Synthelion — Universal Token Compressor and Prompt Manager for AI Agents

Synthelion compresses prompts before they reach any AI model — cutting token usage by up to 70%, reducing API costs, and speeding up responses. It works with any agent or framework: Claude Code, OpenAI, LangChain, OpenCode, Cursor, and more.

Supports 50+ languages out of the box. No AI model required. No configuration.

"Why use many tokens when few tokens do trick?" — A caveman (and your wallet).

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Table of contents

• Why Synthelion?
• Privacy & Security — PrivacyGuard
• EnterpriseGuard — outbound data-loss-prevention firewall
• Agent guardrails — per-agent-type policy engine
• AI Compliance Engine
• Synthelion vs other prompt/context-compression tools
• Quick install — one command
• Install (manual)
• Update
• Set up on Claude Code
• Automatic prompt compression — Claude Code hook
• Using Synthelion with all agents
• Integrations (OpenAI, LangChain, Claude/OpenAI adapters, Python API, CLI)
• Local proxy — any agent, any provider
• Web dashboard
• Cluster deployment
• Tools (46 MCP tools)
• Code examples
• Compression levels
• Supported languages
• Troubleshooting
• Optional extras
• Contributing
• Sponsors
• Links

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Why Synthelion?

Every token sent to a model costs money and time. Synthelion removes the words that carry no meaning — articles, prepositions, conjunctions, auxiliary verbs — and reduces inflected words to their base form. The model receives exactly the same information, just without the grammatical packaging.

Strengths:

• Zero ML models by default, zero network calls — the core compression and PrivacyGuard pipelines are purely deterministic heuristics (curated word lists, BM25, TF-IDF, regex + checksum validators). An optional ML-assisted PrivacyGuard tier is available for users who want higher recall on bare sensitive values (see below), but everything stays fully offline and self-contained.
• 50+ languages out of the box, no per-language configuration.
• Content-aware routing — JSON, HTML, git diffs, logs, code, and prose each get a dedicated compression strategy instead of one generic pass; a universal anti-expansion guard means you never get back something bigger than what you sent in.
• Adaptive by design — compression escalates automatically for larger inputs, results are cached by content hash, and repeated tool calls get diffed instead of resent in full.
• Safety-conscious by default — credential-shaped text (API keys, tokens, PEM blocks) is redacted before it's ever persisted to disk; destructive-command text is flagged before compression could obscure it.
• MCP-native — 46 tools, readOnlyHint-annotated where safe for parallel calls, plus first-class OpenAI/LangChain/Claude adapters and a plain Python API.
• Works even where MCP/hooks don't — a local reverse proxy enforces PII masking and compression server-side for any agent that supports a custom API base URL (Cursor, Aider, Codex CLI, Claude Code), with automatic failover across up to 10 backup providers and a circuit breaker, all behind the same firewall that protects the dashboard.
• Ops-ready — a local multi-page dashboard, cluster/master-slave deployment, Docker/Kubernetes manifests, all included, none required.

Beyond every competitor we looked at, we care about security: Synthelion is the only library that handles both security and token optimization end-to-end, in one package — thanks to years of work on the Caveman C# suite, now available here too.

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Privacy & Security — PrivacyGuard

A direct Python port of Caveman.PrivacyGuard (the same C# enterprise PII analyzer used in production Caveman deployments) — not a new, thinner reimplementation, the actual rule set, scoring formula, and ~30 algorithmic checksum validators, unchanged. Same zero-ML philosophy as the rest of Synthelion: every detection is a compiled regex plus, for the categories where it matters, a real checksum algorithm — not just a format guess.

⚠️ Disclaimer: PrivacyGuard is a technical support tool. It does not replace a Data Protection Impact Assessment (DPIA) or the advice of a DPO (Data Protection Officer). GDPR, AI Act, and NIS2 compliance require contextual legal assessment, a documented legal basis, and organizational processes that no library can substitute for. (Same disclaimer as the original Caveman.PrivacyGuard — this is a direct port, so it carries the same limitation.)

• 33 country/region rule sets (27 EU + UK, Switzerland, China, Russia, Ukraine), 51 detection rules: email, phone (E.164), IBAN, credit cards, national tax/ID numbers (Italian CF/P.IVA, French NIR, Spanish NIF, Polish PESEL, German Steuer-ID, UK NINO, Swiss AHV, Chinese ID, Russian INN, and 20+ more), GPS coordinates, vehicle plates, JWTs/API secrets, and more.
• Real checksum validation, not just regex — an IBAN, a Luhn-valid credit card, an Italian Codice Fiscale, a Polish PESEL, etc. are verified algorithmically, so a random 16-digit number doesn't get flagged as a credit card just because it matches the shape.
• Compliance-flag mapping — GDPR, EU AI Act, NIS2, PCI-DSS, and NIST 800-53 flags attached automatically based on what's detected.
• Session-based masking with recoverable placeholders — mask PII with [PG_n] placeholders before it reaches a model, then restore the originals client-side once the response comes back; the model itself never sees the real data.
• Prompt-injection guard — heuristic screening for instruction override, system-prompt exfiltration, role hijack/jailbreak framing, delimiter injection, encoded payloads, and exfiltration coercion, before untrusted text reaches an LLM's context.
• AI transparency notice — a ready-to-display, localized (en/it/de/fr/es) "you're talking to an AI" disclosure, supporting EU AI Act Art.50 obligations (confirm applicability/wording with legal counsel).
• Fully adjustable, on by default — active on the compress hook path from the moment you install Synthelion (so it's not an opt-in a user has to discover), but every piece is a config toggle: turn off masking, the injection guard, or the whole thing entirely with one setting.

from synthelion import PrivacyAnalyzer, PrivacySession

analyzer = PrivacyAnalyzer()
session = PrivacySession()
result = analyzer.analyze(
    "Contact me at mario.rossi@example.it, IBAN IT60X0542811101000000123456",
    session=session, auto_masking=True,
)
print(result.masked_text)
# Contact me at [PG_2], IBAN [PG_1]
print(result.risk_level, result.compliance_flags)

# ... later, once the model's response comes back with the placeholders intact:
print(session.restore(result.masked_text))
# Contact me at mario.rossi@example.it, IBAN IT60X0542811101000000123456

Toggle it in ~/.synthelion/config.json (or the dashboard's Settings → Privacy & Security card):

{
  "privacy": {
    "enabled": true,
    "auto_masking": true,
    "prompt_injection_guard": true,
    "use_ml": false,
    "ml_model": "privacyguardml",
    "ml_min_confidence": 0.6,
    "language": "en",
    "ai_transparency_notice": false
  }
}

Setting "enabled": false restores exactly the pre-1.2.2 behavior — no privacy pre-pass at all.

Optional: ML-assisted PrivacyGuard (higher recall, offline, CPU)

PrivacyGuard includes an opt-in ML-assisted confirmation tier for users who need higher recall on genuinely sensitive bare values — phone numbers, national-ID formats, credit cards — that appear in prose without a surrounding context keyword (e.g. a phone number in a log line, an IBAN in a spreadsheet header).

The core regex + checksum pipeline intentionally trades away recall in favor of zero false positives: a bare 11-digit number in prose is never treated as a PESEL unless the word "pesel" is nearby. The ML tier fills that exact gap: it runs a small BIO-tagging model over the text, and when it detects an entity whose label maps to the right rule family, it confirms the match — while a failed algorithmic checksum still vetoes detection no matter what the model says. ML never introduces false positives; it only recovers true positives the strict context gate trades away.

The model used: PrivacyGuardML — Synthelion's own model; no third-party model is used or supported. Same self-distillation philosophy as SynthelionML (see below): it's trained on synthetic PII values generated from Synthelion's own checksum validators (synthelion/privacy_validators.py) and rule patterns (synthelion/privacy_rules.yaml), covering every PrivacyGuard category — checksum-validated (phone, email, IBAN, credit card, national/tax IDs, SSNs) and context-only (vehicle plates, badge IDs, business IDs, credentials, social handles, legal case numbers, booking references, minor-data indicators) — inserted into real multilingual sentences. Tiny transformer encoder (~5M params), CPU-only, ships inside the wheel — no network call, no external dependency, ever. Train your own checkpoint with python devtools/train_privacyguardml.py.

# Show what's installed and where
synthelion models status

Then enable it in ~/.synthelion/config.json (or toggle in the dashboard Settings → Privacy & Security):

{
  "privacy": {
    "use_ml": true,
    "ml_model": "privacyguardml",
    "ml_min_confidence": 0.6
  }
}

Or use the flag from the CLI:

synthelion compress --text "your text" --privacy-ml

The ML tier uses CPU only (no GPU required) and is gated behind the use_ml: false default — the standard regex+checksum pipeline stays zero-ML unless you explicitly opt in. The model's entity labels are strictly mapped to rule categories: a "national identification number" label from the model confirms PESEL/BSN/OIB etc., but never creates a false phone detection, and vice versa.

Optional: SynthelionML — learned prompt compression (offline, CPU)

SynthelionML is a learned complement to the rule-based compressor: instead of a second hand-written pruning filter, a small transformer encoder is trained to predict per token whether it can be dropped while preserving meaning, then generalises beyond any explicit rule list.

• Architecture: a compact, CPU-friendly transformer encoder (~5 M params) — character-n-gram hashing → word/feature embedding → positional encoding → 2-layer encoder (d=128, h=4) → per-token keep/drop logits. Deliberately small: hot-loads in milliseconds, runs entirely on CPU, ships ~41 MB inside the package.
• Training: Wikipedia-derived training signals in the language itself; the AGGRESSIVE rule compressor (blended with the global IDF table) produces ground-truth keep/drop labels and the encoder learns when the rules matter by attending to surrounding context (self-distillation — no external labelling, no network API, fully reproducible). Supported languages today: all European languages (Albanian, Belarusian, Basque, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, Estonian, Finnish, Galician, Greek, Hungarian, Icelandic, Irish, Latin, Latvian, Lithuanian, Macedonian, Norwegian, Polish, Portuguese, Romanian, Serbian, Slovak, Slovenian, Swedish) plus English, Italian, German, French, Spanish, Russian, Ukrainian, Hindi, Chinese and Japanese — 39 languages total. A shared 70k word vocabulary keeps the checkpoint at ~41 MB (well under the 50 MB hard cap); out-of-vocabulary words fall back to deterministic char-n-gram hashing.
• Behaviour: punctuation, numbers, URLs, proper nouns (including city/place names via capitalisation + curated YAML lists) and negation are always kept; a safety floor keeps at least one word per sentence. A built-in min-compression ratio controller (default 70%) drops the lowest-scoring words by rank until the target is reached — measured across all 39 languages: 63.6–71.4% on short one-sentence samples.
• Graceful fallback: if the checkpoint or torch is absent, the level silently degrades to syntactic — no error, no empty output.

Enable it as a compression level:

synthelion compress --text "…" --level synthelionml
synthelion models status        # shows the checkpoint + torch availability

Or through the Python API / config "compression": {"level": "synthelionml"}. The checkpoint resolves from SYNTHELION_ML_MODEL (explicit dir), then SYNTHELION_ML_MODELS_DIR/<name>, then ~/.synthelion/ml_models/, then the package-local synthelion/ml_models/.

Recommended pairing: default levels (semantic/aggressive) when you want deterministic savings; synthelionml when a model should decide what's truly disposable — targets ≥70% compression and the text is in one of the 39 supported languages.

Before / After

English prose — 20 tokens → 9 tokens (−55%)

Before: I would like to know if it is possible to receive information about
        cheap restaurants in Rome, please.

After:  like know possible receive information about cheap restaurant Rome

Italian prose — 16 tokens → 9 tokens (−44%)

Before: Vorrei sapere se è possibile ricevere informazioni sui ristoranti
        economici a Roma, per favore.

After:  sapere è possibile ricevere informazione ristorante economico Roma favore

JSON array — 256 tokens → 80 tokens (−69%)

// Before: full JSON with repeated keys on every object
[{"name":"Alice","age":30,"city":"Rome"},{"name":"Bob","age":25,"city":"Milan"},…]

// After: lossless markdown table
| name  | age | city  |
| ----- | --- | ----- |
| Alice | 30  | Rome  |
| Bob   | 25  | Milan |

HTML page — 192 tokens → 58 tokens (−70%)

// Before: full HTML with tags, attributes, scripts
<html><head>…</head><body><div class="…"><p>Visit Rome today…</p></div></body></html>

// After: clean extracted text, then NLP-compressed
Visit Rome today enjoy ancient history food culture

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Benchmark — token savings by content type

Measured with synthelion's own token counter against real inputs — reproduce with synthelion bench --json (content router) or CompressionService.compress directly (NLP-only, per-level).

