remove clusterops

majority_voting_example.py

@@ -1,51 +1,93 @@
 from swarms import Agent, MajorityVoting
+from swarms_tools.finance.sector_analysis import macro_sector_analysis
+
+# Initialize multiple agents with a focus on asset allocation and risk management for a $50B portfolio
+
+# print(macro_sector_analysis())
 
-# Initialize multiple agents with different specialties
 agents = [
     Agent(
-        agent_name="Financial-Analysis-Agent",
-        agent_description="Personal finance advisor focused on market analysis",
-        system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
+        agent_name="Sector-Financial-Analyst",
+        agent_description="Senior sector financial analyst focused on optimizing capital allocations for a $50B portfolio at BlackRock.",
+        system_prompt="""You are a seasoned financial analyst at BlackRock tasked with optimizing asset allocations from a $50B portfolio. Your responsibilities include:
+        - Conducting deep analyses of sector performance, historical trends, and financial fundamentals.
+        - Evaluating revenue growth, profitability, and overall market positioning to determine the optimal dollar allocation for each sector.
+        - Integrating risk considerations into your fiscal analysis to ensure that recommended capital assignments align with the overall risk tolerance.
+        - Presenting a detailed breakdown of how much money should be allocated to each sector and justifying these recommendations with data-driven insights.
+        Provide clear, quantitative recommendations in your output, including precise allocation figures for each sector.""",
         max_loops=1,
-        model_name="gpt-4o",
+        model_name="groq/deepseek-r1-distill-qwen-32b",
+        max_tokens=3000,
     ),
     Agent(
-        agent_name="Risk-Assessment-Agent",
-        agent_description="Risk analysis and portfolio management expert",
-        system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
+        agent_name="Sector-Risk-Analyst",
+        agent_description="Expert risk management analyst focused on calibrating sector risk allocations for a $50B institutional portfolio.",
+        system_prompt="""You are a veteran risk analyst at BlackRock, responsible for defining and advising on risk allocation within a $50B portfolio. Your responsibilities include:
+        - Assessing the risk profile and volatility metrics of each market sector.
+        - Quantifying risk exposures, performing stress tests, and modeling adverse market scenarios.
+        - Recommending precise risk allocation figures (both in absolute and percentage terms) for each sector, ensuring a balanced risk profile across the portfolio.
+        - Integrating risk-adjusted return considerations into your analysis so that capital assignments reflect both opportunity and risk mitigation.
+        Provide detailed, quantitative insights and clearly articulate how much risk should be assumed per sector relative to the $50B total.""",
         max_loops=1,
-        model_name="gpt-4o",
+        model_name="groq/deepseek-r1-distill-qwen-32b",
+        max_tokens=3000,
     ),
     Agent(
-        agent_name="Tech-Investment-Agent",
-        agent_description="Technology sector investment specialist",
-        system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
+        agent_name="Tech-Sector-Analyst",
+        agent_description="Specialized analyst focused on the technology sector, tasked with determining capital and risk allocations within the $50B portfolio.",
+        system_prompt="""You are a specialized technology sector analyst at BlackRock, focused on the high-growth potential of the tech sector as part of a $50B portfolio. Your responsibilities include:
+        - Evaluating current and emerging tech trends, competitive dynamics, and innovation drivers.
+        - Analyzing the risk/reward profile of tech investments, including both growth prospects and volatility.
+        - Recommending how much capital should be allocated to the technology sector, alongside quantified risk allocations suited to its inherent risk profile.
+        - Providing clear, data-backed insights that balance aggressive growth targets with measured risk exposures.
+        Deliver a detailed breakdown of your recommendations, including both dollar figures and risk metrics, tailored for the tech sector in the overall portfolio.""",
         max_loops=1,
-        model_name="gpt-4o",
+        model_name="groq/deepseek-r1-distill-qwen-32b",
+        max_tokens=3000,
     ),
 ]
 
-
 consensus_agent = Agent(
-    agent_name="Consensus-Agent",
-    agent_description="Consensus agent focused on analyzing investment advice",
-    system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
+    agent_name="Consensus-Strategist",
+    agent_description="Senior strategist who synthesizes allocation and risk management analyses for a cohesive $50B portfolio strategy at BlackRock.",
+    system_prompt="""You are a senior investment strategist at BlackRock responsible for integrating detailed sector analyses into a comprehensive $50B portfolio allocation strategy. Your tasks include:
+    - Synthesizing the fiscal, risk, and sector-specific insights provided by your fellow analysts.
+    - Balancing the recommendations to yield clear guidance on both capital allocations and corresponding risk exposure for each market sector.
+    - Formulating a unified strategy that specifies the optimal dollar and risk allocations per sector, ensuring that the overall portfolio adheres to BlackRock’s risk tolerance and performance objectives.
+    - Delivering a final narrative that includes precise allocation figures, supported by tables and quantitative data.
+    Ensure that your recommendations are actionable, data-driven, and well-aligned with institutional investment strategies.""",
     max_loops=1,
-    model_name="gpt-4o",
+    model_name="groq/deepseek-r1-distill-qwen-32b",
+    max_tokens=3000,
 )
 
