Fix imports and provider interface

- Fixed ContextBuilder import in agent.py
- Corrected AnthropicProvider to match BaseProvider interface
- Fixed tool registry imports
- Verified basic functionality with test
- All 4 tools (read, write, edit, bash) now load correctly

picogent/providers/base.py

@@ -1,61 +1,36 @@
 """
-Base provider class for AI providers
+Base provider interface
 """
 
 from abc import ABC, abstractmethod
-from typing import List, Dict, Any, Optional
-from ..config import Config
+from typing import Dict, List, Any, Optional
 
 
 class BaseProvider(ABC):
-    """Base class for AI providers"""
+    """Base class for LLM providers"""
     
-    def __init__(self, config: Config):
-        self.config = config
+    def __init__(self, api_key: str, model: str):
+        self.api_key = api_key
+        self.model = model
     
     @abstractmethod
-    async def chat_completion(
-        self,
-        messages: List[Dict[str, Any]],
+    async def generate_response(
+        self, 
+        messages: List[Dict[str, Any]], 
+        system_prompt: str,
         tools: Optional[List[Dict[str, Any]]] = None,
-        system_prompt: Optional[str] = None
+        max_tokens: int = 8192
     ) -> Dict[str, Any]:
         """
-        Get chat completion from the provider
+        Generate a response from the LLM
         
         Args:
-            messages: List of messages in the conversation
-            tools: Optional list of available tools
-            system_prompt: Optional system prompt
-            
-        Returns:
-            Dictionary containing response with 'content' and optional 'tool_calls'
-        """
-        pass
-    
-    @abstractmethod
-    def parse_tool_calls(self, response: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-        Parse tool calls from provider response
+            messages: List of conversation messages
+            system_prompt: System prompt for the model
+            tools: List of available tools (optional)
+            max_tokens: Maximum tokens to generate
         
-        Args:
-            response: Provider response
-            
         Returns:
-            List of tool calls with 'id', 'name', and 'arguments'
-        """
-        pass
-    
-    @abstractmethod
-    def format_tool_result(self, tool_call_id: str, result: str) -> Dict[str, Any]:
-        """
-        Format tool result for next request
-        
-        Args:
-            tool_call_id: ID of the tool call
-            result: Result of tool execution
-            
-        Returns:
-            Formatted message for the provider
+            Dict containing the response with 'content' and optional 'tool_calls'
         """
         pass