distil-llm 1.11.0

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

Compress your agent's context.
Prove its decisions don't change.

Every other compressor asks you to trust it won't break your agent. Distil is the only one that proves it won't.
On 500 real coding tasks, compressed context didn't just match the full context — it beat it: 42.0% vs 39.2%. _{(SWE-bench Verified)}

_{uvx --from distil-llm distil bench — runs the certificate gate in ~10s, no API key · distil wrap -- claude routes your agent, zero config.}

⚡ Get the savings 2 min, no config pipx install distil-llm distil onboard

🔬 See the proof real harness benchmark ↓ · paper vs the others

_{Honest scope: +2.8pp is a point estimate (CI −0.6..+6.2pp — non-inferiority certified, superiority not yet). Details, incl. what doesn't transfer →}

Use it · Integrations · Install · vs the others · Full Docs →

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Proof first — not a pitch 📊

On a real 500-instance long-horizon agent (SWE-bench Verified, official harness)	task success	tied with full context?	reversible + certified?
Distil (gated + surprise digest, v1.7)	42.0%	✅ +2.8pp over full (CI −0.6..+6.2)	✅
Distil (relevance-gated, E8)	36.8%	✅	✅
Headroom (lossy)	32.6%	❌ −6.6pp	❌
LLMLingua-2 (lossy — only 16/500 runs completed)	2.4%	❌ −36.8pp	❌
no compression (full)	39.2%	—	—

Distil is the only compressor statistically tied with full context — and its v1.7 surprise-preserving digest lands above full context (42.0% vs 39.2%, paired non-inferiority certified) while every lossy tool craters. And on the live head-to-head above (graded by claude-opus-4-8), it certifies 83.2% savings at a 0% decision-change rate, ~1,000× faster than the nearest tool _{(distil is pure-Python heuristics — no local ML model; competitors run transformer inference)}. Full breakdown ↓

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🚀 Use it now

One command sets you up and tells you what to do next:

pipx install distil-llm
distil onboard      # detects your agent + billing, wires the status line, prints a guided tour

It detects your environment (Claude Code · Codex · Gemini CLI; metered vs subscription) and hands you the exact commands. Or wrap your agent directly — no config, no code change:

# Claude Code on a metered API key — saves real $$:
distil wrap --expand -- claude

# Claude Code on a Pro/Max subscription — flat-rate, ToS-safe (trims context, not $):
distil wrap --lossless-only -- claude

# Codex, Gemini CLI, or any agent — same pattern:
distil wrap --expand -- codex

Make it the default — never type distil wrap again

Tired of typing distil wrap every time? Make it the default — once:

distil default            # adds a managed shell alias so `claude` always routes through distil
distil default --undo     # remove it anytime (backed up before any change)

It detects your shell (zsh / bash / fish / PowerShell) and billing mode, writes the right line to the rc file your shell actually reads, and tells you what it detected. Want every SDK covered (not just the agent you type)? distil default --always-on runs a persistent proxy service — powerful, but it's a daemon you keep alive.

Then watch genuine savings from your traffic — measured, not estimated:

distil leaderboard          # cumulative tokens + $ saved, from the local ledger
distil dashboard            # live terminal TUI — token-trim + decision-equiv bars, Ctrl-C to exit

Validate it on your traffic. --shadow runs a fraction of requests twice (compressed and full) and compares the agent's chosen next action:

distil wrap --shadow 0.1 -- claude   # wrap + shadow 10% of requests
distil shadow-stats                  # live decision-equivalence rate

Honest scope: that's next-action equivalence — a proxy, not task success (E7 shows it doesn't fully transfer under aggressive lossy compression). Distil fails safe to full context.

Will it save money? Only on metered billing (API key) — fewer tokens, fewer dollars. On a flat-rate subscription it trims context + latency, not the bill. Coding agents: short sessions ~7%, big wins on long, many-turn sessions the model never re-reads.

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💡 Why Distil is different

You don't need byte-equivalence — you need decision-equivalence: your agent taking the same actions with compressed context. That's measurable and certifiable.

