src/infrastructure/router/cascade.py

@@ -9,12 +9,7 @@ models for image inputs and falls back through capability chains.
 """
 
 import asyncio
-import base64
 import logging
-import time
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from enum import Enum
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
@@ -33,148 +28,25 @@ try:
 except ImportError:
     requests = None  # type: ignore
 
+# Re-export data models so existing ``from …cascade import X`` keeps working.
+from .models import (  # noqa: F401 – re-exports
+    CircuitState,
+    ContentType,
+    ModelCapability,
+    Provider,
+    ProviderMetrics,
+    ProviderStatus,
+    RouterConfig,
+)
+
+# Mixins
+from .health import HealthMixin
+from .providers import ProviderCallsMixin
+
 logger = logging.getLogger(__name__)
 
-# Quota monitor — optional, degrades gracefully if unavailable
-try:
-    from infrastructure.claude_quota import QuotaMonitor, get_quota_monitor
 
-    _quota_monitor: "QuotaMonitor | None" = get_quota_monitor()
-except Exception as _exc:  # pragma: no cover
-    logger.debug("Quota monitor not available: %s", _exc)
-    _quota_monitor = None
-
-
-class ProviderStatus(Enum):
-    """Health status of a provider."""
-
-    HEALTHY = "healthy"
-    DEGRADED = "degraded"  # Working but slow or occasional errors
-    UNHEALTHY = "unhealthy"  # Circuit breaker open
-    DISABLED = "disabled"
-
-
-class CircuitState(Enum):
-    """Circuit breaker state."""
-
-    CLOSED = "closed"  # Normal operation
-    OPEN = "open"  # Failing, rejecting requests
-    HALF_OPEN = "half_open"  # Testing if recovered
-
-
-class ContentType(Enum):
-    """Type of content in the request."""
-
-    TEXT = "text"
-    VISION = "vision"  # Contains images
-    AUDIO = "audio"  # Contains audio
-    MULTIMODAL = "multimodal"  # Multiple content types
-
-
-@dataclass
-class ProviderMetrics:
-    """Metrics for a single provider."""
-
-    total_requests: int = 0
-    successful_requests: int = 0
-    failed_requests: int = 0
-    total_latency_ms: float = 0.0
-    last_request_time: str | None = None
-    last_error_time: str | None = None
-    consecutive_failures: int = 0
-
-    @property
-    def avg_latency_ms(self) -> float:
-        if self.total_requests == 0:
-            return 0.0
-        return self.total_latency_ms / self.total_requests
-
-    @property
-    def error_rate(self) -> float:
-        if self.total_requests == 0:
-            return 0.0
-        return self.failed_requests / self.total_requests
-
-
-@dataclass
-class ModelCapability:
-    """Capabilities a model supports."""
-
-    name: str
-    supports_vision: bool = False
-    supports_audio: bool = False
-    supports_tools: bool = False
-    supports_json: bool = False
-    supports_streaming: bool = True
-    context_window: int = 4096
-
-
-@dataclass
-class Provider:
-    """LLM provider configuration and state."""
-
-    name: str
-    type: str  # ollama, openai, anthropic
-    enabled: bool
-    priority: int
-    tier: str | None = None  # e.g., "local", "standard_cloud", "frontier"
-    url: str | None = None
-    api_key: str | None = None
-    base_url: str | None = None
-    models: list[dict] = field(default_factory=list)
-
-    # Runtime state
-    status: ProviderStatus = ProviderStatus.HEALTHY
-    metrics: ProviderMetrics = field(default_factory=ProviderMetrics)
-    circuit_state: CircuitState = CircuitState.CLOSED
-    circuit_opened_at: float | None = None
-    half_open_calls: int = 0
-
-    def get_default_model(self) -> str | None:
-        """Get the default model for this provider."""
-        for model in self.models:
-            if model.get("default"):
-                return model["name"]
-        if self.models:
-            return self.models[0]["name"]
-        return None
-
-    def get_model_with_capability(self, capability: str) -> str | None:
-        """Get a model that supports the given capability."""
-        for model in self.models:
-            capabilities = model.get("capabilities", [])
-            if capability in capabilities:
-                return model["name"]
-        # Fall back to default
-        return self.get_default_model()
-
-    def model_has_capability(self, model_name: str, capability: str) -> bool:
-        """Check if a specific model has a capability."""
-        for model in self.models:
-            if model["name"] == model_name:
-                capabilities = model.get("capabilities", [])
-                return capability in capabilities
-        return False
-
-
-@dataclass
-class RouterConfig:
-    """Cascade router configuration."""
-
-    timeout_seconds: int = 30
-    max_retries_per_provider: int = 2
-    retry_delay_seconds: int = 1
-    circuit_breaker_failure_threshold: int = 5
-    circuit_breaker_recovery_timeout: int = 60
-    circuit_breaker_half_open_max_calls: int = 2
-    cost_tracking_enabled: bool = True
-    budget_daily_usd: float = 10.0
-    # Multi-modal settings
-    auto_pull_models: bool = True
-    fallback_chains: dict = field(default_factory=dict)
-
-
-class CascadeRouter:
+class CascadeRouter(HealthMixin, ProviderCallsMixin):
     """Routes LLM requests with automatic failover.
 
