erikbern/ann-benchmarks

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

5717 stars · 903 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A benchmarking environment for approximate nearest neighbor (ANN) search algorithms, with pre-generated HDF5 datasets and Docker containers for each evaluated library. It publishes recall vs queries-per-second results across many datasets and distance metrics.

Use cases

• compare approximate nearest neighbor libraries
• benchmark vector search algorithms
• evaluate faiss vs hnswlib vs annoy performance
• measure recall and queries per second for ANN indexes
• find the fastest nearest neighbor search for my dataset
• benchmark similarity search on standard datasets

When to choose

• you need historical, reproducible ANN benchmark results across many algorithms
• you want a ready-made harness with datasets and Docker containers for evaluating a new ANN method
• you need recall/QPS plots across distance metrics like angular, euclidean, hamming, and jaccard

When to avoid

• you need an actively maintained benchmark - the project is no longer maintained and points to alternatives like VIBE
• you need production vector search rather than benchmarking
• you need benchmarks of very recent algorithms not included in the evaluated set

Facets

library · maturity abandoned

benchmarking search-engine vector-database machine-learning databases performance python ann nearest-neighbor-search vector-search hdf5-datasets recall-vs-qps algorithms docker linux

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markdown · JSON · MCP: product_card(name="erikbern/ann-benchmarks")