mjschock's picture
Refactor agent.py and graph.py to enhance agent functionality and logging. Introduce Configuration class for managing parameters, improve state handling in AgentRunner, and update agent graph to support step logging and user interaction. Add new tests for agent capabilities and update requirements for code formatting tools.
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import logging
import os
import uuid
from graph import agent_graph
# Configure logging
logging.basicConfig(level=logging.INFO) # Default to INFO level
logger = logging.getLogger(__name__)
# Enable LiteLLM debug logging only if environment variable is set
import litellm
if os.getenv("LITELLM_DEBUG", "false").lower() == "true":
litellm.set_verbose = True
logger.setLevel(logging.DEBUG)
else:
litellm.set_verbose = False
logger.setLevel(logging.INFO)
class AgentRunner:
"""Runner class for the code agent."""
def __init__(self):
"""Initialize the agent runner with graph and tools."""
logger.info("Initializing AgentRunner")
self.graph = agent_graph
self.last_state = None # Store the last state for testing/debugging
def __call__(self, question: str) -> str:
"""Process a question through the agent graph and return the answer.
Args:
question: The question to process
Returns:
str: The agent's response
"""
try:
logger.info(f"Processing question: {question}")
initial_state = {
"question": question,
"messages": [],
"answer": None,
"step_logs": [],
"is_complete": False, # Initialize is_complete
"step_count": 0, # Initialize step_count
}
# Generate a unique thread_id for this interaction
thread_id = str(uuid.uuid4())
config = {"configurable": {"thread_id": thread_id}}
final_state = self.graph.invoke(initial_state, config)
self.last_state = final_state # Store the final state
return final_state.get("answer", "No answer generated")
except Exception as e:
logger.error(f"Error processing question: {str(e)}")
raise