Open Source Mac Search Software
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Search Software for Mac
View 19 business solutionsBrowse free open source Search software and projects for Mac below. Use the toggles on the left to filter open source Search software by OS, license, language, programming language, and project status.
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DocFetcher
Desktop search application
DocFetcher is an Open Source desktop search application: It allows you to search the contents of files on your computer. — You can think of it as Google for your local files. The application runs on Windows, Linux and Mac OS X. -
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Bowtie, an ultrafast, memory-efficient short read aligner for short DNA sequences (reads) from next-gen sequencers. Please cite: Langmead B, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.
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CLucene is a C++ port of Lucene: the high-performance, full-featured text search engine written in Java. CLucene is faster than lucene as it is written in C++.
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ripgrep
Regex pattern directory search tool that respects your .gitignore
ripgrep is a line-oriented search tool that actively searches the directory you're currently in for a regex pattern. By default, ripgrep will ignore your .gitignore and skip hidden files or directories and binary files automatically. ripgrep has first class support on Windows, macOS and Linux, with binary downloads available for every release. ripgrep is similar to other popular search tools like The Silver Searcher, ack and grep. ripgrep supports arbitrary input preprocessing filters which could be PDF text extraction, less supported decompression, decrypting, automatic encoding detection and so on. In other words, use ripgrep if you like speed, filtering by default, fewer bugs and Unicode support. -
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Meilisearch
An open-source, lightning-fast, and hyper-relevant search engine
An open-source, lightning-fast, and hyper-relevant search engine that fits effortlessly into your apps, websites, and workflow. Meilisearch is a flexible and powerful user-focused search engine that can be added to any website or application. Search-as-you-type returns answers in less than 50 milliseconds. That's faster than the blink of an eye! Deploy in a matter of minutes. Smart presets let you start searching through your data with zero configuration. Send data to Meilisearch however you want, no need to match a schema or convert your dataset to a compatible format. Everyone makes mistakes! If typos break your search experience, many users will leave thinking what they were looking for just wasn't there. Start searching through your dataset in less than 5 minutes and quickly connect your codebase to Meilisearch with our official libraries. Meilisearch is designed to work out-of-the-box, no configuration needed. -
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FSearch
A fast file search utility for Unix-like systems based on GTK3
FSearch is a blazing-fast file search utility for Linux inspired by Windows’ Everything Search Engine, built to allow users to locate files and directories instantly by name rather than content. It achieves its high performance by indexing the filesystem and updating that index in real time, so as you create, rename, or delete files the results stay up to date without noticeable lag. The intuitive user interface supports incremental search — where results filter as you type — plus features like regular expressions, case-sensitivity options, and customizable indexing scopes, making it both powerful and accessible. Designed with GTK and written in C, it integrates nicely with desktop environments like GNOME, XFCE, and Cinnamon while still being lightweight and resource-efficient. Developers and power users appreciate FSearch for dramatically reducing the time it takes to find files compared to traditional search tools, especially on large partitions. -
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fd
A simple, fast and user-friendly alternative to 'find'
fd is a program to find entries in your filesytem. It is a simple, fast and user-friendly alternative to find. While it does not aim to support all of find's powerful functionality, it provides sensible (opinionated) defaults for a majority of use cases. First, to get an overview of all available command line options, you can either run fd -h for a concise help message or fd --help for a more detailed version. fd is designed to find entries in your filesystem. The most basic search you can perform is to run fd with a single argument: the search pattern. Instead of just showing the search results, you often want to do something with them. fd provides two ways to execute external commands for each of your search results: the -x/--exec option runs an external command for each of the search results (in parallel), or the -X/--exec-batch option launches the external command once, with all search results as arguments. -
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fzf
A command-line fuzzy finder
fzf is a general-purpose command-line fuzzy finder. It's an interactive Unix filter for command-line that can be used with any list; files, command history, processes, hostnames, bookmarks, git commits, etc. fzf will launch an interactive finder, read the list from STDIN, and write the selected item to STDOUT. Without STDIN pipe, fzf will use find command to fetch the list of files excluding hidden ones. (You can override the default command with FZF_DEFAULT_COMMAND). fzf by default starts in fullscreen mode, but you can make it start below the cursor with the height option. Unless otherwise specified, fzf starts in "extended-search mode" where you can type in multiple search terms delimited by spaces. Fuzzy completion for files and directories can be triggered if the word before the cursor ends with the trigger sequence, which is by default **. Fuzzy completion for PIDs is provided for the kill command. In this case, there is no trigger sequence. -
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truffleHog
Searches through git repositories for high entropy strings and secrets
truffleHog searches through git repositories for high entropy strings and secrets, digging deep into commit history. TruffleHog runs behind the scenes to scan your environment for secrets like private keys and credentials, so you can protect your data before a breach occurs. Secrets can be found anywhere, so TruffleHog scans more than just code repositories, including SaaS and internally hosted software. With support for custom integrations and new integrations added all the time, you can secure your secrets across your entire environment. TruffleHog is developed by a team entirely comprised of career security experts. Security is our passion and primary concern, and all features are developed with best practices in mind. TruffleHog enables you to track and manage secrets within our intuitive management interface, including links to exactly where secrets have been found. TruffleHog runs quietly in the background, continuously scanning your environment for secrets. -
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Search and replace operations on file content accross multiple files. Recursive operations within entire directory trees. FAR comes with support for regular expressions (regex) over multiple lines, automatic backup and various character encodings. Run grep like extractions to condense or rearrange sources, or perform bulk file renaming.