     Now with multi-modal support:
@@ -487,50 +359,6 @@ class CascadeRouter:
 
         raise RuntimeError("; ".join(errors))
 
-    def _quota_allows_cloud(self, provider: Provider) -> bool:
-        """Check quota before routing to a cloud provider.
-
-        Uses the metabolic protocol via select_model(): cloud calls are only
-        allowed when the quota monitor recommends a cloud model (BURST tier).
-        Returns True (allow cloud) if quota monitor is unavailable or returns None.
-        """
-        if _quota_monitor is None:
-            return True
-        try:
-            suggested = _quota_monitor.select_model("high")
-            # Cloud is allowed only when select_model recommends the cloud model
-            allows = suggested == "claude-sonnet-4-6"
-            if not allows:
-                status = _quota_monitor.check()
-                tier = status.recommended_tier.value if status else "unknown"
-                logger.info(
-                    "Metabolic protocol: %s tier — downshifting %s to local (%s)",
-                    tier,
-                    provider.name,
-                    suggested,
-                )
-            return allows
-        except Exception as exc:
-            logger.warning("Quota check failed, allowing cloud: %s", exc)
-            return True
-
-    def _is_provider_available(self, provider: Provider) -> bool:
-        """Check if a provider should be tried (enabled + circuit breaker)."""
-        if not provider.enabled:
-            logger.debug("Skipping %s (disabled)", provider.name)
-            return False
-
-        if provider.status == ProviderStatus.UNHEALTHY:
-            if self._can_close_circuit(provider):
-                provider.circuit_state = CircuitState.HALF_OPEN
-                provider.half_open_calls = 0
-                logger.info("Circuit breaker half-open for %s", provider.name)
-            else:
-                logger.debug("Skipping %s (circuit open)", provider.name)
-                return False
-
-        return True
-
     def _filter_providers(self, cascade_tier: str | None) -> list["Provider"]:
         """Return the provider list filtered by tier.
 
@@ -641,9 +469,9 @@ class CascadeRouter:
         - Supports image URLs, paths, and base64 encoding
 
         Complexity-based routing (issue #1065):
-        - ``complexity_hint="simple"`` → routes to Qwen3-8B (low-latency)
-        - ``complexity_hint="complex"`` → routes to Qwen3-14B (quality)
-        - ``complexity_hint=None`` (default) → auto-classifies from messages
+        - ``complexity_hint="simple"`` -> routes to Qwen3-8B (low-latency)
+        - ``complexity_hint="complex"`` -> routes to Qwen3-14B (quality)
+        - ``complexity_hint=None`` (default) -> auto-classifies from messages
 
         Args:
             messages: List of message dicts with role and content
@@ -668,7 +496,7 @@ class CascadeRouter:
         if content_type != ContentType.TEXT:
             logger.debug("Detected %s content, selecting appropriate model", content_type.value)
 
-        # Resolve task complexity ─────────────────────────────────────────────
+        # Resolve task complexity
         # Skip complexity routing when caller explicitly specifies a model.
         complexity: TaskComplexity | None = None
         if model is None:
@@ -698,7 +526,7 @@ class CascadeRouter:
                     )
                     continue
 
-            # Complexity-based model selection (only when no explicit model) ──
+            # Complexity-based model selection (only when no explicit model)
             effective_model = model
             if effective_model is None and complexity is not None:
                 effective_model = self._get_model_for_complexity(provider, complexity)
@@ -740,357 +568,6 @@ class CascadeRouter:
 
         raise RuntimeError(f"All providers failed: {'; '.join(errors)}")
 
