),
+            "completion": str(pricing.get("output", "")),
+        }
+        if pricing.get("input_cache_read"):
+            entry["input_cache_read"] = str(pricing["input_cache_read"])
+        if pricing.get("input_cache_write"):
+            entry["input_cache_write"] = str(pricing["input_cache_write"])
+        result[mid] = entry
+
+    _pricing_cache[cache_key] = result
+    return result
+
+
 def _resolve_openrouter_api_key() -> str:
     """Best-effort OpenRouter API key for pricing fetch."""
     return os.getenv("OPENROUTER_API_KEY", "").strip()
@@ -839,7 +993,7 @@ def _resolve_nous_pricing_credentials() -> tuple[str, str]:
 
 
 def get_pricing_for_provider(provider: str, *, force_refresh: bool = False) -> dict[str, dict[str, str]]:
-    """Return live pricing for providers that support it (openrouter, nous)."""
+    """Return live pricing for providers that support it (openrouter, ai-gateway, nous)."""
     normalized = normalize_provider(provider)
     if normalized == "openrouter":
         return fetch_models_with_pricing(
@@ -847,11 +1001,11 @@ def get_pricing_for_provider(provider: str, *, force_refresh: bool = False) -> d
             base_url="https://openrouter.ai/api",
             force_refresh=force_refresh,
         )
+    if normalized == "ai-gateway":
+        return fetch_ai_gateway_pricing(force_refresh=force_refresh)
     if normalized == "nous":
         api_key, base_url = _resolve_nous_pricing_credentials()
         if base_url:
-            # Nous base_url typically looks like https://inference-api.nousresearch.com/v1
-            # We need the part before /v1 for our fetch function
             stripped = base_url.rstrip("/")
             if stripped.endswith("/v1"):
                 stripped = stripped[:-3]
@@ -1253,9 +1407,7 @@ def provider_model_ids(provider: Optional[str], *, force_refresh: bool = False)
         if live:
             return live
     if normalized == "ai-gateway":
-        live = _fetch_ai_gateway_models()
-        if live:
-            return live
+        return ai_gateway_model_ids()
     if normalized == "custom":
         base_url = _get_custom_base_url()
         if base_url:

run_agent.py

@@ -908,6 +908,10 @@ class AIAgent:
                         "X-OpenRouter-Title": "Hermes Agent",
                         "X-OpenRouter-Categories": "productivity,cli-agent",
                     }
+                elif "ai-gateway.vercel.sh" in effective_base.lower():
+                    from agent.auxiliary_client import _AI_GATEWAY_HEADERS
+
+                    client_kwargs["default_headers"] = dict(_AI_GATEWAY_HEADERS)
                 elif "api.githubcopilot.com" in effective_base.lower():
                     from hermes_cli.models import copilot_default_headers
 
@@ -4667,11 +4671,13 @@ class AIAgent:
         return True
 
     def _apply_client_headers_for_base_url(self, base_url: str) -> None:
-        from agent.auxiliary_client import _OR_HEADERS
+        from agent.auxiliary_client import _AI_GATEWAY_HEADERS, _OR_HEADERS
 
         normalized = (base_url or "").lower()
         if "openrouter" in normalized:
             self._client_kwargs["default_headers"] = dict(_OR_HEADERS)
+        elif "ai-gateway.vercel.sh" in normalized:
+            self._client_kwargs["default_headers"] = dict(_AI_GATEWAY_HEADERS)
         elif "api.githubcopilot.com" in normalized:
             from hermes_cli.models import copilot_default_headers
 

