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Get an API key

Before you can start using the SDK, you’ll need to obtain your API key from AI21 Studio.

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Python SDK

Python library GitHub repository

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Installation

Install ai21 Python SDK with your favorite package manager. 
pip install ai21

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Example usage

from ai21 import AI21Client
from ai21.models.chat import ChatMessage

client = AI21Client(
    # defaults to os.environ.get('AI21_API_KEY')
    api_key='my_api_key',
)

system = "You're a support engineer in a SaaS company"
messages = [
    ChatMessage(content=system, role="system"),
    ChatMessage(content="Hello, I need help with a signup process.", role="user"),
]

chat_completions = client.chat.completions.create(
    messages=messages,
    model="jamba-mini-1.6-2025-03",
)

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Async Usage

You can use the AsyncAI21Client to make asynchronous requests. There is no difference between the sync and the async client in terms of usage. 
import asyncio

from ai21 import AsyncAI21Client
from ai21.models.chat import ChatMessage

system = "You're a support engineer in a SaaS company"
messages = [
    ChatMessage(content=system, role="system"),
    ChatMessage(content="Hello, I need help with a signup process.", role="user"),
]

client = AsyncAI21Client(
   # defaults to os.environ.get('AI21_API_KEY')
    api_key='my_api_key',
)


async def main():
    response = await client.chat.completions.create(
        messages=messages,
        model="jamba-mini-1.6-2025-03",
    )

    print(response)


asyncio.run(main())

​
Chat

from ai21 import AI21Client
from ai21.models import RoleType
from ai21.models import ChatMessage

system = "You're a support engineer in a SaaS company"
messages = [
    ChatMessage(text="Hello, I need help with a signup process.", role=RoleType.USER),
    ChatMessage(text="Hi Alice, I can help you with that. What seems to be the problem?", role=RoleType.ASSISTANT),
    ChatMessage(text="I am having trouble signing up for your product with my Google account.", role=RoleType.USER),
]


client = AI21Client()
chat_response = client.chat.create(
    system=system,
    messages=messages,
    model="j2-ultra",
)
For a more detailed example, see the chat examples.

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Completion

from ai21 import AI21Client


client = AI21Client()
completion_response = client.completion.create(
    prompt="This is a test prompt",
    model="j2-mid",
)

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Chat Completion

from ai21 import AI21Client
from ai21.models.chat import ChatMessage

system = "You're a support engineer in a SaaS company"
messages = [
    ChatMessage(content=system, role="system"),
    ChatMessage(content="Hello, I need help with a signup process.", role="user"),
    ChatMessage(content="Hi Alice, I can help you with that. What seems to be the problem?", role="assistant"),
    ChatMessage(content="I am having trouble signing up for your product with my Google account.", role="user"),
]

client = AI21Client()

response = client.chat.completions.create(
    messages=messages,
    model="jamba-large",
    max_tokens=100,
    temperature=0.7,
    top_p=1.0,
    stop=["\n"],
)

print(response)
Note that jamba-large supports async and streaming as well. For a more detailed example, see the chat examples.

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Streaming

We currently support streaming for the Chat Completions API in Jamba. 
from ai21 import AI21Client
from ai21.models.chat import ChatMessage

messages = [ChatMessage(content="What is the meaning of life?", role="user")]

client = AI21Client()

response = client.chat.completions.create(
    messages=messages,
    model="jamba-large",
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content, end="")

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Async Streaming

import asyncio

from ai21 import AsyncAI21Client
from ai21.models.chat import ChatMessage

messages = [ChatMessage(content="What is the meaning of life?", role="user")]

client = AsyncAI21Client()


async def main():
    response = await client.chat.completions.create(
        messages=messages,
        model="jamba-mini-1.6-2025-03",
        stream=True,
    )
    async for chunk in response:
        print(chunk.choices[0].delta.content, end="")


asyncio.run(main())

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Maestro

AI Planning & Orchestration System built for the enterprise. Read more here. 
from ai21 import AI21Client

client = AI21Client()

run_result = client.beta.maestro.runs.create_and_poll(
    input="Write a poem about the ocean",
    requirements=[
        {
            "name": "length requirement",
            "description": "The length of the poem should be less than 1000 characters",
        },
        {
            "name": "rhyme requirement",
            "description": "The poem should rhyme",
        },
    ],
)
For a more detailed example, see maestro sync and async examples.

