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@github-actions github-actions released this 20 Jun 23:18
v0.1.0
2f14656

frugon v0.1.0

Your data never leaves your machine. Your keys go straight to your own providers. Nothing reaches us.

Free, local, open-source LLM cost analyzer. Point it at your real call logs and see — on your machine — how much you'd save by switching or routing models.

Install

# One-shot (no install)
uvx frugon analyze ./logs.jsonl

# Permanent install
pipx install frugon
frugon analyze --demo            # bundled sample log, see it work in 5 seconds
frugon analyze ./logs.jsonl      # your real logs

Cross-platform: macOS, Linux, Windows · Python 3.10 / 3.11 / 3.12 / 3.13.

What's in this release

frugon analyze — read OpenAI-compatible JSONL logs and produce a cost analysis. Counts tokens with tokencost, prices with LiteLLM's registry, picks a cheaper-than-baseline candidate model, and tells you the dollar saving. Cross-platform, fully local, no LLM calls, no network. Honest savings anchored to RouteLLM/LMSYS quality bands — we never inflate the number.

frugon capture — passive OpenAI-compatible logger. Point your app's base URL at http://127.0.0.1:8787 for a day; it records every call to a local JSONL file in the canonical shape and forwards the request unchanged to the real upstream. No data goes anywhere but your local file and your existing upstream.

frugon pricing update — refresh the bundled pricing table from the LiteLLM model_prices_and_context_window.json registry. Atomic write, JSON shape validation, weekly GitHub Actions sync.

frugon models [QUERY] — list the model names frugon can price (the exact names --candidates accepts), optionally filtered by a case-insensitive substring. Pure local read — no network.

frugon quality update — refresh the bundled quality-tier table from the LMArena leaderboard so the Strong / Capable tier labels stay current. Tiers are self-recalibrating percentile bands: Elite (top 10%), Strong (10–30%), Capable (30–60%), Efficient (bottom 40%).

frugon analyze --measure (optional [measure] extra) — sample real prompts through candidate models using your own API keys. Calls go straight to your providers (OpenAI / Anthropic / etc.) — never to us. Two tiers: side-by-side diffs (human judge) or LLM-as-judge win/loss/tie tallies.

frugon analyze --report file.html|file.md — shareable single-page report. Self-contained HTML with inline CSS (deep indigo + cyan + silver), or clean Markdown. The viral surface someone shows their boss.

Realistic savings

Anchored to RouteLLM / LMSYS research bands:

Traffic mix Typical saving
General mixed traffic 30–50%
Easy / repetitive (MT-Bench) up to ~85%
Hard tasks (MMLU) ~30%

Your actual number comes from your logs. Frugon shows what the math says for your data.

Privacy guarantees (tested as code, not promised in prose)

  • Cost analysis is fully local. No LLM, no network, no telemetry.
  • capture never sends data anywhere but your configured upstream. Asserted at the socket layer in tests — any future regression that introduces a side-channel HTTP client breaks CI.
  • --measure calls only the user's own providers with the user's own keys. Keys are never logged, never persisted, never sent anywhere but the provider. Asserted by a defense-in-depth fixture patching socket.socket / socket.create_connection / socket.getaddrinfo.
  • The CLI collects nothing. Open source — anyone can verify.

Quality

  • 1,960 tests, 95% overall coverage, 97% on the cost-math triad (cost.py / pricing.py / routing.py).
  • CI green on 3 OS × 4 Python (ubuntu / macos / windows × 3.10 / 3.11 / 3.12 / 3.13).
  • Every change is code-reviewed before merge; cost-math changes get an extra dedicated review pass.
  • ruff + mypy clean.

Keep the savings

This release is the diagnosis. Want it to keep routing automatically and hold the savings? → https://frugon.rodiun.io

Built by Rodiun. MIT licensed.

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