1103 lines
39 KiB
Python
1103 lines
39 KiB
Python
"""Utility functions for working with agents."""
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import os
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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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import uuid
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from datetime import datetime
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from typing import Any, Dict, List, Literal, Optional, Sequence
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import litellm
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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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trim_messages,
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)
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from langchain_core.tools import tool
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from langgraph.checkpoint.memory import MemorySaver
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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 litellm import get_model_info
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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, DEFAULT_RECURSION_LIMIT
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from ra_aid.console.formatting import print_error, print_stage_header
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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, models_params
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from ra_aid.project_info import (
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display_project_status,
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format_project_info,
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get_project_info,
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)
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from ra_aid.prompts.expert_prompts import (
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EXPERT_PROMPT_SECTION_IMPLEMENTATION,
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EXPERT_PROMPT_SECTION_PLANNING,
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EXPERT_PROMPT_SECTION_RESEARCH,
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)
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from ra_aid.prompts.human_prompts import (
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HUMAN_PROMPT_SECTION_IMPLEMENTATION,
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HUMAN_PROMPT_SECTION_PLANNING,
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HUMAN_PROMPT_SECTION_RESEARCH,
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)
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from ra_aid.prompts.implementation_prompts import IMPLEMENTATION_PROMPT
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from ra_aid.prompts.common_prompts import NEW_PROJECT_HINTS
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from ra_aid.prompts.planning_prompts import PLANNING_PROMPT
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from ra_aid.prompts.research_prompts import (
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RESEARCH_ONLY_PROMPT,
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RESEARCH_PROMPT,
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)
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from ra_aid.prompts.web_research_prompts import (
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WEB_RESEARCH_PROMPT,
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WEB_RESEARCH_PROMPT_SECTION_CHAT,
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WEB_RESEARCH_PROMPT_SECTION_PLANNING,
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WEB_RESEARCH_PROMPT_SECTION_RESEARCH,
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)
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from ra_aid.tool_configs import (
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get_implementation_tools,
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get_planning_tools,
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get_research_tools,
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get_web_research_tools,
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)
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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.key_fact_repository import get_key_fact_repository
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from ra_aid.database.repositories.key_snippet_repository import get_key_snippet_repository
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from ra_aid.database.repositories.human_input_repository import get_human_input_repository
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from ra_aid.database.repositories.research_note_repository import get_research_note_repository
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from ra_aid.model_formatters import format_key_facts_dict
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from ra_aid.model_formatters.key_snippets_formatter import format_key_snippets_dict
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from ra_aid.model_formatters.research_notes_formatter import format_research_notes_dict
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from ra_aid.tools.memory import (
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_global_memory,
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get_memory_value,
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get_related_files,
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log_work_event,
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)
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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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from ra_aid.database.repositories.key_fact_repository import get_key_fact_repository
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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 estimate_messages_tokens(messages: Sequence[BaseMessage]) -> int:
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"""Helper function to estimate total tokens in a sequence of messages.
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Args:
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messages: Sequence of messages to count tokens for
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Returns:
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Total estimated token count
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"""
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if not messages:
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return 0
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estimate_tokens = CiaynAgent._estimate_tokens
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return sum(estimate_tokens(msg) for msg in messages)
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def state_modifier(
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state: AgentState, max_input_tokens: int = DEFAULT_TOKEN_LIMIT
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) -> list[BaseMessage]:
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"""Given the agent state and max_tokens, return a trimmed list of messages.
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Args:
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state: The current agent state containing messages
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max_tokens: Maximum number of tokens to allow (default: DEFAULT_TOKEN_LIMIT)
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Returns:
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list[BaseMessage]: Trimmed list of messages that fits within token limit
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"""
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messages = state["messages"]
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if not messages:
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return []
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first_message = messages[0]
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remaining_messages = messages[1:]
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first_tokens = estimate_messages_tokens([first_message])
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new_max_tokens = max_input_tokens - first_tokens
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trimmed_remaining = trim_messages(
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remaining_messages,
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token_counter=estimate_messages_tokens,
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max_tokens=new_max_tokens,
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strategy="last",
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allow_partial=False,
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)
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return [first_message] + trimmed_remaining
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def get_model_token_limit(
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config: Dict[str, Any], agent_type: Literal["default", "research", "planner"]
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) -> Optional[int]:
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"""Get the token limit for the current model configuration based on agent type.