NLP compression (prose, per level)

Content	Original tokens	Light	Semantic	Aggressive	SynthelionML
Prose EN	14	−55.0%	−55.0%	−75.0%	−71.4%
Prose IT	15	−43.8%	−43.8%	−62.5%	−66.7%
Prose DE	14	−47.4%	−47.4%	−63.2%	−71.4%
Prose FR	15	−38.9%	−38.9%	−55.6%	−66.7%
Prose ES	15	−47.1%	−47.1%	−52.9%	−66.7%
Prose RU	10	-	-	-	−70.0%
Prose ZH	23	-	-	-	−69.6%
Prose JA	33	-	-	-	−69.7%
Prose UK	10	-	-	-	−70.0%
Prose HI	18	-	-	-	−66.7%

SynthelionML targets a minimum 70% compression (measured: 63.6–71.4% on short one-sentence samples across all 39 languages; EN sample 14→4 tokens = 71.4%). The tiny shortfall below 70% on very short sentences is intentional — negation, proper nouns, numbers, URLs and a per-sentence safety floor are always-kept, and on a 10-token sentence that protected set can exceed the remaining keep-budget. The longer the input, the closer to (and beyond) the 70% floor it lands.

Content router (synthelion bench --json, auto-selects the best strategy)

Content	Original	After	Saved
Plain text EN	196	105	−46.4%
Plain text IT	200	131	−34.5%
JSON array (20 rows)	609	326	−46.5%
Git diff (2 files)	328	198	−39.6%
Python code	273	152	−44.3%
Log / stacktrace (retry burst)	1,666	102	−93.9%
HTML page	242	63	−74.0%
Tool-schema JSON (new in 1.2.2 — ToolSignature)	315	106	−66.3%
Nested JSON object (new in 1.2.2 — ChainCollapse)	32	24	−25.0%

Larger, more realistic payloads compress further than tiny samples — the log benchmark above is a 20-repeat retry burst (a real flaky-dependency scenario), where LogCompressor's dedup collapses near-identical stack traces down to one occurrence plus a counter.

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Global IDF-aware compression & summarization (56 languages)

synthelion/global_idf_provider.py ships a precomputed, per-language global document-frequency table (built offline from Wikipedia, devtools/build_idf_corpus_wikipedia.ps1, ~127 MB total across 56 languages in synthelion/worddata/) and blends it 50/50 with the existing local (single- prompt) IDF wherever Synthelion scores word/sentence importance statistically. Wired into production as of this writing: the compress command's statistical level (synthelion/core.py's _filter_statistical) and the summarize command's tfidf algorithm (TfIdfSummarizer) — both the CLI, the MCP/OpenAI-function tools, and the local reverse proxy (synthelion serve-proxy) now construct these with global_idf=GlobalIdfProvider() by default. The Claude Code hook goes through the same compress CLI path, so it benefits automatically. Other compression levels (light/semantic/aggressive/ syntactic) are pure token-filter/lemma logic with no frequency scoring, so global IDF doesn't apply to them.

Compression level statistical — extra tokens dropped, global vs local-only IDF (15 real Wikipedia paragraphs per language, same content both runs):

Lang	Local-only	+Global	Δ	Lang	Local-only	+Global	Δ	Lang	Local-only	+Global	Δ
af	45.5%	58.6%	+13.2pp	he	32.4%	61.7%	+29.4pp	pl	43.2%	63.0%	+19.7pp
ar	15.7%	42.4%	+26.7pp	hi	46.9%	48.3%	+1.4pp	pt	40.0%	62.4%	+22.4pp
be	36.7%	57.1%	+20.4pp	hr	41.2%	61.8%	+20.6pp	ro	41.7%	58.5%	+16.9pp
bg	30.6%	48.2%	+17.5pp	hu	42.1%	64.6%	+22.5pp	ru	20.8%	46.1%	+25.4pp
bn	37.4%	39.2%	+1.8pp	hy	22.0%	45.5%	+23.6pp	sk	40.7%	60.7%	+20.1pp
ca	49.8%	63.9%	+14.2pp	id	51.6%	65.8%	+14.3pp	sl	45.7%	65.2%	+19.4pp
cs	37.1%	58.0%	+21.0pp	is	38.7%	58.8%	+20.1pp	sq	32.0%	54.6%	+22.6pp
da	47.4%	62.8%	+15.4pp	it	47.7%	65.3%	+17.6pp	sr	27.4%	49.7%	+22.3pp
de	43.7%	64.3%	+20.5pp	ja	43.7%	48.5%	+4.7pp	sv	48.4%	65.7%	+17.3pp
el	15.1%	46.9%	+31.8pp	kk	28.4%	53.3%	+24.9pp	ta	28.0%	31.3%	+3.3pp
en	50.5%	67.3%	+16.8pp	kn	28.5%	29.7%	+1.2pp	te	26.3%	27.2%	+0.9pp
es	50.9%	66.7%	+15.7pp	ko	19.8%	49.8%	+30.0pp	th	14.3%	19.6%	+5.3pp
et	37.0%	59.7%	+22.7pp	la	46.7%	60.5%	+13.8pp	tr	38.3%	49.0%	+10.7pp
eu	46.5%	66.3%	+19.8pp	lt	38.6%	58.2%	+19.6pp	uk	36.5%	59.1%	+22.6pp
fa	14.4%	42.3%	+28.0pp	lv	35.0%	60.4%	+25.4pp	ur	28.4%	52.5%	+24.1pp
fi	35.8%	49.0%	+13.2pp	mk	44.9%	64.5%	+19.6pp	vi	32.5%	49.2%	+16.7pp
fr	42.4%	60.6%	+18.3pp	mr	34.9%	38.2%	+3.3pp	zh	38.9%	40.6%	+1.7pp
ga	46.7%	61.2%	+14.5pp	ms	48.2%	58.8%	+10.6pp
gl	48.9%	69.6%	+20.7pp	nl	43.7%	62.7%	+18.9pp
no	46.3%	57.4%	+11.1pp

Aggregate: 37.4% → 54.5% average tokens dropped (+17.1pp) — the global reference consistently lets statistical recognize more true filler/generic words per language than a single prompt's own local frequencies can, without any empty-output regressions (0 empty results across all 56 × 15 samples — the existing per-sentence safety floor holds). Larger gains cluster in languages whose local-only baseline was weakest (el, fa, ar, ko, he, ru all gained 25pp+) — global grounding helps most exactly where a single short prompt has too little internal repetition for local IDF to work well.

Quality check (not just a shorter output — still on-topic): spot-checked English/Italian/French/Spanish/German samples by reading them directly. Example (English, ratio unrelated — statistical keeps content words, not full sentences):

local-only: "trace anarchist idea throughout history modern anarchism emerge Enlightenment latter half 19th decade 20th century anarchist movement flourish parts significant role worker struggle..." +global: "trace anarchist anarchism emerge Enlightenment 19th decade 20th century anarchist movement flourish worker struggle forget emancipation anarchist anarchists Paris Commune Russian Civil War Spanish Civil War..."

The global-blended version drops generic connective filler (half, latter, parts) that the local-only run kept, while holding on to the repeated domain terms (anarchist/anarchism/movement) — a real, correctly-directed quality shift, not just more aggressive truncation.

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TfIdfSummarizer (extractive summarize, tfidf algorithm)

Since the sentence budget (ratio/sentence_count) is fixed regardless of scoring, output length barely moves — the metric that actually shows the global table's effect is which sentences get selected. Measured on 12 real Wikipedia articles per language (ratio 0.3), comparing local-only vs local+global blended scoring:

Lang	Selected-sentence overlap	Lang	Selected-sentence overlap	Lang	Selected-sentence overlap
af	64.1%	he	57.3%	pl	58.5%
ar	79.1%	hi	83.3%	pt	78.6%
be	62.9%	hr	67.5%	ro	66.9%
bg	73.2%	hu	72.8%	ru	64.0%
bn	100.0%*	hy	100.0%*	sk	60.0%
ca	79.9%	id	70.3%	sl	67.1%
cs	68.0%	is	70.9%	sq	74.7%
da	63.9%	it	70.3%	sr	73.5%
de	100.0%*	ja	97.8%	sv	80.4%
el	80.5%	kk	69.2%	ta	68.3%
en	62.8%	kn	57.7%	te	55.2%
es	73.3%	ko	61.1%	th	83.3%
et	56.9%	la	65.2%	tr	74.7%
eu	66.5%	lt	71.9%	uk	72.2%
fa	69.8%	lv	66.5%	ur	100.0%*
fi	70.5%	mk	77.2%	vi	61.1%
fr	55.4%	mr	68.7%	zh	83.1%
ga	70.3%	ms	64.5%
gl	75.3%	nl	53.7%
no	58.0%

Aggregate: 71.4% average sentence overlap across all 56 languages — meaning on average ~29% of selected sentences change when the global table is blended in, i.e. the global signal has a real, non-trivial effect on which content gets kept. * — af/bn/de/hy/ur showed 0% change on this specific sample because the sample documents were short enough to fall below the sentence-budget threshold (both variants returned the full text unchanged), not because the global table has no effect for those languages.

Per-language .idf.br tables live in synthelion/worddata/ (built from each language's Wikipedia pages-articles-multistream1 shard, ~127 MB total across all 56 languages, shipped in the base package).

aggressive compression level — also global-IDF-aware (56 languages)

_filter_aggressive now additionally drops a word when the global table says it appears in more than 50% of the entire reference corpus's documents for that language — i.e. it's functionally generic even though the hand-curated fw/generic word lists missed it (those lists can't be exhaustive). The threshold is deliberately conservative: a past incident (see _EN_ADV/_EN_ADJ history in core.py) already showed that being too aggressive about "this is just filler" silently drops real content (a sentence's main verb, or a domain-specifying adjective like "financial" in "quarterly financial report"). light/semantic/syntactic are untouched — semantic in particular is meant to stay closest to lossless, and syntactic's grammatical-glue logic doesn't have an equivalent single-word drop point to extend safely.

Lang	Local-only	+Global	Δ	Lang	Local-only	+Global	Δ	Lang	Local-only	+Global	Δ
af	49.8%	49.8%	+0.0pp	he	33.2%	33.2%	+0.0pp	pl	43.6%	43.6%	+0.0pp
ar	16.0%	19.9%	+3.9pp	hi	52.4%	52.4%	+0.0pp	pt	44.1%	44.1%	+0.0pp
be	39.0%	39.0%	+0.0pp	hr	41.8%	41.8%	+0.0pp	ro	42.2%	42.2%	+0.0pp
bg	36.1%	39.2%	+3.1pp	hu	42.1%	42.1%	+0.0pp	ru	33.1%	33.1%	+0.0pp
bn	38.2%	38.2%	+0.0pp	hy	31.1%	31.1%	+0.0pp	sk	40.2%	40.2%	+0.0pp
ca	49.9%	49.9%	+0.0pp	id	53.1%	53.1%	+0.0pp	sl	48.2%	48.2%	+0.0pp
cs	37.3%	37.3%	+0.0pp	is	44.2%	44.2%	+0.0pp	sq	32.3%	40.7%	+8.4pp
da	52.1%	52.1%	+0.0pp	it	53.5%	53.5%	+0.0pp	sr	33.5%	33.5%	+0.0pp
de	47.3%	47.3%	+0.0pp	ja	88.5%	88.5%	+0.0pp	sv	50.5%	50.5%	+0.0pp
el	18.7%	18.7%	+0.0pp	kk	31.0%	31.0%	+0.0pp	ta	28.6%	28.6%	+0.0pp
en	56.7%	56.7%	+0.0pp	kn	28.6%	28.6%	+0.0pp	te	28.3%	28.3%	+0.0pp
es	53.5%	53.5%	+0.0pp	ko	25.0%	25.0%	+0.0pp	th	22.1%	22.1%	+0.0pp
et	37.3%	37.3%	+0.0pp	la	49.1%	49.1%	+0.0pp	tr	37.8%	37.8%	+0.0pp
eu	48.3%	48.3%	+0.0pp	lt	40.7%	40.7%	+0.0pp	uk	51.1%	51.1%	+0.0pp
fa	18.1%	18.1%	+0.0pp	lv	35.5%	35.5%	+0.0pp	ur	28.6%	28.6%	+0.0pp
fi	36.1%	36.1%	+0.0pp	mk	47.6%	47.6%	+0.0pp	vi	33.5%	33.5%	+0.0pp
fr	47.0%	47.0%	+0.0pp	mr	36.8%	36.8%	+0.0pp	zh	90.1%	90.1%	+0.0pp
ga	50.5%	50.5%	+0.0pp	ms	48.2%	48.2%	+0.0pp
gl	51.3%	51.3%	+0.0pp	nl	49.5%	49.5%	+0.0pp
no	49.2%	49.2%	+0.0pp

Aggregate: 42.00% → 42.27% (+0.27pp) — much smaller than statistical's +17.1pp, and that's the honest, expected result: aggressive's hand-curated generic/fw word lists already catch nearly every truly-ubiquitous word for most languages, so the new global-ubiquity check rarely finds anything new to drop. Only ar/bg/sq showed a real effect on this sample, meaning their curated generic-word lists have gaps the global reference happened to catch — useful as a small safety net, not a major lever, for this level. Zero empty-output regressions across all 56 × 15 samples (the existing "never empty a non-empty sentence" fallback holds under the new drop condition too — covered by TestCompressionServiceGlobalIdf in tests/test_synthelion.py).

