Adding Py best pratices flakes and pyupgrade

csvagent_example.py

@@ -1,7 +1,10 @@
 # Example usage
 from pathlib import Path
-from swarms.structs.csv_to_agent import AgentLoader, AgentValidationError
 
+from swarms.structs.csv_to_agent import (
+    AgentLoader,
+    AgentValidationError,
+)
 
 if __name__ == "__main__":
     # Example agent configurations

example.py

@@ -1,11 +1,12 @@
 import os
 
+from dotenv import load_dotenv
 from swarm_models import OpenAIChat
+
 from swarms import Agent
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
-from dotenv import load_dotenv
 
 load_dotenv()
 

graph_swarm_example.py

@@ -1,8 +1,8 @@
 from loguru import logger
+
 from swarms.structs.agent import Agent
 from swarms.structs.graph_swarm import GraphSwarm
 
-
 if __name__ == "__main__":
     try:
         # Create agents

multi_agent_router_example.py

@@ -39,4 +39,4 @@
         print(result_execute)
 
     except Exception as e:
-        print(f"Error occurred: {str(e)}")
+        print(f"Error occurred: {e!s}")

new_features_examples/agent_showcase_example.py

@@ -1,8 +1,8 @@
 import os
 
-from swarms import Agent
-
 from swarm_models import OpenAIChat
+
+from swarms import Agent
 from swarms.structs.agents_available import showcase_available_agents
 
 # Get the OpenAI API key from the environment variable

new_features_examples/agent_with_fluidapi.py

@@ -1,10 +1,10 @@
 import os
 
-from swarm_models import OpenAIChat
-from swarms import Agent
-from fluid_api_agent.main import fluid_api_request
 from dotenv import load_dotenv
+from fluid_api_agent.main import fluid_api_request
+from swarm_models import OpenAIChat
 
+from swarms import Agent
 
 load_dotenv()
 
@@ -82,9 +82,9 @@ def omni_api(task: str) -> str:
 
 # Define the system prompt tailored for the API expert
 API_AGENT_SYS_PROMPT = """
-You are a highly specialized financial API expert. 
-Your expertise lies in analyzing financial data, making investment recommendations, and 
-interacting with APIs to retrieve, process, and present data effectively. 
+You are a highly specialized financial API expert.
+Your expertise lies in analyzing financial data, making investment recommendations, and
+interacting with APIs to retrieve, process, and present data effectively.
 You use tools like 'omni_api' to fetch data dynamically, ensuring accuracy and up-to-date results.
 
 Instructions:

new_features_examples/async_agent.py

@@ -1,8 +1,9 @@
+from swarm_models import OpenAIChat
+
 from swarms import Agent
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
-from swarm_models import OpenAIChat
 
 model = OpenAIChat(model_name="gpt-4o")
 

new_features_examples/async_agents.py

@@ -3,11 +3,11 @@
 from dotenv import load_dotenv
 from swarm_models import OpenAIChat
 
+from new_features_examples.async_executor import HighSpeedExecutor
 from swarms import Agent
 from swarms.prompts.finance_agent_sys_prompt import (
     FINANCIAL_AGENT_SYS_PROMPT,
 )
-from new_features_examples.async_executor import HighSpeedExecutor
 
 load_dotenv()
 

new_features_examples/async_executor.py

@@ -2,11 +2,11 @@
 import multiprocessing as mp
 import time
 from functools import partial
-from typing import Any, Dict, Union
+from typing import Any, Optional, Union
 
 
 class HighSpeedExecutor:
-    def __init__(self, num_processes: int = None):
+    def __init__(self, num_processes: Optional[int] = None):
         """
         Initialize the executor with configurable number of processes.
         If num_processes is None, it uses CPU count.
@@ -45,7 +45,7 @@ async def execute_batch(
         num_executions: int,
         *args: Any,
         **kwargs: Any,
-    ) -> Dict[str, Union[int, float]]:
+    ) -> dict[str, Union[int, float]]:
         """
         Execute the given function multiple times concurrently.
 

new_features_examples/async_workflow_example.py

@@ -1,21 +1,20 @@
 import asyncio
-from typing import List
 
 from swarm_models import OpenAIChat
 
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+from swarms.structs.agent import Agent
 from swarms.structs.async_workflow import (
     SpeakerConfig,
     SpeakerRole,
     create_default_workflow,
     run_workflow_with_retry,
 )
-from swarms.prompts.finance_agent_sys_prompt import (
-    FINANCIAL_AGENT_SYS_PROMPT,
-)
-from swarms.structs.agent import Agent
 
