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proxy that compresses Claude Code, Claude Desktop, Codex, Codex Desktop, Aider, Gemini CLI and Ollama context windows to save thousands of tokens per session","maintainers":[{"name":"sergioramosv","email":"sergioramosvicente2004@gmail.com"}],"readme":"# Squeezr\n\n**Token compression proxy for AI coding CLIs.** Sits between your CLI and the API, compresses context on the fly — **without ever breaking Anthropic's prompt cache** — and saves thousands of tokens per session. Real-time dashboard, MCP integration, and an optional AI compression layer powered by **Zest**, Squeezr's own model.\n\n[![npm](https://img.shields.io/npm/v/squeezr-ai)](https://www.npmjs.com/package/squeezr-ai) [![license](https://img.shields.io/npm/l/squeezr-ai)](LICENSE)\n\n## Supported CLIs & apps\n\n| Client | Protocol | Proxy method |\n|--------|----------|--------------|\n| Claude Code | HTTP to Anthropic API | `ANTHROPIC_BASE_URL=http://localhost:8080` |\n| Claude Desktop | HTTP to Anthropic API | Windows: `setx` (via `squeezr setup`); macOS: `launchctl setenv`; Linux: `environment.d` |\n| Aider | HTTP to Anthropic/OpenAI API | `ANTHROPIC_BASE_URL` / `openai_base_url` |\n| OpenCode | HTTP to Anthropic/OpenAI API | `ANTHROPIC_BASE_URL` / `openai_base_url` |\n| Gemini CLI | HTTP to Gemini API | `GEMINI_API_BASE_URL=http://localhost:8080` |\n| Ollama | HTTP (local) | Transparent via dummy API key detection |\n| Codex Desktop | HTTP to OpenAI API | `~/.codex/config.toml` → `openai_base_url` (via `squeezr setup`) |\n| Codex CLI | WebSocket to chatgpt.com | TLS-terminating MITM proxy on :8081 |\n| Cursor IDE | HTTP to OpenAI API (BYOK only) | `localhost:8080` directly (CORS) or `squeezr tunnel` |\n| Continue (VS Code) | HTTP to OpenAI-compat | `apiBase: http://localhost:8080/v1` |\n\nWorks with both API keys and subscription plans (OAuth) — Claude Code Max/Pro, OpenAI Plus, etc.\n\n## Quick start\n\n```bash\nnpm install -g squeezr-ai\nsqueezr setup   # env vars, auto-start, CA trust, MCP server — all automatic\nsqueezr start\n```\n\n## Prompt-cache safety (the core design principle)\n\nAnthropic bills cached context at **0.1x** — but only if the request prefix arrives **byte-for-byte identical** between requests. A proxy that mutates history differently on each request silently invalidates that cache and re-bills the full context at full price, costing far more than compression saves.\n\nSqueezr is built around this constraint:\n\n- **Deterministic compression is byte-stable** — same input always produces the same output (fixed-pressure rules), so the cached prefix never changes → Anthropic's cache keeps hitting.\n- **Unstable passes never touch the cached prefix** — AI compression, cross-turn dedup, diff-reads and stale-turn summaries only operate past the last `cache_control` marker (or freely for clients that don't use caching).\n- **Live cache health monitoring** — the dashboard's *Prompt Cache* card shows `cache_read` vs `cache_creation` tokens and a Hit Health %. Green (≥80%) means you're paying the minimum possible; red means something is invalidating your prefix.\n\nFull write-up: [docs/PROMPT_CACHE.md](docs/PROMPT_CACHE.md)\n\n## How it works\n\nEvery request passes through Squeezr on `localhost:8080`. Compression layers, in order:\n\n1. **MCP tool filtering** (opt-in) — drop tool definitions from MCP servers you block (`mcp_block_servers`) or that aren't allow-listed. A single chatty MCP server can cost ~18k tokens *per request*. Servers used in the conversation are never filtered.\n2. **Tool description compression** — tool descriptions are truncated to their first paragraph (Bash: 10,441 → 53 chars), with the full spec stored in the expand store. The model retrieves it on demand via `squeezr_expand`. Saves ~17k tokens/request on a stock Claude Code session.