tests/agent/test_anthropic_normalize_v2.py

@@ -1,213 +0,0 @@
-"""Regression tests: normalize_anthropic_response_v2 vs v1.
-
-Constructs mock Anthropic responses and asserts that the v2 function
-(returning NormalizedResponse) produces identical field values to the
-original v1 function (returning SimpleNamespace + finish_reason).
-"""
-
-from types import SimpleNamespace
-
-import pytest
-
-from agent.anthropic_adapter import (
-    normalize_anthropic_response,
-    normalize_anthropic_response_v2,
-)
-from agent.transports.types import NormalizedResponse
-
-
-def _text_block(text: str):
-    return SimpleNamespace(type="text", text=text)
-
-
-def _thinking_block(thinking: str, signature: str = "sig_abc"):
-    return SimpleNamespace(type="thinking", thinking=thinking, signature=signature)
-
-
-def _tool_use_block(id: str, name: str, input: dict):
-    return SimpleNamespace(type="tool_use", id=id, name=name, input=input)
-
-
-def _response(content_blocks, stop_reason="end_turn"):
-    return SimpleNamespace(
-        content=content_blocks,
-        stop_reason=stop_reason,
-        usage=SimpleNamespace(input_tokens=10, output_tokens=5),
-    )
-
-
-class TestTextOnly:
-    def setup_method(self):
-        self.resp = _response([_text_block("Hello world")])
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_type(self):
-        assert isinstance(self.v2, NormalizedResponse)
-
-    def test_content_matches(self):
-        assert self.v2.content == self.v1_msg.content
-
-    def test_finish_reason_matches(self):
-        assert self.v2.finish_reason == self.v1_finish
-
-    def test_no_tool_calls(self):
-        assert self.v2.tool_calls is None
-        assert self.v1_msg.tool_calls is None
-
-    def test_no_reasoning(self):
-        assert self.v2.reasoning is None
-        assert self.v1_msg.reasoning is None
-
-
-class TestWithToolCalls:
-    def setup_method(self):
-        self.resp = _response(
-            [
-                _text_block("I'll check that"),
-                _tool_use_block("toolu_abc", "terminal", {"command": "ls"}),
-                _tool_use_block("toolu_def", "read_file", {"path": "/tmp"}),
-            ],
-            stop_reason="tool_use",
-        )
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_finish_reason(self):
-        assert self.v2.finish_reason == "tool_calls"
-        assert self.v1_finish == "tool_calls"
-
-    def test_tool_call_count(self):
-        assert len(self.v2.tool_calls) == 2
-        assert len(self.v1_msg.tool_calls) == 2
-
-    def test_tool_call_ids_match(self):
-        for i in range(2):
-            assert self.v2.tool_calls[i].id == self.v1_msg.tool_calls[i].id
-
-    def test_tool_call_names_match(self):
-        assert self.v2.tool_calls[0].name == "terminal"
-        assert self.v2.tool_calls[1].name == "read_file"
-        for i in range(2):
-            assert self.v2.tool_calls[i].name == self.v1_msg.tool_calls[i].function.name
-
-    def test_tool_call_arguments_match(self):
-        for i in range(2):
-            assert self.v2.tool_calls[i].arguments == self.v1_msg.tool_calls[i].function.arguments
-
-    def test_content_preserved(self):
-        assert self.v2.content == self.v1_msg.content
-        assert "check that" in self.v2.content
-
-
-class TestWithThinking:
-    def setup_method(self):
-        self.resp = _response([
-            _thinking_block("Let me think about this carefully..."),
-            _text_block("The answer is 42."),
-        ])
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_reasoning_matches(self):
-        assert self.v2.reasoning == self.v1_msg.reasoning
-        assert "think about this" in self.v2.reasoning
-
-    def test_reasoning_details_in_provider_data(self):
