Track the latest improvements to our platform, models, and API features. We're constantly working to make our products better.
We published three new cookbooks to help you build with Mixedbread Search. The Image Search cookbook guides you through building visual search applications. Chat with PDFs demonstrates how to create conversational interfaces over your documents. Agent Memory shows how to use Mixedbread as persistent memory for AI agents.
Agentic search is now available, designed for AI agents that need to retrieve information autonomously. The rewritten query is exposed in responses so you can see how the system interprets your searches. Read more in the agentic search documentation.
Stores now support web search as a data source. You can retrieve web results directly via the store compatible API, enabling search across both uploaded documents and web content in a single query.
You can now create API keys with search-only scope. These restricted keys can perform searches but cannot modify stores, upload files, or access other endpoints. This is useful for client-side applications where you want to limit what the key can do.
We launched a free plan that lets you explore Mixedbread Search without commitment. Free tier users get up to 1000 store files and access to core platform features. Token-based pricing is now displayed on the pricing page with an interactive calculator to estimate costs.
Spending limits give you control over your API costs. Set a maximum spend and the system will automatically pause requests when you approach the limit, preventing unexpected charges. Cost tracking is visible in the dashboard with breakdowns by store and product.
The MCP integration now supports OAuth authentication, making it easier to connect Claude Desktop and other MCP-compatible tools to your Mixedbread stores securely. Users can authorize access through the standard OAuth flow rather than manually configuring API keys.
Mixedbread is now available on the Vercel Marketplace as a Vercel Native integration in the Searching (and Agents) categories. Install it to:
Connect projects in one click: we’ll add the required MXBAI_API_KEY and
MXBAI_STORE_ID env vars to your Vercel projects automatically.
Manage from the Vercel dashboard: monitor usage and costs with unified Vercel billing and access controls alongside your other integrations. Read more about it in our Vercel integration docs.
Ship faster with a starter: deploy our example Next.js app to see Mixedbread Search in action and customize from there.
Mixedbread remains fully available as a standalone platform. Use it directly or via Vercel, whichever fits your workflow.
We're excited to announce the public beta of Mixedbread Search, the easy-to-use search API built from the ground up for the AI era. It is a fully-managed search engine that allows you to upload your data and start searching in minutes.
Today we're excited to announce that we've optimized the ingestion speed and concurrency of our Mixedbread Search. We can ingest 1000s of files concurrently with sub 1 second latency. This is a significant improvement over the previous 20 files concurrency limit.
Mixedbread public stores allow users to make stores publicly accessible, so that anyone with an API key can search them. This is useful for public documentation, public knowledge bases, and other use cases where you want to share your data with the world.
We've optimized the hot path for the search latency of our search system, achieving sub 90ms latency without reranking and sub 120ms with reranking. This includes embedding generation, first stage single-vector retrieval, second stage multi-vector retrieval, and (optionally) reranking.
MaxSim CPU is a CPU-optimized version of MaxSim, a state-of-the-art similarity search operator for late-interaction models such as ColBERT and ColPali. It powers our multi-vector retrieval system, achieving sub 5ms latency on AVX2 machines.
Introducing the Mixedbread Model Context Protocol (MCP) server, a TypeScript-based integration that exposes powerful store capabilities to AI assistants like Claude Desktop. Built as an open standard, it enables secure and controlled access to external data sources.
Introducing the Mixedbread CLI, a command-line interface for managing Mixedbread's services directly from your terminal. Built on top of the Mixedbread SDK, it provides efficient command-line access to all core platform features.
Today we're excited to announce the alpha of the Mixedbread platform, bringing together all our capabilities in a unified API. Experience state-of-the-art embeddings, reranking, document parsing, and stores in one integrated solution.
The second generation of our reranking models features reinforcement learning (GRPO), extended context handling, and support for 100+ languages. These models are 8x faster than comparable alternatives while achieving higher accuracy across all benchmarks.
Our smallest and most efficient English embedding model. Perfect for edge deployments and resource-constrained environments, it delivers competitive performance in an extra small footprint.
We've open-sourced Batched, our dynamic batching library that powers Mixedbread's inference infrastructure. Achieve up to 10x throughput improvements with minimal latency impact.
Introducing Baguetter, our open-source framework for testing and evaluating retrieval systems. Make your search better with comprehensive testing tools designed for real-world scenarios.
We've developed BMX, a modern take on the classic BM25 algorithm. Combining lexical and semantic signals, BMX delivers superior hybrid search performance.
In collaboration with deepset, we've released a German/English embedding model that sets new performance standards among open source alternatives.
Our ColBERT model brings late interaction capabilities. Built on our mxbai-embed-large-v1 architecture, it achieves state-of-the-art performance on 13 BEIR benchmarks.
Our flagship English embedding model delivers state-of-the-art performance, outperforming closed source alternatives like OpenAI's text-embedding-v3.
The world's first 2D-Matryoshka embedding model. This innovative approach allows you to reduce both the number of layers and dimensions while maintaining competitive performance.
We're launching our first generation of reranking models, available in three sizes. These models add a powerful semantic layer to existing search systems, dramatically improving result relevance.