multi_agent.py

from smolagents import (
    CodeAgent,
    VisitWebpageTool,
    WebSearchTool,
    WikipediaSearchTool,
    PythonInterpreterTool,
    FinalAnswerTool,
    LiteLLMModel,
)
from vision_tool import image_reasoning_tool
from throttle import consume
import os
import time

# ---- TOOLS ----

common = dict(
    api_key=os.getenv("GROQ_API_KEY"),
    api_base="https://api.groq.com/openai/v1",
    flatten_messages_as_text=True,
)



# ---- MULTI-AGENT SYSTEM ----
class MultyAgentSystem:
    def __init__(self):
        self.deepseek_model = LiteLLMModel(
            "groq/deepseek-r1-distill-llama-70b",
            max_tokens=512,
            **common,
        )
        self.qwen_model = LiteLLMModel("groq/qwen-qwq-32b", **common)
        self.fallback_model = LiteLLMModel("groq/llama3-70b-8k", **common)
        self.llama_model = LiteLLMModel("groq/llama-3.3-70b-versatile", **common)

        self.verification_limit = int(os.getenv("VERIFY_WORD_LIMIT", "75"))

        # --- Web agent definition ---
        self.web_agent = CodeAgent(
            model=self.llama_model,
            tools=[WebSearchTool(), VisitWebpageTool(), WikipediaSearchTool()],
            name="web_agent",
            description=(
                "You are a web browsing agent. Whenever the given {task} involves browsing "
                "the web or a specific website such as Wikipedia or YouTube, you will use "
                "the provided tools. For web-based factual and retrieval tasks, be as precise and source-reliable as possible."
            ),
            additional_authorized_imports=[
                "markdownify",
                "json",
                "requests",
                "urllib.request",
                "urllib.parse",
                "wikipedia-api",
            ],
            verbosity_level=0,
            max_steps=10,
        )

        # --- Info agent definition ---
        self.info_agent = CodeAgent(
            model=self.llama_model,
            tools=[PythonInterpreterTool(), image_reasoning_tool],
            name="info_agent",
            description=(
                "You are an agent tasked with cleaning, parsing, calculating information, and performing OCR if images are provided in the {task}. "
                "You can also analyze images using a vision model. You handle all math, code, and data manipulation. Use numpy, math, and available libraries. "
                "For image or chess tasks, use pytesseract, PIL, chess, or the image_reasoning_tool as required."
            ),
            additional_authorized_imports=[
                "numpy",
                "math",
                "pytesseract",
                "PIL",
                "chess",
            ],
        )

        # --- Manager agent definition ---
        manager_planning_interval = int(os.getenv("MANAGER_PLANNING_INTERVAL", "3"))
        manager_max_steps = int(os.getenv("MANAGER_MAX_STEPS", "8"))

        # The manager starts with the smaller Qwen model to minimize token usage
        # and only relies on DeepSeek when verifying critical answers.
        self.manager_agent = CodeAgent(
            model=self.llama_model,
            tools=[FinalAnswerTool()],
            managed_agents=[self.web_agent, self.info_agent],
            name="manager_agent",
            description=(
                "You are the manager. Given a {task}, plan which agent to use: "
                "If web data is needed, delegate to web_agent. If math, parsing, image reasoning, or code is needed, use info_agent. "
                "After collecting outputs, optionally cross-validate and check correctness, then finalize and submit the best answer using FinalAnswerTool. "
                "For each task, explicitly explain your planning steps and reasons for choosing which agent, and always prefer the most accurate and complete answer possible."
            ),
            additional_authorized_imports=[
                "json",
                "pandas",
                "numpy",
            ],
            planning_interval=manager_planning_interval,
            verbosity_level=2,
            max_steps=manager_max_steps,
        )

        # runtime tracking for fallback switching
        self.total_runtime = 0.0
        self.first_call_duration = None
        self.model_switched = False

    def _switch_to_fallback(self):
        if self.model_switched:
            return
        self.manager_agent.model = self.fallback_model
        self.model_switched = True

    def run(self, question, high_stakes: bool = False, **kwargs):
        start_time = time.time()
        print("Generating initial answer with llama-4-scout")
        max_completion_tokens = kwargs.get("max_completion_tokens", 512)
        prompt_tokens = len(question.split())
        consume(prompt_tokens + max_completion_tokens)
        initial_answer = self.manager_agent(question, **kwargs)
        call_duration = time.time() - start_time

        answer = initial_answer
        if high_stakes or len(initial_answer.split()) > self.verification_limit:
            print("Verifying answer using DeepSeek-70B")
            verification_prompt = (
                "Review the following answer for accuracy and rewrite if needed:"
                f"\n\n{initial_answer}"
            )
            try:
                max_completion_tokens = kwargs.get("max_completion_tokens", 512)
                prompt_tokens = len(verification_prompt.split())
                consume(prompt_tokens + max_completion_tokens)
                answer = self.deepseek_model(
                    verification_prompt, max_completion_tokens=max_completion_tokens
                )
            except Exception as e:
                print(f"Verification failed: {e}. Using initial answer.")
                answer = initial_answer

        if self.first_call_duration is None:
            self.first_call_duration = call_duration
            if self.first_call_duration > 30:
                self._switch_to_fallback()

        self.total_runtime += call_duration
        if self.total_runtime > 300 and not self.model_switched:
            self._switch_to_fallback()

        return answer

    def __call__(self, question, high_stakes: bool = False, **kwargs):

        return self.run(question, high_stakes=high_stakes, **kwargs)