-        v1_details = self.v1_msg.reasoning_details
-        v2_details = self.v2.provider_data.get("reasoning_details") if self.v2.provider_data else None
-        assert v1_details is not None
-        assert v2_details is not None
-        assert len(v2_details) == len(v1_details)
-
-    def test_content_excludes_thinking(self):
-        assert self.v2.content == "The answer is 42."
-
-
-class TestMixed:
-    def setup_method(self):
-        self.resp = _response(
-            [
-                _thinking_block("Planning my approach..."),
-                _text_block("I'll run the command"),
-                _tool_use_block("toolu_xyz", "terminal", {"command": "pwd"}),
-            ],
-            stop_reason="tool_use",
-        )
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_all_fields_present(self):
-        assert self.v2.content is not None
-        assert self.v2.tool_calls is not None
-        assert self.v2.reasoning is not None
-        assert self.v2.finish_reason == "tool_calls"
-
-    def test_content_matches(self):
-        assert self.v2.content == self.v1_msg.content
-
-    def test_reasoning_matches(self):
-        assert self.v2.reasoning == self.v1_msg.reasoning
-
-    def test_tool_call_matches(self):
-        assert self.v2.tool_calls[0].id == self.v1_msg.tool_calls[0].id
-        assert self.v2.tool_calls[0].name == self.v1_msg.tool_calls[0].function.name
-
-
-class TestStopReasons:
-    @pytest.mark.parametrize("stop_reason,expected", [
-        ("end_turn", "stop"),
-        ("tool_use", "tool_calls"),
-        ("max_tokens", "length"),
-        ("stop_sequence", "stop"),
-        ("refusal", "content_filter"),
-        ("model_context_window_exceeded", "length"),
-        ("unknown_future_reason", "stop"),
-    ])
-    def test_stop_reason_mapping(self, stop_reason, expected):
-        resp = _response([_text_block("x")], stop_reason=stop_reason)
-        _v1_msg, v1_finish = normalize_anthropic_response(resp)
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.finish_reason == v1_finish == expected
-
-
-class TestStripToolPrefix:
-    def test_prefix_stripped(self):
-        resp = _response(
-            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
-            stop_reason="tool_use",
-        )
-        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=True)
-        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=True)
-        assert v1_msg.tool_calls[0].function.name == "terminal"
-        assert v2.tool_calls[0].name == "terminal"
-
-    def test_prefix_kept(self):
-        resp = _response(
-            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
-            stop_reason="tool_use",
-        )
-        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=False)
-        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=False)
-        assert v1_msg.tool_calls[0].function.name == "mcp_terminal"
-        assert v2.tool_calls[0].name == "mcp_terminal"
-
-
-class TestEdgeCases:
-    def test_empty_content_blocks(self):
-        resp = _response([])
-        v1_msg, _v1_finish = normalize_anthropic_response(resp)
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.content == v1_msg.content
-        assert v2.content is None
-
-    def test_no_reasoning_details_means_none_provider_data(self):
-        resp = _response([_text_block("hi")])
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.provider_data is None
-
-    def test_v2_returns_dataclass_not_namespace(self):
-        resp = _response([_text_block("hi")])
-        v2 = normalize_anthropic_response_v2(resp)
-        assert isinstance(v2, NormalizedResponse)
-        assert not isinstance(v2, SimpleNamespace)

