config/providers.yaml

@@ -25,6 +25,19 @@ providers:
     tier: local
     url: "http://localhost:11434"
     models:
+      # ── Dual-model routing: Qwen3-8B (fast) + Qwen3-14B (quality) ──────────
+      # Both models fit simultaneously: ~6.6 GB + ~10.5 GB = ~17 GB combined.
+      # Requires OLLAMA_MAX_LOADED_MODELS=2 (set in .env) to stay hot.
+      # Ref: issue #1065 — Qwen3-8B/14B dual-model routing strategy
+      - name: qwen3:8b
+        context_window: 32768
+        capabilities: [text, tools, json, streaming, routine]
+        description: "Qwen3-8B Q6_K — fast router for routine tasks (~6.6 GB, 45-55 tok/s)"
+      - name: qwen3:14b
+        context_window: 40960
+        capabilities: [text, tools, json, streaming, complex, reasoning]
+        description: "Qwen3-14B Q5_K_M — complex reasoning and planning (~10.5 GB, 20-28 tok/s)"
+
       # Text + Tools models
       - name: qwen3:30b
         default: true
@@ -187,6 +200,20 @@ fallback_chains:
     - dolphin3          # base Dolphin 3.0 8B (uncensored, no custom system prompt)
     - qwen3:30b         # primary fallback — usually sufficient with a good system prompt
 
+  # ── Complexity-based routing chains (issue #1065) ───────────────────────
+  # Routine tasks: prefer Qwen3-8B for low latency (~45-55 tok/s)
+  routine:
+    - qwen3:8b              # Primary fast model
+    - llama3.1:8b-instruct  # Fallback fast model
+    - llama3.2:3b           # Smallest available
+
+  # Complex tasks: prefer Qwen3-14B for quality (~20-28 tok/s)
+  complex:
+    - qwen3:14b             # Primary quality model
+    - hermes4-14b           # Native tool calling, hybrid reasoning
+    - qwen3:30b             # Highest local quality
+    - qwen2.5:14b           # Additional fallback
+
 # ── Custom Models ───────────────────────────────────────────────────────────
 # Register custom model weights for per-agent assignment.
 # Supports GGUF (Ollama), safetensors, and HuggingFace checkpoint dirs.

src/config.py

@@ -51,6 +51,13 @@ class Settings(BaseSettings):
     # Set to 0 to use model defaults.
     ollama_num_ctx: int = 32768
 
+    # Maximum models loaded simultaneously in Ollama — override with OLLAMA_MAX_LOADED_MODELS
+    # Set to 2 so Qwen3-8B and Qwen3-14B can stay hot concurrently (~17 GB combined).
+    # Requires Ollama ≥ 0.1.33.  Export this to the Ollama process environment:
+    #   OLLAMA_MAX_LOADED_MODELS=2 ollama serve
+    # or add it to your systemd/launchd unit before starting the harness.
+    ollama_max_loaded_models: int = 2
+
     # Fallback model chains — override with FALLBACK_MODELS / VISION_FALLBACK_MODELS
     # as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:8b,qwen2.5:14b"
     # Or edit config/providers.yaml → fallback_chains for the canonical source.

src/infrastructure/router/__init__.py

@@ -2,6 +2,7 @@
 
 from .api import router
 from .cascade import CascadeRouter, Provider, ProviderStatus, get_router
+from .classifier import TaskComplexity, classify_task
 from .history import HealthHistoryStore, get_history_store
 from .metabolic import (
     DEFAULT_TIER_MODELS,
@@ -27,4 +28,7 @@ __all__ = [
     "classify_complexity",
     "build_prompt",
     "get_metabolic_router",
+    # Classifier
+    "TaskComplexity",
+    "classify_task",
 ]

src/infrastructure/router/cascade.py

@@ -593,6 +593,34 @@ class CascadeRouter:
             "is_fallback_model": is_fallback_model,
         }
 
