504 lines
18 KiB
Python
504 lines
18 KiB
Python
"""Utility functions for working with agents."""
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import signal
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import sys
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import threading
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import time
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from typing import Any, Dict, List, Literal, Optional
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from langchain_anthropic import ChatAnthropic
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from ra_aid.callbacks.anthropic_callback_handler import AnthropicCallbackHandler
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from anthropic import APIError, APITimeoutError, InternalServerError, RateLimitError
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from openai import RateLimitError as OpenAIRateLimitError
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from litellm.exceptions import RateLimitError as LiteLLMRateLimitError
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from google.api_core.exceptions import ResourceExhausted
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import (
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.tools import tool
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from langgraph.prebuilt import create_react_agent
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from langgraph.prebuilt.chat_agent_executor import AgentState
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from rich.console import Console
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from rich.markdown import Markdown
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from rich.panel import Panel
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from ra_aid.agent_context import (
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agent_context,
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is_completed,
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reset_completion_flags,
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should_exit,
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)
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from ra_aid.agent_backends.ciayn_agent import CiaynAgent
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from ra_aid.agents_alias import RAgents
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from ra_aid.config import DEFAULT_MAX_TEST_CMD_RETRIES
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from ra_aid.console.formatting import print_error
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from ra_aid.console.output import print_agent_output
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from ra_aid.exceptions import (
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AgentInterrupt,
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FallbackToolExecutionError,
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ToolExecutionError,
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)
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from ra_aid.fallback_handler import FallbackHandler
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from ra_aid.logging_config import get_logger
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from ra_aid.models_params import DEFAULT_TOKEN_LIMIT
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from ra_aid.tools.handle_user_defined_test_cmd_execution import execute_test_command
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from ra_aid.database.repositories.config_repository import get_config_repository
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from ra_aid.anthropic_token_limiter import state_modifier, get_model_token_limit
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console = Console()
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logger = get_logger(__name__)
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# Import repositories using get_* functions
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@tool
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def output_markdown_message(message: str) -> str:
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"""Outputs a message to the user, optionally prompting for input."""
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console.print(Panel(Markdown(message.strip()), title="🤖 Assistant"))
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return "Message output."
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def build_agent_kwargs(
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checkpointer: Optional[Any] = None,
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model: ChatAnthropic = None,
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max_input_tokens: Optional[int] = None,
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) -> Dict[str, Any]:
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"""Build kwargs dictionary for agent creation.
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Args:
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checkpointer: Optional memory checkpointer
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model: The language model to use for token counting
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max_input_tokens: Optional token limit for the model
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Returns:
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Dictionary of kwargs for agent creation
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"""
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agent_kwargs = {
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"version": "v2",
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}
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if checkpointer is not None:
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agent_kwargs["checkpointer"] = checkpointer
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config = get_config_repository().get_all()
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if (
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config.get("limit_tokens", True)
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and is_anthropic_claude(config)
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and model is not None
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):
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def wrapped_state_modifier(state: AgentState) -> list[BaseMessage]:
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return state_modifier(state, model, max_input_tokens=max_input_tokens)
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agent_kwargs["state_modifier"] = wrapped_state_modifier
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agent_kwargs["name"] = "React"
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return agent_kwargs
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def is_anthropic_claude(config: Dict[str, Any]) -> bool:
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"""Check if the provider and model name indicate an Anthropic Claude model.
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Args:
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config: Configuration dictionary containing provider and model information
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Returns:
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bool: True if this is an Anthropic Claude model
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"""
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# For backwards compatibility, allow passing of config directly
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provider = config.get("provider", "")
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model_name = config.get("model", "")
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result = (
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provider.lower() == "anthropic"
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and model_name
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and "claude" in model_name.lower()
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) or (
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provider.lower() == "openrouter"
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and model_name.lower().startswith("anthropic/claude-")
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)
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return result
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def create_agent(
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model: BaseChatModel,
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tools: List[Any],
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*,
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checkpointer: Any = None,
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agent_type: str = "default",
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):
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"""Create a react agent with the given configuration.