tests/agent/transports/test_transport.py

@@ -1,208 +0,0 @@
-"""Tests for the transport ABC, registry, and AnthropicTransport."""
-
-from types import SimpleNamespace
-
-import pytest
-
-from agent.transports import _REGISTRY, get_transport, register_transport
-from agent.transports.base import ProviderTransport
-from agent.transports.types import NormalizedResponse
-
-
-class TestProviderTransportABC:
-    def test_cannot_instantiate_abc(self):
-        with pytest.raises(TypeError):
-            ProviderTransport()
-
-    def test_concrete_must_implement_all_abstract(self):
-        class Incomplete(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "test"
-
-        with pytest.raises(TypeError):
-            Incomplete()
-
-    def test_minimal_concrete(self):
-        class Minimal(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "test_minimal"
-
-            def convert_messages(self, messages, **kw):
-                return messages
-
-            def convert_tools(self, tools):
-                return tools
-
-            def build_kwargs(self, model, messages, tools=None, **params):
-                return {"model": model, "messages": messages}
-
-            def normalize_response(self, response, **kw):
-                return NormalizedResponse(content="ok", tool_calls=None, finish_reason="stop")
-
-        t = Minimal()
-        assert t.api_mode == "test_minimal"
-        assert t.validate_response(None) is True
-        assert t.extract_cache_stats(None) is None
-        assert t.map_finish_reason("end_turn") == "end_turn"
-
-
-class TestTransportRegistry:
-    def test_get_unregistered_returns_none(self):
-        assert get_transport("nonexistent_mode") is None
-
-    def test_anthropic_registered_on_import(self):
-        import agent.transports.anthropic  # noqa: F401
-
-        t = get_transport("anthropic_messages")
-        assert t is not None
-        assert t.api_mode == "anthropic_messages"
-
-    def test_register_and_get(self):
-        class DummyTransport(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "dummy_test"
-
-            def convert_messages(self, messages, **kw):
-                return messages
-
-            def convert_tools(self, tools):
-                return tools
-
-            def build_kwargs(self, model, messages, tools=None, **params):
-                return {}
-
-            def normalize_response(self, response, **kw):
-                return NormalizedResponse(content=None, tool_calls=None, finish_reason="stop")
-
-        register_transport("dummy_test", DummyTransport)
-        t = get_transport("dummy_test")
-        assert t.api_mode == "dummy_test"
-        _REGISTRY.pop("dummy_test", None)
-
-
-class TestAnthropicTransport:
-    @pytest.fixture
-    def transport(self):
-        import agent.transports.anthropic  # noqa: F401
-
-        return get_transport("anthropic_messages")
-
-    def test_api_mode(self, transport):
-        assert transport.api_mode == "anthropic_messages"
-
-    def test_convert_tools_simple(self, transport):
-        tools = [{
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "description": "A test",
-                "parameters": {"type": "object", "properties": {}},
-            },
-        }]
-        result = transport.convert_tools(tools)
-        assert len(result) == 1
-        assert result[0]["name"] == "test_tool"
-        assert "input_schema" in result[0]
-
-    def test_validate_response_none(self, transport):
-        assert transport.validate_response(None) is False
-
-    def test_validate_response_empty_content(self, transport):
-        r = SimpleNamespace(content=[])
-        assert transport.validate_response(r) is False
-
-    def test_validate_response_valid(self, transport):
-        r = SimpleNamespace(content=[SimpleNamespace(type="text", text="hello")])
-        assert transport.validate_response(r) is True
-
-    def test_map_finish_reason(self, transport):
-        assert transport.map_finish_reason("end_turn") == "stop"
-        assert transport.map_finish_reason("tool_use") == "tool_calls"
-        assert transport.map_finish_reason("max_tokens") == "length"
-        assert transport.map_finish_reason("stop_sequence") == "stop"
-        assert transport.map_finish_reason("refusal") == "content_filter"
-        assert transport.map_finish_reason("model_context_window_exceeded") == "length"
-        assert transport.map_finish_reason("unknown") == "stop"
-
-    def test_extract_cache_stats_none_usage(self, transport):
-        r = SimpleNamespace(usage=None)
-        assert transport.extract_cache_stats(r) is None
-
-    def test_extract_cache_stats_with_cache(self, transport):
-        usage = SimpleNamespace(cache_read_input_tokens=100, cache_creation_input_tokens=50)
-        r = SimpleNamespace(usage=usage)
-        result = transport.extract_cache_stats(r)
-        assert result == {"cached_tokens": 100, "creation_tokens": 50}
-
-    def test_extract_cache_stats_zero(self, transport):
-        usage = SimpleNamespace(cache_read_input_tokens=0, cache_creation_input_tokens=0)
-        r = SimpleNamespace(usage=usage)
-        assert transport.extract_cache_stats(r) is None
-
-    def test_normalize_response_text(self, transport):
-        r = SimpleNamespace(
-            content=[SimpleNamespace(type="text", text="Hello world")],
-            stop_reason="end_turn",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=5),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert isinstance(nr, NormalizedResponse)
-        assert nr.content == "Hello world"
-        assert nr.tool_calls is None or nr.tool_calls == []
-        assert nr.finish_reason == "stop"
-
-    def test_normalize_response_tool_calls(self, transport):
-        r = SimpleNamespace(
-            content=[
-                SimpleNamespace(type="tool_use", id="toolu_123", name="terminal", input={"command": "ls"}),
-            ],
-            stop_reason="tool_use",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=20),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert nr.finish_reason == "tool_calls"
-        assert len(nr.tool_calls) == 1
-        tc = nr.tool_calls[0]
-        assert tc.name == "terminal"
-        assert tc.id == "toolu_123"
-        assert '"command"' in tc.arguments
-
-    def test_normalize_response_thinking(self, transport):
-        r = SimpleNamespace(
-            content=[
-                SimpleNamespace(type="thinking", thinking="Let me think..."),
-                SimpleNamespace(type="text", text="The answer is 42"),
-            ],
-            stop_reason="end_turn",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=15),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert nr.content == "The answer is 42"
-        assert nr.reasoning == "Let me think..."
-
-    def test_build_kwargs_returns_dict(self, transport):
-        messages = [{"role": "user", "content": "Hello"}]
-        kw = transport.build_kwargs(
-            model="claude-sonnet-4-6",
-            messages=messages,
-            max_tokens=1024,
-        )
-        assert isinstance(kw, dict)
-        assert "model" in kw
-        assert "max_tokens" in kw
-        assert "messages" in kw
-
-    def test_convert_messages_extracts_system(self, transport):
-        messages = [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "user", "content": "Hi"},
-        ]
-        system, msgs = transport.convert_messages(messages)
-        assert system is not None
-        assert len(msgs) >= 1

