distil-llm 0.6.0

Compression with a quality contract — cache-aware, causally-pruned context compression for agentic runtimes, gated by a statistical non-inferiority test.

Cut LLM agent costs ~30% — and prove the agent still makes the same decisions.

Most context compressors ship a token-savings estimate.
Distil ships a quality contract: a strategy compresses only as far as a statistical non-inferiority test certifies the agent behaves identically — across 7 domains, as a CI gate.

Quickstart · Integrations · Install · Full Docs →

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🧭 Pick your lens

👔 For decision-makers Agents re-send their whole context every turn — you pay for it every turn. Distil cuts that ~30% with zero quality loss, and proves it: the savings and the accuracy are measured on the same runs, gated in CI. No "trust us."

🛠️ For developers pipx install distil-llm (or uvx --from distil-llm distil …), point your client's base_url at the proxy, done — no code change, any language or SDK. Or wrap(client) in-process. Lossless by default, reversible on demand.

🔬 For researchers Compression reframed as decision-equivalence and certified with TOST non-inferiority + bootstrap CIs over a multi-domain trajectory corpus. Causal ablation discovers what's safe to drop. Reproducible, zero-dep.

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💡 The one idea

You don't need byte-equivalence, you need decision-equivalence. Byte-lossless compression and high savings are information-theoretically in tension. But an agent only has to take the same actions and produce the same outputs whether or not its context was compressed. That's measurable and certifiable — so "100% accuracy" becomes a statistical guarantee on outcomes, not a diff of strings. Everything here makes that real and measured.

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⚡ 60-second start

uvx --from distil-llm distil bench   # certify savings + quality across 7 domains, in seconds

domain            trajectory                $ saved   distil   aggr  pruned
---------------------------------------------------------------------------
ops/sre           sre-disk-incident           33.1%     PASS   FAIL     615
coding            coding-bugfix               28.7%     PASS   FAIL     736
support           support-refund              32.6%     PASS   FAIL     765
research          research-synthesis          25.7%     PASS   FAIL     809
data-analysis     data-analysis-sql           18.1%     PASS   FAIL     965
devops            devops-rollback             25.0%     PASS   FAIL     857
finance           finance-reconcile           29.1%     PASS   FAIL    1014
---------------------------------------------------------------------------
aggregate: distil cuts $0.14212 -> $0.10402 (26.8% cheaper) losslessly; 5761 tokens prunable.
GATE: PASS — every trajectory certified non-inferior; aggressive rejected on all.

Why trust the number? Token-savings numbers are easy to fake — measure quality at low compression, advertise savings at high compression. Distil refuses that: accuracy and compression are measured on the same trajectories, and a strategy that can't pass non-inferiority doesn't ship.

distil certify --strategy distil       # VERDICT: PASS  (100% decision-equivalence)
distil certify --strategy aggressive   # VERDICT: FAIL  (mean diff −1.0, blocked)

The certified compression frontier — distil eval

The artifact no competitor publishes: a savings-vs-quality curve where every point carries its certification verdict. It locates the cliff past which lossy compression drops decisions — and shows distil sitting safely inside it. Reproducible offline; run --runner anthropic over your ingested traces for live task-accuracy.

level                   savings   equiv  certified  curve
--------------------------------------------------------------------------
distil (cache-aware)       8.4%    100%     ✔ PASS   ██
truncate@1200              7.2%     79%        ✘ —    ██
truncate@700              20.0%     36%        ✘ —    ████
truncate@300              41.3%      0%        ✘ —    █████████
--------------------------------------------------------------------------
distil: 8.4% token savings @ 100% decision-equivalence — certified.
certified ceiling beyond which lossy compression drops decisions and the gate rejects it.

