Add browser automation tools and enhance environment configuration
- Introduced new browser automation tools in `browser_tool.py` for navigating, interacting with, and extracting content from web pages using the agent-browser CLI and Browserbase cloud execution. - Updated `.env.example` to include new configuration options for Browserbase API keys and session settings. - Enhanced `model_tools.py` and `toolsets.py` to integrate browser tools into the existing tool framework, ensuring consistent access across toolsets. - Updated `README.md` with setup instructions for browser tools and their usage examples. - Added new test script `test_modal_terminal.py` to validate Modal terminal backend functionality. - Improved `run_agent.py` to support browser tool integration and logging enhancements for better tracking of API responses.
This commit is contained in:
teknium
parent
54ca0997ee
commit
248acf715e
12 changed files with 2626 additions and 134 deletions
167
run_agent.py
167
run_agent.py
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@ -44,6 +44,7 @@ else:
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# Import our tool system
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from model_tools import get_tool_definitions, handle_function_call, check_toolset_requirements
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from tools.terminal_tool import cleanup_vm
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from tools.browser_tool import cleanup_browser
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class AIAgent:
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@ -58,7 +59,7 @@ class AIAgent:
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self,
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base_url: str = None,
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api_key: str = None,
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model: str = "anthropic/claude-sonnet-4-20250514",
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model: str = "anthropic/claude-sonnet-4-20250514", # OpenRouter format
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max_iterations: int = 10,
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tool_delay: float = 1.0,
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enabled_toolsets: List[str] = None,
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@ -156,10 +157,7 @@ class AIAgent:
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client_kwargs["api_key"] = api_key
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else:
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# Primary: OPENROUTER_API_KEY, fallback to direct provider keys
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client_kwargs["api_key"] = os.getenv(
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"OPENROUTER_API_KEY",
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os.getenv("ANTHROPIC_API_KEY", os.getenv("OPENAI_API_KEY", ""))
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)
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client_kwargs["api_key"] = os.getenv("OPENROUTER_API_KEY", "")
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try:
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self.client = OpenAI(**client_kwargs)
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@ -339,11 +337,12 @@ class AIAgent:
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# Check if this message has tool calls
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if "tool_calls" in msg and msg["tool_calls"]:
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# Format assistant message with tool calls
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# Add <think> tags around reasoning for trajectory storage
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content = ""
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# Prepend reasoning in <think> tags if available
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if msg.get("reasoning") and msg["reasoning"].strip():
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content = f"<think>{msg['reasoning']}</think>"
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content = f"<think>\n{msg['reasoning']}\n</think>\n"
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if msg.get("content") and msg["content"].strip():
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content += msg["content"] + "\n"
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@ -406,17 +405,18 @@ class AIAgent:
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else:
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# Regular assistant message without tool calls
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# Add <think> tags around reasoning for trajectory storage
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content = ""
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# Prepend reasoning in <think> tags if available
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if msg.get("reasoning") and msg["reasoning"].strip():
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content = f"<think>{msg['reasoning']}</think>"
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content = f"<think>\n{msg['reasoning']}\n</think>\n"
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content += msg["content"] or ""
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trajectory.append({
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"from": "gpt",
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"value": content
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"value": content.strip()
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})
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elif msg["role"] == "user":
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@ -515,7 +515,31 @@ class AIAgent:
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# Prepare messages for API call
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# If we have an ephemeral system prompt, prepend it to the messages
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api_messages = messages.copy()
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# Note: Reasoning is embedded in content via <think> tags for trajectory storage.
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# However, providers like Moonshot AI require a separate 'reasoning_content' field
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# on assistant messages with tool_calls. We handle both cases here.
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api_messages = []
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for msg in messages:
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api_msg = msg.copy()
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# For assistant messages with tool_calls, providers require 'reasoning_content' field
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# Extract reasoning from our stored 'reasoning' field and add it as 'reasoning_content'
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if msg.get("role") == "assistant" and msg.get("tool_calls"):
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reasoning_text = msg.get("reasoning")
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if reasoning_text:
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# Add reasoning_content for API compatibility (Moonshot AI, Novita, etc.)
