Agent and Agent examples Code Quality fixes

examples/aop_examples/discovery/example_agent_communication.py

@@ -11,15 +11,6 @@
 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 @@
 
 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 @@ def create_pdf_from_string(
         """
         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 @@ def animate(frame):
                 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 @@ def run(
                 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 @@
 
 # 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 @@
 )
 
 # 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 @@
 # 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 @@
     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()
-parse_and_execute_json.return_value = {
+mock_parse_and_execute_json = MagicMock()
+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 @@ def _get_swarms_version(self) -> str:
             import swarms
 
             return swarms.__version__
-        except:
+        except Exception:
             return "Unknown"
 
     def _get_system_info(self) -> SwarmSystemInfo:
@@ -185,7 +185,7 @@ def _get_dependencies_info(self) -> str:
             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

@@ -1,36 +1,62 @@
+#!/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
 
 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 @@
     "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 @@
                     "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 @@
                 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 @@
             initial_memory = (
                 psutil.Process().memory_info().rss / 1024 / 1024
             )
-            psutil.cpu_percent()
 
             # Execute some tasks
             available_agents = aop.list_agents()
@@ -2439,13 +2485,13 @@
         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 @@
     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",