aplan() — langchain Function Reference

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

Function python AgentOrchestration ActionLogic calls 2

Entity Profile

AgentOrchestration→ ActionLogic→ aplan() — langchain Function Reference

Dependency Diagram

graph TD
  6f0df2b5_3eae_dfc5_06c2_f70cf546a695["aplan()"]
  43de2fb1_2cba_0c7f_1a66_b91200c3611f["RunnableMultiActionAgent"]
  6f0df2b5_3eae_dfc5_06c2_f70cf546a695 -->|defined in| 43de2fb1_2cba_0c7f_1a66_b91200c3611f
  330b61d2_ee14_f0e8_72b9_0f8c88b2a488["astream()"]
  6f0df2b5_3eae_dfc5_06c2_f70cf546a695 -->|calls| 330b61d2_ee14_f0e8_72b9_0f8c88b2a488
  3b577de0_fb5e_78d3_db9f_6c9a46325e38["aplan()"]
  6f0df2b5_3eae_dfc5_06c2_f70cf546a695 -->|calls| 3b577de0_fb5e_78d3_db9f_6c9a46325e38
  style 6f0df2b5_3eae_dfc5_06c2_f70cf546a695 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/agents/agent.py lines 567–607

    async def aplan(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> list[AgentAction] | AgentFinish:
        """Async based on past history and current inputs, decide what to do.

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

        Returns:
            Action specifying what tool to use.
        """
        inputs = {**kwargs, "intermediate_steps": intermediate_steps}
        final_output: Any = None
        if self.stream_runnable:
            # Use streaming to make sure that the underlying LLM is invoked in a
            # streaming
            # fashion to make it possible to get access to the individual LLM tokens
            # when using stream_log with the AgentExecutor.
            # Because the response from the plan is not a generator, we need to
            # accumulate the output into final output and return that.
            async for chunk in self.runnable.astream(
                inputs,
                config={"callbacks": callbacks},
            ):
                if final_output is None:
                    final_output = chunk
                else:
                    final_output += chunk
        else:
            final_output = await self.runnable.ainvoke(
                inputs,
                config={"callbacks": callbacks},
            )

        return final_output