entroly

Critical files appear in full. Supporting files appear as signatures. Everything else appears as references. Your AI sees the whole picture — and you pay 70-95% less.

How is this different from RAG?

| | RAG (vector search) | Entroly (context engineering) | |--|---|---| | What it sends | Top-K similar chunks | Entire codebase at optimal resolution | | Handles duplicates | No — sends same code 3x | SimHash dedup in O(1) | | Dependency-aware | No | Yes — auto-includes related files | | Learns from usage | No | Yes — RL optimizes from AI response quality | | Needs embeddings API | Yes (extra cost + latency) | No — runs locally | | Optimal selection | Approximate | Mathematically proven (knapsack solver) |

See It In Action

pip install entroly && entroly demo    # see savings on YOUR codebase

Open the interactive demo for the animated experience.

30-Second Install

pip install entroly[full]
entroly go

That's it. entroly go auto-detects your IDE, configures everything, starts the proxy and dashboard. Then point your AI tool to http://localhost:9377/v1.

Or step by step

pip install entroly                # core engine
entroly init                       # detect IDE + generate config
entroly proxy --quality balanced   # start proxy

Node.js

npm install entroly

Docker

docker pull ghcr.io/juyterman1000/entroly:latest
docker run --rm -p 9377:9377 -p 9378:9378 -v .:/workspace:ro ghcr.io/juyterman1000/entroly:latest

Install options

| Package | What you get | |---------|---| | pip install entroly | Core — MCP server + Python engine | | pip install entroly[proxy] | + HTTP proxy mode | | pip install entroly[native] | + Rust engine (50-100x faster) | | pip install entroly[full] | Everything |

Works With Everything

| AI Tool | Setup | Method | |---------|-------|--------| | Cursor | entroly init | MCP server | | Claude Code | claude mcp add entroly -- entroly | MCP server | | VS Code + Copilot | entroly init | MCP server | | Windsurf | entroly init | MCP server | | Cline | entroly init | MCP server | | OpenClaw | See below | Context Engine | | Cody | entroly proxy | HTTP proxy | | Any LLM API | entroly proxy | HTTP proxy |

Why Developers Choose Entroly

"I stopped manually pasting code into Claude. Entroly just works."

• Zero config — entroly go handles everything. No YAML, no embeddings, no prompt engineering.
• Instant results — See the difference on your first request. No training period.
• Privacy-first — Everything runs locally. Your code never leaves your machine.
• Battle-tested — 436 tests, crash recovery, connection auto-reconnect, cross-platform file locking.
• Built-in security — 55 SAST rules catch hardcoded secrets, SQL injection, command injection across 8 CWE categories.
• Codebase health grades — Clone detection, dead code finder, god file detection. Get an A-F grade.

OpenClaw Integration

OpenClaw users get the deepest integration — Entroly plugs in as a Context Engine:

| Agent Type | What Entroly Does | Token Savings | |------------|---|---| | Main agent | Full codebase at variable resolution | ~95% | | Heartbeat | Only loads changes since last check | ~90% | | Subagents | Inherited context + Nash bargaining budget split | ~92% | | Cron jobs | Minimal context — relevant memories + schedule | ~93% | | Group chat | Entropy-filtered messages — only high-signal kept | ~90% |

from entroly.context_bridge import MultiAgentContext

ctx = MultiAgentContext(workspace_path="~/.openclaw/workspace")
ctx.ingest_workspace()
sub = ctx.spawn_subagent("main", "researcher", "find auth bugs")

How It Works

| Stage | What | Result | |---|---|---| | 1. Ingest | Index codebase, build dependency graph, fingerprint fragments | Complete map in <2s | | 2. Score | Rank by information density — high-value code up, boilerplate down | Every fragment scored | | 3. Select | Mathematically optimal subset fitting your token budget | Proven optimal (knapsack) | | 4. Deliver | 3 resolution levels: full → signatures → references | 100% coverage | | 5. Learn | Track which context produced good AI responses | Gets smarter over time |

Context Engineering, Automated

"The LLM is the CPU, the context window is RAM."

| Layer | What it solves | |---|---| | Documentation tools | Give your agent up-to-date API docs | | Memory systems | Remember things across conversations | | RAG / retrieval | Find relevant code chunks | | Entroly (optimization) | Makes everything fit — optimally compresses codebase + docs + memory into the token budget |

These layers are complementary. Entroly is the optimization layer that ensures everything fits without waste.

CLI Commands

| Command | What it does | |---------|---| | entroly go | One command — auto-detect, init, proxy, dashboard | | entroly demo | Before/after comparison with dollar savings on YOUR project | | entroly dashboard | Live metrics: savings trends, health grade, PRISM weights | | entroly doctor | 7 diagnostic checks — finds problems before you do | | entroly health | Codebase health grade (A-F): clones, dead code, god files | | entroly benchmark | Competitive benchmark: Entroly vs raw context vs top-K | | entroly role | Weight presets: frontend, backend, sre, data, fullstack | | entroly autotune | Auto-optimize engine parameters | | entroly digest | Weekly summary: tokens saved, cost reduction | | entroly status | Check running services |

Quality Presets

entroly proxy --quality speed       # minimal optimization, lowest latency
entroly proxy --quality balanced    # recommended (default)
entroly proxy --quality max         # full pipeline, best results
entroly proxy --quality 0.7         # any float 0.0-1.0

Platform Support

| | Linux | macOS | Windows | |--|---|---|---| | Python 3.10+ | Yes | Yes | Yes | | Rust wheel | Yes | Yes (Intel + Apple Silicon) | Yes | | Docker | Optional | Optional | Optional | | Admin/WSL required | No | No | No |

Production Ready

• Persistent savings tracking — lifetime savings in ~/.entroly/value_tracker.json, trend charts in dashboard
• IDE status bar — /confidence end