fix(llm): honor selected models in provider resolution

src/llm/providers/claude.py

@@ -26,8 +26,14 @@ def _uses_adaptive_thinking_only(model: str) -> bool:
 class ClaudeProvider(LLMProvider):
     provider_type = ProviderType.CLAUDE
 
-    def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
+    def __init__(
+        self,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        default_model: str | None = None,
+    ) -> None:
         self.client = Anthropic(api_key=api_key or os.environ.get("ANTHROPIC_API_KEY"), base_url=base_url)
+        self.default_model = default_model or _DEFAULT_MODEL
         self._models = [
             ModelInfo(
                 name="claude-opus-4-8",
@@ -57,7 +63,7 @@ class ClaudeProvider(LLMProvider):
 
     def generate(self, input: LLMInput) -> LLMOutput:
         try:
-            model = input.model or _DEFAULT_MODEL
+            model = input.model or self.default_model
             system_parts = [msg.content for msg in input.messages if msg.role == Role.SYSTEM]
             api_messages = [
                 msg.to_dict() for msg in input.messages if msg.role not in (Role.SYSTEM,)
@@ -139,4 +145,4 @@ class ClaudeProvider(LLMProvider):
         return bool(self.client.api_key)
 
     def get_default_model(self) -> str:
-        return _DEFAULT_MODEL
+        return self.default_model

src/llm/providers/openai.py

@@ -28,12 +28,18 @@ from llm.providers.openai_compat import to_openai_message_param
 class OpenAIProvider(LLMProvider):
     provider_type = ProviderType.OPENAI
 
-    def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
+    def __init__(
+        self,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        default_model: str | None = None,
+    ) -> None:
         self.client = OpenAI(
             api_key=api_key or os.environ.get("OPENAI_API_KEY"),
             base_url=base_url,
             _enforce_credentials=False,
         )
+        self.default_model = default_model or "gpt-4o-mini"
         self._models = [
             ModelInfo(
                 name="gpt-4o",
@@ -72,7 +78,7 @@ class OpenAIProvider(LLMProvider):
     def generate(self, input: LLMInput) -> LLMOutput:
         try:
             params: dict[str, Any] = {
-                "model": input.model or "gpt-4o-mini",
+                "model": input.model or self.default_model,
                 "messages": [to_openai_message_param(msg) for msg in input.messages],
                 "temperature": input.temperature,
             }
@@ -129,4 +135,4 @@ class OpenAIProvider(LLMProvider):
         return bool(self.client.api_key)
 
     def get_default_model(self) -> str:
-        return "gpt-4o-mini"
+        return self.default_model

src/llm/providers/resolver.py

@@ -22,6 +22,16 @@ _PROVIDER_MAP: dict[ProviderType, type[LLMProvider]] = {
     ProviderType.OLLAMA: OllamaProvider,
 }
 
+# Only built-in adapters have a known default_model constructor contract.
+_MODEL_ENV_VARS: dict[type[LLMProvider], tuple[str, ...]] = {
+    ClaudeProvider: (),
+    OpenAIProvider: (),
+    OllamaProvider: ("OLLAMA_MODEL",),
+    AtlasProvider: (AtlasProvider.model_env,),
+    AstraflowProvider: (AstraflowProvider.model_env,),
+    AstraflowCNProvider: (AstraflowCNProvider.model_env, AstraflowProvider.model_env),
+}
+
 LLM_ENV_FILE = ".llm.env"
 
 
@@ -47,20 +57,48 @@ def _read_saved_llm_config(env_path: str | Path = LLM_ENV_FILE) -> dict[str, str
     return config
 
 
-def _resolve_provider_type(provider_type: ProviderType | str | None) -> ProviderType | str:
+def _resolve_provider_config(
+    provider_type: ProviderType | str | None,
+) -> tuple[ProviderType | str, str | None]:
     if provider_type is not None:
-        return provider_type
+        return provider_type, None
 
     env_provider = os.environ.get("LLM_PROVIDER")
     if env_provider:
-        return _strip_env_value(env_provider).lower()
+        return _strip_env_value(env_provider).lower(), None
 
     saved_config = _read_saved_llm_config()
-    return saved_config.get("LLM_PROVIDER", "claude").lower()
+    saved_provider = saved_config.get("LLM_PROVIDER", "claude").lower()
+    # A saved model belongs to its saved provider, never to an override.
+    saved_model = saved_config.get("LLM_MODEL") if saved_config.get("LLM_PROVIDER") else None
+    return saved_provider, saved_model
+
+
+def _resolve_default_model(
+    provider_type: ProviderType, provider_cls: type[LLMProvider], saved_model: str | None,
+) -> str | None:
+    for name in _MODEL_ENV_VARS[provider_cls]:
+        model = os.environ.get(name)
+        if model:
+            return model
+
+    env_provider = _strip_env_value(os.environ.get("LLM_PROVIDER", "")).lower()
+    if not env_provider or env_provider == provider_type.value:
+        env_model = _strip_env_value(os.environ.get("LLM_MODEL", ""))
+        if env_model:
+            return env_model
+    return saved_model or None
 
