elevenlabs-python: the official Python SDK for ElevenLabs text to speech

What elevenlabs-python solves, and who it is actually for

The ElevenLabs API is an HTTP service. Calling it from Python without a client means constructing requests, serialising JSON bodies, handling pagination on list endpoints, and deciding what to do with a binary audio response. elevenlabs-python removes that layer. It is the official SDK, generated by Fern according to the badge in the README, and the repository's pyproject.toml declares the package name elevenlabs with the source under src/elevenlabs. The audience is narrow and clear: Python developers who are already committed to ElevenLabs as their speech provider and want the API surface expressed as Python objects. If you are evaluating speech vendors, this library will not help you compare them; it is a client for one service. The README frames the value in a single sentence: lifelike voices "in just a few lines of code". The realistic use cases visible in the documentation are narration and content generation (text_to_speech.convert), real-time playback (text_to_speech.stream), voice cloning from sample files (voices.ivc.create), and interactive agents with live audio input and output (the conversational_ai module). Each of those maps to a distinct set of dependencies, which is the first thing to understand about the package.

How the client is structured: one entry point, several sub-clients

Everything starts with the ElevenLabs class from elevenlabs.client. The README shows it constructed with no arguments in one example and with api_key="YOUR_API_KEY" in others, which implies the key is read from the environment when omitted; the example that omits it also calls load_dotenv() from python-dotenv first, so the environment variable is the intended path. From that instance you reach namespaced resources: text_to_speech for synthesis, voices for listing and cloning, models for enumeration, and conversational_ai for agents. The data flow for the common case is short. You pass a string, a voice_id, a model_id and an output_format; the client returns audio, either as a finished object you hand to play() or as an iterator of bytes you consume yourself. The streaming example makes the two consumption styles explicit: pass the iterator to stream() for local playback, or loop over it and check isinstance(chunk, bytes) to process chunks manually. Async is a parallel class, AsyncElevenLabs, with the same resource names, so await elevenlabs.models.list() replaces the synchronous call. The dependency list in pyproject.toml tells you what is happening underneath: httpx for transport, pydantic and pydantic-core for the typed models, websockets for the agent connection, and pyaudio as an optional extra for the default audio interface. Only pyaudio is optional, so a plain pip install pulls the rest.

Installing elevenlabs-python and making a first request

The README gives one install command and no platform caveats. The package is on PyPI as elevenlabs.

pip install elevenlabs

After that, the smallest useful program needs an API key. The README's main example loads it from a .env file via python-dotenv, then constructs the client with no arguments. The convert call takes the text, a voice_id, a model_id and an output_format, and the play helper from elevenlabs.play handles playback locally.

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)

Run that and you should hear the sentence spoken; the voice_id in the example is a fixed identifier from the README, not a default you can rely on for your own account. To see which voices your key can actually use, the README shows a search call instead of a hardcoded identifier.

from elevenlabs.client import ElevenLabs

elevenlabs = ElevenLabs(
  api_key="YOUR_API_KEY",
)

response = elevenlabs.voices.search()
print(response.voices)

That prints the voice objects your account has access to. The README points at the Get Voices API reference for the shape of that output rather than documenting the fields itself, so treat the printed structure as the source of truth for your version. If you want a different model, the README lists four by identifier: eleven_v3, eleven_multilingual_v2, eleven_flash_v2_5 and eleven_turbo_v2_5. The README recommends eleven_multilingual_v2 for most use cases and describes eleven_flash_v2_5 as the low-latency option at 50% lower price per character. Those are the project's own descriptions, not measured results.

Voice cloning and streaming: where the SDK stops being a thin wrapper

Two features need more than a request and a response. Voice cloning, under voices.ivc.create, takes a name, an optional description and a list of local audio file paths. The README's example passes three .mp3 files. This is an upload, which means your process needs those files on disk and the call will take longer than a synthesis request; the SDK does not document chunking, retries or resume behaviour for the upload. The second feature is streaming, and it is the one with a real integration cost. The stream method returns an iterator you either hand to stream() for local playback or consume yourself. The README shows the manual path as a loop that prints byte chunks. For a web backend you would forward those chunks rather than print them, and at that point you are responsible for the framing. The conversational agent path is heavier still. The README's basic example needs an agent_id, a DefaultAudioInterface built on pyaudio, and a Conversation object constructed with requires_auth=True; the session then runs in the background until you call end_session(). That means a microphone and speaker device on the host, which rules out most server environments. The README also documents a ClientTools escape hatch for passing a custom asyncio event loop, with a stated purpose of avoiding "Task got Future attached to a different event loop" errors when you already have an HTTP session or database pool you want to reuse. It also states plainly that when you use a custom loop you are responsible for its lifetime. That is the kind of detail that only matters in production, and it is good that it is written down.

