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import logging
import os
import uuid
from langgraph.types import Command
from services.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
self.thread_id = str(
uuid.uuid4()
) # Generate a unique thread_id for this runner
logger.info(f"Created AgentRunner with thread_id: {self.thread_id}")
def _extract_answer(self, state: dict) -> str:
"""Extract the answer from the state."""
if not state:
return None
# First try to get answer from direct answer field
if "answer" in state and state["answer"]:
logger.info(f"Found answer in direct field: {state['answer']}")
return state["answer"]
# Then try to get answer from messages
if "messages" in state and state["messages"]:
for msg in reversed(state["messages"]):
if hasattr(msg, "content") and msg.content:
logger.info(f"Found answer in message: {msg.content}")
return msg.content
return None
def __call__(self, input_data) -> str:
"""Process a question through the agent graph and return the answer.
Args:
input_data: Either a question string or a Command object for resuming
Returns:
str: The agent's response
"""
try:
config = {"configurable": {"thread_id": self.thread_id}}
logger.info(f"Using config: {config}")
if isinstance(input_data, str):
# Initial question
logger.info(f"Processing initial question: {input_data}")
initial_state = {
"question": input_data,
"messages": [],
"answer": None,
"step_logs": [],
"is_complete": False,
"step_count": 0,
# Initialize new memory fields
"context": {},
"memory_buffer": [],
"last_action": None,
"action_history": [],
"error_count": 0,
"success_count": 0,
}
logger.info(f"Initial state: {initial_state}")
# Use stream to get results
logger.info("Starting graph stream for initial question")
for chunk in self.graph.stream(initial_state, config):
logger.debug(f"Received chunk: {chunk}")
if isinstance(chunk, dict):
if "__interrupt__" in chunk:
logger.info("Detected interrupt in stream")
logger.info(f"Interrupt details: {chunk['__interrupt__']}")
# Let the graph handle the interrupt naturally
continue
answer = self._extract_answer(chunk)
if answer:
self.last_state = chunk
return answer
else:
logger.debug(f"Skipping chunk without answer: {chunk}")
else:
# Resuming from interrupt
logger.info(f"Resuming from interrupt with input: {input_data}")
for result in self.graph.stream(input_data, config):
logger.debug(f"Received resume result: {result}")
if isinstance(result, dict):
answer = self._extract_answer(result)
if answer:
self.last_state = result
return answer
else:
logger.debug(f"Skipping result without answer: {result}")
# If we get here, we didn't find an answer
logger.warning("No answer generated from stream")
return "No answer generated"
except Exception as e:
logger.error(f"Error processing input: {str(e)}")
raise
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