• Certified, not estimated — a strategy ships only if a non-inferiority test passes; can't certify → full context.
• Certified end-to-end, too — distil certify-trajectories bounds how many solvable tasks compression can cost (no other compressor certifies either level).
• Reversible, not lossy — digests behind a handle, keeps the original, hands the agent a distil_expand tool. Compress fearlessly.
• Compounds on outcomes — expansions and matched failures teach the policy what to protect (signatures only, never content) — always more conservative.
• Streams like it isn't there — SSE relays chunk-by-chunk; TTFT preserved.

Fidelity tiers: lossless (--verbatim) · reversible (byte-recoverable on demand — default) · lossy (every other tool). Only Distil certifies the reversible tier (Headroom ships an uncertified retrieve; Distil's recovery is agent-facing — the model expands mid-task — and gated by the decision-equivalence certificate).

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⚡ Prove the numbers yourself — no API key

Don't take the table above on faith. distil bench re-certifies savings and decision-equivalence on a bundled 7-domain corpus, offline, in seconds — the same gate that runs in CI:

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) reversibly; 5761 tokens causally 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)

distil eval plots the certified compression frontier — a savings-vs-quality curve where every point carries its certification verdict, locating the cliff past which lossy compression drops decisions. The artifact no competitor publishes: benchmark.html.

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📊 The proof

Three results, all reproducible, all published with caveats:

• Live head-to-head vs real llmlingua / headroom-ai (graded by claude-opus-4-8): 83.2% savings at 0% decision-change, ~1,000× faster (no ML model loaded vs. competitors' local transformer inference). The live proxy behavior is pinned to the certified strategy by tests/test_live_certified_equivalence.py; the one reviewed delta is a recency carve-out that keeps the last few tool-result turns verbatim (an agent needs its freshest output byte-exact). → benchmark
• E7 (SWE-bench Verified): aggressive lossy compression craters task success (52% → 16%) — a per-step certificate doesn't transfer to multi-turn. The reversible tier survives (56% vs 52%). We publish it because it's true. → E7
• E8–E14 (500-instance agent): the reversible tier is the only compressor non-inferior to full context, generalizes across 5 models / 3 vendors, and the newest digest lands above full (42.0% vs 39.2%). → E8–E14

Full methodology, McNemar tests, per-instance data: docs/PAPER.md · PDF.

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📡 See it working

Measured on your traffic, never estimated, nothing leaves your machine:

• Per request: x-distil-* response headers (tokens-saved, mode, compressible-tokens, expanded).
• Per machine: distil leaderboard (--html for a page).
• Shadow mode: distil proxy --shadow 0.05 reports the live decision-change rate — streaming-aware.
• Org-wide: distil proxy sidecar + set ANTHROPIC_BASE_URL once; every client routes through it.

Dashboard, status-line plugin, federated leaderboard: Deploy & observability.

🔌 Works with every SDK

One proxy. Point any base_url-honoring client at it — Python, TypeScript, any language — and get cache-aware reversible 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
Google Gemini	--upstream https://generativelanguage.googleapis.com	examples/python_gemini.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

Framework hooks (no proxy, no network hop) — for agent frameworks that own the message list, compress it where it lives:

Framework	Hook	Example
LiteLLM	distil.integrations.litellm.compress(kwargs)	examples/python_litellm.py
LangChain	distil.integrations.langchain.compress_messages(msgs)	—
LangGraph	pre_model_hook=pre_model_hook() (compresses graph state before the model node)	examples/python_langgraph.py

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📦 Install your way

New here? pipx install distil-llm, then distil onboard — it sets you up and guides you (see Use it now). Want to see it prove itself first instead? distil bench runs the certified gate in ~10s, no API key. The matrix below is for picking an install format — everything in it is an alternative, not a requirement.

Install gotchas & troubleshooting (package name, old-Python errors, stale mirrors)

⚠️ The one gotcha — the name. The PyPI package is distil-llm but the command is distil (the bare name was taken). So pipx install distil-llm → run distil …. pip install distil installs something else.

🔧 Seeing Could not find a version that satisfies the requirement distil-llm (from versions: none)? The package is on PyPI — that error means your pip/pipx is on a Python older than the package's floor, so pip filters every release out. Distil now supports Python 3.9+ (the version macOS ships), so a current install just works; if you still hit this on a very old Python, let uv provision one for you: uvx --python 3.12 --from distil-llm distil bench (or uv tool install --python 3.12 distil-llm). Check yours with python3 --version.

