SmallestAI Python SDK

pip install smallestai gives you one package with two surfaces, plus a CLI:

Atoms — build, configure, deploy, and phone-call voice AI agents (client.atoms).
Waves — low-latency text-to-speech and speech-to-text, sync/async and streaming (client.waves).
CLI — smallestai for managing agents and deploying agent-crew code.

pip install smallestai

Quickstart
Text-to-speech and speech-to-text
Agent crew: your own LLM
CLI
Async client
Environments
Exception handling
Streaming and websockets
Advanced
Reference and docs
Contributing

Quickstart: create an agent and call it

from smallestai import SmallestAI

client = SmallestAI(api_key="<your-api-key>")

# create an agent (the response .data is the new agent id)
agent_id = client.atoms.agents.create_agent(name="my-first-agent").data

# place an outbound call (from_product_id is a rented number's product id)
client.atoms.calls.start_outbound_call(
    agent_id=agent_id,
    phone_number="+1XXXXXXXXXX",
    from_product_id="<rented-number-product-id>",
)

Text-to-speech and speech-to-text (Waves)

synthesize_tts streams audio bytes. List available voices with client.waves.get_voices().

from smallestai import SmallestAI

client = SmallestAI(api_key="<your-api-key>")

with open("out.wav", "wb") as f:
    for chunk in client.waves.synthesize_tts(text="Hello from Smallest.", voice_id="<voice-id>"):
        f.write(chunk)

Streaming speech-to-text helper:

from smallestai.waves.helpers import stream_speech_to_text

for event in stream_speech_to_text(client, language="en"):
    print(event)

Agent crew: your own LLM in the middle

An agent crew runs the LLM turn on a model you choose while Smallest handles STT and TTS. Point the crew node's OpenAIClient at any OpenAI-compatible endpoint (a hosted API, or a local model via Ollama):

from smallestai.atoms.crew.nodes import OutputCrewNode
from smallestai.atoms.crew.clients.openai import OpenAIClient

class Assistant(OutputCrewNode):
    def __init__(self):
        super().__init__(name="assistant")
        self.llm = OpenAIClient(
            model="claude-haiku-4-5",
            api_key="<your-llm-key>",
            base_url="https://api.anthropic.com/v1/",   # or http://localhost:11434/v1 for Ollama
        )

    async def generate_response(self):
        async for chunk in await self.llm.chat(self.context.messages, stream=True):
            if chunk.content:
                yield chunk.content

Conversation history is handled for you: every turn is appended to self.context, and you send self.context.messages to the model each turn.

Deploy it with the CLI:

smallestai auth login
smallestai agent-crew init --agent-id <agent-id>
smallestai agent-crew deploy --entry-point server.py
smallestai agent-crew builds        # pick the build -> Make Live

A flat directory (server.py + requirements.txt at the root) is the simplest layout; a src/ layout with a pyproject.toml also works (declare all runtime deps in the pyproject).

CLI

smallestai auth login               # store your API key
smallestai agents list              # list, get, call, and manage agents
smallestai agent-crew deploy ...     # package and deploy crew code
smallestai agent-crew chat           # talk to a running crew locally

Async client

The SDK exports an async client with the same surface:

import asyncio
from smallestai import AsyncSmallestAI

async def main():
    client = AsyncSmallestAI(api_key="<your-api-key>")
    agents = await client.atoms.agents.list_agents()
    print(agents.data)

asyncio.run(main())

Environments

from smallestai import SmallestAI
from smallestai.environment import SmallestAIEnvironment

client = SmallestAI(environment=SmallestAIEnvironment.PRODUCTION)

Exception handling

from smallestai.core.api_error import ApiError

try:
    client.atoms.agents.get_agent(id="does-not-exist")
except ApiError as e:
    print(e.status_code, e.body)

Streaming and websockets

Waves supports real-time, low-latency streaming over websockets. stream() returns a context manager; iterate it to process messages as they arrive.

from smallestai import SmallestAI

client = SmallestAI(api_key="<your-api-key>")

# real-time speech-to-text
with client.waves.speech_to_text.stream() as socket:
    for message in socket:
        print(message)

The async client mirrors this with async with / async for. Text-to-speech streams too: synthesize_tts(...) yields audio bytes as they are generated (see above).

Advanced

Access raw response data

Use .with_raw_response to get the response headers and status alongside the parsed data.

response = client.atoms.agents.with_raw_response.get_agent(id="<agent-id>")
print(response.headers)       # response headers
print(response.status_code)   # status code
print(response.data)          # parsed object

Retries

The SDK retries retryable requests with exponential backoff (default 2). Configure it at the client or per request.

client = SmallestAI(api_key="<your-api-key>", max_retries=3)

# or per request
client.atoms.agents.get_agent(id="<agent-id>", request_options={"max_retries": 1})

Timeouts

Defaults to 60 seconds. Configure at the client or per request.

client = SmallestAI(api_key="<your-api-key>", timeout=20.0)

# or per request
client.atoms.agents.get_agent(id="<agent-id>", request_options={"timeout_in_seconds": 5})

Custom client

Override the httpx client for proxies, custom transports, and similar.

import httpx
from smallestai import SmallestAI

client = SmallestAI(
    api_key="<your-api-key>",
    httpx_client=httpx.Client(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

Reference and docs

Full API reference: reference.md
Product docs: https://smallest.ai/docs

Contributing

Most of src/ is generated from an API spec and gets overwritten on regeneration, so hand edits there will not stick. If you spot a bug or a gap, open an issue.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smallestai-5.3.3.tar.gz (445.2 kB view details)

Uploaded Jul 31, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

smallestai-5.3.3-py3-none-any.whl (953.9 kB view details)

Uploaded Jul 31, 2026 Python 3

File details

Details for the file smallestai-5.3.3.tar.gz.

File metadata

Download URL: smallestai-5.3.3.tar.gz
Upload date: Jul 31, 2026
Size: 445.2 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: poetry/1.5.1 CPython/3.9.25 Linux/6.17.0-1020-azure

File hashes

Hashes for smallestai-5.3.3.tar.gz
Algorithm	Hash digest
SHA256	`14e536b27393d9ff50a8ae871c1f81d35b42e0d37ccf8ec1dd97b721e44cbead`
MD5	`faf490d1b0bdc3b0572817263d15854c`
BLAKE2b-256	`22627d593781f285340451ea88b937c212f2cbf943c8bab0a2a77350b05510a9`

See more details on using hashes here.

File details

Details for the file smallestai-5.3.3-py3-none-any.whl.

File metadata

Download URL: smallestai-5.3.3-py3-none-any.whl
Upload date: Jul 31, 2026
Size: 953.9 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: poetry/1.5.1 CPython/3.9.25 Linux/6.17.0-1020-azure

File hashes

Hashes for smallestai-5.3.3-py3-none-any.whl
Algorithm	Hash digest
SHA256	`29a30eb6b924df2a24575fd4e45249f5bcd63e41bc8094a555121619ccb29ab7`
MD5	`62e8ab39b3435dea3cbddd920d8393fa`
BLAKE2b-256	`fe5ee5c55685fc74fc18cb32a461d49826ac1ac9d56cfed4ed188b0473555a6b`