Streaming Events

Strands Agents SDK provides real-time streaming capabilities that allow you to monitor and process events as they occur during agent execution. This enables responsive user interfaces, real-time monitoring, and custom output formatting.

Strands has multiple approaches for handling streaming events:

• Async Iterators: Ideal for asynchronous server frameworks
• Callback Handlers (Python only): Perfect for synchronous applications and custom event processing

Both methods receive the same event types but differ in their execution model and use cases.

Event Types

All streaming methods yield the same set of events:

Lifecycle Events

PythonTypeScript

• init_event_loop: True at the start of agent invocation initializing
• start_event_loop: True when the event loop is starting
• message: Present when a new message is created
• event: Raw event from the model stream
• force_stop: True if the event loop was forced to stop
  • force_stop_reason: Reason for forced stop
• result: The final AgentResult

Each event emitted from the TypeScript agent is a class with a type attribute that has a unique value. When determining an event, you can use instanceof on the class, or an equality check on the event.type value. All events extend HookableEvent, making them both streamable and subscribable via hook callbacks.

• BeforeInvocationEvent: Start of agent loop (before any iterations)
• AfterInvocationEvent: End of agent loop (after all iterations complete)
  • error?: Optional error if loop terminated due to exception
• BeforeModelCallEvent: Before model invocation
  • messages: Array of messages being sent to model
• AfterModelCallEvent: After model invocation
  • message: Assistant message returned by model
  • stopReason: Why generation stopped
• BeforeToolsEvent: Before tools execution
  • message: Assistant message containing tool use blocks
• AfterToolsEvent: After tools execution
  • message: User message containing tool results
• AgentResultEvent: Final agent result
  • result: The AgentResult with stopReason, lastMessage, and optional structuredOutput

Model Stream Events

PythonTypeScript

• data: Text chunk from the model's output
• delta: Raw delta content from the model
• reasoning: True for reasoning events
  • reasoningText: Text from reasoning process
  • reasoning_signature: Signature from reasoning process
  • redactedContent: Reasoning content redacted by the model

• ModelStreamUpdateEvent: Wraps transient model streaming deltas. Access the inner event via .event:
  • ModelMessageStartEvent: Start of a message from the model
  • ModelContentBlockStartEvent: Start of a content block (text, toolUse, reasoning, etc.)
  • ModelContentBlockDeltaEvent: Content deltas for text, tool input, or reasoning
  • ModelContentBlockStopEvent: End of a content block
  • ModelMessageStopEvent: End of a message
  • ModelMetadataEvent: Usage and metrics metadata
• ContentBlockEvent: Wraps a fully assembled content block (TextBlock, ToolUseBlock, ReasoningBlock). Access via .contentBlock
• ModelMessageEvent: Wraps the complete model message after all blocks are assembled. Access via .message

Tool Events

PythonTypeScript

• current_tool_use: Information about the current tool being used, including:
  • toolUseId: Unique ID for this tool use
  • name: Name of the tool
  • input: Tool input parameters (accumulated as streaming occurs)
• tool_stream_event: Information about an event streamed from a tool, including:
  • tool_use: The ToolUse for the tool that streamed the event
  • data: The data streamed from the tool

• BeforeToolCallEvent: Before a tool is executed
  • toolUse: The tool use block with name and input
• AfterToolCallEvent: After a tool finishes execution
  • toolUse: The tool use block
  • result: The tool result block
• ToolStreamUpdateEvent: Wraps streaming progress events from a tool. Access via .event:
  • data: The data streamed from the tool
• ToolResultEvent: Wraps a completed tool result. Access via .result

Multi-Agent Events

PythonTypeScript

Multi-agent systems (Graph and Swarm) emit additional coordination events:

• multiagent_node_start: When a node begins execution
  • type: "multiagent_node_start"
  • node_id: Unique identifier for the node
  • node_type: Type of node ("agent", "swarm", "graph")
• multiagent_node_stream: Forwarded events from agents/multi-agents with node context
  • type: "multiagent_node_stream"
  • node_id: Identifier of the node generating the event
  • event: The original agent event (nested)
• multiagent_node_stop: When a node completes execution
  • type: "multiagent_node_stop"
  • node_id: Unique identifier for the node
  • node_result: Complete NodeResult with execution details, metrics, and status
• multiagent_handoff: When control is handed off between agents (Swarm) or batch transitions (Graph)
  • type: "multiagent_handoff"
  • from_node_ids: List of node IDs completing execution
  • to_node_ids: List of node IDs beginning execution
  • message: Optional handoff message (typically used in Swarm)
• multiagent_result: Final multi-agent result
  • type: "multiagent_result"
  • result: The final GraphResult or SwarmResult

See Graph streaming and Swarm streaming for usage examples.

Coming soon to Typescript!

Quick Examples

PythonTypeScript

Async Iterator Pattern

async for event in agent.stream_async("Calculate 2+2"):
    if "data" in event:
        print(event["data"], end="")

Callback Handler Pattern

def handle_events(**kwargs):
    if "data" in kwargs:
        print(kwargs["data"], end="")

agent = Agent(callback_handler=handle_events)
agent("Calculate 2+2")

Async Iterator Pattern

const agent = new Agent({ tools: [notebook] })

for await (const event of agent.stream('Calculate 2+2')) {
  if (
    event.type === 'modelStreamUpdateEvent' &&
    event.event.type === 'modelContentBlockDeltaEvent' &&
    event.event.delta.type === 'textDelta'
  ) {
    // Print out the model text delta event data
    process.stdout.write(event.event.delta.text)
  }
}
console.log('\nDone!')

Identifying Events Emitted from Agent

This example demonstrates how to identify event emitted from an agent:

PythonTypeScript

from strands import Agent
from strands_tools import calculator

def process_event(event):
    """Shared event processor for both async iterators and callback handlers"""
    # Track event loop lifecycle
    if event.get("init_event_loop", False):
        print("🔄 Event loop initialized")
    elif event.get("start_event_loop", False):
        print("▶️ Event loop cycle starting")
    elif "message" in event:
        print(f"📬 New message created: {event['message']['role']}")
    elif "result" in event:
        print("✅ Agent completed with result")
    elif event.get("force_stop", False):
        print(f"🛑 Event loop force-stopped: {event.get('force_stop_reason', 'unknown reason')}")

    # Track tool usage
    if "current_tool_use" in event and event["current_tool_use"].get("name"):
        tool_name = event["current_tool_use"]["name"]
        print(f"🔧 Using tool: {tool_name}")

    # Show text snippets
    if "data" in event:
        data_snippet = event["data"][:20] + ("..." if len(event["data"]) > 20 else "")
        print(f"📟 Text: {data_snippet}")

agent = Agent(tools=[calculator], callback_handler=None)
async for event in agent.stream_async("What is the capital of France and what is 42+7?"):
    process_event(event)

function processEvent(event: AgentStreamEvent): void {
  // Track agent loop lifecycle
  switch (event.type) {
    case 'beforeInvocationEvent':
      console.log('🔄 Agent loop initialized')
      break
    case 'beforeModelCallEvent':
      console.log('▶️ Agent loop cycle starting')
      break
    case 'afterModelCallEvent':
      console.log(`📬 New message created: ${event.stopData?.message.role}`)
      break
    case 'beforeToolsEvent':
      console.log('About to execute tool!')
      break
    case 'afterToolsEvent':
      console.log('Finished execute tool!')
      break
    case 'afterInvocationEvent':
      console.log('✅ Agent loop completed')
      break
  }

  // Track tool usage
  if (
    event.type === 'modelStreamUpdateEvent' &&
    event.event.type === 'modelContentBlockStartEvent' &&
    event.event.start?.type === 'toolUseStart'
  ) {
    console.log(`\n🔧 Using tool: ${event.event.start.name}`)
  }