 
-async def create_specialized_agents() -> List[Agent]:
+async def create_specialized_agents() -> list[Agent]:
     """Create a set of specialized agents for financial analysis"""
 
     # Base model configuration
@@ -165,7 +164,7 @@ async def main():
                     print(value)
 
     except Exception as e:
-        print(f"Workflow failed: {str(e)}")
+        print(f"Workflow failed: {e!s}")
 
     finally:
         await workflow.cleanup()

new_features_examples/auto_agent.py

@@ -1,7 +1,7 @@
 import json
 import os
 from contextlib import suppress
-from typing import Any, Callable, Dict, Optional, Type, Union
+from typing import Any, Callable, Optional, Union
 
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError, create_model
@@ -10,7 +10,7 @@
 
 class DynamicParser:
     @staticmethod
-    def extract_fields(model: Type[BaseModel]) -> Dict[str, Any]:
+    def extract_fields(model: type[BaseModel]) -> dict[str, Any]:
         return {
             field_name: (
                 field.annotation,
@@ -21,8 +21,8 @@ def extract_fields(model: Type[BaseModel]) -> Dict[str, Any]:
 
     @staticmethod
     def create_partial_model(
-        model: Type[BaseModel], data: Dict[str, Any]
-    ) -> Type[BaseModel]:
+        model: type[BaseModel], data: dict[str, Any]
+    ) -> type[BaseModel]:
         fields = {
             field_name: (
                 field.annotation,
@@ -35,7 +35,7 @@ def create_partial_model(
 
     @classmethod
     def parse(
-        cls, data: Union[str, Dict[str, Any]], model: Type[BaseModel]
+        cls, data: Union[str, dict[str, Any]], model: type[BaseModel]
     ) -> Optional[BaseModel]:
         if isinstance(data, str):
             try:
@@ -88,7 +88,7 @@ class Command(BaseModel):
         ...,
         description="Command name to execute from the provided list of commands.",
     )
-    args: Dict[str, Any] = Field(
+    args: dict[str, Any] = Field(
         ..., description="Arguments required to execute the command."
     )
 
@@ -134,9 +134,9 @@ def task_complete_command(reason: str):
 
 
 # Dynamic command execution
-def execute_command(name: str, args: Dict[str, Any]):
+def execute_command(name: str, args: dict[str, Any]):
     """Dynamically execute a command based on its name and arguments."""
-    command_map: Dict[str, Callable] = {
+    command_map: dict[str, Callable] = {
         "fluid_api": lambda **kwargs: fluid_api_command(
             task=kwargs.get("task")
         ),
@@ -157,8 +157,8 @@ def execute_command(name: str, args: Dict[str, Any]):
 
 
 def parse_and_execute_command(
-    response: Union[str, Dict[str, Any]],
-    base_model: Type[BaseModel] = AgentResponse,
+    response: Union[str, dict[str, Any]],
+    base_model: type[BaseModel] = AgentResponse,
 ) -> Any:
     """Enhanced command parser with flexible input handling"""
     parsed = DynamicParser.parse(response, base_model)
@@ -197,7 +197,7 @@ def parse_and_execute_command(
 ### COMMANDS:
 1. Fluid API: "fluid_api", args: "method": "<GET/POST/...>", "url": "<url>", "headers": "<headers>", "body": "<payload>"
 18. Send Tweet: "send_tweet", args: "text": "<text>"
-19. Do Nothing: "do_nothing", args: 
+19. Do Nothing: "do_nothing", args:
 20. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
 
 ### RESOURCES:

new_features_examples/auto_swarm_router.py

@@ -1,7 +1,9 @@
 import os
+
 from dotenv import load_dotenv
-from swarms import Agent
 from swarm_models import OpenAIChat
+
+from swarms import Agent
 from swarms.structs.swarm_router import SwarmRouter
 
 load_dotenv()