\n3. **System prompt compression** — skill/plugin duplicate blocks deduplicated; optional AI summarization (gated behind `ai_compression`).\n4. **Deterministic preprocessing** — zero-latency regex rules on every tool result: ANSI/progress-bar/timestamp stripping, line dedup, JSON minification, plus ~30 tool-specific patterns (git, vitest/jest, tsc, eslint, cargo, pytest, docker, kubectl, gh…). Byte-stable → cache-safe.\n5. **Cross-turn dedup & diff-reads** — repeated tool outputs collapse to references; repeated file reads become diffs against the latest read. (Only past the cache barrier.)\n6. **Stale-turn summarization** — conversations >40 turns get old assistant prose collapsed to keyword summaries. (Only for clients without prompt caching.)\n7. **AI compression** (opt-in, off by default) — old blocks above the AI floor (~1000 chars, auto-raised by the quality governor) summarized by a small model. Backends: **Zest (local, free, deterministic)**, Haiku, GPT-4o-mini, Gemini Flash. Heavily guarded so it only ever helps:\n   - **Structured-data guard** — JSON / JSONL / record dumps / tables are *never* AI-rewritten (a model can silently blank a field value); they stay in their deterministic form. Prose/logs still get compressed.\n   - **Compressibility probe** — a one-shot `deflate` estimate skips already-dense blocks (path/error/test dumps) that wouldn't beat the min-ratio, so no wasted backend calls.\n   - **Acceptance guardrail + retry-with-correction** — every AI result is validated; if it dropped a critical token (path/URL/error code) the model is re-prompted with the exact tokens to restore, else the result is rejected and the deterministic form is kept. Nothing that loses a hard token is ever used.\n   - **Quality governor** — watches expand-rate and guard-reject-rate and auto-raises the min block size (or pauses) when quality dips.\n   - **Backend-aware limits** — local Zest is free → no rate limit, generous timeout, processed sequentially (Ollama serialises anyway). Cloud backends keep a hard cap (20 calls/5 min) and a short timeout to protect spend.\n   - Plus the persistent on/off toggle and the cache barrier.\n\n### Recovery: nothing is ever lost\n\nEvery compressed block embeds a `squeezr_expand(id)` reference. A `squeezr_expand` tool is injected into each request — if the model needs the original content, it retrieves it in one call. The dashboard tracks expand rate as the compression-quality metric (0 = nothing important was lost).\n\n### Adaptive pressure\n\nCompression aggressiveness scales with context usage: <50% → light (1500-char threshold), 50–75% → normal (800), 75–90% → aggressive (400), >90% → critical (150).\n\n## Web dashboard\n\n`http://localhost:8080/squeezr/dashboard` — 3 pages, SSE-updated:\n\n| Page | What it shows |\n|------|---------------|\n| **Overview** | **Today-scoped** (resets at midnight): tokens saved, two honest ratios — **% of total sent today** and **% of the last request** (changes every turn), cost comparison (today), Cost/Savings-by-type breakdown (today), Top Tools, Session Cache, AI Compression card (calls / saved / spent / net), **Prompt Cache health** (read vs creation + hit %, **persisted across restarts**), by model / by client, compression mode + **Bypass / AI Compression toggles** |\n| **Savings** | Day / Week / Month / All-time filters with period navigation — per-period tokens, cost, sessions, charts, By Model / By Client / Top Tools / AI Compression / Session Cache, all persisted across restarts |\n| **Settings** | Client base-URL reference, ports, version/uptime, bypass & circuit breaker state, **AI Compression on/off**, **Restart / Stop buttons**, update check |\n\n## Safety & resilience\n\nSqueezr sits in the critical path. It is designed to never break your workflow — and never burn your plan:\n\n- **Bypass mode (persisted)** — one click/command disables all compression; survives restarts. The emergency stop.