Where elevenlabs-python is the wrong choice

The library is a client, so it inherits every constraint of the service behind it. There is no offline mode and no local model; if your requirement is on-device synthesis, this is not the package. The README does not document rate-limit handling, retry policy or backoff anywhere in the visible text, so if your workload is bursty you should expect to build that yourself on top of httpx. The version situation deserves attention. pyproject.toml pins the package version at 2.68.0, the release list shows v2.68.0 on 2026-09-11, and there is also a v3.0.0-alpha.1 tag dated 2026-09-08. The README does not describe what changes between the 2.x line and the 3.0 alpha, and it does not document a migration path. If you pin dependencies in production, that ambiguity is a planning problem, not a bug. There is also a licensing boundary worth separating from the code licence: the SDK is MIT, but the voices and the API are a paid hosted service, and the README's cloning example requires an API key. Nothing in the repository grants you rights to the audio you generate or to the voices you clone; that is governed by the service terms, and the README is silent on it. Finally, the conversational agent module is not a general-purpose audio framework. It is built around a specific agent endpoint and a default audio interface that assumes local hardware.

How it compares with calling the HTTP API directly

The honest alternative is not another Python speech library. It is httpx and the ElevenLabs HTTP API documentation, which the README links at elevenlabs.io/docs/api-reference. The difference in approach is concrete. With raw HTTP you write the request body yourself, which means you see exactly which fields the service accepts and you are never waiting on an SDK release to expose a new parameter. You also control retries, timeouts and connection pooling without working around a client's defaults. The trade is typing and maintenance. The SDK gives you pydantic models for responses, a name for every resource, an async class that mirrors the sync one, and a websocket path for agents that you would otherwise implement from the protocol description. The README's own examples are the argument: the streaming and conversational examples are short because the SDK owns the framing. If your integration is one convert call in a script, raw HTTP is defensible. If you are building agents, cloning voices and streaming audio in the same application, reimplementing the websocket layer and the response models is work you would be doing instead of shipping. The MIT licence on this repository means you can read src/elevenlabs to see what the client does before deciding.

Maintenance, versioning and the cost of keeping up

The repository is not archived, and the last push to main was on 2026-09-11, five days before this writing. The release cadence visible in the list is fast: v2.67.0 on 2026-09-07, then v2.68.0 four days later, with a v3.0.0-alpha.1 tag in between. A cadence that tight is a signal about upgrade cost. Because the package is Fern-generated, much of each release is likely regenerated surface area following the HTTP API, which means minor versions can add methods and models without a migration note. The practical consequence is that you should pin the version in your dependency file rather than tracking latest, and read the release notes before bumping. The pyproject.toml classifiers claim support from Python 3.8 through 3.15, and the dependency floor is python = "^3.8", so the package does not force a runtime upgrade on you. The MIT licence covers the code in this repository. It does not cover the service, your API key, the voices, or the audio you generate, and the README does not address any of those. If your organisation has rules about where generated audio may be stored or how cloned voices may be used, that question is answered by the ElevenLabs service terms, not by the LICENSE file, and it is worth resolving before you write code rather than after.

Editorial conclusion

Adopt elevenlabs-python if your application is Python and you want the text to speech, voice cloning, streaming or conversational agent endpoints behind typed methods instead of hand-rolled HTTP. Skip it if you are not on Python, if you need an offline or self-hosted speech engine, or if you want a stable major version: the repository currently carries both a 2.68.0 line and a v3.0.0-alpha.1 tag, and the README does not document a migration path between them. Before you build on it, verify your own key, quota and latency against the live API, and check that the voice_id and model_id you intend to use appear in the models documentation, because the SDK passes those strings through without validating them.

Frequently asked questions

Official sources

1. elevenlabs/elevenlabs-python on GitHub
2. License: MIT
3. Project website
4. README
5. Releases