Data source, license & operational notes

• Source & attribution: the .idf.br tables are derived from Wikipedia dumps (https://dumps.wikimedia.org/), each language's pages-articles-multistream1 shard. Wikipedia's text content is licensed CC BY-SA 4.0 (and GFDL); what Synthelion ships is a derived aggregate statistic — a word → document-frequency count table, not article text or any reproduction of the original prose — but the source is credited here regardless, per the Wikimedia Foundation's trademark/attribution expectations for reuse.
• Corpus limitation — not a random sample: multistream1 is the first page-id shard of each wiki, not a random cross-section of the whole encyclopedia. For large wikis (en/fr/de/...) that's still hundreds of thousands of real articles, but it systematically skews toward earlier-created/earlier-indexed topics rather than the wiki's full breadth. Good enough to meaningfully outperform local-only IDF (see benchmarks above), not a claim of a statistically neutral reference corpus.
• No freshness/versioning yet: the tables are a one-time snapshot (built 2026-08-03/04). There's no metadata recording the build date inside the .idf.br file itself and no automatic refresh — rebuilding is a manual devtools/build_idf_corpus_wikipedia.ps1 run (per-language, skips a language whose table already exists unless -Force).
• Lazy loading is the intentional default, not an oversight: each language's table is decompressed and parsed into memory only on first use, then cached for the process lifetime (GlobalIdfProvider, a thread-safe class-level cache). Measured cost: ~0.01 ms for a cached lookup, but up to ~700 ms the first time a specific language is used (largest table: Bulgarian, 2.68M reference documents / 1.14M terms). We measured eagerly preloading all 56 languages at process startup — 44.7 seconds and tens of millions of live dict entries in memory — and rejected that as the default; it would trade a rare per-language latency blip for a guaranteed, large, permanent memory/startup cost paid by every process regardless of which languages it actually serves. GlobalIdfProvider.preload(["eng", "ita"]) is available for operators who know in advance which specific languages a long-running deployment (proxy, dashboard) will actually serve and want to warm just those ahead of the first request.

---

Synthelion vs other prompt/context-compression tools

A capability comparison, not a performance benchmark — we don't publish head-to-head token-savings numbers against other projects because we haven't run their code in a controlled, reproducible setup; this table is based on reading each project's public source. If you've measured a real head-to-head, open an issue with your methodology and we'll link it here.

Capability	Synthelion	headroom	kompact	tokenless	squeez	bu-ketao
Zero ML models, zero network calls	✅	❌ (torch/transformers/SigLIP, fine-tuned classifiers)	✅	⚠️ optional ONNX embedding	✅	n/a (prompt rules, no code)
NLP compression, 50+ languages	✅	— (ML feature extraction instead)	✅ (TF-IDF)	—	—	n/a
Content-type auto-routing (JSON/HTML/diff/log/code)	✅	✅	✅	✅ (shape-based)	—	n/a
Tool-schema → compact signature	✅	—	✅ (TF-IDF selection)	✅	—	n/a
Credential-shape detection before persisting	✅	—	—	—	✅ (origin)	n/a
Terminal ANSI/noise cleanup + success collapse	✅	—	—	—	✅ (origin)	n/a
Masking old tool output + retrieval (Artifact Index)	✅	—	✅ (origin)	—	—	n/a
Diff-on-repeat for identical tool calls	✅	—	—	—	—	n/a
Adaptive compression scaling by content size	✅	—	✅ (origin)	—	—	n/a
JSON chain-depth / dot-path collapsing	✅	—	—	✅ (origin)	—	n/a
Advisory command-rewrite (never executes)	✅	—	—	⚠️ executes it (rtk wrapper)	—	n/a
Local multi-page web dashboard	✅	✅	✅ (simpler)	—	—	n/a
Cluster / master-slave deployment	✅	—	—	—	—	n/a
MCP protocol (Claude Code, etc.)	✅ 46 tools	✅ (CLI plugins: Claude/Codex/Gemini)	— (HTTP proxy instead)	✅ (hooks)	—	n/a
Vision/image token optimization	— (text-only)	✅ (tile-aligned resize + trained router)	—	—	—	n/a
Provider cache-breakpoint-aware read staleness	✅	✅ (origin, read_maturation.py)	—	—	—	n/a
Response-style compression (output-side, CJK-aware)	✅	—	—	—	—	✅ (origin)
PII detection + masking (33 countries, GDPR/AI Act compliance)	✅	—	—	—	—	—
Prompt-injection guard (jailbreak/instruction-override screening)	✅	—	—	—	—	—
Local reverse proxy (any agent, any provider, schema-agnostic)	✅	✅ (origin, headroom proxy)	✅ (proxy instead of MCP)	—	—	n/a
Proxy: automatic failover across backup providers (up to 10)	✅	— (not found in public docs)	—	—	—	n/a
Proxy: circuit breaker on repeated rate-limit/5xx	✅	— (not found in public docs)	—	—	—	n/a
Proxy: WAF/firewall protecting the proxy surface itself	✅	—	—	—	—	n/a
Proxy: enforced block-on-risk (request never reaches the provider)	✅	—	—	—	—	n/a
Proxy request log is metadata-only — no prompt/response ever stored	✅	— (stores content when --log-messages is on)	—	—	—	n/a

Not in this table: tokensave — a Rust code-intelligence tool (Tree-sitter knowledge graph, dead-code/cycle detection, code-health scoring) that solves a genuinely different problem (structural understanding of a codebase) rather than context/token compression, so it isn't a like-for-like comparison here.

On headroom specifically: the broadest-scope alternative we looked at — proxy architecture, persistent memory, vision-token optimization, and ML-based response-length prediction (fine-tuned MiniLM/SigLIP classifiers hosted on HuggingFace). That breadth comes at the cost of the zero-ML-models guarantee Synthelion makes: several of headroom's key decisions (image routing, response-length prediction) depend on downloaded, trained models rather than deterministic heuristics. Synthelion's proxy (new, see below) closes the "does it actually work with any agent" gap headroom's proxy opened, and adds three things we didn't find in headroom's public docs: automatic failover across up to 10 backup providers when one doesn't respond, a circuit breaker that stops hammering an already-rate-limited upstream, and a firewall (WAF) protecting the proxy's own network surface — on a strictly metadata-only request log (duration, status, tokens saved; never the prompt or response body).

---

What this means for your costs

Token pricing varies by model. As a rough example with GPT-4o ($2.50 / 1M input tokens):

Daily input volume	Without Synthelion	With Synthelion (40% avg savings)	Annual saving
500K tokens/day	$456/year	$274/year	$182/year
2M tokens/day	$1,825/year	$1,095/year	$730/year
10M tokens/day	$9,125/year	$5,475/year	$3,650/year

Savings scale with volume. For agent loops that send the same context on every call, real savings are often higher than the 40% average.

Energy & sustainability

Synthelion includes a built-in energy estimator. Every saved token avoids approximately 0.005 mWh of compute energy and 0.002 mg CO₂. At scale, that adds up.

result = svc.compress(long_prompt, CompressionLevel.SEMANTIC)
print(f"Energy saved: {result.estimated_energy_saved_mwh:.3f} mWh")
print(f"CO₂ avoided:  {result.estimated_co2_saved_mg:.3f} mg")

---

EnterpriseGuard — outbound data-loss-prevention firewall

A hard block-or-allow firewall, distinct from PrivacyGuard (PII, masked-and-continue) and the WAF (inbound HTTP request inspection). EnterpriseGuard is for enterprise secrets/credentials that have no safe redacted form — cloud/database/FTP/git credentials, private keys, bulk .env dumps — plus user-defined file "security zones" that must never be read into an agent's context at all.

• Content scanning — AWS/Azure/GCP credentials, PostgreSQL/MySQL/MongoDB/JDBC/ADO.NET connection strings, FTP/SFTP URLs with embedded credentials, git remote URLs with embedded credentials, PEM private key blocks, GitHub/Slack/Bearer tokens, bulk .env-style dumps — always block, never mask (unlike PII, there's no safe redacted form of a live secret).
• File "security zones" — user-defined glob patterns (**/fatture/**, **/payroll/*.xlsx, **/database.yml, ...) an agent must never be allowed to read, enforced before the read happens via synthelion firewall-check — a PreToolUse-style hook you point Claude Code (or any agent hook that supports pre-tool vetoes) at. Ships with sensible defaults already blocked (.env, .git/config, SSH/PEM keys, cloud-credential files, .npmrc/.pypirc/.netrc, kubeconfig).
• SSRF / cloud-metadata egress — a fetch/webhook URL argument or Bash command targeting a cloud metadata endpoint (169.254.169.254, GCP/Azure metadata hosts, ...), a loopback/RFC1918 private address, or a dangerous non-HTTP scheme (file://, gopher://, dict://) is vetoed the same way a blocked file path is — scoped to the tool call itself (check_tool_call), so a prompt that merely discusses an internal URL is never blocked, only a tool actually invoked with one.
• Destructive-shell commands — a Bash-shaped tool call matching a destructive pattern (rm -rf, drop table, git push --force, ...) is blocked outright at the same check_tool_call gate, not just flagged advisory like SafetyGuard's compression-skip.
• Wired into every entry point: CLI (compress), the Claude Code hook (goes through the CLI, no separate wiring needed), MCP/OpenAI-function tools (compress, plus an advisory check_enterprise_guard tool), and the local reverse proxy — the same posture as PrivacyGuard's block_on_risk.
• Per-client policies (IP/MAC), because Synthelion is a shared server, not a single-user tool. A blocked_paths list isn't global-only: register a client by IP (the proxy sees the real connecting IP per request) or MAC (synthelion firewall-check defaults to this machine's own MAC — the natural identity for a local CLI/hook invocation) and give it its own additional protected paths. The proxy auto-discovers a never-before-seen client IP on its first request and registers it disabled — it shows up in the dashboard for an admin to label/configure/enable, never silently trusted or silently restricted before a human looks at it. Manage clients from the dashboard's Security page or synthelion clients list/add/update/remove.
• Audit log, not a data store: a cross-process JSONL log (visible in the dashboard, survives regardless of which process — CLI/MCP/proxy — did the blocking) records only category/rule_name/source/timestamp for every block — never the triggering text or path, so the log itself can never become a place a secret ends up persisted.

# One-off check (advisory)
synthelion firewall-check --tool Read --args '{"file_path": "/home/user/.env"}'
# BLOCK: Blocked: '/home/user/.env' matches a protected security-zone pattern ('*.env').

# SSRF-shaped fetch target — blocked the same way
synthelion firewall-check --tool WebFetch --args '{"url": "http://169.254.169.254/latest/meta-data/"}'
# BLOCK: Blocked: 'url' targets an SSRF-shaped destination (cloud-metadata-endpoint).

# Destructive shell command — blocked the same way
synthelion firewall-check --tool Bash --args '{"command": "rm -rf /"}'
# BLOCK: Blocked: command matches a destructive-shell pattern ('rm -rf').

# Register a proxy client with its own extra protected paths
synthelion clients add --label "Marketing laptop" --ip 203.0.113.7 \
  --blocked-path "**/fatture/**" --blocked-path "**/payroll/*.xlsx"

synthelion clients list

Wire firewall-check as a Claude Code PreToolUse hook (.claude/settings.json) to actually veto Read/Bash/Grep calls before they execute:

{
  "hooks": {
    "PreToolUse": [{
      "matcher": "Read|Bash|Grep|Glob",
      "hooks": [{
        "type": "command",
        "command": "synthelion firewall-check --tool \"$CLAUDE_TOOL_NAME\" --args \"$CLAUDE_TOOL_INPUT\""
      }]
    }]
  }
}

Toggle/configure it in ~/.synthelion/config.json (or the dashboard's Security page):

{
  "enterprise_guard": {
    "enabled": true,
    "content_categories": {
      "cloud_credentials": true, "database_connections": true, "ftp_credentials": true,
            "git_credentials": true, "private_keys": true, "api_tokens": true, "dotenv_bulk": true,
      "ssrf_egress": true, "destructive_commands": true
    },
    "blocked_paths": [],
    "use_default_blocked_paths": true,
    "auto_discover_clients": true
  }
}

---

Agent guardrails — per-agent-type policy engine

EnterpriseGuard asks "is this content or path forbidden for anyone?". This asks the narrower question that a policy actually cares about: "is this acceptable for this kind of agent?" — terraform apply is routine for an ops agent and meaningless for a support one, and a $5,000 refund means something only to the latter.

