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Overview

Use this guide when you want to subscribe to a CrewAI stream and print or route frames as they arrive. The basic pattern is: 
stream = flow.stream_events(inputs={"topic": "AI agents"})

with stream:
    for frame in stream:
        ...

result = stream.result
Always consume the stream before reading stream.result. If you only care about text generated by LLM calls, subscribe to the llm projection and print frame.content: 
stream = flow.stream_events(inputs={"topic": "AI agents"})

with stream:
    for frame in stream.llm:
        print(frame.content, end="", flush=True)

print()
result = stream.result
frame.content is an empty string for frames that do not carry printable text, so this is also safe: 
with flow.stream_events(inputs={"topic": "AI agents"}) as stream:
    for frame in stream.events:
        if frame.channel == "llm" and frame.content:
            print(frame.content, end="", flush=True)

result = stream.result
Tool events arrive on the tools channel. Use frame.type to distinguish starts, finishes, and errors. 
with flow.stream_events(inputs={"topic": "AI agents"}) as stream:
    for frame in stream.events:
        if frame.channel == "llm" and frame.content:
            print(frame.content, end="", flush=True)

        if frame.channel == "tools" and frame.type == "tool_usage_started":
            print(f"\nTool started: {frame.event.get('tool_name')}")

        if frame.channel == "tools" and frame.type == "tool_usage_finished":
            print(f"\nTool finished: {frame.event.get('tool_name')}")

result = stream.result
frame.event is the structured payload for the source event. Use it for metadata such as tool names, arguments, message roles, and runtime identifiers.

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Watch Flow Progress

Flow lifecycle and method execution frames arrive on the flow channel: 
with flow.stream_events(inputs={"topic": "AI agents"}) as stream:
    for frame in stream.flow:
        print(frame.type, frame.namespace)

result = stream.result
Use this when you want a progress log instead of token-level output.

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Interleave Selected Channels

Use interleave() when you want a subset of channels while preserving their relative order: 
with flow.stream_events(inputs={"topic": "AI agents"}) as stream:
    for frame in stream.interleave(["llm", "tools"]):
        if frame.channel == "llm":
            print(frame.content, end="", flush=True)
        elif frame.type == "tool_usage_started":
            print(f"\nTool: {frame.event.get('tool_name')}")

result = stream.result

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Stream a Direct LLM Call

Direct llm.call(...) returns the final assembled result. To stream a direct LLM call, use llm.stream_events(...): 
from crewai import LLM


llm = LLM(model="gpt-4o-mini")
stream = llm.stream_events("Explain streaming in one sentence.")

with stream:
    for frame in stream.llm:
        print(frame.content, end="", flush=True)

print()
result = stream.result

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Stream a Conversational Turn

Conversational Flows expose stream_turn() for one user message: 
stream = flow.stream_turn(
    "What can you help me with?",
    session_id="session-1",
)

with stream:
    for frame in stream.interleave(["llm", "messages"]):
        if frame.channel == "llm":
            print(frame.content, end="", flush=True)
        elif frame.channel == "messages":
            print(f"\n{frame.event.get('role')}: {frame.event.get('content')}")

reply = stream.result

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Async Consumers

Async streams use the same channel projections: 
stream = flow.astream(inputs={"topic": "AI agents"})

async with stream:
    async for frame in stream.llm:
        print(frame.content, end="", flush=True)

result = stream.result

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Cleanup

Use the stream as a context manager when possible. If a client disconnects or you stop consuming early, close the stream: 
stream = flow.stream_events(inputs={"topic": "AI agents"})

try:
    for frame in stream.events:
        print(frame.content, end="", flush=True)
finally:
    if not stream.is_exhausted:
        stream.close()
For async streams, call await stream.aclose().

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See Also