Flake8cleanup

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+# line too long
+extend-ignore = E501  

concurrent_mix.py

@@ -26,7 +26,7 @@
     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.
-    """,
+    """,  # noqa: W291, W293
     llm=model,
     max_loops=1,
     autosave=False,
@@ -53,7 +53,7 @@
     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.
-    """,
+    """,  # noqa: W291, W293
     llm=model,
     max_loops=1,
     autosave=False,
@@ -75,15 +75,15 @@
     programming languages, and data structures. Outline the key responsibilities, 
     including designing and developing AI agents, integrating with existing systems, 
     and ensuring scalability and performance.
-    """,
+    """,  # noqa: W291, W293
     """
     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.
-    """,
+    """,  # noqa: W291, W293
 ]
 
 # Run agents with tasks concurrently

example_async_vs_multithread.py

@@ -1,6 +1,5 @@
 import os
 import asyncio
-import threading
 from swarms import Agent
 from swarm_models import OpenAIChat
 import time
@@ -40,7 +39,8 @@
     streaming_on=False,
 )
 
-# Function to measure time and memory usage
+
+# Function to measure time and memory usage   # noqa: E302
 def measure_time_and_memory(func):
     def wrapper(*args, **kwargs):
         start_time = time.time()
@@ -52,6 +52,7 @@ def wrapper(*args, **kwargs):
         return result
     return wrapper
 
+
 # Function to run the agent asynchronously
 @measure_time_and_memory
 async def run_agent_async():
@@ -61,11 +62,15 @@ async def run_agent_async():
         )
     )
 
+
 # Function to run the agent on another thread
 @measure_time_and_memory
 def run_agent_thread():
     asyncio.run(run_agent_async())
 
+
 # Run the agent asynchronously and on another thread to test the speed
 asyncio.run(run_agent_async())
 run_agent_thread()
+
+# noqa: W391

new_features_examples/persistent_legal_agent.py

@@ -14,7 +14,7 @@
 5. Maintain consistency across related documents
 6. Output <DONE> only when document is complete and verified
 
-Remember: All output should be marked as 'DRAFT' and require professional legal review."""
+Remember: All output should be marked as 'DRAFT' and require professional legal review."""  # noqa: W291, W293
 
 
 def create_vc_legal_agent():
@@ -67,7 +67,7 @@ def generate_legal_document(agent, document_type, parameters):
 
     Returns:
         str: The generated document content
-    """
+    """  # noqa: W291, W293
     prompt = f"""
     Generate a {document_type} with the following parameters:
     {parameters}
@@ -80,7 +80,7 @@ def generate_legal_document(agent, document_type, parameters):
     5. Output <DONE> when complete
 
     Include [REQUIRES LEGAL REVIEW] tags for sections needing attorney attention.
-    """
+    """  # noqa: W291, W293
 
     return agent.run(prompt)
 

new_features_examples/real_estate_agent.py

@@ -64,7 +64,7 @@ def __init__(self, api_key: str):
 
         Args:
             api_key (str): PropertyRadar API key
-        """
+        """  # noqa: W291, W293
         self.api_key = api_key
         self.base_url = "https://api.propertyradar.com/v1"
         self.session = requests.Session()
@@ -99,7 +99,7 @@ def search_properties(
 
         Returns:
             List[PropertyListing]: List of matching properties
-        """
+        """  # noqa: W291, W293
         try:
             # Build the query parameters
             params = {
@@ -186,7 +186,7 @@ def __init__(
             model_name (str): Name of the LLM model to use
             temperature (float): Temperature setting for the LLM
             saved_state_path (Optional[str]): Path to save agent state
-        """
+        """  # noqa: W291, W293
         self.property_api = PropertyRadarAPI(propertyradar_api_key)
 
         # Initialize OpenAI model
@@ -229,7 +229,7 @@ def _get_system_prompt(self) -> str:
         - Local business development plans
         - Traffic patterns and accessibility
         - Nearby amenities and businesses
-        - Future development potential"""
+        - Future development potential"""  # noqa: W291, W293
 
     def search_properties(
         self,
@@ -251,7 +251,7 @@ def search_properties(
 
         Returns:
             List[Dict[str, Any]]: List of properties with analysis
-        """
+        """  # noqa: W291, W293
         try:
             # Search for properties
             properties = self.property_api.search_properties(