\n- **AI compression master switch (persisted, default OFF)** — with a subscription OAuth token, AI compression calls bill against *your own plan*; Squeezr refuses to auto-route to Haiku on an OAuth token. Use the free local Zest backend or a separately billed API key.\n- **AI rate limiter (cloud only)** — hard cap of 20 AI calls per 5-minute sliding window for paid cloud backends (protects spend). Local Zest is free → not rate-limited.\n- **AI minimum block size (~1000 chars, governed)** — small blocks can't be compressed without loss; Squeezr never AI-compresses below the floor, and the quality governor raises it automatically if reject/expand rates climb.\n- **Structured-data & compressibility guards** — AI never rewrites structured data (JSON/records → no field corruption), and dense/incompressible blocks skip AI entirely.\n- **Acceptance guardrail + retry-with-correction** — AI output that drops a critical token or doesn't save enough is rejected (after one corrective retry); the deterministic form is kept.\n- **Cache barrier** — unstable passes can't touch the cached prefix (see prompt-cache safety above).\n- **Circuit breaker + backend-aware timeouts** — 3 consecutive AI backend failures → AI disabled for 60s, deterministic continues. Local calls get a generous timeout and run sequentially (Ollama serialises) so they don't false-timeout.\n- **Atomic persistence** — stats, history, caches and toggles are written atomically (tmp + rename); a crash can't corrupt them.\n- **Self-test on startup** — detects port squatting (the classic `$.speed` Claude Code error), env-var drift, and pipeline issues.\n\n## Honest metrics\n\nOne source of truth (`~/.squeezr/stats.json`, continuous net counters — never inflated per-session sums):\n\n- **Net saved** = what actually left your requests, after `[squeezr:id]` tag overhead.\n- **Savings by type** — deterministic, dedup, tool descriptions, stale turns, AI, MCP filter, system prompt (gross per-technique, labeled as such).\n- **AI spend tracking** — every compression backend call's real token usage (input/output, per model) is counted and shown against what it saved.\n- **Prompt cache** — `cache_read` vs `cache_creation` from Anthropic's real usage fields. Anthropic's cache discount is shown separately and *not* claimed as Squeezr savings.\n\n## Zest — Squeezr's own compression model\n\nZest (`zest-0.8b`, fine-tuned from Qwen3.5-0.8B with LoRA) is Squeezr's local compression model: free, runs on CPU via Ollama, and **deterministic in greedy decoding** (temperature 0) — which makes AI compression byte-stable and therefore cache-safe. Status: deployed and selectable as the `local` backend (Ollama). Training data is being regenerated against Squeezr's own runtime guard (every example must keep all hard tokens — paths/URLs/error codes — and clear the min-ratio) so the model learns guard-passing compression instead of token-dropping. Design doc: [docs/REINVENT_AI.md](docs/REINVENT_AI.md)\n\n## MCP server\n\nInstalled automatically into Claude Code, Cursor, Windsurf and Cline by `squeezr setup`.\n\nTools: `squeezr_status`, `squeezr_stats`, `squeezr_set_mode`, `squeezr_config`, `squeezr_habits`, `squeezr_stop`, `squeezr_check_updates`, `squeezr_update`, `squeezr_set_project`, `squeezr_bypass`.\n\n## Configuration\n\nUser config lives at **`~/.squeezr/squeezr.toml`** (survives npm updates). A project-local `.squeezr.toml` deep-merges on top.