llm-requesty 0.1.0

LLM plugin for models hosted by requesty

llm-requesty

LLM plugin for models hosted by Requesty

Installation

First, install the LLM command-line utility.

Now install this plugin in the same environment as LLM.

llm install llm-requesty

Configuration

You will need an API key from Requesty. You can obtain one here.

You can set that as an environment variable called requesty_KEY, or add it to the llm set of saved keys using:

llm keys set requesty

Enter key: <paste key here>

Usage

To list available models, run:

llm models list

You should see a list that looks something like this:

requesty: requesty/deepinfra/meta-llama/Meta-Llama-3.1-405B-Instruct
requesty: requesty/deepinfra/Qwen/Qwen2.5-72B-Instruct
requesty: requesty/deepinfra/meta-llama/Llama-3.3-70B-Instruct
...

In requesty, you need to approve the models you want to use before you can prompt them. You can do this by running: Click on Admin Panel and then user "Add Model" to add the models you want to use.

To run a prompt against a model, pass its full model ID to the -m option, like this:

llm -m requesty/google/gemini-2.5-flash-lite-preview-06-17 "Five spooky names for a pet tarantula"

You can set a shorter alias for a model using the llm aliases command like so:

llm aliases set llama3.3 requesty/deepinfra/meta-llama/Llama-3.3-70B-Instruct

Now you can prompt the model using:

cat llm_requesty.py | llm -m llama3.3 -s 'write some pytest tests for this'

Vision models

Some Requesty models can accept image attachments. Run this command:

llm models --options -q requesty

And look for models that list these attachment types:

  Attachment types:
    application/pdf, image/gif, image/jpeg, image/png, image/webp

You can feed these models images as URLs or file paths, for example:

curl https://static.simonwillison.net/static/2024/pelicans.jpg | llm \
    -m requesty/google/gemini-2.5-pro 'describe this image' -a -

Auto caching

Requesty supports auto caching to improve response times and reduce costs for repeated requests. Enable this feature using the -o cache 1 option:

llm -m requesty/deepinfra/meta-llama/Llama-3.3-70B-Instruct -o cache 1 'explain quantum computing'

Listing models

The llm models -q requesty command will display all available models, or you can use this command to see more detailed information:

llm requesty models

Output starts like this:

- id: deepinfra/meta-llama/Meta-Llama-3.1-405B-Instruct
  name: A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.
  context_length: 130,815
  supports_schema: True
  pricing: input $0.8/M, output $0.8/M

- id: deepinfra/Qwen/Qwen2.5-72B-Instruct
  name: Qwen3, the latest generation in the Qwen large language model series...
  context_length: 131,072
  supports_schema: True
  pricing: input $0.23/M, output $0.4/M

Add --json to get back JSON instead:

llm requesty models --json

Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-requesty
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

llm install -e '.[test]'

To run the tests:

pytest

To update recordings and snapshots, run:

PYTEST_REQUESTY_KEY="$(llm keys get requesty)" \
  pytest --record-mode=rewrite --inline-snapshot=fix