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SDKs & Libraries

LiteLLM

LiteLLM provides a unified interface to call 100+ LLMs.

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Apertis is supported as a native provider.

Installation

pip install litellm

Environment Setup

Set your Apertis API key:

export APERTIS_API_KEY="sk-your-api-key"

Or in Python:

import os
os.environ["APERTIS_API_KEY"] = "sk-your-api-key"

Get your API key from Settings → API Keys.

Basic Usage

Completion

import os
from litellm import completion

os.environ["APERTIS_API_KEY"] = "sk-your-api-key"

messages = [{"role": "user", "content": "What is the capital of France?"}]

response = completion(
    model="apertis/gpt-5.2",
    messages=messages
)
print(response.choices[0].message.content)

Streaming

from litellm import completion

messages = [{"role": "user", "content": "Write a short poem about AI"}]

response = completion(
    model="apertis/gpt-5.2",
    messages=messages,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Using Different Models

Use compatible models returned by the Apertis model catalog through the apertis/ prefix:

# OpenAI GPT
response = completion(model="apertis/gpt-5.2", messages=messages)

# Anthropic Claude
response = completion(model="apertis/claude-sonnet-4.5", messages=messages)

# Google Gemini
response = completion(model="apertis/gemini-3-flash-preview", messages=messages)

LiteLLM Proxy Configuration

1. Export API Key

export APERTIS_API_KEY="sk-your-api-key"

2. Configure config.yaml

model_list:
  - model_name: gpt-5.2
    litellm_params:
      model: apertis/gpt-5.2
      api_key: os.environ/APERTIS_API_KEY

  - model_name: claude-sonnet
    litellm_params:
      model: apertis/claude-sonnet-4.5
      api_key: os.environ/APERTIS_API_KEY

  - model_name: gemini-flash
    litellm_params:
      model: apertis/gemini-3-flash-preview
      api_key: os.environ/APERTIS_API_KEY

3. Start Proxy

litellm --config config.yaml

Context Compression

Enable context compression via extra headers to reduce token usage for long conversations:

from litellm import completion

response = completion(
    model="apertis/gpt-4.1",
    messages=[
        {"role": "user", "content": "Explain distributed systems"},
        {"role": "assistant", "content": "Distributed systems are..."},
        # ... long conversation history ...
        {"role": "user", "content": "Summarize the key points"},
    ],
    extra_headers={
        "X-Context-Compression": "on",
        "X-Compression-Model": "gpt-4.1-mini",
    },
)

Supported Parameters

All standard OpenAI-compatible parameters are supported:

Parameter Description
messages Chat messages array
model Model ID with apertis/ prefix
stream Enable streaming responses
temperature Sampling temperature (0-2)
top_p Nucleus sampling
max_tokens Maximum response tokens
frequency_penalty Frequency penalty (-2 to 2)
presence_penalty Presence penalty (-2 to 2)
stop Stop sequences
tools Function/tool definitions
tool_choice Tool selection mode
Provider Model ID
OpenAI apertis/gpt-5.2, apertis/gpt-4.1-mini
Anthropic apertis/claude-sonnet-4.5, apertis/claude-haiku-4.5
Google apertis/gemini-3-pro-preview, apertis/gemini-3-flash-preview

For the full list of models, visit Apertis Models.