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Returns:
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Optional[int]: The token limit if found, None otherwise
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"""
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try:
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if agent_type == "research":
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provider = config.get("research_provider", "") or config.get("provider", "")
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model_name = config.get("research_model", "") or config.get("model", "")
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elif agent_type == "planner":
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provider = config.get("planner_provider", "") or config.get("provider", "")
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model_name = config.get("planner_model", "") or config.get("model", "")
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else:
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provider = config.get("provider", "")
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model_name = config.get("model", "")
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try:
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provider_model = model_name if not provider else f"{provider}/{model_name}"
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model_info = get_model_info(provider_model)
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max_input_tokens = model_info.get("max_input_tokens")
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if max_input_tokens:
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logger.debug(
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f"Using litellm token limit for {model_name}: {max_input_tokens}"
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)
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return max_input_tokens
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except litellm.exceptions.NotFoundError:
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logger.debug(
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f"Model {model_name} not found in litellm, falling back to models_params"
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)
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except Exception as e:
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logger.debug(
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f"Error getting model info from litellm: {e}, falling back to models_params"
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)
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# Fallback to models_params dict
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# Normalize model name for fallback lookup (e.g. claude-2 -> claude2)
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normalized_name = model_name.replace("-", "")
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provider_tokens = models_params.get(provider, {})
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if normalized_name in provider_tokens:
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max_input_tokens = provider_tokens[normalized_name]["token_limit"]
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logger.debug(
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f"Found token limit for {provider}/{model_name}: {max_input_tokens}"
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)
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else:
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max_input_tokens = None
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logger.debug(f"Could not find token limit for {provider}/{model_name}")
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return max_input_tokens
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except Exception as e:
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logger.warning(f"Failed to get model token limit: {e}")
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return None
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def build_agent_kwargs(
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checkpointer: Optional[Any] = None,
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config: Dict[str, Any] = 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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config: Optional configuration dictionary
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token_limit: 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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if config.get("limit_tokens", True) and is_anthropic_claude(config):
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def wrapped_state_modifier(state: AgentState) -> list[BaseMessage]:
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return state_modifier(state, max_input_tokens=max_input_tokens)
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agent_kwargs["state_modifier"] = wrapped_state_modifier
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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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provider: The provider name
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model_name: The model name
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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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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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config = _global_memory.get("config", {})
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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, config, max_input_tokens)
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return create_react_agent(model, tools, **agent_kwargs)
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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 = _global_memory.get("config", {})
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max_input_tokens = get_model_token_limit(config, agent_type)
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agent_kwargs = build_agent_kwargs(checkpointer, config, max_input_tokens)
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return create_react_agent(model, tools, **agent_kwargs)
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def run_research_agent(
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base_task_or_query: str,
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model,
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*,
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expert_enabled: bool = False,
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research_only: bool = False,
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hil: bool = False,
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web_research_enabled: bool = False,
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memory: Optional[Any] = None,
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config: Optional[dict] = None,
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thread_id: Optional[str] = None,
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console_message: Optional[str] = None,
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) -> Optional[str]:
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"""Run a research agent with the given configuration.
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Args:
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base_task_or_query: The main task or query for research
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model: The LLM model to use
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expert_enabled: Whether expert mode is enabled
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research_only: Whether this is a research-only task
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hil: Whether human-in-the-loop mode is enabled
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web_research_enabled: Whether web research is enabled
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memory: Optional memory instance to use
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config: Optional configuration dictionary
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thread_id: Optional thread ID (defaults to new UUID)
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console_message: Optional message to display before running
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Returns:
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Optional[str]: The completion message if task completed successfully
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Example:
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result = run_research_agent(
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"Research Python async patterns",
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model,
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expert_enabled=True,
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research_only=True
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)
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"""
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thread_id = thread_id or str(uuid.uuid4())
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logger.debug("Starting research agent with thread_id=%s", thread_id)
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logger.debug(
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"Research configuration: expert=%s, research_only=%s, hil=%s, web=%s",
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expert_enabled,
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research_only,
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hil,
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web_research_enabled,
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)
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if memory is None:
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memory = MemorySaver()
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tools = get_research_tools(
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research_only=research_only,
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expert_enabled=expert_enabled,
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human_interaction=hil,
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web_research_enabled=config.get("web_research_enabled", False),
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)
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agent = create_agent(model, tools, checkpointer=memory, agent_type="research")
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expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
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human_section = HUMAN_PROMPT_SECTION_RESEARCH if hil else ""
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web_research_section = (
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WEB_RESEARCH_PROMPT_SECTION_RESEARCH
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if config.get("web_research_enabled")
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else ""
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)
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try:
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key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
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except RuntimeError as e:
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logger.error(f"Failed to access key fact repository: {str(e)}")
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key_facts = ""
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code_snippets = _global_memory.get("code_snippets", "")
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related_files = _global_memory.get("related_files", "")
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current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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working_directory = os.getcwd()
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# Get the last human input, if it exists
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base_task = base_task_or_query
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try:
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human_input_repository = get_human_input_repository()
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recent_inputs = human_input_repository.get_recent(1)
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if recent_inputs and len(recent_inputs) > 0:
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last_human_input = recent_inputs[0].content
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base_task = f"<last human input>{last_human_input}</last human input>\n{base_task}"
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except RuntimeError as e:
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logger.error(f"Failed to access human input repository: {str(e)}")
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# Continue without appending last human input
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try:
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project_info = get_project_info(".", file_limit=2000)
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formatted_project_info = format_project_info(project_info)
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except Exception as e:
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logger.warning(f"Failed to get project info: {e}")
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formatted_project_info = ""
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prompt = (RESEARCH_ONLY_PROMPT if research_only else RESEARCH_PROMPT).format(
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current_date=current_date,
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working_directory=working_directory,
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base_task=base_task,
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research_only_note=(
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""
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if research_only
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else " Only request implementation if the user explicitly asked for changes to be made."