+    def _get_model_for_complexity(
+        self, provider: Provider, complexity: "TaskComplexity"
+    ) -> str | None:
+        """Return the best model on *provider* for the given complexity tier.
+
+        Checks fallback chains first (routine / complex), then falls back to
+        any model with the matching capability tag, then the provider default.
+        """
+        from infrastructure.router.classifier import TaskComplexity
+
+        chain_key = "routine" if complexity == TaskComplexity.SIMPLE else "complex"
+
+        # Walk the capability fallback chain — first model present on this provider wins
+        for model_name in self.config.fallback_chains.get(chain_key, []):
+            if any(m["name"] == model_name for m in provider.models):
+                return model_name
+
+        # Direct capability lookup — only return if a model explicitly has the tag
+        # (do not use get_model_with_capability here as it falls back to the default)
+        cap_model = next(
+            (m["name"] for m in provider.models if chain_key in m.get("capabilities", [])),
+            None,
+        )
+        if cap_model:
+            return cap_model
+
+        return None  # Caller will use provider default
+
     async def complete(
         self,
         messages: list[dict],
@@ -600,6 +628,7 @@ class CascadeRouter:
         temperature: float = 0.7,
         max_tokens: int | None = None,
         cascade_tier: str | None = None,
+        complexity_hint: str | None = None,
     ) -> dict:
         """Complete a chat conversation with automatic failover.
 
@@ -608,33 +637,103 @@ class CascadeRouter:
         - Falls back to vision-capable models when needed
         - 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
+
         Args:
             messages: List of message dicts with role and content
-            model: Preferred model (tries this first, then provider defaults)
+            model: Preferred model (tries this first; complexity routing is
+                skipped when an explicit model is given)
             temperature: Sampling temperature
             max_tokens: Maximum tokens to generate
             cascade_tier: If specified, filters providers by this tier.
                 - "frontier_required": Uses only Anthropic provider for top-tier models.
+            complexity_hint: "simple", "complex", or None (auto-detect).
 
         Returns:
-            Dict with content, provider_used, and metrics
+            Dict with content, provider_used, model, latency_ms,
+            is_fallback_model, and complexity fields.
 
         Raises:
             RuntimeError: If all providers fail
         """
+        from infrastructure.router.classifier import TaskComplexity, classify_task
+
         content_type = self._detect_content_type(messages)
         if content_type != ContentType.TEXT:
             logger.debug("Detected %s content, selecting appropriate model", content_type.value)
 
+        # Resolve task complexity ─────────────────────────────────────────────
+        # Skip complexity routing when caller explicitly specifies a model.
+        complexity: TaskComplexity | None = None
+        if model is None:
+            if complexity_hint is not None:
+                try:
+                    complexity = TaskComplexity(complexity_hint.lower())
+                except ValueError:
+                    logger.warning("Unknown complexity_hint %r, auto-classifying", complexity_hint)
+                    complexity = classify_task(messages)
+            else:
+                complexity = classify_task(messages)
+            logger.debug("Task complexity: %s", complexity.value)
+
         errors: list[str] = []
         providers = self._filter_providers(cascade_tier)
 
         for provider in providers:
-            result = await self._try_single_provider(
+            if not self._is_provider_available(provider):
-                provider, messages, model, temperature, max_tokens, content_type, errors
+                continue
+
+            # Metabolic protocol: skip cloud providers when quota is low
+            if provider.type in ("anthropic", "openai", "grok"):
+                if not self._quota_allows_cloud(provider):
+                    logger.info(
+                        "Metabolic protocol: skipping cloud provider %s (quota too low)",
+                        provider.name,
+                    )
+                    continue
+
+            # 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)
+                if effective_model:
+                    logger.debug(
+                        "Complexity routing [%s]: %s → %s",
+                        complexity.value,
+                        provider.name,
+                        effective_model,
+                    )
+
+            selected_model, is_fallback_model = self._select_model(
+                provider, effective_model, content_type
             )
-            if result is not None:
+
-                return result
+            try:
+                result = await self._attempt_with_retry(
+                    provider,
+                    messages,
+                    selected_model,
+                    temperature,
+                    max_tokens,
+                    content_type,
+                )
+            except RuntimeError as exc:
+                errors.append(str(exc))
+                self._record_failure(provider)
+                continue
+
+            self._record_success(provider, result.get("latency_ms", 0))
+            return {
+                "content": result["content"],
+                "provider": provider.name,
+                "model": result.get("model", selected_model or provider.get_default_model()),
+                "latency_ms": result.get("latency_ms", 0),
+                "is_fallback_model": is_fallback_model,
+                "complexity": complexity.value if complexity is not None else None,
+            }
 
         raise RuntimeError(f"All providers failed: {'; '.join(errors)}")
 

