[FEAT][AgentRearrange]

playground/demos/fintech/main.py

@@ -0,0 +1,59 @@
+from swarms import Agent, Anthropic, AgentRearrange
+
+# Define the agents with specific tasks for financial activities
+agent_risk_analysis = Agent(
+    agent_name="RiskAnalysis",
+    agent_description="Analyze the financial risks associated with the portfolio.",
+    system_prompt="Analyze and identify the risks in the financial data provided.",
+    llm=Anthropic(),
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+)
+
+agent_compliance_check = Agent(
+    agent_name="ComplianceCheck",
+    agent_description="Ensure all financial activities adhere to regulatory standards.",
+    system_prompt="Review the financial data to ensure compliance with all relevant regulations.",
+    llm=Anthropic(),
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+)
+
+agent_report_generation = Agent(
+    agent_name="ReportGeneration",
+    agent_description="Generate a detailed report based on the risk analysis and compliance check.",
+    system_prompt="Compile the findings from risk analysis and compliance checks into a comprehensive financial report.",
+    llm=Anthropic(),
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    streaming_on=True,
+    verbose=True,
+    stopping_token="<DONE>",
+)
+
+# Initialize the AgentRearrange system
+financial_workflow = AgentRearrange(
+    agents=[
+        agent_risk_analysis,
+        agent_compliance_check,
+        agent_report_generation,
+    ],
+    flow="RiskAnalysis -> ComplianceCheck -> ReportGeneration",
+    verbose=True,
+)
+
+# Run the workflow on a task
+default_task = (
+    "Prepare a comprehensive financial review for the fiscal quarter."
+)
+results = financial_workflow.run(default_task)
+print("Workflow Results:", results)

playground/structs/agent_rearrange.py

@@ -1,7 +1,6 @@
 from swarms import Agent, AgentRearrange, rearrange, Anthropic
 
 
-
 # Initialize the director agent
 
 director = Agent(

swarms/structs/agent.py

@@ -314,8 +314,7 @@ class Agent:
         self.execute_tool = execute_tool
         self.planning = planning
         self.planning_prompt = planning_prompt
-        
-        
+
         # Name
         self.name = agent_name
         self.description = agent_description
@@ -457,7 +456,7 @@ class Agent:
             logger.info(f"Beginning of Agent {self.agent_name} History")
             logger.info(self.short_memory.return_history_as_string())
             logger.info(f"End of Agent {self.agent_name} History")
-            
+
         # If the user inputs a list of strings for the sop then join them and set the sop
         if self.sop_list:
             self.sop = "\n".join(self.sop_list)
@@ -466,7 +465,6 @@ class Agent:
         if self.sop is not None:
             self.short_memory.add(role=self.user_name, content=self.sop)
 
-
     def set_system_prompt(self, system_prompt: str):
         """Set the system prompt"""
         self.system_prompt = system_prompt

swarms/structs/rearrange.py

@@ -116,30 +116,38 @@ class AgentRearrange(BaseSwarm):
         Returns:
             str: The final processed task.
         """
-        if not self.validate_flow():
-            return "Invalid flow configuration."
-
-        tasks = self.flow.split("->")
-        current_task = task
+        try:
+            if not self.validate_flow():
+                return "Invalid flow configuration."
+
+            tasks = self.flow.split("->")
+            current_task = task
+
+            for task in tasks:
+                agent_names = [name.strip() for name in task.split(",")]
+                if len(agent_names) > 1:
+                    # Parallel processing
+                    logger.info(
+                        f"Running agents in parallel: {agent_names}"
+                    )
+                    results = []
+                    for agent_name in agent_names:
+                        agent = self.agents[agent_name]
+                        result = agent.run(current_task, *args, **kwargs)
+                        results.append(result)
+                    current_task = "; ".join(results)
+                else:
+                    # Sequential processing
+                    logger.info(
+                        f"Running agents sequentially: {agent_names}"
+                    )
+                    agent = self.agents[agent_names[0]]
+                    current_task = agent.run(current_task, *args, **kwargs)
 
-        for task in tasks:
-            agent_names = [name.strip() for name in task.split(",")]
-            if len(agent_names) > 1:
-                # Parallel processing
-                logger.info(f"Running agents in parallel: {agent_names}")
-                results = []
-                for agent_name in agent_names:
-                    agent = self.agents[agent_name]
-                    result = agent.run(current_task, *args, **kwargs)
-                    results.append(result)
-                current_task = "; ".join(results)
-            else:
-                # Sequential processing
-                logger.info(f"Running agents sequentially: {agent_names}")
-                agent = self.agents[agent_names[0]]
-                current_task = agent.run(current_task, *args, **kwargs)
-
-        return current_task
+            return current_task
+        except Exception as e:
+            logger.error(f"An error occurred: {e}")
+            return e
 
 
 def rearrange(