Sugo

Documents, reduced. Feed your LLM the sauce, not the water.

🇮🇹 Leggi in italiano

Sugo turns PDFs, Office files, screenshots and images into clean, compact Markdown for RAG systems, AI agents and LLM chats. It removes what only costs tokens (repeated headers and footers, page numbers, hidden text, tracking parameters, app chrome around a screenshot) and keeps the content, the tables and the structure. Everything runs on your computer: no cloud API, no telemetry, nothing sent anywhere.

In Italian, il sugo del discorso means "the gist", and a sugo is a sauce you reduce until only the concentrated flavour remains. That is what Sugo does to your documents.

What it does

A three-page PDF, as a plain text extractor gives it to your model (pages 1 and 3 look the same):

ACME S.p.A. - Annual Report 2025                Confidential
3.2 Revenue
Revenue grew by 12% in the second half, driven by the export market.
Details: https://acme.example/report?utm_source=newsletter&utm_medium=email
Page 14 of 120

What sugo report.pdf prints:

ACME S.p.A. - Annual Report 2025

# 3.1 Costs

Costs fell by 3% thanks to the new suppliers.
Details: https://acme.example/report

# 3.2 Revenue

Revenue grew by 12% in the second half, driven by the export market.
Details: https://acme.example/report

# 3.3 Outlook

The board expects stable growth next year.
Details: https://acme.example/report

The header is kept once instead of on every page; the page numbers, the repeated "Confidential" and the tracking parameters are gone; the text is the same. sugo stats measures the difference: 180 → 97 tokens, 46% saved on this small example. Every removal is recorded with the cleaner that made it and the reason, so you can always check what was dropped. Text that cannot be read is marked as illegible, never guessed.

The same holds across a benchmark corpus in five languages, with no content lost on any document:

And it stays fast on an ordinary computer, not just a new one: on a 2010 desktop with 2 cores and no GPU, a 100-page PDF converts in 3.6 s and a phone screenshot in 13.7 s (full numbers in TESTING).

How it compares

Measured against two well-known alternatives, on the same 31-document benchmark corpus, with the same tokenizer and the same ground truth — full method, raw numbers and limits in docs/comparisons:

A tool that drops content also "saves" tokens on paper — that's data loss, not compression. Every number above is paired with what actually survived the conversion.

Status

Alpha. The first release, v0.1, is being finished: everything below works today from a source install, or from an early manual alpha already on PyPI. The signed, stable v0.1 release (full supply-chain pipeline: SBOM, Sigstore attestations, PyPI Trusted Publishing) is still being prepared.

See the roadmap for the full plan.

Quick start

Sugo needs Python 3.12 and uv.

uv tool install git+https://github.com/Muralnick/sugo

An early alpha is also on PyPI, published by hand ahead of the signed v0.1 release, under the name sugo-cli (sugo itself is already registered on PyPI by an unrelated, empty project — a name transfer request is open):

uv tool install --prerelease allow "sugo-cli[lite]"

The installed command is sugo either way.

Convert a file and print its Markdown:

sugo report.pdf

More things you can do:

sugo convert *.pdf -o out/            # one NAME.md per file, in out/
sugo convert report.pdf --chunks      # chunks as JSON Lines, ready for RAG
sugo stats screenshot.png             # tokens saved, per LLM provider
sugo clip                             # convert the screenshot in your clipboard
sugo ui                               # open the local web page: drop a file, get Markdown
sugo doctor                           # check the installation

Add --json for output a script can read.

Use it from an AI agent (MCP)

Sugo can serve agents such as Claude Code or Codex over MCP. It reads only inside the folders you name:

claude mcp add sugo -- sugo mcp --root ~/Documents

The agent gets two tools: convert_document and estimate_savings.

On an old computer

Measured on 2026-11-03 on a 2010 desktop: Intel Core i5-650 (2 cores, 4 threads, no AVX), 8 GB RAM, SSD, no usable GPU. Weaker than the 8 GB, 4-core machine Sugo targets.

Native documents run well within the targets even on this machine. OCR is accurate but slow on a CPU without AVX; making it faster is on the roadmap. Details and scripts are in TESTING.

Supported files

Old .doc, .xls and .ppt files and password-protected files are not read: save a new copy first.

How it works

flowchart LR
    F[Your file] --> E[Reader<br/>PDF · Office · OCR · XML]
    E --> IR[Document → Page → Block]
    IR --> C[Cleaners<br/>propose removals]
    C --> K[Core<br/>applies and records them]
    K --> M[Markdown]
    K --> J[JSONL chunks]
    K --> S[Token metrics]

Each reader turns a file into the same structure of pages and blocks. Cleaners only propose changes; the core applies them and marks each removed block with who removed it and why, instead of deleting it. That is what makes the output checkable and reproducible. The full design is in ARCHITECTURE.

Promises

These are written down in PRINCIPLES and checked by tests:

• Local by principle. Conversion never needs the internet. No cloud API, no telemetry.
• No guessing. Unreadable text is marked as illegible, never filled in.
• Nothing silently lost. Every removal is recorded, with its reason.
• Honest metrics. Savings are measured, and Sugo also tells you when sending the original image would be cheaper.
• Safe with untrusted files. Size and time limits, XML parsed safely, models verified by checksum. See the threat model.

Documentation

Contributing and security

See CONTRIBUTING. Please report vulnerabilities privately, as described in SECURITY, not in public issues.

License

AGPL-3.0-or-later (see ADR-010).