\n\n```toml\n[compression]\nthreshold = 800              # min chars to compress a tool result\nkeep_recent = 3              # recent tool results never touched\nai_compression = false       # MASTER switch for AI calls — default OFF (see Safety)\nbackend = \"local\"            # auto | local (Zest) | haiku | gpt-mini | gemini-flash\ncompress_system_prompt = true\ncompress_conversation = true\ncompress_assistant_ai = false # AI-compress long old assistant turns (prose-heavy chats)\nstale_turns = true           # auto-disabled when prompt-cache markers are present\ntool_desc_compress = true    # first-paragraph truncation + expand recovery\ntool_desc_expand = true\n# mcp_block_servers = [\"some-mcp\"]   # drop these servers' tools (~18k tok/req each)\n# mcp_allow_servers = [\"github-mcp\"] # if set, only these survive\n# skip_tools = [\"Read\"]              # never compress these tool types\n# Per-command: append \"# squeezr:skip\" to any Bash command to skip its result\n\n[cache]\nenabled = true\nmax_entries = 1000\n\n[adaptive]\nenabled = true               # pressure-based thresholds (see Adaptive pressure)\n\n[local]\nenabled = true\nupstream_url = \"http://localhost:11434\"\ncompression_model = \"qwen2.5-coder:1.5b\"   # or zest-0.8b once published\n```\n\nEnv vars: `SQUEEZR_PORT`, `SQUEEZR_MITM_PORT`, `SQUEEZR_THRESHOLD`, `SQUEEZR_KEEP_RECENT`, `SQUEEZR_DISABLED`, `SQUEEZR_DRY_RUN`, `SQUEEZR_LOCAL_UPSTREAM`, `SQUEEZR_LOCAL_MODEL`.\n\n## CLI commands\n\n```bash\nsqueezr setup          # configure everything (env, auto-start, CA, MCP)\nsqueezr start          # start the proxy\nsqueezr restart        # stop + start (reloads config)\nsqueezr stop           # stop the proxy\nsqueezr update         # install latest from npm + restart\nsqueezr status         # is it running? version, port, self-test\nsqueezr logs           # last 50 log lines\nsqueezr config         # print current config\nsqueezr ports          # change HTTP / MITM ports\nsqueezr gain           # all-time savings (--session, --details, --reset)\nsqueezr discover       # detect installed AI CLIs\nsqueezr bypass         # toggle bypass (--on / --off) — persisted\nsqueezr tunnel         # Cloudflare Tunnel (Cursor IDE)\nsqueezr mcp install    # register MCP server (mcp uninstall to remove)\nsqueezr desktop start  # separate proxy for Claude/Codex Desktop (stop/status)\nsqueezr uninstall      # remove completely\nsqueezr version\n```\n\n## REST endpoints\n\n`/squeezr/stats` · `/squeezr/history` · `/squeezr/health` · `/squeezr/bypass` (GET/POST) · `/squeezr/ai-compression` (GET/POST) · `/squeezr/config` · `/squeezr/native-compact` · `/squeezr/control/restart` · `/squeezr/control/stop` · `/squeezr/dashboard`\n\n## Requirements\n\nNode.js ≥18, ~140 MB RAM, no GPU. Full details: [docs/HARDWARE_REQUIREMENTS.md](docs/HARDWARE_REQUIREMENTS.md)\n\n## Documentation\n\n| Doc | Contents |\n|-----|----------|\n| [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) | Internal architecture |\n| [docs/PROMPT_CACHE.md](docs/PROMPT_CACHE.md) | How Anthropic's prompt cache works + the 3 cache-breakers we found and fixed |\n| [docs/REINVENT_AI.md](docs/REINVENT_AI.md) | Data-driven design of the AI compression layer (Zest) |\n| [docs/HARDWARE_REQUIREMENTS.md](docs/HARDWARE_REQUIREMENTS.md) | Measured hardware requirements |\n| [docs/TOOLS_ISSUE.md](docs/TOOLS_ISSUE.md) | The 28k-tokens-per-request tool descriptions problem |\n| [CHANGELOG.md](CHANGELOG.md) | Full version history |\n\n## Troubleshooting\n\n**Claude Code throws `undefined is not an object (evaluating '$.speed')`** — something that isn't Squeezr is squatting on the port (often a Docker container on 8080). Run `squeezr status`; if it reports a foreign service: `squeezr ports` to move, or stop the offender. `cat ~/.squeezr/runtime.json` shows the actual bound port.\n\n**Dashboard shows 0 AI calls with AI Compression ON** — expected with Claude Code: the prompt-cache barrier leaves nothing safe for AI to compress (everything cacheable is protected, everything recent is preserved). AI compression shines for clients without prompt caching, or via the (upcoming) stable Zest pipeline.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}