llmloader 0.1.9

Loads a Langchain LLM by model name as a string.

Loads a Langchain LLM by model name as a string.

Installation

pip install llmloader

Or install from GitHub directly:

pip install git+https://github.com/rbturnbull/llmloader.git

Usage

Load the LLM with the llmloader.load function. e.g.

import llmloader

llm = llmloader.load("gpt-4o")
result = llm.invoke("Write me a haiku about love")

llm = llmloader.load("claude-3-5-sonnet-20240620")
result = llm.invoke("Write me a haiku about love")

llm = llmloader.load("grok-4-latest")
result = llm.invoke("Write me a haiku about love")

llm = llmloader.load("mistral-small-latest")
result = llm.invoke("Write me a haiku about love")

llm = llmloader.load("meta-llama/Llama-3.3-70B-Instruct")
result = llm.invoke("Write me a haiku about love")

CLI

You can test out prompts and models on the command line. Make sure you have your API keys set in your environment or add the key with the --api-key flag.

llmloader "Write me a haiku about love" --model gpt-5-mini
llmloader "Write me a haiku about love" --model gpt-5.2
llmloader "Write me a haiku about love" --model claude-sonnet-4-5-20250929
llmloader "Write me a haiku about love" --model grok-4-latest
llmloader "Write me a haiku about love" --model mistral-small-latest
llmloader "Write me a haiku about love" --model gemini-3-pro-preview
# Using OpenRouter
llmloader "Write me a haiku about love" --model openai/gpt-5-mini
# Local deployment models
llmloader "Write me a haiku about love" --model meta-llama/Meta-Llama-3-8B-Instruct
llmloader "Write me a haiku about love" --model meta-llama/Llama-3.3-70B-Instruct
llmloader --help

Environment Variables

You can pass an API key for the model provider using the command line flag --api-key, kwarg api_key=..., or by setting the appropriate environment variable as described below.

Model Provider	Environment Variable
OpenAI	OPENAI_API_KEY
Anthropic	ANTHROPIC_API_KEY
Mistral	MISTRAL_API_KEY
XAI	XAI_API_KEY
OpenRouter	OPENROUTER_API_KEY
Google	GOOGLE_API_KEY

Azure

To use custom models deployed with Azure OpenAI, you need to set the following environment variables:

• CUSTOM_API_KEY: Your Azure API key.

• CUSTOM_ENDPOINT: The endpoint URL for your Azure AI service.

--model should match the deployment name in your Azure AI resource.

Note:

• If llmloader detects the OPENAI_API_KEY environment variable, it will use the OpenAI API by default if a valid model name is provided and CUSTOM_ENDPOINT is not set.

• If both CUSTOM_API_KEY and CUSTOM_ENDPOINT are set, llmloader will use the Azure service.

• CUSTOM_ENDPOINT should be the URL ending with /models, e.g. https://your-resource-name.openai.azure.com/models

Testing

Endpoint Manual Testing

test_manual.py contains tests for models that require API keys. You can run these tests manually after setting the appropriate environment variables.

Once the environment variables are set, you can run the tests with:

pytest -m manual

To specify a particular test, use:

pytest -m manual tests/test_manual.py::test_name

Credit

• Robert Turnbull (Melbourne Data Analytics Platform, University of Melbourne)

• James Quang (Melbourne Data Analytics Platform, University of Melbourne)