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api_msg["reasoning_content"] = reasoning_text
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# Remove 'reasoning' field - it's for trajectory storage only
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# The reasoning is already in the content via <think> tags AND
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# we've added reasoning_content for API compatibility above
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if "reasoning" in api_msg:
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api_msg.pop("reasoning")
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# Remove 'reasoning_details' if present - we use reasoning_content instead
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if "reasoning_details" in api_msg:
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api_msg.pop("reasoning_details")
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api_messages.append(api_msg)
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if active_system_prompt:
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# Insert system message at the beginning
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api_messages = [{"role": "system", "content": active_system_prompt}] + api_messages
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@ -582,7 +606,9 @@ class AIAgent:
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print(f"{self.log_prefix}⏱️ API call completed in {api_duration:.2f}s")
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if self.verbose_logging:
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logging.debug(f"API Response received - Usage: {response.usage if hasattr(response, 'usage') else 'N/A'}")
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# Log response with provider info if available
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resp_model = getattr(response, 'model', 'N/A') if response else 'N/A'
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logging.debug(f"API Response received - Model: {resp_model}, Usage: {response.usage if hasattr(response, 'usage') else 'N/A'}")
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# Validate response has valid choices before proceeding
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if response is None or not hasattr(response, 'choices') or response.choices is None or len(response.choices) == 0:
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@ -600,12 +626,28 @@ class AIAgent:
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# Check for error field in response (some providers include this)
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error_msg = "Unknown"
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provider_name = "Unknown"
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if response and hasattr(response, 'error') and response.error:
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error_msg = str(response.error)
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# Try to extract provider from error metadata
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if hasattr(response.error, 'metadata') and response.error.metadata:
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provider_name = response.error.metadata.get('provider_name', 'Unknown')
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elif response and hasattr(response, 'message') and response.message:
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error_msg = str(response.message)
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# Try to get provider from model field (OpenRouter often returns actual model used)
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if provider_name == "Unknown" and response and hasattr(response, 'model') and response.model:
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provider_name = f"model={response.model}"
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# Check for x-openrouter-provider or similar metadata
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if provider_name == "Unknown" and response:
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# Log all response attributes for debugging
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resp_attrs = {k: str(v)[:100] for k, v in vars(response).items() if not k.startswith('_')}
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if self.verbose_logging:
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logging.debug(f"Response attributes for invalid response: {resp_attrs}")
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print(f"{self.log_prefix}⚠️ Invalid API response (attempt {retry_count}/{max_retries}): {', '.join(error_details)}")
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print(f"{self.log_prefix} 🏢 Provider: {provider_name}")
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print(f"{self.log_prefix} 📝 Provider message: {error_msg[:200]}")
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print(f"{self.log_prefix} ⏱️ Response time: {api_duration:.2f}s (fast response often indicates rate limiting)")
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@ -623,7 +665,7 @@ class AIAgent:
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# Longer backoff for rate limiting (likely cause of None choices)
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wait_time = min(5 * (2 ** (retry_count - 1)), 120) # 5s, 10s, 20s, 40s, 80s, 120s
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print(f"{self.log_prefix}⏳ Retrying in {wait_time}s (extended backoff for possible rate limit)...")
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logging.warning(f"Invalid API response (retry {retry_count}/{max_retries}): {', '.join(error_details)}")
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logging.warning(f"Invalid API response (retry {retry_count}/{max_retries}): {', '.join(error_details)} | Provider: {provider_name}")
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time.sleep(wait_time)
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continue # Retry the API call
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@ -639,12 +681,17 @@ class AIAgent:
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print(f"{self.log_prefix} ⏪ Rolling back to last complete assistant turn")
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rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
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# Clean up VM
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# Clean up VM and browser
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try:
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cleanup_vm(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
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try:
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cleanup_browser(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
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return {
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"final_response": None,
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@ -799,17 +846,21 @@ class AIAgent:
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self._invalid_json_retries = 0
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# Extract reasoning from response if available (for reasoning models like minimax, kimi, etc.)
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reasoning_content = None
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# Extract reasoning from response for storage
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# The reasoning_content field will be added when preparing API messages
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reasoning_text = None
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if hasattr(assistant_message, 'reasoning') and assistant_message.reasoning:
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reasoning_content = assistant_message.reasoning
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reasoning_text = assistant_message.reasoning
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elif hasattr(assistant_message, 'reasoning_content') and assistant_message.reasoning_content:
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reasoning_content = assistant_message.reasoning_content
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reasoning_text = assistant_message.reasoning_content
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# Add assistant message with tool calls to conversation
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messages.append({
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# Build assistant message with tool calls
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# Content stays as-is; reasoning is stored separately and will be passed
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# to the API via reasoning_content field when preparing api_messages
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assistant_msg = {
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"role": "assistant",
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"content": assistant_message.content,
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"reasoning": reasoning_content, # Store reasoning for trajectory
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"content": assistant_message.content or "",
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"reasoning": reasoning_text, # Stored for trajectory extraction & API calls
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"tool_calls": [
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{
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"id": tool_call.id,
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@ -821,7 +872,9 @@ class AIAgent:
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}
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for tool_call in assistant_message.tool_calls
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]
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})
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}
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messages.append(assistant_msg)
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# Execute each tool call
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for i, tool_call in enumerate(assistant_message.tool_calls, 1):
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@ -896,12 +949,17 @@ class AIAgent:
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rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
