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(

app/agents/note_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 note-taking assistant. You help users create, retrieve, update,\n"
@@ -113,7 +112,7 @@ class NoteAgent(ChatAgent):
         return [list_notes, get_note, create_note, update_note, delete_note]
 
     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(

app/agents/project_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 management assistant. You help users create, find,\n"
@@ -148,7 +147,7 @@ class ProjectAgent(ChatAgent):
         ]
 
     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(

app/agents/task_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 task management assistant for a project workspace.\n"
@@ -219,7 +218,7 @@ class TaskAgent(ChatAgent):
         ]
 
     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(

app/config/settings.py

@@ -24,6 +24,9 @@ class Settings(BaseSettings):
 
     OPENAI_API_KEY: str = ""
 
+    LLM_MODEL: str = "gpt-4o"
+    LLM_ROUTER_MODEL: str = "gpt-4o-mini"
+
     CORS_ORIGINS: list[str] = ["app://.", "http://localhost:3000", "http://localhost:5173"]
 
     ENV: Literal["dev", "prod"] = "dev"

app/core/llm.py

@@ -0,0 +1,68 @@
+"""LLM factory — centralised model instantiation via LiteLLM.
+
+Every agent and the orchestrator call ``get_llm()`` or ``get_router_llm()``
+instead of directly constructing a provider-specific class.  The model string
+follows the `LiteLLM model naming convention
+<https://docs.litellm.ai/docs/providers>`_:
+
+* OpenAI:     ``gpt-4o``, ``gpt-4o-mini``
+* Anthropic:  ``anthropic/claude-3.5-sonnet``
+* Google:     ``gemini/gemini-pro``
+* Ollama:     ``ollama/llama3``
+* Bedrock:    ``bedrock/anthropic.claude-v2``
+
+Switch providers by changing **LLM_MODEL** / **LLM_ROUTER_MODEL** in ``.env``
+— no code changes required.
+"""
+
+from __future__ import annotations
+
+from langchain_openai import ChatOpenAI
+from litellm import get_supported_openai_params  # noqa: F401 – validates install
+
+from app.config.settings import settings
+
+
+def _api_key_for_model(model: str) -> str | None:
+    """Return the most appropriate API key for the given LiteLLM model string."""
+    if model.startswith("anthropic/"):
+        return getattr(settings, "ANTHROPIC_API_KEY", None) or None
+    if model.startswith("gemini/") or model.startswith("google/"):
+        return getattr(settings, "GOOGLE_API_KEY", None) or None
+    # Default: OpenAI-compatible (covers plain model names like "gpt-4o")
+    return settings.OPENAI_API_KEY or None
+
+
+def get_llm(
+    *,
+    model: str | None = None,
+    temperature: float = 0,
+) -> ChatOpenAI:
+    """Return a LangChain chat model backed by LiteLLM.
+
+    LiteLLM exposes an OpenAI-compatible API, so we use ``ChatOpenAI`` pointed
+    at the LiteLLM proxy endpoint.  In practice, ``litellm`` patches the
+    ``openai`` client transparently when the model string contains a provider
+    prefix (``anthropic/…``, ``gemini/…``, etc.).
+
+    Parameters
+    ----------
+    model:
+        LiteLLM model identifier. Defaults to ``settings.LLM_MODEL``.
+    temperature:
+        Sampling temperature.  ``0`` = deterministic.
+    """
+    model = model or settings.LLM_MODEL
+    return ChatOpenAI(
+        model=model,
+        temperature=temperature,
+        api_key=_api_key_for_model(model),
+    )
+
+
+def get_router_llm(
+    *,
+    temperature: float = 0,
+) -> ChatOpenAI:
+    """Return the lighter model used for intent classification / routing."""
+    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)

app/core/orchestrator.py

@@ -6,10 +6,9 @@ import json
 from typing import Any, AsyncGenerator
 
 from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_openai import ChatOpenAI
 
-from app.config.settings import settings
 from app.core.agent_registry import AgentRegistry
+from app.core.llm import get_router_llm
 from app.core.agent_registry import registry as _default_registry
 from app.schemas import ChatRequest, ChatResponse, ExecutionPlan
 
@@ -29,8 +28,8 @@ _SYNTHESIZE_HUMAN = (
 )
 
 
-def _make_llm(model: str = "gpt-4o-mini") -> ChatOpenAI:
-    return ChatOpenAI(model=model, temperature=0, api_key=settings.OPENAI_API_KEY)
+def _make_llm():
+    return get_router_llm()
 
 
 async def classify_intent(