diff --git a/app/models/llm_client.py b/app/models/llm_client.py
index e76c078..861f7fa 100644
--- a/app/models/llm_client.py
+++ b/app/models/llm_client.py
@@ -1,3 +1,96 @@
+
+"""
+Concrete LLM implementations (OpenAI + Anthropic)
+Week 2: Switchable LLM client
+"""
+
+import openai
+import anthropic
+from typing import List, Dict, Any, AsyncGenerator
+import asyncio
+from app.models.base import LLMClient, BaseLLMResponse
+from app.config.settings import settings
+
+class OpenAILLM(LLMClient):
+    def __init__(self):
+        self.client = openai.AsyncOpenAI(api_key=settings.openai_api_key)
+    
+    async def chat(
+        self, 
+        messages: List[Dict[str, str]], 
+        model: str = "gpt-4o-mini",
+        **kwargs
+    ) -> BaseLLMResponse:
+        response = await self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            **kwargs
+        )
+        choice = response.choices[0]
+        return BaseLLMResponse(
+            content=choice.message.content or "",
+            usage=response.usage.model_dump() if response.usage else None
+        )
+    
+    async def stream_chat(
+        self,
+        messages: List[Dict[str, str]],
+        model: str = "gpt-4o-mini",
+        **kwargs
+    ) -> AsyncGenerator[BaseLLMResponse, None]:
+        stream = await self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            stream=True,
+            **kwargs
+        )
+        async for chunk in stream:
+            if chunk.choices[0].delta.content:
+                yield BaseLLMResponse(content=chunk.choices[0].delta.content)
+
+class AnthropicLLM(LLMClient):
+    def __init__(self):
+        self.client = anthropic.Anthropic(api_key=settings.anthropic_api_key)
+    
+    async def chat(
+        self, 
+        messages: List[Dict[str, str]], 
+        model: str = "claude-3-5-sonnet-20240620",
+        **kwargs
+    ) -> BaseLLMResponse:
+        # Convert OpenAI format to Anthropic
+        system = next((m["content"] for m in messages if m["role"] == "system"), "")
+        user_messages = [m for m in messages if m["role"] != "system"]
+        
+        response = await self.client.messages.create(
+            model=model,
+            max_tokens=4096,
+            messages=user_messages,
+            system=system,
+            **kwargs
+        )
+        return BaseLLMResponse(content=response.content[0].text)
+    
+    async def stream_chat(
+        self,
+        messages: List[Dict[str, str]],
+        model: str = "claude-3-5-sonnet-20240620",
+        **kwargs
+    ) -> AsyncGenerator[BaseLLMResponse, None]:
+        # Streaming implementation for Anthropic
+        raise NotImplementedError("Anthropic streaming - Week 3 stretch")
+
+class LLMFactory:
+    @staticmethod
+    def create() -> LLMClient:
+        if settings.llm_provider == "openai" or (
+            settings.llm_provider == "auto" and settings.openai_api_key
+        ):
+            return OpenAILLM()
+        elif settings.anthropic_api_key:
+            return AnthropicLLM()
+        else:
+            raise ValueError("No valid LLM provider configured")
 import os
 import logging
 from app.config import settings
@@ -135,4 +228,4 @@ def chat(self, messages: list[dict], **kwargs) -> str:
             logger.error(f"Error during LLM chat with {self._provider}: {e}")
             return '{"error": "LLM call failed", "details": "' + str(e) + '"}'
 
-        return ""
\ No newline at end of file
+        return ""