too much

living_agents/character_agent.py

@@ -71,6 +71,33 @@ class CharacterAgent:
 
         return response
 
+    async def chat_with(self, messages: List[LLMMessage]) -> LLMMessage:
+        """Generate reaction based on memory and character"""
+        # Retrieve relevant memories
+        relevant_memories = await self.memory_stream.retrieve_related_memories(situation, k=8)
+        memory_context = "\n".join([f"- {m.description}" for m in relevant_memories])
+
+        context = {
+            'character': self._get_character_prompt(),
+            'character_name': self.character.name,
+            'memory_context': memory_context,
+            'situation': situation}
+        prompt = PromptManager.get_prompt('react_to_situation', context)
+
+        response = await self.llm.chat([{"role": "user", "content": prompt}])
+
+        # create new memories from interaction
+        interaction_context = {
+            'situation': f'I reacted to: \n{situation}',
+            'response': f'My response was: \n{response}',
+        }
+        prompt, schema = PromptManager.get_prompt_with_schema('extract_interaction_memories', interaction_context)
+        memories_response = await self.llm.client.get_structured_response([{"role": "user", "content": prompt}], schema)
+        for new_memory in memories_response['memories']:
+            await self.perceive(new_memory)
+
+        return response
+
     async def plan_day(self) -> List[str]:
         """Generate high-level daily plan"""
         # Retrieve relevant memories about goals, habits, schedule
@@ -255,5 +282,7 @@ Summary:"""
             memory.importance_score = await instance._score_memory_importance(memory)
             await instance._analyze_trait_impact(memory)
 
+        instance.character.template_file = template['yaml_file']
+
         logger.info(f"Character {instance.character.name} created successfully")
         return instance