tests/agent/transports/test_types.py

@@ -1,130 +0,0 @@
-"""Tests for agent/transports/types.py — dataclass construction + helpers."""
-
-import json
-
-from agent.transports.types import (
-    NormalizedResponse,
-    ToolCall,
-    Usage,
-    build_tool_call,
-    map_finish_reason,
-)
-
-
-class TestToolCall:
-    def test_basic_construction(self):
-        tc = ToolCall(id="call_abc", name="terminal", arguments='{"cmd": "ls"}')
-        assert tc.id == "call_abc"
-        assert tc.name == "terminal"
-        assert tc.arguments == '{"cmd": "ls"}'
-        assert tc.provider_data is None
-
-    def test_none_id(self):
-        tc = ToolCall(id=None, name="read_file", arguments="{}")
-        assert tc.id is None
-
-    def test_provider_data(self):
-        tc = ToolCall(
-            id="call_x",
-            name="t",
-            arguments="{}",
-            provider_data={"call_id": "call_x", "response_item_id": "fc_x"},
-        )
-        assert tc.provider_data["call_id"] == "call_x"
-        assert tc.provider_data["response_item_id"] == "fc_x"
-
-
-class TestUsage:
-    def test_defaults(self):
-        u = Usage()
-        assert u.prompt_tokens == 0
-        assert u.completion_tokens == 0
-        assert u.total_tokens == 0
-        assert u.cached_tokens == 0
-
-    def test_explicit(self):
-        u = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150, cached_tokens=80)
-        assert u.total_tokens == 150
-
-
-class TestNormalizedResponse:
-    def test_text_only(self):
-        r = NormalizedResponse(content="hello", tool_calls=None, finish_reason="stop")
-        assert r.content == "hello"
-        assert r.tool_calls is None
-        assert r.finish_reason == "stop"
-        assert r.reasoning is None
-        assert r.usage is None
-        assert r.provider_data is None
-
-    def test_with_tool_calls(self):
-        tcs = [ToolCall(id="call_1", name="terminal", arguments='{"cmd":"pwd"}')]
-        r = NormalizedResponse(content=None, tool_calls=tcs, finish_reason="tool_calls")
-        assert r.finish_reason == "tool_calls"
-        assert len(r.tool_calls) == 1
-        assert r.tool_calls[0].name == "terminal"
-
-    def test_with_reasoning(self):
-        r = NormalizedResponse(
-            content="answer",
-            tool_calls=None,
-            finish_reason="stop",
-            reasoning="I thought about it",
-        )
-        assert r.reasoning == "I thought about it"
-
-    def test_with_provider_data(self):
-        r = NormalizedResponse(
-            content=None,
-            tool_calls=None,
-            finish_reason="stop",
-            provider_data={"reasoning_details": [{"type": "thinking", "thinking": "hmm"}]},
-        )
-        assert r.provider_data["reasoning_details"][0]["type"] == "thinking"
-
-
-class TestBuildToolCall:
-    def test_dict_arguments_serialized(self):
-        tc = build_tool_call(id="call_1", name="terminal", arguments={"cmd": "ls"})
-        assert tc.arguments == json.dumps({"cmd": "ls"})
-        assert tc.provider_data is None
-
-    def test_string_arguments_passthrough(self):
-        tc = build_tool_call(id="call_2", name="read_file", arguments='{"path": "/tmp"}')
-        assert tc.arguments == '{"path": "/tmp"}'
-
-    def test_provider_fields(self):
-        tc = build_tool_call(
-            id="call_3",
-            name="terminal",
-            arguments="{}",
-            call_id="call_3",
-            response_item_id="fc_3",
-        )
-        assert tc.provider_data == {"call_id": "call_3", "response_item_id": "fc_3"}
-
-    def test_none_id(self):
-        tc = build_tool_call(id=None, name="t", arguments="{}")
-        assert tc.id is None
-
-
-class TestMapFinishReason:
-    ANTHROPIC_MAP = {
-        "end_turn": "stop",
-        "tool_use": "tool_calls",
-        "max_tokens": "length",
-        "stop_sequence": "stop",
-        "refusal": "content_filter",
-    }
-
-    def test_known_reason(self):
-        assert map_finish_reason("end_turn", self.ANTHROPIC_MAP) == "stop"
-        assert map_finish_reason("tool_use", self.ANTHROPIC_MAP) == "tool_calls"
-        assert map_finish_reason("max_tokens", self.ANTHROPIC_MAP) == "length"
-        assert map_finish_reason("refusal", self.ANTHROPIC_MAP) == "content_filter"
-
-    def test_unknown_reason_defaults_to_stop(self):
-        assert map_finish_reason("something_new", self.ANTHROPIC_MAP) == "stop"
-
-    def test_none_reason(self):
-        assert map_finish_reason(None, self.ANTHROPIC_MAP) == "stop"

