AbYousef739

AbYousef739 / -clawcache-free

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The People's LLM cost tracker & cache. 100% local, fiercely private, built for OpenClaw. Track every penny, cache every response.

19
2
89% credibility
Found Feb 17, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ClawCache Free is a Python library that automatically tracks costs for AI language model usage and caches repeated responses to reduce expenses.

How It Works

1
🔍 Discover ClawCache

You hear about ClawCache when you're tired of spending too much money chatting with smart AI helpers.

2
📦 Add it to your project

You easily add ClawCache to your simple program with one quick step, no hassle.

3
✨ Smart wrap your AI chats

You gently wrap your AI conversation calls so ClawCache watches costs and remembers answers automatically.

4
💬 Chat with AI as usual

You keep asking your AI the same questions, and it pulls saved answers instantly, saving you money.

5
📊 Check your savings anytime

You peek at a simple daily report to see exactly how much you've spent and saved that day.

💰 Celebrate your savings

You smile seeing real money saved from reused answers and clear spending insights, building smarter for less.

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AI-Generated Review

What is -clawcache-free?

ClawCache Free is a Python library that tracks every LLM API call's cost across providers like OpenAI, Anthropic, Mistral, and Ollama, while caching exact-match responses locally to cut repeat expenses. Developers decorate their sync or async LLM functions with cost-monitoring hooks, getting automatic token counts, spending logs, and CLI reports via `clawcache --report` showing daily totals, savings, and cache hit rates. It's the people's LLM cost tracker—100% local, fiercely private, built as a free cache for cost-conscious OpenClaw users.

Why is it gaining traction?

It stands out with zero mandatory dependencies, cross-platform file locking for safe concurrent use, and built-in 2026 pricing for accurate projections, delivering 58% cache hits in tested scenarios like code review and data analysis. Developers hook it in minutes for instant visibility into LLM burn rates, unlike generic counters or cloud tools that leak data. The CLI spits out actionable reports on spent vs. saved dollars, making cost control feel effortless.

Who should use this?

Backend engineers wrapping LLM calls in production apps, AI prototyping teams monitoring OpenAI/Anthropic bills, or indie devs building chatbots who hate surprise invoices. Ideal for code review assistants, content generators, or data analysts repeating queries, especially those eyeing people's university LLM experiments or we the people GitHub projects.

Verdict

Grab it for basic LLM cost tracking and caching if you're prototyping—it's functional, MIT-licensed, and pip-installable with solid docs. With just 12 stars and a 0.9% credibility score, it's early-stage and pushes a pro upgrade, so test thoroughly before scaling.

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