[CLEANUP]

docs/swarms_cloud/agent_api.md

@@ -2,6 +2,8 @@
 
 The Swarms API provides endpoints to interact with various language models, manage agent configurations, and handle token counting. This documentation covers the available endpoints, input and output models, and detailed examples for each endpoint.
 
+URL: `https://api.swarms.world`
+
 ## Endpoints
 
 ### `/v1/models`

new_workflow_concurrent.py

@@ -39,7 +39,9 @@ class ConcurrentWorkflow(BaseWorkflow):
     return_results: bool = False
     stopping_condition: Optional[Callable] = None
 
-    def run(self, task: Optional[str] = None, *args, **kwargs) -> Optional[List[Any]]:
+    def run(
+        self, task: Optional[str] = None, *args, **kwargs
+    ) -> Optional[List[Any]]:
         """
         Executes the tasks in parallel using multiple threads.
 
@@ -59,7 +61,12 @@ class ConcurrentWorkflow(BaseWorkflow):
                 logger.warning("No agents found in the workflow.")
                 break
 
-            threads = [threading.Thread(target=self.execute_agent, args=(agent, task)) for agent in self.agents]
+            threads = [
+                threading.Thread(
+                    target=self.execute_agent, args=(agent, task)
+                )
+                for agent in self.agents
+            ]
 
             for thread in threads:
                 thread.start()
@@ -68,11 +75,19 @@ class ConcurrentWorkflow(BaseWorkflow):
                 thread.join()
 
             if self.return_results:
-                results.extend([thread.result for thread in threads if hasattr(thread, 'result')])
+                results.extend(
+                    [
+                        thread.result
+                        for thread in threads
+                        if hasattr(thread, "result")
+                    ]
+                )
 
             loop += 1
-            
-            if self.stopping_condition and self.stopping_condition(results):
+
+            if self.stopping_condition and self.stopping_condition(
+                results
+            ):
                 break
 
         return results if self.return_results else None
@@ -85,8 +100,10 @@ class ConcurrentWorkflow(BaseWorkflow):
     def save(self):
         """Saves the state of the workflow to a file."""
         self.save_state(self.saved_state_filepath)
-        
-    def execute_agent(self, agent: Agent, task: Optional[str] = None, *args, **kwargs):
+
+    def execute_agent(
+        self, agent: Agent, task: Optional[str] = None, *args, **kwargs
+    ):
         try:
             result = agent.run(task, *args, **kwargs)
             if self.print_results:
@@ -95,16 +112,26 @@ class ConcurrentWorkflow(BaseWorkflow):
                 return result
         except Exception as e:
             logger.error(f"Agent {agent} generated an exception: {e}")
-            
 
 
 api_key = os.environ["OPENAI_API_KEY"]
 
 # Model
 swarm = ConcurrentWorkflow(
-    agents = [Agent(llm=OpenAIChat(openai_api_key=api_key, max_tokens=4000,), max_loops=4, dashboard=False)],
+    agents=[
+        Agent(
+            llm=OpenAIChat(
+                openai_api_key=api_key,
+                max_tokens=4000,
+            ),
+            max_loops=4,
+            dashboard=False,
+        )
+    ],
 )
 
 
 # Run the workflow
-swarm.run("Generate a report on the top 3 biggest expenses for small businesses and how businesses can save 20%")
+swarm.run(
+    "Generate a report on the top 3 biggest expenses for small businesses and how businesses can save 20%"
+)

playground/structs/multi_agent_collaboration/graph_workflow_example.py

@@ -1,4 +1,3 @@
-
 import os
 
 from dotenv import load_dotenv
@@ -9,16 +8,16 @@ load_dotenv()
 
 api_key = os.environ.get("OPENAI_API_KEY")
 
-llm = OpenAIChat(
-    temperature=0.5, openai_api_key=api_key, max_tokens=4000
-)
+llm = OpenAIChat(temperature=0.5, openai_api_key=api_key, max_tokens=4000)
 agent1 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
 agent2 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
 
+
 def sample_task():
     print("Running sample task")
     return "Task completed"
 
+
 wf_graph = GraphWorkflow()
 wf_graph.add_node(Node(id="agent1", type=NodeType.AGENT, agent=agent1))
 wf_graph.add_node(Node(id="agent2", type=NodeType.AGENT, agent=agent2))

playground/swarms/geo_economic_forecast_docs/rag_doc_agent.py

@@ -25,7 +25,6 @@ Always prioritize accuracy, depth of analysis, and clarity in your responses. Us
 """
 
 
-
 # Initialize the agent
 agent = Agent(
     agent_name="Geo Expert AI",
@@ -57,7 +56,6 @@ agent = Agent(
 )
 
 
-
 # Initialize the agent
 forecaster_agent = Agent(
     agent_name="Forecaster Agent",
@@ -91,11 +89,13 @@ forecaster_agent = Agent(
 
 # Initialize the swarm
 swarm = MixtureOfAgents(
-    agents = [agent, forecaster_agent],
-    final_agent = forecaster_agent,
-    layers = 1,
+    agents=[agent, forecaster_agent],
+    final_agent=forecaster_agent,
+    layers=1,
 )
 
 # Run the swarm
-out = swarm.run("what is the economic impact of China from technology decoupling, and how is that impact measured? What is the forecast or economic, give some numbers")
-print(out)
+out = swarm.run(
+    "what is the economic impact of China from technology decoupling, and how is that impact measured? What is the forecast or economic, give some numbers"
+)
+print(out)

scripts/cleanup/json_log_cleanup.py

@@ -2,6 +2,7 @@ import os
 import shutil
 from loguru import logger
 
+
 def cleanup_json_logs(name: str = None):
     # Define the root directory and the target directory
     root_dir = os.getcwd()
@@ -46,8 +47,10 @@ def cleanup_json_logs(name: str = None):
         shutil.rmtree(chroma_folder)
         logger.info(f"Deleted Chroma folder at {chroma_folder}")
 
-    logger.info(f"All JSON, LOG and TXT files have been moved to {target_dir}")
+    logger.info(
+        f"All JSON, LOG and TXT files have been moved to {target_dir}"
+    )
 
 
 # Call the function
-cleanup_json_logs("heinz_swarm")
+cleanup_json_logs("heinz_swarm")

swarms/structs/__init__.py

@@ -89,7 +89,12 @@ from swarms.structs.yaml_model import (
     pydantic_type_to_yaml_schema,
 )
 from swarms.structs.mixture_of_agents import MixtureOfAgents
-from swarms.structs.graph_workflow import GraphWorkflow, Node, NodeType, Edge
+from swarms.structs.graph_workflow import (
+    GraphWorkflow,
+    Node,
+    NodeType,
+    Edge,
+)
 
 
 __all__ = [

swarms/structs/base_workflow.py

@@ -38,6 +38,7 @@ class BaseWorkflow(BaseStructure):
         add_objective_to_workflow: Adds an objective to the workflow.
         load_workflow_state: Loads the workflow state from a json file and restores the workflow state.
     """
+
     def __init__(
         self,
         agents: List[Agent] = None,