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Conversational RAG (Beta)

Like chat, but with the ability to retrieve information from your Studio library. 
from ai21 import AI21Client
from ai21.models.chat import ChatMessage

messages = [
    ChatMessage(content="Ask a question about your files", role="user"),
]

client = AI21Client()

client.library.files.create(
  file_path="path/to/file",
  path="path/to/file/in/library",
  labels=["my_file_label"],
)
chat_response = client.beta.conversational_rag.create(
    messages=messages,
    labels=["my_file_label"],
)
For a more detailed example, see the chat sync and async examples.

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File Upload

from ai21 import AI21Client

client = AI21Client()

file_id = client.library.files.create(
    file_path="path/to/file",
    path="path/to/file/in/library",
    labels=["label1", "label2"],
    public_url="www.example.com",
)

uploaded_file = client.library.files.get(file_id)

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Token Counting

By using the count_tokens method, you can estimate the billing for a given request. 
from ai21.tokenizers import get_tokenizer

tokenizer = get_tokenizer(name="jamba-tokenizer")
total_tokens = tokenizer.count_tokens(text="some text")  # returns int
print(total_tokens)

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Async Usage

from ai21.tokenizers import get_async_tokenizer

## Your async function code
#...
tokenizer = await get_async_tokenizer(name="jamba-tokenizer")
total_tokens = await tokenizer.count_tokens(text="some text")  # returns int
print(total_tokens)
Available tokenizers are:
  • jamba-tokenizer
  • j2-tokenizer
For more information on AI21 Tokenizers, see the documentation.

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Environment Variables

You can set several environment variables to configure the client.

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Logging

We use the standard library logging module. To enable logging, set the AI21_LOG_LEVEL environment variable. 
$ export AI21_LOG_LEVEL=debug

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Other Important Environment Variables

  • AI21_API_KEY - Your API key. If not set, you must pass it to the client constructor.
  • AI21_API_VERSION - The API version. Defaults to v1.
  • AI21_API_HOST - The API host. Defaults to https://api.ai21.com/studio/v1/.
  • AI21_TIMEOUT_SEC - The timeout for API requests.
  • AI21_NUM_RETRIES - The maximum number of retries for API requests. Defaults to 3 retries.
  • AI21_AWS_REGION - The AWS region to use for AWS clients. Defaults to us-east-1.

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Error Handling

from ai21 import errors as ai21_errors
from ai21 import AI21Client, AI21APIError
from ai21.models import ChatMessage

client = AI21Client()

system = "You're a support engineer in a SaaS company"
messages = [
        # Notice the given role does not exist and will be the reason for the raised error
        ChatMessage(text="Hello, I need help with a signup process.", role="Non-Existent-Role"),
    ]

try:
    chat_completion = client.chat.create(
        messages=messages,
        model="j2-ultra",
        system=system
    )
except ai21_errors.AI21ServerError as e:
    print("Server error and could not be reached")
    print(e.details)
except ai21_errors.TooManyRequestsError as e:
    print("A 429 status code was returned. Slow down on the requests")
except AI21APIError as e:
    print("A non 200 status code error. For more error types see ai21.errors")

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Cloud Providers

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AWS

AI21 Library provides convenient ways to interact with two AWS clients for use with AWS Bedrock and AWS SageMaker.

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Installation

pip install -U "ai21[AWS]"
This will make sure you have the required dependencies installed, including boto3 >= 1.28.82.

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Usage

Bedrock
from ai21 import AI21BedrockClient, BedrockModelID
from ai21.models.chat import ChatMessage

client = AI21BedrockClient(region='us-east-1') # region is optional, as you can use the env variable instead

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

response = client.chat.completions.create(
    messages=messages,
    model_id=BedrockModelID.JAMBA_1_5_LARGE,
)
Stream
from ai21 import AI21BedrockClient, BedrockModelID
from ai21.models.chat import ChatMessage

system = "You're a support engineer in a SaaS company"
messages = [
    ChatMessage(content=system, role="system"),
    ChatMessage(content="Hello, I need help with a signup process.", role="user"),
    ChatMessage(content="Hi Alice, I can help you with that. What seems to be the problem?", role="assistant"),
    ChatMessage(content="I am having trouble signing up for your product with my Google account.", role="user"),
]

client = AI21BedrockClient()

response = client.chat.completions.create(
    messages=messages,
    model=BedrockModelID.JAMBA_1_5_LARGE,
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].message.content, end="")
Async
import asyncio
from ai21 import AsyncAI21BedrockClient, BedrockModelID
from ai21.models.chat import ChatMessage

client = AsyncAI21BedrockClient(region='us-east-1') # region is optional, as you can use the env variable instead

