AI Developer Tools

szr

szr is a Go-native CLI proxy that reduces noisy terminal output before it reaches an LLM context, saving tokens and preserving command exit codes.

What is szr?

szr is an open-source CLI proxy designed to minimize token usage when feeding terminal command outputs to large language models (LLMs). It wraps commands like git diff, test suites, and grep, compressing noisy output while preserving critical information such as error lines, diagnostics, and exit codes. It also provides deduplication, delta summaries, and full-output recoverability.

szr vs Similar AI Tools

Pricing ModelFreeFreeFreeFree
Free Credits
Key Features
  • Wraps shell commands to produce compact, token-efficient output
  • Preserves critical information (errors, line numbers, exit codes)
  • Deduplication and delta summaries for re-run efficiency
  • Seven breakable boxes covering OWASP Agentic Top-10 vulnerabilities
  • Three guided simulations for cascading failures, human-agent trust, and rogue agents
  • Network-isolated Docker containers for safe execution
  • Code-to-runtime reasoning across cloud, Git, and Kubernetes
  • Action-gate enforces read-only policy on every API call
  • Sandboxed JavaScript execution for concurrent research
  • Append-only, SHA-256-addressed event history through Jaybase
  • AES-256-GCM encryption for stored node payloads
  • Unified RBAC for ledger, notes, snapshots, and audit reads
Pros
  • Significantly reduces token usage for common shell commands
  • Preserves exit codes and critical diagnostic information
  • Covers full OWASP Agentic Top-10 in a realistic manner
  • Docker isolation prevents accidental damage
  • Read-only by construction prevents accidental writes
  • Evidence-backed verification cross-checks every finding
  • Opinionated and secure accounting CLI with immutable audit trail
  • Designed for AI agent integration with JSON output
Cons
  • Only supports a limited set of commands out of the box
  • Requires installation and configuration
  • Requires Docker and technical setup
  • Not for production use; only for lab environments
  • Requires an LLM API key, incurring token costs
  • Limited to read-only operations, cannot remediate
  • Pre-1.0, limited feature set
  • No native QuickBooks import (agents must normalize data)
Best For
  • Developers using AI coding assistants to reduce token consumption
  • Teams running frequent git commands or test suites in LLM contexts
  • Security researchers focusing on AI agent vulnerabilities
  • Developers building MCP-based applications
  • Security engineers
  • DevOps teams
  • Small teams needing secure, auditable accounting with AI agent support
  • Developers integrating automated bookkeeping workflows

How to use szr?

  1. 1Install via Go, Homebrew, npm, or pip.
  2. 2Run any command through szr: szr git status or szr go test ./...
  3. 3Configure by editing config.json or using szr settings.
  4. 4Install AI tool integrations with szr install claude-code or similar.
  5. 5Monitor savings with szr spread and szr discover.

szr Key Features

  • Wraps shell commands to produce compact, token-efficient output
  • Preserves critical information (errors, line numbers, exit codes)
  • Deduplication and delta summaries for re-run efficiency
  • Artifact recovery: full output stored locally with a reference pointer
  • AI tool integration hooks (Claude Code, Cursor, Gemini, Codex)

szr Use Cases

  • Reducing token costs in AI-assisted development workflows
  • Sending cleaner command outputs to LLMs for debugging or explanation
  • Minimizing context window usage when iterating on git diffs or test runs
  • Aggregating savings across sessions and agents

szr Pricing & Free Credits

szr currently operates on a Free model.

This tool is completely free to use

Free

$0

Open-source CLI tool available under Apache 2.0 license

szr Pros & Cons

Pros

  • Significantly reduces token usage for common shell commands
  • Preserves exit codes and critical diagnostic information
  • Open source and free to use
  • Supports integration with major AI coding assistants
  • Deduplication avoids redundant processing of identical output

Cons

  • Only supports a limited set of commands out of the box
  • Requires installation and configuration
  • Some users may prefer raw output for full control

What is szr best for?

  • Developers using AI coding assistants to reduce token consumption
  • Teams running frequent git commands or test suites in LLM contexts
  • Anyone seeking to lower costs when using pay-per-token AI models

szr FAQ

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