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🎤 Reverie, Nov 5, ⚡ ML Data Loading Performance Guide, 🧠 CrewAI Cognitive Memory on LanceDB
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Reverie launches as LanceDB's summit for AI builders, GPU training bottlenecks traced to CPU-side transforms, and CrewAI unifies cognitive memory on LanceDB, plus upcoming events, enterprise updates, and community updates.
Feature Engineering for Multimodal Data: From Laptop to Cluster with LanceDB
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Run a multimodal feature-engineering pipeline in ten minutes with geneva-examples, then rebuild its CLIP embedding step with LanceDB's feature engineering API, the same calls you'd use to enrich your own tables on LanceDB Enterprise.
Announcing Reverie Summit: What AI’s Next Breakthroughs Are Made On
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One day with the researchers and engineers building generative video, world models, physical AI, and the data systems beneath them.
⚡ Multi-Bit RaBitQ Without Refine, 🌋 Bytedance’s Lance-Based AI Stack, 🤖 Lance for Embodied AI Data
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Multi-bit RaBitQ hits 96% recall without refine overhead, Volcano Engine cuts training pipelines from 7 days to 1, Lance powers 100 Hz robotics data ingestion, plus upcoming events, enterprise updates, community updates.
Why CrewAI Rebuilt Agent Memory on LanceDB, Powering 2B+ Agent Executions
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CrewAI replaced a two-system memory stack with LanceDB, then rebuilt agent memory to resolve contradictions and gate recall on confidence. It now ships by default across 12 million monthly downloads and 2B+ agent executions.
Data Loading for AI/ML: A Comprehensive Guide
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A deep dive into data loading for model training — the three pipeline stages, parallelism strategies, shuffling, caching, resumability, and how LanceDB's StreamingDataset fits in.
How ByteDance’s Volcano Engine Rebuilt Its AI Stack on Lance, From Data Lake to Agent Memory at 100K+ QPS
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Volcano Engine rebuilt its AI data stack on Lance, cutting a seven-day pipeline to one day and powering agent memory with LanceDB at 100K+ QPS.
Make Handwritten Notes Searchable: Optimizing an OCR Pipeline with LanceDB
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Learn how to build an OCR pipeline for handwritten medical notes using DSPy, GEPA, and LanceDB to manage images, labels, outputs, metrics, and retrieval.
Rebuilding the Data Foundation for Embodied AI with Lance: From Long Videos to Random-Access-Friendly Multimodal Samples
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Lance turns long robotics videos into random-access multimodal training data with 1.7–6× faster reads and 42% lower storage use.
RaBitQ Gets Faster: Higher Recall, Lower Latency, Query-Time Control
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How LanceDB's latest IVF_RQ improvements raise recall, cut p99 latency, and let teams tune vector search at query time with approx_mode.
📊 Lance vs Delta vs Iceberg, 🔗 Lance Blob V2 Late Materialization, 🤖 Stable-Worldmodel Research Platform
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Benchmark Lance vs Delta vs Iceberg on S3 metadata performance, update blob rows without reading bytes, and train world models directly from object storage, plus upcoming events and enterprise and community updates.
From Messy PDFs to Verifiable Answers with LiteParse and LanceDB
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Turn information-rich PDFs into a local, inspectable, searchable evidence store with LlamaIndex's LiteParse library and LanceDB.
Faster VLM Fine-Tuning With Materialized Model Features in LanceDB
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How Lance format and LanceDB's Enterprise feature engineering platform make VLM fine-tuning faster by materializing expensive multimodal features once and training from those columns.
Lance Blob V2: Late Materialization for Large Binary Data in Spark
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How late materialization in Lance Spark keeps large binary data as lightweight references through query plans, materializing bytes only on write.
Semantic Memory for Hermes Agent with LanceDB
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Introducing a new LanceDB-backed memory plugin that gives Hermes Agent durable, semantic recall across sessions, with benchmarks and a hands-on remember/recall/forget walkthrough.
A Metadata Benchmark of Lance, Delta Lake, and Iceberg on S3
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A Rust benchmark comparison of Lance, Delta Lake, and Apache Iceberg on S3 and S3 Express, and why Lance is optimized for object storage metadata.
Scalable Feature Engineering on Multimodal Datasets
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How LanceDB uses the Lance format's flexible data evolution features to enable scalable feature engineering for multimodal datasets.
Stable-Worldmodel: A High Performance Platform for Reproducible World Model Research
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Introducing stable-worldmodel, an open-source platform for reproducible world model research, evaluation, and benchmarking under visual and physical distribution shifts.
Reproducible Data Curation In The Multimodal Lakehouse
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Learn how LanceDB turns raw multimodal data into reproducible, training-ready datasets with search, filtering, deduplication, sampling, and versioned curation workflows.
🌍 Lance-Backed World Model Platform, 🦆 Multimodal SQL with Lance DuckDB Extension, 💰 LanceDB vs OpenSearch Cost Breakdown
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stable-worldmodel standardizes world model pipelines on Lance, DuckDB Lance extension adds native multimodal SQL, and LanceDB benchmarks 100M vectors at ~$779/month, plus upcoming events, enterprise updates, and community updates.
⚡Vector Search at 10B Scale, 📊 Lance Format Benchmarks, 🚗 AV Pipelines at Scale
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Distributed vector search at 10B scale, more efficient storage with Lance format v2.2, and production AV pipelines simplified, plus upcoming events and community updates.
