mtea prompting agent

README.md

@@ -191,4 +191,8 @@ In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Data
 
 * Integrate guidance and token healing
 
-* Add text to speech [whisper x, youtube script](https://github.com/kyegomez/youtubeURL-to-text) and text to speech code models as tools 
+* Add text to speech [whisper x, youtube script](https://github.com/kyegomez/youtubeURL-to-text) and text to speech code models as tools 
+
+* Add task completion logic with meta prompting, task evaluation as a state from 0.0 to 1.0, and critiquing for meta prompting.
+
+* Integrate meta prompting for every agent boss and worker

swarms/agents/utils.py

@@ -744,4 +744,16 @@ def multi_agent_emotion_analysis_v2(text: str, agents_memory: List[Dict[str, Any
     for emotion in emotion_scores:
         emotion_scores[emotion] /= len(agents_memory)
 
-    return emotion_scores
+    return emotion_scores
+
+def swarm_intelligence(task_prompt, agents_memory):
+    subtasks = generate_tasks(task_prompt)
+    results = []
+    for subtask in subtasks:
+        agent_votes = []
+        for agent_memory in agents_memory:
+            agent_vote, _ = chat(agent_memory + [{"role": "user", "content": f"Propose a solution for: {subtask}"}])
+            agent_votes.append(agent_vote.strip())
+        most_common_solution = max(set(agent_votes), key=agent_votes.count)
+        results.append(most_common_solution)
+    return results