llm_only_agent.py

import os
import re
from smolagents import (
    AgentMemory,
    CodeAgent,
    InferenceClientModel,
    FinalAnswerTool,
    WebSearchTool,
)
from collections.abc import Callable

from smolagents.default_tools import VisitWebpageTool, WikipediaSearchTool
from smolagents.models import OpenAIModel


class LLMOnlyAgent:
    def __init__(self):
        # Instructions prompt
        self.instructions = """finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""

        # Basic inference model
        qwen_model = InferenceClientModel(
            max_tokens=8096,
            model_id="Qwen/Qwen3-Coder-30B-A3B-Instruct",
            custom_role_conversions=None,
            provider="nebius",
        )

        gemini_model = OpenAIModel(
            max_tokens=8096,
            model_id="gemini-2.5-flash",
            # Google Gemini OpenAI-compatible API base URL
            api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
            api_key=os.environ["GEMINI_API_KEY"],
        )

        model = gemini_model

        # Code Agent
        self.agent = CodeAgent(
            model=model,
            instructions=self.instructions,
            tools=[
                FinalAnswerTool(),
                WikipediaSearchTool(),
                WebSearchTool(),
                VisitWebpageTool(),
            ],
            additional_authorized_imports=[
                "markdownify",
                "requests",
                "pandas",
                "numpy",
                "chess",
            ],
            max_steps=5,
            planning_interval=3,
            # final_answer_checks=self.final_answer_checks()
        )

        print("LLM-only Agent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        answer = self.agent.run(question)
        print(f"Agent returning answer: {answer}")
        return answer

    def final_answer_checks(self) -> list[Callable]:
        return [self.check_func]

    def check_func(self, answer: str, memory: AgentMemory) -> bool:
        check = bool(
            re.match(
                r'^(\d+(\.\d+)?|\w+(\s+\w+){0,4}|(\d+(\.\d+)?|"[^"]*"|\w+)(\s*,\s*(\d+(\.\d+)?|"[^"]*"|\w+))+)$',
                answer,
            )
        )
        print(f"FINAL ANSWER CHECK is {check}")
        return check