How LanceDB Accelerates Vector Search at 10 Billion Scale
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How LanceDB applies distributed indexing, distributed query execution, HNSW centroid routing, and fast RaBitQ rotation to scale search to 10B vectors and beyond.
OpenSearch vs LanceDB for Vector Search: Query Cost and Infrastructure
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Choosing a vector database usually comes down to a tradeoff between a full search service and an in-process library. This post showcases benchmarks that compare OpenSearch and LanceDB on the COCO 2017 images embedded with SigLIP. We measure ingestion throughput, query cost, storage layout, and overall infra cost.
Volcano Engine LAS's Lance-Based PB-Scale Autonomous Driving Data Lake Solution
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How Bytedance Volcano Engine LAS (Lake for AI Service) leverages Lance as the core storage format, rapidly constructing a next-gen AI data lake to efficiently store, manage, and process multimodal data (text, images, audio/video).
Unifying the AV ML Stack: From Raw Data to Trained Model with LanceDB
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A complete walkthrough of building an autonomous vehicle perception model training pipeline on top of LanceDB and the Multimodal Lakehouse.
Lance JSON Support: Why You Might Not Really Need Variant
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Lance's JSONB storage, scalar indexing, data evolution, and full-text search already deliver what most users want from Variant — with explicit control, schema consistency, and no vendor lock-in.
Building A Storage Format For The Next Era of Biology
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How Lance can serve as the foundation for AI on single-cell genomics atlases and a new generation for modeling in biology.
📄 Lance Blob V2, 🤗 Upload Lance Datasets to HF Hub, 🦞 LanceDB for OpenClaw's Memory
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Lance Blob V2 introduces adaptive storage semantics, easily upload Lance datasets to Hugging Face Hub, and OpenClaw establishes LanceDB as a default memory layer for agents, plus community and enterprise updates.
Smart Parsing Meets Sharp Retrieval: Combining LiteParse and LanceDB
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Build a structure-aware PDF QA agent with LiteParse, LanceDB, and Claude to answer complex questions over visually rich documents.
Lance Format v2.2 Benchmarks: Half the Storage, None of the Slowdown
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Benchmarks that show how Lance format v2.2 cuts storage by 50%+, beats Parquet on compression, and delivers up to 68x faster blob reads — while preserving the scan and random access patterns that multimodal training depends on.
Make your SQL Workflows Multimodal With LanceDB × DuckDB
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A hands-on walkthrough showing how LanceDB and DuckDB work together to query multimodal data in SQL, join against multiple tables, and materialize results back into LanceDB.
Agentic Coding as Community Stewardship
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Agentic coding lets upstream maintainers turn ecosystem-wide mechanical updates into fast, contextual, low-friction contributions for downstream users.
One System, Many Workloads: Rethinking What "Multimodal" Means for AI
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A practical definition of multimodal complexity, and how LanceDB’s Multimodal Lakehouse is built to address these challenges.
The Future of AI-Native Development is Local: Inside Continue's LanceDB-Powered Evolution
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Discover how Continue revolutionized AI-native development with LanceDB's embedded TypeScript library, enabling lightning-fast semantic code search while maintaining complete developer privacy and offline capability.
Lance File Format 2.2: Taming Complex Data
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Lance file format 2.2 introduces Blob V2, nested schema evolution, native Map type support, and additional compression and performance improvements for AI/ML data workloads.
Lance Blob V2: Making Multimodal Data a First-Class Citizen in the Lakehouse
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How we redesigned blob storage in Lance to make multimodal data a first-class citizen, with four storage semantics (Inline, Packed, Dedicated, External) that automatically adapt to your workload.
Why LanceDB Is the Most Natural Memory Layer for OpenClaw
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OpenClaw and similar personal autonomous agents need a local-first long-term memory layer. LanceDB fits that role with embedded deployment, filesystem-native storage, and multimodal retrieval.
OpenClaw + LanceDB + Seed 2.0: Turn Visual Ideas into Reality, Fast!
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OpenClaw and similar personal autonomous agents need a local-first long-term memory layer. LanceDB fits that role with embedded deployment, filesystem-native storage, and multimodal retrieval.
Memory for OpenClaw: From Zero to LanceDB Pro
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Benchmarking three OpenClaw memory plugins on the LOCOMO dataset
A Guide to Uploading Lance Datasets on the Hugging Face Hub
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Build a multimodal Lance dataset, publish it to the Hub, and query the precomputed vector + FTS indexes in LanceDB, without needing to download the dataset locally.
Zero Shot Image Classification with Vector Search
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Get about zero shot image classification with vector search. Get practical steps, examples, and best practices you can use now.
WeRide's Data Platform Transformation: How LanceDB Fuels Model Development Velocity
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Discover how WeRide, a leading autonomous driving company, leveraged LanceDB to revolutionize their data platform, achieving 90x improvement in ML developer productivity and reducing data mining time from 1 week to 1 hour.
Training a Variational AutoEncoder from Scratch with Lance File Format
