chat_agent ¶

@chat_agent.tool
async def ask_job_status_agent(
    ctx: RunContext[SensAIDeps], request: str
) -> JobStatus | None:
    """
    Forward a job-status request to the job-status agent and return its answer.

    Args:
        ctx: The calling agent's run context - `ctx.model` (the same `FallbackModel` this run was given) has to be passed through explicitly, since `job_status_agent` has no `model=` of its own to fall back on; `ctx.deps`/`ctx.usage` share dashboard state and token accounting with the nested run the same way.
        request: The request to hand off, in natural language (e.g. "how far along is the job?" or "what happened on trial 12?").

    Returns:
        The current job status, or None if no job is loaded.

    """
    result = await job_status_agent.run(
        request, model=_delegate_model(ctx), deps=ctx.deps, usage=ctx.usage
    )
    return result.output

@chat_agent.tool
async def ask_column_manifest_agent(
    ctx: RunContext[SensAIDeps], request: str
) -> ColumnManifest:
    """
    Forward a signal-column request to the column-manifest agent and return its answer.

    Args:
        ctx: The calling agent's run context - `ctx.model` (the same `FallbackModel` this run was given) has to be passed through explicitly, since `column_manifest_agent` has no `model=` of its own to fall back on; `ctx.deps`/`ctx.usage` share dashboard state and token accounting with the nested run the same way.
        request: The request to hand off, in natural language (e.g. "what signals can I plot?").

    Returns:
        The current column manifest.

    """
    result = await column_manifest_agent.run(
        request, model=_delegate_model(ctx), deps=ctx.deps, usage=ctx.usage
    )
    return result.output

@chat_agent.tool
async def ask_plot_config_agent(
    ctx: RunContext[SensAIDeps], request: str
) -> PlotConfig | None:
    """
    Forward a plot request to the plot-config agent and return its answer.

    Use this for any request about the trial viewer plot: reading its current state, editing \
    it (colors, axes, grid, signals, ...), or saving/loading/deleting it as a named profile.

    Args:
        ctx: The calling agent's run context - `ctx.model` (the same `FallbackModel` this run was given) has to be passed through explicitly, since `plot_config_agent` has no `model=` of its own to fall back on; `ctx.deps`/`ctx.usage` share dashboard state and token accounting with the nested run the same way.
        request: The request to hand off, in natural language (e.g. "make the box_acc:y line red" or "save this as robotics/baseline").

    Returns:
        The resulting plot config (current, edited, saved, or loaded).

    """
    result = await plot_config_agent.run(
        request, model=_delegate_model(ctx), deps=ctx.deps, usage=ctx.usage
    )
    return result.output