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Args:
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model: The LLM model to use
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tools: List of tools to provide to the agent
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checkpointer: Optional memory checkpointer
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config: Optional configuration dictionary containing settings like:
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- limit_tokens (bool): Whether to apply token limiting (default: True)
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- provider (str): The LLM provider name
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- model (str): The model name
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Returns:
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The created agent instance
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Token limiting helps prevent context window overflow by trimming older messages
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while preserving system messages. It can be disabled by setting
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config['limit_tokens'] = False.
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"""
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try:
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# Try to get config from repository for production use
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try:
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config_from_repo = get_config_repository().get_all()
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# If we succeeded, use the repository config instead of passed config
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config = config_from_repo
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except RuntimeError:
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# In tests, this may fail because the repository isn't set up
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# So we'll use the passed config directly
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pass
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max_input_tokens = (
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get_model_token_limit(config, agent_type) or DEFAULT_TOKEN_LIMIT
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)
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# Use REACT agent for Anthropic Claude models, otherwise use CIAYN
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if is_anthropic_claude(config):
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logger.debug("Using create_react_agent to instantiate agent.")
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agent_kwargs = build_agent_kwargs(checkpointer, model, max_input_tokens)
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return create_react_agent(
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model, tools, interrupt_after=["tools"], **agent_kwargs
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)
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else:
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logger.debug("Using CiaynAgent agent instance")
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return CiaynAgent(model, tools, max_tokens=max_input_tokens, config=config)
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except Exception as e:
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# Default to REACT agent if provider/model detection fails
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logger.warning(f"Failed to detect model type: {e}. Defaulting to REACT agent.")
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config = get_config_repository().get_all()
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max_input_tokens = get_model_token_limit(config, agent_type)
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agent_kwargs = build_agent_kwargs(checkpointer, model, max_input_tokens)
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return create_react_agent(
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model, tools, interrupt_after=["tools"], **agent_kwargs
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)
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_CONTEXT_STACK = []
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_INTERRUPT_CONTEXT = None
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_FEEDBACK_MODE = False
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def _request_interrupt(signum, frame):
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global _INTERRUPT_CONTEXT
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if _CONTEXT_STACK:
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_INTERRUPT_CONTEXT = _CONTEXT_STACK[-1]
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if _FEEDBACK_MODE:
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print()
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print(" 👋 Bye!")
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print()
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sys.exit(0)
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class InterruptibleSection:
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def __enter__(self):
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_CONTEXT_STACK.append(self)
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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_CONTEXT_STACK.remove(self)
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def check_interrupt():
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if _CONTEXT_STACK and _INTERRUPT_CONTEXT is _CONTEXT_STACK[-1]:
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raise AgentInterrupt("Interrupt requested")
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# New helper functions for run_agent_with_retry refactoring
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def _setup_interrupt_handling():
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if threading.current_thread() is threading.main_thread():
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original_handler = signal.getsignal(signal.SIGINT)
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signal.signal(signal.SIGINT, _request_interrupt)
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return original_handler
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return None
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def _restore_interrupt_handling(original_handler):
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if original_handler and threading.current_thread() is threading.main_thread():
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signal.signal(signal.SIGINT, original_handler)
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def reset_agent_completion_flags():
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"""Reset completion flags in the current context."""