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Katalog
Catalog your devices to search, analyze, and backup your files.
Catalog your devices to search, analyze, and backup your files: - Create catalogs from different sources or devices - Search and explore files even when the devices are disconnected - Organize and backup your collection of files, and get statistics User documentation: - Get started/Tutorial: https://stephanecouturier.github.io/Katalog/docs/tutorial - Download (Linux Flathub): https://flathub.org/en/apps/io.github.stephanecouturier.Katalog - Documentation: https://stephanecouturier.github.io/Katalog/docs/Overview - Development: https://stephanecouturier.github.io/Katalog/docs/Development-Overview - Facebook: https://www.facebook.com/Katalog-107117844916308 -
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Elasticsearch
A Distributed RESTful Search Engine
Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types. -
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searchmonkey
Powerful desktop search app for Windows, Linux and Mac
Power searching on your desktop without the pain. Perform powerful desktop searches without having to index your system using regular expressions. Graphical equivalent to find/grep. Available for Windows (JAVA) and Linux (Gnome & KDE) and easy to translate into your locale. -
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Career-Ops
AI-powered job search system built on Claude Code
Career Ops is an open-source platform designed to help individuals manage their job search process with a structured, operations-style approach that treats career development like a pipeline. It provides a system for organizing job applications, tracking progress across different stages, and maintaining visibility into opportunities, much like a lightweight CRM tailored for job seekers. The project emphasizes clarity and accountability, enabling users to monitor applications, follow-ups, and outcomes in a centralized environment. It is typically built with modern web technologies, allowing for easy customization and self-hosting by developers who want full control over their data. Career Ops focuses on reducing the chaos and fragmentation that often comes with job searching by introducing a repeatable workflow that can be iterated and improved over time. It also supports the idea of data-driven job searching. -
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Fuse.js
Lightweight fuzzy-search, in JavaScript
Fuse.js is a powerful, lightweight fuzzy-search library, with zero dependencies. Generally speaking, fuzzy searching (more formally known as approximate string matching) is the technique of finding strings that are approximately equal to a given pattern (rather than exactly). It might not make sense for every situation, but can be ideal depending on your search requirements. For example, qhen you want client-side fuzzy searching of small to moderately large data sets. Also, when you can't justify setting up a dedicated backend simply to handle search. ElasticSearch or Algolia, although both great services, may be overkill for your particular use cases. You can access all options above via Fuse.config. This is useful if you want to override default options for all Fuse instances. Fuse.js supports logical query operators. These operators are used for filtering the data and getting precise results based on the given conditions. -
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Ransack
Object-based searching
Create search forms for your Ruby on Rails application with Ransack! Ransack is available in two modes: simple and advanced, allowing you to create either simple or advanced search forms. Simple mode works much like MetaSearch and is very easy to set up. The advanced mode makes use of Rails' nested attributes functionality in order to generate complex queries with nested AND/OR groupings, etc. This involves more complexity but does produce some very interesting search interfaces and puts more power in the hands of users. Ransack is compatible with Rails 5.0, 5.1, 5.2 and 6.0, and on Ruby 2.3 and later. -
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Sonic
Fast, lightweight & schema-less search backend
Sonic is a super fast and lightweight, schema-less search backend that can be used in place of super-heavy and full-featured search backends like Elasticsearch. It is able to normalize language search queries, auto-complete search queries and offer the most relevant results. Being an identifier index rather than a document index, when queried it provides IDs that can be used to refer to matched documents in an external database. -
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Cerebro
Open-source productivity booster with a brain
Cerebro is an open source electron-based productivity software that lets you search and see everything you need on your PC in one place. With just a few clicks you can search on your machine or on the internet everything you need; see everything you need without having to open another application; and easily find and use what you want. -
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SWR
