tareksanger

from agents import Agent, Runner, function_tool  # OpenAI Agents SDK components
from celery import Celery, chain
import openai

# Define a simple tool for the agent (e.g., a web search stub)
@function_tool
def find_articles(query: str) -> list:
    # In a real scenario, this function might call an API or database.
    # Here we simulate by returning static content.
    return [f"Result 1 about {query}", f"Result 2 about {query}"]

tareksanger

import math
from datetime import datetime

DECAY_LAMBDA = 0.01
TYPE_MULTIPLIER = {
    "primary_research":   0.8,
    "secondary_research": 0.6,
    "feedback":           0.4,
}

tareksanger

import asyncio
import logging
from typing import Dict
from lumis.agents.base import BaseAgent
from lumis.agents.graph_based_agent import GraphBasedAgent
from lumis.llm.openai_llm import LLM
from lumis.memory.simple_memory import SimpleMemory


class MyGraphPipeline(GraphBasedAgent[Dict, str]):