fixed reflexion agent death spiral

swarms/agents/flexion_agent.py

@@ -1,4 +1,5 @@
 from typing import List, Dict, Any, Tuple
+import re
 import time
 from datetime import datetime
 
@@ -7,6 +8,10 @@ from swarms.structs.conversation import Conversation
 
 from loguru import logger
 
+# Configuration constants for ReflexionAgent
+EARLY_TERMINATION_THRESHOLD = 0.8  # Lower than 0.9 for more realistic termination
+DEFAULT_SCORE = 0.6  # Higher than 0.5 to increase chance of early termination
+SCORE_IMPROVEMENT_THRESHOLD = 0.05  # Minimum improvement to continue iterating
 
 # Define Reflexion prompt with detailed instructions
 REFLEXION_PROMPT = """
@@ -298,6 +303,73 @@ Focus on extracting lasting insights that will be valuable for improving future
             f"Initialized {self.agent_name} with model {self.model_name}"
         )
 
+    def _extract_score_robust(self, evaluation: str) -> float:
+        """
+        Robustly extract a score from evaluation response using multiple strategies.
+
+        Args:
+            evaluation: The evaluation text from the evaluator agent
+
+        Returns:
+            float: Extracted score between 0.0 and 1.0, or DEFAULT_SCORE if extraction fails
+        """
+        # Strategy 1: Look for "final score: X/10" or "overall score: X" (existing pattern)
+        # Handle markdown formatting by removing asterisks
+        eval_clean = evaluation.replace('*', '').lower()
+
+        score_patterns = [
+            r"(?:final|overall)\s+score:?\s*(\d+(?:\.\d+)?)",
+            r"score:?\s*(\d+(?:\.\d+)?)\s*/\s*10",
+            r"(?:rating|grade):?\s*(\d+(?:\.\d+)?)\s*/\s*10",
+            r"(?:rating|grade):?\s*(\d+(?:\.\d+)?)",
+        ]
+
+        for pattern in score_patterns:
+            matches = re.findall(pattern, eval_clean)
+            if matches:
+                try:
+                    score = float(matches[-1])
+                    # Normalize to 0-1 range if needed
+                    normalized = score / 10.0 if score > 1.0 else score
+                    return max(0.0, min(1.0, normalized))
+                except (ValueError, IndexError):
+                    continue
+
+        # Strategy 2: Look for any number between 0-10 with context
+        context_patterns = [
+            r"(\d+(?:\.\d+)?)\s*/\s*10",
+            r"(\d+(?:\.\d+)?)\s*out of\s*10",
+        ]
+
+        for pattern in context_patterns:
+            matches = re.findall(pattern, eval_clean)
+            if matches:
+                try:
+                    score = float(matches[-1])
+                    return max(0.0, min(1.0, score / 10.0))
+                except (ValueError, IndexError):
+                    continue
+
+        # Strategy 3: Sentiment analysis fallback
+        positive_keywords = ['excellent', 'great', 'strong', 'good', 'well done', 'impressive', 'thorough', 'comprehensive']
+        negative_keywords = ['poor', 'weak', 'lacking', 'insufficient', 'needs improvement', 'unclear', 'incomplete']
+
+        eval_lower = evaluation.lower()
+        positive_count = sum(1 for kw in positive_keywords if kw in eval_lower)
+        negative_count = sum(1 for kw in negative_keywords if kw in eval_lower)
+
+        if positive_count > negative_count and positive_count > 0:
+            logger.debug(f"Score extraction via sentiment: positive ({positive_count} keywords)")
+            return 0.75  # Likely good
+        elif negative_count > positive_count and negative_count > 0:
+            logger.debug(f"Score extraction via sentiment: negative ({negative_count} keywords)")
+            return 0.4   # Likely poor
+
+        # Default fallback
+        logger.warning(f"Could not extract score from evaluation, using default: {DEFAULT_SCORE}")
+        logger.debug(f"Evaluation text (first 200 chars): {evaluation[:200]}")
+        return DEFAULT_SCORE
+
     def act(
         self,
         task: str,
@@ -364,24 +436,10 @@ Evaluate this response thoroughly according to the criteria in your instructions
 
         evaluation = self.evaluator.run(task=prompt)
 
-        # Extract numerical score from evaluation (in a production system, you'd want a more
-        # robust parsing method here, potentially using structured output)
-        try:
-            # Look for a final score in the format "Final Score: X/10" or similar
-            import re
-
-            score_matches = re.findall(
-                r"(?:final|overall)\s+score:?\s*(\d+(?:\.\d+)?)",
-                evaluation.lower(),
-            )
-            score = float(score_matches[-1]) if score_matches else 5.0
-            # Normalize to 0-1 range
-            normalized_score = score / 10.0
-        except Exception as e:
-            logger.error(f"Failed to extract score: {e}")
-            normalized_score = 0.5  # Default mid-range score
+        # Use robust score extraction with multiple fallback strategies
+        normalized_score = self._extract_score_robust(evaluation)
 
-        logger.debug(
+        logger.info(
             f"Evaluation complete. Score: {normalized_score:.2f}"
         )
 
@@ -542,16 +600,20 @@ Based on the original response, evaluation, and reflection, provide an improved
         all_results = []
 
         for task_idx, task in enumerate(tasks):
-            logger.info(f"Processing task {task_idx+1}/{len(tasks)}")
+            logger.info(
+                f"\n{'='*60}\nProcessing task {task_idx+1}/{len(tasks)}\n{'='*60}"
+            )
+            logger.info(f"Task: {task[:100]}...")
 
             iterations = []
             best_response = None
             best_score = -1
+            prev_score = -1
 
             # Run through multiple iterations of reflection
             for iteration in range(self.max_loops):
-                logger.debug(
-                    f"Starting iteration {iteration+1}/{self.max_loops}"
+                logger.info(
+                    f"\n--- Iteration {iteration+1}/{self.max_loops} ---"
                 )
 
                 # In first iteration, generate new response
@@ -581,13 +643,36 @@ Based on the original response, evaluation, and reflection, provide an improved
                     best_response = step_result["response"]
                     best_score = step_result["score"]
 
-                # If score is very high, we can stop early
-                if step_result["score"] > 0.9:
-                    logger.debug(
-                        f"Score {step_result['score']} exceeds threshold. Stopping early."
+                current_score = step_result["score"]
+                logger.info(
+                    f"Iteration {iteration+1} complete | Score: {current_score:.2f} | Best: {best_score:.2f}"
+                )
+
+                # Early termination condition 1: Score is high enough
+                if current_score >= EARLY_TERMINATION_THRESHOLD:
+                    logger.info(
+                        f"✓ High score achieved ({current_score:.2f} >= {EARLY_TERMINATION_THRESHOLD}). Stopping early."
+                    )
+                    break
+
+                # Early termination condition 2: Score not improving
+                if iteration > 0 and (current_score - prev_score) < SCORE_IMPROVEMENT_THRESHOLD:
+                    logger.info(
+                        f"✓ Score improvement minimal ({current_score:.2f} vs {prev_score:.2f}). Stopping early."
                     )
                     break
 
+                prev_score = current_score
+
+            # Summary logging
+            iterations_used = len(iterations)
+            logger.info(
+                f"\n{'='*60}\n"
+                f"Task complete | Iterations used: {iterations_used}/{self.max_loops} | "
+                f"Best score: {best_score:.2f}\n"
+                f"{'='*60}"
+            )
+
             # Add to conversation history (simplified)
             self.conversation.add("user", task)
             self.conversation.add("assistant", best_response)