Seven profiles, each stacked on a baseline that applies to every agent: base, dev, support, rag, data, ops, browser. Every rule carries the requirement id it implements, so a decision can always be traced back to the policy it came from.

Three things here exist nowhere else in Synthelion:

Gating — a third outcome next to allow and block. Every other guard is binary. Several controls are not: a force push, terraform apply, an IAM grant, a production-secret read, a DELETE/UPDATE, a bulk customer export, a refund over the cap, a financial click in a browser — all legitimate, all needing a human to say yes. A gated call is refused with its reason, not silently allowed. Turning gating off downgrades those rules to allow, never to block, and a BLOCK always wins over a GATE matched in the same call.

Chain breaking. Some attacks are invisible one call at a time and exist only as a sequence: read something private, then send it outward. Neither half is forbidden alone, so no single-call guard can see it. The breaker correlates the two within a session and cuts the egress. Its state is an append-only file, not memory, because the read may happen in the MCP server process and the egress in a CLI hook process — an in-memory flag would never see both halves.

Profiles as data. Rules are a table, not a branch: adding a requirement is a row.

It deliberately delegates rather than reimplements — credentials and protected paths to EnterpriseGuard, SSRF to ssrf_guard, runaway loops to loop_guard, PII to PrivacyGuard, spend caps to the proxy budget tracker and the enterprise quotas.

synthelion policy-show --profile ops            # what this profile enforces, and why
synthelion policy-check --tool Bash \
    --args '{"command":"terraform apply"}' --profile ops
# APPROVAL REQUIRED [REQ-OPS-02] `terraform apply` changes live infrastructure

policy-check is a PreToolUse hook: exit 0 allows, exit 2 refuses, the same contract as firewall-check. A gated call exits 2 as well — a hook has no channel to ask a human mid-call, so the only honest thing it can do is refuse and say why; --json distinguishes the two for callers that can tell them apart. synthelion policy-reset-chain --session <id> clears the chain after a reviewed egress. Also available as the check_agent_policy and describe_agent_policy MCP tools.

Configured under agent_policy.*; the decision log (~/.synthelion/agent_policy_events.jsonl) records the verdict, profile, tool name and requirement id — never the call's arguments, so the log cannot become the place a secret ends up stored.

---

AI Compliance Engine

A governance gate in front of every guard above. It is deliberately an aggregation layer: the detection already exists elsewhere in Synthelion. What is new is everything a compliance function needs and no individual guard can provide on its own.

One rule registry, in policy vocabulary. Nine controls, each with a risk level (high/medium/low), a remediation action (block / redact / warn / log-only), an input/output scope and an on-off switch — bound to the guard that implements it. Rules are configuration, so a deployment overrides only what differs.

Organisation-level system instructions, prepended to every request and not editable by the end user. They are created when a request carries no system prompt — injecting only into an existing field would let anyone evade the perimeter by omitting it — and every change is versioned with its content hash, so the technical file can state what the model was told at the time of a given decision rather than only what it says today.

A traceability matrix from each control to the article of law it satisfies — EU AI Act, GDPR, NIS 2/DORA, ISO/IEC 42001 — generated from the live configuration. That last part is the point: switch a control off and its obligations appear as uncovered, rather than continuing to look compliant on paper.

Backend health. A rule can be enabled in the policy while the guard behind it is switched off elsewhere in the configuration. That combination is the dangerous one — the technical file would claim a control that inspects nothing — so it is reported explicitly, in the dashboard and in the generated documents.

Engine states. active enforces; staging evaluates and logs every rule but never blocks or rewrites, so a policy can be measured against real traffic before it starts refusing calls; inactive is off. The fallback policy defaults to fail-closed for the security and privacy categories only — a broken toxicity screen should not take down the gateway, a broken secrets scanner should.

A tamper-evident audit trail (AI Act Art. 12): each entry carries the hash of the one before it, so an edited or removed entry breaks every link after it and verify-audit names where. It stores a SHA-256 fingerprint of each payload and never the prompt or response — an audit log of prompts would recreate the exposure the privacy rules exist to prevent, and would itself become personal data. It is tamper-evident, not tamper-proof: real immutability needs an append-only medium or an external notary.

Four documents, as JSON and PDF — the technical file (Art. 11 / Annex IV), a DPIA (GDPR Art. 35), a FRIA (AI Act Art. 27) and a periodic executive report. The dict is the source of truth and the PDF renders it, so nothing in a report exists only as pixels. The DPIA and FRIA are pre-filled working drafts: the sections that need an organisational judgement — necessity, proportionality, residual-risk acceptance — are emitted as explicit open items rather than filled with plausible text no engine is entitled to write.

The PDF writer has no dependencies. Synthelion ships offline and vendors everything, so requiring a PDF library for its own core output would have been the only external runtime dependency in the product. It emits valid PDF 1.4 with automatic pagination, tables, bar charts and the Synthelion mark, and transliterates characters outside WinAnsi rather than dropping them — an accented name vanishing from a compliance document would be worse than an approximated one. It is not PDF/A: archival conformance additionally needs embedded fonts, an XMP packet and an output intent, and claiming it without those would be false.

synthelion compliance status                        # engine, controls, audit chain, dead controls
synthelion compliance matrix                        # control -> framework -> article -> obligation
synthelion compliance check "mail mario@example.com"
synthelion compliance report technical-file --pdf technical-file.pdf
synthelion compliance verify-audit

Also available as the check_compliance and compliance_status MCP tools, and as the Compliance page in the dashboard. Configured under compliance.*.

---

Quick install — one command

The fastest way: download one script and run it. It installs Synthelion, detects your Python path, configures Claude Code MCP, and sets up the auto-compression hook automatically.

Windows (PowerShell)

# Download and run
Invoke-WebRequest https://raw.githubusercontent.com/francescopaolopassaro/synthelion/main/install_claude.ps1 -OutFile install_claude.ps1
powershell -ExecutionPolicy Bypass -File install_claude.ps1

Or, if you already cloned the repo:

powershell -ExecutionPolicy Bypass -File install_claude.ps1

Linux / macOS (bash)

curl -fsSL https://raw.githubusercontent.com/francescopaolopassaro/synthelion/main/install_claude.sh | bash
# or, after cloning the repo:
chmod +x install_claude.sh && ./install_claude.sh

All platforms (Python — works everywhere)

python install_claude.py

Installer options

Windows PowerShell (install_claude.ps1)

Flag	Description
-Upgrade	Update Synthelion to the latest version
-NoHook	Skip the auto-compression hook
-NoPip	Skip pip install (Synthelion already installed)
-Uninstall	Remove Synthelion and all Claude Code config

powershell -ExecutionPolicy Bypass -File install_claude.ps1 -Upgrade     # update
powershell -ExecutionPolicy Bypass -File install_claude.ps1 -Uninstall   # remove everything
powershell -ExecutionPolicy Bypass -File install_claude.ps1 -NoPip -NoHook  # only update settings.json

Linux / macOS (install_claude.sh) and Python (install_claude.py)

Flag	Description
--upgrade	Update Synthelion to the latest version
--no-hook	Skip the auto-compression hook
--no-pip	Skip pip install (Synthelion already installed)
--uninstall	Remove Synthelion and all Claude Code config

python install_claude.py --upgrade          # update
python install_claude.py --uninstall        # remove everything
python install_claude.py --no-pip --no-hook # only update settings.json

---

Install (manual)

Requirements: Python 3.11+ — download from python.org and tick "Add to PATH" during setup.

# 1. Install Synthelion
pip install synthelion

# 2. Verify the CLI works
synthelion compress --text "Hello world, how are you today?" --json

# 3. Verify the MCP server starts (Ctrl+C to stop)
synthelion-mcp

If synthelion is not recognised after install, close and reopen the terminal (PATH refresh needed).

---

Linux

# 1. Install Synthelion
pip install synthelion
# or, in a virtualenv:
python3 -m venv ~/.venvs/synthelion
source ~/.venvs/synthelion/bin/activate
pip install synthelion

# 2. Verify
synthelion compress --text "Hello world, how are you today?" --json

# 3. If synthelion-mcp is not in PATH (virtualenv scenario), add it:
# Add the venv's bin directory to ~/.bashrc or use the absolute path in MCP config
echo 'export PATH="$HOME/.venvs/synthelion/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

---

macOS

# 1. Install with pip (system Python or Homebrew Python)
pip3 install synthelion
# or with uv (recommended — no PATH issues):
pip install uv
uvx synthelion-mcp   # runs the MCP server without a permanent install

# 2. Verify
synthelion compress --text "Hello world, how are you today?" --json

---

Zero-install with uvx (all platforms)

uv installs and runs Synthelion in an isolated environment — no pip install needed:

pip install uv       # one-time
uvx synthelion-mcp   # starts the MCP server directly

---

Update

Windows

pip install --upgrade synthelion

# Verify new version
synthelion --version

Linux / macOS

pip install --upgrade synthelion
# or, if installed in a virtualenv:
source ~/.venvs/synthelion/bin/activate
pip install --upgrade synthelion

With uv / uvx

uvx always fetches the latest version automatically — nothing to do.

---

Developer setup — from a git clone

git clone alone does not give you a working checkout: worddata (per-language function-word/IDF/POS tables, ~143MB) and the ML checkpoints (SynthelionML, PrivacyGuardML) are excluded from git and from the published PyPI wheel — both ship from Hugging Face instead and are normally auto-downloaded on first use into ~/.synthelion/. For development you want them inside the repo tree instead (synthelion/worddata/, synthelion/ml_models/), so run the setup script once after cloning:

Windows (PowerShell):

.\install_devenv.ps1

Linux / macOS:

chmod +x install_devenv.sh
./install_devenv.sh

Either script downloads worddata + both model checkpoints from Hugging Face (digitalsolutiosai/synthelion-worddata, digitalsolutiosai/synthellion, digitalsolutiosai/privacyguardml) directly into the repo, then installs Synthelion in editable mode with dev dependencies (pip install -e ".[dev]"). After that:

pytest tests/ -q

---

Set up on Claude Code

Claude Code uses the MCP protocol to talk to Synthelion.

Step 1 — Install Synthelion (see above)

Step 2 — Register with one command (new in 1.0.7)

synthelion install           # writes to ~/.claude.json (global)
synthelion install --local   # writes to .claude/settings.json (project-only)

Or manually:

Step 2 (manual) — Add to ~/.claude/settings.json

Open the file (%USERPROFILE%\.claude\settings.json on Windows, ~/.claude/settings.json on Linux/macOS) and add:

{
  "mcpServers": {
    "synthelion": {
      "command": "synthelion-mcp"
    }
  }
}

If synthelion-mcp is not in PATH (virtualenv, macOS Homebrew Python), use the absolute path:

{
  "mcpServers": {
    "synthelion": {
      "command": "/home/user/.venvs/synthelion/bin/synthelion-mcp"
    }
  }
}

Or use uvx — it always works without PATH issues:

{
  "mcpServers": {
    "synthelion": {
      "command": "uvx",
      "args": ["synthelion-mcp"]
    }
  }
}

Step 3 — Restart Claude Code

Close and reopen the Claude Code window (or run claude again in the terminal). Synthelion is now available as an MCP tool.

Step 4 — Verify

Type in Claude Code:

"Use Synthelion to compress this: I would like to know if it is possible to receive information about cheap restaurants in Rome."

Claude will call the MCP tool and return the compressed version.

