Offline-web-search: A Local Google Search Alternative for AI Agents

What This Is

offline-web-search is a fully local Google Search alternative designed specifically for AI agents that need offline web search capabilities. It was created as a drop-in replacement for Claude's web tools, allowing LLMs to use it without complex prompting.

Core Problem Being Solved

The developer identified that existing offline search solutions for AI agents have significant limitations. Most tools either dump raw HTML files into the context window or have basic search functionality that prevents agents from finding specific documentation. This is particularly problematic in air-gapped environments, when handling sensitive data, or when building fully local stacks.

Key Technical Features

• Search Engine Behavior: Instead of text dumps, it indexes content into a local SQLite FTS5 database using BM25 ranking, title boosting, synonym expansion, prefix matching, and non-English demotion.
• Content Sources: Natively supports Kiwix ZIM archives (containing offline snapshots of Stack Overflow, Python docs, DevDocs, Wikipedia) and includes an indexing API and crawler for custom content like internal Confluence, company docs, or random HTML pages.
• Architecture: Uses a client-server model with an HTTP API. The "heavy" content server runs centrally on your network, while lightweight clients connect via MCP server for Claude Desktop or native Claude Code skill.
• Exposed Tools: Provides two standard tools: Google Search for BM25 ranked search and visit_page to return clean Markdown of full pages.

How It Works

The tool was built by reverse-engineering Claude's Web-Fetch and Web-Search tools, their system prompts, and functionality. Search results provide highly relevant, ranked snippets to the LLM, which can then use the visit_page tool to access clean, readable Markdown versions of full pages.

Who It's For

Developers building offline AI agents or heavily local setups who need reliable search capabilities without internet access.