tests/hermes_cli/test_ai_gateway_models.py

@@ -0,0 +1,222 @@
+"""AI Gateway provider UX, live pricing, and model promotion tests."""
+
+from __future__ import annotations
+
+import json
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from hermes_cli import models as models_module
+from hermes_cli.models import (
+    CANONICAL_PROVIDERS,
+    VERCEL_AI_GATEWAY_MODELS,
+    _ai_gateway_model_is_free,
+    ai_gateway_model_ids,
+    fetch_ai_gateway_models,
+    fetch_ai_gateway_pricing,
+    get_pricing_for_provider,
+)
+
+
+def _mock_urlopen(payload):
+    resp = MagicMock()
+    resp.read.return_value = json.dumps(payload).encode()
+    ctx = MagicMock()
+    ctx.__enter__.return_value = resp
+    ctx.__exit__.return_value = False
+    return ctx
+
+
+def _reset_caches():
+    models_module._ai_gateway_catalog_cache = None
+    models_module._pricing_cache.clear()
+
+
+@pytest.fixture
+def config_home(tmp_path, monkeypatch):
+    home = tmp_path / "hermes"
+    home.mkdir()
+    (home / "config.yaml").write_text("model: some-old-model\n")
+    (home / ".env").write_text("")
+    monkeypatch.setenv("HERMES_HOME", str(home))
+    monkeypatch.delenv("AI_GATEWAY_API_KEY", raising=False)
+    monkeypatch.delenv("AI_GATEWAY_BASE_URL", raising=False)
+    return home
+
+
+def test_ai_gateway_provider_is_promoted_near_top_of_picker():
+    slugs = [entry.slug for entry in CANONICAL_PROVIDERS]
+    assert "ai-gateway" in slugs[:3]
+
+
+def test_ai_gateway_pricing_translates_input_output_to_prompt_completion():
+    _reset_caches()
+    payload = {
+        "data": [
+            {
+                "id": "moonshotai/kimi-k2.5",
+                "type": "language",
+                "pricing": {
+                    "input": "0.0000006",
+                    "output": "0.0000025",
+                    "input_cache_read": "0.00000015",
+                    "input_cache_write": "0.0000006",
+                },
+            }
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_pricing(force_refresh=True)
+
+    entry = result["moonshotai/kimi-k2.5"]
+    assert entry["prompt"] == "0.0000006"
+    assert entry["completion"] == "0.0000025"
+    assert entry["input_cache_read"] == "0.00000015"
+    assert entry["input_cache_write"] == "0.0000006"
+
+
+def test_get_pricing_for_provider_supports_ai_gateway():
+    _reset_caches()
+    payload = {
+        "data": [
+            {
+                "id": "moonshotai/kimi-k2.5",
+                "type": "language",
+                "pricing": {"input": "0.0001", "output": "0.0002"},
+            }
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = get_pricing_for_provider("ai-gateway", force_refresh=True)
+    assert result["moonshotai/kimi-k2.5"] == {"prompt": "0.0001", "completion": "0.0002"}
+
+
+def test_ai_gateway_pricing_returns_empty_on_fetch_failure():
+    _reset_caches()
+    with patch("urllib.request.urlopen", side_effect=OSError("network down")):
+        result = fetch_ai_gateway_pricing(force_refresh=True)
+    assert result == {}
+
+
+def test_ai_gateway_pricing_skips_entries_without_pricing_dict():
+    _reset_caches()
+    payload = {
+        "data": [
+            {"id": "x/y", "pricing": None},
+            {"id": "a/b", "pricing": {"input": "0", "output": "0"}},
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_pricing(force_refresh=True)
+    assert "x/y" not in result
+    assert result["a/b"] == {"prompt": "0", "completion": "0"}
+
+
+def test_ai_gateway_free_detector():
+    assert _ai_gateway_model_is_free({"input": "0", "output": "0"}) is True
+    assert _ai_gateway_model_is_free({"input": "0", "output": "0.01"}) is False
+    assert _ai_gateway_model_is_free({"input": "0.01", "output": "0"}) is False
+    assert _ai_gateway_model_is_free(None) is False
+    assert _ai_gateway_model_is_free({"input": "not a number"}) is False