-    async def _try_provider(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None,
-        content_type: ContentType = ContentType.TEXT,
-    ) -> dict:
-        """Try a single provider request."""
-        start_time = time.time()
-
-        if provider.type == "ollama":
-            result = await self._call_ollama(
-                provider=provider,
-                messages=messages,
-                model=model or provider.get_default_model(),
-                temperature=temperature,
-                max_tokens=max_tokens,
-                content_type=content_type,
-            )
-        elif provider.type == "openai":
-            result = await self._call_openai(
-                provider=provider,
-                messages=messages,
-                model=model or provider.get_default_model(),
-                temperature=temperature,
-                max_tokens=max_tokens,
-            )
-        elif provider.type == "anthropic":
-            result = await self._call_anthropic(
-                provider=provider,
-                messages=messages,
-                model=model or provider.get_default_model(),
-                temperature=temperature,
-                max_tokens=max_tokens,
-            )
-        elif provider.type == "grok":
-            result = await self._call_grok(
-                provider=provider,
-                messages=messages,
-                model=model or provider.get_default_model(),
-                temperature=temperature,
-                max_tokens=max_tokens,
-            )
-        elif provider.type == "vllm_mlx":
-            result = await self._call_vllm_mlx(
-                provider=provider,
-                messages=messages,
-                model=model or provider.get_default_model(),
-                temperature=temperature,
-                max_tokens=max_tokens,
-            )
-        else:
-            raise ValueError(f"Unknown provider type: {provider.type}")
-
-        latency_ms = (time.time() - start_time) * 1000
-        result["latency_ms"] = latency_ms
-
-        return result
-
-    async def _call_ollama(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None = None,
-        content_type: ContentType = ContentType.TEXT,
-    ) -> dict:
-        """Call Ollama API with multi-modal support."""
-        import aiohttp
-
-        url = f"{provider.url or settings.ollama_url}/api/chat"
-
-        # Transform messages for Ollama format (including images)
-        transformed_messages = self._transform_messages_for_ollama(messages)
-
-        options = {"temperature": temperature}
-        if max_tokens:
-            options["num_predict"] = max_tokens
-
-        payload = {
-            "model": model,
-            "messages": transformed_messages,
-            "stream": False,
-            "options": options,
-        }
-
-        timeout = aiohttp.ClientTimeout(total=self.config.timeout_seconds)
-
-        async with aiohttp.ClientSession(timeout=timeout) as session:
-            async with session.post(url, json=payload) as response:
-                if response.status != 200:
-                    text = await response.text()
-                    raise RuntimeError(f"Ollama error {response.status}: {text}")
-
-                data = await response.json()
-                return {
-                    "content": data["message"]["content"],
-                    "model": model,
-                }
-
-    def _transform_messages_for_ollama(self, messages: list[dict]) -> list[dict]:
-        """Transform messages to Ollama format, handling images."""
-        transformed = []
-
-        for msg in messages:
-            new_msg = {
-                "role": msg.get("role", "user"),
-                "content": msg.get("content", ""),
-            }
-
-            # Handle images
-            images = msg.get("images", [])
-            if images:
-                new_msg["images"] = []
-                for img in images:
-                    if isinstance(img, str):
-                        if img.startswith("data:image/"):
-                            # Base64 encoded image
-                            new_msg["images"].append(img.split(",")[1])
-                        elif img.startswith("http://") or img.startswith("https://"):
-                            # URL - would need to download, skip for now
-                            logger.warning("Image URLs not yet supported, skipping: %s", img)
-                        elif Path(img).exists():
-                            # Local file path - read and encode
-                            try:
-                                with open(img, "rb") as f:
-                                    img_data = base64.b64encode(f.read()).decode()
-                                    new_msg["images"].append(img_data)
-                            except Exception as exc:
-                                logger.error("Failed to read image %s: %s", img, exc)
-
-            transformed.append(new_msg)
-
-        return transformed
-
-    async def _call_openai(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None,
-    ) -> dict:
-        """Call OpenAI API."""
-        import openai
-
-        client = openai.AsyncOpenAI(
-            api_key=provider.api_key,
-            base_url=provider.base_url,
-            timeout=self.config.timeout_seconds,
-        )
-
-        kwargs = {
-            "model": model,
-            "messages": messages,
-            "temperature": temperature,
-        }
-        if max_tokens:
-            kwargs["max_tokens"] = max_tokens
-
-        response = await client.chat.completions.create(**kwargs)
-
-        return {
-            "content": response.choices[0].message.content,
-            "model": response.model,
-        }
-
-    async def _call_anthropic(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None,
-    ) -> dict:
-        """Call Anthropic API."""
-        import anthropic
-
-        client = anthropic.AsyncAnthropic(
-            api_key=provider.api_key,
