Streaming Events

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

=== "Python"

- **`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`](../../../api-reference/python/agent/agent_result.md#strands.agent.agent_result.AgentResult)

=== "TypeScript"

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.

- **`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
- **`BeforeModelEvent`**: Before model invocation
    - **`messages`**: Array of messages being sent to model
- **`AfterModelEvent`**: 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

Model Stream Events

=== "Python"

- **`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

=== "TypeScript"

- **`ModelMessageStartEvent`**: Start of a message from the model
- **`ModelContentBlockStartEvent`**: Start of a content block from a model for 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

Tool Events

=== "Python" - 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 === "TypeScript" - BeforeToolsEvent: Information about the current tool being used, including: - message: The assistant message containing tool use blocks - ToolStreamEvent: Information about an event streamed from a tool, including: - data: The data streamed from the tool

Multi-Agent Events

=== "Python"

Multi-agent systems ([Graph](../multi-agent/graph.md) and [Swarm](../multi-agent/swarm.md)) 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](../multi-agent/graph.md#streaming-events) and [Swarm streaming](../multi-agent/swarm.md#streaming-events) for usage examples.

=== "TypeScript"

```typescript
Coming soon to Typescript!
```

Quick Examples

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

**Callback Handler Pattern**
```python
def handle_events(**kwargs):
    if "data" in kwargs:
        print(kwargs["data"], end="")

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

=== "TypeScript"

**Async Iterator Pattern**
```typescript
--8<-- "user-guide/concepts/streaming/overview.ts:quick_example_async_iterator"
```

Identifying Events Emitted from Agent

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

=== "Python"

```python
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)

```

=== "TypeScript"

```typescript
--8<-- "user-guide/concepts/streaming/overview.ts:agent_loop_lifecycle"
```

Sub-Agent Streaming Example

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

=== "Python"

```python
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?")
```

=== "TypeScript"

```typescript
--8<-- "user-guide/concepts/streaming/overview.ts:sub_agent_basic"
```

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