# ANNLib: A Development Framework for Efficient Approximate Nearest Neighbor Search

arXiv: https://arxiv.org/abs/2607.17582 · HTML: https://mlsanity.com/paper/2607.17582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 66 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph](https://mlsanity.com/paper/1707.00143/) (2017)
- [FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search](https://mlsanity.com/paper/2105.09613/) (2021)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings](https://mlsanity.com/paper/2405.19504/) (2024)
- [SPFresh: Incremental In-Place Update for Billion-Scale Vector Search](https://mlsanity.com/paper/2410.14452/) (2024)
- [Visual Search at Alibaba](https://mlsanity.com/paper/2102.04674/) (2021)
- [ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms](https://mlsanity.com/paper/2305.04359/) (2023)
- [Results of the Big ANN: NeurIPS'23 competition](https://mlsanity.com/paper/2409.17424/) (2024)
- [Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search](https://mlsanity.com/paper/2202.06212/) (2022)
- [The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform](https://mlsanity.com/paper/1908.07389/) (2019)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Approximate Nearest Neighbor Search with Window Filters](https://mlsanity.com/paper/2402.00943/) (2024)
- [Low-Latency Graph Streaming Using Compressed Purely-Functional Trees](https://mlsanity.com/paper/1904.08380/) (2019)
- [Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data](https://mlsanity.com/paper/2505.06501/) (2025)
- [PAM: Parallel Augmented Maps](https://mlsanity.com/paper/1612.05665/) (2016)
- [kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search](https://mlsanity.com/paper/2501.06121/) (2025)
- [Parallel Nearest Neighbors in Low Dimensions with Batch Updates](https://mlsanity.com/paper/2111.04182/) (2021)
- [Efficient Graph-Based Approximate Nearest Neighbor Search Achieving: Low Latency Without Throughput Loss](https://mlsanity.com/paper/2504.20461/) (2025)
- [VecFlow: A High-Performance Vector Data Management System for Filtered-Search on GPUs](https://mlsanity.com/paper/2506.00812/) (2025)

## Abstract (arXiv)

Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines. Recently, many ANNS systems have been proposed to provide broad, flexible functionalities or achieve high performance. However, it is inherently difficult to achieve both. We propose ANNLib to address this gap. ANNLib is a library that provides a programming framework to achieve high performance and flexible functionalities for ANNS systems, based on popular graph-based ANNS algorithms. We carefully decouple and independently optimize both the algorithm and the data structure components in an ANNS system. In addition, we integrate state-of-the-art algorithms and data structures as modules in ANNLib, as well as our new designs. Users can choose combinations of components to support sophisticated settings with high performance, such as filtered search, fully dynamic updates, historical queries on snapshots, and range searches. Our experiments show that our new solution provides a simple interface for various applications, and achieves comparable or even better performance to previous work specifically for each application.