+
+
+def test_fetch_ai_gateway_models_filters_against_live_catalog():
+    _reset_caches()
+    preferred = [mid for mid, _ in VERCEL_AI_GATEWAY_MODELS]
+    live_ids = preferred[:3]
+    payload = {
+        "data": [
+            {"id": mid, "pricing": {"input": "0.001", "output": "0.002"}}
+            for mid in live_ids
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_models(force_refresh=True)
+
+    assert [mid for mid, _ in result] == live_ids
+    assert result[0][1] == "recommended"
+    assert ai_gateway_model_ids(force_refresh=False) == live_ids
+
+
+def test_fetch_ai_gateway_models_tags_free_models():
+    _reset_caches()
+    first_id = VERCEL_AI_GATEWAY_MODELS[0][0]
+    second_id = VERCEL_AI_GATEWAY_MODELS[1][0]
+    payload = {
+        "data": [
+            {"id": first_id, "pricing": {"input": "0.001", "output": "0.002"}},
+            {"id": second_id, "pricing": {"input": "0", "output": "0"}},
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_models(force_refresh=True)
+
+    by_id = dict(result)
+    assert by_id[first_id] == "recommended"
+    assert by_id[second_id] == "free"
+
+
+def test_free_moonshot_model_auto_promoted_to_top_even_if_not_curated():
+    _reset_caches()
+    first_curated = VERCEL_AI_GATEWAY_MODELS[0][0]
+    unlisted_free_moonshot = "moonshotai/kimi-coder-free-preview"
+    payload = {
+        "data": [
+            {"id": first_curated, "pricing": {"input": "0.001", "output": "0.002"}},
+            {"id": unlisted_free_moonshot, "pricing": {"input": "0", "output": "0"}},
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_models(force_refresh=True)
+
+    assert result[0] == (unlisted_free_moonshot, "recommended")
+    assert any(mid == first_curated for mid, _ in result)
+
+
+def test_paid_moonshot_does_not_get_auto_promoted():
+    _reset_caches()
+    first_curated = VERCEL_AI_GATEWAY_MODELS[0][0]
+    payload = {
+        "data": [
+            {"id": first_curated, "pricing": {"input": "0.001", "output": "0.002"}},
+            {"id": "moonshotai/some-paid-variant", "pricing": {"input": "0.001", "output": "0.002"}},
+        ]
+    }
+    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
+        result = fetch_ai_gateway_models(force_refresh=True)
+
+    assert result[0][0] == first_curated
+
+
+def test_fetch_ai_gateway_models_falls_back_on_error():
+    _reset_caches()
+    with patch("urllib.request.urlopen", side_effect=OSError("network")):
+        result = fetch_ai_gateway_models(force_refresh=True)
+    assert result == list(VERCEL_AI_GATEWAY_MODELS)
+
+
+def test_ai_gateway_setup_flow_shows_deeplink_and_passes_pricing(config_home, monkeypatch, capsys):
+    from hermes_cli.main import _model_flow_ai_gateway
+    from hermes_cli.config import load_config
+
+    pricing = {"moonshotai/kimi-k2.6": {"prompt": "0", "completion": "0"}}
+    monkeypatch.setenv("HERMES_HOME", str(config_home))
+
+    with patch("getpass.getpass", return_value="vercel-key"), \
+         patch("hermes_cli.models.ai_gateway_model_ids", return_value=["moonshotai/kimi-k2.6"]), \
+         patch("hermes_cli.models.get_pricing_for_provider", return_value=pricing), \
+         patch("hermes_cli.auth._prompt_model_selection", return_value="moonshotai/kimi-k2.6") as prompt_selection, \
+         patch("hermes_cli.auth.deactivate_provider"):
+        _model_flow_ai_gateway(load_config(), "")
+
+    out = capsys.readouterr().out
+    assert "vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai-gateway&title=AI+Gateway" in out
+    assert "free credits" in out.lower()
+    assert prompt_selection.call_args.kwargs["pricing"] == pricing
+
+    import yaml
+    config = yaml.safe_load((config_home / "config.yaml").read_text()) or {}
+    model = config["model"]
+    assert model["provider"] == "ai-gateway"
+    assert model["api_mode"] == "chat_completions"

