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Mixedbread

AI-ASSISTANTS
Velocity0.0

Search and embeddings platform for building AI retrieval and agentic search

mixedbread builds embedding models and retrieval tooling, shipping in occasional bursts.

embeddingsretrievalopen-sourceinfrastructuredeveloper-tools
Current state
mixedbread works across the retrieval stack: embedding models, open-source libraries for batching and retrieval testing, and ingestion-performance work, with a Vercel Marketplace integration lowering the bar to adoption. The changelog is sparse and intermittent, with entries spanning model releases, developer libraries, and infrastructure optimization rather than a single product surface.
Where it's heading
The pattern points to a company building both the models (embeddings) and the developer tooling around them (Baguetter for retrieval testing, Batched for dynamic batching), with periodic platform integrations. Cadence is low and uneven, so the direction is best read as steady infrastructure investment rather than a fast-moving roadmap.
Prediction
The entries are too sparse to predict a specific next move with confidence; the consistent thread is embedding models plus open-source retrieval tooling, so more of both is the safe read.

Recent moves

  1. 8mo ago

    Vercel Marketplace Integration

    A Vercel Marketplace integration makes mixedbread easier to adopt within Vercel-based stacks, extending distribution without changing the core product.

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  2. 9mo ago

    Ingestion Speed Optimization (fast track)

    An ingestion-speed optimization on the fast track improves how quickly data is processed into the retrieval pipeline — a performance gain for users running larger corpora.

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  3. 1y ago

    Batched - Dynamic Batching Library

    Batched, a dynamic batching library, is open-source tooling that helps developers run model inference more efficiently — part of mixedbread's pattern of shipping infrastructure around its models.

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  4. 1y ago

    Baguetter - Retrieval Testing Framework

    Baguetter, a retrieval testing framework, gives teams a way to evaluate retrieval quality — reinforcing mixedbread's positioning across both models and the tooling to measure them.

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  5. 1y ago

    deepset-mxbai-embed-de-large-v1

    A German embedding model (deepset-mxbai-embed-de-large-v1) extends language coverage of the embedding lineup, though the entry carries no detail beyond the release itself.

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