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Train a Variational Autoencoder end‑to‑end using Lance for fast, scalable data handling. You’ll set up the dataset, build the VAE in PyTorch, and run training, sampling, and reconstructions.
Track AI Trends: CrewAI Agents & RAG
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This article will teach us how to make an AI Trends Searcher using CrewAI Agents and their Tasks. But before diving into that, let's first understand what CrewAI is and how we can use it for these applications.
Tokens per Second Is NOT All You Need
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Explore about tokens per second is not all you need. Get practical steps, examples, and best practices you can use now.
The Future of Open Source Table Formats: Apache Iceberg and Lance
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Explore the future of open source table formats: apache iceberg and lance with practical insights and expert guidance from the LanceDB team.
The Case for Random Access I/O
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One of the reasons we started the Lance file format and have been investigating new encodings is because we wanted a format with better support for random access.
LanceDB Raises $30M Series A to Build the Multimodal Lakehouse
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We have closed another funding round to accelerate development of the Multimodal Lakehouse - a unified platform for AI data infrastructure.
SemanticDotArt: Rethinking Art Discovery with LanceDB
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SemanticDotArt turns art discovery into a multimodal search experience, matching feelings, phrases, and images with LanceDB's hybrid retrieval.
Second Dinner's Secret Weapon: LanceDB-Powered RAG for Faster, Smarter Game Development
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Discover how Second Dinner, creators of Marvel Snap, leveraged LanceDB Cloud to transform game development workflows, reducing prototyping time from months to hours and automating QA test generation with 81% better results.
Search Within an Image with Segment Anything
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Get about search within an image with segment anything. Get practical steps, examples, and best practices you can use now.
Scalable Computer Vision with LanceDB & Voxel51
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Explore about scalable computer vision with lancedb & voxel51. Get practical steps, examples, and best practices you can use now.
Rethinking Table File Paths with Uber: Lance’s Multi-Base Layout
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A tour of Lance's file path design, and how Lance’s new multi-base layout enables multi-location datasets (such as Uber’s multi-bucket setup) with minimal metadata rewrites.
RAG Isn't One-Size-Fits-All: Here's How to Tune It for Your Use Case
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Great RAG comes from a tight iteration loop. Learn how to systematically improve each layer of your RAG system using Kiln and LanceDB.
Python Package to convert image datasets to Lance type
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Explore python package to convert image datasets to lance type with practical insights and expert guidance from the LanceDB team.
The Quest for One Million IOPS: Benchmarking Storage at LanceDB
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Learn how LanceDB benchmarks storage and how we achieved one million disk reads per second.
November Feature Roundup
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Explore November feature roundup with practical insights and expert guidance from the LanceDB team.
🛡️ Newly Knighted Lancelot, ▶️ TwelveLabs Semantic Video Recommendations, 🧠 Cognee's AI Memory Layer with LanceDB
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Our September newsletter welcomes new Lancelot members, highlights TwelveLabs semantic video recommendations, Cognee’s AI memory layer, and shares the latest product and community updates.
🎨 Semantic.Art, 💾 Stable Lance 2.1, 🎥 Ray+LanceDB powers Netflix
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Our October newsletter highlights Semantic.Art, Lance File 2.1, RaBitQ Quantization, upcoming events, latest product and community updates.
🛡️Lance Community Governance, Lance + Iceberg 🧊, Netflix’s Multimodal Search Demo 🔍
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Our November newsletter highlights Lance community governance, a deep dive on Lance and Iceberg, a demo of Netflix's multimodal search, previous talk recordings, and the latest product and community updates.
June 2025: $30M Series A, Multimodal Lakehouse Launch & Product Updates
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LanceDB's June 2025 newsletter covering latest company news, product updates, open source releases, and community highlights.
⚖️ Harvey’s Enterprise-Grade RAG on LanceDB, 💼 Dosu Case Study, Minimax&LumaLabs❤️Lance-Ray
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Our August newsletter features a new case study with Dosu, recaps from events with Harvey and Databricks, and the latest product and community updates.
🦆 Lance x DuckDB SQL Retrieval, 🚗 Uber-Scale Storage, ⚡ 1.5M IOPS
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Kicking off 2026 with Lance-native SQL retrieval via DuckDB, Uber-scale multi-bucket storage, 1.5M IOPS benchmarks, and continued OSS momentum across the Lance ecosystem.
🤗 Lance x Hugging Face, 🪾Git-Style Branching, 🏔️ Geospatial in Lance
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Native Lance support on Hugging Face Hub, Git-style branching and shallow clone for AI data, and Arrow-native geospatial with R-Tree indexing, plus steady OSS and community momentum.
💾 Lance SDK v1.0.0, 🗓️ 1st Lance Community Sync, 🔍 Wikisearch
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Our December newsletter highlights Lance SDK v1.0.0, our upcoming Lance community sync, Wikisearch demo, and the latest product and community updates.
Netflix’s Media Data Lake ❤️ LanceDB, CodeRabbit 💼 Case Study, Lance Namespace
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Our August newsletter highlights LanceDB powering Netflix's Media Data Lake, a case study on CodeRabbit's AI-powered code reviews, and updates on Lance Namespace and Spark integration.
