8 Best LEANN Alternatives in 2026 (Open Source)

LEANN — [MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% priva. Achieves 97% storage savings over traditional vector databases by computing embeddings on-demand instead of storing them, enabling large-scale RAG on personal laptops.

These 8 open-source tools do the same job. They are ordered by how closely they match LEANN, with live GitHub data so you can see which projects are actively maintained.

1. 1. Chroma

   Data infrastructure for AI

   What sets it apart: Unlike Pinecone (closed, managed-only) or Weaviate (complex schema), Chroma offers the simplest developer experience with a 4-function API, automatic embedding, and zero-config in-memory mode — making it the fastest path from idea to working vector search.

   Best for: Developers who need the simplest possible vector database to prototype and build RAG applications; Projects needing an open-source, self-hosted alternative to Pinecone with minimal API surface

   ⭐ 29.4kLEANN vs Chroma →Full profile →

2. 2. Qdrant

   Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

   What sets it apart: vs Milvus: simpler setup with Rust performance and richer payload filtering; vs Pinecone: self-hostable open-source with on-disk quantization for cost efficiency; vs Chroma: production-grade with distributed deployment and hardware acceleration

   Best for: RAG applications with rich metadata filtering; Teams wanting Rust-performance vector DB with easy setup; Prototyping with in-memory mode before production

   ⭐ 34.9kLEANN vs Qdrant →Full profile →

3. 3. Milvus

   Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

   What sets it apart: vs Qdrant: designed for billion-scale with K8s-native distributed architecture and GPU acceleration; vs Pinecone: fully open-source with self-hosting option and hybrid sparse/dense vector search

   Best for: Large-scale RAG applications needing billion-vector search; Production AI apps requiring real-time vector updates; Hybrid search combining semantic and keyword matching

   ⭐ 46.3kLEANN vs Milvus →Full profile →

4. 4. Weaviate

   Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a c

   What sets it apart: Combines vector + keyword + generative search in a single query — vs Pinecone (vector-only) or Elasticsearch (keyword-first with vector bolt-on)

   Best for: Production RAG systems needing hybrid search; Semantic search applications at scale

   ⭐ 16.9kLEANN vs Weaviate →Full profile →

5. 5. zvec

   A lightweight, lightning-fast, in-process vector database

   What sets it apart: An open-source, lightweight vector database designed to run in-process within applications rather than as a separate service.

   Best for: Embedding vector search directly into AI agent applications; Local-first workspace search for AI agents and humans; Production-grade, low-latency similarity search with minimal setup

   ⭐ 16.0kLEANN vs zvec →Full profile →

6. 6. txtai

   💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

   What sets it apart: All-in-one framework combining vector search, LLM orchestration, agents, and multi-modal pipelines — unlike LangChain (orchestration-only) or Weaviate (DB-only), txtai covers the full stack from indexing to agents

   Best for: Building end-to-end semantic search + RAG applications in Python; Teams wanting a single framework for embeddings, LLM orchestration, and agents; Multi-modal search across text, images, audio, and video

   ⭐ 13.0kLEANN vs txtai →Full profile →

7. 7. embedbase

   A dead-simple API to build LLM-powered apps

   What sets it apart: Dead-simple hosted API for embeddings and semantic search with built-in LLM text generation, no vector DB hosting needed

   Best for: quick-semantic-search-setup; embedding-based-applications; building-recommendation-engines

   ⭐ 522LEANN vs embedbase →Full profile →

8. 8. memvid

   Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

   What sets it apart: Packages complete memory system into a single portable file without requiring databases or complex infrastructure.

   Best for: Long-running AI agents; Offline-first AI systems; Auditable AI workflows

   ⭐ 16.6kLEANN vs memvid →Full profile →