MixedBread
Embeddings, rerankers, and retrieval infrastructure for RAG and agents.
About
MixedBread is a Berlin-based AI search and retrieval platform providing the building blocks for RAG and agent retrieval: mxbai-embed embedding models, mxbai-rerank rerankers (including v2 and the v3-listwise instruction-following reranker), multimodal stores, document parsing, structured extraction, and connector-based ingestion across text, PDFs, images, audio, and video in 100+ languages.
Models are published as open weights on Hugging Face and served through a hosted REST API, with first-party integrations for LangChain and the Vercel AI SDK. The platform targets developers who want state-of-the-art retrieval quality without operating their own embedding infrastructure.
Caveats: the embedding and reranking market is crowded (Cohere, Voyage, Jina, open models), so evaluate benchmark claims against your own data, and compare hosted-API costs with self-hosting the open weights at scale.
What it can do
- mxbai embedding models
- mxbai reranking models
- Document parsing and chunking
- Multimodal retrieval stores
- Open weights plus hosted API
Setup
Sign up for an API key at mixedbread.com, or self-host the open-weight models from Hugging Face.
Prerequisites
API key for the hosted service; documents to index.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 82; Trending score 78/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.