tests/run_agent/test_provider_attribution_headers.py

@@ -0,0 +1,62 @@
+"""Attribution default_headers applied per provider via base-URL detection."""
+
+from unittest.mock import MagicMock, patch
+
+from run_agent import AIAgent
+
+
+@patch("run_agent.OpenAI")
+def test_openrouter_base_url_applies_or_headers(mock_openai):
+    mock_openai.return_value = MagicMock()
+    agent = AIAgent(
+        api_key="test-key",
+        base_url="https://openrouter.ai/api/v1",
+        model="test/model",
+        quiet_mode=True,
+        skip_context_files=True,
+        skip_memory=True,
+    )
+
+    agent._apply_client_headers_for_base_url("https://openrouter.ai/api/v1")
+
+    headers = agent._client_kwargs["default_headers"]
+    assert headers["HTTP-Referer"] == "https://hermes-agent.nousresearch.com"
+    assert headers["X-OpenRouter-Title"] == "Hermes Agent"
+
+
+@patch("run_agent.OpenAI")
+def test_ai_gateway_base_url_applies_attribution_headers(mock_openai):
+    mock_openai.return_value = MagicMock()
+    agent = AIAgent(
+        api_key="test-key",
+        base_url="https://openrouter.ai/api/v1",
+        model="test/model",
+        quiet_mode=True,
+        skip_context_files=True,
+        skip_memory=True,
+    )
+
+    agent._apply_client_headers_for_base_url("https://ai-gateway.vercel.sh/v1")
+
+    headers = agent._client_kwargs["default_headers"]
+    assert headers["HTTP-Referer"] == "https://hermes-agent.nousresearch.com"
+    assert headers["X-Title"] == "Hermes Agent"
+    assert headers["User-Agent"].startswith("HermesAgent/")
+
+
+@patch("run_agent.OpenAI")
+def test_unknown_base_url_clears_default_headers(mock_openai):
+    mock_openai.return_value = MagicMock()
+    agent = AIAgent(
+        api_key="test-key",
+        base_url="https://openrouter.ai/api/v1",
+        model="test/model",
+        quiet_mode=True,
+        skip_context_files=True,
+        skip_memory=True,
+    )
+    agent._client_kwargs["default_headers"] = {"X-Stale": "yes"}
+
+    agent._apply_client_headers_for_base_url("https://api.example.com/v1")
+
+    assert "default_headers" not in agent._client_kwargs