Topics / #prompt-caching

Prompt-caching projects on GitHub

Caching LLM prompt prefixes to cut latency and token costs

As of , the most-starred Prompt-caching projects on GitHub are DeepSeek-Reasonix (35.8K stars), pxpipe (7.5K stars) and OpenExecutive (5.6K stars). GitHub.Locker tracks 5 Prompt-caching repositories with 58.7K stars combined. The fastest-growing on October 16 was cost-xray, with +206 stars in a day.

5Projects
58.7KStars in total
+240Stars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

5 projects · by stars

  1. #1 · esengine/DeepSeek-Reasonix

    DeepSeek-Reasonix

    Reasonix is a coding agent for complex software engineering tasks, built as a single local engine that you access through a terminal, desktop app, browser, or your editor via ACP.

    35.8K
  2. #2 · teamchong/pxpipe

    pxpipe

    pxpipe is a local proxy that cuts Claude Code's (and other LLM clients') input token usage by rendering bulky context — system prompts, tool docs, and conversation history — as compact PNG images instead of raw text, since an image's token cost depends on its pixel size, not the amount of text packed into it.

    7.5K
  3. #3 · SenteLabsAI/OpenExecutive

    OpenExecutive

    Open Executive is an AI system that acts as a company's virtual executive team — presented as one senior advisor with Harvard MBA-level knowledge, backed by eight specialist agents (CSO, CFO, HR, General Counsel, COO, CMO, CPO, Board Communications).

    5.6K
  1. 4

    cost-xray

    5.4Ktigerless-labs/cost-xray · Python

    cost-xray is a local tool that captures the raw API traffic between Claude Code or Codex and their providers, then breaks down exactly what's in each request and what each part costs — system prompts, tool schemas, MCP blocks, cached vs fresh tokens, and more.

  2. 5

    opencode-dynamic-context-pruning

    4.4KTarquinen/opencode-dynamic-context-pruning · TypeScript

    Dynamic Context Pruning (DCP) is a plugin for OpenCode that automatically cuts token usage by intelligently managing conversation context — compressing stale history into summaries, deduplicating repeated tool calls, and purging failed tool-call inputs.

Often tagged together