Refactor LLM instantiation across agents and orchestrator

- Replaced direct instantiation of ChatOpenAI with a centralized get_llm function in CheckpointAgent, NoteAgent, ProjectAgent, and TaskAgent.
- Introduced a new llm.py module to handle LLM model instantiation and API key management.
- Updated settings.py to include LLM_MODEL and LLM_ROUTER_MODEL configurations.
- Modified orchestrator.py to use get_router_llm for intent classification.
- Updated requirements.txt to include litellm for LLM management.
- Adjusted tests to mock get_llm instead of ChatOpenAI directly.

app/agents/checkpoint_agent.py

@@ -7,10 +7,9 @@ from typing import Any
 
 from langchain_core.messages import HumanMessage, SystemMessage
 from langchain_core.tools import tool
-from langchain_openai import ChatOpenAI
 
-from app.config.settings import settings
 from app.core.agent_registry import ChatAgent, registry
+from app.core.llm import get_llm
 
 _SYSTEM_PROMPT = (
     "You are a project checkpoint assistant. Checkpoints are milestone dates that\n"
@@ -112,7 +111,7 @@ class CheckpointAgent(ChatAgent):
         return [list_checkpoints, create_checkpoint, update_checkpoint, delete_checkpoint]
 
     async def handle(self, query: str, context: dict[str, Any]) -> str:
-        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
+        llm = get_llm()
         messages = [
             SystemMessage(content=_SYSTEM_PROMPT),
             HumanMessage(