feat(agent): add answer extraction module and integrate into run function

Add new answer_extractor module that processes agent responses to extract clean answers.
Refactor main entry point to use the simplified run() function instead of interactive loop.

agent/agent.py

@@ -7,6 +7,7 @@ from agent.tools.math_solver import math_solver
 
 from agent.agents.websearchagents import web_search_agents
 from agent.agents.websearchagent import websearch_agent
+from agent.agents.answer_extractor import extract_answer
 
 load_dotenv()
 
@@ -35,21 +36,24 @@ def run(query: str) -> str:
     result = agent.invoke({"messages": [HumanMessage(content=query)]})
     content = result["messages"][-1].content
     if isinstance(content, list):
-        return content[0].get("text", "")
-    return str(content)
+        content = content[0].get("text", "")
+    else:
+        content = str(content)
+    return extract_answer(content, query)
 
 
 if __name__ == "__main__":
-    agent = supervisor_agent()
-    chat_history: list = []
-    while True:
-        query = input("\nYou: ")
-        if query.lower() in ("exit", "quit"):
-            break
-        chat_history.append(HumanMessage(content=query))
-        result = agent.invoke({"messages": chat_history})
-        chat_history = result["messages"]
-        content = chat_history[-1].content
-        if isinstance(content, list):
-            content = content[0].get("text", "")
-        print(f"Agent: {content}")
+    run(input("Query:"))
+    # agent = supervisor_agent()
+    # chat_history: list = []
+    # while True:
+    #     query = input("\nYou: ")
+    #     if query.lower() in ("exit", "quit"):
+    #         break
+    #     chat_history.append(HumanMessage(content=query))
+    #     result = agent.invoke({"messages": chat_history})
+    #     chat_history = result["messages"]
+    #     content = chat_history[-1].content
+    #     if isinstance(content, list):
+    #         content = content[0].get("text", "")
+    #     print(f"Agent: {content}")

agent/agents/answer_extractor.py

@@ -0,0 +1,84 @@
+from colorama import Fore, Style  # type: ignore[import]
+from langchain.agents import create_agent
+from pydantic import BaseModel, Field
+
+
+class ExtractedAnswer(BaseModel):
+    """Structured output for answer extraction."""
+
+    extract_process_description: str = Field(
+        description="Description of the extraction process and reasoning."
+    )
+    answer: str = Field(
+        description="The pure, concise answer without any extra text or spaces."
+    )
+
+
+def extract_answer(raw_answer: str, question: str) -> str:
+    """Extract a concise, plain-text answer from a verbose agent response."""
+    print(f"{Fore.CYAN}[AnswerExtractor] Extracting...{Style.RESET_ALL}")
+
+    agent = create_agent(
+        model="google_genai:gemini-3-flash-preview",
+        response_format=ExtractedAnswer,
+        system_prompt="""\
+You are part of a system that demands extreme precision.
+Your job is to extract the pure answer from a verbose agent response.
+The answer field must contain ONLY the answer itself —
+no extra description, no explanation, no units unless explicitly required,
+no leading or trailing whitespace, no punctuation beyond what is part of the answer.
+Do NOT add any surrounding text. Do NOT pad with spaces.
+
+```example
+Input:
+'... the answer is 114. Thank.'
+
+Output:
+'114'
+```
+
+```example
+Input:
+'... Serial number is BC34ACD2 and ...'
+
+Output:
+'BC34ACD2'
+```
+
+```example
+Input:
+'Result is 6, 7, 1, 10'
+
+Output:
+'6, 7, 1, 10'
+```
+
+```example
+Input:
+'Answer is 73!'
+
+Output:
+'73'
+```
+
+""",
+    )
+
+    result = agent.invoke(
+        {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": (
+                        f"Question: {question}\n\nAgent's verbose answer:\n{raw_answer}"
+                    ),
+                }
+            ]
+        }
+    )
+    extracted: ExtractedAnswer = result["structured_response"]
+    print(
+        f"{Fore.CYAN}[AnswerExtractor] Process: {extracted.extract_process_description}{Style.RESET_ALL}"
+    )
+    print(f"{Fore.CYAN}[AnswerExtractor] Answer: {extracted.answer}{Style.RESET_ALL}")
+    return extracted.answer