Enhance answer_message function to include tool execution and result integration. The function now processes tool calls from the AI response, executes them, and appends the results before generating the final response.

agent.py

@@ -96,9 +96,24 @@ def answer_message(state: AgentState) -> AgentState:
     {messages}
     
     """
-    response = model_with_tools.invoke(prompt)
+    ai_msg = model_with_tools.invoke(prompt)
+    messages.append(ai_msg)
+
+    # Step 2: Execute tools and collect results
+    for tool_call in ai_msg.tool_calls:
+        # Execute the tool with the generated arguments
+        name = tool_call['name']
+        args = tool_call['args']
+        tool = tools_by_name[name]
+        tool_result = tool.invoke(args)
+        messages.append(tool_result)
+
+    # Step 3: Pass results back to model for final response
+    final_response = model_with_tools.invoke(messages)
+    print(final_response.text)
+
     # Append the model's answer to the messages list
-    return {"messages": messages + [response]}
+    return {"messages": messages + [final_response]}