fix(config): canonicalize LLM env and respect cleared vars

Drop saved LLM config if any current LLM env var differs, and treat
explicit empty env vars as cleared so saved values are removed and
not re-applied.

strix/config/config.py

@@ -19,6 +19,18 @@ class Config:
     strix_llm_max_retries = "5"
     strix_memory_compressor_timeout = "30"
     llm_timeout = "300"
+    _LLM_CANONICAL_NAMES = (
+        "strix_llm",
+        "llm_api_key",
+        "llm_api_base",
+        "openai_api_base",
+        "litellm_base_url",
+        "ollama_api_base",
+        "strix_reasoning_effort",
+        "strix_llm_max_retries",
+        "strix_memory_compressor_timeout",
+        "llm_timeout",
+    )
 
     # Tool & Feature Configuration
     perplexity_api_key = None
@@ -45,6 +57,20 @@ class Config:
     def tracked_vars(cls) -> list[str]:
         return [name.upper() for name in cls._tracked_names()]
 
+    @classmethod
+    def _llm_env_vars(cls) -> set[str]:
+        return {name.upper() for name in cls._LLM_CANONICAL_NAMES}
+
+    @classmethod
+    def _llm_env_changed(cls, saved_env: dict[str, Any]) -> bool:
+        for var_name in cls._llm_env_vars():
+            current = os.getenv(var_name)
+            if current is None:
+                continue
+            if saved_env.get(var_name) != current:
+                return True
+        return False
+
     @classmethod
     def get(cls, name: str) -> str | None:
         env_name = name.upper()
@@ -88,10 +114,24 @@ class Config:
     def apply_saved(cls) -> dict[str, str]:
         saved = cls.load()
         env_vars = saved.get("env", {})
+        if not isinstance(env_vars, dict):
+            env_vars = {}
+        cleared_vars = {
+            var_name
+            for var_name in cls.tracked_vars()
+            if var_name in os.environ and os.environ.get(var_name) == ""
+        }
+        if cleared_vars:
+            for var_name in cleared_vars:
+                env_vars.pop(var_name, None)
+            cls.save({"env": env_vars})
+        if cls._llm_env_changed(env_vars):
+            cls.save({"env": {}})
+            return {}
         applied = {}
 
         for var_name, var_value in env_vars.items():
-            if var_name in cls.tracked_vars() and not os.getenv(var_name):
+            if var_name in cls.tracked_vars() and var_name not in os.environ:
                 os.environ[var_name] = var_value
                 applied[var_name] = var_value