aafter_model() — langchain Function Reference

Architecture documentation for the aafter_model() function in human_in_the_loop.py from the langchain codebase.

Function python LangChainCore LanguageModelBase calls 1

Entity Profile

LangChainCore→ LanguageModelBase→ aafter_model() — langchain Function Reference

Dependency Diagram

graph TD
  7648034b_ec9a_ba69_ba13_196efa046263["aafter_model()"]
  b706912e_28f0_afbf_eb10_723aa3e74c52["HumanInTheLoopMiddleware"]
  7648034b_ec9a_ba69_ba13_196efa046263 -->|defined in| b706912e_28f0_afbf_eb10_723aa3e74c52
  c27d424a_40fd_dcec_b4d0_8dbf62b73ff2["after_model()"]
  7648034b_ec9a_ba69_ba13_196efa046263 -->|calls| c27d424a_40fd_dcec_b4d0_8dbf62b73ff2
  style 7648034b_ec9a_ba69_ba13_196efa046263 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain_v1/langchain/agents/middleware/human_in_the_loop.py lines 375–387

    async def aafter_model(
        self, state: AgentState[Any], runtime: Runtime[ContextT]
    ) -> dict[str, Any] | None:
        """Async trigger interrupt flows for relevant tool calls after an `AIMessage`.

        Args:
            state: The current agent state.
            runtime: The runtime context.

        Returns:
            Updated message with the revised tool calls.
        """
        return self.after_model(state, runtime)