langchain_experimental.autonomous_agents.autogpt.agent.AutoGPT

class langchain_experimental.autonomous_agents.autogpt.agent.AutoGPT(ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, chat_history_memory: Optional[BaseChatMessageHistory] = None)

Agent for interacting with AutoGPT.

Methods

__init__(ai_name, memory, chain, ...[, ...])
from_llm_and_tools(ai_name, ai_role, memory, ...)
run(goals)

Parameters

• ai_name (str) –

• memory (VectorStoreRetriever) –

• chain (LLMChain) –

• output_parser (BaseAutoGPTOutputParser) –

• tools (List[BaseTool]) –

• feedback_tool (Optional[HumanInputRun]) –

• chat_history_memory (Optional[BaseChatMessageHistory]) –

__init__(ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, chat_history_memory: Optional[BaseChatMessageHistory] = None)

Parameters

• ai_name (str) –

• memory (VectorStoreRetriever) –

• chain (LLMChain) –

• output_parser (BaseAutoGPTOutputParser) –

• tools (List[BaseTool]) –

• feedback_tool (Optional[HumanInputRun]) –

• chat_history_memory (Optional[BaseChatMessageHistory]) –

classmethod from_llm_and_tools(ai_name: str, ai_role: str, memory: VectorStoreRetriever, tools: List[BaseTool], llm: BaseChatModel, human_in_the_loop: bool = False, output_parser: Optional[BaseAutoGPTOutputParser] = None, chat_history_memory: Optional[BaseChatMessageHistory] = None) → AutoGPT

Parameters

• ai_name (str) –

• ai_role (str) –

• memory (VectorStoreRetriever) –

• tools (List[BaseTool]) –

• llm (BaseChatModel) –

• human_in_the_loop (bool) –

• output_parser (Optional[BaseAutoGPTOutputParser]) –

• chat_history_memory (Optional[BaseChatMessageHistory]) –

Return type

AutoGPT

run(goals: List[str]) → str

Parameters

goals (List[str]) –

Return type

str