let aider figure out which model to use
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AI Christianson
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ed04230267
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250bf0a84c
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@ -82,9 +82,6 @@ Examples:
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# Create console instance
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console = Console()
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# Create the base model
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model = initialize_llm(parse_arguments().provider, parse_arguments().model)
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# Create individual memory objects for each agent
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research_memory = MemorySaver()
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planning_memory = MemorySaver()
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@ -125,10 +122,6 @@ def get_research_tools(research_only: bool = False) -> list:
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planning_tools = [list_directory_tree, emit_expert_context, ask_expert, emit_plan, emit_task, emit_related_files, emit_key_facts, delete_key_facts, emit_key_snippets, delete_key_snippets, read_file_tool, fuzzy_find_project_files, ripgrep_search]
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implementation_tools = [list_directory_tree, run_shell_command, emit_expert_context, ask_expert, run_programming_task, emit_related_files, emit_key_facts, delete_key_facts, emit_key_snippets, delete_key_snippets, read_file_tool, fuzzy_find_project_files, ripgrep_search]
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# Create stage-specific agents with individual memory objects
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planning_agent = create_react_agent(model, planning_tools, checkpointer=planning_memory)
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implementation_agent = create_react_agent(model, implementation_tools, checkpointer=implementation_memory)
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def is_informational_query() -> bool:
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"""Determine if the current query is informational based on implementation_requested state.
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@ -188,7 +181,7 @@ def run_agent_with_retry(agent, prompt: str, config: dict):
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time.sleep(delay)
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continue
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def run_implementation_stage(base_task, tasks, plan, related_files):
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def run_implementation_stage(base_task, tasks, plan, related_files, model):
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"""Run implementation stage with a distinct agent for each task."""
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if not is_stage_requested('implementation'):
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print_stage_header("Implementation Stage Skipped")
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@ -224,7 +217,7 @@ def run_implementation_stage(base_task, tasks, plan, related_files):
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run_agent_with_retry(task_agent, task_prompt, {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100})
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def run_research_subtasks(base_task: str, config: dict):
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def run_research_subtasks(base_task: str, config: dict, model):
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"""Run research subtasks with separate agents."""
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subtasks = _global_memory.get('research_subtasks', [])
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if not subtasks:
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@ -255,11 +248,13 @@ def run_research_subtasks(base_task: str, config: dict):
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run_agent_with_retry(subtask_agent, subtask_prompt, config)
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def validate_environment():
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"""Validate required environment variables and dependencies."""
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missing = []
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def validate_environment(args):
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"""Validate required environment variables and dependencies.
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args = parse_arguments()
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Args:
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args: The parsed command line arguments
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"""
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missing = []
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provider = args.provider
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# Check API keys based on provider
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@ -288,8 +283,11 @@ def main():
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"""Main entry point for the ra-aid command line tool."""
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try:
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try:
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validate_environment()
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args = parse_arguments()
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validate_environment(args) # Will exit if env vars missing
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# Create the base model after validation
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model = initialize_llm(args.provider, args.model)
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# Validate message is provided
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if not args.message:
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@ -309,11 +307,16 @@ def main():
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# Store config in global memory for access by is_informational_query
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_global_memory['config'] = config
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# Create research agent now that config is available
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research_agent = create_react_agent(model, get_research_tools(research_only=_global_memory.get('config', {}).get('research_only', False)), checkpointer=research_memory)
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# Run research stage
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print_stage_header("Research Stage")
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# Create research agent with local model
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research_agent = create_react_agent(
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model,
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get_research_tools(research_only=_global_memory.get('config', {}).get('research_only', False)),
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checkpointer=research_memory
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)
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research_prompt = f"""User query: {base_task} --keep it simple
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{RESEARCH_PROMPT}
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@ -327,11 +330,15 @@ Be very thorough in your research and emit lots of snippets, key facts. If you t
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raise # Re-raise to be caught by outer handler
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# Run any research subtasks
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run_research_subtasks(base_task, config)
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run_research_subtasks(base_task, config, model)
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# Proceed with planning and implementation if not an informational query
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if not is_informational_query():
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print_stage_header("Planning Stage")
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# Create planning agent
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planning_agent = create_react_agent(model, planning_tools, checkpointer=planning_memory)
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planning_prompt = PLANNING_PROMPT.format(
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research_notes=get_memory_value('research_notes'),
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key_facts=get_memory_value('key_facts'),
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@ -348,7 +355,8 @@ Be very thorough in your research and emit lots of snippets, key facts. If you t
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base_task,
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get_memory_value('tasks'),
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get_memory_value('plan'),
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get_related_files()
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get_related_files(),
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model
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)
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except TaskCompletedException:
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sys.exit(0)
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@ -4,33 +4,41 @@ from langchain_anthropic import ChatAnthropic
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from langchain_core.language_models import BaseChatModel
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def initialize_llm(provider: str, model_name: str) -> BaseChatModel:
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"""Initialize a language model client based on the specified provider and model.
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Note: Environment variables must be validated before calling this function.
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Use validate_environment() to ensure all required variables are set.
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Args:
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provider: The LLM provider to use ('openai', 'anthropic', 'openrouter', 'openai-compatible')
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model_name: Name of the model to use
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Returns:
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BaseChatModel: Configured language model client
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Raises:
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ValueError: If the provider is not supported
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"""
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if provider == "openai":
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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raise ValueError("OPENAI_API_KEY environment variable is not set.")
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return ChatOpenAI(openai_api_key=api_key, model=model_name)
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elif provider == "anthropic":
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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raise ValueError("ANTHROPIC_API_KEY environment variable is not set.")
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return ChatAnthropic(anthropic_api_key=api_key, model=model_name)
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elif provider == "openrouter":
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api_key = os.getenv("OPENROUTER_API_KEY")
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if not api_key:
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raise ValueError("OPENROUTER_API_KEY environment variable is not set.")
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return ChatOpenAI(
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openai_api_key=api_key,
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openai_api_key=os.getenv("OPENAI_API_KEY"),
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model=model_name
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)
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elif provider == "anthropic":
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return ChatAnthropic(
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anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"),
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model=model_name
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)
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elif provider == "openrouter":
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return ChatOpenAI(
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openai_api_key=os.getenv("OPENROUTER_API_KEY"),
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openai_api_base="https://openrouter.ai/api/v1",
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model=model_name
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)
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elif provider == "openai-compatible":
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api_key = os.getenv("OPENAI_API_KEY")
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api_base = os.getenv("OPENAI_API_BASE")
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if not api_key or not api_base:
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raise ValueError("Both OPENAI_API_KEY and OPENAI_API_BASE environment variables must be set.")
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return ChatOpenAI(
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openai_api_key=api_key,
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openai_api_base=api_base,
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openai_api_key=os.getenv("OPENAI_API_KEY"),
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openai_api_base=os.getenv("OPENAI_API_BASE"),
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model=model_name
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)
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else:
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@ -42,7 +42,6 @@ def run_programming_task(input: RunProgrammingTaskInput) -> Dict[str, Union[str,
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# Build command
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command = [
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"aider",
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"--sonnet",
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"--yes-always",
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"--no-auto-commits",
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"--dark-mode",
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