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🔌 Works with every SDK

One proxy. Point any base_url-honoring client at it — Python, TypeScript, any language — and get cache-aware lossless compression with no code change.

distil proxy --upstream https://api.anthropic.com   # localhost:8788

SDK / framework	Change	Example
Anthropic SDK (Py/TS)	base_url="http://127.0.0.1:8788"	examples/python_anthropic.py
OpenAI SDK	base_url="http://127.0.0.1:8788/v1"	examples/python_openai.py
Vercel AI SDK	createAnthropic({ baseURL: '…:8788' })	examples/js_vercel_ai_sdk.ts
LangChain (py/js)	anthropicApiUrl / base URL	examples/js_langchain.ts
LiteLLM	api_base="http://127.0.0.1:8788"	examples/python_litellm.py

Prefer in-process? Wrap the client directly — still no call-site change:

from distil.adapters.anthropic import wrap
client = wrap(anthropic.Anthropic())   # compresses the request, keeps the cache warm

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📦 Install your way

Format	Command	Prereq
Zero install	uvx --from distil-llm distil bench	uv
Isolated CLI	pipx install distil-llm → distil bench	Python 3.11+, pipx
Homebrew	brew install dshakes/tap/distil	Homebrew
Docker	docker build -t distil . && docker run distil bench	Docker
Single file	make pyz → python dist/distil.pyz bench	Python 3.11+
In a venv	pip install distil-llm (inside an active virtualenv)	Python 3.11+

The import package and CLI are distil; the PyPI distribution is distil-llm (the bare name was taken — so uvx/pip must reference distil-llm, not distil). Distil is a CLI: install it isolated (pipx/uv/brew/Docker), because modern macOS/Linux block system-wide pip install (PEP 668). Node / any language: point your SDK's base_url at distil proxy, or use distil wrap -- <agent> — no Distil-specific package needed.

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🧠 How it works

Two techniques carry most of the win — they target where the money actually is in an agent loop, not where it looks like it is.

① Cache-aware compression — the dominant lever

You re-send the growing context every step. With prompt caching a cache read is ~10× cheaper than fresh input, so the real cost is cache misses, not context size. Distil keeps the prefix byte-stable (schema canonicalization + lifting volatile fields like timestamps/UUIDs out of the prefix) and compresses only the volatile tail.

Naive recompression sends fewer tokens yet costs more than not compressing at all, because it rewrites the cached prefix every turn. Distil doesn't — that's the whole game most tools miss.

② Causal / counterfactual pruning — the discovery engine

The eval isn't a ruler bolted on the side; it's a discovery engine. Remove a context block, replay, did any decision change? Blocks that never change a decision are provably free to drop.

distil prune
# doc-0   PRUNE (causally inert)     # speculative retrieval, never cited
# obs-0   keep (changed a decision)  # carries the decision-driving signal

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🧩 What's inside (all real, all wired, no stubs)

Capability	Module	Loss profile
Cache-aware priced cost engine	compress/cache_aware.py	—
Schema canonicalization + volatile-field extraction	compress/stabilize.py	lossless · reversible
Tier-0 reversible transforms · Tier-1 decision-aware digest	compress/tier0.py, tier1.py	lossless / reversible
Causal / counterfactual pruning	replay/ablation.py	certified
TOST non-inferiority gate + 7-domain corpus + distil bench	certify/, corpus.py	the contract
Provider proxy — drop-in across SDKs	proxy.py, distil proxy	reversible
Managed gateway — multi-tenant + live savings dashboard	gateway.py, distil gateway	—
In-process adapter (wrap)	adapters/anthropic.py	reversible
Learned keep-model (logistic, 96.4% acc / 0.98 F1 held-out)	codec/learned.py	pluggable
Transformer keep-model — ONNX adapter + training pipeline	codec/transformer.py, codec/train_transformer.py	pluggable
Auth-mode gating (lossless-only on subscription/OAuth)	policy.py	safety
Holdout A/B savings + bootstrap CI	certify/holdout.py	—
Byte-fidelity invariants (reversible + append-only)	fidelity.py, distil verify	—
BM25 partial retrieval · delta context · gist caching	retrieval.py, delta.py, gist.py	lossless
Output compression — gated shaping + lossless re-entry digest + A/B harness	output.py, distil output-savings	gated / lossless
Real-trace ingestion — run the gate on your own traffic	ingest.py, distil ingest	—
Performance benchmark — p50/p95 latency + throughput	perf.py, distil perf	—
Billing-grade tokenizer + live runner	tokenizer.py, replay/anthropic_runner.py	opt-in
Savings ledger + leaderboard (privacy-preserving)	ledger.py	local-first
Certified compression frontier — savings-vs-accuracy curve	eval.py, distil eval	the proof
Self-distilling keep-model — learns from causal labels, gated by the contract	online.py, distil online	never-regressing
Verifiable federated telemetry — signed, content-free savings + verdict	telemetry.py, distil federated-leaderboard	tamper-evident
Async high-concurrency proxy	aproxy.py, distil proxy --async	[async] extra
Rust hot-path core + pure-Python parity fallback	rust/distil-core, distil/native.py	opt-in speed