-            timeout=self.config.timeout_seconds,
-        )
-
-        # Convert messages to Anthropic format
-        system_msg = None
-        conversation = []
-        for msg in messages:
-            if msg["role"] == "system":
-                system_msg = msg["content"]
-            else:
-                conversation.append(
-                    {
-                        "role": msg["role"],
-                        "content": msg["content"],
-                    }
-                )
-
-        kwargs = {
-            "model": model,
-            "messages": conversation,
-            "temperature": temperature,
-            "max_tokens": max_tokens or 1024,
-        }
-        if system_msg:
-            kwargs["system"] = system_msg
-
-        response = await client.messages.create(**kwargs)
-
-        return {
-            "content": response.content[0].text,
-            "model": response.model,
-        }
-
-    async def _call_grok(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None,
-    ) -> dict:
-        """Call xAI Grok API via OpenAI-compatible SDK."""
-        import httpx
-        import openai
-
-        client = openai.AsyncOpenAI(
-            api_key=provider.api_key,
-            base_url=provider.base_url or settings.xai_base_url,
-            timeout=httpx.Timeout(300.0),
-        )
-
-        kwargs = {
-            "model": model,
-            "messages": messages,
-            "temperature": temperature,
-        }
-        if max_tokens:
-            kwargs["max_tokens"] = max_tokens
-
-        response = await client.chat.completions.create(**kwargs)
-
-        return {
-            "content": response.choices[0].message.content,
-            "model": response.model,
-        }
-
-    async def _call_vllm_mlx(
-        self,
-        provider: Provider,
-        messages: list[dict],
-        model: str,
-        temperature: float,
-        max_tokens: int | None,
-    ) -> dict:
-        """Call vllm-mlx via its OpenAI-compatible API.
-
-        vllm-mlx exposes the same /v1/chat/completions endpoint as OpenAI,
-        so we reuse the OpenAI client pointed at the local server.
-        No API key is required for local deployments.
-        """
-        import openai
-
-        base_url = provider.base_url or provider.url or "http://localhost:8000"
-        # Ensure the base_url ends with /v1 as expected by the OpenAI client
-        if not base_url.rstrip("/").endswith("/v1"):
-            base_url = base_url.rstrip("/") + "/v1"
-
-        client = openai.AsyncOpenAI(
-            api_key=provider.api_key or "no-key-required",
-            base_url=base_url,
-            timeout=self.config.timeout_seconds,
-        )
-
-        kwargs: dict = {
-            "model": model,
-            "messages": messages,
-            "temperature": temperature,
-        }
-        if max_tokens:
-            kwargs["max_tokens"] = max_tokens
-
-        response = await client.chat.completions.create(**kwargs)
-
-        return {
-            "content": response.choices[0].message.content,
-            "model": response.model,
-        }
-
-    def _record_success(self, provider: Provider, latency_ms: float) -> None:
-        """Record a successful request."""
-        provider.metrics.total_requests += 1
-        provider.metrics.successful_requests += 1
-        provider.metrics.total_latency_ms += latency_ms
-        provider.metrics.last_request_time = datetime.now(UTC).isoformat()
-        provider.metrics.consecutive_failures = 0
-
-        # Close circuit breaker if half-open
-        if provider.circuit_state == CircuitState.HALF_OPEN:
-            provider.half_open_calls += 1
-            if provider.half_open_calls >= self.config.circuit_breaker_half_open_max_calls:
-                self._close_circuit(provider)
-
-        # Update status based on error rate
-        if provider.metrics.error_rate < 0.1:
-            provider.status = ProviderStatus.HEALTHY
-        elif provider.metrics.error_rate < 0.3:
-            provider.status = ProviderStatus.DEGRADED
-
-    def _record_failure(self, provider: Provider) -> None:
-        """Record a failed request."""
-        provider.metrics.total_requests += 1
-        provider.metrics.failed_requests += 1
-        provider.metrics.last_error_time = datetime.now(UTC).isoformat()
-        provider.metrics.consecutive_failures += 1
-
-        # Check if we should open circuit breaker
-        if provider.metrics.consecutive_failures >= self.config.circuit_breaker_failure_threshold:
-            self._open_circuit(provider)
-
-        # Update status
-        if provider.metrics.error_rate > 0.3:
-            provider.status = ProviderStatus.DEGRADED
-        if provider.metrics.error_rate > 0.5:
-            provider.status = ProviderStatus.UNHEALTHY
-
-    def _open_circuit(self, provider: Provider) -> None:
-        """Open the circuit breaker for a provider."""
-        provider.circuit_state = CircuitState.OPEN
-        provider.circuit_opened_at = time.time()
-        provider.status = ProviderStatus.UNHEALTHY
-        logger.warning("Circuit breaker OPEN for %s", provider.name)
-
-    def _can_close_circuit(self, provider: Provider) -> bool:
-        """Check if circuit breaker can transition to half-open."""
-        if provider.circuit_opened_at is None:
-            return False
-        elapsed = time.time() - provider.circuit_opened_at
-        return elapsed >= self.config.circuit_breaker_recovery_timeout
-
-    def _close_circuit(self, provider: Provider) -> None:
-        """Close the circuit breaker (provider healthy again)."""
-        provider.circuit_state = CircuitState.CLOSED
-        provider.circuit_opened_at = None
-        provider.half_open_calls = 0
-        provider.metrics.consecutive_failures = 0
-        provider.status = ProviderStatus.HEALTHY
-        logger.info("Circuit breaker CLOSED for %s", provider.name)
-
     def reload_config(self) -> dict:
         """Hot-reload providers.yaml, preserving runtime state.
 

