llm-codegen-research 2.2

Useful classes and methods for researching code-generation by LLMs.

llm-codegen-research

about

A collection of methods and classes I repeatedly use when conducting research on LLM code-generation. Covers both prompting various LLMs, and analysing the markdown responses.

installation

Install directly from PyPI, using pip:

pip install llm-codegen-research

usage

First configure environment vairables for the APIs you want to use:

export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...
export TOGETHER_API_KEY=...
export MISTRAL_API_KEY=...
export DEEPSEEK_API_KEY=...

You can get a quick response from an LLM:

from llm_cgr import generate, Markdown

response = generate("Write python code to generate the nth fibonacci number.")

markdown = Markdown(text=response)

Or define a client to generate multiple repsonses, or have a chat interaction:

from llm_cgr import get_llm

# create the llm
llm = get_llm(
    model="gpt-4.1-mini",
    system="You're a really funny comedian.",
)

# get multiple responses and see the difference
responses = llm.generate(
    user="Tell me a joke I haven't heard before!",
    samples=3,
)
print(responses)

# or have a multi-prompt chat interaction
llm.chat(user="Tell me a knock knock joke?")
llm.chat(user="Wait, I'm meant to say who's there!")
print(llm.history)

development

Clone the repository code:

git clone https://github.com/itsluketwist/llm-codegen-research.git

We use uv for project management. Once cloned, create a virtual environment and install uv and the project:

python -m venv .venv

. .venv/bin/activate

pip install uv

uv sync

Use make commands to lint and test:

make lint

make test

Use uv to add new dependencies into the project and uv.lock:

uv add openai