src/infrastructure/router/classifier.py

@@ -0,0 +1,166 @@
+"""Task complexity classifier for Qwen3 dual-model routing.
+
+Classifies incoming tasks as SIMPLE (route to Qwen3-8B for low-latency)
+or COMPLEX (route to Qwen3-14B for quality-sensitive work).
+
+Classification is fully heuristic — no LLM inference required.
+"""
+
+import re
+from enum import Enum
+
+
+class TaskComplexity(Enum):
+    """Task complexity tier for model routing."""
+
+    SIMPLE = "simple"   # Qwen3-8B Q6_K: routine, latency-sensitive
+    COMPLEX = "complex"  # Qwen3-14B Q5_K_M: quality-sensitive, multi-step
+
+
+# Keywords strongly associated with complex tasks
+_COMPLEX_KEYWORDS: frozenset[str] = frozenset(
+    [
+        "plan",
+        "review",
+        "analyze",
+        "analyse",
+        "triage",
+        "refactor",
+        "design",
+        "architecture",
+        "implement",
+        "compare",
+        "debug",
+        "explain",
+        "prioritize",
+        "prioritise",
+        "strategy",
+        "optimize",
+        "optimise",
+        "evaluate",
+        "assess",
+        "brainstorm",
+        "outline",
+        "summarize",
+        "summarise",
+        "generate code",
+        "write a",
+        "write the",
+        "code review",
+        "pull request",
+        "multi-step",
+        "multi step",
+        "step by step",
+        "backlog prioriti",
+        "issue triage",
+        "root cause",
+        "how does",
+        "why does",
+        "what are the",
+    ]
+)
+
+# Keywords strongly associated with simple/routine tasks
+_SIMPLE_KEYWORDS: frozenset[str] = frozenset(
+    [
+        "status",
+        "list ",
+        "show ",
+        "what is",
+        "how many",
+        "ping",
+        "run ",
+        "execute ",
+        "ls ",
+        "cat ",
+        "ps ",
+        "fetch ",
+        "count ",
+        "tail ",
+        "head ",
+        "grep ",
+        "find file",
+        "read file",
+        "get ",
+        "query ",
+        "check ",
+        "yes",
+        "no",
+        "ok",
+        "done",
+        "thanks",
+    ]
+)
+
+# Content longer than this is treated as complex regardless of keywords
+_COMPLEX_CHAR_THRESHOLD = 500
+
+# Short content defaults to simple
+_SIMPLE_CHAR_THRESHOLD = 150
+
+# More than this many messages suggests an ongoing complex conversation
+_COMPLEX_CONVERSATION_DEPTH = 6
+
+
+def classify_task(messages: list[dict]) -> TaskComplexity:
+    """Classify task complexity from a list of messages.
+
+    Uses heuristic rules — no LLM call required.  Errs toward COMPLEX
+    when uncertain so that quality is preserved.
+
+    Args:
+        messages: List of message dicts with ``role`` and ``content`` keys.
+
+    Returns:
+        TaskComplexity.SIMPLE or TaskComplexity.COMPLEX
+    """
+    if not messages:
+        return TaskComplexity.SIMPLE
+
+    # Concatenate all user-turn content for analysis
+    user_content = " ".join(
+        msg.get("content", "")
+        for msg in messages
+        if msg.get("role") in ("user", "human")
+        and isinstance(msg.get("content"), str)
+    ).lower().strip()
+
+    if not user_content:
+        return TaskComplexity.SIMPLE
+
+    # Complexity signals override everything -----------------------------------
+
+    # Explicit complex keywords
+    for kw in _COMPLEX_KEYWORDS:
+        if kw in user_content:
+            return TaskComplexity.COMPLEX
+
+    # Numbered / multi-step instruction list: "1. do this  2. do that"
+    if re.search(r"\b\d+\.\s+\w", user_content):
+        return TaskComplexity.COMPLEX
+
+    # Code blocks embedded in messages
+    if "```" in user_content:
+        return TaskComplexity.COMPLEX
+
+    # Long content → complex reasoning likely required
+    if len(user_content) > _COMPLEX_CHAR_THRESHOLD:
+        return TaskComplexity.COMPLEX
+
+    # Deep conversation → complex ongoing task
+    if len(messages) > _COMPLEX_CONVERSATION_DEPTH:
+        return TaskComplexity.COMPLEX
+
+    # Simplicity signals -------------------------------------------------------
+
+    # Explicit simple keywords
+    for kw in _SIMPLE_KEYWORDS:
+        if kw in user_content:
+            return TaskComplexity.SIMPLE
+
+    # Short single-sentence messages default to simple
+    if len(user_content) <= _SIMPLE_CHAR_THRESHOLD:
+        return TaskComplexity.SIMPLE
+
+    # When uncertain, prefer quality (complex model)
+    return TaskComplexity.COMPLEX

