entroly-context-engine

⚡ Live Tokenomics

Live means measured by Entroly, not a fabricated global number. Exact token and dollar totals stay local; privacy-safe product-health telemetry uses coarse buckets rather than exact global savings. Run entroly value or open entroly dashboard for live cumulative totals on your installation. For proxy observability, scrape /metrics and see Metrics & Monitoring.

Related MCP server: Portable MCP Toolkit

What is Entroly? (in plain English)

AI coding assistants have a memory limit. Hand one your whole codebase and it gets slow, expensive, and distracted — like giving someone a 500-page manual when they only needed page 47.

Entroly finds page 47.

It sits between your code and the AI, reads everything, and passes along only the parts that matter for the question actually being asked. Three things make that safe to do:

Do I need to pay for anything to try it? No. The two commands in the Install section below run entirely on your own machine, with no API key, and show you real numbers on your own project before you connect anything paid.

Install

Not sure which one? Pick Python. It's the complete version and what most people use. The others are alternate ways to run the same engine.

Platform	Install	What you get
🐍 Python (pip) — recommended	pip install -U entroly	Everything: the command-line tool, the server your AI editor talks to, and the code library
📦 Node / npm	npm install -g entroly	The same engine, nothing Python required
🦀 Rust (source build)	cd entroly-core && cargo build --release --bin entroly-rs --features proxy	One self-contained program, no Python or Node needed
🍺 Homebrew	brew install juyterman1000/entroly/entroly	The command-line tool on macOS/Linux
🐳 Docker	docker pull ghcr.io/juyterman1000/entroly:latest	Runs in a container, nothing installed on your machine
Now check that it worked — free, offline, no API key:

cd /your/repo
entroly verify-claims
entroly simulate

Both run locally. Neither one calls an AI or costs anything.

Extras (entroly[proxy], entroly[native], entroly[full]), the standalone Rust binary, and uninstall steps: Engine & install options.

Quickstart — by how you work

Just want it working? pip install -U entroly && entroly go — that's the whole thing. It finds your editor, sets itself up, and shows you a before/after dashboard. The rest of this table is for specific setups.

Your situation	Do this	What it gets you
🟢 "I just want it on." (pip / Python user)	pip install -U entroly && entroly go	Auto-detects your editor, wraps your agent, opens a dashboard showing tokens before and after
"I use Node, not Python." (npm user)	npm install -g entroly && entroly init	Same engine, nothing Python required
"I want one binary, no runtime." (Rust user)	cargo build --release --bin entroly-rs --features proxy (from entroly-core/)	A single native program with no dependencies
"I use Claude Code / Cursor / Windsurf / VS Code." (MCP user)	entroly attach create --client claude --project . --ttl 4h --install (or entroly init for Cursor/VS Code)	Your editor gets compression, receipts, and recovery as built-in tools — access expires on its own, and you change zero code
"I'm building my own app in Python." (SDK user)	from entroly import compress, compress_messages, optimize	Call it straight from your code, anywhere you assemble a prompt
"I have an API key and my own app." (proxy user)	entroly proxy → point ANTHROPIC_BASE_URL / OPENAI_BASE_URL / GOOGLE_GEMINI_BASE_URL at localhost:9377	Every request gets optimized on the way past — no code changes on your side
Why bother: less unnecessary context reaches the model (lower bill, less
distraction for the model), nothing is silently lost (every drop is
recoverable and receipted), and you can prove it — entroly verify-claims
and entroly simulate show real numbers on your own repo before you connect
a paid key.

from entroly import compress, compress_messages, optimize
compressed = compress(api_response, budget=2000)
messages   = compress_messages(messages, budget=30000)
context    = optimize(fragments, budget=8000, query="fix the login bug")

entroly compress response.json --out small.json
entroly recover sha256:0b957c79... --out restored.json

Full setup paths for every agent, IDE, and CI use case: Get started in depth · Command reference.

See it work in 30 seconds

Not mocked recordings — each video is rendered from a checked-in command that verifies its source artifact before printing a number.

Full protocols, sample sizes, and every caveat: docs/BENCHMARKS.md.

Benchmarks

The question that matters: if you send less, does the AI start getting things wrong? These are standard public tests, run with and without Entroly.

How to read this: Retention is how well the AI still answered — 100% means it did just as well on far less text. Token savings is how much less was sent (and therefore paid for). Measured with gpt-4o-mini; intervals are Wilson 95% CIs.

Being straight with you: look at the SQuAD 2.0 row — accuracy went down (80% → 72%). Compression is a trade, not magic, and it doesn't win everywhere. That's why entroly simulate exists: run it on your own project and see your own numbers before you commit to anything.

Hallucination detection (WITNESS, local, no API): 84.92% accuracy / 0.7976 AUROC on 20,000 HaluEval-QA decisions — within the reported uncertainty of gpt-4o-mini as an API judge on the same shared sample.

Frozen evidence-selection benchmark (opt-in PRISM-R research prototype, not the default compressor): a disagreement guard kept the answer-bearing passage in 298 of 300 cases while selecting an average of 1.02 of 16 passages (paired exact McNemar p=0.21875 vs. BM25 alone) — this experiment measures retrieval of the known-answer passage, not generated-answer quality. Full protocol: PRISM-R neural evidence frontier.