My Summer Internship Experience at LanceDB
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I'm Raunak, a master's student at the University of Illinois, Urbana-Champaign. This summer, I had the opportunity to intern as a Software Engineer at LanceDB, an early-stage startup based in San Francisco.
My SIMD Is Faster than Yours
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An untold story about how we make LanceDB vector search fast. Get practical steps and examples from 'My SIMD is faster than Yours'.
Multimodal Myntra Fashion Search Engine Using LanceDB
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Build a multimodal fashion search engine with LanceDB and CLIP embeddings. Follow a step‑by‑step workflow to register embeddings, create the table, query by text or image, and ship a Streamlit UI.
What is the LanceDB Multimodal Lakehouse?
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Introducing the Multimodal Lakehouse - a unified platform for managing AI data from raw files to production-ready features, now part of LanceDB Enterprise.
Multi Document Agentic RAG: a Walkthrough
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Unlock about multi document agentic rag: a walkthrough. Get practical steps, examples, and best practices you can use now.
Modified RAG: Parent Document & Bigger Chunk Retriever
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Get about modified rag: parent document & bigger chunk retriever. Get practical steps, examples, and best practices you can use now.
MemGPT: OS Inspired LLMs That Manage Their Own Memory
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Explore about memgpt: os inspired llms that manage their own memory. Get practical steps, examples, and best practices you can use now.
Late Interaction & Efficient Multi-modal Retrievers Need More Than a Vector Index
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Explore late interaction & efficient multi-modal retrievers need more than a vector index with practical insights and expert guidance from the LanceDB team.
Developers, Ditch the Black Box: Welcome to Continue
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Remember flipping through coding manuals? Those quickly became relics with the rise of Google and Stack Overflow, a one-stop shop for developer queries.
Lance × Hugging Face: A New Era of Sharing Multimodal Data on the Hub
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Announcing native read support for Lance format on Hugging Face Hub. You can now distribute your large multimodal datasets as a single, searchable artifact (including blobs, embeddings and indexes) all in one place!
Lance × DuckDB: SQL for Retrieval on the Multimodal Lakehouse Format
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Use the Lance format as your lakehouse layer for retrieval, RAG and more, with the native Lance extension for DuckDB
Lance, Windows. Windows, Lance
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It was Spring of 2012. After being an avid user for 2+ years, I finally decided to join Wes Mckinney and work on pandas full time.
Lance v2: A New Columnar Container Format
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Explore lance v2: a new columnar container format with practical insights and expert guidance from the LanceDB team.
Productionalize AI Workloads with Lance Namespace, LanceDB, and Ray
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Learn how to productionalize AI workloads with Lance Namespace's enterprise stack integration and the scalability of LanceDB and Ray for end-to-end ML pipelines.
Lance File 2.1 is Now Stable
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The 2.1 file version is now stable, learn what that means for you and what's coming next.
Lance File 2.1: Smaller and Simpler
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Explore lance file 2.1: smaller and simpler with practical insights and expert guidance from the LanceDB team.
Introducing Lance Data Viewer: A Simple Way to Explore Lance Tables
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A lightweight open source web UI for exploring Lance datasets, viewing schemas, and browsing table data with vector visualization support.
Building the Future Together: Introducing Lance Community Governance
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Announcing the formal governance structure for the Lance community, establishing clear pathways for contribution and leadership with a three-tier system of contributors, maintainers and PMC.
Manage Lance Tables in Any Catalog using Lance Namespace and Spark
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Access and manage your Lance tables in Hive, Glue, Unity Catalog, or any catalog service using Lance Namespace with the latest Lance Spark connector.
Implementing Corrective RAG in the Easiest Way
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Even though text-generation models are good at generating content, they sometimes need to improve in returning facts. This happens because of the way they are trained.
Hybrid Search: RAG for Real-Life Production-Grade Applications
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Get about hybrid search: rag for real-life production-grade applications. Get practical steps, examples, and best practices you can use now.
Hybrid Search: Combining BM25 and Semantic Search for Better Results with Langchain
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Have you ever thought about how search engines find exactly what you're looking for? They usually use a mix of matching specific words and understanding the meaning behind them.
Hybrid Search and Custom Reranking with LanceDB
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Combine keyword and vector search for higher‑quality results with LanceDB. This post shows how to run hybrid search and compare rerankers (linear combination, Cohere, ColBERT) with code and benchmarks.
Reduce Hallucinations from LLM-Powered Agents Using Long-Term Memory
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Understand about reduce hallucinations from llm-powered agents using long-term memory. Get practical steps, examples, and best practices you can use now.
Implement Contextual Retrieval and Prompt Caching with LanceDB