src/infrastructure/router/health.py

@@ -0,0 +1,137 @@
+"""Health monitoring and circuit breaker mixin for the Cascade Router.
+
+Provides failure tracking, circuit breaker state transitions,
+and quota-based cloud provider gating.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from datetime import UTC, datetime
+
+from .models import CircuitState, Provider, ProviderMetrics, ProviderStatus
+
+logger = logging.getLogger(__name__)
+
+# Quota monitor — optional, degrades gracefully if unavailable
+try:
+    from infrastructure.claude_quota import QuotaMonitor, get_quota_monitor
+
+    _quota_monitor: "QuotaMonitor | None" = get_quota_monitor()
+except Exception as _exc:  # pragma: no cover
+    logger.debug("Quota monitor not available: %s", _exc)
+    _quota_monitor = None
+
+
+class HealthMixin:
+    """Mixin providing health tracking, circuit breaker, and quota checks.
+
+    Expects the consuming class to have:
+      - self.config: RouterConfig
+      - self.providers: list[Provider]
+    """
+
+    def _record_success(self, provider: Provider, latency_ms: float) -> None:
+        """Record a successful request."""
+        provider.metrics.total_requests += 1
+        provider.metrics.successful_requests += 1
+        provider.metrics.total_latency_ms += latency_ms
+        provider.metrics.last_request_time = datetime.now(UTC).isoformat()
+        provider.metrics.consecutive_failures = 0
+
+        # Close circuit breaker if half-open
+        if provider.circuit_state == CircuitState.HALF_OPEN:
+            provider.half_open_calls += 1
+            if provider.half_open_calls >= self.config.circuit_breaker_half_open_max_calls:
+                self._close_circuit(provider)
+
+        # Update status based on error rate
+        if provider.metrics.error_rate < 0.1:
+            provider.status = ProviderStatus.HEALTHY
+        elif provider.metrics.error_rate < 0.3:
+            provider.status = ProviderStatus.DEGRADED
+
+    def _record_failure(self, provider: Provider) -> None:
+        """Record a failed request."""
+        provider.metrics.total_requests += 1
+        provider.metrics.failed_requests += 1
+        provider.metrics.last_error_time = datetime.now(UTC).isoformat()
+        provider.metrics.consecutive_failures += 1
+
+        # Check if we should open circuit breaker
+        if provider.metrics.consecutive_failures >= self.config.circuit_breaker_failure_threshold:
+            self._open_circuit(provider)
+
+        # Update status
+        if provider.metrics.error_rate > 0.3:
+            provider.status = ProviderStatus.DEGRADED
+        if provider.metrics.error_rate > 0.5:
+            provider.status = ProviderStatus.UNHEALTHY
+
+    def _open_circuit(self, provider: Provider) -> None:
+        """Open the circuit breaker for a provider."""
+        provider.circuit_state = CircuitState.OPEN
+        provider.circuit_opened_at = time.time()
+        provider.status = ProviderStatus.UNHEALTHY
+        logger.warning("Circuit breaker OPEN for %s", provider.name)
+
+    def _can_close_circuit(self, provider: Provider) -> bool:
+        """Check if circuit breaker can transition to half-open."""
+        if provider.circuit_opened_at is None:
+            return False
+        elapsed = time.time() - provider.circuit_opened_at
+        return elapsed >= self.config.circuit_breaker_recovery_timeout
+
+    def _close_circuit(self, provider: Provider) -> None:
+        """Close the circuit breaker (provider healthy again)."""
+        provider.circuit_state = CircuitState.CLOSED
+        provider.circuit_opened_at = None
+        provider.half_open_calls = 0
+        provider.metrics.consecutive_failures = 0
+        provider.status = ProviderStatus.HEALTHY
+        logger.info("Circuit breaker CLOSED for %s", provider.name)
+
+    def _is_provider_available(self, provider: Provider) -> bool:
+        """Check if a provider should be tried (enabled + circuit breaker)."""
+        if not provider.enabled:
+            logger.debug("Skipping %s (disabled)", provider.name)
+            return False
+
+        if provider.status == ProviderStatus.UNHEALTHY:
+            if self._can_close_circuit(provider):
+                provider.circuit_state = CircuitState.HALF_OPEN
+                provider.half_open_calls = 0
+                logger.info("Circuit breaker half-open for %s", provider.name)
+            else:
+                logger.debug("Skipping %s (circuit open)", provider.name)
+                return False
+
+        return True
+
+    def _quota_allows_cloud(self, provider: Provider) -> bool:
+        """Check quota before routing to a cloud provider.
+
+        Uses the metabolic protocol via select_model(): cloud calls are only
+        allowed when the quota monitor recommends a cloud model (BURST tier).
+        Returns True (allow cloud) if quota monitor is unavailable or returns None.
+        """
+        if _quota_monitor is None:
+            return True
+        try:
+            suggested = _quota_monitor.select_model("high")
+            # Cloud is allowed only when select_model recommends the cloud model
+            allows = suggested == "claude-sonnet-4-6"
+            if not allows:
+                status = _quota_monitor.check()
+                tier = status.recommended_tier.value if status else "unknown"
+                logger.info(
+                    "Metabolic protocol: %s tier — downshifting %s to local (%s)",
+                    tier,
+                    provider.name,
+                    suggested,
+                )
+            return allows
+        except Exception as exc:
+            logger.warning("Quota check failed, allowing cloud: %s", exc)
+            return True