new_features_examples/rearrange_test.py

@@ -25,7 +25,7 @@
     so the finance team can take actionable steps to cut off unproductive spending. You also monitor and 
     dynamically adapt the swarm to optimize their performance. Finally, you summarize their findings 
     into a coherent report.
-    """,
+    """,   # noqa: W291, W293
     llm=model,
     max_loops=1,
     dashboard=False,
@@ -45,7 +45,7 @@
     (e.g., marketing, operations, utilities, etc.), and flagging areas where there seems to be excessive spending. 
     You will provide a detailed breakdown of each category, along with specific recommendations for cost-cutting. 
     Pay close attention to monthly recurring subscriptions, office supplies, and non-essential expenditures.
-    """,
+    """,   # noqa: W291, W293
     llm=model,
     max_loops=1,
     dashboard=False,
@@ -65,7 +65,7 @@
     such as highlighting the specific transactions that can be immediately cut off and summarizing the areas 
     where the company is overspending. Your summary will be used by the BossAgent to generate the final report.
     Be clear and to the point, emphasizing the urgency of cutting unnecessary expenses.
-    """,
+    """,  # noqa: W291, W293
     llm=model,
     max_loops=1,
     dashboard=False,
@@ -85,7 +85,7 @@
     and providing recommendations for potential cost reduction. After the analysis, the SummaryGenerator will then 
     consolidate all the findings into an actionable summary that the finance team can use to immediately cut off unnecessary expenses. 
     Together, your collaboration is essential to streamlining and improving the company’s financial health.
-"""
+"""  # noqa: W291, W293
 
 # Create a list of agents
 agents = [boss_agent, worker1, worker2]
@@ -112,7 +112,7 @@
     analysis of recent transactions to identify which expenses can be cut off to improve profitability. 
     Analyze the provided transaction data and create a detailed report on cost-cutting opportunities, 
     focusing on recurring transactions and non-essential expenditures. 
-"""
+"""  # noqa: W291, W293
 
 # Run the swarm system with the task
 output = agent_system.run(task)

new_features_examples/spike/agent_rearrange_test.py

@@ -4,7 +4,7 @@
 - You send structured data to the swarm through the users form they make
 - then connect rag for every agent using llama index to remember all the students data
 - structured outputs
-"""
+"""  # noqa: W291, W293
 
 import os
 from dotenv import load_dotenv
@@ -50,7 +50,7 @@ class CollegesRecommendation(BaseModel):
     Focus on creating actionable, well-reasoned final recommendations that 
     balance all relevant factors and stakeholder input.
 
-"""
+"""  # noqa: W291, W293
 
 function_caller = OpenAIFunctionCaller(
     system_prompt=FINAL_AGENT_PROMPT,
@@ -71,7 +71,7 @@ class CollegesRecommendation(BaseModel):
     6. Create a comprehensive student profile summary
 
     Always consider both quantitative metrics (GPA, test scores) and qualitative aspects 
-    (personal growth, challenges overcome, unique perspectives).""",
+    (personal growth, challenges overcome, unique perspectives).""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -94,7 +94,7 @@ class CollegesRecommendation(BaseModel):
     6. Track historical admission data and acceptance rates
 
     Focus on providing accurate, comprehensive information about each institution
-    while considering both academic and cultural fit factors.""",
+    while considering both academic and cultural fit factors.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -117,7 +117,7 @@ class CollegesRecommendation(BaseModel):
     6. Explain the reasoning behind each match
 
     Always provide a balanced list with realistic expectations while 
-    considering both student preferences and admission probability.""",
+    considering both student preferences and admission probability.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -140,7 +140,7 @@ class CollegesRecommendation(BaseModel):
     6. Document key points of agreement and disagreement
 
     Maintain objectivity while ensuring all important factors are thoroughly discussed
-    and evaluated.""",
+    and evaluated.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -163,7 +163,7 @@ class CollegesRecommendation(BaseModel):
     6. Suggest alternative options when appropriate
 
     Focus on constructive criticism that helps improve the final college list
-    while maintaining realistic expectations.""",
+    while maintaining realistic expectations.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -188,7 +188,7 @@ class CollegesRecommendation(BaseModel):
 
     Focus on creating actionable, well-reasoned final recommendations that 
     balance all relevant factors and stakeholder input.
-    """,
+    """,  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,

new_features_examples/spike/test.py

@@ -4,7 +4,7 @@
 - You send structured data to the swarm through the users form they make
 - then connect rag for every agent using llama index to remember all the students data
 - structured outputs
-"""
+"""  # noqa: W291, W293
 
 import os
 from dotenv import load_dotenv
@@ -50,7 +50,7 @@ class CollegesRecommendation(BaseModel):
     Focus on creating actionable, well-reasoned final recommendations that 
     balance all relevant factors and stakeholder input.
 