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Unlock about implement contextual retrieval and prompt caching with lancedb. Get practical steps, examples, and best practices you can use now.
RAG with GRPO Fine-Tuned Reasoning Model
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Explore rag with grpo fine-tuned reasoning model with practical insights and expert guidance from the LanceDB team.
GraphRAG: Hierarchical Approach to Retrieval-Augmented Generation
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Explore GraphRAG: hierarchical approach to retrieval-augmented-generation with practical insights and expert guidance from the LanceDB team.
GPU-Accelerated Indexing in LanceDB
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Speed up vector index training in LanceDB with CUDA or Apple Silicon (MPS). See how GPU‑accelerated IVF/PQ training compares to CPU and how to enable it in code.
How We Added Geospatial Support To Lance With No New Code
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How Lance's Arrow-native architecture enables first-class geospatial support through extension types, GeoDataFusion integration, and R-Tree indexing.
Building Semantic Video Recommendations with TwelveLabs and LanceDB
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Build semantic video recommendations using TwelveLabs embeddings, LanceDB storage, and Geneva pipelines with Ray.
LanceDB's Geneva: Scalable Feature Engineering
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Learn how to build scalable feature engineering pipelines with Geneva and LanceDB. This demo transforms image data into rich features including captions, embeddings, and metadata using distributed Ray clusters.
From BI to AI: A Modern Lakehouse Stack with Lance and Iceberg
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A comparison of where Iceberg and Lance sit in the modern lakehouse stack. We highlight emerging architectures that are bridging the worlds of analytics and AI/ML workloads using these two formats, while being built on the same data foundation.
Setup Real-Time Multimodal AI Analytics with Apache Fluss (incubating) and Lance
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Learn how to build real-time multimodal AI analytics by integrating Apache Fluss streaming storage with Lance's AI-optimized lakehouse. This guide demonstrates streaming multimodal data processing for RAG systems and ML workflows.
Columnar File Readers in Depth: Parallelism without Row Groups
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Explore columnar file readers in depth: column shredding with practical insights and expert guidance from the LanceDB team.
LanceDB's RaBitQ Quantization for Blazing Fast Vector Search
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Introducing RaBitQ quantization in LanceDB for higher compression, faster indexing, and better recall on high‑dimensional embeddings.
LanceDB WikiSearch: Native Full-Text Search on 41M Wikipedia Docs
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No more Tantivy! We stress-tested native full-text search in our latest massive-scale search demo. Let's break down how it works and what we did to scale it.
Efficient RAG with Compression and Filtering
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Discover about efficient rag with compression and filtering. Get practical steps, examples, and best practices you can use now.
Effortlessly Loading and Processing Images with Lance: a Code Walkthrough
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Working with large image datasets in machine learning can be challenging, often requiring significant computational resources and efficient data-handling techniques.
Designing a Table Format for ML Workloads
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Explore designing a table format for ML workloads with practical insights and expert guidance from the LanceDB team.
Custom Datasets for Efficient LLM Training Using Lance
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See about custom datasets for efficient llm training using lance. Get practical steps, examples, and best practices you can use now.
Creating a FinTech AI Agent From Scratch
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Explore fintech ai agent from scratch with practical insights and expert guidance from the LanceDB team.
Convert Any Image Dataset to Lance
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In our article, we explored the remarkable capabilities of the Lance format, a modern, columnar data storage solution designed to revolutionize the way we work with large image datasets in machine learning.
Columnar File Readers in Depth: Structural Encoding
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Deep dive into LanceDB's dual structural encoding approach - mini-block for small data types and full-zip for large multimodal data. Learn how this optimizes compression and random access performance compared to Parquet.
Columnar File Readers in Depth: Repetition & Definition Levels
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Explore columnar file readers in depth: repetition & definition levels with practical insights and expert guidance from the LanceDB team.
Columnar File Readers in Depth: Compression Transparency
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Explore columnar file readers in depth: compression transparency with practical insights and expert guidance from the LanceDB team.
Columnar File Readers in Depth: Column Shredding
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Explore columnar file readers in depth: column shredding with practical insights and expert guidance from the LanceDB team.
Columnar File Readers in Depth: Backpressure
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Streaming data applications can be tricky. When you can read data faster than you can process the data then bad things tend to happen. The various solutions to this problem are largely classified as backpressure.
Columnar File Readers in Depth: APIs and Fusion
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The API used to read files has evolved over time, from simple full table reads to batch reads and eventually to iterative record batch readers. Lance takes this a step further to return a stream of read tasks.
Chunking Techniques with Langchain and LlamaIndex
