Code quality fixes

examples/aop_examples/discovery/example_agent_communication.py

@@ -11,15 +11,6 @@ from swarms.structs.aop import AOP
 def simulate_agent_discovery():
     """Simulate how an agent would use the discovery tool."""
 
-    # Create a sample agent that will use the discovery tool
-    Agent(
-        agent_name="ProjectCoordinator",
-        agent_description="Coordinates projects and assigns tasks to other agents",
-        system_prompt="You are a project coordinator who helps organize work and delegate tasks to the most appropriate team members. You can discover information about other agents to make better decisions.",
-        model_name="gpt-4o-mini",
-        temperature=0.4,
-    )
-
     # Create the AOP cluster
     aop = AOP(
         server_name="Project Team",

examples/aop_examples/example_new_agent_tools.py

@@ -7,19 +7,12 @@ This example shows how to use the new MCP tools for getting agent information.
 
 import json
 import asyncio
-from swarms.structs.aop import AOPCluster
 from swarms.tools.mcp_client_tools import execute_tool_call_simple
 
 
 async def demonstrate_new_agent_tools():
     """Demonstrate the new agent information tools."""
 
-    # Create AOP cluster connection
-    AOPCluster(
-        urls=["http://localhost:5932/mcp"],
-        transport="streamable-http",
-    )
-
     print("🔧 New AOP Agent Information Tools Demo")
     print("=" * 50)
     print()
@@ -77,7 +70,6 @@ async def demonstrate_new_agent_tools():
         if isinstance(result, list) and len(result) > 0:
             data = result[0]
             if data.get("success"):
-                data.get("agent_info", {})
                 discovery_info = data.get("discovery_info", {})
                 print(
                     f"   Agent: {discovery_info.get('agent_name', 'Unknown')}"

examples/demos/legal/legal_swarm.py

@@ -303,7 +303,6 @@ class LegalSwarm:
         """
         try:
             from reportlab.lib.pagesizes import letter
-            from reportlab.pdfgen import canvas
             from reportlab.lib.styles import (
                 getSampleStyleSheet,
                 ParagraphStyle,

examples/guides/graphworkflow_guide/quick_start_guide.py

@@ -337,7 +337,7 @@ def step_4_advanced_patterns():
     print("\n📊 Workflow structure:")
     try:
         advanced_workflow.visualize_simple()
-    except:
+    except Exception:
         print("   (Text visualization not available)")
 
     # Execute advanced workflow

examples/guides/graphworkflow_guide/setup_and_test.py

@@ -109,7 +109,7 @@ def test_basic_import() -> bool:
     print("\n🧪 Testing basic GraphWorkflow import...")
 
     try:
-        from swarms.structs.graph_workflow import GraphWorkflow
+        from swarms.structs.graph_workflow import GraphWorkflow  # noqa: F401
 
         print("✅ GraphWorkflow imported successfully")
         return True
@@ -123,7 +123,7 @@ def test_agent_import() -> bool:
     print("\n🧪 Testing Agent import...")
 
     try:
-        from swarms import Agent
+        from swarms import Agent  # noqa: F401
 
         print("✅ Agent imported successfully")
         return True

examples/multi_agent/graphworkflow_examples/test_enhanced_json_export.py

@@ -213,7 +213,7 @@ def test_file_save_load():
     try:
         os.remove("test_workflow.json")
         print("\n🧹 Cleaned up test file")
-    except:
+    except Exception:
         pass
 
 

examples/multi_agent/simulations/agent_map/agent_map_simulation.py

@@ -1202,7 +1202,7 @@ class AgentMapSimulation:
                 self.fig.canvas.manager.window.wm_attributes(
                     "-topmost", 0
                 )
-            except:
+            except Exception:
                 pass  # Not all backends support this
 
             plt.show(block=False)
@@ -1535,7 +1535,7 @@ class AgentMapSimulation:
                 if with_visualization and self.fig is not None:
                     try:
                         self.update_visualization()
-                    except:
+                    except Exception:
                         pass  # Ignore visualization errors
 
                 # Print status every 10 seconds

examples/multi_agent/simulations/agent_map/v0/demo_simulation.py

@@ -250,7 +250,7 @@ def main():
             if simulation.fig is not None:
                 try:
                     simulation.update_visualization()
-                except:
+                except Exception:
                     pass  # Ignore visualization errors
 
             # Check if we have enough conversations to make it interesting

examples/multi_agent/simulations/map_generation/game_map.py

@@ -25,7 +25,6 @@ warnings.filterwarnings("ignore")
 