tests/infrastructure/test_router_cascade.py

@@ -1512,3 +1512,195 @@ class TestTrySingleProvider:
         assert len(errors) == 1
         assert "boom" in errors[0]
         assert provider.metrics.failed_requests == 1
+
+
+class TestComplexityRouting:
+    """Tests for Qwen3-8B / Qwen3-14B dual-model routing (issue #1065)."""
+
+    def _make_dual_model_provider(self) -> Provider:
+        """Build an Ollama provider with both Qwen3 models registered."""
+        return Provider(
+            name="ollama-local",
+            type="ollama",
+            enabled=True,
+            priority=1,
+            url="http://localhost:11434",
+            models=[
+                {
+                    "name": "qwen3:8b",
+                    "capabilities": ["text", "tools", "json", "streaming", "routine"],
+                },
+                {
+                    "name": "qwen3:14b",
+                    "default": True,
+                    "capabilities": ["text", "tools", "json", "streaming", "complex", "reasoning"],
+                },
+            ],
+        )
+
+    def test_get_model_for_complexity_simple_returns_8b(self):
+        """Simple tasks should select the model with 'routine' capability."""
+        from infrastructure.router.classifier import TaskComplexity
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        provider = self._make_dual_model_provider()
+
+        model = router._get_model_for_complexity(provider, TaskComplexity.SIMPLE)
+        assert model == "qwen3:8b"
+
+    def test_get_model_for_complexity_complex_returns_14b(self):
+        """Complex tasks should select the model with 'complex' capability."""
+        from infrastructure.router.classifier import TaskComplexity
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        provider = self._make_dual_model_provider()
+
+        model = router._get_model_for_complexity(provider, TaskComplexity.COMPLEX)
+        assert model == "qwen3:14b"
+
+    def test_get_model_for_complexity_returns_none_when_no_match(self):
+        """Returns None when provider has no matching model in chain."""
+        from infrastructure.router.classifier import TaskComplexity
+
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {}  # empty chains
+
+        provider = Provider(
+            name="test",
+            type="ollama",
+            enabled=True,
+            priority=1,
+            models=[{"name": "llama3.2:3b", "default": True, "capabilities": ["text"]}],
+        )
+
+        # No 'routine' or 'complex' model available
+        model = router._get_model_for_complexity(provider, TaskComplexity.SIMPLE)
+        assert model is None
+
+    @pytest.mark.asyncio
+    async def test_complete_with_simple_hint_routes_to_8b(self):
+        """complexity_hint='simple' should use qwen3:8b."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "fast answer", "model": "qwen3:8b"}
+            result = await router.complete(
+                messages=[{"role": "user", "content": "list tasks"}],
+                complexity_hint="simple",
+            )
+
+        assert result["model"] == "qwen3:8b"
+        assert result["complexity"] == "simple"
+
+    @pytest.mark.asyncio
+    async def test_complete_with_complex_hint_routes_to_14b(self):
+        """complexity_hint='complex' should use qwen3:14b."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "detailed answer", "model": "qwen3:14b"}
+            result = await router.complete(
+                messages=[{"role": "user", "content": "review this PR"}],
+                complexity_hint="complex",
+            )
+
+        assert result["model"] == "qwen3:14b"
+        assert result["complexity"] == "complex"
+
+    @pytest.mark.asyncio
+    async def test_explicit_model_bypasses_complexity_routing(self):
+        """When model is explicitly provided, complexity routing is skipped."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "response", "model": "qwen3:14b"}
+            result = await router.complete(
+                messages=[{"role": "user", "content": "list tasks"}],
+                model="qwen3:14b",  # explicit override
+            )
+
+        # Explicit model wins — complexity field is None
+        assert result["model"] == "qwen3:14b"
+        assert result["complexity"] is None
+
+    @pytest.mark.asyncio
+    async def test_auto_classification_routes_simple_message(self):
+        """Short, simple messages should auto-classify as SIMPLE → 8B."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "ok", "model": "qwen3:8b"}
+            result = await router.complete(
+                messages=[{"role": "user", "content": "status"}],
+                # no complexity_hint — auto-classify
+            )
+
+        assert result["complexity"] == "simple"
+        assert result["model"] == "qwen3:8b"
+
+    @pytest.mark.asyncio
+    async def test_auto_classification_routes_complex_message(self):
+        """Complex messages should auto-classify → 14B."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "deep analysis", "model": "qwen3:14b"}
+            result = await router.complete(
+                messages=[{"role": "user", "content": "analyze and prioritize the backlog"}],
+            )
+
+        assert result["complexity"] == "complex"
+        assert result["model"] == "qwen3:14b"
+
+    @pytest.mark.asyncio
+    async def test_invalid_complexity_hint_falls_back_to_auto(self):
+        """Invalid complexity_hint should log a warning and auto-classify."""
+        router = CascadeRouter(config_path=Path("/nonexistent"))
+        router.config.fallback_chains = {
+            "routine": ["qwen3:8b"],
+            "complex": ["qwen3:14b"],
+        }
+        router.providers = [self._make_dual_model_provider()]
+
+        with patch.object(router, "_call_ollama") as mock_call:
+            mock_call.return_value = {"content": "ok", "model": "qwen3:8b"}
+            # Should not raise
+            result = await router.complete(
+                messages=[{"role": "user", "content": "status"}],
+                complexity_hint="INVALID_HINT",
+            )
+
+        assert result["complexity"] in ("simple", "complex")  # auto-classified