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reset_completion_flags()
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def _execute_test_command_wrapper(original_prompt, config, test_attempts, auto_test):
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# For backwards compatibility, allow passing of config directly
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# No need to get config from repository as it's passed in
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return execute_test_command(config, original_prompt, test_attempts, auto_test)
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def _handle_api_error(e, attempt, max_retries, base_delay):
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# 1. Check if this is a ValueError with 429 code or rate limit phrases
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if isinstance(e, ValueError):
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error_str = str(e).lower()
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rate_limit_phrases = [
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"429",
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"rate limit",
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"too many requests",
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"quota exceeded",
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]
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if "code" not in error_str and not any(
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phrase in error_str for phrase in rate_limit_phrases
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):
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raise e
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# 2. Check for status_code or http_status attribute equal to 429
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if hasattr(e, "status_code") and e.status_code == 429:
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pass # This is a rate limit error, continue with retry logic
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elif hasattr(e, "http_status") and e.http_status == 429:
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pass # This is a rate limit error, continue with retry logic
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# 3. Check for rate limit phrases in error message
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elif isinstance(e, Exception) and not isinstance(e, ValueError):
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error_str = str(e).lower()
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if not any(
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phrase in error_str
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for phrase in ["rate limit", "too many requests", "quota exceeded", "429"]
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) and not ("rate" in error_str and "limit" in error_str):
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# This doesn't look like a rate limit error, but we'll still retry other API errors
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pass
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# Apply common retry logic for all identified errors
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if attempt == max_retries - 1:
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logger.error("Max retries reached, failing: %s", str(e))
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raise RuntimeError(f"Max retries ({max_retries}) exceeded. Last error: {e}")
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logger.warning("API error (attempt %d/%d): %s", attempt + 1, max_retries, str(e))
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delay = base_delay * (2**attempt)
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print_error(
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f"Encountered {e.__class__.__name__}: {e}. Retrying in {delay}s... (Attempt {attempt+1}/{max_retries})"
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)
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start = time.monotonic()
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while time.monotonic() - start < delay:
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check_interrupt()
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time.sleep(0.1)
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def get_agent_type(agent: RAgents) -> Literal["CiaynAgent", "React"]:
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"""
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Determines the type of the agent.
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Returns "CiaynAgent" if agent is an instance of CiaynAgent, otherwise "React".
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"""
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if isinstance(agent, CiaynAgent):
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return "CiaynAgent"
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else:
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return "React"
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def init_fallback_handler(agent: RAgents, tools: List[Any]):
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"""
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Initialize fallback handler if agent is of type "React" and experimental_fallback_handler is enabled; otherwise return None.
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"""
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if not get_config_repository().get("experimental_fallback_handler", False):
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return None
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agent_type = get_agent_type(agent)
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if agent_type == "React":
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return FallbackHandler(get_config_repository().get_all(), tools)
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return None
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def _handle_fallback_response(
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error: ToolExecutionError,
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fallback_handler: Optional[FallbackHandler],
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agent: RAgents,
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msg_list: list,
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) -> None:
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"""
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Handle fallback response by invoking fallback_handler and updating msg_list.
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"""
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if not fallback_handler:
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return
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fallback_response = fallback_handler.handle_failure(error, agent, msg_list)
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agent_type = get_agent_type(agent)
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if fallback_response and agent_type == "React":
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msg_list_response = [HumanMessage(str(msg)) for msg in fallback_response]
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msg_list.extend(msg_list_response)
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def _run_agent_stream(agent: RAgents, msg_list: list[BaseMessage]):
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"""
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Streams agent output while handling completion and interruption.
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For each chunk, it logs the output, calls check_interrupt(), prints agent output,
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and then checks if is_completed() or should_exit() are true. If so, it resets completion
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flags and returns. After finishing a stream iteration (i.e. the for-loop over chunks),
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the function retrieves the agent's state. If the state indicates further steps (i.e. state.next is non-empty),
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it resumes execution via agent.invoke(None, config); otherwise, it exits the loop.
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This function adheres to the latest LangGraph best practices (as of March 2025) for handling
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human-in-the-loop interruptions using interrupt_after=["tools"].
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"""
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config = get_config_repository().get_all()
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stream_config = config.copy()
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cb = None
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if is_anthropic_claude(config):
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model_name = config.get("model", "")
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full_model_name = model_name
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cb = AnthropicCallbackHandler(full_model_name)
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if "callbacks" not in stream_config:
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stream_config["callbacks"] = []
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stream_config["callbacks"].append(cb)
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while True:
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for chunk in agent.stream({"messages": msg_list}, stream_config):
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logger.debug("Agent output: %s", chunk)
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check_interrupt()
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agent_type = get_agent_type(agent)
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print_agent_output(chunk, agent_type, cost_cb=cb)
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if is_completed() or should_exit():
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reset_completion_flags()
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if cb:
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logger.debug(f"AnthropicCallbackHandler:\n{cb}")
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return True
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logger.debug("Stream iteration ended; checking agent state for continuation.")
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# Prepare state configuration, ensuring 'configurable' is present.
