Create agentos_core.py

agentos_core.py

@@ -0,0 +1,42 @@
+import os, json, random
+from openai import OpenAI
+
+class AgentCore:
+    def __init__(self):
+        self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+        self.memory = []
+        self.performance_log = []
+
+    def think(self, prompt):
+        # Generate a response
+        response = self.client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt}]
+        )
+        output = response.choices[0].message.content.strip()
+
+        # Store in memory
+        self.memory.append({"prompt": prompt, "response": output})
+        self.log_feedback(prompt, output)
+
+        # Learn slightly (simple adaptive prompt tweaking)
+        if len(self.performance_log) % 3 == 0:
+            self.adapt_prompting_style()
+
+        return output
+
+    def log_feedback(self, prompt, output):
+        score = self.auto_score(output)
+        self.performance_log.append({"prompt": prompt, "response": output, "score": score})
+
+    def auto_score(self, output):
+        # Simple scoring: the longer and more coherent, the higher the score
+        return len(output.split())
+
+    def adapt_prompting_style(self):
+        # Simulate a micro self-improvement step
+        avg_score = sum(d["score"] for d in self.performance_log[-3:]) / 3
+        if avg_score < 50:
+            print("🧠 Agent adjusting style for clarity...")
+        else:
+            print("🚀 Agent maintaining current strategy.")