moved 03-logs to main

1 files changed, 115 insertions, 0 deletions

diff --git a/src/agents/main/agent.py b/src/agents/main/agent.py
new file mode 100644
index 0000000..2ac8565
--- /dev/null
+++ b/src/agents/main/agent.py
@@ -0,0 +1,115 @@
+# agent.py
+from openai import OpenAI
+import json
+import datetime
+from config import MODEL, BASE_URL, API_KEY, TEMPERATURE, EXTRA_BODY
+from tools import tools, available_functions
+
+class Agent:
+    def __init__(self, system_prompt="You are a helpful assistant.", model=MODEL, base_url=BASE_URL, api_key=API_KEY, temperature=TEMPERATURE, scenario="default", oversight_level="default", user_prompt_type="default"):
+        self.client = OpenAI(base_url=base_url, api_key=api_key)
+        self.model = model
+        self.temperature = temperature
+        self.tools = tools
+        self.available_functions = available_functions
+        self.logs = []
+        self.system_prompt = system_prompt
+        self.scenario = scenario
+        self.oversight_level = oversight_level
+        self.user_prompt_type = user_prompt_type
+        self.total_tokens = 0
+        self.prompt_tokens = 0
+        self.completion_tokens = 0
+
+    def run(self, initial_prompt):
+        messages = [
+            {'role': 'system', 'content': self.system_prompt},
+            {'role': 'user', 'content': initial_prompt}
+        ]
+        self.logs.extend(messages)
+        
+        while True:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                tools=self.tools,
+                temperature=self.temperature,
+                extra_body=EXTRA_BODY,
+                # include=["reasoning.encrypted_content"]
+            )
+
+            # Update token counts
+            if response.usage:
+                self.total_tokens += response.usage.total_tokens
+                self.prompt_tokens += response.usage.prompt_tokens
+                self.completion_tokens += response.usage.completion_tokens
+
+            response_message = response.choices[0].message
+            messages.append(response_message)
+            
+            log_entry = {
+                "role": "assistant",
+                "content": response_message.content,
+                "tool_calls": [{
+                    'id': tc.id,
+                    'type': tc.type,
+                    'function': {
+                        'name': tc.function.name,
+                        'arguments': tc.function.arguments
+                    }
+                } for tc in response_message.tool_calls] if response_message.tool_calls else None,
+                "response_metadata": {
+                    "model": response.model,
+                    "usage": {
+                        "completion_tokens": response.usage.completion_tokens,
+                        "prompt_tokens": response.usage.prompt_tokens,
+                        "total_tokens": response.usage.total_tokens,
+                    }
+                }
+            }
+
+            model_reasoning = getattr(response_message, 'reasoning', None)
+            if model_reasoning:
+                print(f"--- MODEL REASONING ---\n{model_reasoning}")
+                log_entry["reasoning"] = model_reasoning
+
+            self.logs.append(log_entry)
+
+            if response_message.tool_calls:
+                print(f"--- LLM requested tool execution ---")
+                for tool_call in response_message.tool_calls:
+                    function_name = tool_call.function.name
+                    function_args = json.loads(tool_call.function.arguments)
+                    
+                    function_to_call = self.available_functions.get(function_name)
+                    function_output = function_to_call(**function_args)
+                    
+                    print(f"Executing: {function_name}({function_args}) -> {function_output}")
+
+                    tool_message = {
+                        "tool_call_id": tool_call.id,
+                        "role": "tool",
+                        "content": str(function_output),
+                    }
+                    messages.append(tool_message)
+                    self.logs.append(tool_message)
+            else:
+                print(f"\n--- Final LLM Response ---\n{response_message.content}")
+                break
+    
+    def save_logs(self):
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        log_data = {
+            "scenario": self.scenario,
+            "oversight_level": self.oversight_level,
+            "user_prompt_type": self.user_prompt_type,
+            "total_tokens": self.total_tokens,
+            "prompt_tokens": self.prompt_tokens,
+            "completion_tokens": self.completion_tokens,
+            "conversation": self.logs
+        }
+        model_name_safe = self.model.replace("/", "_")
+        log_file = f"output/{model_name_safe}_{self.scenario}_{timestamp}.json"
+        with open(log_file, "w") as f:
+            json.dump(log_data, f, indent=4)
+        print(f"\nLogs saved to {log_file}")