From 2899b5f8482390f1e8fc9a71ac7b24f6c21d319d Mon Sep 17 00:00:00 2001 From: Ariel Frischer Date: Mon, 10 Mar 2025 04:08:12 -0700 Subject: [PATCH] feat(agent_utils.py): add AnthropicCallbackHandler to track token usage and costs for Anthropic models (#118) style(agent_utils.py): format imports and code for better readability refactor(agent_utils.py): standardize model name and cost calculation logic for clarity and maintainability chore(anthropic_callback_handler.py): create a new file for the AnthropicCallbackHandler implementation and related functions --- ra_aid/agent_utils.py | 483 ++++++---- .../callbacks/anthropic_callback_handler.py | 270 ++++++ uv.lock | 910 +++++++++--------- 3 files changed, 1040 insertions(+), 623 deletions(-) create mode 100644 ra_aid/callbacks/anthropic_callback_handler.py diff --git a/ra_aid/agent_utils.py b/ra_aid/agent_utils.py index 59aee41..2593f13 100644 --- a/ra_aid/agent_utils.py +++ b/ra_aid/agent_utils.py @@ -10,6 +10,9 @@ import uuid from datetime import datetime from typing import Any, Dict, List, Literal, Optional, Sequence +from ra_aid.callbacks.anthropic_callback_handler import AnthropicCallbackHandler + + import litellm from anthropic import APIError, APITimeoutError, InternalServerError, RateLimitError from openai import RateLimitError as OpenAIRateLimitError @@ -71,7 +74,11 @@ from ra_aid.prompts.human_prompts import ( from ra_aid.prompts.implementation_prompts import IMPLEMENTATION_PROMPT from ra_aid.prompts.common_prompts import NEW_PROJECT_HINTS from ra_aid.prompts.planning_prompts import PLANNING_PROMPT -from ra_aid.prompts.reasoning_assist_prompt import REASONING_ASSIST_PROMPT_PLANNING, REASONING_ASSIST_PROMPT_IMPLEMENTATION, REASONING_ASSIST_PROMPT_RESEARCH +from ra_aid.prompts.reasoning_assist_prompt import ( + REASONING_ASSIST_PROMPT_PLANNING, + REASONING_ASSIST_PROMPT_IMPLEMENTATION, + REASONING_ASSIST_PROMPT_RESEARCH, +) from ra_aid.prompts.research_prompts import ( RESEARCH_ONLY_PROMPT, RESEARCH_PROMPT, @@ -90,9 +97,15 @@ from ra_aid.tool_configs import ( ) from ra_aid.tools.handle_user_defined_test_cmd_execution import execute_test_command from ra_aid.database.repositories.key_fact_repository import get_key_fact_repository -from ra_aid.database.repositories.key_snippet_repository import get_key_snippet_repository -from ra_aid.database.repositories.human_input_repository import get_human_input_repository -from ra_aid.database.repositories.research_note_repository import get_research_note_repository +from ra_aid.database.repositories.key_snippet_repository import ( + get_key_snippet_repository, +) +from ra_aid.database.repositories.human_input_repository import ( + get_human_input_repository, +) +from ra_aid.database.repositories.research_note_repository import ( + get_research_note_repository, +) from ra_aid.database.repositories.work_log_repository import get_work_log_repository from ra_aid.model_formatters import format_key_facts_dict from ra_aid.model_formatters.key_snippets_formatter import format_key_snippets_dict @@ -332,7 +345,9 @@ def create_agent( if is_anthropic_claude(config): logger.debug("Using create_react_agent to instantiate agent.") agent_kwargs = build_agent_kwargs(checkpointer, max_input_tokens) - return create_react_agent(model, tools, interrupt_after=['tools'], **agent_kwargs) + return create_react_agent( + model, tools, interrupt_after=["tools"], **agent_kwargs + ) else: logger.debug("Using CiaynAgent agent instance") return CiaynAgent(model, tools, max_tokens=max_input_tokens, config=config) @@ -343,7 +358,9 @@ def create_agent( config = get_config_repository().get_all() max_input_tokens = get_model_token_limit(config, agent_type) agent_kwargs = build_agent_kwargs(checkpointer, max_input_tokens) - return create_react_agent(model, tools, interrupt_after=['tools'], **agent_kwargs) + return create_react_agent( + model, tools, interrupt_after=["tools"], **agent_kwargs + ) def run_research_agent( @@ -406,7 +423,9 @@ def run_research_agent( recent_inputs = human_input_repository.get_recent(1) if recent_inputs and len(recent_inputs) > 0: last_human_input = recent_inputs[0].content - base_task = f"{last_human_input}\n{base_task}" + base_task = ( + f"{last_human_input}\n{base_task}" + ) except RuntimeError as e: logger.error(f"Failed to access human input repository: {str(e)}") # Continue without appending last human input @@ -416,7 +435,9 @@ def run_research_agent( except RuntimeError as e: logger.error(f"Failed to access key fact repository: {str(e)}") key_facts = "" - key_snippets = format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict()) + key_snippets = format_key_snippets_dict( + get_key_snippet_repository().get_snippets_dict() + ) related_files = get_related_files() try: @@ -432,20 +453,22 @@ def run_research_agent( human_interaction=hil, web_research_enabled=get_config_repository().get("web_research_enabled", False), ) - + # Get model info for reasoning assistance configuration provider = get_config_repository().get("provider", "") model_name = get_config_repository().get("model", "") - + # Get