aplan() — langchain Function Reference

Architecture documentation for the aplan() function in base.py from the langchain codebase.

Function python AgentOrchestration ActionLogic calls 1

Entity Profile

AgentOrchestration→ ActionLogic→ aplan() — langchain Function Reference

Dependency Diagram

graph TD
  37bbde8a_7a2d_f623_0793_d10a9ca94fd6["aplan()"]
  4079b11a_3240_0a70_5830_e3af3b13b06e["OpenAIMultiFunctionsAgent"]
  37bbde8a_7a2d_f623_0793_d10a9ca94fd6 -->|defined in| 4079b11a_3240_0a70_5830_e3af3b13b06e
  77654667_c7de_ebc2_15c2_9c00aae7bd56["_parse_ai_message()"]
  37bbde8a_7a2d_f623_0793_d10a9ca94fd6 -->|calls| 77654667_c7de_ebc2_15c2_9c00aae7bd56
  style 37bbde8a_7a2d_f623_0793_d10a9ca94fd6 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/agents/openai_functions_multi_agent/base.py lines 235–264

    async def aplan(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> list[AgentAction] | AgentFinish:
        """Async given input, decided what to do.

        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations.
            callbacks: Callbacks to use.
            **kwargs: User inputs.

        Returns:
            Action specifying what tool to use.
        """
        agent_scratchpad = format_to_openai_function_messages(intermediate_steps)
        selected_inputs = {
            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
        }
        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
        prompt = self.prompt.format_prompt(**full_inputs)
        messages = prompt.to_messages()
        predicted_message = await self.llm.ainvoke(
            messages,
            functions=self.functions,
            callbacks=callbacks,
        )
        return _parse_ai_message(predicted_message)