Initialize the BabyAGI Controller.
get_next_task(result: str, task_description: str, objective: str) → List[Dict][source]#
Get the next task.
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores.base.VectorStoreRetriever, chain: langchain.chains.llm.LLMChain, output_parser: langchain.experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser, tools: List[langchain.tools.base.BaseTool], feedback_tool: Optional[langchain.tools.human.tool.HumanInputRun] = None, chat_history_memory: Optional[langchain.schema.BaseChatMessageHistory] = None)[source]#
Agent class for interacting with Auto-GPT.
Generative Agents#
Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
class langchain.experimental.GenerativeAgent(*, name: str, age: Optional[int] = None, traits: str = 'N/A', status: str, memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory, llm: langchain.base_language.BaseLanguageModel, verbose: bool = False, summary: str = '', summary_refresh_seconds: int = 3600, last_refreshed: datetime.datetime = None, daily_summaries: List[str] = None)[source]#
A character with memory and innate characteristics.
model Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
field age: Optional[int] = None#
The optional age of the character.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html