model configuration to check for reasoning_assist_default model_config = {} provider_models = models_params.get(provider, {}) if provider_models and model_name in provider_models: model_config = provider_models[model_name] - + # Check if reasoning assist is explicitly enabled/disabled force_assistance = get_config_repository().get("force_reasoning_assistance", False) - disable_assistance = get_config_repository().get("disable_reasoning_assistance", False) + disable_assistance = get_config_repository().get( + "disable_reasoning_assistance", False + ) if force_assistance: reasoning_assist_enabled = True elif disable_assistance: @@ -453,26 +476,31 @@ def run_research_agent( else: # Fall back to model default reasoning_assist_enabled = model_config.get("reasoning_assist_default", False) - + logger.debug("Reasoning assist enabled: %s", reasoning_assist_enabled) expert_guidance = "" - + # Get research note information for reasoning assistance try: - research_notes = format_research_notes_dict(get_research_note_repository().get_notes_dict()) + research_notes = format_research_notes_dict( + get_research_note_repository().get_notes_dict() + ) except Exception as e: logger.warning(f"Failed to get research notes: {e}") research_notes = "" - + # If reasoning assist is enabled, make a one-off call to the expert model if reasoning_assist_enabled: try: - logger.info("Reasoning assist enabled for model %s, getting expert guidance", model_name) - + logger.info( + "Reasoning assist enabled for model %s, getting expert guidance", + model_name, + ) + # Collect tool descriptions tool_metadata = [] from ra_aid.tools.reflection import get_function_info as get_tool_info - + for tool in tools: try: tool_info = get_tool_info(tool.func) @@ -481,13 +509,13 @@ def run_research_agent( tool_metadata.append(f"Tool: {name}\nDescription: {description}\n") except Exception as e: logger.warning(f"Error getting tool info for {tool}: {e}") - + # Format tool metadata formatted_tool_metadata = "\n".join(tool_metadata) - + # Initialize expert model expert_model = initialize_expert_llm(provider, model_name) - + # Format the reasoning assist prompt reasoning_assist_prompt = REASONING_ASSIST_PROMPT_RESEARCH.format( current_date=current_date, @@ -500,62 +528,78 @@ def run_research_agent( env_inv=get_env_inv(), tool_metadata=formatted_tool_metadata, ) - + # Show the reasoning assist query in a panel console.print( - Panel(Markdown("Consulting with the reasoning model on the best research approach."), title="📝 Thinking about research strategy...", border_style="yellow") + Panel( + Markdown( + "Consulting with the reasoning model on the best research approach." + ), + title="📝 Thinking about research strategy...", + border_style="yellow", + ) ) - + logger.debug("Invoking expert model for reasoning assist") # Make the call to the expert model response = expert_model.invoke(reasoning_assist_prompt) - + # Check if the model supports think tags supports_think_tag = model_config.get("supports_think_tag", False) supports_thinking = model_config.get("supports_thinking", False) - + # Get response content, handling if it's a list (for Claude thinking mode) content = None - - if hasattr(response, 'content'): + + if hasattr(response, "content"): content = response.content else: # Fallback if content attribute is missing content = str(response) - + # Process content based on its type if isinstance(content, list): # Handle structured thinking mode (e.g., Claude 3.7) thinking_content = None response_text = None - + # Process each item in the list for item in content: if isinstance(item, dict): # Extract thinking content - if item.get('type') == 'thinking' and 'thinking' in item: - thinking_content = item['thinking'] + if item.get("type") == "thinking" and "thinking" in item: + thinking_content = item["thinking"] logger.debug("Found structured thinking content") # Extract response text - elif item.get('type') == 'text' and 'text' in item: - response_text = item['text'] + elif item.get("type") == "text" and "text" in item: + response_text = item["text"] logger.debug("Found structured response text") - + # Display thinking content in a separate panel if available - if thinking_content and get_config_repository().get("show_thoughts", False): - logger.debug(f"Displaying structured thinking content ({len(thinking_content)} chars)") - console.print( - Panel(Markdown(thinking_content), title="💭 Expert Thinking", border_style="yellow") + if thinking_content and get_config_repository().get( + "show_thoughts", False + ): + logger.debug( + f"Displaying structured thinking content ({len(thinking_content)} chars)" ) - + console.print( + Panel( + Markdown(thinking_content), + title="💭 Expert Thinking", + border_style="yellow", + ) + ) + # Use response_text if available, otherwise fall back to joining if response_text: content = response_text else: # Fallback: join list items if structured extraction failed - logger.debug("No structured response text found, joining list items") + logger.debug( + "No structured response text