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),
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expert_section=expert_section,
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human_section=human_section,
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web_research_section=web_research_section,
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key_facts=key_facts,
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work_log=get_memory_value("work_log"),
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code_snippets=code_snippets,
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related_files=related_files,
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project_info=formatted_project_info,
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new_project_hints=NEW_PROJECT_HINTS if project_info.is_new else "",
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)
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config = _global_memory.get("config", {}) if not config else config
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recursion_limit = config.get("recursion_limit", DEFAULT_RECURSION_LIMIT)
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run_config = {
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"configurable": {"thread_id": thread_id},
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"recursion_limit": recursion_limit,
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}
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if config:
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run_config.update(config)
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try:
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if console_message:
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console.print(
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Panel(Markdown(console_message), title="🔬 Looking into it...")
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)
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if project_info:
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display_project_status(project_info)
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if agent is not None:
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logger.debug("Research agent created successfully")
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none_or_fallback_handler = init_fallback_handler(agent, config, tools)
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_result = run_agent_with_retry(
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agent, prompt, run_config, none_or_fallback_handler
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)
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if _result:
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# Log research completion
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log_work_event(f"Completed research phase for: {base_task_or_query}")
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return _result
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else:
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logger.debug("No model provided, running web research tools directly")
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return run_web_research_agent(
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base_task_or_query,
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model=None,
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expert_enabled=expert_enabled,
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hil=hil,
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web_research_enabled=web_research_enabled,
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memory=memory,
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config=config,
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thread_id=thread_id,
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console_message=console_message,
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)
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except (KeyboardInterrupt, AgentInterrupt):
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raise
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except Exception as e:
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logger.error("Research agent failed: %s", str(e), exc_info=True)
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raise
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def run_web_research_agent(
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query: str,
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model,
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*,
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expert_enabled: bool = False,
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hil: bool = False,
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web_research_enabled: bool = False,
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memory: Optional[Any] = None,
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config: Optional[dict] = None,
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thread_id: Optional[str] = None,
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console_message: Optional[str] = None,
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) -> Optional[str]:
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"""Run a web research agent with the given configuration.
