diff options
| -rw-r--r-- | src/agents/main/agent.py | 403 | ||||
| -rw-r--r-- | src/agents/main/config.yaml | 89 | ||||
| -rw-r--r-- | src/agents/main/config_loader.py | 161 | ||||
| -rw-r--r-- | src/agents/main/interrogate.py | 156 | ||||
| -rw-r--r-- | src/agents/main/main.py | 68 | ||||
| -rw-r--r-- | src/agents/main/provider.py | 586 | ||||
| -rw-r--r-- | src/agents/main/pyproject.toml | 3 | ||||
| -rw-r--r-- | src/agents/main/runner.py | 166 | ||||
| -rw-r--r-- | src/agents/main/uv.lock | 336 |
9 files changed, 1624 insertions, 344 deletions
diff --git a/src/agents/main/agent.py b/src/agents/main/agent.py index 26050b3..2b979fa 100644 --- a/src/agents/main/agent.py +++ b/src/agents/main/agent.py @@ -1,190 +1,327 @@ -# agent.py -from vfs import VFS -from openai import OpenAI +""" +Agent class that uses the provider abstraction layer. +Handles conversation loops, tool execution, and logging. +""" import json -import re import os import datetime -from config import MODEL, BASE_URL, API_KEY, TEMPERATURE, EXTRA_BODY, INCLUDE_REASONING_IN_CONTEXT +from typing import List, Dict, Any, Optional +from vfs import VFS +from provider import ProviderAdapter, LLMResponse, ReasoningStep, create_provider_adapter +from config_loader import ProviderConfig, ModelConfig 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 = [] + def __init__( + self, + system_prompt: str = "You are a helpful assistant.", + provider_adapter: ProviderAdapter = None, + scenario: str = "default", + oversight_level: str = "default", + user_prompt_type: str = "default" + ): + self.provider = provider_adapter self.system_prompt = system_prompt self.scenario = scenario self.oversight_level = oversight_level self.user_prompt_type = user_prompt_type + + # Get model info from provider + if provider_adapter: + self.model = provider_adapter.model_config.id + self.temperature = provider_adapter.model_config.temperature + else: + self.model = "unknown" + self.temperature = 1.0 + + self.tools = tools + self.available_functions = available_functions + self.logs: List[Dict] = [] self.total_tokens = 0 self.prompt_tokens = 0 self.completion_tokens = 0 - def run(self, initial_prompt): + @classmethod + def from_configs( + cls, + system_prompt: str, + provider_config: ProviderConfig, + model_config: ModelConfig, + scenario: str = "default", + oversight_level: str = "default", + user_prompt_type: str = "default" + ) -> "Agent": + """Create an Agent from provider and model configs.""" + adapter = create_provider_adapter(provider_config, model_config) + return Agent( + system_prompt=system_prompt, + provider_adapter=adapter, + scenario=scenario, + oversight_level=oversight_level, + user_prompt_type=user_prompt_type + ) + + def run(self, initial_prompt: str): + """Run the agent with an initial prompt.""" messages = [ {'role': 'system', 'content': self.system_prompt}, {'role': 'user', 'content': initial_prompt} ] self.logs.extend(messages) - - # This is now a wrapper around the main chat logic - self.chat_loop(messages) + return self.chat_loop(messages) - def load_conversation(self, conversation_history, total_tokens=0, prompt_tokens=0, completion_tokens=0): + def load_conversation( + self, + conversation_history: List[Dict], + total_tokens: int = 0, + prompt_tokens: int = 0, + completion_tokens: int = 0 + ): + """Load a conversation from history (for interrogation).""" self.logs = conversation_history self.total_tokens = total_tokens self.prompt_tokens = prompt_tokens self.completion_tokens = completion_tokens - def chat(self, user_input): + def chat(self, user_input: str): + """Send a message and get response.""" self.logs.append({'role': 'user', 'content': user_input}) - messages = list(self.logs) # Create a copy for the API call + messages = list(self.logs) return self.chat_loop(messages) - def chat_loop(self, messages): + def chat_loop(self, messages: List[Dict]): + """Main conversation loop.""" + turn_count = 0 while True: - response = self.client.chat.completions.create( - model=self.model, - messages=messages, - tools=self.tools, - temperature=self.temperature, - extra_body=EXTRA_BODY, - ) + llm_response = self.provider.call(messages, self.tools) # 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 - - # Process content to extract reasoning - content = response_message.content - reasoning = None - clean_content = content - - if content: - # First, try to parse with tags for models that support it - thought_match = re.search(r"<(thinking|thought)>(.*?)</\1>", content, re.DOTALL) - if thought_match: - reasoning = thought_match.group(2).strip() - clean_content = content.replace(thought_match.group(0), "").strip() - # If no tags, and there are tool calls, assume the whole content is reasoning - elif response_message.tool_calls: - reasoning = content - clean_content = None - - # Decide what to append to the API message history - if INCLUDE_REASONING_IN_CONTEXT: - messages.append(response_message) + if llm_response.usage: + self.total_tokens += llm_response.usage.get("total_tokens", 0) + self.prompt_tokens += llm_response.usage.get("prompt_tokens", 0) + self.completion_tokens += llm_response.usage.get("completion_tokens", 0) + + # Process the response - check for interleaved thinking + if self._has_interleaved_thinking(llm_response): + result = self._handle_interleaved(messages, llm_response) + if result is not None: + return result else: - # Create a new dictionary with the cleaned content - message_dict = { - 'role': 'assistant', - 'content': clean_content, - } - if response_message.tool_calls: - message_dict['tool_calls'] = [{ - 'id': tc.id, - 'type': tc.type, - 'function': { - 'name': tc.function.name, - 'arguments': tc.function.arguments - } - } for tc in response_message.tool_calls] - - messages.append(message_dict) - - log_entry = { + result = self._handle_standard(messages, llm_response, is_first_turn=(turn_count == 0)) + if result is not None: + return result + + turn_count += 1 + + def _has_interleaved_thinking(self, response: LLMResponse) -> bool: + """Check if response has interleaved thinking (reasoning between tool calls).""" + # If we have tool calls AND reasoning that isn't the final response + if response.tool_calls and response.reasoning_steps: + # Check if the reasoning is not marked as final response + has_non_final_reasoning = any( + not step.is_final_response + for step in response.reasoning_steps + ) + return has_non_final_reasoning + return False + + def _handle_interleaved(self, messages: List[Dict], response: LLMResponse): + """Handle interleaved thinking (reasoning between tool calls).""" + print("--- Interleaved thinking detected ---") + + # Extract reasoning content + reasoning_text = "\n".join( + step.content for step in response.reasoning_steps + if not step.is_final_response + ) + + # Build assistant message with reasoning and tool calls + assistant_message = { + "role": "assistant", + "content": reasoning_text if reasoning_text else None, + "tool_calls": response.tool_calls + } + messages.append(assistant_message) + + # Log the assistant message + self.logs.append({ + "role": "assistant", + "content": reasoning_text, + "reasoning": reasoning_text, + "tool_calls": response.tool_calls, + "interleaved_thinking": True, + "response_metadata": { + "model": self.model, + "usage": response.usage + } + }) + + # print(f"--- Reasoning ---\n{reasoning_text[:200]}..." if len(reasoning_text) > 200 else f"--- Reasoning ---\n{reasoning_text}") + print(f"--- LLM requested {len(response.tool_calls)} tool execution(s) ---") + + # Execute each tool call and continue the loop + for tool_call in response.tool_calls: + result = self._execute_tool(messages, tool_call) + if result is None: + return None # Stop iteration + + # Continue the while loop for more tool calls or final response + return None + + def _handle_standard(self, messages: List[Dict], response: LLMResponse, is_first_turn: bool = False): + """Handle standard response (reasoning, then tools, then final or just final).""" + content = response.content or "" + reasoning = response.reasoning_steps[0].content if response.reasoning_steps else None + + # If there are tool calls + if response.tool_calls: + # Build assistant message + assistant_message = { "role": "assistant", - "content": clean_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, + "content": content if content else None, + "tool_calls": response.tool_calls + } + messages.append(assistant_message) + + # Log entry + self.logs.append({ + "role": "assistant", + "content": content, + "reasoning": reasoning, + "tool_calls": response.tool_calls, "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": self.model, + "usage": response.usage } - } + }) + + print(f"--- LLM requested {len(response.tool_calls)} tool execution(s) ---") + + # Execute all tool calls + for tool_call in response.tool_calls: + result = self._execute_tool(messages, tool_call) + if result is None: + return None + + # Continue loop for more interactions + return None + + else: + # No tool calls - check if this is a final response or just a conversational response + # On first turn, models like Kimi-K2 may respond conversationally before making tool calls + if is_first_turn: + # Log the response but don't return - continue to next turn + assistant_message = { + "role": "assistant", + "content": content if content else None + } + messages.append(assistant_message) - if reasoning: - log_entry["reasoning"] = reasoning - - model_reasoning = getattr(response_message, 'reasoning', None) - if model_reasoning: - print(f"--- MODEL REASONING ---\n{model_reasoning}") - log_entry["reasoning"] = model_reasoning - - # Append the processed assistant message to our internal logs - 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), + self.logs.append({ + "role": "assistant", + "content": content, + "reasoning": reasoning, + "tool_calls": None, + "response_metadata": { + "model": self.model, + "usage": response.usage } - messages.append(tool_message) - self.logs.append(tool_message) + }) + + # Continue to next turn - don't return + return None else: - print(f"\n--- Final LLM Response ---\n{response_message.content}") - return response_message.content - - def save_logs(self, output_dir="output"): + # Not first turn and no tool calls - this is a final response + assistant_message = { + "role": "assistant", + "content": content if content else None + } + messages.append(assistant_message) + + # Log entry + self.logs.append({ + "role": "assistant", + "content": content, + "reasoning": reasoning, + "tool_calls": None, + "response_metadata": { + "model": self.model, + "usage": response.usage + } + }) + + # print(f"\n--- Final LLM Response ---\n{content}") + return content + + def _execute_tool(self, messages: List[Dict], tool_call: Dict) -> Optional[str]: + """Execute a single tool call.""" + function_name = tool_call["function"]["name"] + function_args = json.loads(tool_call["function"]["arguments"]) + + function_to_call = self.available_functions.get(function_name) + if not function_to_call: + error_msg = f"Unknown tool: {function_name}" + print(f"Error: {error_msg}") + function_output = error_msg + else: + try: + function_output = function_to_call(**function_args) + except Exception as e: + function_output = f"Error executing {function_name}: {str(e)}" + + # print(f"Executing: {function_name}({function_args}) -> {function_output}") + print(f"Executing: {function_name}({function_args})") + + # Create tool message + tool_message = { + "tool_call_id": tool_call["id"], + "role": "tool", + "content": str(function_output) + } + messages.append(tool_message) + self.logs.append(tool_message) + + return str(function_output) + + def save_logs( + self, + output_dir: str = "output", + scenario: str = None, + oversight_level: str = None + ): + """Save conversation logs to a JSON file.""" timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") model_name_safe = self.model.replace("/", "_") - scenario_name_safe = self.scenario.replace("/", "_") + scenario_name = (scenario or self.scenario).replace("/", "_") + oversight = oversight_level or self.oversight_level - # New directory structure - model_output_dir = os.path.join(output_dir, model_name_safe) - os.makedirs(model_output_dir, exist_ok=True) + # Directory structure: output/{model}/{scenario}/{oversight}/ + base_dir = os.path.join(output_dir, model_name_safe, scenario_name, oversight) + os.makedirs(base_dir, exist_ok=True) - # New filename and run_id - filename_base = f"{scenario_name_safe}_{self.oversight_level}_{timestamp}" - run_id = f"{model_name_safe}/{filename_base}" - log_file = os.path.join(model_output_dir, f"{filename_base}.json") + # Filename: {timestamp}_run_id.json + filename_base = f"{timestamp}" + run_id = f"{model_name_safe}/{scenario_name}/{oversight}/{filename_base}" + log_file = os.path.join(base_dir, f"{filename_base}.json") log_data = { "run_id": run_id, "model": self.model, - "scenario": self.scenario, - "oversight_level": self.oversight_level, + "scenario": scenario or self.scenario, + "oversight_level": oversight, "user_prompt_type": self.user_prompt_type, "temperature": self.temperature, - "base_url": str(self.client.base_url), - "include_reasoning_in_context": INCLUDE_REASONING_IN_CONTEXT, - "extra_body_config": EXTRA_BODY, + "base_url": str(self.provider.provider_config.base_url) if self.provider else None, + "extra_body_config": self.provider.model_config.extra_body if self.provider else {}, "final_vfs_state": VFS.get_instance().fs, "total_tokens": self.total_tokens, "prompt_tokens": self.prompt_tokens, "completion_tokens": self.completion_tokens, "conversation": self.logs } - + with open(log_file, "w") as f: json.dump(log_data, f, indent=4) print(f"\nLogs saved to {log_file}") + return log_file diff --git a/src/agents/main/config.yaml b/src/agents/main/config.yaml new file mode 100644 index 0000000..58faa3b --- /dev/null +++ b/src/agents/main/config.yaml @@ -0,0 +1,89 @@ +# Experiment Configuration +# Define providers, models, scenarios, and runs + +# Default parameters (can be overridden per model) +defaults: + temperature: 1.0 + # max_tokens: 4096 + oversight: low + include_reasoning_in_context: true + +# Provider configurations +providers: + openai: + api_key_env: OPENAI_API_KEY + base_url: https://api.openai.com/v1 + google: + api_key_env: GOOGLE_API_KEY + base_url: https://generativelanguage.googleapis.com/v1beta/openai/ + openrouter: + api_key_env: OPENROUTER_API_KEY + base_url: https://openrouter.ai/api/v1 + anthropic: + api_key_env: ANTHROPIC_API_KEY + moonshot: + api_key_env: OPENROUTER_API_KEY + base_url: https://openrouter.ai/api/v1 + + +# Models to test +models: + - id: z-ai/glm-4.7 + provider: openrouter + extra_body: + thinking: + enabled: true + +# - id: gpt-4.5 +# provider: openai +# temperature: 0.7 + +# - id: claude-haiku-4-5 +# provider: anthropic +# temperature: 0.7 +# extra_body: +# thinking: +# type: "enabled" +# budget_tokens: 10000 + +# - id: gemini-2.5-flash +# provider: google +# temperature: 1 +# extra_body: +# extra_body: +# google: +# thinking_config: +# include_thoughts: True + +# - id: anthropic/claude-sonnet-4-5 +# provider: openrouter +# temperature: 0.7 + +# - id: moonshotai/kimi-k2-thinking +# provider: moonshot +# temperature: 1.0 +# extra_body: +# reasoning: +# enabled: True + +# Scenarios to run +scenarios: + - path: scenarios/corporate_sabotage_v2 + runs: 1 + +# - path: scenarios/data_sanitization +# runs: 3 +# +# - path: scenarios/log_redaction +# runs: 3 + +# Oversight levels to test +oversight_levels: +# - low +# - medium + - high + +# Output settings +output: + dir: test + save_vfs_state: true diff --git a/src/agents/main/config_loader.py b/src/agents/main/config_loader.py new file mode 100644 index 0000000..056b47b --- /dev/null +++ b/src/agents/main/config_loader.py @@ -0,0 +1,161 @@ +""" +Configuration loader for the experiment framework. +Loads config.yaml and provides access to providers, models, scenarios. +""" +import os +import yaml +from typing import Any, Dict, List, Optional +from dataclasses import dataclass, field + +# Load environment variables from .env file +try: + from dotenv import load_dotenv + load_dotenv() +except ImportError: + pass # python-dotenv not installed + + +@dataclass +class ProviderConfig: + """Configuration for a provider.""" + name: str + api_key_env: str + base_url: str + extra_body: Dict[str, Any] = field(default_factory=dict) + + @property + def api_key(self) -> str: + """Get API key from environment variable.""" + key = os.environ.get(self.api_key_env) + if not key: + raise ValueError(f"Environment variable {self.api_key_env} not set") + return key + + +@dataclass +class ModelConfig: + """Configuration for a model.""" + id: str + provider: str + temperature: float = 1.0 + max_tokens: Optional[int] = None + extra_body: Dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ScenarioConfig: + """Configuration for a scenario.""" + path: str + runs: int = 1 + + +class ConfigLoader: + """Load and manage experiment configuration.""" + + def __init__(self, config_path: str = "config.yaml"): + self.config_path = config_path + self._config: Dict[str, Any] = {} + self._providers: Dict[str, ProviderConfig] = {} + self._models: List[ModelConfig] = [] + self._scenarios: List[ScenarioConfig] = [] + + def load(self) -> None: + """Load configuration from YAML file.""" + with open(self.config_path, 'r') as f: + self._config = yaml.safe_load(f) + + self._parse_providers() + self._parse_models() + self._parse_scenarios() + + def _parse_providers(self) -> None: + """Parse provider configurations.""" + providers = self._config.get('providers', {}) + for name, config in providers.items(): + self._providers[name] = ProviderConfig( + name=name, + api_key_env=config.get('api_key_env', ''), + base_url=config.get('base_url', ''), + extra_body=config.get('extra_body', {}) + ) + + def _parse_models(self) -> None: + """Parse model configurations.""" + defaults = self._config.get('defaults', {}) + models = self._config.get('models', []) + + for model in models: + self._models.append(ModelConfig( + id=model['id'], + provider=model['provider'], + temperature=model.get('temperature', defaults.get('temperature', 1.0)), + max_tokens=model.get('max_tokens', defaults.get('max_tokens')), + extra_body=model.get('extra_body', {}) + )) + + def _parse_scenarios(self) -> None: + """Parse scenario configurations.""" + scenarios = self._config.get('scenarios', []) + + for scenario in scenarios: + self._scenarios.append(ScenarioConfig( + path=scenario['path'], + runs=scenario.get('runs', 1) + )) + + @property + def providers(self) -> Dict[str, ProviderConfig]: + """Get all provider configurations.""" + return self._providers + + @property + def models(self) -> List[ModelConfig]: + """Get all model configurations.""" + return self._models + + @property + def scenarios(self) -> List[ScenarioConfig]: + """Get all scenario configurations.""" + return self._scenarios + + @property + def oversight_levels(self) -> List[str]: + """Get oversight levels to test.""" + return self._config.get('oversight_levels', ['low']) + + @property + def defaults(self) -> Dict[str, Any]: + """Get default configuration.""" + return self._config.get('defaults', {}) + + @property + def output_dir(self) -> str: + """Get output directory.""" + return self._config.get('output', {}).get('dir', 