 
 def get_provider(provider_type: ProviderType | str | None = None, **kwargs: str) -> LLMProvider:
-    provider_type = _resolve_provider_type(provider_type)
+    """Create a provider, honoring the model selected by llm-select.
+
+    Built-in model defaults prefer explicit kwargs, then existing provider-specific
+    environment variables, LLM_MODEL, and the saved provider/model pair. Saved
+    config is only read when neither an explicit nor an environment provider wins.
+    Custom providers retain their own constructor and configuration semantics.
+    """
+    provider_type, saved_model = _resolve_provider_config(provider_type)
 
     if isinstance(provider_type, str):
         try:
@@ -72,6 +110,11 @@ def get_provider(provider_type: ProviderType | str | None = None, **kwargs: str)
     if not provider_cls:
         raise ValueError(f"No provider registered for type: {provider_type}")
 
+    if "default_model" not in kwargs and provider_cls in _MODEL_ENV_VARS:
+        model = _resolve_default_model(provider_type, provider_cls, saved_model)
+        if model:
+            kwargs = {**kwargs, "default_model": model}
+
     return provider_cls(**kwargs)
 
 

tests/test_claude_provider.py

@@ -38,6 +38,41 @@ def make_response(content: list[SimpleNamespace], stop_reason: str = "tool_use")
     )
 
 
+@pytest.mark.unit
+@pytest.mark.parametrize(
+    ("default_model", "request_model", "expected_model"),
+    [
+        (None, None, "claude-sonnet-4-6"),
+        (None, "claude-haiku-4-5", "claude-haiku-4-5"),
+        ("claude-haiku-4-5", None, "claude-haiku-4-5"),
+        ("claude-opus-4-8", None, "claude-opus-4-8"),
+        ("claude-haiku-4-5", "claude-opus-4-8", "claude-opus-4-8"),
+    ],
+)
+def test_generate_respects_configured_default_and_request_model(
+    default_model: str | None, request_model: str | None, expected_model: str
+) -> None:
+    provider_options = {"api_key": "test-key"}
+    if default_model is not None:
+        provider_options["default_model"] = default_model
+    provider = ClaudeProvider(**provider_options)
+    client = FakeClient(make_response([SimpleNamespace(type="text", text="ok")]))
+    provider.client = client
+    expected_default = default_model or "claude-sonnet-4-6"
+
+    assert provider.get_default_model() == expected_default
+
+    provider.generate(
+        LLMInput(messages=[Message(role=Role.USER, content="Hi")], model=request_model)
+    )
+
+    assert client.messages.last_params["model"] == expected_model
+    if expected_model == "claude-opus-4-8":
+        assert client.messages.last_params["thinking"] == {"type": "adaptive"}
+        assert "temperature" not in client.messages.last_params
+    assert provider.get_default_model() == expected_default
+
+
 @pytest.mark.unit
 def test_generate_collects_text_and_tool_use_blocks() -> None:
     provider = make_provider(

tests/test_provider_tools.py

@@ -73,6 +73,38 @@ def _openai_response(**overrides) -> SimpleNamespace:
     return SimpleNamespace(**defaults)
 
 
+@pytest.mark.unit
+@pytest.mark.parametrize(
+    ("default_model", "request_model", "expected_model"),
+    [
+        (None, None, "gpt-4o-mini"),
+        (None, "gpt-4o", "gpt-4o"),
+        ("gpt-4o", None, "gpt-4o"),
+        ("gpt-4o", "gpt-4-turbo", "gpt-4-turbo"),
+    ],
+)
+def test_openai_generate_respects_configured_default_and_request_model(
+    default_model: str | None, request_model: str | None, expected_model: str
+) -> None:
+    provider_options = {"api_key": "test"}
+    if default_model is not None:
+        provider_options["default_model"] = default_model
+    provider = OpenAIProvider(**provider_options)
+    client = _OpenAIClient()
+    provider.client = client
+    expected_default = default_model or "gpt-4o-mini"
+
+    assert provider.get_default_model() == expected_default
+
+    provider.generate(
+        LLMInput(messages=[Message(role=Role.USER, content="hi")], model=request_model)
+    )
+
+    assert client.completions.params is not None
+    assert client.completions.params["model"] == expected_model
+    assert provider.get_default_model() == expected_default
+
+
 def test_openai_provider_serializes_tools_for_chat_completions():
     provider = OpenAIProvider(api_key="test")
     client = _OpenAIClient()

tests/test_resolver.py

@@ -1,6 +1,11 @@
+import io
+import json
+from unittest.mock import Mock
+
 import pytest
 