src/infrastructure/router/models.py

@@ -0,0 +1,138 @@
+"""Data models for the Cascade LLM Router.
+
+Enums, dataclasses, and configuration objects shared across router modules.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import Enum
+
+
+class ProviderStatus(Enum):
+    """Health status of a provider."""
+
+    HEALTHY = "healthy"
+    DEGRADED = "degraded"  # Working but slow or occasional errors
+    UNHEALTHY = "unhealthy"  # Circuit breaker open
+    DISABLED = "disabled"
+
+
+class CircuitState(Enum):
+    """Circuit breaker state."""
+
+    CLOSED = "closed"  # Normal operation
+    OPEN = "open"  # Failing, rejecting requests
+    HALF_OPEN = "half_open"  # Testing if recovered
+
+
+class ContentType(Enum):
+    """Type of content in the request."""
+
+    TEXT = "text"
+    VISION = "vision"  # Contains images
+    AUDIO = "audio"  # Contains audio
+    MULTIMODAL = "multimodal"  # Multiple content types
+
+
+@dataclass
+class ProviderMetrics:
+    """Metrics for a single provider."""
+
+    total_requests: int = 0
+    successful_requests: int = 0
+    failed_requests: int = 0
+    total_latency_ms: float = 0.0
+    last_request_time: str | None = None
+    last_error_time: str | None = None
+    consecutive_failures: int = 0
+
+    @property
+    def avg_latency_ms(self) -> float:
+        if self.total_requests == 0:
+            return 0.0
+        return self.total_latency_ms / self.total_requests
+
+    @property
+    def error_rate(self) -> float:
+        if self.total_requests == 0:
+            return 0.0
+        return self.failed_requests / self.total_requests
+
+
+@dataclass
+class ModelCapability:
+    """Capabilities a model supports."""
+
+    name: str
+    supports_vision: bool = False
+    supports_audio: bool = False
+    supports_tools: bool = False
+    supports_json: bool = False
+    supports_streaming: bool = True
+    context_window: int = 4096
+
+
+@dataclass
+class Provider:
+    """LLM provider configuration and state."""
+
+    name: str
+    type: str  # ollama, openai, anthropic
+    enabled: bool
+    priority: int
+    tier: str | None = None  # e.g., "local", "standard_cloud", "frontier"
+    url: str | None = None
+    api_key: str | None = None
+    base_url: str | None = None
+    models: list[dict] = field(default_factory=list)
+
+    # Runtime state
+    status: ProviderStatus = ProviderStatus.HEALTHY
+    metrics: ProviderMetrics = field(default_factory=ProviderMetrics)
+    circuit_state: CircuitState = CircuitState.CLOSED
+    circuit_opened_at: float | None = None
+    half_open_calls: int = 0
+
+    def get_default_model(self) -> str | None:
+        """Get the default model for this provider."""
+        for model in self.models:
+            if model.get("default"):
+                return model["name"]
+        if self.models:
+            return self.models[0]["name"]
+        return None
+
+    def get_model_with_capability(self, capability: str) -> str | None:
+        """Get a model that supports the given capability."""
+        for model in self.models:
+            capabilities = model.get("capabilities", [])
+            if capability in capabilities:
+                return model["name"]
+        # Fall back to default
+        return self.get_default_model()
+
+    def model_has_capability(self, model_name: str, capability: str) -> bool:
+        """Check if a specific model has a capability."""
+        for model in self.models:
+            if model["name"] == model_name:
+                capabilities = model.get("capabilities", [])
+                return capability in capabilities
+        return False
+
+
+@dataclass
+class RouterConfig:
+    """Cascade router configuration."""
+
+    timeout_seconds: int = 30
+    max_retries_per_provider: int = 2
+    retry_delay_seconds: int = 1
+    circuit_breaker_failure_threshold: int = 5
+    circuit_breaker_recovery_timeout: int = 60
+    circuit_breaker_half_open_max_calls: int = 2
+    cost_tracking_enabled: bool = True
+    budget_daily_usd: float = 10.0
+    # Multi-modal settings
+    auto_pull_models: bool = True
+    fallback_chains: dict = field(default_factory=dict)

