lmfpd (Python)

Language-model fingerprint detection: score saved replies against the lmfpd reference bank, generate challenge prompts, and run one live detection round against an OpenAI- or Anthropic-compatible API.

Documentation: https://lm.ikale.io/docs/en/sdk

Install and build

pip install lmfpd
uv build                   # from sdk/python; writes the sdist and wheel to dist/

Python 3.10 or newer. Dependencies: httpx (with SOCKS support), zstandard, and numpy.

Loading reference data

lmfpd.load(source=DEFAULT_DATA_URLS, cache=None, ttl=86400.0) is the one entry point.

• Local directory: a single source that is not an http:// or https:// URL is a directory holding unified_bank.json and shared_detector.bin (for example projects/data). It is read as is, with no cache and no network.
• URLs: sources are tried in order: https://lm.ikale.io/data/, then the mainland-China mirror https://cdn.jsdmirror.com/npm/lmfpd-data@latest/. Only bundle.json and the detector are downloaded (about 3.6 MB compressed, 4.5 MB uncompressed). Files are not checksum-verified.
• Cache: one file, cache, defaulting to <OS cache dir>/lmfpd/data.bin ($XDG_CACHE_HOME, ~/Library/Caches, %LOCALAPPDATA%, or ~/.cache). Its modification time is the last check against the sources.
  • With a cache, load() returns it at once. When the last check is ttl seconds old or more (0 checks on every load), a background thread fetches bundle.json and, only if the detector changed, the new detector for the next load. Failures there are ignored.
  • Without a cache, or with a damaged one, load() downloads before returning and raises lmfpd: cannot download reference data from ... when every source fails.

HTTP requests honor HTTPS_PROXY, HTTP_PROXY, ALL_PROXY (including socks5h://), and NO_PROXY.

API

Results are plain dicts with the same keys in every lmfpd SDK; lmfpd.Analysis, Sample, and DetectResult are their TypedDicts. Invalid input raises lmfpd.LmfpdError.

• load(source, cache, ttl) -> Detector: load reference data from URLs (cached) or a local directory.
• Detector.built_at: the data build identifier.
• Detector.analyze(outputs, strict=False) -> Analysis: score one to three {"text", "expected_count"} replies.
• Detector.detect(base_url, api_key, model, *, api="responses", effort="", service_tier="default", stream=True, strict=False, count=3, parallel=3, timeout=120.0) -> DetectResult: send count challenges and score the replies. api is responses, chat_completions, or messages.
• generate_challenges(count=3) -> list[Challenge]: random challenge prompts.
• parse_numbers(text) -> list[int]: the numbers 1 to 355 of the longest run in a reply.
• build_request(config, prompt) -> dict: the URL, headers, and body detect sends for one prompt.
• request_sample(config, challenge) -> Sample: send one challenge and check the reply.

config dicts use the detect keyword names (base_url, api_key, model, api, ...).

import lmfpd

detector = lmfpd.load()
result = detector.detect("https://api.openai.com/v1", "sk-...", "gpt-5")
print(result["analysis"]["prediction"] if result["analysis"] else result["error"])