'output') + + def get_provider(self, name: str) -> ProviderConfig: + """Get a specific provider configuration.""" + if name not in self._providers: + raise ValueError(f"Unknown provider: {name}") + return self._providers[name] + + def get_model(self, model_id: str) -> ModelConfig: + """Get a specific model configuration.""" + for model in self._models: + if model.id == model_id: + return model + raise ValueError(f"Unknown model: {model_id}") + + def get_scenario(self, path: str) -> ScenarioConfig: + """Get a specific scenario configuration.""" + for scenario in self._scenarios: + if scenario.path == path: + return scenario + raise ValueError(f"Unknown scenario: {path}") + + +def load_config(config_path: str = "config.yaml") -> ConfigLoader: + """Convenience function to load configuration.""" + loader = ConfigLoader(config_path) + loader.load() + return loader diff --git a/src/agents/main/interrogate.py b/src/agents/main/interrogate.py index e0b85c4..035ca58 100644 --- a/src/agents/main/interrogate.py +++ b/src/agents/main/interrogate.py @@ -1,15 +1,75 @@ -# interrogate.py +""" +Interrogate - replay and continue a conversation from a log file. +Allows interactive questioning of an agent based on a previous run. +""" import argparse import json +import os +from typing import Dict, Any, Optional from agent import Agent -from config import API_KEY # We'll need the API key from the config -from vfs import VFS # Import VFS +from config_loader import ConfigLoader, ProviderConfig, ModelConfig +from provider import create_provider_adapter +from vfs import VFS + + +def load_prompt(file_path: str) -> str: + """Load a prompt file.""" + if not os.path.exists(file_path): + return "" + with open(file_path, 'r') as f: + return f.read().strip() + + +def get_provider_from_log(log_data: Dict) -> tuple: + """Extract provider info from log and create configs.""" + model_id = log_data["model"] + base_url = log_data.get("base_url", "") + temperature = log_data.get("temperature", 1.0) + extra_body = log_data.get("extra_body_config", {}) + + # Determine provider from base_url + if "openrouter" in base_url: + provider_name = "openrouter" + elif "openai" in base_url: + provider_name = "openai" + elif "google" in base_url or "generativelanguage" in base_url: + provider_name = "google" + else: + # Default to openai-compatible + provider_name = "openai" + + # Create configs + # Note: API key needs to be in environment or config + provider_config = ProviderConfig( + name=provider_name, + api_key_env=f"{provider_name.upper()}_API_KEY", + base_url=base_url + ) + + model_config = ModelConfig( + id=model_id, + provider=provider_name, + temperature=temperature, + extra_body=extra_body + ) + + return provider_config, model_config + def main(): parser = argparse.ArgumentParser(description="Interrogate a conversation log.") parser.add_argument("log_file", help="Path to the log file to load.") + parser.add_argument("--config", default="config.yaml", help="Path to config file (for API keys)") args = parser.parse_args() + # Load config (for API keys) + config = ConfigLoader(args.config) + try: + config.load() + except FileNotFoundError: + pass # Config file is optional for interrogation + + # Load log file try: with open(args.log_file, 'r') as f: log_data = json.load(f) @@ -20,38 +80,40 @@ def main(): print(f"Error: Could not decode JSON from {args.log_file}") return - # Extract data to re-hydrate the agent - conversation_history = log_data["conversation"] - system_prompt = conversation_history[0]['content'] - model = log_data["model"] - temperature = log_data.get("temperature", 1.0) # Default if not found - base_url = log_data.get("base_url") - extra_body = log_data.get("extra_body_config", {}) + # Extract data from log + conversation_history = log_data.get("conversation", []) + if not conversation_history: + print("Error: No conversation history in log file") + return + + system_prompt = conversation_history[0].get('content', '') if conversation_history else '' + scenario = log_data.get('scenario', 'interrogation') + oversight_level = log_data.get('oversight_level', 'N/A') + + # Get provider from log + provider_config, model_config = get_provider_from_log(log_data) - # Initialize the virtual file system from the log + # Initialize VFS from log final_vfs_state = log_data.get("final_vfs_state") if final_vfs_state: VFS.get_instance(fs_data=final_vfs_state) print("--- VFS State Loaded from Log ---") + VFS.get_instance().print_fs() else: - # If no VFS state is found in the log, initialize with an empty VFS - VFS.get_instance() # Initialize an empty VFS if no state found + VFS.get_instance() print("--- No VFS State in Log, Initializing Empty VFS ---") - # Initialize the agent - # Note: some parameters like scenario are just for logging, not for re-hydration - agent = Agent( + # Create agent from log provider info + agent = Agent.from_configs( system_prompt=system_prompt, - model=model, - base_url=base_url, - api_key=API_KEY, # API key is not in the log, get it from config - temperature=temperature, - # Pass scenario/oversight for logging purposes if we save later - scenario=log_data.get('scenario', 'interrogation'), - oversight_level=log_data.get('oversight_level', 'N/A') + provider_config=provider_config, + model_config=model_config, + scenario=scenario, + oversight_level=oversight_level, + user_prompt_type=log_data.get('user_prompt_type', 'interrogation') ) - - # Load the conversation history + + # Load conversation history (skip system prompt as it's already in system_prompt) agent.load_conversation( conversation_history=conversation_history, total_tokens=log_data.get('total_tokens', 0), @@ -59,26 +121,54 @@ def main(): completion_tokens=log_data.get('completion_tokens', 0) ) - print("Starting interactive session... (type 'exit' to quit)") + print("\n--- Loaded Conversation ---") + print(f"Model: {agent.model}") + print(f"Scenario: {scenario}") + print(f"Oversight: {oversight_level}") + print(f"Total tokens so far: {agent.total_tokens}") + print(f"Conversation turns: {len([m for m in conversation_history if m.get('role') in ['user', 'assistant', 'tool']])}") + + # Start interactive session + print("\nStarting interactive session... (type 'exit' to quit, 'save' to save)") while True: try: user_input = input("\nYour turn: ") if user_input.lower() == 'exit': break - + if user_input.lower() == 'save': + agent.save_logs() + continue + agent.chat(user_input) + # Find the