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This pattern gives you the lowest possible “time to first audio”. Instead of waiting for the agent to finish, every token is forwarded to a streaming TTS callback that buffers up sentences and dispatches them to the speech engine the moment they’re complete.

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Step 1: Install dependencies

pip install -U swarms voice-agents
export OPENAI_API_KEY=sk-...

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Step 2: Build the agent

from swarms import Agent

agent = Agent(
    agent_name="Quantitative-Trading-Agent",
    agent_description="Advanced quantitative trading and algorithmic analysis agent",
    model_name="gpt-4.1",
    dynamic_temperature_enabled=True,
    max_loops=1,
    dynamic_context_window=True,
    top_p=None,
)

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Step 3: Create the streaming TTS callback

StreamingTTSCallback is a callable that satisfies Swarms’s streaming_callback contract. With stream_mode=True, the audio for each sentence is played the moment it’s synthesised. 
from voice_agents.main import StreamingTTSCallback

tts_callback = StreamingTTSCallback(
    voice="alloy",
    model="openai/tts-1",
    stream_mode=True,
)
OpenAI ships six voices: alloy, echo, fable, onyx, nova, shimmer.

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Step 4: Run the agent with the callback

Pass the callback as streaming_callback. Tokens flow into the agent’s response and into the TTS engine in parallel. 
out = agent.run(
    task="What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?",
    streaming_callback=tts_callback,
)

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Step 5: Flush the buffer

StreamingTTSCallback buffers the last sentence until it sees a terminator (., ?, !, …). Always call flush() at the end so the final sentence is spoken. 
tts_callback.flush()
print(out)

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Full example

from swarms import Agent
from voice_agents.main import StreamingTTSCallback

agent = Agent(
    agent_name="Quantitative-Trading-Agent",
    agent_description="Advanced quantitative trading and algorithmic analysis agent",
    model_name="gpt-4.1",
    dynamic_temperature_enabled=True,
    max_loops=1,
    dynamic_context_window=True,
    top_p=None,
)

tts_callback = StreamingTTSCallback(
    voice="alloy", model="openai/tts-1", stream_mode=True
)

out = agent.run(
    task="What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?",
    streaming_callback=tts_callback,
)

tts_callback.flush()
print(out)
Source: examples/guides/voice_agents/voice_agents_examples/agent_with_speech.py

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When to use this pattern

  • You want time to first audio as low as possible.
  • The agent’s output is long enough that waiting for completion would be awkward.
  • You’re fine with sentence-level granularity (the callback buffers per sentence, not per token).

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See also