---
id: litellm
title: LiteLLM
sidebar_label: LiteLLM
---

DeepEval allows you to use any model supported by LiteLLM to run evals, either through the CLI or directly in Python.

:::note
Before getting started, make sure you have LiteLLM installed. It will not be installed automatically with DeepEval, you need to install it separately:

```bash
pip install litellm
```

:::

### Command Line

To configure your LiteLLM model through the CLI, run the following command. You must specify the provider in the model name:

```bash
# OpenAI
deepeval set-litellm --model=openai/gpt-3.5-turbo

# Anthropic
deepeval set-litellm --model=anthropic/claude-3-opus

# Google
deepeval set-litellm --model=google/gemini-pro
```

You can also specify a custom API base:

```bash
deepeval set-litellm \
    --model=openai/gpt-3.5-turbo \
    --base-url="https://your-custom-endpoint.com"
```

:::info
The CLI command above sets LiteLLM as the default provider for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset LiteLLM:

```bash
deepeval unset-litellm
```

:::

:::tip[Persisting settings]
You can persist CLI settings with the optional `--save` flag.
See [Flags and Configs -> Persisting CLI settings](/docs/evaluation-flags-and-configs#persisting-cli-settings-with---save).
:::

### Python

When using LiteLLM in Python, you must always specify the provider in the model name. Here's how to use `LiteLLMModel` from DeepEval's model collection:

<Tabs items={["Python", "ENV"]}>
<Tab value="Python">

```python
from deepeval.models import LiteLLMModel
from deepeval.metrics import AnswerRelevancyMetric

model = LiteLLMModel(
    model="openai/gpt-3.5-turbo",  # Provider must be specified
    api_key="your-api-key",  # optional, can be set via environment variable
    base_url="your-api-base",  # optional, for custom endpoints
    temperature=0
)

answer_relevancy = AnswerRelevancyMetric(model=model)
```

</Tab>
<Tab value="ENV">

To use any LiteLLM model directly in `deepeval`, set the `USE_LITELLM=1` in your `env` and simply pass the name of your desired model in your metric initialization:

```python
from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy = AnswerRelevancyMetric(
    model="openai/gpt-3.5-turbo",
)
```

You should also set the other necessary vars like `LITELLM_API_KEY` to be able to use the LiteLLM models as shown above.

</Tab>
</Tabs>

There are **ZERO** mandatory and **SEVEN** optional parameters when creating a `LiteLLMModel`:

- [Optional] `model`: A string specifying the provider and model name (e.g. `openai/gpt-3.5-turbo`, `anthropic/claude-3-opus`). Defaults to `LITELLM_MODEL_NAME` if not passed; raises an error at runtime if unset.
- [Optional] `api_key`: A string specifying the API key for the model. If not passed, `deepeval` attempts (in order) `LITELLM_API_KEY`, `LITELLM_PROXY_API_KEY`, `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, then `GOOGLE_API_KEY`. If none are set, the key is left unset and the underlying LiteLLM/provider behavior applies.
- [Optional] `base_url`: A string specifying the base URL for the model API. Defaults to `LITELLM_API_BASE`, then `LITELLM_PROXY_API_BASE` if not passed.
- [Optional] `temperature`: A float specifying the model temperature. Defaults to `TEMPERATURE` if not passed; falls back to `0.0` if unset.
- [Optional] `cost_per_input_token`: A float specifying the cost for each input token for the provided model. Defaults to `None` if not passed; when unset, the cost is taken from LiteLLM's response if it reports one, otherwise reported as unknown.
- [Optional] `cost_per_output_token`: A float specifying the cost for each output token for the provided model. Defaults to `None` if not passed; when unset, the cost is taken from LiteLLM's response if it reports one, otherwise reported as unknown.
- [Optional] `generation_kwargs`: A dictionary of additional generation parameters forwarded to LiteLLM's `completion(...)` / `acompletion(...)` call.

:::tip
Any `**kwargs` you would like to use for your model can be passed through the `generation_kwargs` parameter. However, we request you to double check the params supported by the model and your model provider in their [official docs](https://docs.litellm.ai/docs/providers/custom_llm_server).
:::

### Available Models

:::note
This list only displays some of the available models. For a complete list of supported models and their capabilities, refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/providers).
:::

Below is a list of commonly used models (always prefix the model with its provider):

#### OpenAI Models

- `openai/gpt-3.5-turbo`
- `openai/gpt-4`
- `openai/gpt-4-turbo-preview`

#### Anthropic Models

- `anthropic/claude-3-opus`
- `anthropic/claude-3-sonnet`
- `anthropic/claude-3-haiku`

#### Google Models

- `google/gemini-pro`
- `google/gemini-ultra`

#### Mistral Models

- `mistral/mistral-small`
- `mistral/mistral-medium`
- `mistral/mistral-large`

#### LM Studio Models

- `lm-studio/Meta-Llama-3.1-8B-Instruct-GGUF`
- `lm-studio/Mistral-7B-Instruct-v0.2-GGUF`
- `lm-studio/Phi-2-GGUF`

#### Ollama Models

- `ollama/llama2`
- `ollama/mistral`
- `ollama/codellama`
- `ollama/neural-chat`
- `ollama/starling-lm`

:::note
When using LM Studio, you need to specify the API base URL. By default, LM Studio runs on `http://localhost:1234/v1`.

When using Ollama, you need to specify the API base URL. By default, Ollama runs on `http://localhost:11434/v1`.
:::