A privacy respecting metasearch engine that aggregates results from multiple search services and databases.
SearXNG is an open-source, self-hosted metasearch engine that aggregates results from multiple search services and databases. It functions as a proxy between the user and various search engines, ensuring that users are neither tracked nor profiled during their search activities. By acting as an intermediary, the software hides the identity of the requester from the destination search engines.
The software is deployed as a server application, allowing individuals or organizations to host their own private search instance. It processes queries by fetching data from a variety of external sources and presenting the combined results in a single interface. This deployment model gives the administrator full control over the data flow and the specific search engines used to populate the results page.
Built with Python, the application focuses on privacy by stripping identifying information before forwarding requests to the underlying search engines. The architecture allows the administrator to fine-tune the instance through a dedicated configuration guide, enabling the selection of specific engines and the adjustment of how results are aggregated. This makes the software suitable for technical users who require a centralized search hub that does not collect personal data or build user profiles for advertising purposes.
Because it is licensed under the GNU Affero General Public License, the software encourages community contributions and transparency in its codebase. Users can connect with other operators and developers via Matrix to discuss installation and optimization strategies for their specific server environments.
SearXNG serves as a privacy-focused tool for users who prefer to manage their own search infrastructure.
A lightweight search engine providing full text indexing with minimal resource requirements and a built-in UI.
A community driven search and analytics suite for ingesting, searching, visualizing, and analyzing unstructured data at scale.
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