refactor(llm): enable hot-reload for model config and client runtime

make LLM task config resolution dynamic in LLMRequest
load model clients on demand from latest config
clear client instance cache on config reload
remove stale module-level model_config usage in llm_api
add hot-reload tests for LLM/config watcher flow

pytests/config_test/test_llm_request_hot_reload.py

@@ -0,0 +1,76 @@
+from types import SimpleNamespace
+from importlib import util
+from pathlib import Path
+
+from src.config.config import config_manager
+from src.config.model_configs import TaskConfig
+from src.llm_models.utils_model import LLMRequest
+
+
+def _load_llm_api_module():
+    file_path = Path(__file__).parent.parent.parent / "src" / "plugin_system" / "apis" / "llm_api.py"
+    spec = util.spec_from_file_location("test_llm_api_module", file_path)
+    assert spec is not None and spec.loader is not None
+    module = util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _make_model_config(task_config: TaskConfig, attr_name: str = "utils"):
+    model_task_config = SimpleNamespace(**{attr_name: task_config})
+    return SimpleNamespace(model_task_config=model_task_config, models=[], api_providers=[])
+
+
+def test_llm_request_resolve_task_config_by_signature(monkeypatch):
+    old_task = TaskConfig(model_list=["gpt-a"], max_tokens=512, temperature=0.3, slow_threshold=15.0)
+    current_task = TaskConfig(model_list=["gpt-a"], max_tokens=512, temperature=0.3, slow_threshold=15.0)
+
+    monkeypatch.setattr(config_manager, "get_model_config", lambda: _make_model_config(current_task, "utils"))
+
+    req = LLMRequest(model_set=old_task, request_type="test")
+
+    assert req._task_config_name == "utils"
+
+
+def test_llm_request_refresh_task_config_updates_runtime_state(monkeypatch):
+    old_task = TaskConfig(model_list=["gpt-a"], max_tokens=512, temperature=0.3, slow_threshold=15.0)
+    initial_task = TaskConfig(model_list=["gpt-a"], max_tokens=512, temperature=0.3, slow_threshold=15.0)
+    updated_task = TaskConfig(model_list=["gpt-b", "gpt-c"], max_tokens=1024, temperature=0.5, slow_threshold=20.0)
+
+    current = {"task": initial_task}
+
+    def get_model_config_stub():
+        return _make_model_config(current["task"], "replyer")
+
+    monkeypatch.setattr(config_manager, "get_model_config", get_model_config_stub)
+
+    req = LLMRequest(model_set=old_task, request_type="test")
+    assert req._task_config_name == "replyer"
+
+    current["task"] = updated_task
+    req._refresh_task_config()
+
+    assert req.model_for_task.model_list == ["gpt-b", "gpt-c"]
+    assert list(req.model_usage.keys()) == ["gpt-b", "gpt-c"]
+
+
+def test_llm_api_get_available_models_reads_latest_config(monkeypatch):
+    llm_api = _load_llm_api_module()
+
+    first_utils = TaskConfig(model_list=["gpt-a"])
+    second_utils = TaskConfig(model_list=["gpt-z"])
+
+    state = {"task": first_utils}
+
+    def get_model_config_stub():
+        model_task_config = SimpleNamespace(utils=state["task"], planner=TaskConfig(model_list=["gpt-p"]))
+        return SimpleNamespace(model_task_config=model_task_config)
+
+    monkeypatch.setattr(config_manager, "get_model_config", get_model_config_stub)
+
+    first = llm_api.get_available_models()
+    assert first["utils"].model_list == ["gpt-a"]
+
+    state["task"] = second_utils
+    second = llm_api.get_available_models()
+    assert second["utils"].model_list == ["gpt-z"]