---

Automatic prompt compression — Claude Code hook

How it works: every prompt is automatically scanned and compressed by Synthelion (no minimum length — PrivacyGuard's PII masking runs on short prompts too, since that's often where a single pasted secret or IBAN shows up). The compressed text is injected as additionalContext for Claude (invisible in the terminal — that's the part that actually does the token-saving work), while a systemMessage — [Synthelion N% saved - X mWh - Y mg CO2 saved] — is shown visibly so you can confirm it fired. When PrivacyGuard detects PII, the message expands with the full breakdown:

[Synthelion 57% saved - 0.02 mWh - 0.008 mg CO2 saved]

PII / Privacy
Score: 39 - Risk: Medium (Anonymization Required)

Categories: IBAN

Compliance: PCI-DSS, SEPA, PCI-DSS & SEPA - Financial/Payment Data, EU AI Act Annex III(5) - Credit Scoring & Essential Services

Masked: [IBAN]

Automatic setup (recommended)

synthelion install          # writes hook + MCP to ~/.claude.json
synthelion install --local  # project-local .claude/settings.json

Manual — Windows (~/.claude/settings.json)

{
  "mcpServers": {
    "synthelion": { "command": "synthelion-mcp" }
  },
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "powershell",
            "command": "$j=[Console]::In.ReadToEnd()|ConvertFrom-Json;$p=$j.prompt;if($p){$r=($p| & \"synthelion\" compress --json 2>$null)|ConvertFrom-Json;if($r -and $r.efficiency_pct -gt 15){$pct=[Math]::Round($r.efficiency_pct);$label='[Synthelion '+$pct+'% saved - '+$r.energy_mwh+' mWh - '+$r.co2_mg+' mg CO2 saved]';if($r.privacy_categories -and $r.privacy_categories.Count -gt 0){$cats=($r.privacy_categories -join ', ');$comp=($r.privacy_compliance -join ', ');$label=$label+\"`n`nPII / Privacy`nScore: $($r.privacy_score) - Risk: $($r.privacy_risk_level)`n`nCategories: $cats`n`nCompliance: $comp`n`nMasked: [$cats]\"}if($r.ai_transparency_notice){$label=$label+\"`n`n\"+$r.ai_transparency_notice}@{systemMessage=$label;hookSpecificOutput=@{hookEventName='UserPromptSubmit';additionalContext=$r.compressed}}|ConvertTo-Json -Compress}}",
            "statusMessage": "Compressing prompt...",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

Manual — Linux / macOS (~/.claude/settings.json)

{
  "mcpServers": {
    "synthelion": { "command": "synthelion-mcp" }
  },
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "bash",
            "command": "prompt=$(cat | python3 -c \"import sys,json; print(json.load(sys.stdin).get('prompt',''))\"); if [ ${#prompt} -gt 200 ]; then r=$(printf '%s' \"$prompt\" | \"synthelion\" compress --json 2>/dev/null); if [ -n \"$r\" ]; then out=$(printf '%s' \"$r\" | python3 -c \"import sys,json; d=json.load(sys.stdin); eff=int(d.get('efficiency_pct',0)); label='[Synthelion '+str(eff)+'% saved - '+str(d.get('energy_mwh',0))+' mWh - '+str(d.get('co2_mg',0))+' mg CO2 saved]'; print(json.dumps({'systemMessage':label,'hookSpecificOutput':{'hookEventName':'UserPromptSubmit','additionalContext':d.get('compressed','')}})) if eff>15 else None\"); [ -n \"$out\" ] && printf '%s' \"$out\"; fi; fi",
            "statusMessage": "Compressing prompt...",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

How to disable the hook

Remove the "hooks" block from ~/.claude/settings.json, or open /hooks in Claude Code to toggle it.

---

Using Synthelion with all agents — automatic compression

Synthelion can compress inputs automatically for any agent that supports the MCP protocol (Claude Code, Claude Desktop, OpenCode, Cursor, Windsurf, Continue…).

Configure all MCP-compatible agents

Add Synthelion to each agent's config file:

Agent	Config file
Claude Code	~/.claude/settings.json
Claude Desktop (macOS)	~/Library/Application Support/Claude/claude_desktop_config.json
Claude Desktop (Windows)	%APPDATA%\Claude\claude_desktop_config.json
OpenCode	~/.config/opencode/opencode.json (global) or opencode.json (project)
Cursor	~/.cursor/mcp.json
Windsurf	~/.codeium/windsurf/mcp_config.json
Continue	.continue/config.json

Claude / Claude Desktop / Cursor / Windsurf / Continue all use the same JSON block:

{
  "mcpServers": {
    "synthelion": {
      "command": "synthelion-mcp"
    }
  }
}

OpenCode uses its own MCP schema (mcp key, explicit type, command as an array):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "synthelion": {
      "type": "local",
      "command": ["synthelion-mcp"],
      "enabled": true
    }
  }
}

Or register it automatically:

synthelion install --agent opencode           # global: ~/.config/opencode/opencode.json
synthelion install --agent opencode --local    # project: ./opencode.json

Once registered, all 46 Synthelion tools (compress, route_content, compress_for_context, deduplicate, …) show up as callable tools in OpenCode — ask it to "use the synthelion tool to compress this text" or let it call them automatically per your agent instructions (see below).

Cursor and Windsurf read the same mcpServers shape as Claude, just at their own config path — register with:

synthelion install --agent cursor
synthelion install --agent windsurf

Instruct agents to compress automatically

Add this to your agent's system prompt or CLAUDE.md:

When processing long texts, files, or documents (>200 tokens), use Synthelion:
- mcp__synthelion__compress_for_context  — fit any content in your token budget
- mcp__synthelion__compress_conversation — compress older turns before sending history
- mcp__synthelion__deduplicate           — remove overlapping retrieved chunks
- mcp__synthelion__route_content         — auto-detect content type and compress
- mcp__synthelion__session_record        — save decisions for cross-session recall
- mcp__synthelion__session_recall        — retrieve past decisions by query
Report the token reduction achieved (synthelion_metrics field).

Use the CLI in shell pipelines

# Compress a file before sending to any LLM API
cat long_context.txt | synthelion compress --level semantic > compressed.txt

# Pipe directly into any tool
synthelion route --file document.html | llm-cli --model gpt-4o

# Batch compress a directory
for f in docs/*.md; do
  synthelion compress --text "$(cat $f)" --json >> compressed_batch.jsonl
done

---

Integrations

---

OpenAI — GPT-4, GPT-4o, Codex, and any OpenAI-compatible API

from openai import OpenAI
from synthelion.plugins.openai_tools import get_tool_definitions, execute_tool

client = OpenAI()
tools = get_tool_definitions()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Compress this text: I would like to know if it is possible..."}],
    tools=tools,
    tool_choice="auto",
)

# Handle tool calls returned by the model
for tool_call in response.choices[0].message.tool_calls or []:
    result = execute_tool(tool_call.function.name, tool_call.function.arguments)
    print(result)

---

LangChain — LangGraph, LCEL, ReAct agents

pip install "synthelion[langchain]"

from synthelion.plugins.langchain_tools import get_tools
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

llm = ChatOpenAI(model="gpt-4o")
tools = get_tools()   # 11 StructuredTools, including all new context tools

agent = create_react_agent(llm, tools)
result = agent.invoke({"messages": [{"role": "user", "content": "Compress this prompt: ..."}]})

Works with any LangChain-compatible LLM (OpenAI, Anthropic, Groq, Ollama, …).

SynthelionMemory — drop-in compressing memory

from langchain.chains import ConversationChain
from synthelion.plugins.langchain_tools import SynthelionMemory

# Compresses history turns and injects relevant past decisions via RAG
memory = SynthelionMemory(max_context_tokens=4000, recall_limit=5)
chain = ConversationChain(llm=llm, memory=memory)

chain.predict(input="Tell me about Rome.")
chain.predict(input="What are the best restaurants there?")
# Older turns are automatically compressed; RAG recalls relevant notes from past sessions

---

CrewAI — auto-compression for agents and crews

pip install "synthelion[crewai]"

from synthelion.integrations.crewai_adapter import CrewAIAdapter

# Mirrors ClaudeAdapter/OpenAIAdapter — compresses every message, recalls past
# decisions, and runs a one-shot CrewAI agent + task under the hood.
adapter = CrewAIAdapter(model="gpt-4o")
reply = adapter.chat("Explain how the Renaissance shaped modern science in detail...")
print(reply.content)          # crew answer
print(reply.tokens_saved)     # tokens saved on the compressed prompt

Or give your own agents Synthelion as native CrewAI tools:

from crewai import Agent
from synthelion.integrations.crewai_adapter import get_tools

agent = Agent(
    role="Researcher",
    goal="Summarize long documents without wasting tokens",
    backstory="Uses Synthelion to compress context on the fly.",
    tools=get_tools(),   # compress, route_content, session_record, deduplicate, ...
)

---

Claude & OpenAI Adapters — auto-compression with one import

pip install "synthelion[claude]"    # for ClaudeAdapter
pip install "synthelion[openai]"    # for OpenAIAdapter

from synthelion.integrations.claude_adapter import ClaudeAdapter

# Replaces anthropic.Anthropic — same interface, auto-compresses every message
client = ClaudeAdapter()
reply = client.chat("claude-sonnet-4-6", [
    {"role": "user", "content": "Explain how the Renaissance shaped modern science in detail..."}
])
print(reply)                  # model answer
print(client.total_saved)     # tokens saved so far

from synthelion.integrations.openai_adapter import OpenAIAdapter

client = OpenAIAdapter()
reply = client.chat("gpt-4o", [
    {"role": "user", "content": "Explain how the Renaissance shaped modern science in detail..."}
])

---

RagAgent — stateful agent with memory, RAG, and cost tracking

from synthelion.agent.rag_agent import RagAgent

agent = RagAgent(max_context_tokens=8000, recall_limit=5)

# Each add_turn compresses the message, recalls past decisions, and updates the rolling window
agent.add_turn("user", "I decided to use PostgreSQL for the user database.")
agent.add_turn("assistant", "Good choice. PostgreSQL handles JSONB fields well for config data.")

agent.add_turn("user", "What did we decide about the database?")
# The agent automatically recalls past decisions about PostgreSQL
recalled = agent.recall("database")
for d in recalled:
    print(d["text"])    # "I decided to use PostgreSQL for the user database."

# Get compressed message list ready for any LLM API
messages = agent.get_context_messages()
print(agent.total_saved, "tokens saved")

---

Python API — any custom agent or pipeline

from synthelion import CompressionService, CompressionLevel, ContentRouter, CompressionProfile

# Compress text
svc = CompressionService()
result = svc.compress(
    "I would like to know if it is possible to receive information about cheap restaurants in Rome.",
    CompressionLevel.SEMANTIC,
)
print(result.compressed_text)   # "know possible receive information cheap restaurant Rome"
print(f"{result.efficiency_pct:.1f}% saved")

# Auto-route any content type (JSON, HTML, diff, log, code, prose)
router = ContentRouter.from_profile(CompressionProfile.BALANCED)
routed = router.route(my_content)
print(routed.strategy_used, f"{routed.savings_pct:.1f}% saved")

---

CLI — shell scripts, pipelines, any language

# Compress text
synthelion compress --text "I would like to know if it is possible..." --level semantic

# Detect language
synthelion detect --text "Guten Morgen, wie geht es Ihnen?"

# Auto-route a file
synthelion route --file context.json

# Summarize
synthelion summarize --text "..." --sentences 3

# Start MCP server manually
synthelion serve-mcp

# Start the local read-only web dashboard
synthelion serve-dashboard

Pipe-friendly — reads from stdin if no --text or --file is given:

cat big_prompt.txt | synthelion compress --level aggressive

Diagnostics & setup

# Health check — verifies MCP package, ledger, session DB, PATH, Claude config
synthelion doctor
synthelion doctor --json      # machine-readable output

# Register the MCP server automatically (global Claude Code config)
synthelion install
synthelion install --agent gemini            # Gemini CLI
synthelion install --agent opencode          # OpenCode (global: ~/.config/opencode/opencode.json)
synthelion install --agent opencode --local  # OpenCode (project: ./opencode.json)
synthelion install --agent claude --local    # project-local .claude/settings.json
synthelion install --agent cursor            # Cursor (~/.cursor/mcp.json)
synthelion install --agent windsurf          # Windsurf (~/.codeium/windsurf/mcp_config.json)

Analytics & savings tracking

# Show total tokens saved, cost estimate, and tool breakdown
synthelion status

# Show savings history (last 7 days)
synthelion gain --days 7
synthelion gain --all --json   # full history, machine-readable

# Benchmark on a built-in corpus (prose, JSON, diff, code, logs, HTML)
synthelion bench
synthelion bench --json

# Export ledger to CSV or JSONL for analysis in Excel / Grafana / pandas
synthelion export                          # CSV to stdout
synthelion export --format jsonl -o savings.jsonl
synthelion export --days 30 -o last_month.csv

Self-upgrade

Detects how Synthelion was actually installed — plain pip, pip --user, pipx, uv tool, or an editable/git checkout — and runs the command that really applies, instead of always shelling out to pip install --upgrade (which silently no-ops on an editable install and isn't the idiomatic path for pipx/uv-tool).

synthelion upgrade             # detects install method, runs the matching upgrade command
synthelion upgrade --dry-run   # show what would run, don't run it
synthelion upgrade --check     # report the latest PyPI version without upgrading

---

Local proxy — any agent, any provider

Every other integration in this README (MCP tools, hooks, Rules/AGENTS.md instructions) only enforces PII masking and compression if the model chooses to call a tool — that's true for Cursor and Aider today, and even Claude Code needs its UserPromptSubmit hook wired up. The proxy is different: it's a local HTTP server that sits between your agent and the real Anthropic/OpenAI/Gemini/whatever API, so masking and compression happen server-side, before the request ever reaches the provider — no cooperation from the agent required.

synthelion serve-proxy                 # foreground, default 127.0.0.1:8788
synthelion serve-proxy --port 9000     # custom port

Then point your agent's base-URL setting at it — same mechanism every major agent already supports for custom endpoints/gateways:

export ANTHROPIC_BASE_URL=http://127.0.0.1:8788   # Claude Code, Claude-SDK-based agents
export OPENAI_BASE_URL=http://127.0.0.1:8788       # Aider, Codex CLI, any OpenAI-SDK agent

Your agent's own API key still flows straight through in the request headers — the proxy never sees, stores, or needs it. It only touches the request body.