found, joining list items" + ) content = "\n".join(str(item) for item in content) - elif (supports_think_tag or supports_thinking): + elif supports_think_tag or supports_thinking: # Process thinking content using the centralized function content, _ = process_thinking_content( content=content, @@ -563,22 +607,28 @@ def run_research_agent( supports_thinking=supports_thinking, panel_title="💭 Expert Thinking", panel_style="yellow", - logger=logger + logger=logger, ) - + # Display the expert guidance in a panel console.print( - Panel(Markdown(content), title="Research Strategy Guidance", border_style="blue") + Panel( + Markdown(content), + title="Research Strategy Guidance", + border_style="blue", + ) ) - + # Use the content as expert guidance - expert_guidance = content + "\n\nCONSULT WITH THE EXPERT FREQUENTLY DURING RESEARCH" - + expert_guidance = ( + content + "\n\nCONSULT WITH THE EXPERT FREQUENTLY DURING RESEARCH" + ) + logger.info("Received expert guidance for research") except Exception as e: logger.error("Error getting expert guidance for research: %s", e) expert_guidance = "" - + agent = create_agent(model, tools, checkpointer=memory, agent_type="research") expert_section = EXPERT_PROMPT_SECTION_RESEARCH if expert_enabled else "" @@ -588,7 +638,7 @@ def run_research_agent( if get_config_repository().get("web_research_enabled") else "" ) - + # Prepare expert guidance section if expert guidance is available expert_guidance_section = "" if expert_guidance: @@ -600,7 +650,7 @@ def run_research_agent( # We get research notes earlier for reasoning assistance # Get environment inventory information - + prompt = (RESEARCH_ONLY_PROMPT if research_only else RESEARCH_PROMPT).format( current_date=current_date, working_directory=working_directory, @@ -643,9 +693,7 @@ def run_research_agent( if agent is not None: logger.debug("Research agent created successfully") none_or_fallback_handler = init_fallback_handler(agent, tools) - _result = run_agent_with_retry( - agent, prompt, none_or_fallback_handler - ) + _result = run_agent_with_retry(agent, prompt, none_or_fallback_handler) if _result: # Log research completion log_work_event(f"Completed research phase for: {base_task_or_query}") @@ -731,7 +779,9 @@ def run_web_research_agent( logger.error(f"Failed to access key fact repository: {str(e)}") key_facts = "" try: - key_snippets = format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict()) + 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 = "" @@ -741,7 +791,7 @@ def run_web_research_agent( working_directory = os.getcwd() # Get environment inventory information - + prompt = WEB_RESEARCH_PROMPT.format( current_date=current_date, working_directory=working_directory, @@ -771,9 +821,7 @@ def run_web_research_agent( logger.debug("Web research agent completed successfully") none_or_fallback_handler = init_fallback_handler(agent, tools) - _result = run_agent_with_retry( - agent, prompt, none_or_fallback_handler - ) + _result = run_agent_with_retry(agent, prompt, none_or_fallback_handler) if _result: # Log web research completion log_work_event(f"Completed web research phase for: {query}") @@ -835,17 +883,19 @@ def run_planning_agent( provider = get_config_repository().get("provider", "") model_name = get_config_repository().get("model", "") logger.debug("Checking for reasoning_assist_default on %s/%s", provider, model_name) - + # Get model configuration to check for reasoning_assist_default model_config = {} provider_models = models_params.get(provider, {}) if provider_models and model_name in provider_models: model_config = provider_models[model_name] - + # Check if reasoning assist is explicitly enabled/disabled force_assistance = get_config_repository().get("force_reasoning_assistance", False) - disable_assistance = get_config_repository().get("disable_reasoning_assistance", False) - + disable_assistance = get_config_repository().get( + "disable_reasoning_assistance", False + ) + if force_assistance: reasoning_assist_enabled = True elif disable_assistance: @@ -853,27 +903,29 @@ def run_planning_agent( else: # Fall back to model default reasoning_assist_enabled = model_config.get("reasoning_assist_default", False) - + logger.debug("Reasoning assist enabled: %s", reasoning_assist_enabled) - + # Get all the context information (used both for normal planning and reasoning assist) 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()) + 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() @@ -882,28 +934,31 @@ def run_planning_agent( except RuntimeError as e: logger.error(f"Failed to access research note repository: {str(e)}") formatted_research_notes = "" - + # Get related files related_files = "\n".join(get_related_files()) - + # Get environment inventory information env_inv = get_env_inv() - + # Display the planning stage header before any reasoning assistance print_stage_header("Planning Stage") - + # Initialize expert guidance section expert_guidance = "" - + # If reasoning assist is enabled, make a one-off call to the expert model if reasoning_assist_enabled: try: - logger.info("Reasoning assist enabled for model %s, getting expert guidance", model_name) - + logger.info( + "Reasoning assist enabled for model %s, getting expert guidance", + model_name, + ) + # Collect tool descriptions tool_metadata = [] from ra_aid.tools.reflection import get_function_info as get_tool_info - + for tool in tools: try: tool_info = get_tool_info(tool.func) @@ -912,13 +967,13 @@ def run_planning_agent( tool_metadata.append(f"Tool: {name}\nDescription: {description}\n") except Exception as e: logger.warning(f"Error getting tool info for {tool}: {e}") - + # Format tool metadata formatted_tool_metadata = "\n".join(tool_metadata) - + # Initialize expert model expert_model = initialize_expert_llm(provider, model_name) - + # Format the reasoning assist prompt reasoning_assist_prompt = REASONING_ASSIST_PROMPT_PLANNING.format( current_date=current_date, @@ -931,62 +986,78 @@ def run_planning_agent( env_inv=env_inv, tool_metadata=formatted_tool_metadata, ) - + # Show the reasoning assist query in a panel console.print( - Panel(Markdown("Consulting with the reasoning model on the best way to do this."), title="📝 Thinking about the plan...", border_style="yellow") + Panel( + Markdown( + "Consulting with the reasoning model on the best way to do this." + ), + title="📝 Thinking about the plan...", + border_style="yellow", + ) ) - + logger.debug("Invoking expert model for reasoning assist") # Make the call to the expert model response = expert_model.invoke(reasoning_assist_prompt) - + # Check if the model supports think tags supports_think_tag = model_config.get("supports_think_tag", False) supports_thinking = model_config.get("supports_thinking", False) - + # Get response content, handling if it's a list (for Claude thinking mode) content = None - - if hasattr(response, 'content'): + + if hasattr(response, "content"): content = response.content else: # Fallback if content attribute is missing content = str(response) - + # Process content based on its type if isinstance(content, list): # Handle structured thinking mode (e.g., Claude 3.7) thinking_content = None response_text = None - + # Process each item in the list for item in content: if isinstance(item, dict): # Extract thinking content - if item.get('type') == 'thinking' and 'thinking' in item: - thinking_content = item['thinking'] + if item.get("type") == "thinking" and "thinking" in item: + thinking_content = item["thinking"] logger.debug("Found structured thinking content") # Extract response text - elif item.get('type') == 'text' and 'text' in item: - response_text = item['text'] + elif item.get("type") == "text" and "text" in item: + response_text = item["text"] logger.debug("Found structured response text") - + # Display thinking content in a separate panel if available - if thinking_content and get_config_repository().get("show_thoughts", False): - logger.debug(f"Displaying structured thinking content ({len(thinking_content)} chars)") - console.print( - Panel(Markdown(thinking_content), title="💭 Expert Thinking", border_style="yellow") + if thinking_content and get_config_repository().get( + "show_thoughts", False + ): + logger.debug( + f"Displaying structured thinking content ({len(thinking_content)} chars)" ) - + console.print( + Panel( + Markdown(thinking_content), + title="💭 Expert Thinking", + border_style="yellow", + ) + ) + # Use response_text if available, otherwise fall back to joining if response_text: content = response_text else: # Fallback: join list items if structured extraction failed - logger.debug("No structured response text found, joining list items") + logger.debug( + "No structured response text found, joining list items" + ) content = "\n".join(str(item) for item in content) - elif (supports_think_tag or supports_thinking): + elif supports_think_tag or supports_thinking: # Process thinking content using the centralized function content, _ = process_thinking_content( content=content, @@ -994,24 +1065,28 @@ def run_planning_agent( supports_thinking=supports_thinking, panel_title="💭 Expert Thinking", panel_style="yellow", - logger=logger + logger=logger, ) - + # Display the expert guidance in a panel console.print( - Panel(Markdown(content), title="Reasoning Guidance", border_style="blue") + Panel( + Markdown(content), title="Reasoning Guidance", border_style="blue" + ) ) - + # Use the content as expert guidance - expert_guidance = content + "\n\nCONSULT WITH THE EXPERT FREQUENTLY ON THIS TASK" - + expert_guidance = ( + content + "\n\nCONSULT WITH THE EXPERT FREQUENTLY ON THIS TASK" + ) + logger.info("Received expert guidance for planning") except Exception as e: logger.error("Error getting expert guidance for planning: %s", e) expert_guidance = "" - + 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 = ( @@ -1019,7 +1094,7 @@ def run_planning_agent( if get_config_repository().get("web_research_enabled", False) else "" ) - + # Prepare expert guidance section if expert guidance is available expert_guidance_section = "" if expert_guidance: @@ -1050,7 +1125,9 @@ def