New archi: domain driven development

Working but need to check out code

agent/agent.py

@@ -3,9 +3,10 @@ from typing import Any, Dict, List
 import json
 
 from .llm import DeepSeekClient
-from .memory import Memory
+from infrastructure.persistence.memory import Memory
 from .registry import make_tools, Tool
 from .prompts import PromptBuilder
+from .config import settings
 
 class Agent:
     def __init__(self, llm: DeepSeekClient, memory: Memory, max_tool_iterations: int = 5):
@@ -69,18 +70,35 @@ class Agent:
         # Build system prompt using PromptBuilder
         system_prompt = self.prompt_builder.build_system_prompt(self.memory.data)
 
-        # Initialize conversation with user input
+        # Initialize conversation with system prompt
         messages: List[Dict[str, Any]] = [
             {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_input},
         ]
 
+        # Add conversation history from memory (last N messages for context)
+        # Only add user/assistant messages, NOT system messages
+        history = self.memory.get("history", [])
+        max_history = settings.max_history_messages
+        if history and max_history > 0:
+            # Filter to keep only user and assistant messages
+            filtered_history = [
+                msg for msg in history
+                if msg.get("role") in ("user", "assistant")
+            ]
+            recent_history = filtered_history[-max_history:]
+            messages.extend(recent_history)
+            print(f"Added {len(recent_history)} messages from history (filtered)")
+
+        # Add current user input
+        messages.append({"role": "user", "content": user_input})
+
         # Tool execution loop
         iteration = 0
         while iteration < self.max_tool_iterations:
             print(f"\n--- Iteration {iteration + 1} ---")
 
             # Get LLM response
+            print(messages)
             llm_response = self.llm.complete(messages)
             print("LLM response:", llm_response)