🔧 Got an old version (e.g. 0.25.1) instead of the latest? Public PyPI always serves the newest (pip index versions distil-llm lists them). If you got an older one, your pip/pipx is not resolving against public PyPI — almost always a stale internal mirror (Artifactory / CodeArtifact / Nexus that hasn't synced the latest yet — common right after a release) or a <1.0 version pin in a constraints file / pip.conf. Diagnose and fix:

pip index versions distil-llm     # stops at an old version? → your index/mirror is stale
pip config list ; env | grep -i pip   # look for an index-url or PIP_CONSTRAINT pin
# unblock now — force public PyPI:
pipx install --pip-args="--index-url https://pypi.org/simple/" distil-llm
# (or, if you must use the mirror, ask your platform team to sync distil-llm; it exists upstream)

Format	Command	Prereq
Zero install	uvx --from distil-llm distil bench	uv — auto-provisions Python 3.9+
Isolated CLI	pipx install distil-llm → distil bench	Python 3.9+ (else pipx install --python python3.12 distil-llm)
Homebrew	brew install dshakes/tap/distil	Homebrew
Docker	docker run ghcr.io/dshakes/distil:latest bench (or docker build -t distil .)	Docker
Single file	make pyz → python dist/distil.pyz bench	Python 3.9+
In a venv	pip install distil-llm (inside an active virtualenv)	Python 3.9+

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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🧰 Cheat-sheet

Basics are in Use it now and Works with every SDK. Beyond that:

Goal	Command
Set up + a guided tour (start here)	distil onboard
Make distil the default (no per-session wrap)	distil default · undo: distil default --undo
Remove distil's footprint (before uninstalling)	distil offboard · also clear data: distil offboard --purge
Diagnose your setup (ledger, shadow, proxy self-test, wiring)	distil doctor
Wire the savings status line into Claude Code	distil setup (compact segment: DISTIL_STATUSLINE=minimal)
Watch genuine savings accumulate	distil leaderboard · live TUI: distil dashboard
Live decision-equivalence on real traffic	distil wrap --shadow 0.1 -- claude → distil shadow-stats
Certify on your domain	distil ingest --input prod.jsonl --out ./mycorpus → distil conformal --corpus ./mycorpus
Recover digested detail from any agent (MCP)	distil mcp
Self-improving keep policy	distil learn / distil online

Status line — one pattern in every state: distil · <live> · total ▼<lifetime>.

state	you see	means
saving	distil · ▼12.0K · 40% smaller · $0.31 · total ▼27.0M · ✓eq 99%	compressing your recent traffic (last 15 min, all terminals)
watching	distil · ✓ on · waiting for a large read · total ▼27.0M	on, but no large content yet — savings come from big file/command output
idle	distil · ✓ on · total ▼27.0M	set up and on, no recent traffic

▼ = tokens saved · total = lifetime · ✓eq = decision-equivalence (shown past 25 shadow samples). Sharing the line with git/cwd/model? DISTIL_STATUSLINE=minimal → distil ▼7.8K · 27M total. On a flat-rate subscription, dollars are notional and auto-hidden (DISTIL_SUBSCRIPTION=0/1).

Rule of thumb: subscription/interactive → --lossless-only (verbatim is implied; no separate flag needed) · PAYG/autonomous → default digest (+--expand) · coding re-reads → add --session-delta.

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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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🎓 The certificate (DERC)

The gate answers "is this strategy non-inferior on my corpus?". The Decision-Equivalence Risk Certificate answers the operational one: "for a risk budget I choose (say ≤5% decision-change), how hard can I compress with a guarantee that holds on my real traffic?"

distil conformal --corpus ./mycorpus --alpha 0.05 --delta 0.05
# ✔ CERTIFIED 'lossless' → 57.4% savings; decision-change ≤ 5.0% at 95% confidence (Learn-Then-Test)

It's conformal risk control (Learn-Then-Test / CRC — distribution-free, finite-sample), not a heuristic threshold. The one load-bearing caveat: the guarantee requires exchangeability (calibration traffic ≈ live traffic) and is marginal over that distribution — recalibrate on drift. Full theory + citations: Concepts · docs/PAPER.md.

