Consuming Streams

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.

Print LLM Output

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

Print Tool Activity

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.

Print Streamed Tool Call Arguments

When a model supports streamed tool calling, CrewAI emits partial tool-call arguments as llm_stream_chunk frames on the llm channel. These frames are different from tool execution events:

llm channel: the model is constructing the tool call.
tools channel: CrewAI is executing the tool.

The current delta is stored in frame.event["chunk"]. The accumulated arguments are stored in frame.event["tool_call"]["function"]["arguments"].

with llm.stream_events("Get the weather in Paris.", tools=[weather_tool]) as stream:
    for frame in stream.interleave(["llm", "tools"]):
        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
            tool_call = frame.event.get("tool_call")
            if tool_call:
                function = tool_call["function"]
                print(f"\nPreparing tool: {function['name']}")
                print(f"Arguments so far: {function['arguments']}")
            elif frame.content:
                print(frame.content, end="", flush=True)

        elif frame.channel == "tools" and frame.type == "tool_usage_started":
            print(f"\nRunning tool: {frame.event.get('tool_name')}")

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

result = stream.result

Some providers emit arguments in small JSON fragments. For display, prefer the accumulated argument string over the latest delta.

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.

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

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

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

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

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().

Get Started

Guides

Core Concepts

MCP Integration

Tools

Observability

Learn

Telemetry

Consuming Streams

Overview

Print LLM Output

Print Tool Activity

Print Streamed Tool Call Arguments

Watch Flow Progress

Interleave Selected Channels

Stream a Direct LLM Call

Stream a Conversational Turn

Async Consumers

Cleanup

See Also

​Overview

​Print LLM Output

​Print Tool Activity

​Print Streamed Tool Call Arguments

​Watch Flow Progress

​Interleave Selected Channels

​Stream a Direct LLM Call

​Stream a Conversational Turn

​Async Consumers

​Cleanup

​See Also

Overview

Print LLM Output

Print Tool Activity

Print Streamed Tool Call Arguments

Watch Flow Progress

Interleave Selected Channels

Stream a Direct LLM Call

Stream a Conversational Turn

Async Consumers

Cleanup

See Also