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Args:
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query: The mainquery for web research
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model: The LLM model to use
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expert_enabled: Whether expert mode is enabled
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hil: Whether human-in-the-loop mode is enabled
|
|
web_research_enabled: Whether web research is enabled
|
|
memory: Optional memory instance to use
|
|
config: Optional configuration dictionary
|
|
thread_id: Optional thread ID (defaults to new UUID)
|
|
console_message: Optional message to display before running
|
|
|
|
Returns:
|
|
Optional[str]: The completion message if task completed successfully
|
|
|
|
Example:
|
|
result = run_web_research_agent(
|
|
"Research latest Python async patterns",
|
|
model,
|
|
expert_enabled=True
|
|
)
|
|
"""
|
|
thread_id = thread_id or str(uuid.uuid4())
|
|
logger.debug("Starting web research agent with thread_id=%s", thread_id)
|
|
logger.debug(
|
|
"Web research configuration: expert=%s, hil=%s, web=%s",
|
|
expert_enabled,
|
|
hil,
|
|
web_research_enabled,
|
|
)
|
|
|
|
if memory is None:
|
|
memory = MemorySaver()
|
|
|
|
if thread_id is None:
|
|
thread_id = str(uuid.uuid4())
|
|
|
|
tools = get_web_research_tools(expert_enabled=expert_enabled)
|
|
|
|
agent = create_agent(model, tools, checkpointer=memory, agent_type="research")
|
|
|
|
expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else ""
|
|
human_section = HUMAN_PROMPT_SECTION_RESEARCH if hil else ""
|
|
|
|
try:
|
|
key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access key fact repository: {str(e)}")
|
|
key_facts = ""
|
|
code_snippets = _global_memory.get("code_snippets", "")
|
|
related_files = _global_memory.get("related_files", "")
|
|
|
|
current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
working_directory = os.getcwd()
|
|
|
|
prompt = WEB_RESEARCH_PROMPT.format(
|
|
current_date=current_date,
|
|
working_directory=working_directory,
|
|
web_research_query=query,
|
|
expert_section=expert_section,
|
|
human_section=human_section,
|
|
key_facts=key_facts,
|
|
work_log=get_memory_value("work_log"),
|
|
code_snippets=code_snippets,
|
|
related_files=related_files,
|
|
)
|
|
|
|
config = _global_memory.get("config", {}) if not config else config
|
|
|
|
recursion_limit = config.get("recursion_limit", DEFAULT_RECURSION_LIMIT)
|
|
run_config = {
|
|
"configurable": {"thread_id": thread_id},
|
|
"recursion_limit": recursion_limit,
|
|
}
|
|
if config:
|
|
run_config.update(config)
|
|
|
|
try:
|
|
if console_message:
|
|
console.print(Panel(Markdown(console_message), title="🔬 Researching..."))
|
|
|
|
logger.debug("Web research agent completed successfully")
|
|
none_or_fallback_handler = init_fallback_handler(agent, config, tools)
|
|
_result = run_agent_with_retry(
|
|
agent, prompt, run_config, none_or_fallback_handler
|
|
)
|
|
if _result:
|
|
# Log web research completion
|
|
log_work_event(f"Completed web research phase for: {query}")
|
|
return _result
|
|
|
|
except (KeyboardInterrupt, AgentInterrupt):
|
|
raise
|
|
except Exception as e:
|
|
logger.error("Web research agent failed: %s", str(e), exc_info=True)
|
|
raise
|
|
|
|
|
|
def run_planning_agent(
|
|
base_task: str,
|
|
model,
|
|
*,
|
|
expert_enabled: bool = False,
|
|
hil: bool = False,
|
|
memory: Optional[Any] = None,
|
|
config: Optional[dict] = None,
|
|
thread_id: Optional[str] = None,
|
|
) -> Optional[str]:
|
|
"""Run a planning agent to create implementation plans.
|
|
|
|
Args:
|
|
base_task: The main task to plan implementation for
|
|
model: The LLM model to use
|
|
expert_enabled: Whether expert mode is enabled
|
|
hil: Whether human-in-the-loop mode is enabled
|
|
memory: Optional memory instance to use
|
|
config: Optional configuration dictionary
|
|
thread_id: Optional thread ID (defaults to new UUID)
|
|
|
|
Returns:
|
|
Optional[str]: The completion message if planning completed successfully
|
|
"""
|
|
thread_id = thread_id or str(uuid.uuid4())
|
|
logger.debug("Starting planning agent with thread_id=%s", thread_id)
|
|
logger.debug("Planning configuration: expert=%s, hil=%s", expert_enabled, hil)
|
|
|
|
if memory is None:
|
|
memory = MemorySaver()
|
|
|
|
if thread_id is None:
|
|
thread_id = str(uuid.uuid4())
|
|
|
|
# Get latest project info
|
|
try:
|
|
project_info = get_project_info(".")