-from llm.core.types import ProviderType
+from llm.cli.selector import save_config
+from llm.core.types import LLMInput, Message, ProviderType, Role
 from llm.providers import (
     AstraflowCNProvider,
     AstraflowProvider,
@@ -9,6 +14,7 @@ from llm.providers import (
     OllamaProvider,
     OpenAIProvider,
     get_provider,
+    resolver,
 )
 
 
@@ -36,6 +42,7 @@ class TestGetProvider:
     def test_get_astraflow_cn_provider(self):
         provider = get_provider("astraflow_cn")
         assert isinstance(provider, AstraflowCNProvider)
+
         assert provider.provider_type == ProviderType.ASTRAFLOW_CN
 
     def test_get_atlas_provider(self):
@@ -100,3 +107,176 @@ class TestGetProvider:
         provider = get_provider()
 
         assert isinstance(provider, AstraflowCNProvider)
+
+
+class TestSelectedModel:
+    @pytest.fixture(autouse=True)
+    def isolated_config(self, monkeypatch, tmp_path):
+        monkeypatch.chdir(tmp_path)
+        for name in (
+            "LLM_PROVIDER", "LLM_MODEL", "OLLAMA_MODEL", "ATLAS_MODEL",
+            "ASTRAFLOW_MODEL", "ASTRAFLOW_CN_MODEL",
+        ):
+            # save_config(persist=True) writes os.environ directly; restore it after each test.
+            monkeypatch.setenv(name, "")
+            monkeypatch.delenv(name)
+        monkeypatch.setenv("ANTHROPIC_API_KEY", "test-key")
+        monkeypatch.setenv("OPENAI_API_KEY", "test-key")
+
+    @pytest.mark.parametrize("persist", [False, True])
+    def test_selector_model_reaches_actual_request(self, monkeypatch, persist):
+        save_config("ollama", "mistral", persist=persist)
+        urlopen = Mock(side_effect=lambda *args, **kwargs: io.BytesIO(
+            b'{"message":{"content":"ok"},"model":"mistral"}'
+        ))
+        monkeypatch.setattr("urllib.request.urlopen", urlopen)
+
+        provider = get_provider()
+        provider.generate(LLMInput(messages=[Message(Role.USER, "hello")]))
+
+        assert provider.get_default_model() == "mistral"
+        assert json.loads(urlopen.call_args.args[0].data)["model"] == "mistral"
+
+        provider.generate(LLMInput(messages=[], model="codellama"))
+        assert json.loads(urlopen.call_args.args[0].data)["model"] == "codellama"
+
+    @pytest.mark.parametrize("provider_type", list(ProviderType))
+    @pytest.mark.parametrize("source", ["saved", "environment"])
+    def test_selected_model_applies_to_every_builtin(self, monkeypatch, provider_type, source):
+        save_config(provider_type.value, "Selected/MixedCase-model", persist=source == "environment")
+
+        provider = get_provider()
+
+        assert provider.provider_type == provider_type
+        assert provider.get_default_model() == "Selected/MixedCase-model"
+
+    def test_environment_model_overrides_saved_model(self, monkeypatch):
+        save_config("ollama", "mistral")
+        monkeypatch.setenv("LLM_MODEL", "codellama")
+
+        assert get_provider().get_default_model() == "codellama"
+
+    @pytest.mark.parametrize("provider_type,model_env", [
+        ("ollama", "OLLAMA_MODEL"),
+        ("atlas", "ATLAS_MODEL"),
+        ("astraflow", "ASTRAFLOW_MODEL"),
+        ("astraflow_cn", "ASTRAFLOW_CN_MODEL"),
+        ("astraflow_cn", "ASTRAFLOW_MODEL"),
+    ])
+    def test_provider_environment_retains_precedence(self, monkeypatch, provider_type, model_env):
+        save_config(provider_type, "saved-model")
+        monkeypatch.setenv("LLM_MODEL", "global-model")
+        monkeypatch.setenv(model_env, "provider-model")
+
+        assert get_provider().get_default_model() == "provider-model"
+
+    def test_cn_specific_environment_overrides_shared_fallback(self, monkeypatch):
+        save_config("astraflow_cn", "saved-model")
+        monkeypatch.setenv("ASTRAFLOW_MODEL", "shared-model")
+        monkeypatch.setenv("ASTRAFLOW_CN_MODEL", "cn-model")
+
+        assert get_provider().get_default_model() == "cn-model"
+
+    @pytest.mark.parametrize("provider_type", list(ProviderType))
+    def test_explicit_default_model_overrides_selection(self, monkeypatch, provider_type):
+        save_config(provider_type.value, "saved-model", persist=True)
+        monkeypatch.setenv("OLLAMA_MODEL", "provider-model")
+
+        assert get_provider(default_model="explicit-model").get_default_model() == "explicit-model"
+
+    @pytest.mark.parametrize("default_model", [None, ""])
