overseek944

overseek944 / twotrim

Public

ultra-lightweight, mathematically robust prompt compression middleware

twotrim.com ai ai-cost-optimization compression-algorithm token-compression
28
2
100% credibility
Found Apr 18, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TwoTrim is an open-source tool that acts as an invisible helper between apps and AI services to shorten lengthy prompts by up to 65% while keeping answer quality high.

How It Works

1
📰 Discover TwoTrim

You hear about a simple tool that shrinks long messages to AI chat services, saving up to 65% on costs without losing smarts.

2
📦 Get it ready

Download and set it up with one easy command, like adding a helpful app to your computer.

3
Pick your way
🌐
Helper background

Start a silent sidekick that catches all your AI talks and trims them automatically.

🔧
Blend into project

Mix it directly into your app so it smartens messages before sending.

4
🔗 Link your AI friend

Point it to your favorite AI service, like chatting with a smart helper online.

5
✨ Send a big message

Type a huge story or question, watch it shrink instantly while keeping every key fact.

6
📊 Check the savings

See reports of tokens saved, costs cut, and quality stayed perfect on tough tests.

🎉 AI chats for less

Now your apps talk smarter to AI, use way fewer resources, and feel just as clever.

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Star Growth

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AI-Generated Review

What is twotrim?

Twotrim is a Python middleware that compresses LLM prompts ultra-lightweight and mathematically robust way, slashing token usage by up to 65% before hitting APIs like OpenAI or Anthropic. Drop it in as an invisible proxy server—pip install twotrim, run `twotrim serve`, tweak your OpenAI client's base_url—and watch massive prompts shrink without accuracy loss. Or use the SDK as a drop-in OpenAI client replacement for in-process compression.

Why is it gaining traction?

It stands out with zero-code proxy mode for instant deployment, CPU-only speed under 100ms, and modes like lossless (strips fluff), balanced (~40% cut), or aggressive (~65%) tuned for RAG/QA. Benchmarks on GSM8K, LongBench, and HumanEval match or beat Microsoft’s LLMLingua-2 on accuracy while skipping GPU needs. Developers love the OpenAI compatibility and LiteLLM chaining for Claude/Gemini.

Who should use this?

Backend engineers building RAG apps or chatbots with ballooning OpenAI bills. Python devs in production handling long contexts like meeting transcripts or docs. Teams using LangChain/LlamaIndex wanting token savings without refactoring prompts.

Verdict

Promising for cost-conscious LLM stacks—try the proxy first for quick wins—but at 28 stars and 1.0% credibility, it's early alpha with solid benchmarks/docs yet light tests. Worth a spin if tokens hurt; monitor evals before prod. (187 words)

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