Merge 0ce21dada8 into ce0f267efe

new_features_examples/swarmarrange/swarm_arange_demo.py

@@ -171,49 +171,41 @@ agents = [
     regulatory_specialist,
 ]
 
-# Define multiple flow patterns
-flows = [
-    "Industry-Analyst -> Tech-Expert -> Market-Researcher -> Regulatory-Specialist -> Managing-Director -> VP-Finance",
-    "Managing-Director -> VP-Finance -> Industry-Analyst -> Tech-Expert -> Market-Researcher -> Regulatory-Specialist",
-    "Tech-Expert -> Market-Researcher -> Regulatory-Specialist -> Industry-Analyst -> Managing-Director -> VP-Finance",
-]
-
-# Create instances of AgentRearrange for each flow pattern
-blackstone_acquisition_analysis = AgentRearrange(
-    name="Blackstone-Acquisition-Analysis",
+# Example 1: Automatic sequential flow
+acquisition_analysis = AgentRearrange(
+    name="Acquisition-Analysis",
     description="A system for analyzing potential acquisitions",
-    agents=agents,
-    flow=flows[0],
+    agents=agents
 )
+acquisition_analysis.set_flow_from_task("Analyze this acquisition step by step, starting with industry analysis and ending with financial recommendations")
 
-blackstone_investment_strategy = AgentRearrange(
-    name="Blackstone-Investment-Strategy",
-    description="A system for evaluating investment opportunities",
-    agents=agents,
-    flow=flows[1],
+# Example 2: Automatic parallel flow
+market_research = AgentRearrange(
+    name="Market-Research",
+    description="A system for parallel market research",
+    agents=agents
 )
-
-blackstone_market_analysis = AgentRearrange(
-    name="Blackstone-Market-Analysis",
-    description="A system for analyzing market trends and opportunities",
-    agents=agents,
-    flow=flows[2],
+market_research.set_flow_from_task("Analyze multiple market segments simultaneously to identify opportunities")
+
+# Example 3: Hybrid flow with agent groups
+agent_groups = {
+    "research": ["Industry-Analyst", "Tech-Expert", "Market-Researcher"],
+    "compliance": ["Regulatory-Specialist"],
+    "management": ["Managing-Director", "VP-Finance"]
+}
+
+investment_strategy = AgentRearrange(
+    name="Investment-Strategy",
+    description="A system for comprehensive investment analysis",
+    agents=agents
 )
+investment_strategy.flow = investment_strategy.generate_flow("hybrid", agent_groups)
 
-swarm_arrange = SwarmRearrange(
-    name="Blackstone-Swarm",
-    description="A swarm that processes tasks concurrently using multiple agents and rearranges the flow based on the task requirements.",
-    swarms=[
-        blackstone_acquisition_analysis,
-        blackstone_investment_strategy,
-        blackstone_market_analysis,
-    ],
-    flow=f"{blackstone_acquisition_analysis.name} -> {blackstone_investment_strategy.name} -> {blackstone_market_analysis.name}",
-    max_loops=1,
-)
+# Run examples
+print("Acquisition Analysis Flow:", acquisition_analysis.flow)
+print("Market Research Flow:", market_research.flow)
+print("Investment Strategy Flow:", investment_strategy.flow)
 
-print(
-    swarm_arrange.run(
-        "Analyze NVIDIA's performance, market trends, and potential for growth in the AI industry"
-    )
-)
+# Example task execution
+result = acquisition_analysis.run("Analyze potential acquisition of a tech startup valued at $50M")
+print("\nAcquisition Analysis Result:", result)

swarms/structs/rearrange.py

@@ -775,6 +775,67 @@ class AgentRearrange(BaseSwarm):
             for attr_name, attr_value in self.__dict__.items()
         }
 
+    def generate_flow(self, task_type: str = "sequential", agent_groups: Dict[str, List[str]] = None) -> str:
+        """
+        Automatically generates a flow pattern based on task type and agent groupings.
+        
+        Args:
+            task_type (str): Type of flow pattern ('sequential', 'parallel', or 'hybrid')
+            agent_groups (Dict[str, List[str]], optional): Groups of agents that should work together
+                Format: {"group1": ["agent1", "agent2"], "group2": ["agent3", "agent4"]}
+        
+        Returns:
+            str: Generated flow pattern
+        """
+        if not self.agents:
+            raise ValueError("No agents available to generate flow.")
+        
+        agent_names = list(self.agents.keys())
+        
+        if task_type == "sequential":
+            # Simple sequential flow: a -> b -> c
+            return " -> ".join(agent_names)
+        
+        elif task_type == "parallel":
+            # All agents work in parallel: a, b, c
+            return ", ".join(agent_names)
+        
+        elif task_type == "hybrid" and agent_groups:
+            # Hybrid flow using agent groups: (a, b) -> (c, d) -> e
+            flow_parts = []
+            for group in agent_groups.values():
+                valid_agents = [a for a in group if a in agent_names]
+                if valid_agents:
+                    flow_parts.append(", ".join(valid_agents))
+            return " -> ".join(flow_parts)
+        
+        else:
+            raise ValueError("Invalid task_type or missing agent_groups for hybrid flow")
+
+    def set_flow_from_task(self, task: str):
+        """
+        Analyzes the task description and automatically sets an appropriate flow pattern.
+        
+        Args:
+            task (str): Task description to analyze
+        """
+        # Keywords indicating parallel processing needs
+        parallel_keywords = ["compare", "analyze multiple", "parallel", "simultaneously"]
+        # Keywords indicating sequential processing needs
+        sequential_keywords = ["step by step", "in sequence", "following", "then"]
+        
+        task_lower = task.lower()
+        
+        if any(keyword in task_lower for keyword in parallel_keywords):
+            self.flow = self.generate_flow("parallel")
+        elif any(keyword in task_lower for keyword in sequential_keywords):
+            self.flow = self.generate_flow("sequential")
+        else:
+            # Default to sequential flow if no clear indicators
+            self.flow = self.generate_flow("sequential")
+        
+        logger.info(f"Automatically set flow pattern: {self.flow}")
+
 
 def rearrange(
     agents: List[Agent] = None,