 # Third-party model imports
 try:
-    import timm
     from segment_anything import (
         SamAutomaticMaskGenerator,
         sam_model_registry,

examples/utils/agent_loader/multi_agents_loader_demo.py

@@ -1,4 +1,5 @@
 from swarms.utils import load_agents_from_markdown
+from swarms.structs.sequential_workflow import SequentialWorkflow
 
 agents = load_agents_from_markdown(
     [
@@ -9,7 +10,6 @@ agents = load_agents_from_markdown(
 )
 
 # Example 3: Use agents in a workflow
-from swarms.structs.sequential_workflow import SequentialWorkflow
 
 workflow = SequentialWorkflow(agents=agents, max_loops=1)
 

scripts/docker/test_docker.py

@@ -22,7 +22,7 @@ def test_swarms_import() -> Dict[str, Any]:
         )
 
         # Test basic functionality
-        from swarms import Agent
+        from swarms import Agent  # noqa: F401
 
         print(" Agent class imported successfully")
 

swarms/structs/multi_model_gpu_manager.py

@@ -34,7 +34,7 @@ from contextlib import contextmanager
 # Try to import transformers, but don't fail if not available
 try:
     import transformers
-    from transformers import AutoModel, AutoTokenizer
+    from transformers import AutoModel
 
     TRANSFORMERS_AVAILABLE = True
 except ImportError:

swarms/tools/py_func_to_openai_func_str.py

@@ -16,7 +16,6 @@ from typing import (
     Type,
     TypeVar,
     Union,
-    get_args,
 )
 
 from pydantic import BaseModel, Field

swarms/utils/audio_processing.py

@@ -117,7 +117,6 @@ def process_audio_with_model(
         from litellm import (
             completion,
             supports_audio_input,
-            supports_audio_output,
         )
 
         if not supports_audio_input(model):

tests/agent/agents/test_agent_logging.py

@@ -1,11 +1,10 @@
 from unittest.mock import MagicMock
 import unittest
 from swarms.structs.agent import Agent
-from swarms.tools.tool_parse_exec import parse_and_execute_json
 
 # Mock parse_and_execute_json for testing
-parse_and_execute_json = MagicMock()
+mock_parse_and_execute_json = MagicMock()
-parse_and_execute_json.return_value = {
+mock_parse_and_execute_json.return_value = {
     "tool_name": "calculator",
     "args": {"numbers": [2, 2]},
     "output": "4",

tests/agent/benchmark_agent/test_auto_test_eval.py

@@ -92,7 +92,7 @@ class SwarmsIssueReporter:
             import swarms
 
             return swarms.__version__
-        except:
+        except Exception:
             return "Unknown"
 
     def _get_system_info(self) -> SwarmSystemInfo:
@@ -185,7 +185,7 @@ class SwarmsIssueReporter:
             for dist in pkg_resources.working_set:
                 deps.append(f"- {dist.key} {dist.version}")
             return "\n".join(deps)
-        except:
+        except Exception:
             return "Unable to fetch dependency information"
 
     # First, add this method to your SwarmsIssueReporter class

tests/aop/aop_benchmark.py

@@ -1,13 +1,47 @@
+#!/usr/bin/env python3
+"""
+AOP Framework Benchmarking Suite
+
+This comprehensive benchmarking suite tests the scaling laws of the AOP (Agent Orchestration Platform)
+framework by measuring latency, throughput, memory usage, and other performance metrics across different
+agent counts and configurations.
+
+Features:
+- Scaling law analysis (1 to 100+ agents)
+- Latency and throughput measurements
+- Memory usage profiling
+- Concurrent execution testing
+- Error rate analysis
+- Performance visualization with charts
+- Statistical analysis and reporting
+- Real agent testing with actual LLM calls
+
+Usage:
+1. Set your OpenAI API key: export OPENAI_API_KEY="your-key-here"
+2. Install required dependencies: pip install swarms
+3. Run the benchmark: python aop_benchmark.py
+4. Check results in the generated charts and reports
+
+Configuration:
+- Edit BENCHMARK_CONFIG at the top of the file to customize settings
+- Adjust model_name, max_agents, and other parameters as needed
+- This benchmark ONLY uses real agents with actual LLM calls
+
+Author: AI Assistant
+Date: 2024
+"""
+
 import gc
 import json
 import os
+import psutil
 import random
 import statistics
 import time
-import uuid
 import warnings
+import uuid
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass, asdict
 from datetime import datetime, timedelta
 from typing import Any, Dict, List, Tuple
 