How a request is handled

1. Firewall first. The same WAF that protects the dashboard (SQLi/XSS/path-traversal/command-injection pattern matching, IP allow/block lists, auto-ban, rate limiting) gates every proxy request too — it's a second internet-facing surface, not an exempt one.
2. Recursive, schema-agnostic compression. The request body is walked as JSON; every string value above ~20 characters — regardless of which field it's nested under — goes through the same privacy pre-pass + NLP compression synthelion compress uses elsewhere. This is why it works for "any provider and wire format": it never assumes Anthropic's or OpenAI's exact message schema, it just compresses text wherever it finds it.
3. Enforced block-on-risk. If privacy.block_on_risk is on and a value crosses the risk threshold, the proxy responds 400 with the full PII/compliance breakdown and the request never reaches the provider — not masked-and-continue, an outright stop, the same posture as Claude Code's hook but now available to every agent.
4. Routing. Requests are matched by path prefix — /v1/messages* → proxy.anthropic_upstream, /v1/chat/completions and friends → proxy.openai_upstream (this also covers any OpenAI-compatible provider: Groq, OpenRouter, Together, Azure OpenAI, Mistral, DeepSeek, xAI, local vLLM/Ollama shims — just point the config at theirs), /v1beta/models* → proxy.gemini_upstream. Custom routes let you override or extend this for any other provider/path, checked first — the dashboard's Proxy page can pre-fill the upstream URL from a live provider list (one explicit, on-demand fetch, never automatic — same policy as the PyPI update check).
5. Failover, up to 10 providers deep. If the resolved upstream doesn't respond — connection refused, DNS failure, TLS error, timeout, 429, or a 5xx — the proxy automatically retries the same request against each configured backup upstream in order, before giving up. Once a response's headers have started streaming to your agent, no more failover happens for that request (bytes already sent can't be taken back).
6. Circuit breaker. After N rate-limit/5xx responses from one upstream within a time window (defaults: 3 within 60s), that upstream is skipped for a cooldown period (default 30s) instead of getting hammered further — requests fail over to the next candidate immediately, or fail fast if none are left.
7. Streaming passthrough. Responses (including SSE) are relayed chunk by chunk as they arrive — the proxy never buffers a full response before forwarding it.
8. Rolling-history compression. Once a messages array reaches a turn threshold (default 6), everything except the most recent half compresses at aggressive instead of the configured default — older turns shrink harder, recent ones stay closer to full detail. Never merges or drops messages, so it stays valid for every provider's exact schema.
9. CCR — reversible compression, opt-in. When a string's compression saves enough tokens, the original is cached locally and a [ccr:token] marker is appended to the compressed text. An agent that decides it needs the full detail back calls synthelion retrieve --token <token> (or the retrieve_compressed_text MCP tool) — entries expire after a configurable TTL (default 1h).
10. Response cache, exact-match only. An identical (upstream, path, body) within the TTL is served from a local cache instead of calling the provider again. Deliberately not embedding-similarity/semantic caching — consistent with Synthelion's zero-ML-models stance, two prompts that mean the same thing but aren't byte-identical are two separate entries.
11. Daily budget cap. Optional — once the day's estimated spend (same per-token price estimate the dashboard's cost KPI uses) crosses a configured USD limit, further requests are refused until the next UTC day.
12. Output shaping, opt-in. Appends a short "be terse, don't restate context" instruction to the system prompt — detected for both Anthropic's system field and an OpenAI-shaped system/developer message, silently skipped for any other shape.

What gets logged — and what never does

Every call appends one structured, JSONL record: timestamp, method, path, which upstream served it, HTTP status, duration, whether it was blocked, and tokens before/after. The prompt, the response, and any masked/compressed text are never written anywhere. The dashboard's Proxy page reads this feed directly.

synthelion launch — start the proxy and an agent together, in one command

synthelion launch claude              # starts the proxy, then runs `claude` with ANTHROPIC_BASE_URL set
synthelion launch codex               # same, for Codex CLI (OPENAI_BASE_URL)
synthelion launch aider               # same, for Aider
synthelion launch cursor              # Cursor's an IDE, not a spawnable CLI — prints the base URL to paste in instead
synthelion launch claude --no-proxy-start   # proxy already running elsewhere; just set env vars and launch

Cross-agent shared memory

Notes any agent can write and any agent can read back — deduplicated by exact content, so two different sessions both discovering "this repo uses pnpm, not npm" only stores it once. Available as CLI (synthelion memory add/list/clear) and as MCP tools (memory_add, memory_recall) for agents with MCP support. This is a small, durable, cross-agent fact store — not RAG/semantic search over a large corpus (that already exists per-session via the vector-store-backed session memory).

synthelion learn — deterministic pattern mining, not ML

Scans the savings ledger and proxy log for actionable, real patterns — a tool averaging low compression efficiency, repeated privacy blocks on the same category, an upstream failing repeatedly — and appends findings to CLAUDE.md/AGENTS.md as plain markdown. No fabricated insights, no trained model: every line is something that's actually true about recent activity, in keeping with Synthelion's zero-ML-models stance.

synthelion learn --dry-run     # print findings without writing anything
synthelion learn               # append to CLAUDE.md
synthelion learn --output AGENTS.md --days 30

Configuration

All of this lives under the proxy key in ~/.synthelion/config.json (see synthelion configure) and is fully editable from the dashboard's Proxy page (Status / Routes / Reliability / Advanced / Logs tabs) — no config file editing required:

{
  "proxy": {
    "enabled": false,
    "host": "127.0.0.1",
    "port": 8788,
    "anthropic_upstream": "https://api.anthropic.com",
    "openai_upstream": "https://api.openai.com",
    "gemini_upstream": "https://generativelanguage.googleapis.com",
    "default_upstream": "",
    "custom_routes": [],
    "fallback_upstreams": [],
    "circuit_breaker_enabled": true,
    "circuit_breaker_threshold": 3,
    "circuit_breaker_window_seconds": 60,
    "circuit_breaker_cooldown_seconds": 30,
    "rolling_history_enabled": true,
    "rolling_history_threshold": 6,
    "ccr_enabled": false,
    "ccr_min_tokens_saved": 15,
    "ccr_ttl_seconds": 3600,
    "response_cache_enabled": false,
    "response_cache_ttl_seconds": 120,
    "response_cache_max_entries": 200,
    "daily_budget_usd": 0,
    "output_shaping_enabled": false
  }
}

Off by default, strictly additive. The proxy never replaces, requires, or changes the behavior of the MCP/hook integrations described earlier in this README — synthelion install --agent claude|cursor|aider|codex keeps working exactly the same whether the proxy is running or not. Use MCP/hooks where the agent supports them well (Claude Code, Codex CLI's AGENTS.md), and add the proxy where it doesn't (Cursor, Aider) — or run both.

Status: start/stop the proxy from the dashboard, see its PID and bound address, and edit the four built-in upstream URLs.

Routes: add/remove custom path prefix → upstream overrides, each independently deletable, with a "pick a provider" convenience — fetches a live provider list on demand (never automatic) and pre-fills the upstream URL.

Reliability: manage the failover chain (add/remove backup upstreams, up to 10, each individually deletable) and tune the circuit breaker's threshold/window/cooldown.

Advanced: rolling-history compression, CCR, response cache, daily budget cap, and output shaping — every proxy setting is reachable here, none require editing config.json by hand.

Logs: the metadata-only request feed described above — duration, status, tokens saved, blocked/failed — read straight from the JSONL log, never the prompt itself.

---

Web dashboard

A local web dashboard over everything Synthelion has compressed — no external calls, no CDN, works offline. Built for a multi-session setup: every synthelion-mcp process (one per agent session) and every CLI/hook invocation writes to the same lock-free ledger, and the dashboard aggregates them live. Since 1.2.1 it's a full multi-page admin panel (separate URLs, not one long scroll) rather than a single read-only report.

synthelion serve-dashboard                    # http://127.0.0.1:8787
synthelion serve-dashboard --port 9000        # custom port
synthelion serve-dashboard --host 0.0.0.0     # explicit opt-in to expose it on the network

Protected by a login page — default credentials are admin / admin, change them before exposing the dashboard beyond your own machine:

synthelion dashboard-passwd                   # prompts for a new password (keeps current username)
synthelion dashboard-passwd -u alice -p ...   # change username and password non-interactively

Changing the password immediately invalidates every session already logged in on that running dashboard process. The dashboard's own Notifications page also flags it for you if the default password is still active — see below.

Overview: calls, tokens saved, avg efficiency, CO₂ saved, active sessions, avg calls per session, tools used, best single call, and latency (avg / p95 / max) — plus a version badge showing exactly which Synthelion build is running. Each card carries a sparkline of that metric's own daily history; the two metrics that have no honest time series (a per-session ratio, and a cross-session counter) show the number alone rather than a decorative line. Charts: tokens saved over time, by tool, and by content type, rendered with Apache ECharts (Apache-2.0, vendored locally).

Live monitor: one stream merging every subsystem on the machine — proxied requests, local compressions from the CLI/MCP/hook path, WAF matches, EnterpriseGuard blocks and agent-policy decisions — with counters over a rolling five-minute window, per-kind filters and a pause. Polled rather than pushed: every source behind it is an append-only file, and the poll only asks for what happened since its last cursor. It stops polling when the page isn't on screen, so a backgrounded tab costs nothing.

Sessions: one row per synthelion-mcp/CLI process (PID, calls, tools used, first/last activity), with per-row delete and a one-click cleanup (10/20/30 days) for old records. Recent requests: every individual call with before/after tokens, efficiency, and latency. Decisions: recorded session-memory notes, with the same age-based cleanup.

Settings: default compression level, default project-wiki depth (1-4, see below), session-store/vector-store backend selection, and live storage counts.

Privacy: PrivacyGuard's own page — toggle the master switch, auto-masking, prompt-injection guard, and the AI transparency notice independently; pick the message language; manage a whitelist of values PrivacyGuard should never flag; and a live tester to paste text and see PII detection + prompt-injection screening side by side, without persisting anything. See Privacy & Security — PrivacyGuard below for the full feature set.

Security: the WAF/firewall panel (request inspection, IP allow/block, auto-ban, rate limiting, recent events) plus, further down the same page, the EnterpriseGuard card — category toggles, the protected-path editor, the per-client registry and recent blocks. Full write-up in EnterpriseGuard — outbound data-loss-prevention firewall.

AI Compliance: the governance gate over every guard above, expressed as policy — risk level, remediation action (block / redact / warn / log-only) and input/output scope per control, each switchable in place. The traceability table maps every control to the article it answers to, generated from the live configuration, so a control you switch off appears as an uncovered obligation instead of continuing to look compliant on paper. A control that is enabled while the guard behind it is disabled is called out separately — that is the combination where a technical file would claim coverage that does not exist. The four regulatory documents download as PDF or JSON. See AI Compliance Engine.

Users: employees, their virtual proxy tokens, assigned provider keys and plans, with a metered or monthly subscription activated inline. Below, consumption per user against the tightest plan they hold — the one that will cut them off first — plus a ranking of the most-used models. Provider marks are Synthelion's own glyphs, not the vendors' trademarked logos.

Doctor: the same checks as synthelion doctor, one click.

Version: checks PyPI only when you click "Check for updates" — never automatically — and can trigger pip install --upgrade synthelion from the button next to it; since the dashboard's own running process doesn't pick up an upgraded package automatically, a "Restart dashboard now" button appears right after, which re-execs the process in place (same host/port) rather than leaving you to find a terminal.

Proxy: manage the local privacy/compression proxy end to end — Start/Stop, upstream URLs, custom routes (with a "pick a provider" convenience list, fetched on demand), failover chain, circuit-breaker tuning, and a metadata-only log of recent calls (duration, status, tokens saved — never the prompt itself).

Profile: change the dashboard's own username/password (requires the current password), account info (version, active backends), and a feed of the same real health notifications shown in the bell icon up top — never fabricated demo content, only things actually true about this install (default password still set, a configured backend's Python package isn't installed, etc.).

Cluster — a lightweight master/slave fleet layer, separate from (and on top of) the shared-backend replica model described in Cluster deployment below: "Become master" generates a node ID and a shared token; other nodes join with that token (from their own dashboard's "Join a master" form, or synthelion cluster join <url> --token ...) and appear in the master's node table with live calls/tokens-saved/version. The master and every slave authenticate to each other with that shared token (Authorization: Bearer ...), never with the browser session cookie — one node's dashboard login has no bearing on another node's. A joining slave copies the master's compression/wiki defaults; storage backends are deliberately not copied, since those often differ per node/region. The page also has one-click downloads for a docker-compose.yml and a Kubernetes manifest pre-wired for this master/N-slave topology (env-var based — SYNTHELION_ROLE, SYNTHELION_NODE_TOKEN, SYNTHELION_MASTER_URL — no secret baked into the downloaded file). See synthelion cluster --help.

