Qdrant
Open source · RAG and vector databases · qdrant/qdrant
What it does
Qdrant is a vector database: it stores embeddings of your text or images and finds the closest matches quickly, with filters on extra fields. It is written in Rust and has a hosted cloud option.
Good for: The search layer behind a "chat with your documents" app.
Qdrant on GitHub (opens in a new tab) Project homepage (opens in a new tab)
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The signals we checked
- Licence
- Apache-2.0 (OSI-approved open-source licence)
- Popularity
- Stars: 35,091 (checked 16 Oct 2026)
- Last commit
- 15 Oct 2026
- Open issues and PRs
- 782
- Status
- Active (not archived)
- Self-host
- EasyDocker one-liner.
- GitHub topics
- ai-search
- ai-search-engine
- embeddings-similarity
- hnsw
- hybrid-search
- image-search
- knn-algorithm
- machine-learning
- mlops
- nearest-neighbor-search
More in rag and vector databases
- RAGFlow A self-hosted knowledge base over company documents, with answers that cite their source.
- LlamaIndex Developers building document Q&A or data agents in Python or TypeScript.
- Milvus Vector search that may need to grow to very large collections.
- Chroma Adding document search to a prototype or small app with very little setup.
- Weaviate Search over your own content that mixes keyword matching with meaning-based matching.
No affiliate links on this page, and we are not paid by this project. The summary is our own plain-English description; check the repo for current features before you rely on it.
Stars, licences and commit dates come from the GitHub API, checked 16 Oct 2026. "Last commit" is the last push to the repo. Star counts are a rough popularity signal, not a quality score. There are no affiliate links in this section: links go straight to each project.