src/infrastructure/router/providers.py

@@ -0,0 +1,318 @@
+"""Provider API call mixin for the Cascade Router.
+
+Contains methods for calling individual LLM provider APIs
+(Ollama, OpenAI, Anthropic, Grok, vllm-mlx).
+"""
+
+from __future__ import annotations
+
+import base64
+import logging
+import time
+from pathlib import Path
+from typing import Any
+
+from config import settings
+
+from .models import ContentType, Provider
+
+logger = logging.getLogger(__name__)
+
+
+class ProviderCallsMixin:
+    """Mixin providing LLM provider API call methods.
+
+    Expects the consuming class to have:
+      - self.config: RouterConfig
+    """
+
+    async def _try_provider(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+        content_type: ContentType = ContentType.TEXT,
+    ) -> dict:
+        """Try a single provider request."""
+        start_time = time.time()
+
+        if provider.type == "ollama":
+            result = await self._call_ollama(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+                content_type=content_type,
+            )
+        elif provider.type == "openai":
+            result = await self._call_openai(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        elif provider.type == "anthropic":
+            result = await self._call_anthropic(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        elif provider.type == "grok":
+            result = await self._call_grok(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        elif provider.type == "vllm_mlx":
+            result = await self._call_vllm_mlx(
+                provider=provider,
+                messages=messages,
+                model=model or provider.get_default_model(),
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        else:
+            raise ValueError(f"Unknown provider type: {provider.type}")
+
+        latency_ms = (time.time() - start_time) * 1000
+        result["latency_ms"] = latency_ms
+
+        return result
+
+    async def _call_ollama(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None = None,
+        content_type: ContentType = ContentType.TEXT,
+    ) -> dict:
+        """Call Ollama API with multi-modal support."""
+        import aiohttp
+
+        url = f"{provider.url or settings.ollama_url}/api/chat"
+
+        # Transform messages for Ollama format (including images)
+        transformed_messages = self._transform_messages_for_ollama(messages)
+
+        options: dict[str, Any] = {"temperature": temperature}
+        if max_tokens:
+            options["num_predict"] = max_tokens
+
+        payload = {
+            "model": model,
+            "messages": transformed_messages,
+            "stream": False,
+            "options": options,
+        }
+
+        timeout = aiohttp.ClientTimeout(total=self.config.timeout_seconds)
+
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            async with session.post(url, json=payload) as response:
+                if response.status != 200:
+                    text = await response.text()
+                    raise RuntimeError(f"Ollama error {response.status}: {text}")
+
+                data = await response.json()
+                return {
+                    "content": data["message"]["content"],
+                    "model": model,
+                }
+
+    def _transform_messages_for_ollama(self, messages: list[dict]) -> list[dict]:
+        """Transform messages to Ollama format, handling images."""
+        transformed = []
+
+        for msg in messages:
+            new_msg: dict[str, Any] = {
+                "role": msg.get("role", "user"),
+                "content": msg.get("content", ""),
+            }
+
+            # Handle images
+            images = msg.get("images", [])
+            if images:
+                new_msg["images"] = []
+                for img in images:
+                    if isinstance(img, str):
+                        if img.startswith("data:image/"):
+                            # Base64 encoded image
+                            new_msg["images"].append(img.split(",")[1])
+                        elif img.startswith("http://") or img.startswith("https://"):
+                            # URL - would need to download, skip for now
+                            logger.warning("Image URLs not yet supported, skipping: %s", img)
+                        elif Path(img).exists():
+                            # Local file path - read and encode
+                            try:
+                                with open(img, "rb") as f:
+                                    img_data = base64.b64encode(f.read()).decode()
+                                    new_msg["images"].append(img_data)
+                            except Exception as exc:
+                                logger.error("Failed to read image %s: %s", img, exc)
+
+            transformed.append(new_msg)
+
+        return transformed
+
+    async def _call_openai(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call OpenAI API."""
+        import openai
+
+        client = openai.AsyncOpenAI(
+            api_key=provider.api_key,
+            base_url=provider.base_url,
+            timeout=self.config.timeout_seconds,
+        )
+
+        kwargs: dict[str, Any] = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+        }
+        if max_tokens:
+            kwargs["max_tokens"] = max_tokens
+
+        response = await client.chat.completions.create(**kwargs)
+
+        return {
+            "content": response.choices[0].message.content,
+            "model": response.model,
+        }
+
+    async def _call_anthropic(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call Anthropic API."""
+        import anthropic
+
+        client = anthropic.AsyncAnthropic(
+            api_key=provider.api_key,
+            timeout=self.config.timeout_seconds,
+        )
+
+        # Convert messages to Anthropic format
+        system_msg = None
+        conversation = []
+        for msg in messages:
+            if msg["role"] == "system":
+                system_msg = msg["content"]
+            else:
+                conversation.append(
+                    {
+                        "role": msg["role"],
+                        "content": msg["content"],
+                    }
+                )
+
+        kwargs: dict[str, Any] = {
+            "model": model,
+            "messages": conversation,
+            "temperature": temperature,
+            "max_tokens": max_tokens or 1024,
+        }
+        if system_msg:
+            kwargs["system"] = system_msg
+
+        response = await client.messages.create(**kwargs)
+
+        return {
+            "content": response.content[0].text,
+            "model": response.model,
+        }
+
+    async def _call_grok(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call xAI Grok API via OpenAI-compatible SDK."""
+        import httpx
+        import openai
+
+        client = openai.AsyncOpenAI(
+            api_key=provider.api_key,
+            base_url=provider.base_url or settings.xai_base_url,
+            timeout=httpx.Timeout(300.0),
+        )
+
+        kwargs: dict[str, Any] = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+        }
+        if max_tokens:
+            kwargs["max_tokens"] = max_tokens
+
+        response = await client.chat.completions.create(**kwargs)
+
+        return {
+            "content": response.choices[0].message.content,
+            "model": response.model,
+        }
+
+    async def _call_vllm_mlx(
+        self,
+        provider: Provider,
+        messages: list[dict],
+        model: str,
+        temperature: float,
+        max_tokens: int | None,
+    ) -> dict:
+        """Call vllm-mlx via its OpenAI-compatible API.
+
+        vllm-mlx exposes the same /v1/chat/completions endpoint as OpenAI,
+        so we reuse the OpenAI client pointed at the local server.
+        No API key is required for local deployments.
+        """
+        import openai
+
+        base_url = provider.base_url or provider.url or "http://localhost:8000"
+        # Ensure the base_url ends with /v1 as expected by the OpenAI client
+        if not base_url.rstrip("/").endswith("/v1"):
+            base_url = base_url.rstrip("/") + "/v1"
+
+        client = openai.AsyncOpenAI(
+            api_key=provider.api_key or "no-key-required",
+            base_url=base_url,
+            timeout=self.config.timeout_seconds,
+        )
+
+        kwargs: dict[str, Any] = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+        }
+        if max_tokens:
+            kwargs["max_tokens"] = max_tokens
+
+        response = await client.chat.completions.create(**kwargs)
+
+        return {
+            "content": response.choices[0].message.content,
+            "model": response.model,
+        }

