llm‑router-LIB — Python client library#

llm‑router is a lightweight Python client for interacting with the LLM‑Router API. It provides typed request models, convenient service wrappers, and robust error handling so you can focus on building LLM‑driven applications rather than dealing with raw HTTP calls.

Overview#

llm_router_lib is the official Python SDK for the LLM‑Router project https://github.com/radlab-dev-group/llm-router.

It abstracts the HTTP layer behind a small, well‑typed API:

• Typed payloads built with pydantic (e.g., GenerativeConversationModel).
• Service objects that know the endpoint URL and the model class they expect.
• Automatic token handling, request retries, and exponential back‑off.
• Rich exception hierarchy (LLMRouterError, AuthenticationError, RateLimitError, ValidationError).

Features#

Installation#

The library is pure Python and works with Python 3.10+.

```shell script

Create a virtualenv (recommended)#

python -m venv .venv source .venv/bin/activate

Install from the repository (editable mode)#

pip install -e .

If you prefer a regular installation from a wheel or source distribution, use:

```shell script
pip install .

Note – The project relies only on the packages listed in the repository’s requirements.txt (pydantic, requests, etc.), all of which are installed automatically by pip.

Quick start#

from llm_router_lib.client import LLMRouterClient
from llm_router_lib.data_models.builtin_chat import GenerativeConversationModel

# Initialise the client (replace with your own endpoint and token)
client = LLMRouterClient(
    api="https://api.your-llm-router.com",
    token="YOUR_ACCESS_TOKEN"
)

# Build a request payload
payload = GenerativeConversationModel(
    model_name="google/gemma-3-12b-it",
    user_last_statement="Hello, how are you?",
    historical_messages=[{"user": "Hi"}],
    temperature=0.7,
    max_new_tokens=128,
)

# Call the API
response = client.conversation_with_model(payload)

print(response)  # → dict with the model's answer and metadata

Extended conversation#

from llm_router_lib.data_models.builtin_chat import ExtendedGenerativeConversationModel

payload = ExtendedGenerativeConversationModel(
    model_name="google/gemma-3-12b-it",
    user_last_statement="Explain quantum entanglement.",
    system_prompt="Answer as a friendly professor.",
    temperature=0.6,
    max_new_tokens=256,
)

response = client.extended_conversation_with_model(payload)
print(response)

Core concepts#

Client#

LLMRouterClient is the entry point. It handles:

• Base URL normalization.
• Optional bearer token injection.
• Construction of the internal HttpRequester.

All public methods accept either a dict or a pydantic model; models are automatically serialized with .model_dump().

Data models#

Located in llm_router_lib/data_models/.
Key models:

All models inherit from a common _GenerativeOptions base that defines temperature, token limits, language, etc.

Services#

Implemented in llm_router_lib/services/.
Each service extends _BaseConversationService and defines:

• endpoint – the API path (e.g., /api/conversation_with_model).
• model_cls – the Pydantic model class used for validation.

The service’s call() method performs the HTTP POST and returns a parsed JSON dictionary, raising LLMRouterError on malformed responses.

Utilities#

• llm_router_lib/utils/http.py – thin wrapper around requests with retry logic, response validation, and logging.
• Logging is integrated via the standard library logging module; you can inject your own logger when constructing the client.

Error handling#

All exceptions inherit from LLMRouterError, allowing a single except LLMRouterError: clause to catch any SDK‑related problem.

License#

llm_router_lib is released under the MIT License. See the LICENSE file for details.