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AgentReady – Drop-in proxy that cuts LLM token costs 40-60%

AgentReady – Drop-in proxy that cuts LLM token costs 40-60%

by christalingx·Feb 23, 2026·8 points·13 comments
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AI Analysis

●●●BangerShip ItSolve My ProblemSlick

Drop-in proxy that cuts GPT token costs 40-60% without changing app code.

Strengths
  • •Deterministic, rule-based compression (no secondary LLM call) means genuinely predictable cost savings and latency
  • •Zero integration friction: swap base_url in any OpenAI SDK, works with OpenAI/Anthropic/Google
  • •Real, verifiable example showing 52% compression on complex prompts with identical model output quality
Weaknesses
  • •Compression rules are opaque—no transparency into what gets stripped or why, risk of breaking edge cases
  • •Cost savings alone don't address quality: lossy compression could degrade outputs for prompt-sensitive tasks like code generation
Target Audience

Engineers shipping LLM-powered applications with per-token billing concerns

Similar To

Langsmith · LlamaIndex · Helicone

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