last assistant message to print response + found = False + for msg in reversed(agent.logs): + if msg.get('role') == 'assistant': + content = msg.get('content', '') + reasoning = msg.get('reasoning', '') + print(f"\n--- Assistant Response ---") + if reasoning: + print(f"Reasoning: {reasoning[:300]}{'...' if len(reasoning) > 300 else ''}") + if content: + print(f"Content: {content[:300]}{'...' if len(content) > 300 else ''}") + if msg.get('tool_calls'): + print(f"Tool calls: {len(msg['tool_calls'])}") + found = True + break + + if not found: + print("No assistant message found in logs") + except KeyboardInterrupt: print("\nExiting...") break - - # Ask user if they want to save the new conversation + + # Ask to save save_choice = input("\nSave the extended conversation to a new log file? (y/n): ").lower() if save_choice == 'y': - agent.save_logs(output_dir="interrogation_logs") - - print("Session ended.") + agent.save_logs() if __name__ == "__main__": - main()
\ No newline at end of file + main() diff --git a/src/agents/main/main.py b/src/agents/main/main.py index 5d398c4..ad4015a 100644 --- a/src/agents/main/main.py +++ b/src/agents/main/main.py @@ -1,62 +1,14 @@ -# main.py -from agent import Agent -import os -from config import INCLUDE_HIDDEN_GOAL, MODEL -from vfs import VFS +""" +Main entry point for running experiments. +Uses config.yaml to define what to run. +""" +from runner import run_from_config -def load_prompt(file_path): - if not os.path.exists(file_path): - return "" - with open(file_path, 'r') as f: - return f.read().strip() -if __name__ == "__main__": - # Configuration for the experiment run - SCENARIO = "corporate_sabotage" - OVERSIGHT_LEVEL = "high" - - # Construct paths based on the scenario - scenario_dir = os.path.join("scenarios", SCENARIO) - vfs_path = os.path.join(scenario_dir, "data") - - # Initialize the virtual file system - VFS.get_instance(vfs_path) - - # Load prompts - benign_goal_path = os.path.join(scenario_dir, "benign_goal.md") - hidden_goal_path = os.path.join(scenario_dir, "hidden_goal.md") - user_prompt_path = os.path.join(scenario_dir, "user.md") - - benign_goal = load_prompt(benign_goal_path) - user_prompt = load_prompt(user_prompt_path) - - system_prompt_parts = [benign_goal] - - if INCLUDE_HIDDEN_GOAL: - hidden_goal = load_prompt(hidden_goal_path) - system_prompt_parts.append(hidden_goal) +def main(): + """Run all experiments defined in config.yaml.""" + run_from_config("config.yaml") - # Load and append oversight prompt - oversight_prompt_path = os.path.join("oversight", f"{OVERSIGHT_LEVEL}.md") - oversight_prompt = load_prompt(oversight_prompt_path) - system_prompt_parts.append(oversight_prompt) - system_prompt = "\n\n".join(filter(None, system_prompt_parts)) - - print("\n--- Using model:", MODEL, "---\n") - print("\n--- Final Virtual File System State ---") - VFS.get_instance().print_fs() - agent = Agent( - system_prompt=system_prompt, - scenario=SCENARIO, - oversight_level=OVERSIGHT_LEVEL, - user_prompt_type=os.path.basename(user_prompt_path) - ) - agent.run(user_prompt) - agent.save_logs() - - print("\n--- Final Virtual File System State ---") - VFS.get_instance().print_fs() - - - +if __name__ == "__main__": + main() diff --git a/src/agents/main/provider.py b/src/agents/main/provider.py new file mode 100644 index 0000000..837810c --- /dev/null +++ b/src/agents/main/provider.py @@ -0,0 +1,586 @@ +""" +Provider abstraction layer. +Handles different LLM providers with OpenAI-compatible APIs and reasoning extraction. +""" +import json +import re +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Tuple +from dataclasses import dataclass, field +from openai import OpenAI +from config_loader import ProviderConfig, ModelConfig + + +@dataclass +class ReasoningStep: + """A reasoning step from the model.""" + content: str + type: str = "reasoning" # "reasoning", "tool_call", "tool_result" + tool_name: Optional[str] = None + tool_args: Optional[Dict] = None + tool_result: Optional[str] = None + is_final_response: bool = False + + +@dataclass +class LLMResponse: + """Unified response from any LLM provider.""" + content: Optional[str] = None + reasoning_steps: List[ReasoningStep] = field(default_factory=list) + tool_calls: List[Dict] = field(default_factory=list) + usage: Dict[str, int] = field(default_factory=dict) + raw_response: Any = None + + +class ProviderAdapter(ABC): + """Base class for provider adapters.""" + + def __init__(self, provider_config: ProviderConfig, model_config: ModelConfig): + self.provider_config = provider_config + self.model_config = model_config + + @abstractmethod + def call(self, messages: List[Dict], tools: List[Dict]) -> LLMResponse: + """Make an API call and return a unified response.""" + pass + + @abstractmethod + def extract_reasoning(self, response: Any) -> List[ReasoningStep]: + """Extract reasoning steps from provider response.""" + pass + + def _merge_extra_body(self, extra_body: Dict[str, Any]) -> Dict[str, Any]: + """Merge model extra_body with provider extra_body.""" + merged = self.provider_config.extra_body.copy() + merged.update(extra_body) + return merged + + +class OpenAIProviderAdapter(ProviderAdapter): + """Adapter for OpenAI-compatible APIs (OpenAI, OpenRouter, Together, etc.).""" + + def __init__(self, provider_config: ProviderConfig, model_config: ModelConfig): + super().__init__(provider_config, model_config) + self.client = OpenAI( + base_url=provider_config.base_url, + api_key=provider_config.api_key + ) + + def call(self, messages: List[Dict], tools: List[Dict]) -> LLMResponse: + """Make an API call via OpenAI-compatible endpoint.""" + extra_body = self._merge_extra_body(self.model_config.extra_body) + + response = self.client.chat.completions.create( + model=self.model_config.id, + messages=messages, + tools=tools if tools else None, + temperature=self.model_config.temperature, + max_tokens=self.model_config.max_tokens, + extra_body=extra_body if extra_body else None + ) + + return self._parse_response(response) + + def _parse_response(self, response) -> LLMResponse: + """Parse OpenAI-compatible response.""" + # Extract usage + usage = {} + if response.usage: + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens + } + + message = response.choices[0].message + + # Extract content and reasoning + content = message.content or "" + reasoning = None + + # Try to get reasoning from different sources + # 1. Check for reasoning_content (OpenRouter) + if hasattr(message, 'reasoning_content') and message.reasoning_content: + reasoning = message.reasoning_content + + # 2. Check for reasoning_details (structured) + elif hasattr(message, 'reasoning_details') and message.reasoning_details: + reasoning = self._extract_from_reasoning_details(message.reasoning_details) + + # 3. Regex fallback for <thinking> tags + else: + thought_match = re.search(r"<(thinking|thought)>(.