  // Show text snippets
  if (
    event.type === 'modelStreamUpdateEvent' &&
    event.event.type === 'modelContentBlockDeltaEvent' &&
    event.event.delta.type === 'textDelta'
  ) {
    process.stdout.write(event.event.delta.text)
  }
}
const responseGenerator = agent.stream('What is the capital of France and what is 42+7? Record in the notebook.')
for await (const event of responseGenerator) {
  processEvent(event)
}

Sub-Agent Streaming Example

Utilizing both agents as a tool and tool streaming, this example shows how to stream events from sub-agents:

PythonTypeScript

from typing import AsyncIterator
from dataclasses import dataclass
from strands import Agent, tool
from strands_tools import calculator

@dataclass
class SubAgentResult:
    agent: Agent
    event: dict

@tool
async def math_agent(query: str) -> AsyncIterator:
    """Solve math problems using the calculator tool."""
    agent = Agent(
        name="Math Expert",
        system_prompt="You are a math expert. Use the calculator tool for calculations.",
        callback_handler=None,
        tools=[calculator]
    )

    result = None
    async for event in agent.stream_async(query):
        yield SubAgentResult(agent=agent, event=event)
        if "result" in event:
            result = event["result"]

    yield str(result)

def process_sub_agent_events(event):
    """Shared processor for sub-agent streaming events"""
    tool_stream = event.get("tool_stream_event", {}).get("data")

    if isinstance(tool_stream, SubAgentResult):
        current_tool = tool_stream.event.get("current_tool_use", {})
        tool_name = current_tool.get("name")

        if tool_name:
            print(f"Agent '{tool_stream.agent.name}' using tool '{tool_name}'")

    # Also show regular text output
    if "data" in event:
        print(event["data"], end="")

# Using with async iterators
orchestrator_async_iterator = Agent(
    system_prompt="Route math questions to the math_agent tool.",
    callback_handler=None,
    tools=[math_agent]
)


# With async-iterator
async for event in orchestrator_async_iterator.stream_async("What is 3+3?"):
    process_sub_agent_events(event)


# With callback handler
def handle_events(**kwargs):
    process_sub_agent_events(kwargs)

orchestrator_callback = Agent(
    system_prompt="Route math questions to the math_agent tool.",
    callback_handler=handle_events,
    tools=[math_agent]
)

orchestrator_callback("What is 3+3?")

// Create the math agent
const mathAgent = new Agent({
  systemPrompt: 'You are a math expert. Answer a math problem in one sentence',
  printer: false,
})

const calculator = tool({
  name: 'mathAgent',
  description: 'Agent that calculates the answer to a math problem input.',
  inputSchema: z.object({ input: z.string() }),
  callback: async function* (input): AsyncGenerator<string, string, unknown> {
    // Stream from the sub-agent
    const generator = mathAgent.stream(input.input)
    let result = await generator.next()
    while (!result.done) {
      // Process events from the sub-agent
      if (
        result.value.type === 'modelStreamUpdateEvent' &&
        result.value.event.type === 'modelContentBlockDeltaEvent' &&
        result.value.event.delta.type === 'textDelta'
      ) {
        yield result.value.event.delta.text
      }
      result = await generator.next()
    }
    return result.value.lastMessage.content[0]!.type === 'textBlock'
      ? result.value.lastMessage.content[0]!.text
      : result.value.lastMessage.content[0]!.toString()
  },
})

const agent = new Agent({ tools: [calculator] })
for await (const event of agent.stream('What is 2 * 3? Use your tool.')) {
  if (event.type === 'toolStreamUpdateEvent') {
    console.log(`Tool Event: ${JSON.stringify(event.event.data)}`)
  }
}
console.log('\nDone!')

Next Steps

• Learn about Async Iterators for asynchronous streaming
• Explore Callback Handlers for synchronous event processing
• See the Agent API Reference for complete method documentation