> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Basic Speech Agent

> Run an agent normally, then narrate its final response with text-to-speech.

The simplest voice-agent pattern: let the agent finish thinking, then hand the final text to `stream_tts_openai` for narration. This is ideal when you only care about the final answer, not intermediate tokens.

## Step 1: Install dependencies

```bash theme={null}
pip install -U swarms voice-agents
export OPENAI_API_KEY=sk-...
```

## Step 2: Build the agent

Use any LiteLLM-compatible model. Here we use a quantitative trading agent.

```python theme={null}
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,
)
```

## Step 3: Run the agent

The agent runs to completion and returns the full response as a string.

```python theme={null}
out = agent.run(
    task="What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?",
)
```

## Step 4: Stream the result through TTS

`stream_tts_openai` accepts a list of strings and streams them through OpenAI's TTS engine. With `stream_mode=True`, audio chunks play as they're synthesised.

```python theme={null}
from voice_agents.main import stream_tts_openai

stream_tts_openai(
    [out],
    stream_mode=True,
)
```

## Full example

```python theme={null}
from swarms import Agent
from voice_agents.main import stream_tts_openai

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,
)

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

stream_tts_openai(
    [out],
    stream_mode=True,
)
```

<Note>
  Source: [examples/guides/voice\_agents/voice\_agents\_examples/agent\_speech.py](https://github.com/kyegomez/swarms/blob/master/examples/guides/voice_agents/voice_agents_examples/agent_speech.py)
</Note>

## When to use this pattern

* You only need to narrate the **final** answer.
* Latency to first audio is not critical (you wait for the agent to finish before any speech).
* Simplicity wins — no callback wiring, no `flush()` calls.

For sentence-by-sentence narration as the agent generates, see [Streaming Voice Agent](/examples/voice-agents/agent-with-streaming-speech).