overllm 0.1.3

Catch the LLM/AI calls you didn't need. A fast, deterministic linter that flags LLM API calls where plain code is simpler, cheaper, and more reliable.

overllm

Catch the LLM/AI calls you didn't need.

overllm is a small, fast linter with one job: find the places in your code where you call an AI model to do something plain code does better. You called GPT to parse a date. You called a model to extract JSON that json.loads already handles. You are paying latency, money, and nondeterminism for a regex.

It reads your code with Python's own ast module. No model runs, no network, no API key. Same code in, same result out. Fast enough for a pre-commit hook.

Everyone else lints the code the AI wrote. overllm catches where you are paying an AI to do what a library already does.

Install

pip install overllm

Use it

overllm .            # scan the current project
overllm src/         # scan a folder
overllm app.py       # scan one file

Example output:

app.py:42:5 llm-mechanical  LLM call asks the model to sort
    resp = client.chat.completions.create(model="gpt-4o", messages=[...])
    -> use sorted()

app.py:88:1 llm-in-loop  LLM call inside a loop: one API round-trip per iteration
    completion(model="gpt-4o", messages=[{"role": "user", "content": f"tag {x}"}])
    -> batch the inputs into a single call, cache repeated results, or use a function

2 needless LLM calls in 1 file.

overllm exits non-zero when it finds something, so it gates a commit or a CI check. Pass --exit-zero to report without failing.

Rules

Every rule fires only on a concrete code pattern, and every finding names the deterministic replacement. It stays silent when it is not sure.

Rule	Fires when	Suggests
static-prompt	The user prompt is a compile-time constant, no variables. The input is fixed, so the call buys nothing.	precompute or cache the result
llm-extraction	The prompt asks the model to extract an email, URL, date, or number.	a regex, datetime, or urllib.parse
llm-mechanical	The prompt asks for a mechanical transform: sort, reverse, count, sum, deduplicate, change case, base64, arithmetic on literals.	the one-line stdlib equivalent
llm-in-loop	An LLM call runs once per loop iteration (real N calls, not streaming).	batch, cache, or move it out of the loop
prompt-injection	Untrusted web-request input (request.args, request.json, ...) flows straight into the prompt.	keep it in a separate user message, validate it, constrain the model

It detects calls to the OpenAI, Anthropic, Google, Mistral, Cohere, Groq, LangChain, LiteLLM, and Ollama SDKs, and raw HTTP requests to those hosts.

Silence a false positive

resp = client.chat.completions.create(...)  # overllm: ignore
resp = client.chat.completions.create(...)  # overllm: ignore=llm-in-loop

Put # overllm: ignore-file at the top of a file to skip the whole file.

Configure

In pyproject.toml (Python 3.11+):

[tool.overllm]
ignore = ["llm-in-loop"]
exclude = ["examples/", "migrations/"]

Or on the command line: --select, --ignore, --exclude via config, --config PATH.

Pre-commit hook

In .pre-commit-config.yaml:

repos:
  - repo: https://github.com/theadamdanielsson/overllm
    rev: v0.1.1
    hooks:
      - id: overllm

GitHub Action

overllm ships an Action that scans a pull request and leaves one grounded comment. It stays silent when there is nothing to say.

name: overllm
on:
  pull_request:

permissions:
  contents: read
  pull-requests: write

jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: theadamdanielsson/overllm@v1
        with:
          paths: "."

Other output formats

overllm --format json .      # machine-readable
overllm --format sarif .     # upload to GitHub code scanning
overllm --format markdown .  # the PR-comment body

Why not just use an AI code reviewer?

AI reviewers and AI-slop linters look at the code the model produced: comments, dead code, structure. None of them ask the question overllm asks, which is whether you needed the model at all. It is a different axis, and it is one plain static analysis can answer with high precision and zero cost.

License

MIT © Adam Danielsson