Evenemang

// Workflow lifecycle events
WorkflowStartedEvent     // Workflow execution begins
WorkflowOutputEvent      // Workflow outputs data
WorkflowErrorEvent       // Workflow encounters an error
WorkflowWarningEvent     // Workflow encountered a warning

// Executor events
ExecutorInvokedEvent     // Executor starts processing
ExecutorCompletedEvent   // Executor finishes processing
ExecutorFailedEvent      // Executor encounters an error
AgentResponseEvent       // An agent run produces output
AgentResponseUpdateEvent // An agent run produces a streaming update

// Superstep events
SuperStepStartedEvent    // Superstep begins
SuperStepCompletedEvent  // Superstep completes

// Request events
RequestInfoEvent         // A request is issued

# All events use the unified WorkflowEvent class with a type discriminator:

# Workflow lifecycle events
WorkflowEvent.type == "started"             # Workflow execution begins
WorkflowEvent.type == "status"              # Workflow state changed (use .state)
WorkflowEvent.type == "output"              # Workflow produces an output
WorkflowEvent.type == "failed"              # Workflow terminated with error (use .details)
WorkflowEvent.type == "error"               # Non-fatal error from user code
WorkflowEvent.type == "warning"             # Workflow encountered a warning

# Executor events
WorkflowEvent.type == "executor_invoked"    # Executor starts processing
WorkflowEvent.type == "executor_completed"  # Executor finishes processing
WorkflowEvent.type == "executor_failed"     # Executor encounters an error
WorkflowEvent.type == "data"                # Executor emitted data (e.g., AgentResponse)

# Superstep events
WorkflowEvent.type == "superstep_started"   # Superstep begins
WorkflowEvent.type == "superstep_completed" # Superstep completes

# Request events
WorkflowEvent.type == "request_info"        # A request is issued

using Microsoft.Agents.AI.Workflows;

await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
    switch (evt)
    {
        case ExecutorInvokedEvent invoke:
            Console.WriteLine($"Starting {invoke.ExecutorId}");
            break;

        case ExecutorCompletedEvent complete:
            Console.WriteLine($"Completed {complete.ExecutorId}: {complete.Data}");
            break;

        case WorkflowOutputEvent output:
            Console.WriteLine($"Workflow output: {output.Data}");
            return;

        case WorkflowErrorEvent error:
            Console.WriteLine($"Workflow error: {error.Exception}");
            return;
    }
}

from agent_framework import WorkflowEvent

async for event in workflow.run_stream(input_message):
    if event.type == "executor_invoked":
        print(f"Starting {event.executor_id}")
    elif event.type == "executor_completed":
        print(f"Completed {event.executor_id}: {event.data}")
    elif event.type == "output":
        print(f"Workflow produced output: {event.data}")
        return
    elif event.type == "error":
        print(f"Workflow error: {event.data}")
        return

Definiera en anpassad händelse genom att underklassa WorkflowEvent. Baskonstruktorn accepterar en valfri object? data nyttolast som exponeras via egenskapen Data .

using Microsoft.Agents.AI.Workflows;

// Simple event with a string payload
internal sealed class ProgressEvent(string step) : WorkflowEvent(step) { }

// Event with a structured payload
internal sealed class MetricsEvent(MetricsData metrics) : WorkflowEvent(metrics) { }

I Python skapar du anpassade händelser med hjälp av WorkflowEvent klassen direkt med en diskriminerande sträng av anpassad typ. Parametrarna type och data bär all information.

from agent_framework import WorkflowEvent

# Create a custom event with a custom type string and payload
event = WorkflowEvent(type="progress", data="Step 1 complete")

# Custom event with a structured payload
event = WorkflowEvent(type="metrics", data={"latency_ms": 42, "tokens": 128})

Generera anpassade händelser från en exekutors meddelandehanterare genom att anropa AddEventAsync på IWorkflowContext:

using Microsoft.Agents.AI.Workflows;

internal sealed class ProgressEvent(string step) : WorkflowEvent(step) { }

internal sealed partial class CustomExecutor() : Executor("CustomExecutor")
{
    [MessageHandler]
    private async ValueTask HandleAsync(string message, IWorkflowContext context)
    {
        await context.AddEventAsync(new ProgressEvent("Validating input"));

        // Executor logic...

        await context.AddEventAsync(new ProgressEvent("Processing complete"));
    }
}

Generera anpassade händelser från en hanterare genom att anropa add_event på WorkflowContext:

from agent_framework import (
    handler,
    Executor,
    WorkflowContext,
    WorkflowEvent,
)

class CustomExecutor(Executor):

    @handler
    async def handle(self, message: str, ctx: WorkflowContext[str]) -> None:
        await ctx.add_event(WorkflowEvent(type="progress", data="Validating input"))

        # Executor logic...

        await ctx.add_event(WorkflowEvent(type="progress", data="Processing complete"))

Använd mönstermatchning för att filtrera efter din anpassade händelsetyp i händelseströmmen:

await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
    switch (evt)
    {
        case ProgressEvent progress:
            Console.WriteLine($"Progress: {progress.Data}");
            break;

        case WorkflowOutputEvent output:
            Console.WriteLine($"Done: {output.Data}");
            return;
    }
}

Filtrera på den anpassade typdiskriminatorsträngen:

async for event in workflow.run(input_message, stream=True):
    if event.type == "progress":
        print(f"Progress: {event.data}")
    elif event.type == "output":
        print(f"Done: {event.data}")
        return