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juyterman1000/entroly

juyterman1000/entroly

Context control layer for c
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Config template (adjust for your client)
claude mcp add entroly -- npx -y entroly
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
476 ★ · juyterman1000/entroly · read from the repository page · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 476 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository juyterman1000/entroly;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

Entroly — Select AI context with auditable recovery.

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Receipt-backed selection records: what was kept, what was omitted, and the handle that recovers the exact original bytes.
Compression you can undo, on your own repository, in one command — without replacing your model or agent architecture.
pip install -U entroly  ·  entroly verify-claims  ·  entroly simulate
Entroly is an open-source, local-first AI token-efficiency and Context Assurance layer: budgeted evidence selection, recoverable context compression, content-addressed evidence recovery, and auditable receipts. Works through VS Code, Claude Code, Cursor, Codex, OpenClaw, GitHub Copilot, Aider, and OpenAI/Anthropic-compatible apps.
English · 简体中文 · 繁體中文 · 日本語 · 한국어 · Español · हिन्दी · Français · Deutsch · Português · Italiano · Türkçe · Tiếng Việt · Bahasa Indonesia · Polski · Nederlands · ไทย · Svenska · Čeština · Tagalog · Română
What is it? · Install · Quickstart · Benchmarks · Questions · Contribute
---
What is Entroly? (in plain English)
Entroly is a local-first context control plane for AI coding tools. It selects
evidence within a token budget, preserves recoverable omissions, and records
selection decisions in receipts. Three properties to evaluate on your task:
| | |
|---|---|
| 💰 **Avoidable input cost** | Smaller requests can reduce input cost when they reach a provider through an Entroly-controlled route. The result depends on the workload and provider cache — see the real numbers below. |
| 🔍 **Recoverable originals** | Receipt-backed recovery retains source material locally. Exact recovery requires the referenced store and source bytes to remain available; it does not guarantee answer quality. |
| 🧾 **You can check its work** | Every decision comes with a receipt: what was kept, what was left out, and why. |
**Do I have to change my code?** No. On hosts with a verified prompt hook,
Entroly runs before the model plans. MCP-only integrations remain callable
tools that an agent may skip; API traffic is intercepted only when it is routed
through the Entroly proxy. Check entroly activation status --json instead of
assuming an installed integration is active.
**Do I need to pay for anything to try it?** No. The two commands in the
Install section below run on your own machine, with no API key, and
show you real numbers on your own project before you connect anything paid.
(Missing native-engine support is reported without downloading packages — see
the note under Install.)
**Switching coding agents mid-task?** entroly-work resume reconstructs
unfinished work from durable repository evidence, even if the previous agent
left no handoff. The receiving agent must review the inferred state and
acknowledge it before claiming work through Entroly. Start with
entroly-work --project . --json resume --to-agent codex;
the agent continuity guide shows the complete flow
and how to measure whether it actually saves rediscovery work on your tasks.
---
Contributing
Start with development setup and repository layout, then
follow the architecture, style guide,
and test instructions for the surface
being changed. Source, delivery adapters, and benchmark evidence have different
review requirements; the contributor guide explains their boundaries.
Install
> **Not sure which one?** Pick **Python**. It's the complete version and what
> most people use. The other packages expose different runtime surfaces.
| 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 | Local Node/WASM CLI; the Python package has a broader CLI, SDK, and proxy surface |
| 🦀 **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 |
**Prefer a package runner instead of a global install?** These commands use
the same published artifacts in an isolated tool cache:
The Node commands provide the local WASM CLI. The Python commands provide the
complete CLI, SDK, MCP, proxy, verification, and native-engine path described
above. Entroly's release workflow smoke-tests all five runners against the
exact version before a release is considered complete.
Source
awesome · https://github.com/juyterman1000/entroly
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