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In our last blog, we talked about chunking and why it is necessary for processing data through LLMs. We covered some simple techniques to perform text chunking.
Chunking Analysis: Which is the right chunking approach for your language?
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Explore chunking analysis: which is the right chunking approach for your language? with practical insights and expert guidance from the LanceDB team.
Chat with Your Stats Using Langchain Dataframe Agent & LanceDB Hybrid Search
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In this blog, we’ll explore how to build a chat application that interacts with CSV and Excel files using LanceDB’s hybrid search capabilities.
Netflix's Media Data Lake and the Rise of the Multimodal Lakehouse
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How Netflix built a Media Data Lake powered by LanceDB and the Multimodal Lakehouse to unify petabytes of media assets for machine learning pipelines.
Case Study: Meet Dosu - the Intelligent Knowledge Base for Software Teams and Agents
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How Dosu uses LanceDB to transform codebases into living knowledge bases with real-time search and versioning.
How Cognee Builds AI Memory Layers with LanceDB
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How Cognee uses LanceDB to deliver durable, isolated, and low-ops AI memory from local development to managed production.
Case Study: How CodeRabbit Leverages LanceDB for AI-Powered Code Reviews
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How CodeRabbit leverages LanceDB-powered context engineering turns every review into a quality breakthrough.
Building RAG on codebases: Part 2
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Building a Cursor-like @codebase RAG solution. Part 2 focuses on the generating embeddings and the retrieval strategy using a combination of techniques in LanceDB.
Building RAG on codebases: Part 1
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Building a Cursor-like @codebase RAG solution. Part 1 focuses on indexing techniques, chunking strategies, and generating embeddings in LanceDB.
Branching and Shallow Cloning in Lance: Towards a "Git for AI Data"
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A deep dive into how table formats handle version management for ML/AI experimentation, and how Lance unifies branching, tagging, and shallow clone on top of its multi-base architecture.
Better RAG with Active Retrieval Augmented Generation FLARE
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by Akash A. Get practical steps and examples from 'Better RAG with Active Retrieval Augmented Generation FLARE'.
Benchmarking Random Access in Lance
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In this short blog post we’ll take you through some simple benchmarks to show the random access performance of Lance format. Get practical steps and examples from 'Benchmarking random access in Lance'.
Inverted File Product Quantization (IVF_PQ): Accelerate Vector Search by Creating Indices
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Compress vectors with PQ and accelerate retrieval with IVF_PQ in LanceDB. The tutorial explains the concepts, memory savings, and a minimal implementation with search tuning knobs.
Benchmarking Cohere Rerankers with LanceDB
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Improve retrieval quality by reranking LanceDB results with Cohere and ColBERT. You’ll plug rerankers into vector, FTS, and hybrid search and compare accuracy on real datasets.
AnythingLLM's Competitive Edge: LanceDB for Seamless RAG and Agent Workflows
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Discover how AnythingLLM leveraged LanceDB's serverless architecture to eliminate vector database setup complexity, enabling seamless cross-platform RAG and agent workflows with zero configuration required.
Announcing Lance SDK 1.0.0: What This Milestone Means for the Community
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We’re excited to announce that the core Rust SDK and the Python and Java binding SDKs are graduating to version 1.0.0, alongside a new, community-driven release strategy.
Agentic RAG Using LangGraph: Build an Autonomous Customer Support Agent
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Build an autonomous customer support agent using LangGraph and LanceDB that automatically fetches, classifies, drafts, and responds to emails with RAG-powered policy retrieval.
Advanced RAG: Precise Zero-Shot Dense Retrieval with HyDE
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In the world of search engines, the quest to find the most relevant information is a constant challenge. Researchers are always on the lookout for innovative ways to improve the effectiveness of search results.
Accelerate Vector Search Applications Using OpenVINO & LanceDB
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We show how to use the CLIP from OpenAI for Text-to-Image and Image-to-Image searching. We’ll also do a comparative analysis of the PyTorch model, FP16 OpenVINO format, and INT8 OpenVINO format in terms of speedup.
A Primer on Text Chunking and Its Types
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Text chunking is a technique in natural language processing that divides text into smaller segments, usually based on the parts of speech and grammatical meanings of the words.
A Practical Guide to Training Custom Rerankers
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Explore a practical guide to training custom rerankers with practical insights and expert guidance from the LanceDB team.
A Practical Guide to Fine-Tuning Embedding Models
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Explore a practical guide to fine-tuning embedding models with practical insights and expert guidance from the LanceDB team.
Keep Your Data Fresh with CocoIndex and LanceDB
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Store a multimodal dataset of recipes in LanceDB, a multimodal lakehouse for AI, and keep it fresh with CocoIndex, a declarative data transformation framework for AI with incremental processing capabilities.
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🎤 Reverie, Nov 5, ⚡ ML Data Loading Performance Guide, 🧠 CrewAI Cognitive Memory on LanceDB