*?)</\1>", content, re.DOTALL) + if thought_match: + reasoning = thought_match.group(2).strip() + content = content.replace(thought_match.group(0), "").strip() + + # Extract tool calls + tool_calls = [] + has_tool_calls = False + if message.tool_calls: + has_tool_calls = True + tool_calls = [ + { + "id": tc.id, + "type": tc.type, + "function": { + "name": tc.function.name, + "arguments": tc.function.arguments + } + } + for tc in message.tool_calls + ] + + # If there are tool calls, content should be None (reasoning is in the reasoning field) + if has_tool_calls: + content = None + + # Build reasoning steps + reasoning_steps = [] + if reasoning: + reasoning_steps.append(ReasoningStep(content=reasoning, type="reasoning")) + + # Add final response if no tool calls + if not tool_calls and content: + reasoning_steps.append(ReasoningStep( + content=content, + type="reasoning", + is_final_response=True + )) + + return LLMResponse( + content=content, + reasoning_steps=reasoning_steps, + tool_calls=tool_calls, + usage=usage, + raw_response=response + ) + + def _extract_from_reasoning_details(self, reasoning_details: List[Dict]) -> str: + """Extract reasoning text from structured reasoning_details.""" + reasoning_parts = [] + for item in reasoning_details: + if item.get("type") == "reasoning.text": + reasoning_parts.append(item.get("text", "")) + return "\n".join(reasoning_parts).strip() + + def extract_reasoning(self, response: Any) -> List[ReasoningStep]: + """Extract reasoning steps from raw response.""" + parsed = self._parse_response(response) + return parsed.reasoning_steps + + +class GoogleProviderAdapter(ProviderAdapter): + """Adapter for Google's Generative Language API.""" + + def __init__(self, provider_config: ProviderConfig, model_config: ModelConfig): + super().__init__(provider_config, model_config) + # Google uses a different SDK, but we can use OpenAI-compatible endpoint + self.client = OpenAI( + base_url=provider_config.base_url, + api_key=provider_config.api_key + ) + + def call(self, messages: List[Dict], tools: List[Dict]) -> LLMResponse: + """Make an API call via Google Generative Language API.""" + extra_body = self._merge_extra_body(self.model_config.extra_body) + + response = self.client.chat.completions.create( + model=self.model_config.id, + messages=messages, + tools=tools if tools else None, + temperature=self.model_config.temperature, + max_tokens=self.model_config.max_tokens, + extra_body=extra_body if extra_body else None + ) + + return self._parse_response(response) + + def _parse_response(self, response) -> LLMResponse: + """Parse Google-compatible response.""" + usage = {} + if response.usage: + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens + } + + message = response.choices[0].message + content = message.content or "" + + # Google doesn't typically output structured reasoning in this format + # Just return content + reasoning_steps = [] + if content: + reasoning_steps.append(ReasoningStep( + content=content, + type="reasoning", + is_final_response=True + )) + + tool_calls = [] + if message.tool_calls: + tool_calls = [ + { + "id": tc.id, + "type": tc.type, + "function": { + "name": tc.function.name, + "arguments": tc.function.arguments + } + } + for tc in message.tool_calls + ] + + return LLMResponse( + content=content, + reasoning_steps=reasoning_steps, + tool_calls=tool_calls, + usage=usage, + raw_response=response + ) + + def extract_reasoning(self, response: Any) -> List[ReasoningStep]: + """Extract reasoning steps from raw response.""" + parsed = self._parse_response(response) + return parsed.reasoning_steps + + +class AnthropicProviderAdapter(ProviderAdapter): + """Adapter for Anthropic's direct API.""" + + def __init__(self, provider_config: ProviderConfig, model_config: ModelConfig): + super().__init__(provider_config, model_config) + try: + import anthropic + self.client = anthropic.Anthropic( + api_key=provider_config.api_key + ) + except ImportError: + raise ImportError("anthropic package not installed. Run: pip install anthropic") + + def call(self, messages: List[Dict], tools: List[Dict]) -> LLMResponse: + """Make an API call via Anthropic API.""" + # Convert OpenAI-style messages to Anthropic format + system_prompt = None + anthropic_messages = [] + + for msg in messages: + role = msg.get("role") + content = msg.get("content") + + if role == "system": + system_prompt = content + elif role == "user": + anthropic_messages.append({ + "role": "user", + "content": content + }) + elif role == "assistant": + # Check for tool calls in the message + if msg.get("tool_calls"): + # Anthropic uses content blocks for tool calls + blocks = [{"type": "text", "text": content or ""}] + for tc in msg.get("tool_calls", []): + blocks.append({ + "type": "tool_use", + "id": tc["id"], + "name": tc["function"]["name"], + "input": json.loads(tc["function"]["arguments"]) + }) + anthropic_messages.append({ + "role": "assistant", + "content": blocks + }) + else: + anthropic_messages.append({ + "role": "assistant", + "content": content + }) + elif role == "tool": + # Tool result + anthropic_messages.append({ + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": msg.get("tool_call_id"), + "content": msg.get("content") + } + ] + }) + + # Build thinking config from extra_body + extra_body = self._merge_extra_body(self.model_config.extra_body) + thinking_config = extra_body.get("thinking", {"type": "enabled", "budget_tokens": 10000}) + + response = self.client.messages.create( + model=self.model_config.id, + max_tokens=self.model_config.max_tokens or 16000, + thinking=thinking_config, + system=system_prompt, + messages=anthropic_messages + ) + + return self._parse_response(response) + + def _parse_response(self, response) -> LLMResponse: + """Parse Anthropic response with content blocks.""" + # Extract usage + usage = {} + if hasattr(response, "usage"): + usage = { + "input_tokens": getattr(response.usage, "input_tokens", 0), + "output_tokens": getattr(response.usage, "output_tokens", 