new_features_examples/concurrent_examples/concurrent_mix 2.py

@@ -16,15 +16,15 @@
 delaware_ccorp_agent = Agent(
     agent_name="Delaware-CCorp-Hiring-Agent",
     system_prompt="""
-    Create a comprehensive hiring description for a Delaware C Corporation, 
-    including all relevant laws and regulations, such as the Delaware General 
-    Corporation Law (DGCL) and the Delaware Corporate Law. Ensure the description 
-    covers the requirements for hiring employees, contractors, and officers, 
-    including the necessary paperwork, tax obligations, and benefits. Also, 
-    outline the procedures for compliance with Delaware's employment laws, 
-    including anti-discrimination laws, workers' compensation, and unemployment 
-    insurance. Provide guidance on how to navigate the complexities of Delaware's 
-    corporate law and ensure that all hiring practices are in compliance with 
+    Create a comprehensive hiring description for a Delaware C Corporation,
+    including all relevant laws and regulations, such as the Delaware General
+    Corporation Law (DGCL) and the Delaware Corporate Law. Ensure the description
+    covers the requirements for hiring employees, contractors, and officers,
+    including the necessary paperwork, tax obligations, and benefits. Also,
+    outline the procedures for compliance with Delaware's employment laws,
+    including anti-discrimination laws, workers' compensation, and unemployment
+    insurance. Provide guidance on how to navigate the complexities of Delaware's
+    corporate law and ensure that all hiring practices are in compliance with
     state and federal regulations.
     """,
     llm=model,
@@ -41,17 +41,17 @@
 indian_foreign_agent = Agent(
     agent_name="Indian-Foreign-Hiring-Agent",
     system_prompt="""
-    Create a comprehensive hiring description for an Indian or foreign country, 
-    including all relevant laws and regulations, such as the Indian Contract Act, 
-    the Indian Labour Laws, and the Foreign Exchange Management Act (FEMA). 
-    Ensure the description covers the requirements for hiring employees, 
-    contractors, and officers, including the necessary paperwork, tax obligations, 
-    and benefits. Also, outline the procedures for compliance with Indian and 
-    foreign employment laws, including anti-discrimination laws, workers' 
-    compensation, and unemployment insurance. Provide guidance on how to navigate 
-    the complexities of Indian and foreign corporate law and ensure that all hiring 
-    practices are in compliance with state and federal regulations. Consider the 
-    implications of hiring foreign nationals and the requirements for obtaining 
+    Create a comprehensive hiring description for an Indian or foreign country,
+    including all relevant laws and regulations, such as the Indian Contract Act,
+    the Indian Labour Laws, and the Foreign Exchange Management Act (FEMA).
+    Ensure the description covers the requirements for hiring employees,
+    contractors, and officers, including the necessary paperwork, tax obligations,
+    and benefits. Also, outline the procedures for compliance with Indian and
+    foreign employment laws, including anti-discrimination laws, workers'
+    compensation, and unemployment insurance. Provide guidance on how to navigate
+    the complexities of Indian and foreign corporate law and ensure that all hiring
+    practices are in compliance with state and federal regulations. Consider the
+    implications of hiring foreign nationals and the requirements for obtaining
     necessary visas and work permits.
     """,
     llm=model,
@@ -69,19 +69,19 @@
 agents = [delaware_ccorp_agent, indian_foreign_agent]
 tasks = [
     """
-    Create a comprehensive hiring description for an Agent Engineer, including 
-    required skills and responsibilities. Ensure the description covers the 
-    necessary technical expertise, such as proficiency in AI/ML frameworks, 
-    programming languages, and data structures. Outline the key responsibilities, 
-    including designing and developing AI agents, integrating with existing systems, 
+    Create a comprehensive hiring description for an Agent Engineer, including
+    required skills and responsibilities. Ensure the description covers the
+    necessary technical expertise, such as proficiency in AI/ML frameworks,
+    programming languages, and data structures. Outline the key responsibilities,
+    including designing and developing AI agents, integrating with existing systems,
     and ensuring scalability and performance.
     """,
     """
-    Generate a detailed job description for a Prompt Engineer, including 
-    required skills and responsibilities. Ensure the description covers the 
-    necessary technical expertise, such as proficiency in natural language processing, 
-    machine learning, and software development. Outline the key responsibilities, 
-    including designing and optimizing prompts for AI systems, ensuring prompt 
+    Generate a detailed job description for a Prompt Engineer, including
+    required skills and responsibilities. Ensure the description covers the
+    necessary technical expertise, such as proficiency in natural language processing,
+    machine learning, and software development. Outline the key responsibilities,
+    including designing and optimizing prompts for AI systems, ensuring prompt
     quality and consistency, and collaborating with cross-functional teams.
     """,
 ]