feat: add Claude backend with automatic Ollama fallback (Claudification Phase 1)

All agents now prefer Claude API when ANTHROPIC_API_KEY is configured,
with automatic fallback to Ollama when offline or unconfigured. New
src/anthropic/ module provides model selection via get_model() factory.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/agents/tatlock.py

@@ -138,10 +138,7 @@ class TatlockAgent(AgentInterface):
     """
 
     def __init__(self):
-        """Initialize Tatlock configuration (lazy agent creation)."""
-        # Store Ollama configuration
-        self.ollama_host = str(config.OLLAMA_HOST)
-        self.model_name = config.OLLAMA_DEFAULT_MODEL
+        """Initialize Tatlock (lazy agent creation)."""
         self._agent = None  # Lazy initialization
 
     def _ensure_agent(self):
@@ -149,30 +146,21 @@ class TatlockAgent(AgentInterface):
         if self._agent is not None:
             return
 
+        from src.anthropic.model_selector import get_model, get_model_info
+
+        model_info = get_model_info()
         logger.info(
             "tatlock_agent_initializing",
-            ollama_host=self.ollama_host,
-            model=self.model_name,
+            backend=model_info["backend"],
+            model=model_info["model"],
         )
 
-        # Import required classes for Ollama configuration
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        # Get best available model (Claude if available, else Ollama)
+        model = get_model()
 
-        # PydanticAI expects Ollama base URL to end with /v1
-        # Remove trailing slash from ollama_host if present
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        # Create Ollama model with provider
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
-
-        # Create PydanticAI agent with Ollama model
+        # Create PydanticAI agent
         self._agent = Agent(
-            ollama_model,
+            model,
             system_prompt=TATLOCK_SYSTEM_PROMPT,
         )
 
@@ -461,8 +449,7 @@ class TatlockAgent(AgentInterface):
             ...     tool_tracker=tracker,
             ... )
         """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        from src.anthropic.model_selector import get_model
 
         logger.info(
             "tatlock_run_with_scoped_tools",
@@ -473,18 +460,12 @@ class TatlockAgent(AgentInterface):
 
         # Create a fresh agent instance with scoped tools only
         # This ensures Tatlock can ONLY use tools recommended by the Steward
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()
 
         # Create agent with scoped tools
         # Tools from household registry are already PydanticAI Tool objects
         scoped_agent = Agent(
-            ollama_model,
+            model,
             system_prompt=TATLOCK_SYSTEM_PROMPT,
             tools=scoped_tools,  # Pass tools directly to Agent constructor
         )
@@ -555,8 +536,7 @@ class TatlockAgent(AgentInterface):
         Yields:
             Text chunks from the streaming response
         """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        from src.anthropic.model_selector import get_model
 
         logger.info(
             "tatlock_run_with_scoped_tools_stream",
@@ -566,17 +546,11 @@ class TatlockAgent(AgentInterface):
         )
 
         # Create a fresh agent instance with scoped tools only
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()
 
         # Create agent with scoped tools
         scoped_agent = Agent(
-            ollama_model,
+            model,
             system_prompt=TATLOCK_SYSTEM_PROMPT,
             tools=scoped_tools,
         )
@@ -650,8 +624,6 @@ class TatlockAgent(AgentInterface):
                 - tool_outputs: Dict mapping tool names to their outputs
                 - raw_output: The agent's raw text output
         """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
         from pydantic_ai.settings import ModelSettings
         from pydantic_ai.messages import (
             ModelRequest,
@@ -661,6 +633,7 @@ class TatlockAgent(AgentInterface):
             ToolCallPart,
             ToolReturnPart,
         )
+        from src.anthropic.model_selector import get_model
 
         logger.info(
             "tatlock_orchestrate_tool_calls",
@@ -680,17 +653,11 @@ class TatlockAgent(AgentInterface):
         )
 
         # Create a fresh agent instance with scoped tools only
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()
 
         # Create agent with scoped tools
         scoped_agent = Agent(
-            ollama_model,
+            model,
             system_prompt=TATLOCK_SYSTEM_PROMPT,
             tools=scoped_tools,
         )
@@ -799,9 +766,8 @@ class TatlockAgent(AgentInterface):
         Returns:
             str: Butler-toned response synthesized from all results
         """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
         from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
+        from src.anthropic.model_selector import get_model
 
         logger.info(
             "tatlock_synthesize_from_results",
@@ -848,17 +814,11 @@ class TatlockAgent(AgentInterface):
         synthesis_prompt = "\n".join(synthesis_parts)
 
         # Create synthesis agent (no tools needed)
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()
 
         # Synthesis agent uses butler prompt but no tools
         synthesis_agent = Agent(
-            ollama_model,
+            model,
             system_prompt=TATLOCK_SYSTEM_PROMPT,
             # No tools for synthesis phase
         )