---
title: "SDK Quickstart"
url: "/docs/learn/sdk_quickstart"
canonical_url: "https://docs.litellm.ai/docs/learn/sdk_quickstart"
type: "docs"
last_updated: "2026-10-06"
summary: "Make your first LiteLLM SDK call, then jump to the right docs for the next feature you need."
related:
  - "/docs/learn"
  - "/docs/learn/gateway_quickstart"
---
# SDK Quickstart

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt

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

## 1. Install LiteLLM

```bash
uv add 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](/docs/providers).

## 3. Make Your First Call

```python
from litellm import completion
import os

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

response = completion(
    model="openai/gpt-5.6-terra",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
)

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

## 4. Check The Response

The line below:

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

prints the assistant text, for example:

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

If you print the full object with:

```python
print(response)
```

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

```python
ModelResponse(
    id='chatcmpl-abc123',
    created=1773782130,
    model='gpt-5.6-terra',
    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:

```json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "gpt-5.6-terra",
  "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](/docs/completion/output).

Need more provider examples? See the main [Getting Started](/docs/#quick-start) page.

## 5. Pick Your Next Step

- [Stream Responses](https://docs.litellm.ai/docs/completion/stream): Receive tokens incrementally with stream=True.
- [Use Tools](https://docs.litellm.ai/docs/completion/function_call): Add function calling in a provider-agnostic way.
- [Return JSON](https://docs.litellm.ai/docs/completion/json_mode): Constrain responses to structured JSON output.
- [Add Routing](https://docs.litellm.ai/docs/routing): Use retries, fallbacks, and load balancing in app code.
- [Choose A Provider](https://docs.litellm.ai/docs/providers): Find provider-specific auth, model naming, and params.

## When To Use Gateway Instead

Use LiteLLM Gateway if you need centralized auth, virtual keys, spend tracking, shared logging, or one OpenAI-compatible endpoint for multiple apps.

[Go to Gateway Quickstart →](/docs/learn/gateway_quickstart)

## Related pages

- [Learn](https://docs.litellm.ai/docs/learn.md)
- [Gateway Quickstart](https://docs.litellm.ai/docs/learn/gateway_quickstart.md)