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agentchat.contrib.agent_builder

AgentBuilder​

class AgentBuilder()

AgentBuilder can help user build an automatic task solving process powered by multi-agent system. Specifically, our building pipeline includes initialize and build.

__init__​

def __init__(config_file_or_env: Optional[str] = "OAI_CONFIG_LIST",
             config_file_location: Optional[str] = "",
             builder_model: Optional[Union[str, list]] = [],
             agent_model: Optional[Union[str, list]] = [],
             builder_model_tags: Optional[list] = [],
             agent_model_tags: Optional[list] = [],
             max_agents: Optional[int] = 5)

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

Arguments:

  • config_file_or_env - path or environment of the OpenAI api configs.
  • builder_model - specify a model as the backbone of build manager.
  • agent_model - specify a model as the backbone of participant agents.
  • endpoint_building_timeout - timeout for building up an endpoint server.
  • max_agents - max agents for each task.

clear_agent​

def clear_agent(agent_name: str, recycle_endpoint: Optional[bool] = True)

Clear a specific agent by name.

Arguments:

  • agent_name - the name of agent.
  • recycle_endpoint - trigger for recycle the endpoint server. If true, the endpoint will be recycled when there is no agent depending on.

clear_all_agents​

def clear_all_agents(recycle_endpoint: Optional[bool] = True)

Clear all cached agents.

build​

def build(building_task: str,
          default_llm_config: Dict,
          coding: Optional[bool] = None,
          code_execution_config: Optional[Dict] = None,
          use_oai_assistant: Optional[bool] = False,
          user_proxy: Optional[autogen.ConversableAgent] = None,
          max_agents: Optional[int] = None,
          **kwargs) -> Tuple[List[autogen.ConversableAgent], Dict]

Auto build agents based on the building task.

Arguments:

  • building_task - instruction that helps build manager (gpt-4) to decide what agent should be built.
  • coding - use to identify if the user proxy (a code interpreter) should be added.
  • code_execution_config - specific configs for user proxy (e.g., last_n_messages, work_dir, ...).
  • default_llm_config - specific configs for LLM (e.g., config_list, seed, temperature, ...).
  • use_oai_assistant - use OpenAI assistant api instead of self-constructed agent.
  • user_proxy - user proxy's class that can be used to replace the default user proxy.

Returns:

  • agent_list - a list of agents.
  • cached_configs - cached configs.

build_from_library​

def build_from_library(
        building_task: str,
        library_path_or_json: str,
        default_llm_config: Dict,
        top_k: int = 3,
        coding: Optional[bool] = None,
        code_execution_config: Optional[Dict] = None,
        use_oai_assistant: Optional[bool] = False,
        embedding_model: Optional[str] = "all-mpnet-base-v2",
        user_proxy: Optional[autogen.ConversableAgent] = None,
        **kwargs) -> Tuple[List[autogen.ConversableAgent], Dict]

Build agents from a library. The library is a list of agent configs, which contains the name and system_message for each agent. We use a build manager to decide what agent in that library should be involved to the task.

Arguments:

  • building_task - instruction that helps build manager (gpt-4) to decide what agent should be built.
  • library_path_or_json - path or JSON string config of agent library.
  • default_llm_config - specific configs for LLM (e.g., config_list, seed, temperature, ...).
  • coding - use to identify if the user proxy (a code interpreter) should be added.
  • code_execution_config - specific configs for user proxy (e.g., last_n_messages, work_dir, ...).
  • use_oai_assistant - use OpenAI assistant api instead of self-constructed agent.
  • embedding_model - a Sentence-Transformers model use for embedding similarity to select agents from library. As reference, chromadb use "all-mpnet-base-v2" as default.
  • user_proxy - user proxy's class that can be used to replace the default user proxy.

Returns:

  • agent_list - a list of agents.
  • cached_configs - cached configs.

save​

def save(filepath: Optional[str] = None) -> str

Save building configs. If the filepath is not specific, this function will create a filename by encrypt the building_task string by md5 with "save_config_" prefix, and save config to the local path.

Arguments:

  • filepath - save path.

Returns:

  • filepath - path save.

load​

def load(filepath: Optional[str] = None,
         config_json: Optional[str] = None,
         use_oai_assistant: Optional[bool] = False,
         **kwargs) -> Tuple[List[autogen.ConversableAgent], Dict]

Load building configs and call the build function to complete building without calling online LLMs' api.

Arguments:

  • filepath - filepath or JSON string for the save config.
  • config_json - JSON string for the save config.
  • use_oai_assistant - use OpenAI assistant api instead of self-constructed agent.

Returns:

  • agent_list - a list of agents.
  • cached_configs - cached configs.