cleanup and format

examples/aop_examples/discovery/example_agent_communication.py

@@ -12,7 +12,7 @@ def simulate_agent_discovery():
     """Simulate how an agent would use the discovery tool."""
 
     # Create a sample agent that will use the discovery tool
-    coordinator_agent = Agent(
+    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.",
@@ -118,34 +118,6 @@ def simulate_agent_discovery():
 
     # Show what the MCP tool response would look like
     print("📡 Sample MCP tool response structure:")
-    sample_response = {
-        "success": True,
-        "agents": [
-            {
-                "tool_name": "data_specialist",
-                "agent_name": "DataSpecialist",
-                "description": "Handles all data-related tasks and analysis",
-                "short_system_prompt": "You are a data specialist with expertise in data processing, analysis, and visualization...",
-                "tags": [
-                    "data",
-                    "analysis",
-                    "python",
-                    "sql",
-                    "statistics",
-                ],
-                "capabilities": [
-                    "data_processing",
-                    "statistical_analysis",
-                    "visualization",
-                ],
-                "role": "specialist",
-                "model_name": "gpt-4o-mini",
-                "max_loops": 1,
-                "temperature": 0.5,
-                "max_tokens": 4096,
-            }
-        ],
-    }
 
     print("   discover_agents() -> {")
     print("     'success': True,")

examples/aop_examples/example_new_agent_tools.py

@@ -15,7 +15,7 @@ async def demonstrate_new_agent_tools():
     """Demonstrate the new agent information tools."""
 
     # Create AOP cluster connection
-    aop_cluster = AOPCluster(
+    AOPCluster(
         urls=["http://localhost:5932/mcp"],
         transport="streamable-http",
     )
@@ -77,7 +77,7 @@ async def demonstrate_new_agent_tools():
         if isinstance(result, list) and len(result) > 0:
             data = result[0]
             if data.get("success"):
-                agent_info = data.get("agent_info", {})
+                data.get("agent_info", {})
                 discovery_info = data.get("discovery_info", {})
                 print(
                     f"   Agent: {discovery_info.get('agent_name', 'Unknown')}"

tests/aop/aop_benchmark.py

@@ -1377,7 +1377,7 @@ class AOPBenchmarkSuite:
             # Execute with first available agent
             agent_name = available_agents[0]
             try:
-                response = aop._execute_agent_with_timeout(
+                aop._execute_agent_with_timeout(
                     agent_name, task, timeout=30
                 )
                 execution_time = time.time() - execution_start
@@ -1485,7 +1485,7 @@ class AOPBenchmarkSuite:
                     "data": [response2],
                     "analysis_type": "classification",
                 }
-                response3 = aop._execute_agent_with_timeout(
+                aop._execute_agent_with_timeout(
                     available_agents[2], task3, timeout=30
                 )
 
@@ -1664,7 +1664,7 @@ class AOPBenchmarkSuite:
             initial_memory = (
                 psutil.Process().memory_info().rss / 1024 / 1024
             )
-            initial_cpu = psutil.cpu_percent()
+            psutil.cpu_percent()
 
             # Execute some tasks
             available_agents = aop.list_agents()
@@ -1813,7 +1813,7 @@ class AOPBenchmarkSuite:
                 tool_start = time.time()
                 try:
                     # Execute tool test
-                    response = aop._execute_agent_with_timeout(
+                    aop._execute_agent_with_timeout(
                         available_agents[0], test, timeout=15
                     )
                     tool_time = time.time() - tool_start
@@ -2501,7 +2501,7 @@ class AOPBenchmarkSuite:
 
         # 3. Tool Quality vs Cost by Model
         ax3 = axes[1, 0]
-        scatter = ax3.scatter(
+        ax3.scatter(
             df["cost_usd"],
             df["response_quality_score"],
             s=100,

tests/structs/test_auto_swarm_builder_fix.py

@@ -8,7 +8,6 @@ Bug: https://github.com/kyegomez/swarms/issues/1115
 """
 
 import pytest
-from pydantic import BaseModel
 
 from swarms.structs.agent import Agent
 from swarms.structs.auto_swarm_builder import (