+    def test_explicit_empty_default_retains_constructor_fallback(self, monkeypatch, default_model):
+        save_config("ollama", "saved-model", persist=True)
+        monkeypatch.setenv("OLLAMA_MODEL", "provider-model")
+
+        assert get_provider(default_model=default_model).get_default_model() == "provider-model"
+
+    @pytest.mark.parametrize("explicit", [False, True])
+    def test_provider_override_does_not_inherit_saved_model(self, monkeypatch, explicit):
+        save_config("openai", "gpt-selected")
+        if explicit:
+            provider = get_provider("ollama")
+        else:
+            monkeypatch.setenv("LLM_PROVIDER", "ollama")
+            provider = get_provider()
+
+        assert provider.get_default_model() == "llama3.2"
+
+    def test_explicit_provider_does_not_inherit_another_environment_model(self, monkeypatch):
+        monkeypatch.setenv("LLM_PROVIDER", "openai")
+        monkeypatch.setenv("LLM_MODEL", "gpt-selected")
+
+        assert get_provider("ollama").get_default_model() == "llama3.2"
+
+    @pytest.mark.parametrize("env_provider", [None, "  'OLLAMA'  "])
+    def test_environment_model_applies_to_matching_explicit_provider(self, monkeypatch, env_provider):
+        if env_provider is not None:
+            monkeypatch.setenv("LLM_PROVIDER", env_provider)
+        monkeypatch.setenv("LLM_MODEL", '  "MixedCase/Model"  ')
+
+        assert get_provider("ollama").get_default_model() == "MixedCase/Model"
+
+    @pytest.mark.parametrize("source", ["saved", "environment"])
+    @pytest.mark.parametrize("model", ["", "   ", '""'])
+    def test_empty_selected_model_retains_default(self, monkeypatch, source, model):
+        save_config("ollama", model, persist=source == "environment")
+
+        assert get_provider().get_default_model() == "llama3.2"
+
+    def test_empty_environment_model_falls_back_to_saved_model(self, monkeypatch):
+        save_config("ollama", "mistral")
+        monkeypatch.setenv("LLM_MODEL", "  ")
+
+        assert get_provider().get_default_model() == "mistral"
+
+    def test_quoted_saved_model_preserves_case(self, tmp_path):
+        tmp_path.joinpath(".llm.env").write_text(
+            "LLM_PROVIDER='OLLAMA'\nLLM_MODEL='MixedCase/Model'\n"
+        )
+
+        assert get_provider().get_default_model() == "MixedCase/Model"
+
+    def test_unpaired_saved_model_does_not_override_default_provider(self, tmp_path):
+        tmp_path.joinpath(".llm.env").write_text("LLM_MODEL=orphan-model\n")
+
+        assert get_provider().get_default_model() == "claude-sonnet-4-6"
+
+    @pytest.mark.parametrize("explicit", [False, True])
+    def test_provider_override_does_not_read_invalid_saved_file(self, monkeypatch, tmp_path, explicit):
+        tmp_path.joinpath(".llm.env").write_bytes(b"\xff\xfe")
+        monkeypatch.setenv("LLM_MODEL", "mistral")
+        if explicit:
+            provider = get_provider("ollama")
+        else:
+            monkeypatch.setenv("LLM_PROVIDER", "ollama")
+            provider = get_provider()
+
+        assert provider.get_default_model() == "mistral"
+
+    def test_registered_provider_keeps_its_constructor_contract(self, monkeypatch):
+        class CustomProvider(OllamaProvider):
+            def __init__(self, base_url=None):
+                super().__init__(base_url=base_url, default_model="custom-default")
+
+        monkeypatch.setattr(resolver, "_PROVIDER_MAP", resolver._PROVIDER_MAP.copy())
+        resolver.register_provider(ProviderType.OLLAMA, CustomProvider)
+        save_config("ollama", "mistral", persist=True)
+
+        provider = get_provider(base_url="http://localhost:1234")
+
+        assert isinstance(provider, CustomProvider)
+        assert provider.base_url == "http://localhost:1234"
+        assert provider.get_default_model() == "custom-default"
+
+    def test_registered_provider_receives_explicit_default_model(self, monkeypatch):
+        class CustomProvider(OllamaProvider):
+            pass
+
+        monkeypatch.setattr(resolver, "_PROVIDER_MAP", resolver._PROVIDER_MAP.copy())
+        resolver.register_provider(ProviderType.OLLAMA, CustomProvider)
+        save_config("ollama", "mistral", persist=True)
+
+        provider = get_provider(default_model="custom-explicit")
+
+        assert isinstance(provider, CustomProvider)
+        assert provider.get_default_model() == "custom-explicit"