Quell reads your docstrings, Pydantic models, and type annotations, extracts every testable requirement, finds which ones have no test, generates pytest tests via a rule engine, verifies each test through a 5-gate pipeline, and writes only proven tests to disk.

Does Quell require an LLM API key?

The rule engine runs entirely in-process — no source code is ever transmitted. ~75% of edge cases are handled with no network call and no API key. LLM fallback is opt-in and only sends the function signature, never the full body.

What is the 5-gate pipeline?

Every generated test must pass: Gate 1 (AST valid Python), Gate 2 (not already in a test file), Gate 3 (no shell calls or file writes), Gate 4 (passes against original code), Gate 5 (fails when the requirement is violated). Only gate-5-verified tests are written to disk.

What is the Production Readiness Score (PRS)?

PRS = (WRITTEN × 1.0 + SCAFFOLDED × 0.5) / total_requirements × 100. Tiers: 80-100 Production Ready, 60-79 Review Needed, 0-59 Needs Work.

How is Quell different from GitHub Copilot or Qodo for test generation?

Quell reads specifications that already exist in your code — it does not generate tests from scratch. It finds requirements already documented in your docstrings, Pydantic models, and type annotations that have no test. The 5-gate pipeline, especially Gate 5 (violation injection), verifies each test actually catches the bug it claims to catch. This verification step is not present in Copilot, Qodo, or Hypothesis.

Can Quell be used in CI pipelines?

Yes. Run quell ci src/ --threshold 80 to fail CI if PRS falls below 80. Set prs_threshold in pyproject.toml under [tool.quell]. Works with GitHub Actions, GitLab CI, and any system that checks exit codes.

LLM Providers

The rule engine handles ~75% of edge cases with no LLM required. For the remaining complex cases, Quell falls back to an LLM.

No LLM needed for most codebases

If you're seeing rule-engine coverage above 75%, you can skip LLM configuration entirely. Quell will still find and write tests for every case the rule engine handles.

Groq (default, recommended)

Groq provides fast, cheap inference. Sign up at groq.com.

quell auth set GROQ_API_KEY=gsk_...

Or set as environment variable:

export GROQ_API_KEY=gsk_...

Enable in config:

[tool.quell]
enable_llm = true
llm_provider = "groq"

OpenAI-compatible endpoints

Any OpenAI-compatible endpoint works:

export OPENAI_API_KEY=sk-...
export OPENAI_BASE_URL=https://api.openai.com/v1  # or your endpoint

[tool.quell]
enable_llm = true
llm_provider = "openai"

What data is sent to the LLM

Only the source code of the function being tested is sent. No config files, no secrets, no unrelated code. The prompt includes:

The function source code
The requirement description extracted from the docstring
A system prompt instructing the LLM to generate a pytest test

No file paths, no project structure, no environment variables.

Privacy mode

Set enable_llm = false to guarantee nothing leaves your machine:

[tool.quell]
enable_llm = false

In this mode, complex cases become SCAFFOLDED instead of WRITTEN.