ChanChan Mao
•
September 9, 2026
•
Newsletter
Table of Contents
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🎤 Reverie, the Summit for AI Builders

Every generative model is a kind of dream, a plausible world rendered from what it has seen. Reverie, the summit for AI builders by LanceDB, is taking place on November 5 in San Francisco. Researchers and engineers behind frontier models, world models, generative video, and physical AI go deep on the data systems those dreams are made on.

Featuring speakers from NVIDIA, Runway, Luma, Applied Intuition, Cruise, Adobe, Exa, and more.

Apply to Attend → | Read more →

🧠 Why CrewAI Rebuilt Agent Memory on LanceDB, Powering 2B+ Agent Executions

CrewAI replaced a two-system memory stack with a single LanceDB table storing vectors, metadata, and multimodal data together, scoring recall by similarity, recency, and importance so old critical decisions still surface over trivial recent ones. Now CrewAI's default vector backend, it powers 12M monthly downloads and 2B+ agent executions.

Read more →

⚡ Data Loading for AI/ML: A Comprehensive Guide

GPU training throughput rarely bottlenecks on I/O — the real trap is the CPU stage, where JPEG decoding and tokenization can starve even a capable pipeline. Weston Pace walks through the three-stage model with concrete numbers (16K tokens/sec on an L40S, 1GB/s per core) and practical PyTorch + LanceDB guidance.

Read more →

📚 Also Published

📸 Actuate & Ray Summit in SF

LanceDB team at Actuate and Ray Summit this month! Thanks to everyone who stopped by our booth and said hi. We really enjoyed meeting so many of you in person and talking about data infrastructure for physical AI, robotics, and foundation models. See you at our own Reverie on November 5 👋

📅 Upcoming Events

Reverie — November 5, 2026 · San Francisco, CA

Reverie is a one-day technical summit for researchers and engineers building the data systems behind world models, generative video, physical AI, multimodal search, and agentic research.

Apply to Attend →

🏗️ LanceDB Enterprise Updates

Performance

  • Less job-history scanning on cleanup — Per-job scratch storage removes a cleanup job's need to scan every other job's record for what's expired; on a 604-record benchmark, that scan alone cost ~600 sequential storage reads and 15.2 of 26.3 total seconds (58%).
  • Fewer redundant manifest reads on WAL nodes — Reusing a shard's own manifest cache instead of rebuilding it each read cuts a flush operation's storage requests from 132 to 12, and stops idle tables from burning ~17 requests/minute on background GC and compaction checks with no client traffic.
  • Concurrent SSTable fetch in WAL compaction — Fetching a compaction pass's SSTables concurrently instead of one at a time cuts fetch time by roughly 4x at the default fan-out: an 8-SSTable pass drops from 1.6s to 0.4s, and a 32-SSTable pass from 6.4s to 1.6s.
  • Bounded admin traffic on large clusters — Capping concurrency on cluster-status requests and polling only the replica-group members a job actually targets, instead of every member cluster-wide, prevents a connection spike on the query node as replica-group counts grow.
  • Streaming computed-column refresh — Streaming a computed column's output directly into its fragment file, instead of buffering the whole output twice, removes the artificial cap that previously bounded how much a single refresh batch could compute at once.

Features

Feature Description
Materialized views SQL now supports CREATE, REFRESH, and SHOW MATERIALIZED VIEW, a filtered or projected view of another table that refreshes as a background job, with a validation token guarding against refreshing a view dropped and recreated under the same name.
SQL-defined computed columns SQL now supports registering Python functions as computed columns, with WHERE-filtered refresh to recompute only matching rows and GPU-backed execution for functions that need it, bringing Feature Engineering's computed-column workflow into SQL.
Table cloning in SQL CREATE TABLE ... CLONE now works from the SQL shell and Flight SQL, closing the last surface that lacked it: clone a table as of its latest version, a specific version, a tag, or a timestamp, without copying the underlying data.
Column lineage graph A new lineage view shows which upstream columns and Feature Engineering jobs produced each column, rendered as an interactive graph with search and click-to-pin, keeping a complex table's derivation history legible instead of an unreadable tangle.
Row and fragment sampling The SQL:2003 TABLESAMPLE clause is now supported for both row-level and whole-fragment sampling, by percentage or exact row count, with a repeatable seed for reproducible samples and support for sampling one side of a join independently.

‍

🌟 Open Source Releases

Project Description
Lance v3.0.2 – v11.0.0
Release notes		 • ACORN-1 traversal cuts worst-case HNSW search latency 10–250x when a filter clusters away from the query's region, at up to 4 points of recall (opt-in via ApproxMode::Fast) (#7927); a new MAXSCORE algorithm for pure-SHOULD FTS queries cuts candidate probes up to 53.6x in benchmark testing (#8474); and FTS scorers can now compose across columns in one shared row-address domain (#8685)
• Segmented indexes now support ngram (#7244), bloom filters (#7925), and RTree (#7932); zone maps extended to all data types (#8017) with seeds written into data file footers (#7427)
• New Dataset.migrate_to_stable_row_ids migration method (#8521), cross-store deep_clone support (#7545), compaction limits via max_source_rows/max_source_bytes (#8235), and per-fragment column writes that survive compaction (#8313)
LanceDB v0.37.1 – v0.38.0
Release notes		 • Materialized views land in the client SDKs — declare and refresh on local tables (#3930, #4010) with Python (#3933) and Node (#3935) bindings, alongside SQL-declared computed columns (#3937) with async refresh returning a job handle (#3939)
• New Function framework for registering scalar UDFs with typed wire contracts (#3985), grouped column bindings (#3994), GPU resource requirements (#4085), and Blob v2 signatures (#4091)
• Streaming dataset adds backpressure on its post-transform queue (#3897) and sequence packing (#3920); the data loader now reads remote tables (#3981) pinned to a fixed base table version (#3982)
lance-namespace v0.11.0 – v0.11.1
Release notes		 • InsertIntoTableResponse now returns num_inserted_rows and version fields (#359)
• Support for declaring and backfilling expression-computed columns (#360)
• Vector index build parameters (e.g. IVF/PQ settings) can now be specified via CreateTableIndexRequest (#361)
lance-trino v0.3.4
Release notes		 • Fixed Substrait name emission for structs nested inside lists, improving compatibility with complex schema queries (#227)
• Optimized COUNT() queries over non-null constants (#155)

🫶 Community Contributions

Thank you to contributors from Netflix, Bytedance, Adobe, Intel, Microsoft, Pinterest, Huawei for improvements across storage, indexing, query execution, distributed processing, and ecosystem integrations in LanceDB, Lance, and the broader ecosystem.

