sqz: context compression for AI coding agents

sqz compresses tool output before it enters the model’s context window. It runs as a hook and an MCP server for Claude Code, Cursor, Windsurf, Cline, Gemini CLI, Codex, Kiro, Zed and Copilot CLI, and as a proxy in front of any other MCP server. Deterministic Rust, no LLM calls, offline, and every compressed result can be recovered byte-exact.

cargo install sqz-cli sqz-mcp     # or: brew install ojuschugh1/sqz/sqz · npm i -g sqz-cli · pipx install sqz
sqz init                          # installs hooks, MCP server and agent guidance for every client it finds

Then work as usual. sqz stats --breakdown shows what it saved, per command, from your own sessions. sqz doctor verifies the whole chain: binary, database, shell hook, which clients are actually routed through sqz, and whether anything was compressed recently.

Not installed yet? If you already use Claude Code or Kiro, sqz discover --replay replays your existing transcripts through the engine and reports what it would have saved — a counterfactual estimate on your own sessions, computed locally.

Where the tokens actually go

Across 3,003 real compressions sqz saved 178,442 tokens, a 24.7% average reduction. That number is deliberately not the headline, because most of the tools in this space are graded on ratio alone, and a compressor that hits 90% by dropping the line the agent needed has made the session more expensive.

What the data shows instead:

• Command output dominates. Build logs, test runs, git status, package installs. A 1,200-token cargo test with one failure becomes the failure, the assertion and the file:line, about 120 tokens. Forty-five commands have purpose-built formatters; unknown output goes through a generic pipeline that folds repeated lines and never touches source code, stack traces or secrets.
• Identical file re-reads are rare. Measured on 92 Claude Code sessions by a user who counted: 1 in 542 reads. What repeats is unchanged command output (a 13-token §ref§), line ranges of a file already read in full (a §ref:HASH:L40-80§), and files re-read after a small edit (a line-level delta).
• Over-compression is tracked, not hidden. sqz stats reports how often the agent re-ran a command whose output had not changed, or asked for an original back. Those regret signals sit next to the savings so you can discount the number yourself.

Guides

• Stop AI coding agents from re-reading the same files: what repeats, the three kinds of reference, and the one path sqz cannot see.
• MCP context compression: the sqz-mcp tools, the proxy for any MCP server, per-client configuration.
• Context rot vs context compression: why compaction arrives late and what proactive context management looks like.
• Choosing a context compression approach: shell wrappers, session dedup, MCP proxies, retrieval and LLM summarisation compared by approach, plus how to compare tools yourself.

Measurements

• Quality benchmark: compression ratio against information preservation, run as a regression test on every change. Includes the zeros and the bugs it found.
• Token savings benchmark: session-level dedup and delta numbers.
• White paper: pre-injection context compression for agentic code generation.
• Per-formatter reductions, backed by fixtures.

Integrations

• Overview: three integration levels and a config snippet for every client.
• Claude Code, Cursor, Codex CLI, all platforms.
• VS Code, JetBrains, Firefox, Chrome, API proxy.

Reference

• Source and issues on GitHub. If sqz mangles a command you use daily, the best report is the command and its output; it becomes a fixture in the quality benchmark.
• API docs: sqz-engine, sqz-mcp on docs.rs, or this site’s copy.
• Changelog. Machine-readable index for AI tools: llms.txt.