Full docs: Getting started · Concepts · Techniques · CLI · Output & I/O · Architecture · Integrations · Deploy & security · FAQ

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🔒 Security & deployment

• Localhost-only by default — the proxy binds 127.0.0.1 and forwards only to the single configured upstream (no SSRF).
• No secret/body logging — request bodies and credentials are never logged.
• Auth-mode gating — --lossless-only keeps subscription/OAuth sessions lossless and never injects tools (provider-ToS-safe).
• Stateless — nothing is persisted; ZDR-compatible.

See Deploy & security for topologies (local sidecar, container sidecar, shared gateway) and the threat model.

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✅ What we won't pretend

• Default tokenizer is an offline heuristic (zero deps); ratios are robust, dollars are approximate. Use --tokenizer anthropic for billing-grade counts (the correct Claude tokenizer — tiktoken undercounts Claude).
• The default runner is a deterministic stand-in so the gate runs offline with ground truth. --runner anthropic certifies against the live model — implemented, UNVERIFIED until you run it with a key.
• The learned keep-model is a real trained logistic classifier (96.4%/0.98 on held-out lines). The transformer path ships a real ONNX adapter + training pipeline; a demo checkpoint (96.3%/0.98, trained on the bundled corpus) is on the v0.1.0 release, and you retrain on your own traces for production (distil train-transformer). We don't fabricate weights to claim "done."
• Numbers here are reproducible from the bundled corpus with the heuristic tokenizer. No vanity metrics.

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🎯 Both sides of the bill — input and output

Input/context (Tier-0/1, cache stabilization, causal pruning, proxy + adapter) — comprehensive.

Output — two real mechanisms (distil/output.py):

• Generation-side shaping — a gated role:"system" verbosity directive (distil proxy --shape-output light|aggressive) so the model emits fewer tokens. Lossy by nature, so it's PAYG-only and measured: distil output-savings reports the token cut and the rate the answer survived, with a bootstrap CI.
• Lossless output-on-re-entry digest — long answers that become history are digested reversibly, so verbose past output stops costing full price as context.

$ distil output-savings
output tokens cut 72.5% (95% CI 67.5–77.1%), answer preserved 100.0% of the time, n=6

Run the gate on your traffic, not just the synthetic corpus:

$ distil ingest --input prod-requests.jsonl --out ./mycorpus   # Anthropic/OpenAI logs → trajectories
$ distil bench --corpus ./mycorpus --savings-only

Performance (distil perf, stdlib, single core): ~27,000 distil-compressions/sec; the in-process adapter compresses a request in ~0.006 ms (p50).

Honest limits

• Production keep-model weights. A logistic model (96.4%/0.98) ships built-in; the transformer is a real adapter + pipeline with a demo checkpoint on the v0.1.0 release — retrain on your traces (distil train-transformer).
• Output shaping's realized savings are live — the A/B harness measures it on recorded pairs; the token reduction lands when a real model generates against the directive.
• Live-model certification is offline by default; --runner anthropic is implemented but UNVERIFIED without a key.

No vanity metrics — every number here is reproducible from the bundled corpus.

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🤝 Contributing

PRs welcome — see CONTRIBUTING.md. The one rule that matters: a new compression strategy must pass make gate (non-inferior on every domain, byte-reversible). No green gate, no merge. That's the whole philosophy in one sentence.

License

Apache-2.0 · “Same potency, less volume.”