---
title: Azure OpenAI
slug: guides/server/providers/azure
---


<Tip> To use Letta with Azure OpenAI, set the environment variables `AZURE_API_KEY` and `AZURE_BASE_URL`. You can also optionally specify `AZURE_API_VERSION` (default is `2024-09-01-preview`) </Tip>

You can use Letta with OpenAI if you have an OpenAI account and API key. Once you have set your `AZURE_API_KEY` and `AZURE_BASE_URL` specified in your environment variables, you can select what model and configure the context window size

Currently, Letta supports the following OpenAI models:
- `gpt-4` (recommended for advanced reasoning)
- `gpt-4o-mini` (recommended for low latency and cost)
- `gpt-4o`
- `gpt-4-turbo` (*not* recommended, should use `gpt-4o-mini` instead)
- `gpt-3.5-turbo` (*not* recommended, should use `gpt-4o-mini` instead)


## Enabling Azure OpenAI models
To enable the Azure provider, set your key as an environment variable:
```bash
export AZURE_API_KEY="..."
export AZURE_BASE_URL="..."

# Optional: specify API version (default is 2024-09-01-preview)
export AZURE_API_VERSION="2024-09-01-preview"
```
Now, Azure OpenAI models will be enabled with you run `letta run` or the letta service.

### Using the `docker run` server with OpenAI
To enable Azure OpenAI models, simply set your `AZURE_API_KEY` and `AZURE_BASE_URL` as an environment variables:
```bash
# replace `~/.letta/.persist/pgdata` with wherever you want to store your agent data
docker run \
  -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
  -p 8283:8283 \
  -e AZURE_API_KEY="your_azure_api_key" \
  -e AZURE_BASE_URL="your_azure_base_url" \
  -e AZURE_API_VERSION="your_azure_api_version" \
  letta/letta:latest
```

<Accordion icon="square-terminal" title="CLI (pypi only)">
### Using `letta run` and `letta server` with Azure OpenAI
To chat with an agent, run:
```bash
export AZURE_API_KEY="..."
export AZURE_BASE_URL="..."
letta run
```
To run the Letta server, run:
```bash
export AZURE_API_KEY="..."
export AZURE_BASE_URL="..."
letta server
```
To select the model used by the server, use the dropdown in the ADE or specify a `LLMConfig` object in the Python SDK.
</Accordion>

## Specifying agent models
When creating agents, you must specify the LLM and embedding models to use via a *handle*. You can additionally specify a context window limit (which must be less than or equal to the maximum size).

```python
from letta_client import Letta

client = Letta(base_url="http://localhost:8283")

azure_agent = client.agents.create(
    model="azure/gpt-4o-mini",
    embedding="azure/text-embedding-3-small",
    # optional configuration
    context_window_limit=16000,
)
```