Recovery, latency, and head-to-head frontier results are in docs/BENCHMARKS.md with raw artifacts linked. None of these numbers are a universal or production-savings guarantee for your workload — reproduce them on your own repo with entroly simulate and entroly value.

Features

• Picks first, shrinks second — it works out which files actually answer your question, then compresses them.

• Gives you the original back, exactly — anything left out can be restored character-for-character and checked against a fingerprint.

• Shows its work — a receipt for every decision: what was kept, what was left out and why, and what risk remains.

• Fact-checks answers — compares what the AI said against the evidence it was given, on your machine, without paying for a second AI call.

• Doesn't wreck your caching — keeps the unchanging parts of your prompt stable so your provider's discount for repeated text still applies.

• Rescues sessions before they crash — when a conversation grows too big, it trims recoverable output instead of letting the provider reject the request mid-task.

• Can route cheap work to cheap models — optional and fail-closed when uncertain.

Runs as a CLI, Python/TypeScript SDK, MCP server, HTTP proxy, or library import. Full surface map: docs/product-surface.md. Architecture and Rust internals: docs/DETAILS.md.

Works with your stack

Status describes integration depth, not a savings guarantee — provider-observed savings require requests to actually traverse an Entroly proxy route. Entroly does not claim interception of GitHub-hosted subscription inference on Copilot's native path. Full compatibility matrix: docs/agent-compatibility.md.

Current model support

Entroly carries verified public metadata for GPT-5.6 Sol, Terra, and Luna; Gemini 3.6 Flash; and Gemini 3.5 Flash-Lite, and it can discover installed NVIDIA Nemotron 3.5 Lightning Ollama tags. Gated or private-preview announcements are not promoted into the verified matrix without a usable public model ID and limits. For example, Gemini 3.5 Flash Cyber remains outside the generally available matrix because its documented CodeMender access is restricted to selected governments and trusted partners. See Verified model support for model IDs, transport paths, limits, and availability boundaries.

NVIDIA Nemotron 3.5 Lightning with Ollama

Entroly supports nemotron-3.5-lightning through its existing local Ollama discovery and OpenAI-compatible proxy path. This is a model-neutral integration: Entroly manages evidence selection, budgets, recovery handles, Context Receipts, and optional verification around the request; Ollama runs the model.

ollama pull nemotron-3.5-lightning
python -m entroly.models discover ollama --inspect-ollama-context
# Set ENTROLY_OPENAI_BASE=http://127.0.0.1:11434 in your shell, then:
entroly proxy

Ollama lists the standard nemotron-3.5-lightning tag as a 30B mixture-of-experts model with 3B active parameters and a 1M context window. Its Apple-silicon 30b-mlx tag is listed separately with a 256K window, so Entroly discovers the installed tag's metadata instead of assuming that every build has the same limit. Local Ollama inference can keep model prompts on the device; agent tools, configured remote providers, and other applications retain their own network and privacy boundaries. Compatibility, setup, and official sources.

When to use it · when to skip it

Great fit: large repos where the agent only sees a few files at a time · chatty multi-turn agents · anywhere you want answers checked against evidence · cutting a real, growing AI bill.

Skip it: tiny repos or short prompts that already fit the budget · judgment-heavy tasks where you always want the full flagship model.

More commands

Also available: entroly wrap, entroly unwrap, entroly serve, entroly daemon, entroly dashboard, entroly demo, entroly capabilities, entroly ingest, entroly select, entroly receipt, entroly explain, entroly context-commit, entroly proof, entroly benchmark, entroly cache, entroly ravs, entroly perf, entroly batch. Full description: command reference.

Common questions

Docs & community

• AI efficiency hub — token economics, AI cost optimization, memory, hallucination reduction, model routing, adaptive context, and verified code intelligence.

• AI cost optimization — provider-bound input savings, billing boundaries, and workload-specific measurement.

• Token economics — token saving, context compression, cache-aware context control, and more room in the context window.

• Memory OS — budget-aware working, episodic, and semantic memory.

• Hallucination reduction — WITNESS evidence-support verification.

• Guarded model routing — RAVS routing, uncertainty control, and fail-closed escalation.

• Adaptive context — bounded self-improving context.

• Verified Code Context — parser-backed repository intelligence, typed graphs, architecture, value flow, LSP enrichment, source verification, and refactoring contracts.

• Full benchmark evidence — every number, protocol, artifact, and caveat.

• Model-triggered recovery holdout — frozen recovery protocol, evidence boundary, and reproduction details.

• Context Commit conformance artifact — checked-in conformance evidence for Context Commit contracts.

• Product surface map — CLI, SDK, MCP, proxy, verification, memory, security.

• Architecture & full spec — Rust modules, compression, provenance, command reference.

• Agent compatibility — every supported client and its exact authentication boundary.

• First-run trust guide — exactly what to run before wiring a paid model key.

• For teams — ROI, security, deployment one-pager.

• Limitations — where Entroly helps, where it passes through, what it doesn't guarantee.

• Public evidence policy — claim tiers and package links.

• Context Commits · Context Receipts · Proof-guided recovery

• Cookbook — copy-paste recipes.

• Discord · Discussions · Issues

Compressing a bad selection is still a bad selection. Entroly ranks first, then compresses — so the model gets structure, not just fewer tokens.