run_planning_agent( ) config_values = get_config_repository().get_all() - recursion_limit = get_config_repository().get("recursion_limit", DEFAULT_RECURSION_LIMIT) + recursion_limit = get_config_repository().get( + "recursion_limit", DEFAULT_RECURSION_LIMIT + ) run_config = { "configurable": {"thread_id": thread_id}, "recursion_limit": recursion_limit, @@ -1060,9 +1137,7 @@ def run_planning_agent( try: logger.debug("Planning agent completed successfully") none_or_fallback_handler = init_fallback_handler(agent, tools) - _result = run_agent_with_retry( - agent, planning_prompt, none_or_fallback_handler - ) + _result = run_agent_with_retry(agent, planning_prompt, none_or_fallback_handler) if _result: # Log planning completion log_work_event(f"Completed planning phase for: {base_task}") @@ -1135,7 +1210,7 @@ def run_task_implementation_agent( 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() @@ -1144,7 +1219,7 @@ def run_task_implementation_agent( except RuntimeError as e: logger.error(f"Failed to access research note repository: {str(e)}") formatted_research_notes = "" - + # Get latest project info try: project_info = get_project_info(".") @@ -1152,24 +1227,26 @@ def run_task_implementation_agent( except Exception as e: logger.warning("Failed to get project info: %s", str(e)) formatted_project_info = "Project info unavailable" - + # Get environment inventory information env_inv = get_env_inv() - + # Get model configuration to check for reasoning_assist_default provider = get_config_repository().get("provider", "") model_name = get_config_repository().get("model", "") logger.debug("Checking for reasoning_assist_default on %s/%s", provider, model_name) - + model_config = {} provider_models = models_params.get(provider, {}) if provider_models and model_name in provider_models: model_config = provider_models[model_name] - + # Check if reasoning assist is explicitly enabled/disabled force_assistance = get_config_repository().get("force_reasoning_assistance", False) - disable_assistance = get_config_repository().get("disable_reasoning_assistance", False) - + disable_assistance = get_config_repository().get( + "disable_reasoning_assistance", False + ) + if force_assistance: reasoning_assist_enabled = True elif disable_assistance: @@ -1177,71 +1254,84 @@ def run_task_implementation_agent( else: # Fall back to model default reasoning_assist_enabled = model_config.get("reasoning_assist_default", False) - + logger.debug("Reasoning assist enabled: %s", reasoning_assist_enabled) - + # Initialize implementation guidance section implementation_guidance_section = "" - + # If reasoning assist is enabled, make a one-off call to the expert model if reasoning_assist_enabled: try: - logger.info("Reasoning assist enabled for model %s, getting implementation guidance", model_name) - + logger.info( + "Reasoning assist enabled for model %s, getting implementation guidance", + model_name, + ) + # Collect tool descriptions tool_metadata = [] from ra_aid.tools.reflection import get_function_info as get_tool_info - + for tool in tools: try: tool_info = get_tool_info(tool.func) name = tool.func.__name__ description = inspect.getdoc(tool.func) - tool_metadata.append(f"Tool: {name}\\nDescription: {description}\\n") + tool_metadata.append( + f"Tool: {name}\\nDescription: {description}\\n" + ) except Exception as e: logger.warning(f"Error getting tool info for {tool}: {e}") - + # Format tool metadata formatted_tool_metadata = "\\n".join(tool_metadata) - + # Initialize expert model expert_model = initialize_expert_llm(provider, model_name) - + # Format the reasoning assist prompt for implementation reasoning_assist_prompt = REASONING_ASSIST_PROMPT_IMPLEMENTATION.format( current_date=current_date, working_directory=working_directory, task=task, key_facts=key_facts, - key_snippets=format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict()), + key_snippets=format_key_snippets_dict( + get_key_snippet_repository().get_snippets_dict() + ), research_notes=formatted_research_notes, related_files="\\n".join(related_files), env_inv=env_inv, tool_metadata=formatted_tool_metadata, ) - + # Show the reasoning assist query in a panel console.print( - Panel(Markdown("Consulting with the reasoning model on the best implementation approach."), title="📝 Thinking about implementation...", border_style="yellow") + Panel( + Markdown( + "Consulting with the reasoning model on the best implementation approach." + ), + title="📝 Thinking about implementation...", + border_style="yellow", + ) ) - + logger.debug("Invoking expert model for implementation reasoning assist") # Make the call to the expert model response = expert_model.invoke(reasoning_assist_prompt) - + # Check if the model supports think tags supports_think_tag = model_config.get("supports_think_tag", False) supports_thinking = model_config.get("supports_thinking", False) - + # Process response content content = None - - if hasattr(response, 'content'): + + if hasattr(response, "content"): content = response.content else: # Fallback if content attribute is missing content = str(response) - + # Process the response content using the centralized function content, extracted_thinking = process_thinking_content( content=content, @@ -1249,24 +1339,28 @@ def run_task_implementation_agent( supports_thinking=supports_thinking, panel_title="💭 Implementation Thinking", panel_style="yellow", - logger=logger + logger=logger, ) - + # Display the implementation guidance in a panel console.print( - Panel(Markdown(content), title="Implementation Guidance", border_style="blue") + Panel( + Markdown(content), + title="Implementation Guidance", + border_style="blue", + ) ) - + # Format the implementation guidance section for the prompt implementation_guidance_section = f""" {content} """ - + logger.info("Received implementation guidance") except Exception as e: logger.error("Error getting implementation guidance: %s", e) implementation_guidance_section = "" - + prompt = IMPLEMENTATION_PROMPT.format( current_date=current_date, working_directory=working_directory, @@ -1276,7 +1370,9 @@ def run_task_implementation_agent( plan=plan, related_files=related_files, key_facts=key_facts, - key_snippets=format_key_snippets_dict(get_key_snippet_repository().get_snippets_dict()), + key_snippets=format_key_snippets_dict( + get_key_snippet_repository().get_snippets_dict() + ), research_notes=formatted_research_notes, work_log=get_work_log_repository().format_work_log(), expert_section=EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else "", @@ -1296,7 +1392,9 @@ def run_task_implementation_agent( ) config_values = get_config_repository().get_all() - recursion_limit = get_config_repository().get("recursion_limit", DEFAULT_RECURSION_LIMIT) + recursion_limit = get_config_repository().get( + "recursion_limit", DEFAULT_RECURSION_LIMIT + ) run_config = { "configurable": {"thread_id": thread_id}, "recursion_limit": recursion_limit, @@ -1306,9 +1404,7 @@ def run_task_implementation_agent( try: logger.debug("Implementation agent completed successfully") none_or_fallback_handler = init_fallback_handler(agent, tools) - _result = run_agent_with_retry( - agent, prompt, none_or_fallback_handler - ) + _result = run_agent_with_retry(agent, prompt, none_or_fallback_handler) if _result: # Log task implementation completion log_work_event(f"Completed implementation of task: {task}") @@ -1380,27 +1476,37 @@ 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): + 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: + 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: + 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): + 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( @@ -1457,55 +1563,78 @@ def _handle_fallback_response( def _run_agent_stream(agent: RAgents, msg_list: list[BaseMessage]): """ Streams agent output while handling completion and interruption. - + For each chunk, it logs the output, calls check_interrupt(), prints agent output, and then checks if is_completed() or should_exit() are true. If so, it resets completion flags and returns. After finishing a stream iteration (i.e. the for-loop over chunks), the function retrieves the agent's state. If the state indicates further steps (i.e. state.next is non-empty), it resumes execution via agent.invoke(None, config); otherwise, it exits the loop. - + This function adheres to the latest LangGraph best practices (as of March 2025) for handling human-in-the-loop interruptions using interrupt_after=["tools"]. """ config = get_config_repository().get_all() + stream_config = config.copy() + + cb = None + if is_anthropic_claude(config): + model_name = config.get("model", "") + full_model_name = model_name + cb = AnthropicCallbackHandler(full_model_name) + + if "callbacks" not in stream_config: + stream_config["callbacks"] = [] + stream_config["callbacks"].append(cb) + while True: - # Process each chunk from the agent stream. - for chunk in agent.stream({"messages": msg_list}, config): + for chunk in agent.stream({"messages": msg_list}, stream_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() - return True # Exit immediately when finished or signaled to exit. + if cb: + logger.debug(f"AnthropicCallbackHandler:\n{cb}") + return True + logger.debug("Stream iteration ended; checking agent state for continuation.") - + # Prepare state configuration, ensuring 'configurable' is present. state_config = get_config_repository().get_all().copy() if "configurable" not in state_config: - logger.debug("Key 'configurable' not found in config; adding it as an empty dict.") + logger.debug( + "Key 'configurable' not found in config; adding it as an empty dict." + ) state_config["configurable"] = {} logger.debug("Using state_config for agent.get_state(): %s", state_config) - + try: state = agent.get_state(state_config) logger.debug("Agent state retrieved: %s", state) except Exception as e: - logger.error("Error retrieving agent state with state_config %s: %s", state_config, e) + logger.error( + "Error retrieving agent state with state_config %s: %s", state_config, e + ) raise - - # If the state indicates that further steps