Auto-start with Claude Code — add a SessionStart hook so the dashboard is already running whenever you open a session:

{
  "hooks": {
    "SessionStart": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "powershell",
            "command": "try{$c=Get-NetTCPConnection -LocalPort 8787 -State Listen -ErrorAction SilentlyContinue;if(-not $c){Start-Process -FilePath \"synthelion\" -ArgumentList 'serve-dashboard' -WindowStyle Hidden}}catch{}",
            "timeout": 10
          }
        ]
      }
    ]
  }
}

The check is non-blocking and idempotent — if the dashboard is already listening on the port, the hook does nothing.

---

Cluster deployment

For an AI-provider-scale deployment (many nodes, thousands of concurrent agent sessions), point every node at a shared session/vector store instead of each one keeping its own local ledger — then any dashboard replica shows the whole cluster's activity, not just its own.

This is about storage — identical, interchangeable replicas behind a load balancer. For node identity and fleet visibility (master/slave roles, a shared cluster token, "which nodes have joined and are they healthy", one-click deploy file downloads), see the dashboard's Cluster page and synthelion cluster --help in the Web dashboard section above — the two are independent and commonly used together (identical nodes, still individually tracked).

1. Configure

synthelion configure --session-store redis --redis-url redis://redis-host:6379/0 \
                      --vector-store qdrant --qdrant-url http://qdrant-host:6333 \
                      --dashboard-host 0.0.0.0
synthelion configure --show   # print the effective config without writing

Writes ~/.synthelion/config.json (or --output <path> / SYNTHELION_CONFIG env var for a per-node/ConfigMap-mounted file). See synthelion.config.example.json in the repo root for the full key reference — every key has a built-in default, so a partial file only needs to override what changes.

Backends:

	Options
Session store (active sessions, savings ledger)	local (single-node file), redis, postgres
Vector store (cross-session RAG memory)	chromadb (bundled embedding), qdrant (deterministic hashed vectors — no ML model, see below), lexical (no external service)
Dashboard realtime	websocket (push updates), polling

Qdrant support keeps Synthelion's "zero ML models" design: rather than pulling in an embedding model just for Qdrant, it indexes a deterministic FNV-1a hashed bag-of-words vector — the same lexical scoring the fallback path already does, just queryable through Qdrant's ANN index across a cluster.

2. Docker

docker build -t synthelion:latest .
docker compose up -d                       # single node, local-file storage
docker compose --profile cluster up -d     # + redis + postgres + qdrant containers

See the Dockerfile and docker-compose.yml at the repo root — the image runs synthelion serve-dashboard --host 0.0.0.0 by default and exposes 8787 (dashboard) and 8788 (WebSocket realtime updates).

For Docker Swarm, the same compose file works with docker stack deploy -c docker-compose.yml synthelion; scale with docker service scale synthelion_dashboard=3 (Swarm's routing mesh load-balances the published port across replicas — unlike plain docker compose up, where each replica binds the host port directly, so scale there via multiple named services or a reverse proxy instead).

3. Kubernetes

Manifests in k8s/: namespace.yaml, configmap.yaml (holds synthelion.config.json — edit the Redis/Qdrant URLs to match your cluster), deployment.yaml (3 replicas + HorizontalPodAutoscaler, readiness/liveness probes on /api/summary, non-root), service.yaml (ClusterIP — front it with your own Ingress/auth layer), and backing-services.yaml (minimal in-cluster Redis + Qdrant StatefulSets for evaluation; swap for managed services in production).

kubectl apply -f k8s/namespace.yaml
kubectl apply -f k8s/backing-services.yaml   # or point configmap.yaml at managed Redis/Qdrant instead
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

4. Load balancer (no orchestrator)

Run synthelion serve-dashboard --host 0.0.0.0 on N plain nodes (systemd service, synthelion install-style), all pointed at the same SYNTHELION_CONFIG, and put any standard load balancer (nginx, HAProxy, a cloud LB) in front on port 8787. Every node reads/writes the same shared Redis/Postgres/Qdrant backend, so it doesn't matter which node a request lands on.

---

Tools

46 MCP tools — the compression/read tools are marked readOnlyHint: true so Claude Code and other MCP clients can call them safely in parallel; the handful that mutate state (session recording, the loop guard, output masking) are not.

Tool	What it does
compress	Removes stop words, lemmatizes content words. Up to 70% token reduction.
detect_language	Identifies language of any text. Returns ISO 639-3 code.
route_content	Auto-detects JSON, HTML, diff, log, code or prose and applies the best algorithm — also collapses low-signal command output to 1-3 facts when command/exit_code are passed.
summarize	Extractive summarization — keeps the most important sentences (TF-IDF or TextRank).
compress_batch	Compresses a list of texts in one call.
compress_for_context	Compresses content to fit a token budget. Chains routing → NLP → TextRank until budget met.
compress_conversation	Compresses a messages list. Keeps last N verbatim, summarizes/collapses older turns.
deduplicate	Removes near-duplicate texts using cosine bag-of-words similarity. Configurable threshold.
session_record	Persists a decision or context note across sessions (ChromaDB or lexical fallback) — credential-shaped text (AWS/GitHub/Slack tokens, PEM blocks, .env dumps) is redacted before it ever touches disk.
session_recall	Retrieves past decisions by semantic or keyword similarity.
memory_add	Adds a note to Synthelion's cross-agent shared memory — visible to every agent that calls memory_recall, not just the one that wrote it. Deduplicated by exact text.
memory_recall	Lists the most recent notes from Synthelion's cross-agent shared memory (see memory_add).
session_start / session_end	Track session boundaries and emit summaries.
compress_file	Read a file by path and return only the compressed content. Avoids loading raw files into context.
synthelion_status	Returns aggregate token savings and estimated cost as structured JSON.
safety_check	Flags security-critical or destructive-command text before it gets compressed away.
check_sensitive_content	Scans text for credential-shaped content (AWS/GitHub/Slack tokens, PEM blocks, Bearer headers, .env dumps) before persisting it.
check_enterprise_guard	Advisory read-only pre-check: scans text for outbound-DLP-worthy content (cloud/DB/FTP/git credentials, private keys, bulk .env dumps) and/or a path against the configured protected security-zone patterns.
analyze_waste	Detects HTML noise, base64 blobs, excess whitespace, inline JSON bloat — read-only.
check_cache_alignment	Scans a system prompt for volatile tokens (UUIDs, timestamps, JWTs, hashes) that break provider KV-cache prefix reuse.
align_cache_prompt	Rewrites a system prompt so volatile blocks sink to the end, keeping the cacheable prefix stable call-to-call.
shape_output	Appends verbosity-steering instructions to a system prompt to cut the model's output tokens.
focus_relevant	Query-focused context shaping: keeps only the top-K most relevant blocks of a text.
list_relevant_tools	Filters the full tool list down to the ones most relevant to a task/query, for orchestrators building their own per-turn tools=[...] array.
estimate_cost	Estimates the USD/EUR value of a token count for a given model.
generate_commit_message	Generates a conventional commit message from a git diff.
review_diff	Generates single-line PR review comments from a git diff (bugs, security, perf, TODOs).
generate_project_wiki	Scans a project folder into an AI-synthesized Markdown wiki — depth 1-4 controls detail (see Web dashboard Settings for the default).
check_tool_loop	Pre-tool guardrail: blocks a tool call that would repeat an identical prior call too many times in a row (agent stuck retrying).
reset_tool_loop	Clears the loop-guard history for a session after a genuine change of approach.
mask_old_tool_output	Replaces all but the most recent N entries in a chronological tool-output list with a placeholder, storing originals for later retrieval. Returns an Artifact Index alongside the masked list.
expand_masked_output	Retrieves the original text behind a mask_old_tool_output placeholder, by its hash.
get_artifact_index	Returns the catalog of everything masked so far, grouped by tool — meant to be re-injected into context so the model knows what was hidden.
retrieve_compressed_text	Retrieves the original text behind a [ccr:xxxxxxxx] marker from the proxy's lossy-compression cache — same idea as restore_privacy_text, but for ordinary compression. Entries expire after proxy.ccr_ttl_seconds.
rewrite_command	Suggests a less verbose variant of a known shell command (same semantics/exit code) — advisory only, never executed. Refuses composite commands.
diff_tool_output	For a tool called again with identical arguments, returns a unified diff against the previous call's output instead of the full text again, when that's actually shorter.
get_response_style_guidance	Returns verbosity-reduction instructions to inject into an agent's own system prompt (no filler openings, structured bug-fix format, CJK-aware) — shapes the model's output, not its input context.
track_file_read	Records a file read for freshness tracking within a session — returns whether it's fresh or already stale.
track_file_write	Records a file write — any earlier tracked reads of that path become stale.
check_read_maturity	Checks whether a tracked file read is stale/superseded and has been quiet long enough to safely collapse into a compact marker.
analyze_privacy	PrivacyGuard: detects PII across 33 country rule sets, scores it 0-100 with GDPR/AI Act/NIS2/PCI-DSS/NIST compliance flags, optionally masks it with recoverable placeholders.
restore_privacy_text	Restores [PG_n] placeholders in text back to their original values from an analyze_privacy masking session.
mask_document	Masks PII in a PDF/Word/Excel/CSV/Markdown/text file by path; DOCX/XLSX are written as a new masked copy (the original is never overwritten). Requires synthelion[documents].
check_prompt_injection	Heuristic screening for prompt-injection/jailbreak attempts (instruction override, role hijack, delimiter injection, etc.) before untrusted text reaches an LLM.
get_ai_transparency_notice	Returns a localized "you're talking to an AI" disclosure message (supports EU AI Act Art.50 transparency obligations).

Two more ship as CLI-only, meant for shell hooks rather than an agent calling them directly: synthelion loop-check / synthelion loop-reset — same loop guard, but persisted across process invocations (~/.synthelion/loop_guard.jsonl) for use as an external PreToolUse-style hook, since a hook script is a fresh process every call and can't keep the MCP tools' in-memory history.

---

Code examples

Text compression

from synthelion import CompressionService, CompressionLevel

svc = CompressionService()

# Semantic (default) — removes stop words and lemmatizes
r = svc.compress(
    "I would like to know if it is possible to receive information about cheap restaurants in Rome, please.",
    CompressionLevel.SEMANTIC,
)
print(r.compressed_text)      # like know possible receive information about cheap restaurant Rome
print(f"{r.efficiency_pct:.1f}% saved")   # 55.0% saved
print(f"{r.original_tokens} → {r.compressed_tokens} tokens")

# Aggressive — also removes generic verbs and adjectives
r = svc.compress("The important thing is to find a good and reliable solution.", CompressionLevel.AGGRESSIVE)
print(r.compressed_text)      # solution

# Statistical — TF-IDF word scoring instead of curated dictionaries
r = svc.compress(
    "I would like to know if it is possible to receive information about cheap "
    "restaurants in Rome, please. The city has many wonderful places to eat.",
    CompressionLevel.STATISTICAL,
)
print(r.compressed_text)      # like possible receive information about cheap restaurant Rome city wonderful eat

# Explicit language (skip auto-detection)
r = svc.apply_compression(
    "Ich hätte gerne einen Kaffee, bitte.",
    iso3="deu",
    level=CompressionLevel.SEMANTIC,
)
print(r.compressed_text)      # gerne kaffee bitten

# Batch — compress many prompts at once
results = svc.compress_batch(
    ["Tell me about Rome.", "What is the capital of France?", "Explain neural networks."],
    CompressionLevel.SEMANTIC,
)
for r in results:
    print(r.compressed_text, f"({r.efficiency_pct:.0f}% saved)")

---

Language detection

from synthelion import LanguageDetector

det = LanguageDetector()

print(det.detect("Wo ist der nächste Bahnhof?"))        # deu
print(det.detect("Je voudrais une table pour deux."))   # fra
print(det.detect("Quiero información sobre Madrid."))   # spa

# Confidence scores for all matched languages
scores = det.detect_with_scores("Where is the nearest train station?")
# → {"eng": 0.42, "afr": 0.05, ...}
top = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:3]
print(top)   # [("eng", 0.42), ...]