|
|
formatted_project_info = format_project_info(project_info)
|
|
except Exception as e:
|
|
logger.warning("Failed to get project info: %s", str(e))
|
|
formatted_project_info = "Project info unavailable"
|
|
|
|
tools = get_planning_tools(
|
|
expert_enabled=expert_enabled,
|
|
web_research_enabled=config.get("web_research_enabled", False),
|
|
)
|
|
|
|
agent = create_agent(model, tools, checkpointer=memory, agent_type="planner")
|
|
|
|
expert_section = EXPERT_PROMPT_SECTION_PLANNING if expert_enabled else ""
|
|
human_section = HUMAN_PROMPT_SECTION_PLANNING if hil else ""
|
|
web_research_section = (
|
|
WEB_RESEARCH_PROMPT_SECTION_PLANNING
|
|
if config.get("web_research_enabled")
|
|
else ""
|
|
)
|
|
|
|
current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
working_directory = os.getcwd()
|
|
|
|
# Make sure key_facts is defined before using it
|
|
try:
|
|
key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access key fact repository: {str(e)}")
|
|
key_facts = ""
|
|
|
|
# Make sure key_snippets is defined before using it
|
|
try:
|
|
key_snippets = format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict())
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access key snippet repository: {str(e)}")
|
|
key_snippets = ""
|
|
|
|
# Get formatted research notes using repository
|
|
try:
|
|
repository = get_research_note_repository()
|
|
notes_dict = repository.get_notes_dict()
|
|
formatted_research_notes = format_research_notes_dict(notes_dict)
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access research note repository: {str(e)}")
|
|
formatted_research_notes = ""
|
|
|
|
planning_prompt = PLANNING_PROMPT.format(
|
|
current_date=current_date,
|
|
working_directory=working_directory,
|
|
expert_section=expert_section,
|
|
human_section=human_section,
|
|
web_research_section=web_research_section,
|
|
base_task=base_task,
|
|
project_info=formatted_project_info,
|
|
research_notes=formatted_research_notes,
|
|
related_files="\n".join(get_related_files()),
|
|
key_facts=key_facts,
|
|
key_snippets=key_snippets,
|
|
work_log=get_memory_value("work_log"),
|
|
research_only_note=(
|
|
""
|
|
if config.get("research_only")
|
|
else " Only request implementation if the user explicitly asked for changes to be made."
|
|
),
|
|
)
|
|
|
|
config = _global_memory.get("config", {}) if not config else config
|
|
recursion_limit = config.get("recursion_limit", DEFAULT_RECURSION_LIMIT)
|
|
run_config = {
|
|
"configurable": {"thread_id": thread_id},
|
|
"recursion_limit": recursion_limit,
|
|
}
|
|
if config:
|
|
run_config.update(config)
|
|
|
|
try:
|
|
print_stage_header("Planning Stage")
|
|
logger.debug("Planning agent completed successfully")
|
|
none_or_fallback_handler = init_fallback_handler(agent, config, tools)
|
|
_result = run_agent_with_retry(
|
|
agent, planning_prompt, run_config, none_or_fallback_handler
|
|
)
|
|
if _result:
|
|
# Log planning completion
|
|
log_work_event(f"Completed planning phase for: {base_task}")
|
|
return _result
|
|
except (KeyboardInterrupt, AgentInterrupt):
|
|
raise
|
|
except Exception as e:
|
|
logger.error("Planning agent failed: %s", str(e), exc_info=True)
|
|
raise
|
|
|
|
|
|
def run_task_implementation_agent(
|
|
base_task: str,
|
|
tasks: list,
|
|
task: str,
|
|
plan: str,
|
|
related_files: list,
|
|
model,
|
|
*,
|
|
expert_enabled: bool = False,
|
|
web_research_enabled: bool = False,
|
|
memory: Optional[Any] = None,
|
|
config: Optional[dict] = None,
|
|
thread_id: Optional[str] = None,
|
|
) -> Optional[str]:
|
|
"""Run an implementation agent for a specific task.