tests/infrastructure/test_router_cascade.py

@@ -677,7 +677,7 @@ class TestVllmMlxProvider:
         router.providers = [provider]
 
         # Quota monitor downshifts to local (ACTIVE tier) — vllm_mlx should still be tried
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             mock_qm.select_model.return_value = "qwen3:14b"
             mock_qm.check.return_value = None
 
@@ -713,7 +713,7 @@ class TestMetabolicProtocol:
         router = CascadeRouter(config_path=Path("/nonexistent"))
         router.providers = [self._make_anthropic_provider()]
 
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             # select_model returns cloud model → BURST tier
             mock_qm.select_model.return_value = "claude-sonnet-4-6"
             mock_qm.check.return_value = None
@@ -732,7 +732,7 @@ class TestMetabolicProtocol:
         router = CascadeRouter(config_path=Path("/nonexistent"))
         router.providers = [self._make_anthropic_provider()]
 
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             # select_model returns local 14B → ACTIVE tier
             mock_qm.select_model.return_value = "qwen3:14b"
             mock_qm.check.return_value = None
@@ -750,7 +750,7 @@ class TestMetabolicProtocol:
         router = CascadeRouter(config_path=Path("/nonexistent"))
         router.providers = [self._make_anthropic_provider()]
 
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             # select_model returns local 8B → RESTING tier
             mock_qm.select_model.return_value = "qwen3:8b"
             mock_qm.check.return_value = None
@@ -776,7 +776,7 @@ class TestMetabolicProtocol:
         )
         router.providers = [provider]
 
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             mock_qm.select_model.return_value = "qwen3:8b"  # RESTING tier
 
             with patch.object(router, "_call_ollama") as mock_call:
@@ -793,7 +793,7 @@ class TestMetabolicProtocol:
         router = CascadeRouter(config_path=Path("/nonexistent"))
         router.providers = [self._make_anthropic_provider()]
 
-        with patch("infrastructure.router.cascade._quota_monitor", None):
+        with patch("infrastructure.router.health._quota_monitor", None):
             with patch.object(router, "_call_anthropic") as mock_call:
                 mock_call.return_value = {"content": "Cloud response", "model": "claude-sonnet-4-6"}
                 result = await router.complete(
@@ -1200,7 +1200,7 @@ class TestCascadeTierFiltering:
 
     async def test_frontier_required_uses_anthropic(self):
         router = self._make_router()
-        with patch("infrastructure.router.cascade._quota_monitor", None):
+        with patch("infrastructure.router.health._quota_monitor", None):
             with patch.object(router, "_call_anthropic") as mock_call:
                 mock_call.return_value = {
                     "content": "frontier response",
@@ -1464,7 +1464,7 @@ class TestTrySingleProvider:
         router = self._router()
         provider = self._provider(ptype="anthropic")
         errors: list[str] = []
-        with patch("infrastructure.router.cascade._quota_monitor") as mock_qm:
+        with patch("infrastructure.router.health._quota_monitor") as mock_qm:
             mock_qm.select_model.return_value = "qwen3:14b"  # non-cloud → ACTIVE tier
             mock_qm.check.return_value = None
             result = await router._try_single_provider(