Cut your Claude / OpenAI / Gemini bill 70–95% on AI coding. Local proxy that compresses context, keeps provider caches hot, and verifies LLM output ($0 hallucination guard). Drop-in for Cursor, Claude Code, Codex, Aider + 34 more and custom providers — 30s, no code changes
# Add to your Claude Code skills
git clone https://github.com/juyterman1000/entrolyLast scanned: 5/23/2026
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"status": "PASSED",
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}entroly is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by juyterman1000. Cut your Claude / OpenAI / Gemini bill 70–95% on AI coding. Local proxy that compresses context, keeps provider caches hot, and verifies LLM output ($0 hallucination guard). Drop-in for Cursor, Claude Code, Codex, Aider + 34 more and custom providers — 30s, no code changes. It has 419 GitHub stars.
Yes. entroly passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/juyterman1000/entroly" and add it to your Claude Code skills directory (see the Installation section above).
entroly is primarily written in Python. It is open-source under juyterman1000 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh entroly against similar tools.
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Entroly is an auditable context control plane for AI agents. It decides what context to send, records what it left out, and produces a receipt you can inspect before trusting a hard multi-file answer.
Most compression tools shrink whatever text the agent already chose. Entroly starts one step earlier: it chooses the highest-value evidence first, compresses only after selection, keeps originals recoverable, then verifies the answer against the evidence.
Use it however you work: wrap your agent, run it as a proxy, plug it in as an MCP server, or import the library.
| What usually breaks AI coding at scale | What Entroly adds |
|---|---|
| Context windows fill with logs, duplicate files, and irrelevant chunks | Budgeted selection that favors answer-critical files, dependency links, failures, and anomalies |
| Token savings look good but quality silently drops | Accuracy-retention benchmarks, receipts, and WITNESS verification |
| Agents lose the exact line, stack trace, or omitted file they later need | Reversible compressed fragments and retrieval handles |
| First-time setup depends on one IDE or one provider | CLI, SDK, MCP, proxy, npm, PyPI, Docker, and local simulation paths |
| Enterprise teams need proof, not screenshots | Committed JSON artifacts, local self-tests, and reproducible commands |
Entroly ships as a full local runtime, not one proxy command:
| Surface | What users get |
|---|---|
| CLI | verify-claims, simulate, perf, wrap, proxy, serve, daemon, benchmark, witness, receipt, audit, doctor, health, batch, learn, ravs, cache, and more |
| SDK | compress, compress_messages, optimize, verify, hallucination detection, Context Receipts, localizers, cache alignment, cost cortex, Memory OS |
| MCP server | Context optimization, exact retrieval, receipts, recovery, feedback, security scans, codebase health, smart reads, belief verification, response verification |
| Proxy | Anthropic/OpenAI-compatible local optimization path for API-key users and custom apps |
| Node/WASM | entroly, entroly-mcp, and entroly-wasm packages for npm users |
| Trust layer | WITNESS, EICV, STAVE, receipt proofs, provenance checks, prompt-injection scanning, and local verification reports |
| Memory/session intelligence | Memory OS, Memory Fabric, long-term memory, session digests, checkpoint relevance, cache-retention forecasting, and lifetime value tracking |
| Multimodal intake | Diff, diagram, voice, image, and structured-context ingestion with provider-aware image token estimates and compliance-gated optimization |
| Gateway/accounting | Provider capability planning, failover policy, redaction receipts, usage ledger, cache routing, spend math, and budget harnesses |
| Knowledge vault/CogOps | Belief compilation, vault search, workspace change sync, epistemic routing, verification engines, and flow orchestration |
| Framework/event gateways | LangChain helpers, Ebbiforge provenance auditing, AgentSkills export, Hermes, Slack, Discord, and Telegram gateway hooks for teams that want operational feedback loops |
| Self-improvement | PRISM/RAVS feedback, autotune, skill crystallization, promotion gates, evolution logging, and budget-gated skill synthesis |
| Observability | Dashboard, daemon supervisor, control plane, health reports, value tracker, release-surface checks, and local JSON proof reports |
Under the hood, the Python control plane is backed by a Rust/WASM engine with BM25, entropy scoring, SimHash dedup, dependency graphs, knapsack/IOS selection, EGSC caching, PRISM learning, SAST, QCCR, EICV, witness checks, CogOps, cache economics, and memory primitives.
See the full code-derived map in docs/product-surface.md.
your agent ──► Entroly (local) ──► LLM provider
│
├─ rank the repo (BM25 + entropy + dep-graph)
├─ select under budget (knapsack, reversible)
├─ emit receipt (included, omitted, risks)
├─ cache-align prefix (keep provider cache hot)
└─ verify the reply (WITNESS hallucination guard)
Critical files go in full. Supporting files become signatures. Everything else becomes a reference you can expand on demand — so the model gets a broader view of your codebase in a smaller prompt. Nothing is lost: every compressed fragment is fully retrievable.
The best first run is local and proof-driven. It should work before you connect an API key, proxy, paid model, or enterprise setup.
pip install -U entroly # or: npm i -g entroly · brew install juyterman1000/entroly/entroly
1. Prove the package works on your machine:
entroly verify-claims # SDK import, indexing, optimization, exact recovery, engine mode
entroly simulate # local no-LLM savings estimate on your current repo
2. Pick one integration path:
| You are using | Run this | Why |
|---|---|---|
| Claude Code subscription | claude mcp add entroly -- entroly |
Adds Entroly tools without proxy/API-key assumptions |
| Cursor, VS Code, Windsurf, or another MCP client | entroly init or entroly serve |
Local MCP tools for context, receipts, recovery, and feedback |
| Pay-as-you-go API keys or a custom app | entroly proxy |
Transparent Anthropic/OpenAI-compatible optimization path |
| Python app | from entroly import compress, compress_messages, optimize |
Direct SDK control |
| Node/npm workflow | npm install -g entroly |
WASM runtime without a Python-first setup |
| CI or release gate | entroly batch --budget 8000 --fail-over-budget |
Enforce prompt budgets before merge |
3. Best setup for Claude Code subscription users:
claude mcp add entroly -- entroly
Claude Code stays your client. Entroly adds local tools for compression, retrieval, receipts, and savings reports.
4. One command — auto-detects your IDE, wraps your agent, opens the dashboard:
cd /your/re