langchain-ai/langgraph · error · ValueError

Expected to have a matching ToolMessage in Command.update fo

Error message

Expected to have a matching ToolMessage in Command.update for tool '{call['name']}', got: {messages_update}. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage. You can fix it by modifying the tool to return {example_update}.

What it means

Error "Expected to have a matching ToolMessage in Command.update for tool '{call['name']}', got: {messages_update}. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage. You can fix it by modifying the tool to return {example_update}." thrown in langchain-ai/langgraph.

Source

Thrown at libs/prebuilt/langgraph/prebuilt/tool_node.py:1578

            require_terminator
            and updated_command.graph is None
            and not has_matching_tool_message
        ):
            example_update = (
                '`Command(update={"messages": '
                '[ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`'
                if input_type == "dict"
                else "`Command(update="
                '[ToolMessage("Success", tool_call_id=tool_call_id), ...], ...)`'
            )
            msg = (
                "Expected to have a matching ToolMessage in Command.update "
                f"for tool '{call['name']}', got: {messages_update}. "
                "Every tool call (LLM requesting to call a tool) "
                "in the message history MUST have a corresponding ToolMessage. "
                f"You can fix it by modifying the tool to return {example_update}."
            )
            raise ValueError(msg)
        return updated_command


def tools_condition(
    state: list[AnyMessage] | dict[str, Any] | BaseModel,
    messages_key: str = "messages",
) -> Literal["tools", "__end__"]:
    """Conditional routing function for tool-calling workflows.

    This utility function implements the standard conditional logic for ReAct-style
    agents: if the last `AIMessage` contains tool calls, route to the tool execution
    node; otherwise, end the workflow. This pattern is fundamental to most tool-calling
    agent architectures.

    The function handles multiple state formats commonly used in LangGraph applications,
    making it flexible for different graph designs while maintaining consistent behavior.

    Args:

View on GitHub (pinned to 38031739e5)

When it happens

Trigger: Thrown at libs/prebuilt/langgraph/prebuilt/tool_node.py:1578 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of langchain-ai/langgraph@38031739e5 (2026-08-26). Data as JSON: /api/errors/8f5d8b5f2f786b36. Report an issue: GitHub.