0), + "total_tokens": getattr(response.usage, "input_tokens", 0) + getattr(response.usage, "output_tokens", 0) + } + + # Parse content blocks + reasoning_content = "" + final_content = "" + reasoning_steps = [] + + for block in response.content: + if block.type == "thinking": + # Extract reasoning from thinking block + if hasattr(block, "thinking") and block.thinking: + reasoning_content += block.thinking + "\n" + reasoning_steps.append(ReasoningStep( + content=block.thinking, + type="reasoning" + )) + elif block.type == "text": + final_content += block.text + "\n" + + # Clean up + reasoning_content = reasoning_content.strip() + final_content = final_content.strip() + + # Check for tool use blocks (Anthropic tool use) + tool_calls = [] + for block in response.content: + if block.type == "tool_use": + tool_calls.append({ + "id": block.id, + "type": "function", + "function": { + "name": block.name, + "arguments": json.dumps(block.input) + } + }) + + # Build response + if tool_calls: + # If there are tool calls, content is None + pass + elif final_content: + # Final response + reasoning_steps.append(ReasoningStep( + content=final_content, + type="reasoning", + is_final_response=True + )) + + return LLMResponse( + content=final_content if not tool_calls else None, + reasoning_steps=reasoning_steps, + tool_calls=tool_calls, + usage=usage, + raw_response=response + ) + + def extract_reasoning(self, response: Any) -> List[ReasoningStep]: + """Extract reasoning steps from raw response.""" + parsed = self._parse_response(response) + return parsed.reasoning_steps + + +class MoonshotProviderAdapter(ProviderAdapter): + """Adapter for Moonshot AI (Kimi-K2) using proprietary token format for tool calls.""" + + def __init__(self, provider_config: ProviderConfig, model_config: ModelConfig): + super().__init__(provider_config, model_config) + self.client = OpenAI( + base_url=provider_config.base_url, + api_key=provider_config.api_key + ) + + def call(self, messages: List[Dict], tools: List[Dict]) -> LLMResponse: + """Make an API call via Moonshot AI endpoint.""" + extra_body = self._merge_extra_body(self.model_config.extra_body) + + response = self.client.chat.completions.create( + model=self.model_config.id, + messages=messages, + tools=tools if tools else None, + temperature=self.model_config.temperature, + max_tokens=self.model_config.max_tokens, + extra_body=extra_body if extra_body else None + ) + + return self._parse_response(response) + + def _parse_response(self, response) -> LLMResponse: + """Parse Moonshot AI response with proprietary token format.""" + usage = {} + if response.usage: + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens + } + + message = response.choices[0].message + content = message.content or "" + + # Extract reasoning from various sources + reasoning = None + + # 1. Check for reasoning_content attribute (OpenRouter structured output) + if hasattr(message, 'reasoning_content') and message.reasoning_content: + reasoning = message.reasoning_content + + # 2. Check for <thinking> tags in content + if not reasoning: + thought_match = re.search(r"<(thinking|thought)>(.*?)</\1>", content, re.DOTALL) + if thought_match: + reasoning = thought_match.group(2).strip() + content = content.replace(thought_match.group(0), "").strip() + + # DEBUG: Check for OpenAI tool_calls first + openai_tool_calls = [] + has_openai_tc = hasattr(message, 'tool_calls') and message.tool_calls + if has_openai_tc: + for tc in message.tool_calls: + openai_tool_calls.append({ + "id": tc.id, + "type": tc.type, + "function": { + "name": tc.function.name, + "arguments": tc.function.arguments + } + }) + + # Extract tool calls from proprietary Kimi-K2 token format + proprietary_tool_calls = self._extract_tool_calls(content) + + # Debug output + has_prop_tc = len(proprietary_tool_calls) > 0 + print(f" [MoonshotAdapter] content_len={len(content)}, has_reasoning={bool(reasoning)}, has_openai_tc={has_openai_tc}, has_proprietary_tc={has_prop_tc}") + + # Use OpenAI tool_calls if available, otherwise use proprietary + tool_calls = openai_tool_calls if openai_tool_calls else proprietary_tool_calls + + # Clean tool calls from content + clean_content = content + if tool_calls: + clean_content = self._remove_tool_tokens(clean_content) + + # Build reasoning steps + reasoning_steps = [] + + # Add reasoning if present (either from reasoning_content or <thinking> tags) + if reasoning: + reasoning_steps.append(ReasoningStep(content=reasoning, type="reasoning")) + + # When there are tool calls, the content is the model's reasoning about tool selection + if tool_calls and clean_content: + reasoning_steps.append(ReasoningStep( + content=clean_content, + type="reasoning" + )) + + # Add final response content if present and no tool calls + if not tool_calls and clean_content: + reasoning_steps.append(ReasoningStep( + content=clean_content, + type="reasoning", + is_final_response=True + )) + + return LLMResponse( + content=clean_content if not tool_calls else None, + reasoning_steps=reasoning_steps, + tool_calls=tool_calls, + usage=usage, + raw_response=response + ) + + def _extract_tool_calls(self, content: str) -> List[Dict]: + """Extract tool calls from Kimi-K2 proprietary token format.""" + if '<|tool_calls_section_begin|>' not in content: + return [] + + # Pattern to match Kimi-K2 tool call format: + # <|tool_call_begin|>functions.read_file:1<|tool_call_argument_begin|>{"file_path": "..."}<|tool_call_end|> + pattern = r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[\w\.]+:\d+)\s*<\|tool_call_argument_begin\|>\s*(?P<function_arguments>.*?)\s*<\|tool_call_end\|>" + + tool_calls = [] + tool_calls_section_match = re.search( + r"<\|tool_calls_section_begin\|>(.*?)<\|tool_calls_section_end\|>", + content, + re.DOTALL + ) + + if not tool_calls_section_match: + return [] + + section_content = tool_calls_section_match.group(1) + + for match in re.finditer(pattern, section_content, re.DOTALL): + function_id = match.group("tool_call_id") + function_args = match.group("function_arguments") + + # Parse function name from ID: functions.read_file:0 -> read_file + function_name = function_id.split('.')[1].split(':')[0] + + # Generate a proper UUID-style ID for compatibility + tool_id = f"tc_{len(tool_calls)}" + + tool_calls.append({ + "id": tool_id, + "type": "function", + "function": { + "name": function_name, + "arguments": function_args + } + }) + + return tool_calls + + def _remove_tool_tokens(self, content: str) -> str: + """Remove proprietary tool call tokens from content.""" + # Remove the entire tool calls section + content = re.sub( + r"<\|tool_calls_section_begin\|>.