pip install elevenlabs

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
from elevenlabs.play import play

load_dotenv()
elevenlabs = ElevenLabs()
audio = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v3",
    output_format="mp3_44100_128",
)
play(audio)

from elevenlabs.client import ElevenLabs

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)
response = elevenlabs.voices.search()
print(response.voices)

Clone Voice

Clone your voice in an instant. Note that voice cloning requires an API key, see below.

from elevenlabs.client import ElevenLabs
from elevenlabs.play import play

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)
voice = elevenlabs.voices.ivc.create(
    name="Alex",
    description="An old American male voice with a slight hoarseness in his throat. Perfect for news", # Optional
    files=["./sample_0.mp3", "./sample_1.mp3", "./sample_2.mp3"],
)

Streaming

Stream audio in real-time, as it's being generated.

from elevenlabs import stream
from elevenlabs.client import ElevenLabs

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)
audio_stream = elevenlabs.text_to_speech.stream(
    text="This is a test",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2"
)
option 1: play the streamed audio locally
stream(audio_stream)
option 2: process the audio bytes manually
for chunk in audio_stream:
    if isinstance(chunk, bytes):
        print(chunk)

Async Client

Use AsyncElevenLabs if you want to make API calls asynchronously.

import asyncio

from elevenlabs.client import AsyncElevenLabs
elevenlabs = AsyncElevenLabs(
  api_key="MY_API_KEY"
)
async def print_models() -> None:
    models = await elevenlabs.models.list()
    print(models)
asyncio.run(print_models())

ElevenAgents

Build interactive AI agents with real-time audio capabilities using ElevenAgents.

Basic Usage

from elevenlabs.client import ElevenLabs
from elevenlabs.conversational_ai.conversation import Conversation, ClientTools
from elevenlabs.conversational_ai.default_audio_interface import DefaultAudioInterface

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)
Create audio interface for real-time audio input/output
audio_interface = DefaultAudioInterface()
Create conversation
conversation = Conversation(
    client=elevenlabs,
    agent_id="your-agent-id",
    requires_auth=True,
    audio_interface=audio_interface,
)
Start the conversation
conversation.start_session()
The conversation runs in background until you call:
conversation.end_session()

Custom Event Loop Support

For advanced use cases involving context propagation, resource reuse, or specific event loop management, ClientTools supports custom asyncio event loops:

import asyncio
from elevenlabs.conversational_ai.conversation import ClientTools

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)
async def main():
    # Get the current event loop
    custom_loop = asyncio.get_running_loop()

    

      
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# Create ClientTools with custom loop to prevent "different event loop" errors
client_tools = ClientTools(loop=custom_loop)

# Register your tools
async def get_weather(params):
    location = params.get("location", "Unknown")
    # Your async logic here
    return f"Weather in {location}: Sunny, 72°F"

client_tools.register("get_weather", get_weather, is_async=True)

# Use with conversation
conversation = Conversation(
    client=elevenlabs,
    agent_id="your-agent-id",
    requires_auth=True,
    audio_interface=audio_interface,
    client_tools=client_tools
)
    
  
asyncio.run(main())

Benefits of Custom Event Loop:

• Context Propagation: Maintain request-scoped state across async operations
• Resource Reuse: Share existing async resources like HTTP sessions or database pools
• Loop Management: Prevent "Task got Future attached to a different event loop" errors
• Performance: Better control over async task scheduling and execution

Important: When using a custom loop, you're responsible for its lifecycle
Don't close the loop while ClientTools are still using it.

Tool Registration

Register custom tools that the AI agent can call during conversations:

client_tools = ClientTools()

Sync tool
def calculate_sum(params):
    numbers = params.get("numbers", [])
    return sum(numbers)
Async tool
async def fetch_data(params):
    url = params.get("url")
    # Your async HTTP request logic
    return {"data": "fetched"}
client_tools.register("calculate_sum", calculate_sum, is_async=False)
client_tools.register("fetch_data", fetch_data, is_async=True)

Languages Supported

Explore all models & languages.

Contributing

While we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!

On the other hand, contributions to the README are always very welcome!