langgraph_agent/subagent_factory.py

"""
Subagent Factory

Creates specialized agents with filtered tool subsets.
"""
from typing import List, Dict, Any
from langchain_core.language_models import BaseChatModel
from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver
from .subagent_config import SubAgentConfig
from .config import AgentConfig

class SubAgentFactory:
    """Factory for creating specialized subagents."""

    @staticmethod
    async def create_subagent(
        subagent_name: str,
        all_tools: List[Any],
        llm: BaseChatModel,
        provider: str = "openai"
    ):
        """
        Create a specialized subagent with filtered tools.

        Args:
            subagent_name: Name of the subagent (e.g., "image_identifier")
            all_tools: Full list of available tools
            llm: Language model instance
            provider: LLM provider name ("openai", "anthropic", "huggingface")

        Returns:
            LangGraph agent configured for the subagent
        """
        # Get subagent configuration with provider-specific prompts
        definitions = SubAgentConfig.get_subagent_definitions(provider=provider)

        if subagent_name not in definitions:
            raise ValueError(f"Unknown subagent: {subagent_name}")

        config = definitions[subagent_name]

        # Filter tools for this subagent
        allowed_tool_names = set(config["tools"])
        subagent_tools = [
            tool for tool in all_tools
            if tool.name in allowed_tool_names
        ]

        print(f"[SUBAGENT]: Creating {config['name']}")
        print(f"  • Tools: {', '.join([t.name for t in subagent_tools])}")
        print(f"  • Prompt preview: {config['prompt'][:80]}...")

        # Create specialized agent with filtered tools and name
        # Note: create_agent auto-compiles, so we pass name directly
        agent = create_agent(
            model=llm,
            tools=subagent_tools,
            system_prompt=config["prompt"],
            name=subagent_name
        )

        return agent

    @staticmethod
    async def create_all_subagents(
        all_tools: List[Any],
        llm: BaseChatModel,
        provider: str = "openai"
    ) -> Dict[str, Any]:
        """
        Create all specialized subagents.

        Args:
            all_tools: Full list of available tools
            llm: Language model instance
            provider: LLM provider name ("openai", "anthropic", "huggingface")

        Returns:
            Dict mapping subagent names to agent instances
        """
        definitions = SubAgentConfig.get_subagent_definitions(provider=provider)
        subagents = {}

        for name in definitions.keys():
            subagents[name] = await SubAgentFactory.create_subagent(
                name, all_tools, llm, provider=provider
            )

        return subagents