tests/infrastructure/test_router_classifier.py

@@ -0,0 +1,134 @@
+"""Tests for Qwen3 dual-model task complexity classifier."""
+
+import pytest
+
+from infrastructure.router.classifier import TaskComplexity, classify_task
+
+
+class TestClassifyTask:
+    """Tests for classify_task heuristics."""
+
+    # ── Simple / routine tasks ──────────────────────────────────────────────
+
+    def test_empty_messages_is_simple(self):
+        assert classify_task([]) == TaskComplexity.SIMPLE
+
+    def test_no_user_content_is_simple(self):
+        messages = [{"role": "system", "content": "You are Timmy."}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    def test_short_status_query_is_simple(self):
+        messages = [{"role": "user", "content": "status"}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    def test_list_command_is_simple(self):
+        messages = [{"role": "user", "content": "list all tasks"}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    def test_get_command_is_simple(self):
+        messages = [{"role": "user", "content": "get the latest log entry"}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    def test_short_message_under_threshold_is_simple(self):
+        messages = [{"role": "user", "content": "run the build"}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    def test_affirmation_is_simple(self):
+        messages = [{"role": "user", "content": "yes"}]
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+    # ── Complex / quality-sensitive tasks ──────────────────────────────────
+
+    def test_plan_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "plan the sprint"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_review_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "review this code"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_analyze_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "analyze performance"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_triage_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "triage the open issues"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_refactor_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "refactor the auth module"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_explain_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "explain how the router works"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_prioritize_keyword_is_complex(self):
+        messages = [{"role": "user", "content": "prioritize the backlog"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_long_message_is_complex(self):
+        long_msg = "do something " * 50  # > 500 chars
+        messages = [{"role": "user", "content": long_msg}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_numbered_list_is_complex(self):
+        messages = [
+            {
+                "role": "user",
+                "content": "1. Read the file  2. Analyze it  3. Write a report",
+            }
+        ]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_code_block_is_complex(self):
+        messages = [
+            {"role": "user", "content": "Here is the code:\n```python\nprint('hello')\n```"}
+        ]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_deep_conversation_is_complex(self):
+        messages = [
+            {"role": "user", "content": "hi"},
+            {"role": "assistant", "content": "hello"},
+            {"role": "user", "content": "ok"},
+            {"role": "assistant", "content": "yes"},
+            {"role": "user", "content": "ok"},
+            {"role": "assistant", "content": "yes"},
+            {"role": "user", "content": "now do the thing"},
+        ]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_analyse_british_spelling_is_complex(self):
+        messages = [{"role": "user", "content": "analyse this dataset"}]
+        assert classify_task(messages) == TaskComplexity.COMPLEX
+
+    def test_non_string_content_is_ignored(self):
+        """Non-string content should not crash the classifier."""
+        messages = [{"role": "user", "content": ["part1", "part2"]}]
+        # Should not raise; result doesn't matter — just must not blow up
+        result = classify_task(messages)
+        assert isinstance(result, TaskComplexity)
+
+    def test_system_message_not_counted_as_user(self):
+        """System message alone should not trigger complex keywords."""
+        messages = [
+            {"role": "system", "content": "analyze everything carefully"},
+            {"role": "user", "content": "yes"},
+        ]
+        # "analyze" is in system message (not user) — user says "yes" → simple
+        assert classify_task(messages) == TaskComplexity.SIMPLE
+
+
+class TestTaskComplexityEnum:
+    """Tests for TaskComplexity enum values."""
+
+    def test_simple_value(self):
+        assert TaskComplexity.SIMPLE.value == "simple"
+
+    def test_complex_value(self):
+        assert TaskComplexity.COMPLEX.value == "complex"
+
+    def test_lookup_by_value(self):
+        assert TaskComplexity("simple") == TaskComplexity.SIMPLE
+        assert TaskComplexity("complex") == TaskComplexity.COMPLEX