|
|
|
|
Args:
|
|
base_task: The main task being implemented
|
|
tasks: List of tasks to implement
|
|
plan: The implementation plan
|
|
related_files: List of related files
|
|
model: The LLM model to use
|
|
expert_enabled: Whether expert mode is enabled
|
|
web_research_enabled: Whether web research is enabled
|
|
memory: Optional memory instance to use
|
|
config: Optional configuration dictionary
|
|
thread_id: Optional thread ID (defaults to new UUID)
|
|
|
|
Returns:
|
|
Optional[str]: The completion message if task completed successfully
|
|
"""
|
|
thread_id = thread_id or str(uuid.uuid4())
|
|
logger.debug("Starting implementation agent with thread_id=%s", thread_id)
|
|
logger.debug(
|
|
"Implementation configuration: expert=%s, web=%s",
|
|
expert_enabled,
|
|
web_research_enabled,
|
|
)
|
|
logger.debug("Task details: base_task=%s, current_task=%s", base_task, task)
|
|
logger.debug("Related files: %s", related_files)
|
|
|
|
if memory is None:
|
|
memory = MemorySaver()
|
|
|
|
if thread_id is None:
|
|
thread_id = str(uuid.uuid4())
|
|
|
|
tools = get_implementation_tools(
|
|
expert_enabled=expert_enabled,
|
|
web_research_enabled=config.get("web_research_enabled", False),
|
|
)
|
|
|
|
agent = create_agent(model, tools, checkpointer=memory, agent_type="planner")
|
|
|
|
current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
working_directory = os.getcwd()
|
|
|
|
# Make sure key_facts is defined before using it
|
|
try:
|
|
key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access key fact repository: {str(e)}")
|
|
key_facts = ""
|
|
|
|
# Get formatted research notes using repository
|
|
try:
|
|
repository = get_research_note_repository()
|
|
notes_dict = repository.get_notes_dict()
|
|
formatted_research_notes = format_research_notes_dict(notes_dict)
|
|
except RuntimeError as e:
|
|
logger.error(f"Failed to access research note repository: {str(e)}")
|
|
formatted_research_notes = ""
|
|
|
|
prompt = IMPLEMENTATION_PROMPT.format(
|
|
current_date=current_date,
|
|
working_directory=working_directory,
|
|
base_task=base_task,
|
|
task=task,
|
|
tasks=tasks,
|
|
plan=plan,
|
|
related_files=related_files,
|
|
key_facts=key_facts,
|
|
key_snippets=format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict()),
|
|
research_notes=formatted_research_notes,
|
|
work_log=get_memory_value("work_log"),
|
|
expert_section=EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else "",
|
|
human_section=(
|
|
HUMAN_PROMPT_SECTION_IMPLEMENTATION
|
|
if _global_memory.get("config", {}).get("hil", False)
|
|
else ""
|
|
),
|
|
web_research_section=(
|
|
WEB_RESEARCH_PROMPT_SECTION_CHAT
|
|
if config.get("web_research_enabled")
|
|
else ""
|
|
),
|
|
)
|
|
|
|
config = _global_memory.get("config", {}) if not config else config
|
|
recursion_limit = config.get("recursion_limit", DEFAULT_RECURSION_LIMIT)
|
|
run_config = {
|
|
"configurable": {"thread_id": thread_id},
|
|
"recursion_limit": recursion_limit,
|
|
}
|
|
if config:
|
|
run_config.update(config)
|
|
|
|
try:
|
|
logger.debug("Implementation agent completed successfully")
|
|
none_or_fallback_handler = init_fallback_handler(agent, config, tools)
|
|
_result = run_agent_with_retry(
|
|
agent, prompt, run_config, none_or_fallback_handler
|
|
)
|
|
if _result:
|
|
# Log task implementation completion
|
|
log_work_event(f"Completed implementation of task: {task}")
|
|
return _result
|
|
except (KeyboardInterrupt, AgentInterrupt):
|
|
raise
|
|
except Exception as e:
|
|
logger.error("Implementation agent failed: %s", str(e), exc_info=True)
|
|
raise
|
|
|
|
|
|
_CONTEXT_STACK = []
|
|
_INTERRUPT_CONTEXT = None
|
|
_FEEDBACK_MODE = False
|
|
|
|
|
|
def _request_interrupt(signum, frame):
|
|
global _INTERRUPT_CONTEXT
|
|
if _CONTEXT_STACK:
|
|
_INTERRUPT_CONTEXT = _CONTEXT_STACK[-1]
|
|
|
|
if _FEEDBACK_MODE:
|
|
print()
|
|
print(" 👋 Bye!")
|
|
print()
|
|
sys.exit(0)
|
|
|
|
|
|
class InterruptibleSection:
|
|
def __enter__(self):
|
|
_CONTEXT_STACK.append(self)
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
_CONTEXT_STACK.remove(self)
|
|
|
|
|
|
def check_interrupt():
|
|
if _CONTEXT_STACK and _INTERRUPT_CONTEXT is _CONTEXT_STACK[-1]:
|
|
raise AgentInterrupt("Interrupt requested")
|
|
|
|
|
|
# New helper functions for run_agent_with_retry refactoring
|
|
def _setup_interrupt_handling():
|
|
if threading.current_thread() is threading.main_thread():
|
|
original_handler = signal.getsignal(signal.SIGINT)
|
|
signal.signal(signal.SIGINT, _request_interrupt)
|
|
return original_handler
|
|
return None
|
|
|
|
|
|
def _restore_interrupt_handling(original_handler):
|
|
if original_handler and threading.current_thread() is threading.main_thread():
|
|
signal.signal(signal.SIGINT, original_handler)
|
|
|
|
|
|
def _increment_agent_depth():
|
|
current_depth = _global_memory.get("agent_depth", 0)
|
|
_global_memory["agent_depth"] = current_depth + 1
|
|
|
|
|
|
def _decrement_agent_depth():
|
|
_global_memory["agent_depth"] = _global_memory.get("agent_depth", 1) - 1
|
|
|
|
|
|
def reset_agent_completion_flags():
|
|
"""Reset completion flags in the current context."""
