MarcellM01

MarcellM01 / TinySearch

Public

Shrink the web for your local LLMs!

14
2
100% credibility
Found May 14, 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

TinySearch is a lightweight, local tool that performs web searches, crawls websites, ranks relevant content chunks using hybrid methods, and assembles source-grounded prompts for AI language models.

How It Works

1
🔍 Discover TinySearch

You hear about TinySearch, a simple tool that helps your AI assistant research the web and provide reliable answers with sources.

2
📥 Get it on your computer

Download the files to a folder on your computer and follow the easy steps to prepare it, like creating a safe space for it to work.

3
🔧 Set it up quickly

Run a simple command to install what it needs and let it grab a helpful thinking tool if missing, all happening automatically.

4
🔗 Connect to your AI helper

Tell your AI coding tool or assistant where TinySearch lives so they can team up for better research.

5
🚀 Start researching

Ask a question like 'How do I fix this code?' and watch TinySearch search the web, read pages, and pick the best info.

6
📄 Get ready-to-use info

It hands back a clear summary prompt packed with the most relevant snippets and exact web links for proof.

✅ Your AI shines

Feed the prompt to your AI and get spot-on, trustworthy answers with citations, making your work faster and more accurate.

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Star Growth

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AI-Generated Review

What is TinySearch?

TinySearch is a Python-based research engine that shrinks the web for local LLMs, turning raw queries into concise, source-grounded prompts. It queries DuckDuckGo, ranks results with hybrid search, crawls top pages to extract relevant chunks, and delivers a ready-to-use prompt with cited URLs—no full pages dumped into context. Run it via MCP for agents, FastAPI for APIs, or direct pipelines, all locally with optional ONNX embeddings.

Why is it gaining traction?

It stands out by staying tiny and private: no dashboards, accounts, or persistent caches, just on-demand web access that keeps LLMs grounded in verifiable snippets. Developers dig the MCP integration for tools like Cursor, plus swappable local/OpenAI embeddings and tunable hybrid ranking that beats dumping unfiltered results. The config JSON and trace logs make tweaking dead simple without vendor lock-in.

Who should use this?

LLM agent builders wiring web research into Cursor, Cline, or Roo Code setups. Local AI devs needing tiny search for LLMs without API costs, or Python scripters prototyping RAG pipelines. Ideal for those shrinking web pages to fit tight contexts in offline or edge deployments.

Verdict

Grab it if you're experimenting with local LLM agents—solid docs, tests, and MCP/FastAPI entrypoints make setup fast despite 14 stars and 1.0% credibility signaling early maturity. Polish the config for production; it's a smart shrink on web bloat today.

(198 words)

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