React Hooks library for remote data fetching
The name “SWR” is derived from stale-while-revalidate, a HTTP cache invalidation strategy popularized by HTTP RFC 5861. SWR is a strategy to first return the data from cache (stale), then send the fetch request (revalidate), and finally come with the up-to-date data. With SWR, components will get a stream of data updates constantly and automatically. And the UI will be always fast and reactive. With just one single line of code, you can simplify the logic of data fetching in your project, and also have all many amazing features out-of-the-box. SWR has you covered in all aspects of speed, correctness, and stability to help you build better experiences. Fast page navigation, polling on interval, data dependency, revalidation on focus, revalidation on network recovery, local mutation (Optimistic UI), smart error retry, pagination and scroll position recovery, React Suspense, and more. -
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turbovec
A vector index built on TurboQuant, written in Rust with Python
turbovec is a Rust-based vector index with Python bindings for fast similarity search. It is built around TurboQuant, a quantization approach designed to reduce vector storage while preserving useful distance information. The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. TurboVec is useful for developers building retrieval, ranking, semantic search, recommendation, or AI memory systems. Its main value is combining Rust performance with a Python-facing workflow for modern vector search experiments and applications. -
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A small application for listing the contents(files/folders/extensions) of a folder. The results includes file name length/full path length/maximum extensions. It supports exporting a report in .txt/.csv format with fully customizable contents.
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AnySearch Skill
Unified real-time search engine skill for AI agents
AnySearch Skill is a real-time search engine skill for AI agents. It gives agents a structured way to search the web, run vertical searches, perform parallel batch searches, and extract full-page content. The project is packaged as a skill rather than a standalone search application, so it is meant to be installed into compatible AI-agent environments. It supports multiple domain-specific search categories, making it useful when general web search is too broad. The skill can also fetch page content after a URL is found, which helps agents answer questions from source material rather than snippets alone. Its main value is turning web search into a reusable, agent-friendly capability with consistent inputs and outputs. -
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Apache Lucene
Apache Lucene open-source search software
The Apache Lucene™ project develops open-source search software. The project releases a core search library, named Lucene™ core, as well as PyLucene, a Python binding for Lucene. Lucene Core is a Java library providing powerful indexing and search features, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. The PyLucene sub-project provides Python bindings for Lucene Core. The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are defined by collaborative consensus-based processes, an open, pragmatic software license and a desire to create high-quality software that leads the way in its field. Apache Lucene, Apache Solr, Apache PyLucene, Apache Open Relevance Project and their respective logos are trademarks of The Apache Software Foundation. -
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ENAS in PyTorch
PyTorch implementation of "Efficient Neural Architecture Search
ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and educational codebase, helping practitioners understand how ENAS works in practice and how to reproduce results on benchmark datasets. The project includes training scripts, model definitions, and search procedures that show the full workflow from architecture sampling to evaluation. Because ENAS relies on shared weights among candidate models, the implementation emphasizes efficiency and experiment reproducibility. -
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MiniSearch
Minimalist web-searching platform with an AI assistant
MiniSearch is a minimalist web search application with a built-in AI assistant that runs largely inside the browser for privacy-focused information retrieval. The project combines metasearch capabilities with local or remote language model inference to provide conversational answers alongside traditional search results. It is designed to be lightweight, easy to deploy with Docker, and configurable for both personal and hosted use cases. The platform supports browser-level integration so users can set it as their default search engine for quick access. Its architecture emphasizes privacy by avoiding tracking and minimizing data collection while still enabling advanced AI features. Overall, MiniSearch targets users who want a self-hosted, Perplexity-style search experience with strong control over data and models.