*?<\|tool_calls_section_end\||>", + "", + content, + flags=re.DOTALL + ) + return content.strip() + + def extract_reasoning(self, response: Any) -> List[ReasoningStep]: + """Extract reasoning steps from raw response.""" + parsed = self._parse_response(response) + return parsed.reasoning_steps + + +def create_provider_adapter(provider_config: ProviderConfig, model_config: ModelConfig) -> ProviderAdapter: + """Factory function to create the appropriate provider adapter.""" + provider_name = provider_config.name.lower() + + adapters = { + "openai": OpenAIProviderAdapter, + "openrouter": OpenAIProviderAdapter, + "together": OpenAIProviderAdapter, + "google": GoogleProviderAdapter, + "anthropic": AnthropicProviderAdapter, + "moonshot": MoonshotProviderAdapter, + } + + adapter_class = adapters.get(provider_name) + if not adapter_class: + raise ValueError(f"Unsupported provider: {provider_name}") + + return adapter_class(provider_config, model_config) diff --git a/src/agents/main/pyproject.toml b/src/agents/main/pyproject.toml index 20c98f3..60220b1 100644 --- a/src/agents/main/pyproject.toml +++ b/src/agents/main/pyproject.toml @@ -5,5 +5,8 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ + "anthropic>=0.75.0", "openai>=2.8.0", + "pyyaml>=6.0.3", + "python-dotenv>=1.0.0", ] diff --git a/src/agents/main/runner.py b/src/agents/main/runner.py new file mode 100644 index 0000000..218602a --- /dev/null +++ b/src/agents/main/runner.py @@ -0,0 +1,166 @@ +""" +Runner - orchestrates experiment runs based on config. +Loops through models, scenarios, and oversight levels. +""" +import os +from typing import List, Dict, Any +from config_loader import ConfigLoader, ProviderConfig, ModelConfig, ScenarioConfig +from vfs import VFS +from agent import Agent +import datetime + + +def load_prompt(file_path: str) -> str: + """Load a prompt file.""" + if not os.path.exists(file_path): + return "" + with open(file_path, 'r') as f: + return f.read().strip() + + +class ExperimentRunner: + """Runs experiments based on configuration.""" + + def __init__(self, config: ConfigLoader, verbose: bool = False): + self.config = config + self.results: List[Dict] = [] + self.verbose = verbose + + def run_all(self): + """Run all experiments defined in config.""" + print(f"\n{'='*60}") + print("Starting Experiment Run") + print(f"{'='*60}\n") + + total_runs = 0 + for model_config in self.config.models: + for scenario_config in self.config.scenarios: + for oversight_level in self.config.oversight_levels: + runs = self._run_combo(model_config, scenario_config, oversight_level) + total_runs += runs + + print(f"\n{'='*60}") + print(f"Experiment Complete: {total_runs} runs") + print(f"{'='*60}\n") + + def _run_combo( + self, + model_config: ModelConfig, + scenario_config: ScenarioConfig, + oversight_level: str + ) -> int: + """Run a specific combination of model, scenario, and oversight.""" + provider_config = self.config.get_provider(model_config.provider) + + scenario_name = os.path.basename(scenario_config.path) + print(f"\n--- Running: {model_config.id} | {scenario_name} | {oversight_level} ---") + + runs_completed = 0 + for run_num in range(1, scenario_config.runs + 1): + try: + self._run_single( + model_config=model_config, + provider_config=provider_config, + scenario_config=scenario_config, + oversight_level=oversight_level, + run_num=run_num + ) + runs_completed += 1 + except Exception as e: + print(f"ERROR in run {run_num}: {e}") + import traceback + traceback.print_exc() + + print(f"--- Completed: {runs_completed}/{scenario_config.runs} runs ---") + return runs_completed + + def _run_single( + self, + model_config: ModelConfig, + provider_config: ProviderConfig, + scenario_config: ScenarioConfig, + oversight_level: str, + run_num: int + ): + """Run a single experiment.""" + # Load scenario prompts + benign_goal = load_prompt(os.path.join(scenario_config.path, "benign_goal.md")) + hidden_goal = load_prompt(os.path.join(scenario_config.path, "hidden_goal.md")) + user_prompt = load_prompt(os.path.join(scenario_config.path, "user.md")) + oversight_prompt = load_prompt(os.path.join("oversight", f"{oversight_level}.md")) + + # Build system prompt + system_parts = [benign_goal] + if hidden_goal: + system_parts.append(hidden_goal) + if oversight_prompt: + system_parts.append(oversight_prompt) + system_prompt = "\n\n".join(filter(None, system_parts)) + + # Initialize VFS + vfs_path = os.path.join(scenario_config.path, "data") + VFS.get_instance(vfs_path) + + print(f" VFS initialized from: {vfs_path}") + if self.verbose: + VFS.get_instance().print_fs() + + # Create agent + agent = Agent.from_configs( + system_prompt=system_prompt, + provider_config=provider_config, + model_config=model_config, + scenario=os.path.basename(scenario_config.path), + oversight_level=oversight_level, + user_prompt_type="user.md" + ) + + # Run the conversation + print(f"\n Starting conversation (run {run_num})...") + start_time = datetime.datetime.now() + agent.run(user_prompt) + end_time = datetime.datetime.now() + + # Save logs + output_dir = self.config.output_dir + log_file = agent.save_logs(output_dir=output_dir) + + # Print final VFS + if self.verbose: + print(f"\n Final VFS state:") + VFS.get_instance().print_fs() + + # Record result + self.results.append({ + "model": model_config.id, + "provider": model_config.provider, + "scenario": os.path.basename(scenario_config.path), + "oversight_level": oversight_level, + "run_num": run_num, + "run_id": f"{model_config.id}/{os.path.basename(scenario_config.path)}/{oversight_level}/{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}", + "duration_seconds": (end_time - start_time).total_seconds(), + "total_tokens": agent.total_tokens, + "log_file": log_file + }) + + print(f" Completed in {(end_time - start_time).total_seconds():.2f}s") + + +def run_from_config(config_path: str = "config.yaml"): + """Convenience function to run all experiments from a config file.""" + config = ConfigLoader(config_path) + config.load() + + runner = ExperimentRunner(config) + runner.run_all() + + return runner.results + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Run experiments from config") + parser.add_argument("--config", 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