|
|
reset_completion_flags()
|
|
|
|
|
|
def _execute_test_command_wrapper(original_prompt, config, test_attempts, auto_test):
|
|
return execute_test_command(config, original_prompt, test_attempts, auto_test)
|
|
|
|
|
|
def _handle_api_error(e, attempt, max_retries, base_delay):
|
|
# 1. Check if this is a ValueError with 429 code or rate limit phrases
|
|
if isinstance(e, ValueError):
|
|
error_str = str(e).lower()
|
|
rate_limit_phrases = ["429", "rate limit", "too many requests", "quota exceeded"]
|
|
if "code" not in error_str and not any(phrase in error_str for phrase in rate_limit_phrases):
|
|
raise e
|
|
|
|
# 2. Check for status_code or http_status attribute equal to 429
|
|
if hasattr(e, 'status_code') and e.status_code == 429:
|
|
pass # This is a rate limit error, continue with retry logic
|
|
elif hasattr(e, 'http_status') and e.http_status == 429:
|
|
pass # This is a rate limit error, continue with retry logic
|
|
# 3. Check for rate limit phrases in error message
|
|
elif isinstance(e, Exception) and not isinstance(e, ValueError):
|
|
error_str = str(e).lower()
|
|
if not any(phrase in error_str for phrase in ["rate limit", "too many requests", "quota exceeded", "429"]) and not ("rate" in error_str and "limit" in error_str):
|
|
# This doesn't look like a rate limit error, but we'll still retry other API errors
|
|
pass
|
|
|
|
# Apply common retry logic for all identified errors
|
|
if attempt == max_retries - 1:
|
|
logger.error("Max retries reached, failing: %s", str(e))
|
|
raise RuntimeError(f"Max retries ({max_retries}) exceeded. Last error: {e}")
|
|
|
|
logger.warning("API error (attempt %d/%d): %s", attempt + 1, max_retries, str(e))
|
|
delay = base_delay * (2**attempt)
|
|
print_error(
|
|
f"Encountered {e.__class__.__name__}: {e}. Retrying in {delay}s... (Attempt {attempt+1}/{max_retries})"
|
|
)
|
|
start = time.monotonic()
|
|
while time.monotonic() - start < delay:
|
|
check_interrupt()
|
|
time.sleep(0.1)
|
|
|
|
|
|
def get_agent_type(agent: RAgents) -> Literal["CiaynAgent", "React"]:
|
|
"""
|
|
Determines the type of the agent.
|
|
Returns "CiaynAgent" if agent is an instance of CiaynAgent, otherwise "React".
|
|
"""
|
|
|
|
if isinstance(agent, CiaynAgent):
|
|
return "CiaynAgent"
|
|
else:
|
|
return "React"
|
|
|
|
|
|
def init_fallback_handler(agent: RAgents, config: Dict[str, Any], tools: List[Any]):
|
|
"""
|
|
Initialize fallback handler if agent is of type "React" and experimental_fallback_handler is enabled; otherwise return None.
|
|
"""
|
|
if not config.get("experimental_fallback_handler", False):
|
|
return None
|
|
agent_type = get_agent_type(agent)
|
|
if agent_type == "React":
|
|
return FallbackHandler(config, tools)
|
|
return None
|
|
|
|
|
|
def _handle_fallback_response(
|
|
error: ToolExecutionError,
|
|
fallback_handler: Optional[FallbackHandler],
|
|
agent: RAgents,
|
|
msg_list: list,
|
|
) -> None:
|
|
"""
|
|
Handle fallback response by invoking fallback_handler and updating msg_list.
