fixes to merge

core/framework/llm/anthropic.py

@@ -1,29 +1,12 @@
 """Anthropic Claude LLM provider - backward compatible wrapper around LiteLLM."""
 
+import os
 from typing import Any
 
 from framework.llm.provider import LLMProvider, LLMResponse, Tool
 from framework.llm.litellm import LiteLLMProvider
 
 
-def _get_api_key_from_credential_manager() -> str | None:
-    """Get API key from CredentialManager or environment.
-
-    Priority:
-    1. CredentialManager (supports .env hot-reload)
-    2. os.environ fallback
-    """
-    try:
-        from aden_tools.credentials import CredentialManager
-
-        creds = CredentialManager()
-        if creds.is_available("anthropic"):
-            return creds.get("anthropic")
-    except ImportError:
-        pass
-    return os.environ.get("ANTHROPIC_API_KEY")
-
-
 def _get_api_key_from_credential_manager() -> str | None:
     """Get API key from CredentialManager or environment.
 
@@ -64,7 +47,7 @@ class AnthropicProvider(LLMProvider):
                      or ANTHROPIC_API_KEY env var.
             model: Model to use (default: claude-haiku-4-5-20251001)
         """
-                # Delegate to LiteLLMProvider internally.
+        # Delegate to LiteLLMProvider internally.
         self.api_key = api_key or _get_api_key_from_credential_manager()
         if not self.api_key:
             raise ValueError(
@@ -78,12 +61,6 @@ class AnthropicProvider(LLMProvider):
             api_key=self.api_key,
         )
 
-        
-        
-
-        self.model = model
-        self.api_key = api_key
-
     def complete(
         self,
         messages: list[dict[str, Any]],
@@ -108,88 +85,10 @@ class AnthropicProvider(LLMProvider):
         max_iterations: int = 10,
     ) -> LLMResponse:
         """Run a tool-use loop until Claude produces a final response."""
-        current_messages = list(messages)
-        total_input_tokens = 0
-        total_output_tokens = 0
-
-        for _ in range(max_iterations):
-            response = self.client.messages.create(
-                model=self.model,
-                max_tokens=1024,
-                system=system,
-                messages=current_messages,
-                tools=[self._tool_to_dict(t) for t in tools],
-            )
-
-            total_input_tokens += response.usage.input_tokens
-            total_output_tokens += response.usage.output_tokens
-
-            # Check if we're done (no more tool use)
-            if response.stop_reason == "end_turn":
-                content = ""
-                for block in response.content:
-                    if block.type == "text":
-                        content += block.text
-
-                return LLMResponse(
-                    content=content,
-                    model=response.model,
-                    input_tokens=total_input_tokens,
-                    output_tokens=total_output_tokens,
-                    stop_reason=response.stop_reason,
-                    raw_response=response,
-                )
-
-            # Process tool uses
-            tool_uses = []
-            assistant_content = []
-            for block in response.content:
-                if block.type == "tool_use":
-                    tool_uses.append(
-                        ToolUse(id=block.id, name=block.name, input=block.input)
-                    )
-                    assistant_content.append({
-                        "type": "tool_use",
-                        "id": block.id,
-                        "name": block.name,
-                        "input": block.input,
-                    })
-                elif block.type == "text":
-                    assistant_content.append({
-                        "type": "text",
-                        "text": block.text,
-                    })
-
-            # Add assistant message with tool uses
-            current_messages.append({
-                "role": "assistant",
-                "content": assistant_content,
-            })
-
-            # Execute tools and add results
-            tool_results = []
-            for tool_use in tool_uses:
-                result = tool_executor(tool_use)
-                # Ensure content is never empty (Anthropic API requires non-empty content)
-                content = result.content if result.content else "(empty result)"
-                tool_results.append({
-                    "type": "tool_result",
-                    "tool_use_id": result.tool_use_id,
-                    "content": content,
-                    "is_error": result.is_error,
-                })
-
-            current_messages.append({
-                "role": "user",
-                "content": tool_results,
-            })
-
-        # Max iterations reached
-        return LLMResponse(
-            content="Max tool iterations reached",
-            model=self.model,
-            input_tokens=total_input_tokens,
-            output_tokens=total_output_tokens,
-            stop_reason="max_iterations",
-            raw_response=None,
+        return self._provider.complete_with_tools(
+            messages=messages,
+            system=system,
+            tools=tools,
+            tool_executor=tool_executor,
+            max_iterations=max_iterations,
         )

core/tests/test_litellm_provider.py

@@ -250,7 +250,7 @@ class TestAnthropicProviderBackwardCompatibility:
     def test_anthropic_provider_init_defaults(self):
         """Test AnthropicProvider initialization with defaults."""
         provider = AnthropicProvider(api_key="test-key")
-        assert provider.model == "claude-sonnet-4-20250514"
+        assert provider.model == "claude-haiku-4-5-20251001"
         assert provider.api_key == "test-key"
 
     def test_anthropic_provider_init_custom_model(self):