deerflow2/src/agents/agents.py

56 lines
1.8 KiB
Python

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT
import logging
from typing import List, Optional
from langgraph.prebuilt import create_react_agent
from src.config.agents import AGENT_LLM_MAP
from src.llms.llm import get_llm_by_type
from src.prompts import apply_prompt_template
from src.agents.tool_interceptor import wrap_tools_with_interceptor
logger = logging.getLogger(__name__)
# Create agents using configured LLM types
def create_agent(
agent_name: str,
agent_type: str,
tools: list,
prompt_template: str,
pre_model_hook: callable = None,
interrupt_before_tools: Optional[List[str]] = None,
):
"""Factory function to create agents with consistent configuration.
Args:
agent_name: Name of the agent
agent_type: Type of agent (researcher, coder, etc.)
tools: List of tools available to the agent
prompt_template: Name of the prompt template to use
pre_model_hook: Optional hook to preprocess state before model invocation
interrupt_before_tools: Optional list of tool names to interrupt before execution
Returns:
A configured agent graph
"""
# Wrap tools with interrupt logic if specified
processed_tools = tools
if interrupt_before_tools:
logger.info(
f"Creating agent '{agent_name}' with tool-specific interrupts: {interrupt_before_tools}"
)
processed_tools = wrap_tools_with_interceptor(tools, interrupt_before_tools)
return create_react_agent(
name=agent_name,
model=get_llm_by_type(AGENT_LLM_MAP[agent_type]),
tools=processed_tools,
prompt=lambda state: apply_prompt_template(
prompt_template, state, locale=state.get("locale", "en-US")
),
pre_model_hook=pre_model_hook,
)