SDK Quickstart

Use this path if you are integrating LiteLLM directly into application code.

1. Install LiteLLM

pip install litellm

2. Set Provider Credentials

Start with one provider and set its environment variables.

OpenAI: OPENAI_API_KEY
Anthropic: ANTHROPIC_API_KEY
Azure OpenAI: AZURE_API_KEY, AZURE_API_BASE, AZURE_API_VERSION
Bedrock: standard AWS credentials
Vertex AI: VERTEXAI_PROJECT, VERTEXAI_LOCATION

If you have not picked a provider yet, browse all supported providers.

3. Make Your First Call

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-api-key"

response = completion(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
)

print(response.choices[0].message.content)

4. Check The Response

The line below:

print(response.choices[0].message.content)

prints the assistant text, for example:

Hello! I'm doing well, thanks for asking.

If you print the full object with:

print(response)

you will see a Python ModelResponse(...) object. For an OpenAI-backed model, it can look like this:

ModelResponse(
    id='chatcmpl-abc123',
    created=1773782130,
    model='gpt-4o-2024-08-06',
    object='chat.completion',
    system_fingerprint='fp_4ff89bf575',
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="Hello! I'm just a program, but I'm here to help you. How can I assist you today?",
                role='assistant',
                tool_calls=None,
                function_call=None,
                provider_specific_fields={'refusal': None},
                annotations=[]
            ),
            provider_specific_fields={}
        )
    ],
    usage=Usage(
        completion_tokens=21,
        prompt_tokens=13,
        total_tokens=34,
        completion_tokens_details=CompletionTokensDetailsWrapper(...),
        prompt_tokens_details=PromptTokensDetailsWrapper(...)
    ),
    service_tier='default'
)

The same response follows an OpenAI-style shape. Conceptually, it looks like this:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm doing well, thanks for asking."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 12,
    "total_tokens": 25
  }
}

id, created, token counts, and message text will vary by request.

If you call an OpenAI-backed model, you may also see extra fields such as system_fingerprint, service_tier, tool_calls, function_call, annotations, provider_specific_fields, and detailed token usage. For the full output reference, see completion output.

Need more provider examples? See the main Getting Started page.