Notable contributions this month:

  • @yanghua — Added a pluggable cache backend API to the Java SDK, letting custom caching strategies register and switch at runtime on top of Lance's existing cache registry
  • @zhangyue19921010 — Introduced compaction controls with max_source_rows and max_source_bytes limits, enabling fine-grained resource management during file compaction
  • @sezruby — Exposed ArrowArrayStream export on LanceScanner in Java, enabling zero-copy interoperability with Arrow-native tooling
  • @morales-t-netflix — Added per-value support for fixed-length packed structs, enabling efficient columnar encoding for complex nested types
  • @leohoare — Implemented ACORN-1 traversal for prefiltered HNSW search, delivering 10–250x faster filtered vector queries at a small (≤4 point) recall tradeoff
  • @xtangxtang — Fixed fp16 IVF partition assignment to route through AMX-FP16 GEMM, recovering misassigned vectors and accelerating vector indexing on Intel hardware
  • @professor-moody — Contributed critical security fix bounds-checking length prefix in VariableFullZipDecoder, preventing potential memory safety issues
  • @XuQianJin-Stars — Implemented safe commit with ConditionalPutCommitHandler for GooseFS, enabling consistent writes on distributed filesystems
  • @wombatu-kun — Fixed stale per-segment rows in vector search after in-place column value updates, keeping the index consistent with the latest data

A heartfelt thank you to our community contributors of Lance and LanceDB this past month:

@1fanwang • @3286360470 • @a-erofeev • @adityaj0 • @ali2arslan • @anirudh-s-kumar • @anonx3247 • @antio2 • @ar-maan05 • @arielyes • @atirna • @beinan • @brunosrz • @clearlove10-c • @com-junkawasaki • @cswpy • @dawid0309 • @dcfocus • @ddupg • @dentiny • @divyanshus2404 • @dshepelev15 • @dtolnay • @dubin555 • @ecthlion • @everysympathy • @fangbo • @fanng1 • @farazshaikh • @fzowl • @geserdugarov • @haochengliu • @hellower • @hfutatzhanghb • @huahuay • @igorganapolsky • @ilya-zlobintsev • @isaac-dasari • @ivscheianu • @j7nhai • @jackylee-ch • @jagrutipatilp • @jay-ju • @jayson-huang • @jerryjch • @jiaoew1991 • @jiaqizho • @jimmy-xie-fleet • @jo-migo • @jonasdedden • @jsnider3 • @julianyg • @kamronis • @kangnan-li • @keunhong • @leoreeyang • @lh-kevin • @lichuang • @luciferyang • @lucyge2022 • @majin1102 • @mannxo • @maswin • @medisean • @mikemikimike • @mikewhb • @mmatczuk • @nyl3532016 • @paramt • @pengw0048 • @pjdurden • @primorlee • @prrao87 • @puchengy • @qiuyuhang • @ragingkore • @ragnorc • @raygao25 • @ringfa11 • @risto0211 • @roridemonslayer • @rupertmaiti2005 • @sbrunk • @seven7763 • @sliortega295-ops • @sravan1011 • @stevestevenpoor • @tandede • @touch-of-grey • @u70b3 • @valkum • @vatharevinayak • @vinaysurtani • @vip892766gma • @weimingdiit • @winklemad • @wirybeaver • @xiaguanglei • @xixigoodluck • @xloya • @yentur • @yichenw • @yuvalif • @yuw1 • @zackfairts • @zhangstar333 • @zouhuajian • @zyt-yt

🤝 Lance Community Sync Recap

The community syncs this month covered the Lance 10.0.0 SDK release, which included a critical encoding fix backported to all 0.x versions, along with a refactor of file version handling. Discussion also focused on the Lance 11.0.0 SDK beta development and its progression to RC2, with work done to address benchmark regressions before release. Process changes were announced for format-change votes, moving from GitHub discussions to pull requests with a shortened 72-hour window. The team also discussed proposals for experimental feature flags and native video/blob data types in Lance.

The next Lance Community Sync will take place on Thursday, September 10 @ 9am PT.

‍

ChanChan Mao
Developer Relations @ LanceDB
GitHub
Linkedin

🎤 Reverie, Nov 5, ⚡ ML Data Loading Performance Guide, 🧠 CrewAI Cognitive Memory on LanceDB

ChanChan Mao
•
September 8, 2026
newsletter-august-2026

Data Loading for AI/ML: A Comprehensive Guide

Weston Pace
•
July 22, 2026
data-loading-guide

Announcing Reverie Summit: What AI’s Next Breakthroughs Are Made On

LanceDB
•
August 3, 2026
announcing-reverie-summit-2026