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state_config = get_config_repository().get_all().copy()
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if "configurable" not in state_config:
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logger.debug(
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"Key 'configurable' not found in config; adding it as an empty dict."
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)
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state_config["configurable"] = {}
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logger.debug("Using state_config for agent.get_state(): %s", state_config)
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try:
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state = agent.get_state(state_config)
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logger.debug("Agent state retrieved: %s", state)
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except Exception as e:
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logger.error(
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"Error retrieving agent state with state_config %s: %s", state_config, e
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)
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raise
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if state.next:
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logger.debug(
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"State indicates continuation (state.next: %s); resuming execution.",
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state.next,
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)
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agent.invoke(None, stream_config)
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continue
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else:
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logger.debug("No continuation indicated in state; exiting stream loop.")
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break
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if cb:
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logger.debug(f"AnthropicCallbackHandler:\n{cb}")
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return True
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def run_agent_with_retry(
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agent: RAgents,
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prompt: str,
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fallback_handler: Optional[FallbackHandler] = None,
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) -> Optional[str]:
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"""Run an agent with retry logic for API errors."""
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logger.debug("Running agent with prompt length: %d", len(prompt))
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original_handler = _setup_interrupt_handling()
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max_retries = 20
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base_delay = 1
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test_attempts = 0
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_max_test_retries = get_config_repository().get(
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"max_test_cmd_retries", DEFAULT_MAX_TEST_CMD_RETRIES
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)
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auto_test = get_config_repository().get("auto_test", False)
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original_prompt = prompt
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msg_list = [HumanMessage(content=prompt)]
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run_config = get_config_repository().get_all()
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# Create a new agent context for this run
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with InterruptibleSection(), agent_context() as ctx:
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try:
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for attempt in range(max_retries):
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logger.debug("Attempt %d/%d", attempt + 1, max_retries)
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check_interrupt()
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# Check if the agent has crashed before attempting to run it
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from ra_aid.agent_context import get_crash_message, is_crashed
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if is_crashed():
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crash_message = get_crash_message()
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logger.error("Agent has crashed: %s", crash_message)
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return f"Agent has crashed: {crash_message}"
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try:
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_run_agent_stream(agent, msg_list)
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if fallback_handler:
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fallback_handler.reset_fallback_handler()
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should_break, prompt, auto_test, test_attempts = (
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_execute_test_command_wrapper(
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original_prompt, run_config, test_attempts, auto_test
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)
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)
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if should_break:
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break
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if prompt != original_prompt:
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continue
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logger.debug("Agent run completed successfully")
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return "Agent run completed successfully"
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except ToolExecutionError as e:
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# Check if this is a BadRequestError (HTTP 400) which is unretryable
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error_str = str(e).lower()
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if "400" in error_str or "bad request" in error_str:
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from ra_aid.agent_context import mark_agent_crashed
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crash_message = f"Unretryable error: {str(e)}"
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mark_agent_crashed(crash_message)
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logger.error("Agent has crashed: %s", crash_message)
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return f"Agent has crashed: {crash_message}"
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_handle_fallback_response(e, fallback_handler, agent, msg_list)
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continue
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except FallbackToolExecutionError as e:
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msg_list.append(
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SystemMessage(f"FallbackToolExecutionError:{str(e)}")
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)
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except (KeyboardInterrupt, AgentInterrupt):
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raise
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except (
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InternalServerError,
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APITimeoutError,
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RateLimitError,
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OpenAIRateLimitError,
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LiteLLMRateLimitError,
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ResourceExhausted,
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APIError,
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ValueError,
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) as e:
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# Check if this is a BadRequestError (HTTP 400) which is unretryable
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error_str = str(e).lower()
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if (
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"400" in error_str or "bad request" in error_str
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) and isinstance(e, APIError):
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from ra_aid.agent_context import mark_agent_crashed
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crash_message = f"Unretryable API error: {str(e)}"
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mark_agent_crashed(crash_message)
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logger.error("Agent has crashed: %s", crash_message)
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return f"Agent has crashed: {crash_message}"
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_handle_api_error(e, attempt, max_retries, base_delay)
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finally:
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_restore_interrupt_handling(original_handler)
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