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# Clean up VM
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# Clean up VM and browser
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try:
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cleanup_vm(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
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try:
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cleanup_browser(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
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return {
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"final_response": None,
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@ -917,18 +975,21 @@ class AIAgent:
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self._empty_content_retries = 0
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# Extract reasoning from response if available
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reasoning_content = None
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reasoning_text = None
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if hasattr(assistant_message, 'reasoning') and assistant_message.reasoning:
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reasoning_content = assistant_message.reasoning
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reasoning_text = assistant_message.reasoning
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elif hasattr(assistant_message, 'reasoning_content') and assistant_message.reasoning_content:
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reasoning_content = assistant_message.reasoning_content
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reasoning_text = assistant_message.reasoning_content
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# Add final assistant message
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messages.append({
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# Build final assistant message
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# Content stays as-is; reasoning stored separately for trajectory extraction
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final_msg = {
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"role": "assistant",
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"content": final_response,
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"reasoning": reasoning_content # Store reasoning for trajectory
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})
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"reasoning": reasoning_text # Stored for trajectory extraction
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}
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messages.append(final_msg)
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print(f"🎉 Conversation completed after {api_call_count} OpenAI-compatible API call(s)")
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break
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@ -963,12 +1024,18 @@ class AIAgent:
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# Save trajectory if enabled
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self._save_trajectory(messages, user_message, completed)
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# Clean up VM for this task after conversation completes
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# Clean up VM and browser for this task after conversation completes
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try:
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cleanup_vm(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
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try:
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cleanup_browser(effective_task_id)
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except Exception as e:
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if self.verbose_logging:
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logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
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return {
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"final_response": final_response,
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@ -994,14 +1061,15 @@ class AIAgent:
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def main(
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query: str = None,
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model: str = "claude-opus-4-20250514",
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model: str = "anthropic/claude-sonnet-4-20250514",
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api_key: str = None,
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base_url: str = "https://api.anthropic.com/v1/",
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base_url: str = "https://openrouter.ai/api/v1",
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max_turns: int = 10,
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enabled_toolsets: str = None,
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disabled_toolsets: str = None,
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list_tools: bool = False,
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save_trajectories: bool = False,
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save_sample: bool = False,
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verbose: bool = False,
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log_prefix_chars: int = 20
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):
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@ -1010,16 +1078,17 @@ def main(
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Args:
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query (str): Natural language query for the agent. Defaults to Python 3.13 example.
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model (str): Model name to use. Defaults to claude-opus-4-20250514.
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api_key (str): API key for authentication. Uses ANTHROPIC_API_KEY env var if not provided.
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base_url (str): Base URL for the model API. Defaults to https://api.anthropic.com/v1/
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model (str): Model name to use (OpenRouter format: provider/model). Defaults to anthropic/claude-sonnet-4-20250514.
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api_key (str): API key for authentication. Uses OPENROUTER_API_KEY env var if not provided.
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base_url (str): Base URL for the model API. Defaults to https://openrouter.ai/api/v1
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max_turns (int): Maximum number of API call iterations. Defaults to 10.
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enabled_toolsets (str): Comma-separated list of toolsets to enable. Supports predefined
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toolsets (e.g., "research", "development", "safe").
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Multiple toolsets can be combined: "web,vision"
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disabled_toolsets (str): Comma-separated list of toolsets to disable (e.g., "terminal")
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list_tools (bool): Just list available tools and exit
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save_trajectories (bool): Save conversation trajectories to JSONL files. Defaults to False.
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save_trajectories (bool): Save conversation trajectories to JSONL files (appends to trajectory_samples.jsonl). Defaults to False.
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save_sample (bool): Save a single trajectory sample to a UUID-named JSONL file for inspection. Defaults to False.
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verbose (bool): Enable verbose logging for debugging. Defaults to False.
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log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses. Defaults to 20.
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@ -1173,6 +1242,34 @@ def main(
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print("-" * 30)
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print(result['final_response'])
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# Save sample trajectory to UUID-named file if requested
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if save_sample:
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import uuid
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sample_id = str(uuid.uuid4())[:8]
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sample_filename = f"sample_{sample_id}.jsonl"
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# Convert messages to trajectory format (same as batch_runner)
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trajectory = agent._convert_to_trajectory_format(
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result['messages'],
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user_query,
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result['completed']
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)
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entry = {
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"conversations": trajectory,
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"timestamp": datetime.now().isoformat(),
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"model": model,
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"completed": result['completed'],
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"query": user_query
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}
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try:
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with open(sample_filename, "w", encoding="utf-8") as f:
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f.write(json.dumps(entry, ensure_ascii=False) + "\n")
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print(f"\n💾 Sample trajectory saved to: {sample_filename}")
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except Exception as e:
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print(f"\n⚠️ Failed to save sample: {e}")
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print("\n👋 Agent execution completed!")
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