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

async def main():
    response = await client.chat.completions.create(
        messages=messages,
        model_id=BedrockModelID.JAMBA_1_5_LARGE,
    )


asyncio.run(main())

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With Boto3 Session

import boto3

from ai21 import AI21BedrockClient, BedrockModelID
from ai21.models.chat import ChatMessage

boto_session = boto3.Session(region_name="us-east-1")

client = AI21BedrockClient(session=boto_session)

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

response = client.chat.completions.create(
    messages=messages,
    model_id=BedrockModelID.JAMBA_1_5_LARGE,
)

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Async

import boto3
import asyncio

from ai21 import AsyncAI21BedrockClient, BedrockModelID
from ai21.models.chat import ChatMessage

boto_session = boto3.Session(region_name="us-east-1")

client = AsyncAI21BedrockClient(session=boto_session)

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

async def main():
  response = await client.chat.completions.create(
      messages=messages,
      model_id=BedrockModelID.JAMBA_1_5_LARGE,
  )

asyncio.run(main())

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SageMaker

from ai21 import AI21SageMakerClient

client = AI21SageMakerClient(endpoint_name="j2-endpoint-name")
response = client.summarize.create(
    source="Text to summarize",
    source_type="TEXT",
)
print(response.summary)
Async
import asyncio
from ai21 import AsyncAI21SageMakerClient

client = AsyncAI21SageMakerClient(endpoint_name="j2-endpoint-name")

async def main():
  response = await client.summarize.create(
      source="Text to summarize",
      source_type="TEXT",
  )
  print(response.summary)

asyncio.run(main())

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With Boto3 Session

from ai21 import AI21SageMakerClient
import boto3
boto_session = boto3.Session(region_name="us-east-1")

client = AI21SageMakerClient(
    session=boto_session,
    endpoint_name="j2-endpoint-name",
)

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Azure

If you wish to interact with your Azure endpoint on Azure AI Studio, use the AI21AzureClient and AsyncAI21AzureClient clients. The following models are supported on Azure:
  • jamba-large
from ai21 import AI21AzureClient
from ai21.models.chat import ChatMessage

client = AI21AzureClient(
  base_url="https://<YOUR-ENDPOINT>.inference.ai.azure.com",
  api_key="<your Azure API key>",
)

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

response = client.chat.completions.create(
  model="jamba-mini-1.6-2025-03",
  messages=messages,
)
Async
import asyncio
from ai21 import AsyncAI21AzureClient
from ai21.models.chat import ChatMessage

client = AsyncAI21AzureClient(
  base_url="https://<YOUR-ENDPOINT>.inference.ai.azure.com/v1/chat/completions",
  api_key="<your Azure api key>",
)

messages = [
  ChatMessage(content="You are a helpful assistant", role="system"),
  ChatMessage(content="What is the meaning of life?", role="user")
]

async def main():
  response = await client.chat.completions.create(
    model="jamba-large",
    messages=messages,
  )

asyncio.run(main())

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Vertex

If you wish to interact with your Vertex AI endpoint on GCP, use the AI21VertexClient and AsyncAI21VertexClient clients. The following models are supported on Vertex:
  • jamba-1.5-mini
  • jamba-1.5-large
from ai21 import AI21VertexClient

from ai21.models.chat import ChatMessage

# You can also set the project_id, region, access_token and Google credentials in the constructor
client = AI21VertexClient()

messages = ChatMessage(content="What is the meaning of life?", role="user")

response = client.chat.completions.create(
    model="jamba-1.5-mini",
    messages=[messages],
)
Async
import asyncio

from ai21 import AsyncAI21VertexClient
from ai21.models.chat import ChatMessage

# You can also set the project_id, region, access_token and Google credentials in the constructor
client = AsyncAI21VertexClient()


async def main():
    messages = ChatMessage(content="What is the meaning of life?", role="user")

    response = await client.chat.completions.create(
        model="jamba-1.5-mini",
        messages=[messages],
    )

asyncio.run(main())
Happy prompting! 🚀