spranab

spranab / contextcache

Public

Persistent KV cache with content-hash addressing for tool-augmented LLMs

15
0
100% credibility
Found Mar 05, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ContextCache is an open-source middleware that caches tool definitions for AI models to dramatically speed up responses regardless of tool count.

How It Works

1
🔍 Find a speed fix

You're building an AI helper with lots of tools but responses start slow every time.

2
🚀 Grab ContextCache

Download the free tool and run its simple starter command—no tech skills needed.

3
🌐 Open your dashboard

Visit the web page in your browser to see your speedy assistant up and running.

4
📝 Add your tools

Paste descriptions of your tools once and watch them get saved for instant reuse.

5
💬 Chat super fast

Ask questions naturally and get the right tool picked in just 200 milliseconds—feels magical!

6
🔗 Link smart thinkers

Connect popular AI services so they handle the thinking and full answers automatically.

🎉 Blazing assistant ready

Your AI helper now zips through hundreds of tools without slowing down, saving tons of time daily.

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Star Growth

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AI-Generated Review

What is contextcache?

Contextcache is a Python middleware for tool-augmented LLMs that caches KV states of tool definitions persistently on disk using content-hash addressing, slashing prefill time from seconds to ~200ms per request. Register tools once via a simple API or SDK, and every query reuses the github persistent storage across sessions—only the user prompt gets processed fresh. It offers GPU-accelerated servers for blazing speed or CPU-only mode with llama.cpp, plus a browser dashboard for monitoring.

Why is it gaining traction?

Unlike naive tool calling where schemas reprefill every time, this delivers flat TTFT scaling to 50+ tools with zero accuracy loss, as proven by benchmarks skipping 99% of tokens. Persistent cache python means states survive restarts via SHA-256 keys, enabling multi-tenant setups without duplicate storage. Devs love the pip-installable SDK, FastAPI endpoints like /route and /pipeline, and server-side LLM credential config to keep keys secure.

Who should use this?

Backend engineers building production LLM agents with 10-100 tools, like retail bots routing "check inventory" or merchant apps querying GMV. Teams on RTX GPUs chasing sub-300ms latency, or CPU-only setups needing quick tool selection before piping to Claude/OpenAI. Ideal for FastAPI apps integrating local models like Qwen3 with external synthesis.

Verdict

Worth a quickstart spin for anyone fighting tool prefill latency—docs, benchmarks, and demo shine despite 15 stars and 1.0% credibility score signaling early days. Solid for persistent cache python prototypes, but watch for multi-GPU scaling in vNext.

(198 words)

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