Open Source Search Apps

Find the best open source search solutions to power your applications! Index, store & retrieve data efficiently. Improve site search and accelerate discovery.

16 apps available

Apps in Search

Go
97%
BSD 3

Weaviate

Databases · Search

16,854

Open-source vector database combining semantic search, hybrid queries, RAG, and image search in a single cloud-native system built for production scale.

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91
Repo Health
85
Technical
67
Dependency
Built with
Go 97%
Updated 5 days ago
Python
67%
Apache 2.0

SurfSense

AI Assistants · Search

16,270

The open-source, unlimited NotebookLM alternative with real-time collaboration, a desktop app, and no vendor lock-in.

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87
Repo Health
71
Technical
66
Dependency
Built with
Python 67%
TypeScript 30%
Updated 1 weeks ago
Python
82%
AGPL 3.0

SearXNG

Search

37,694

Privacy-first metasearch engine that aggregates results from 250+ search services — no tracking, no profiling, full control when self-hosted.

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79
Repo Health
82
Technical
83
Dependency
Built with
Python 82%
Updated 1 weeks ago
Python
94%
Apache 2.0

SWIRL

Data Engineering · Databases · Search

3,047

Federated AI search and RAG across 100+ enterprise sources—no data extraction, no vector database required.

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62
Repo Health
83
Technical
65
Dependency
Built with
Python 94%
Updated 1 weeks ago
Python
66%
Other

Morphik

AI Development · Databases · Search

3,716

Morphik is an AI-native ingestion and retrieval engine that lets developers store, search, and reason over visually rich documents — scanned PDFs, manuals, slides, and video — without duct-taping together OCR, an embedding model, and a vector database.

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60
Repo Health
71
Technical
67
Dependency
Built with
Python 66%
TypeScript 23%
Updated 1 weeks ago

About Search

The Search category encompasses applications designed to index, store, and retrieve information efficiently. These tools are critical for any system dealing with significant amounts of data where rapid access is a requirement.

Key features commonly found in these applications include:

  • Indexing: Building searchable representations of data sources.
  • Query Parsing: Understanding user intent and translating it into effective searches.
  • Ranking Algorithms: Ordering search results by relevance, ensuring the most useful information appears first.
  • Faceted Search: Allowing users to refine results based on specific criteria (e.g., date, author, category).
  • Full-Text Search: Searching within the content of documents and files.
  • Autocompletion & Suggestions: Helping users formulate effective queries.

Open source search solutions are valuable for a wide range of use cases. They power site search on websites, enable document retrieval within organizations, facilitate e-commerce product discovery, and are core components of many data analytics pipelines. They solve the problem of information overload, making it easier for users to access the data they need quickly and reliably. Choosing an open source solution provides flexibility, control over data privacy, and the ability to customize functionality for unique requirements.

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