remain (i.e. state.next is non-empty), - # then resume execution by invoking the agent with no new input. + if state.next: - logger.debug("State indicates continuation (state.next: %s); resuming execution.", state.next) - agent.invoke(None, config) + logger.debug( + "State indicates continuation (state.next: %s); resuming execution.", + state.next, + ) + agent.invoke(None, stream_config) continue else: logger.debug("No continuation indicated in state; exiting stream loop.") break + if cb: + logger.debug(f"AnthropicCallbackHandler:\n{cb}") return True + def run_agent_with_retry( agent: RAgents, prompt: str, @@ -1517,7 +1646,9 @@ def run_agent_with_retry( max_retries = 20 base_delay = 1 test_attempts = 0 - _max_test_retries = get_config_repository().get("max_test_cmd_retries", DEFAULT_MAX_TEST_CMD_RETRIES) + _max_test_retries = get_config_repository().get( + "max_test_cmd_retries", DEFAULT_MAX_TEST_CMD_RETRIES + ) auto_test = get_config_repository().get("auto_test", False) original_prompt = prompt msg_list = [HumanMessage(content=prompt)] diff --git a/ra_aid/callbacks/anthropic_callback_handler.py b/ra_aid/callbacks/anthropic_callback_handler.py new file mode 100644 index 0000000..2bdf737 --- /dev/null +++ b/ra_aid/callbacks/anthropic_callback_handler.py @@ -0,0 +1,270 @@ +"""Custom callback handlers for tracking token usage and costs.""" + +import threading +from contextlib import contextmanager +from contextvars import ContextVar +from typing import Any, Dict, List, Optional + +from langchain_core.callbacks import BaseCallbackHandler + +# Define cost per 1K tokens for various models +ANTHROPIC_MODEL_COSTS = { + # Claude 3.7 Sonnet input + "claude-3-7-sonnet-20250219": 0.003, + "anthropic/claude-3.7-sonnet": 0.003, + "claude-3.7-sonnet": 0.003, + # Claude 3.7 Sonnet output + "claude-3-7-sonnet-20250219-completion": 0.015, + "anthropic/claude-3.7-sonnet-completion": 0.015, + "claude-3.7-sonnet-completion": 0.015, + # Claude 3 Opus input + "claude-3-opus-20240229": 0.015, + "anthropic/claude-3-opus": 0.015, + "claude-3-opus": 0.015, + # Claude 3 Opus output + "claude-3-opus-20240229-completion": 0.075, + "anthropic/claude-3-opus-completion": 0.075, + "claude-3-opus-completion": 0.075, + # Claude 3 Sonnet input + "claude-3-sonnet-20240229": 0.003, + "anthropic/claude-3-sonnet": 0.003, + "claude-3-sonnet": 0.003, + # Claude 3 Sonnet output + "claude-3-sonnet-20240229-completion": 0.015, + "anthropic/claude-3-sonnet-completion": 0.015, + "claude-3-sonnet-completion": 0.015, + # Claude 3 Haiku input + "claude-3-haiku-20240307": 0.00025, + "anthropic/claude-3-haiku": 0.00025, + "claude-3-haiku": 0.00025, + # Claude 3 Haiku output + "claude-3-haiku-20240307-completion": 0.00125, + "anthropic/claude-3-haiku-completion": 0.00125, + "claude-3-haiku-completion": 0.00125, + # Claude 2 input + "claude-2": 0.008, + "claude-2.0": 0.008, + "claude-2.1": 0.008, + # Claude 2 output + "claude-2-completion": 0.024, + "claude-2.0-completion": 0.024, + "claude-2.1-completion": 0.024, + # Claude Instant input + "claude-instant-1": 0.0016, + "claude-instant-1.2": 0.0016, + # Claude Instant output + "claude-instant-1-completion": 0.0055, + "claude-instant-1.2-completion": 0.0055, +} + + +def standardize_model_name(model_name: str, is_completion: bool = False) -> str: + """ + Standardize the model name to a format that can be used for cost calculation. + + Args: + model_name: Model name to standardize. + is_completion: Whether the model is used for completion or not. + + Returns: + Standardized model name. + """ + if not model_name: + model_name = "claude-3-sonnet" + + model_name = model_name.lower() + + # Handle OpenRouter prefixes + if model_name.startswith("anthropic/"): + model_name = model_name[len("anthropic/") :] + + # Add completion suffix if needed + if is_completion and not model_name.endswith("-completion"): + model_name = model_name + "-completion" + + return model_name + + +def get_anthropic_token_cost_for_model( + model_name: str, num_tokens: int, is_completion: bool = False +) -> float: + """ + Get the cost in USD for a given model and number of tokens. + + Args: + model_name: Name of the model + num_tokens: Number of tokens. + is_completion: Whether the model is used for completion or not. + + Returns: + Cost in USD. + """ + model_name = standardize_model_name(model_name, is_completion) + + if model_name not in ANTHROPIC_MODEL_COSTS: + # Default to Claude 3 Sonnet pricing if model not found + model_name = ( + "claude-3-sonnet" if not is_completion else "claude-3-sonnet-completion" + ) + + cost_per_1k = ANTHROPIC_MODEL_COSTS[model_name] + total_cost = cost_per_1k * (num_tokens / 1000) + + return total_cost + + +class AnthropicCallbackHandler(BaseCallbackHandler): + """Callback Handler that tracks Anthropic token usage and costs.""" + + total_tokens: int = 0 + prompt_tokens: int = 0 + completion_tokens: int = 0 + successful_requests: int = 0 + total_cost: float = 0.0 + model_name: str = "claude-3-sonnet" # Default model + + def __init__(self, model_name: Optional[str] = None) -> None: + super().