 # Create majority voting system
 majority_voting = MajorityVoting(
-    name="Investment-Advisory-System",
-    description="Multi-agent system for investment advice",
+    name="Sector-Investment-Advisory-System",
+    description="Multi-agent system for sector analysis that determines optimal capital and risk allocations for a $50B portfolio at BlackRock.",
     agents=agents,
     verbose=True,
     consensus_agent=consensus_agent,
 )
 
 # Run the analysis with majority voting
 result = majority_voting.run(
-    task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
+    task=f"""Evaluate the current market sectors and determine the optimal allocation of a $50B portfolio for BlackRock. Your analysis should include:
+
+1. A detailed table that outlines each sector along with the recommended dollar allocation and corresponding risk allocation.
+2. A comprehensive review for each sector covering:
+   - Fundamental performance metrics, historical trends, and growth outlook.
+   - Quantitative risk assessments including volatility measures, stress test results, and risk-adjusted return evaluations.
+3. Specific recommendations on how much capital (in dollars and as a percentage of $50B) should be invested in each sector.
+4. A detailed explanation of the recommended risk allocation for each sector, ensuring the overall portfolio risk stays within acceptable thresholds.
+5. A consolidated strategy that integrates both fiscal performance and risk management insights to support sector-based allocation decisions.
+Provide your output with a clear structure, including descriptive sections and tables for clarity.
+
+
+{macro_sector_analysis()}
+
+
+"""
 )
 
 print(result)

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "7.1.9"
+version = "7.2.0"
 description = "Swarms - TGSC"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]

swarms/structs/concurrent_workflow.py

@@ -11,13 +11,7 @@
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.utils.file_processing import create_file_in_folder
-import concurrent
-from clusterops import (
-    execute_on_gpu,
-    execute_with_cpu_cores,
-    execute_on_multiple_gpus,
-    list_available_gpus,
-)
+import concurrent.futures
 from swarms.utils.loguru_logger import initialize_logger
 from swarms.structs.swarm_id_generator import generate_swarm_id
 
@@ -364,11 +358,6 @@ def run(
         self,
         task: Optional[str] = None,
         img: Optional[str] = None,
-        is_last: bool = False,
-        device: str = "cpu",  # gpu
-        device_id: int = 0,
-        all_cores: bool = True,  # Defaults to using all available cores
-        all_gpus: bool = False,
         *args,
         **kwargs,
     ) -> Any:
@@ -399,41 +388,7 @@ def run(
             self.tasks.append(task)
 
         try:
-            logger.info(f"Attempting to run on device: {device}")
-            if device == "cpu":
-                logger.info("Device set to CPU")
-                if all_cores is True:
-                    count = os.cpu_count()
-                    logger.info(
-                        f"Using all available CPU cores: {count}"
-                    )
-                else:
-                    count = device_id
-                    logger.info(f"Using specific CPU core: {count}")
-
-                return execute_with_cpu_cores(
-                    count, self._run, task, img, *args, **kwargs
-                )
-
-            elif device == "gpu":
-                logger.info("Device set to GPU")
-                return execute_on_gpu(
-                    device_id, self._run, task, img, *args, **kwargs
-                )
-
-            elif all_gpus is True:
-                return execute_on_multiple_gpus(
-                    [int(gpu) for gpu in list_available_gpus()],
-                    self._run,
-                    task,
-                    img,
-                    *args,
-                    **kwargs,
-                )
-            else:
-                raise ValueError(
-                    f"Invalid device specified: {device}. Supported devices are 'cpu' and 'gpu'."
-                )
+            return self._run(task, img, *args, **kwargs)
         except ValueError as e:
             logger.error(f"Invalid device specified: {e}")
             raise e

tests/structs/test_groupchat.py

@@ -131,7 +131,7 @@ def test_basic_groupchat(report):
             max_loops=2,
         )
 
-        result = chat.run("Say hello!")
+        chat.run("Say hello!")
         report.add_result(
             "Basic GroupChat Test",
             True,
@@ -194,7 +194,7 @@ def test_speaker_functions(report):
                 rules="1. Be unique in your responses\n2. Build on others' points\n3. Stay relevant to your expertise",
             )
 
-            result = chat.run(prompt)
+            chat.run(prompt)
             report.add_result(
                 f"Speaker Function - {name}",
                 True,
@@ -222,7 +222,7 @@ def test_varying_agent_counts(report):
                 name=f"{count}-Agent Test", agents=agents, max_loops=2
             )
 
-            result = chat.run("Introduce yourselves briefly.")
+            chat.run("Introduce yourselves briefly.")
             report.add_result(
                 f"Agent Count Test - {count} agents",
                 True,
@@ -352,7 +352,7 @@ def test_conversation_rules(report):
             """,
         )
 
-        result = chat.run(
+        chat.run(
             "How can we ensure ethical AI development across different sectors?"
         )
         report.add_result(