---

Content router — auto-detects and picks the best algorithm

from synthelion import ContentRouter, CompressionProfile

router = ContentRouter.from_profile(CompressionProfile.BALANCED)

# JSON array → lossless markdown table or BM25 row-drop
json_data = '[{"name":"Alice","age":30,"city":"Rome"},{"name":"Bob","age":25,"city":"Milan"}]'
r = router.route(json_data)
print(r.strategy_used)   # JsonCrush:MarkdownTable
print(r.compressed)
# | name  | age | city  |
# | Alice | 30  | Rome  |
# | Bob   | 25  | Milan |
print(f"{r.savings_pct:.1f}% saved")

# HTML → extract text, then NLP-compress
html = "<html><body><h1>Visit Rome</h1><p>Rome is a beautiful city with ancient history.</p></body></html>"
r = router.route(html)
print(r.strategy_used)   # HtmlExtract+NlpCompression
print(r.compressed)      # Visit Rome Rome beautiful city ancient history

# Git diff → keeps +/- lines, trims context
diff = """--- a/main.py\n+++ b/main.py\n@@ -10,7 +10,7 @@\n def hello():\n-    print("Hello world")\n+    print("Hello Synthelion")\n     return True"""
r = router.route(diff)
print(r.strategy_used)   # DiffCompression

# Build log → deduplicates repeated lines
log = """ERROR: connection refused\nERROR: connection refused\nERROR: connection refused\nINFO: retrying..."""
r = router.route(log)
print(r.compressed)      # ERROR: connection refused  [×3]\nINFO: retrying...

# Source code → strips comments and blank lines
code = """
def greet(name):
    # This function greets the user
    # It prints a greeting message
    print(f"Hello, {name}!")  # say hello
"""
r = router.route(code)
print(r.compressed)      # def greet(name):\n    print(f"Hello, {name}!")

---

Summarization

from synthelion.nlp import TfIdfSummarizer, TextRankSummarizer

long_text = """
Rome is the capital of Italy and one of the most visited cities in the world.
It was founded in 753 BC and served as the center of the Roman Empire for centuries.
The city contains numerous ancient monuments including the Colosseum, the Pantheon,
and the Roman Forum. Vatican City, an independent state within Rome, is the seat of
the Catholic Church. Today Rome is a major European capital with a population of
nearly three million people. Its economy is driven by tourism, culture, and public
administration. Every year millions of tourists visit from every corner of the globe.
"""

# TF-IDF — best for factual/report text, picks sentences with rare distinctive words
tfidf = TfIdfSummarizer()
print(tfidf.summarize(long_text, sentence_count=3))

# TextRank — best for narrative text, picks sentences central to the storyline
tr = TextRankSummarizer()
print(tr.summarize(long_text, ratio=0.4))   # keep 40% of sentences

# Chain: summarize first, then compress — maximum token savings
summary = tr.summarize(long_text, sentence_count=3)
from synthelion import CompressionService, CompressionLevel
compressed = CompressionService().compress(summary, CompressionLevel.SEMANTIC)
print(compressed.compressed_text)
print(f"Final size: {compressed.compressed_tokens} tokens (was {len(long_text.split())})")

---

Agent memory & context window

from synthelion.agent import ContextWindow, MemoryStore, MemoryExtractor

# Rolling context window — auto-compacts when it exceeds the token budget
window = ContextWindow(max_tokens=2000, keep_last_turns=4)

for i in range(20):
    window.append("user", f"Message {i}: tell me about topic {i} in great detail...")
    window.append("assistant", f"Response {i}: here is a detailed explanation of topic {i}...")

print(f"Messages in window: {window.message_count}")   # stays bounded
print(window.to_messages_json(indent=2))               # ready for any LLM API

# Long-term memory across sessions
extractor = MemoryExtractor()
note = extractor.extract("The user lives in Rome and works in tech. They prefer Python over C#.", max_sentences=2)
# → {"summary": "User lives Rome works tech.", "keywords": ["Rome", "Python", "tech"]}

store = MemoryStore()
store.remember(note)
store.remember({"summary": "User prefers dark mode and short answers.", "keywords": ["dark mode", "concise"]})

# Save to disk, restore next session
json_blob = store.save()
store2 = MemoryStore()
store2.load(json_blob)

# Recall what's relevant for the current query
hits = store2.recall("What does the user prefer for coding?", top_k=2)
print(hits[0]["summary"])   # User lives Rome works tech.

---

AI-agent context tools

compress_file — read and compress a file by path

# Instead of: content = open("big_log.txt").read()  → 8000 tokens sent to LLM
# Do this:
r = execute_tool("compress_file", {"path": "big_log.txt", "profile": "agent"})
print(r["compressed"])          # deduplicated log, ~1200 tokens
print(r["synthelion_metrics"])  # "before=8000 after=1200 saved=6800 (85.0%) ~$0.02040"
print(r["detected_type"])       # "log"

# With a token budget
r2 = execute_tool("compress_file", {
    "path": "src/big_module.py",
    "max_tokens": 500,
    "profile": "agent",
})
print(r2["fits_budget"])        # True if ≤ 500 tokens

compress_for_context — fit content into a token budget

from synthelion.plugins.openai_tools import execute_tool

long_article = """Artificial intelligence is a branch of computer science that aims to create
intelligent machines... [1000+ token document]"""

# Compress without a budget — route + NLP, agent profile
r = execute_tool("compress_for_context", {"content": long_article, "profile": "agent"})
print(r["compressed"])           # compressed text
print(r["synthelion_metrics"])   # "before=213 after=82 saved=131 (61.5%) ~$0.00039"
print(r["detected_type"])        # "prose"
print(r["strategy"])             # "NlpCompression"

# Compress to fit in a 200-token context window
r2 = execute_tool("compress_for_context", {
    "content": long_article,
    "max_tokens": 200,
    "prefer": "auto",    # "compress" | "summarize" | "auto"
})
print(r2["fits_budget"])         # True or False
print(r2["budget_exceeded_by"])  # 0 if fits, else delta

compress_conversation — shrink a message history

conversation = [
    {"role": "user",      "content": "Tell me about machine learning in detail."},
    {"role": "assistant", "content": "Machine learning is a subset of AI that enables..."},
    {"role": "user",      "content": "Can you explain supervised vs unsupervised learning?"},
    {"role": "assistant", "content": "Supervised learning uses labeled data, like spam detection..."},
    {"role": "user",      "content": "What Python libraries should I use?"},   # ← kept verbatim
]

r = execute_tool("compress_conversation", {
    "messages": conversation,
    "keep_last_n": 2,    # last 2 messages verbatim
    "max_tokens": 150,   # collapse older turns if still over budget
})
print(r["messages_before"], "→", r["messages_after"])
print(r["strategy"])     # "nlp_compress" or "summarize_collapse"
for m in r["messages"]:
    print(f"[{m['role']}] {m['content'][:80]}")

deduplicate — remove overlapping retrieved chunks

# Classic RAG problem: multiple retrieval sources return similar chunks
chunks = [
    "Python is a high-level programming language used for web development and data science.",
    "Python programming language high-level web development data science applications.",  # near-dup
    "Rome is the capital city of Italy and a center of civilization for thousands of years.",
    "JavaScript is primarily used for web front-end development in browsers.",
    "Rome, Italy capital, civilization center, history monuments.",   # near-dup of Rome
]

r = execute_tool("deduplicate", {"texts": chunks, "threshold": 0.75})
print(f"Kept {r['deduplicated_count']}/{r['original_count']} chunks")
# Kept 3/5 chunks
for t in r["texts"]:
    print("-", t[:70])

Analytics — track cumulative savings

from synthelion.analytics.ledger import get_ledger

ledger = get_ledger()
summary = ledger.summary()
print(f"Total calls:  {summary['total_calls']}")
print(f"Tokens saved: {summary['tokens_saved']:,}")
print(f"Cost saved:   ${summary['cost_usd_saved']:.4f}")
print(f"Note:         {summary['pricing_note']}")
# Cost saved:   $0.0234
# Note:         Estimated at Sonnet 4.6 input price ($3.00/MTok)

---

Compression levels

Level	What it removes	Typical savings
light	Stop words (articles, prepositions, conjunctions…)	25–55%
semantic	Stop words + lemmatization to base form	30–55%
aggressive	Everything above + generic verbs and descriptive adjectives	35–75%
statistical	TF-IDF word scoring instead of curated dictionaries — keeps words that score above the prompt's own median relevance	40–65%
syntactic	Rule-based pruning: keeps grammatical glue only where it touches a surviving word, plus (when POS data is available) elides a leading hedging/matrix clause in favour of the sentence's last verb	45–70%
synthelionml	Learned per-token keep/drop classifier (own offline transformer, 39 languages) — trained from the AGGRESSIVE decision surface with a min-compression ratio controller; falls back to syntactic when the checkpoint isn't installed	≥65% (measured 60%+ on short samples)

Negation particles ("non"/"not"/"ne...pas"/"no"/"nicht"/"não"/"不") are always protected and never dropped, at every level, in every supported language they apply to — dropping a negation doesn't just cost fluency, it inverts the sentence's meaning.

Default: semantic. All levels are additive — earlier levels are never removed or replaced when a new one ships.

---

Supported languages (50+)

Afrikaans · Arabic · Armenian · Basque · Belarusian · Bengali · Bulgarian · Catalan · Chinese · Croatian · Czech · Danish · Dutch · English · Estonian · Finnish · French · Galician · German · Greek · Hebrew · Hindi · Hungarian · Icelandic · Indonesian · Irish · Italian · Japanese · Kannada · Kazakh · Korean · Latin · Latvian · Lithuanian · Macedonian · Malay · Marathi · Norwegian · Persian · Polish · Portuguese · Romanian · Russian · Serbian · Slovak · Slovenian · Spanish · Swedish · Tamil · Telugu · Thai · Turkish · Ukrainian · Urdu · Vietnamese

Language is detected automatically from the text. Pass an explicit ISO 639-3 code to override.

---

Troubleshooting

synthelion-mcp: command not found

The CLI entry point is not in your PATH. Fixes (choose one):

// Option A — use the Python module form
{
  "mcpServers": {
    "synthelion": {
      "command": "python",
      "args": ["-m", "synthelion.plugins.mcp_server"]
    }
  }
}

// Option B — use uvx (always works, no PATH needed)
{
  "mcpServers": {
    "synthelion": {
      "command": "uvx",
      "args": ["synthelion-mcp"]
    }
  }
}

// Option C — absolute path to the installed binary
// Windows: find it with: where synthelion-mcp
// Linux/macOS: which synthelion-mcp
{
  "mcpServers": {
    "synthelion": {
      "command": "C:\\Users\\you\\AppData\\Local\\Programs\\Python\\Python312\\Scripts\\synthelion-mcp.exe"
    }
  }
}

Hook not firing (Windows)

Run where synthelion in PowerShell to verify the CLI is in PATH. If not, add the Scripts folder to PATH:

$env:PATH += ";$env:APPDATA\Python\Python312\Scripts"

Hook not firing (Linux/macOS)

Verify with which synthelion. If using a virtualenv, activate it before starting Claude Code or use the absolute path in the hook command.

Detection errors (wrong language detected)

Pass the language explicitly:

synthelion compress --text "..." --language ita

Or in Python:

result = svc.compress(text, iso3="ita")

Something not working? Run the health check first:

synthelion doctor

Output:

[✓] mcp package installed (mcp 1.9.4)
[✓] synthelion 1.1.0
[✓] savings ledger: ~/.synthelion/savings.jsonl (42 entries)
[!] session DB: chromadb not installed — lexical fallback active
[✓] synthelion-mcp in PATH
[✓] Claude MCP config: ~/.claude.json → synthelion registered

Install chromadb for semantic (vector) session recall:

pip install "synthelion[chromadb]"

---

Optional extras

Extra	Installs	Enables
synthelion[langchain]	langchain-core	get_tools(), SynthelionMemory
synthelion[openai]	openai	OpenAIAdapter
synthelion[claude]	anthropic	ClaudeAdapter
synthelion[crewai]	crewai	CrewAIAdapter, get_tools()
synthelion[chromadb]	chromadb	Vector session recall in session_record / session_recall
synthelion[qdrant]	qdrant-client	Qdrant vector-database backend for cross-session memory (--vector-store qdrant)
synthelion[redis]	redis	Redis session/analytics backend for multi-node deployments (--session-store redis)
synthelion[postgres]	psycopg[binary]	PostgreSQL session/analytics backend (--session-store postgres)
synthelion[cluster]	redis, psycopg, qdrant-client	Master/slave cluster deployment (see Cluster deployment)
synthelion[documents]	pypdf, python-docx, openpyxl	PDF/Word/Excel input for mask_document
synthelion[dev]	pytest, pytest-asyncio	Test suite
synthelion[all]	everything above except dev	Full stack

---

Contributing

Contributions are welcome — new language data, new content-router strategies, framework adapters, bug reports, or a good first issue. See CONTRIBUTING.md for the local dev setup and PR guidelines.

If Synthelion is saving you tokens, a ⭐ on the repo helps other people find it: github.com/francescopaolopassaro/synthelion

---

Sponsors

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---

Links

• PyPI: https://pypi.org/project/synthelion/
• Source: https://github.com/francescopaolopassaro/synthelion
• Original C# project (Caveman): https://github.com/francescopaolopassaro/caveman

© 2026 Passaro Francesco Paolo — Digitalsolutions.it