Method execute

internagent/mas/agents/evolution_agent.py:326–505

Evolve hypothesis by addressing critiques and incorporating feedback. Generates improved hypothesis versions that systematically address identified weaknesses while preserving core strengths. Incorporates evidence and feedback to produce scientifically stronger, mor

(self, context: Dict[str, Any], params: Dict[str, Any])

            return []

    async def execute(self, context: Dict[str, Any], params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Evolve hypothesis by addressing critiques and incorporating feedback.

        Generates improved hypothesis versions that systematically address identified
        weaknesses while preserving core strengths. Incorporates evidence and feedback
        to produce scientifically stronger, more testable hypotheses. Documents specific
        improvements and changes made during evolution.

        Args:
            context (Dict[str, Any]): Execution context with keys:
                - goal (Dict): Research goal and constraints
                - hypothesis (Dict): Hypothesis to evolve with text/rationale
                - critiques (List[Dict]): Identified weaknesses to address
                - evidence (List[Dict]): Supporting evidence (optional)
                - feedback (List[Dict]): Scientist feedback (optional)
                - iteration (int): Current iteration number
            params (Dict[str, Any]): Runtime parameters:
                - evolution_count (int): Override evolution count (optional)

        Returns:
            Dict[str, Any]: Evolution results containing:
                - evolved_hypotheses (List[Dict]): Improved versions with text/rationale/improvements
                - reasoning (str): Evolution strategy explanation
                - changes (List[Dict]): Documented changes by type
                - metadata (Dict): Evolution context

        Raises:
            AgentExecutionError: If goal/hypothesis missing or evolution fails
        """
        # Extract parameters
        goal = context.get("goal", {})
        hypothesis = context.get("hypothesis", {})
        critiques = context.get("critiques", [])
        evidence = context.get("evidence", [])
        feedback = context.get("feedback", [])
        
        if not goal or not hypothesis:
            raise AgentExecutionError("Research goal and hypothesis are required for evolution")
        
        # Extract text from hypothesis
        hypothesis_text = hypothesis.get("text", "")
        if not hypothesis_text:
            raise AgentExecutionError("Hypothesis text is required for evolution")
            
        # Extract optional parameters
        iteration = context.get("iteration", 0)
        evolution_count = params.get("evolution_count", self.evolution_count)

        # Initialize memory retriever if enabled and not yet initialized
        if self.use_memory and context.get("task_name") and not self.memory_retriever:
            from ..tools.memory_retrieval import TaskMemoryRetriever
            self.memory_retriever = TaskMemoryRetriever(
                task_name=context.get("task_name"),
                memory_dir=self.memory_dir,
                top_k=self.memory_top_k,
                alpha=self.memory_alpha,
                include_details=self.memory_include_details,