Entroly is an Apache-2.0, local-first context layer that selects evidence within a token budget before an agent's request leaves the machine, records every omission in a receipt, and recovers the exact original bytes from a content-addressed store on demand. **Entroly is the first member of this category whose product is the audit rather than the compression: the savings are modest and self-run, but every drop is receipted, byte-exact recoverable, and verifiable offline with one command, which is a guarantee none of the bigger compressors here offers.** ## What it is A Python package (`pip install entroly`) carrying the full surface (CLI, SDK, localhost proxy, MCP server, dashboard), plus a Node/WASM CLI on npm, a Rust binary, a Homebrew tap, and a Docker image, all published at the same version and smoke-tested by the release workflow, by a solo maintainer (juyterman1000). Selection is budgeted and query-conditioned: `compress`, `compress_messages`, and `optimize` return smaller payloads whose omitted spans get content hashes, and `entroly recover sha256:...` restores any of them exactly. Delivery is wherever the request already flows: a proxy on localhost:9377 you point `ANTHROPIC_BASE_URL` or `OPENAI_BASE_URL` at, an MCP server, a reversible prompt hook for Claude Code, Codex, Cursor, Kiro, and VS Code, and SDK calls in your own prompt assembly. A runaway-session rescue compacts bulky tool output in flight while keeping the prompt prefix byte-stable so the provider's warm cache survives. Two offline commands are the trust surface: `entroly verify-claims` runs twelve local checks with no API key, and `entroly simulate` measures selection on your own repo before you connect a paid key. ## Status Young, active, and barely discovered: 475 stars, 72 forks, created 2026-03-07, pushed 2026-10-12, PyPI and npm both at 1.0.86 across 104 releases, as of 2026-10-12 (GitHub, PyPI, and npm APIs). Star History Chart Adoption is near zero for now: about 1,455 PyPI downloads and 1,044 npm downloads in the trailing month (PyPIstats and npm API, as of 2026-10-12), and a Hacker News search returns no story about the product (checked 2026-10-12, every hit unrelated entropy content). Its one external listing is the [Awesome-Context-Engineering](https://github.com/Meirtz/Awesome-Context-Engineering) survey's Context Optimization Engines section, the same list that surfaced LLMLingua here. The missing community footprint is itself the finding to weigh: at this size the project is one person's bet with no independent signal in either direction. ## Strengths - **The receipt model answers LLMLingua's worst property and Headroom's open question at once**: nothing is silently dropped, recovery is byte-exact (the README ships 13-of-13 public SDK recovery probes and a 5,117-of-5,117 source-span fidelity check as repository artifacts), and `entroly verify-claims` proves the installed path works without an API key. - The honesty markers are above the field's baseline: the README's own benchmark table shows the SQuAD 2.0 regression (80 to 72 percent) in bold, marks GSM8K as pass-through rather than a win, and prints Wilson confidence intervals with reproduction scripts. - The proxy's session rescue keeps the prompt prefix byte-stable during in-flight compaction, which protects provider prompt-cache pricing, a detail most compression layers ignore because it never shows in a benchmark. - Activation is checkable: `entroly activation status --json` reports whether a host hook actually ran, and MCP-only integrations are labeled advisory because the model can skip them. ## Cautions - **Every performance number is self-run at hobby scale**: the README table runs 20 to 50 samples per row on gpt-4o-mini, a toy next to the multi-thousand-run tables Sourcegraph and Augment publish, and the vendor's own docs say savings exist only "when requests traverse an Entroly-controlled route". - No independent evaluation, no practitioner threads, and four-digit monthly downloads mean the tool has never been fight-tested outside its author's benchmarks, as of 2026-10-12. - The native engine is a separate install with runtime repair off by default; without it, query-conditioned selection is unavailable and the README labels reduction figures "unearned", so the out-of-box experience can be much weaker than the tables. - Solo human maintainer (2,089 of 2,141 contributions), 104 releases in seven months, and a 20-topic keyword spread on the repository all say the bus factor and the focus are unproven. ## Pricing Free and open source under Apache-2.0; there is no paid tier, so pricing does not apply. The costs are your own machine for the selection pass and the tokens you fail to save. ## Compared to - [Headroom](../headroom/index.md): the closest sibling, also a local multi-channel compressor with a retrieve path; Headroom ships more surfaces and bigger claimed savings, Entroly ships receipts, byte-exact recovery, and the verification commands. - [LLMLingua](../llmlingua/index.md): the peer-reviewed ancestor whose deletions are permanent; Entroly's selection is the reversible counter-design, with almost none of its evidence base. - [rtk](../rtk/index.md): compresses the shell-output channel per command; Entroly sits at the request boundary across all channels and adds the audit layer. ## Bottom line **Recommended for teams on metered provider bills who want compression they can audit, starting with `entroly simulate` on their own repo and treating every published percentage as a hypothesis at n=20.** Not for anyone who needs an independently validated tool, a supported product, or the largest claimed savings, because none of those exist here yet. My disagreeable claim: this category's arms race measures the wrong thing, and a smaller saving a buyer can prove beats a bigger saving nobody can check, which would make receipts, not ratios, the feature the next generation of compressors copies. ## Changes - 2026-10-12 - Created from the 2026-10-12 Meirtz/Awesome-Context-Engineering entrant scan (the list's Entroly entry under Context Optimization Engines), with the zero-community-footprint finding and the small-n self-run-benchmark caveat recorded as the critical angle. ## See also - [Headroom](../headroom/index.md) - the bigger, better-known compression sibling Entroly adds the audit layer to - [LLMLingua](../llmlingua/index.md) - the destructive-pruning ancestor whose failure mode the receipts answer - [rtk](../rtk/index.md) - the output-channel filter in the same token-bill fight - [Context Engines Feature Matrix](../context-engines-feature-matrix/index.md) - the category comparison this note joins as the nineteenth column ## References - https://api.github.com/repos/juyterman1000/entroly - stars, forks, created and pushed dates, Apache-2.0 license, and topics as of 2026-10-12 - https://raw.githubusercontent.com/juyterman1000/entroly/main/README.md - mechanism, install surfaces, receipt and recovery model, the README benchmark table with the disclosed SQuAD regression, and the activation-status honesty notes (fetched 200, 2026-10-12) - https://raw.githubusercontent.com/juyterman1000/entroly/main/docs/BENCHMARKS.md - full benchmark protocols, sample sizes, the 24/24 versus 18/24 model-recovery holdout, and the WITNESS hallucination-detection numbers (fetched 200, 2026-10-12) - https://pypi.org/pypi/entroly/json - version 1.0.86 (uploaded 2026-10-04) and the 104-release train - https://registry.npmjs.org/entroly - the npm distribution at 1.0.86 - https://api.npmjs.org/downloads/point/last-month/entroly - 1,044 downloads in the month to 2026-10-10 - https://pypistats.org/api/packages/entroly/recent - 1,455 downloads in the trailing month as of 2026-10-12 - https://hn.algolia.com/api/v1/search?query=entroly&hitsPerPage=5 - the footprint check: no story about the product, all hits unrelated (fetched 2026-10-12) - https://marketplace.visualstudio.com/items?itemName=entroly.entroly-vscode - the VS Code extension surface (fetched 200, 2026-10-12) - https://github.com/Meirtz/Awesome-Context-Engineering - the entrant source: Entroly listed under Context Optimization Engines (repository and README fetched via the GitHub API, 2026-10-12)