@@ -15,22 +49,14 @@ import matplotlib.pyplot as plt
 import numpy as np
 import openpyxl
 import pandas as pd
-import psutil
 import seaborn as sns
 from dotenv import load_dotenv
 from loguru import logger
 from openpyxl.styles import Font
 from openpyxl.utils.dataframe import dataframe_to_rows
 
-from swarms.structs.agent import Agent
 from swarms.structs.aop import AOP
-from swarms.utils.litellm_wrapper import LiteLLM
-
-# Suppress warnings for cleaner output
-warnings.filterwarnings("ignore")
 
-# Load environment variables
-load_dotenv()
 # Configuration
 BENCHMARK_CONFIG = {
     "models": [
@@ -60,6 +86,21 @@ BENCHMARK_CONFIG = {
     "detailed_logging": True,  # Enable detailed logging
 }
 
+# Suppress warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# Load environment variables
+load_dotenv()
+
+# Import swarms Agent directly to avoid uvloop dependency
+try:
+    from swarms.structs.agent import Agent
+    from swarms.utils.litellm_wrapper import LiteLLM
+
+    SWARMS_AVAILABLE = True
+except ImportError:
+    SWARMS_AVAILABLE = False
+
 
 @dataclass
 class BenchmarkResult:
@@ -375,6 +416,12 @@ class AOPBenchmarkSuite:
                     "SWARMS_API_KEY or OPENAI_API_KEY environment variable is required for real agent testing"
                 )
 
+            # Check if swarms is available
+            if not SWARMS_AVAILABLE:
+                raise ImportError(
+                    "Swarms not available - install swarms: pip install swarms"
+                )
+
             # Create LiteLLM instance for the specific model
             llm = LiteLLM(
                 model_name=model_name,
@@ -877,7 +924,7 @@ class AOPBenchmarkSuite:
                 try:
                     if len(str(cell.value)) > max_length:
                         max_length = len(str(cell.value))
-                except:
+                except Exception:
                     pass
             adjusted_width = min(max_length + 2, 50)
             ws.column_dimensions[column_letter].width = adjusted_width
@@ -1616,7 +1663,6 @@ class AOPBenchmarkSuite:
             initial_memory = (
                 psutil.Process().memory_info().rss / 1024 / 1024
             )
-            psutil.cpu_percent()
 
             # Execute some tasks
             available_agents = aop.list_agents()
@@ -2439,13 +2485,13 @@ class AOPBenchmarkSuite:
         ax2.grid(True, alpha=0.3)
 
         # Add value labels on bars
-        for i, (bar, time) in enumerate(
+        for i, (bar, exec_time) in enumerate(
             zip(bars2, df["avg_tool_execution_time"])
         ):
             ax2.text(
                 bar.get_x() + bar.get_width() / 2,
                 bar.get_height() + 0.01,
-                f"{time:.2f}s",
+                f"{exec_time:.2f}s",
                 ha="center",
                 va="bottom",
                 fontsize=8,
@@ -2905,6 +2951,33 @@ def main():
     print(f"   Context Length: {BENCHMARK_CONFIG['context_length']}")
     print()
 
+    # Check for required environment variables
+    api_key = os.getenv("SWARMS_API_KEY") or os.getenv(
+        "OPENAI_API_KEY"
+    )
+    if not api_key:
+        print(
+            "❌  Error: SWARMS_API_KEY or OPENAI_API_KEY not found in environment variables"
+        )
+        print(
+            "   This benchmark requires real LLM calls for accurate performance testing"
+        )
+        print(
+            "   Set your API key: export SWARMS_API_KEY='your-key-here' or export OPENAI_API_KEY='your-key-here'"
+        )
+        return 1
+
+    # Check for required imports
+    if not SWARMS_AVAILABLE:
+        print("❌  Error: swarms not available")
+        print(
+            "   Install required dependencies: pip install swarms openpyxl"
+        )
+        print(
+            "   This benchmark requires swarms framework and Excel support"
+        )
+        return 1
+
     # Initialize benchmark suite
     benchmark = AOPBenchmarkSuite(
         output_dir="aop_benchmark_results",

tests/utils/test_md_output.py

@@ -2,9 +2,6 @@ import os
 
 from dotenv import load_dotenv
 
-# Load environment variables
-load_dotenv()
-
 from swarms import (
     Agent,
     ConcurrentWorkflow,
@@ -14,6 +11,9 @@ from swarms import (
 
 from swarms.utils.formatter import Formatter
 
+# Load environment variables
+load_dotenv()
+
 
 class MarkdownTestSwarm:
     """A test swarm that demonstrates markdown output capabilities"""