zgjja
3b4e993531
2. use tavily from`langchain-tavily` rather than the older one from `langchain-community` Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
20 lines
696 B
Python
20 lines
696 B
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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from langgraph.prebuilt import create_react_agent
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from src.config.agents import AGENT_LLM_MAP
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from src.llms.llm import get_llm_by_type
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from src.prompts import apply_prompt_template
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# Create agents using configured LLM types
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def create_agent(agent_name: str, agent_type: str, tools: list, prompt_template: str):
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"""Factory function to create agents with consistent configuration."""
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return create_react_agent(
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name=agent_name,
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model=get_llm_by_type(AGENT_LLM_MAP[agent_type]),
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tools=tools,
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prompt=lambda state: apply_prompt_template(prompt_template, state),
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)
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