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Squeez

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Squeeze verbose LLM agent tool output down to only the relevant lines.

LLM coding agents waste 80-95% of context tokens on irrelevant tool output. Squeez trains a small (2-3B) generative model to identify and extract only the lines that matter — compressing tool output by ~86% on average.

Quick Example

$ cat django/middleware.py | squeez "Fix the CSRF validation bug"
class CsrfViewMiddleware(MiddlewareMixin):
    def _check_referer(self, request):
        referer = request.META.get('HTTP_REFERER')
        if referer is None:
            raise RejectRequest('No referer')
        good_referer = request.get_host()
        if not same_origin(referer, good_referer):
            raise RejectRequest('Bad referer')

From 42 lines of middleware code, squeez extracts only the 8 lines relevant to the CSRF referer check.

Highlights

  • ~86% compression — keeps only the lines the agent needs
  • CLI + Python API — cat file | squeez "task" or ToolOutputExtractor
  • Two backends — vLLM server (fast) or local transformers (no server)
  • Config-driven — YAML config, env vars, or CLI args
  • Agent-ready — works with Claude Code, Codex CLI, OpenCode via instruction files
  • Open dataset — 9K samples from real SWE-bench tool execution + synthetic multi-ecosystem data