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Unify flow streaming frame items

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lorenzejay
2026-06-29 14:50:17 -07:00
parent a48f45c917
commit 90b06a4523
15 changed files with 739 additions and 220 deletions
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53
docs/edge/en/learn/streaming-flow-execution.mdx
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@@ -52,7 +52,7 @@ class ResearchFlow(Flow):
## Synchronous Streaming
When you call `kickoff()` on a flow with streaming enabled, it returns a `FlowStreamingOutput` object that you can iterate over:
When you call `kickoff()` on a flow with streaming enabled, it returns a stream session that yields ordered `StreamFrame` items:
```python Code
flow = ResearchFlow()
@@ -60,44 +60,43 @@ flow = ResearchFlow()
# Start streaming execution
streaming = flow.kickoff()
# Iterate over chunks as they arrive
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Iterate over stream items as they arrive
for item in streaming:
print(item.content, end="", flush=True)
# Access the final result after streaming completes
result = streaming.result
print(f"\n\nFinal output: {result}")
```
### Stream Chunk Information
### Stream Item Information
Each chunk provides context about where it originated in the flow:
Each item provides both printable content and structured event data:
```python Code
streaming = flow.kickoff()
for chunk in streaming:
print(f"Agent: {chunk.agent_role}")
print(f"Task: {chunk.task_name}")
print(f"Content: {chunk.content}")
print(f"Type: {chunk.chunk_type}") # TEXT or TOOL_CALL
for item in streaming:
print(f"Channel: {item.channel}")
print(f"Type: {item.type}")
print(f"Content: {item.content}")
print(f"Event payload: {item.event}")
```
### Accessing Streaming Properties
The `FlowStreamingOutput` object provides useful properties and methods:
The stream session provides useful properties and methods:
```python Code
streaming = flow.kickoff()
# Iterate and collect chunks
for chunk in streaming:
print(chunk.content, end="", flush=True)
# Iterate and collect items
for item in streaming:
print(item.content, end="", flush=True)
# After iteration completes
print(f"\nCompleted: {streaming.is_completed}")
print(f"Full text: {streaming.get_full_text()}")
print(f"Total chunks: {len(streaming.chunks)}")
print(f"Total frames: {len(streaming.frames)}")
print(f"Final result: {streaming.result}")
```
@@ -114,9 +113,9 @@ async def stream_flow():
# Start async streaming
streaming = await flow.kickoff_async()
# Async iteration over chunks
async for chunk in streaming:
print(chunk.content, end="", flush=True)
# Async iteration over stream items
async for item in streaming:
print(item.content, end="", flush=True)
# Access final result
result = streaming.result
@@ -422,7 +421,7 @@ except Exception as e:
## Cancellation and Resource Cleanup
`FlowStreamingOutput` supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
The stream session supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
### Async Context Manager
@@ -430,8 +429,8 @@ except Exception as e:
streaming = await flow.kickoff_async()
async with streaming:
async for chunk in streaming:
print(chunk.content, end="", flush=True)
async for item in streaming:
print(item.content, end="", flush=True)
```
### Explicit Cancellation
@@ -439,8 +438,8 @@ async with streaming:
```python Code
streaming = await flow.kickoff_async()
try:
async for chunk in streaming:
print(chunk.content, end="", flush=True)
async for item in streaming:
print(item.content, end="", flush=True)
finally:
await streaming.aclose() # async
# streaming.close() # sync equivalent
@@ -451,7 +450,7 @@ After cancellation, `streaming.is_cancelled` and `streaming.is_completed` are bo
## Important Notes
- Streaming automatically enables LLM streaming for any crews used within the flow
- You must iterate through all chunks before accessing the `.result` property
- You must iterate through all stream items before accessing the `.result` property
- Streaming works with both structured and unstructured flow state
- Flow streaming captures output from all crews and LLM calls in the flow
- Each chunk includes context about which agent and task generated it
@@ -475,4 +474,4 @@ result = streaming.result
print(f"\nFlow complete! View structure at: research_flow.html")
```
By leveraging flow streaming, you can build sophisticated, responsive applications that provide users with real-time visibility into complex multi-stage workflows, making your AI automations more transparent and engaging.
By leveraging flow streaming, you can build sophisticated, responsive applications that provide users with real-time visibility into complex multi-stage workflows, making your AI automations more transparent and engaging.