🏔 The trajectory-level certificate

DERC certifies the step; this certifies the task. Our E7 experiment — and the 2024–26 agent-compression literature — shows per-step fidelity can pass while end-to-end success collapses, so distil also certifies the level users actually feel: run your eval suite twice (full context vs compressed), feed the matched outcomes in, and get a distribution-free bound on how many solvable tasks compression may cost you:

distil certify-trajectories outcomes.jsonl --alpha 0.05 --delta 0.05
# each line: {"task_id": "...", "full_success": true, "compressed_success": true}
# → With confidence 95%, compression degrades at most 5.0% of tasks the full
#   context would have solved (observed 0.5% over 200 matched trajectories).

It refuses to certify on small samples, states its exchangeability assumptions in the certificate itself, and ships an anytime-valid drift monitor (trajectory_risk.drift_monitor) that tells you when live traffic has shifted enough that the certificate is stale. Matched failures also feed the outcome-guided policy (distil.compress.guideline): content classes that break tasks when digested get protected byte-exact, automatically.

🧩 What's inside

40+ shipped capabilities, all real (no stubs): the cache-aware cost engine, causal pruning, the TOST gate + conformal certificate, the proxy + Anthropic/OpenAI/Gemini adapters, an MCP server, LiteLLM/LangChain/LangGraph hooks, learned keep-models, output compression, and an optional Rust hot-path core (build-from-source via maturin; published wheels run the pure-Python engine, same API) — with zero runtime dependencies in the core.

Full module-by-module map: Architecture · Techniques · CLI reference.

🔒 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 to Tier-0 verbatim only: no Tier-1 digest stubs, no tool injection (provider-ToS-safe). Without an injected expand tool the agent cannot recover a stub, so --lossless-only folds directly into verbatim — no separate --verbatim flag needed.
• Minimal local persistence — digest originals are written to ~/.distil/restore/ (respects DISTIL_HOME) so handles survive proxy restarts, and age out after DISTIL_RESTORE_TTL_DAYS (default 14). For strict ZDR deployments, point DISTIL_HOME at an ephemeral path or clear that directory between sessions. No data is forwarded upstream.
• Ops-ready — unauthenticated GET /distil/health liveness probe on every entry point (never touches the billed upstream); gateway accounting checkpoints to disk every 30 s (crash-safe, not just on graceful shutdown); DISTIL_DEBUG=1 surfaces everything the fail-open compression path swallows.

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 — ratios robust, dollars approximate. --tokenizer anthropic for billing-grade counts.
• Default runner is a deterministic stand-in (offline gate with ground truth). Non-circular eval grades real agent traces with a real model — proof harness.
• Credible grading, enforced: majority-vote (single samples let grader noise look like a decision change), a same-family grader, and grading the reversible tier with its distil_expand recovery loop.
• No fabricated weights — the keep-model is a real logistic classifier (96.4%); the transformer ships a demo checkpoint you retrain on your traces.

Deliberately not a platform

Distil is a compression engine with a correctness gate, not a context suite. We declined what can't go under the certificate:

Adjacent feature	Our stance
Persistent memory / knowledge graph	Out of scope — a lossy store is the opposite of byte-reversible.
Hosted semantic cache	Out of scope — we make the provider's prompt cache pay off, not a second lossy one.
Editor/Copilot auth	Out of scope — Distil sits on the wire or in-process; never brokers credentials.

What we did adopt (it survives the gate): a pluggable salience scorer to protect entities, cache-prefix observability, and framework hooks.

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🎯 Both sides of the bill

Distil compresses input/context (comprehensive) and output — generation-side verbosity shaping (PAYG, measured with distil output-savings) plus a reversible output-on-re-entry digest, so verbose past answers stop costing full price as history. Details: Output & I/O.

🔬 Reproducible evaluation & the paper

Every number reproduces from the bundled corpus (distil bench, no key). The non-circular proof harness grades real agent traces with a real model (τ-bench / SWE-bench): benchmarks/PROVE.md. Compiled paper, LaTeX source, and all committed results: docs/PAPER.md · docs/paper/ · paper PDF.

Stop paying to re-send context your agent never reads.

pipx install distil-llm && distil bench
_{certified savings across 7 domains in ~10 seconds — zero API key, zero runtime deps}

Get started → · Wire it into your SDK · Read the proof · PyPI

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⭐ If distil saved you tokens

A star is how the next engineer finds provable savings instead of a lossy guess — and distil stats --badge gives you a shareable badge of your own measured number to show alongside it. That badge + this repo are the whole marketing department.

🤝 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.”