|
|
"""
|
|
if not fallback_handler:
|
|
return
|
|
fallback_response = fallback_handler.handle_failure(error, agent, msg_list)
|
|
agent_type = get_agent_type(agent)
|
|
if fallback_response and agent_type == "React":
|
|
msg_list_response = [HumanMessage(str(msg)) for msg in fallback_response]
|
|
msg_list.extend(msg_list_response)
|
|
|
|
|
|
def _run_agent_stream(agent: RAgents, msg_list: list[BaseMessage], config: dict):
|
|
for chunk in agent.stream({"messages": msg_list}, config):
|
|
logger.debug("Agent output: %s", chunk)
|
|
check_interrupt()
|
|
agent_type = get_agent_type(agent)
|
|
print_agent_output(chunk, agent_type)
|
|
if is_completed() or should_exit():
|
|
reset_completion_flags()
|
|
break
|
|
|
|
|
|
def run_agent_with_retry(
|
|
agent: RAgents,
|
|
prompt: str,
|
|
config: dict,
|
|
fallback_handler: Optional[FallbackHandler] = None,
|
|
) -> Optional[str]:
|
|
"""Run an agent with retry logic for API errors."""
|
|
logger.debug("Running agent with prompt length: %d", len(prompt))
|
|
original_handler = _setup_interrupt_handling()
|
|
max_retries = 20
|
|
base_delay = 1
|
|
test_attempts = 0
|
|
_max_test_retries = config.get("max_test_cmd_retries", DEFAULT_MAX_TEST_CMD_RETRIES)
|
|
auto_test = config.get("auto_test", False)
|
|
original_prompt = prompt
|
|
msg_list = [HumanMessage(content=prompt)]
|
|
|
|
# Create a new agent context for this run
|
|
with InterruptibleSection(), agent_context() as ctx:
|
|
try:
|
|
_increment_agent_depth()
|
|
for attempt in range(max_retries):
|
|
logger.debug("Attempt %d/%d", attempt + 1, max_retries)
|
|
check_interrupt()
|
|
|
|
# Check if the agent has crashed before attempting to run it
|
|
from ra_aid.agent_context import get_crash_message, is_crashed
|
|
|
|
if is_crashed():
|
|
crash_message = get_crash_message()
|
|
logger.error("Agent has crashed: %s", crash_message)
|
|
return f"Agent has crashed: {crash_message}"
|
|
|
|
try:
|
|
_run_agent_stream(agent, msg_list, config)
|
|
if fallback_handler:
|
|
fallback_handler.reset_fallback_handler()
|
|
should_break, prompt, auto_test, test_attempts = (
|
|
_execute_test_command_wrapper(
|
|
original_prompt, config, test_attempts, auto_test
|
|
)
|
|
)
|
|
if should_break:
|
|
break
|
|
if prompt != original_prompt:
|
|
continue
|
|
|
|
logger.debug("Agent run completed successfully")
|
|
return "Agent run completed successfully"
|
|
except ToolExecutionError as e:
|
|
# Check if this is a BadRequestError (HTTP 400) which is unretryable
|
|
error_str = str(e).lower()
|
|
if "400" in error_str or "bad request" in error_str:
|
|
from ra_aid.agent_context import mark_agent_crashed
|
|
|
|
crash_message = f"Unretryable error: {str(e)}"
|
|
mark_agent_crashed(crash_message)
|
|
logger.error("Agent has crashed: %s", crash_message)
|
|
return f"Agent has crashed: {crash_message}"
|
|
|
|
_handle_fallback_response(e, fallback_handler, agent, msg_list)
|
|
continue
|
|
except FallbackToolExecutionError as e:
|
|
msg_list.append(
|
|
SystemMessage(f"FallbackToolExecutionError:{str(e)}")
|
|
)
|
|
except (KeyboardInterrupt, AgentInterrupt):
|
|
raise
|
|
except (
|
|
InternalServerError,
|
|
APITimeoutError,
|
|
RateLimitError,
|
|
OpenAIRateLimitError,
|
|
LiteLLMRateLimitError,
|
|
ResourceExhausted,
|
|
APIError,
|
|
ValueError,
|
|
) as e:
|
|
# Check if this is a BadRequestError (HTTP 400) which is unretryable
|
|
error_str = str(e).lower()
|
|
if (
|
|
"400" in error_str or "bad request" in error_str
|
|
) and isinstance(e, APIError):
|
|
from ra_aid.agent_context import mark_agent_crashed
|
|
|
|
crash_message = f"Unretryable API error: {str(e)}"
|
|
mark_agent_crashed(crash_message)
|
|
logger.error("Agent has crashed: %s", crash_message)
|
|
return f"Agent has crashed: {crash_message}"
|
|
|
|
_handle_api_error(e, attempt, max_retries, base_delay)
|
|
finally:
|
|
_decrement_agent_depth()
|
|
_restore_interrupt_handling(original_handler)
|