-"""
+"""  # noqa: W291, W293
 
 function_caller = OpenAIFunctionCaller(
     system_prompt=FINAL_AGENT_PROMPT,
@@ -71,7 +71,7 @@ class CollegesRecommendation(BaseModel):
     6. Create a comprehensive student profile summary
 
     Always consider both quantitative metrics (GPA, test scores) and qualitative aspects 
-    (personal growth, challenges overcome, unique perspectives).""",
+    (personal growth, challenges overcome, unique perspectives).""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -94,7 +94,7 @@ class CollegesRecommendation(BaseModel):
     6. Track historical admission data and acceptance rates
 
     Focus on providing accurate, comprehensive information about each institution
-    while considering both academic and cultural fit factors.""",
+    while considering both academic and cultural fit factors.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -117,7 +117,7 @@ class CollegesRecommendation(BaseModel):
     6. Explain the reasoning behind each match
 
     Always provide a balanced list with realistic expectations while 
-    considering both student preferences and admission probability.""",
+    considering both student preferences and admission probability.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -140,7 +140,7 @@ class CollegesRecommendation(BaseModel):
     6. Document key points of agreement and disagreement
 
     Maintain objectivity while ensuring all important factors are thoroughly discussed
-    and evaluated.""",
+    and evaluated.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -163,7 +163,7 @@ class CollegesRecommendation(BaseModel):
     6. Suggest alternative options when appropriate
 
     Focus on constructive criticism that helps improve the final college list
-    while maintaining realistic expectations.""",
+    while maintaining realistic expectations.""",  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -188,7 +188,7 @@ class CollegesRecommendation(BaseModel):
 
     Focus on creating actionable, well-reasoned final recommendations that 
     balance all relevant factors and stakeholder input.
-    """,
+    """,  # noqa: W291, W293
     llm=model,
     max_loops=1,
     verbose=True,
@@ -227,7 +227,7 @@ class CollegesRecommendation(BaseModel):
     - Extracurriculars: Robotics Club President, Math Team
     - Budget: Need financial aid
     - Preferred Environment: Medium-sized urban campus
-    """
+    """  # noqa: W291, W293
 
     # Run the comprehensive college selection analysis
     result = college_selection_workflow.run(

scripts/auto_tests_docs/auto_docs.py

@@ -1,4 +1,4 @@
-###### VERISON2
+# VERSION2
 import inspect
 import os
 import threading

scripts/auto_tests_docs/docs.py

@@ -111,7 +111,6 @@ def TEST_WRITER_SOP_PROMPT(
 
    Create 5,000 lines of extensive and thorough tests for the code below using the guide, do not worry about your limits you do not have any
    just write the best tests possible, the module is {module}, the file path is {path} return all of the code in one file, make sure to test all the functions and methods in the code.
-
 
 
    ######### TESTING GUIDE #############

scripts/platform_update/parse_prompts_and_submit_to_marketplace 2.py

@@ -110,7 +110,7 @@ def is_duplicate(new_prompt, published_prompts):
 def extract_use_cases(prompt):
     """Extract use cases from the prompt by chunking it into meaningful segments."""
     # This is a simple placeholder; you can use a more advanced method to extract use cases
-    chunks = [prompt[i : i + 50] for i in range(0, len(prompt), 50)]
+    chunks = [prompt[i: i + 50] for i in range(0, len(prompt), 50)]
     return [
         {"title": f"Use case {idx+1}", "description": chunk}
         for idx, chunk in enumerate(chunks)

scripts/platform_update/parse_prompts_and_submit_to_marketplace.py

@@ -110,7 +110,7 @@ def is_duplicate(new_prompt, published_prompts):
 def extract_use_cases(prompt):
     """Extract use cases from the prompt by chunking it into meaningful segments."""
     # This is a simple placeholder; you can use a more advanced method to extract use cases
-    chunks = [prompt[i : i + 50] for i in range(0, len(prompt), 50)]
+    chunks = [prompt[i: i + 50] for i in range(0, len(prompt), 50)]
     return [
         {"title": f"Use case {idx+1}", "description": chunk}
         for idx, chunk in enumerate(chunks)