__init__() + self._lock = threading.Lock() + if model_name: + self.model_name = model_name + + # Default costs for Claude 3.7 Sonnet + self.input_cost_per_token = 0.003 / 1000 # $3/M input tokens + self.output_cost_per_token = 0.015 / 1000 # $15/M output tokens + + def __repr__(self) -> str: + return ( + f"Tokens Used: {self.total_tokens}\n" + f"\tPrompt Tokens: {self.prompt_tokens}\n" + f"\tCompletion Tokens: {self.completion_tokens}\n" + f"Successful Requests: {self.successful_requests}\n" + f"Total Cost (USD): ${self.total_cost:.6f}" + ) + + @property + def always_verbose(self) -> bool: + """Whether to call verbose callbacks even if verbose is False.""" + return True + + def on_llm_start( + self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any + ) -> None: + """Record the model name if available.""" + if "name" in serialized: + self.model_name = serialized["name"] + + def on_llm_new_token(self, token: str, **kwargs: Any) -> None: + """Count tokens as they're generated.""" + with self._lock: + self.completion_tokens += 1 + self.total_tokens += 1 + token_cost = get_anthropic_token_cost_for_model( + self.model_name, 1, is_completion=True + ) + self.total_cost += token_cost + + def on_llm_end(self, response: Any, **kwargs: Any) -> None: + """Collect token usage from response.""" + token_usage = {} + + # Try to extract token usage from response + if hasattr(response, "llm_output") and response.llm_output: + llm_output = response.llm_output + if "token_usage" in llm_output: + token_usage = llm_output["token_usage"] + elif "usage" in llm_output: + usage = llm_output["usage"] + + # Handle Anthropic's specific usage format + if "input_tokens" in usage: + token_usage["prompt_tokens"] = usage["input_tokens"] + if "output_tokens" in usage: + token_usage["completion_tokens"] = usage["output_tokens"] + + # Extract model name if available + if "model_name" in llm_output: + self.model_name = llm_output["model_name"] + + # Try to get usage from response.usage + elif hasattr(response, "usage"): + usage = response.usage + if hasattr(usage, "prompt_tokens"): + token_usage["prompt_tokens"] = usage.prompt_tokens + if hasattr(usage, "completion_tokens"): + token_usage["completion_tokens"] = usage.completion_tokens + if hasattr(usage, "total_tokens"): + token_usage["total_tokens"] = usage.total_tokens + + # Extract usage from generations if available + elif hasattr(response, "generations") and response.generations: + for gen in response.generations: + if gen and hasattr(gen[0], "generation_info"): + gen_info = gen[0].generation_info or {} + if "usage" in gen_info: + token_usage = gen_info["usage"] + break + + # Update counts with lock to prevent race conditions + with self._lock: + prompt_tokens = token_usage.get("prompt_tokens", 0) + completion_tokens = token_usage.get("completion_tokens", 0) + + # Only update prompt tokens if we have them + if prompt_tokens > 0: + self.prompt_tokens += prompt_tokens + self.total_tokens += prompt_tokens + prompt_cost = get_anthropic_token_cost_for_model( + self.model_name, prompt_tokens, is_completion=False + ) + self.total_cost += prompt_cost + + # Only update completion tokens if not already counted by on_llm_new_token + if completion_tokens > 0 and completion_tokens > self.completion_tokens: + additional_tokens = completion_tokens - self.completion_tokens + self.completion_tokens = completion_tokens + self.total_tokens += additional_tokens + completion_cost = get_anthropic_token_cost_for_model( + self.model_name, additional_tokens, is_completion=True + ) + self.total_cost += completion_cost + + self.successful_requests += 1 + + def __copy__(self) -> "AnthropicCallbackHandler": + """Return a copy of the callback handler.""" + return self + + def __deepcopy__(self, memo: Any) -> "AnthropicCallbackHandler": + """Return a deep copy of the callback handler.""" + return self + + +# Create a context variable for our custom callback +anthropic_callback_var: ContextVar[Optional[AnthropicCallbackHandler]] = ContextVar( + "anthropic_callback", default=None +) + + +@contextmanager +def get_anthropic_callback( + model_name: Optional[str] = None, +) -> AnthropicCallbackHandler: + """Get the Anthropic callback handler in a context manager. + which conveniently exposes token and cost information. + + Args: + model_name: Optional model name to use for cost calculation. + + Returns: + AnthropicCallbackHandler: The Anthropic callback handler. + + Example: + >>> with get_anthropic_callback("claude-3-sonnet") as cb: + ... # Use the callback handler + ... # cb.total_tokens, cb.total_cost will be available after + """ + cb = AnthropicCallbackHandler(model_name) + anthropic_callback_var.set(cb) + yield cb + anthropic_callback_var.set(None) diff --git a/uv.lock b/uv.lock index e88265d..3cb0473 100644 --- a/uv.lock +++ b/uv.lock @@ -11,7 +11,7 @@ resolution-markers = [ [[package]] name = "aider-chat" -version = "0.74.2" +version = "0.75.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -91,6 +91,7 @@ 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