agentchat.contrib.agent_optimizer

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execute_func

def execute_func(name, packages, code, **args)

The wrapper for generated functions.

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AgentOptimizer

class AgentOptimizer()

Base class for optimizing AutoGen agents. Specifically, it is used to optimize the functions used in the agent. More information could be found in the following paper: https://arxiv.org/abs/2402.11359.

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__init__

def __init__(max_actions_per_step: int,
             llm_config: dict,
             optimizer_model: Optional[str] = "gpt-4-1106-preview")

(These APIs are experimental and may change in the future.)

Arguments:

• max_actions_per_step int - the maximum number of actions that the optimizer can take in one step.
• llm_config dict - llm inference configuration. Please refer to OpenAIWrapper.create for available options. When using OpenAI or Azure OpenAI endpoints, please specify a non-empty ‘model’ either in llm_config or in each config of ‘config_list’ in llm_config.
• optimizer_model - the model used for the optimizer.

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record_one_conversation

def record_one_conversation(conversation_history: list[dict],
                            is_satisfied: bool = None)

record one conversation history.

Arguments:

• conversation_history List[Dict] - the chat messages of the conversation.
• is_satisfied bool - whether the user is satisfied with the solution. If it is none, the user will be asked to input the satisfaction.

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step

def step()

One step of training. It will return register_for_llm and register_for_executor at each iteration, which are subsequently utilized to update the assistant and executor agents, respectively. See example: https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_agentoptimizer.ipynb

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reset_optimizer

def reset_optimizer()

reset the optimizer.