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lmflux

openai_agent(agent_id, model_id, tools=None, system_prompt='You are a helpful assistant.', options=LLMOptions())

Creates a new OpenAI compatible agent. It will use the OpenAICompatibleEndpoint as its base LLM, so It will take the OPENAI_API_BASE and OPENAI_API_KEY enviroment variables to create a OAI client.

Args: - agent_id (str) : A unique identifier for the agent. It will be used to identify the agent in the conversation. - model_id (str) : The ID of the OpenAI model to use. - system_prompt (SystemPrompt, optional): The prompt to use as a starting point for the conversation. Defaults to "You are a helpful assistant.". - tools (list[callable], optional): A list of functions that will be used by the agent to perform tasks. Defaults to None. - options (LLMOptions, optional): Additional options to pass to the LLM. Defaults to LLMOptions().

Returns: - Agent

Source code in src/lmflux/__init__.py 
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def openai_agent(
    agent_id:str, model_id:str, tools:list[callable]=None, 
    system_prompt="You are a helpful assistant.", 
    options=LLMOptions()
) -> Agent:
    """
    Creates a new OpenAI compatible agent.
    It will use the `OpenAICompatibleEndpoint` as its base LLM, so It will take the OPENAI_API_BASE and OPENAI_API_KEY enviroment variables to create a OAI client.

    Args:
    - agent_id (str) : A unique identifier for the agent. It will be used to identify the agent in the conversation.
    - model_id (str) : The ID of the OpenAI model to use.
    - system_prompt (SystemPrompt, optional): The prompt to use as a starting point for the conversation. Defaults to "You are a helpful assistant.".
    - tools (list[callable], optional): A list of functions that will be used by the agent to perform tasks. Defaults to None.
    - options (LLMOptions, optional): Additional options to pass to the LLM. Defaults to LLMOptions().

    Returns:
    - Agent
    """
    llm = OpenAICompatibleEndpoint(model_id, SystemPrompt(content=system_prompt), options=options)
    agent = create_agent(llm, agent_id=agent_id)
    if tools:
        agent.with_tools(*tools)
    return agent.build()