feat: Add NaliiaTools

src/naliiabot/bot/agent/agent.py

@@ -1,5 +1,6 @@
 from langgraph.graph import StateGraph, START, END
 from langchain_core.messages import SystemMessage, ToolMessage, BaseMessage
+import logging
 from typing import Literal, Callable, Any
 from .schemas import MessagesState
 from dataclasses import dataclass
@@ -38,10 +39,9 @@ class Agent:
                 - Si es None: sin herramientas
                 - Si es list: lista de herramientas
         """
+
         self._model = model
         self._config = config or AgentConfig()
-        
-        # Normalizar tools: usar la lista proporcionada o lista vacía
         self._tools = tools or []
             
         self._tools_by_name: dict[str, Any] = {
@@ -49,7 +49,6 @@ class Agent:
         }
         self._compiled_agent = self._compile_agent()
 
-        # Hooks para testing (monkey-patching friendly)
         self._pre_llm_hook: Callable | None = None
         self._post_tool_hook: Callable | None = None
 
@@ -92,26 +91,28 @@ class Agent:
 
     def set_pre_llm_hook(self, hook: Callable[[dict], None]):
         """Registra hook pre-llm para testing."""
+
         self._pre_llm_hook = hook
 
     def set_post_tool_hook(self, hook: Callable[[dict, Any], None]):
         """Registra hook post-tool para testing."""
+
         self._post_tool_hook = hook
 
     def _compile_agent(self):
         """Compila el grafo una sola vez."""
+
         agent = self._build_agent()
+
         return agent.compile()
 
     def _build_agent(self) -> StateGraph:
         """Construye el grafo de estado completo."""
         agent_builder = StateGraph(MessagesState)
 
-        # Nodos
         agent_builder.add_node("llm_call", self._llm_call_node)
         agent_builder.add_node("tool_node", self._tool_node)
 
-        # Flujo
         agent_builder.add_edge(START, "llm_call")
         agent_builder.add_conditional_edges(
             "llm_call",
@@ -125,8 +126,6 @@ class Agent:
 
         return agent_builder
 
-    # ============ Nodos del Grafo ============
-
     def _llm_call_node(self, state: MessagesState) -> dict:
         """
         Nodo LLM: Decide siguiente acción o responde.
@@ -143,6 +142,7 @@ class Agent:
         ] + state["messages"]
 
         model_with_tools = self._model.bind_tools(self._tools)
+
         response = model_with_tools.invoke(messages)
 
         current_calls = state.get("llm_calls", 0)
@@ -185,29 +185,30 @@ class Agent:
         Ejecuta una herramienta individual con manejo de errores.
         """
         tool_name = tool_call.get("name")
-        tool_id = tool_call.get("id", "unknown")
+        tool_id = tool_call.get("id")
         tool_args = tool_call.get("args", {})
 
         if tool_name not in self._tools_by_name:
             return ToolMessage(
                 content=f"Error: Tool '{tool_name}' not found",
                 tool_call_id=tool_id,
-                status="error"
+                name=tool_name,
             )
 
         try:
             tool = self._tools_by_name[tool_name]
             observation = tool.invoke(tool_args)
+
             return ToolMessage(
                 content=str(observation),
                 tool_call_id=tool_id,
-                status="success"
-            )
+                name=tool_name)
+
         except Exception as e:
             return ToolMessage(
                 content=f"Error executing tool: {str(e)}",
                 tool_call_id=tool_id,
-                status="error"
+                name=tool_name,
             )
 
     def _should_continue(