A Developer's View of OceanBase's Open-Source AI Product Trio

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An OceanBase engineer offers a developer's take on three open-source AI products—seekdb, PowerRAG, and PowerMem—dissecting the three big challenges of the AI era (multimodal data, context rot, and memory management) and explaining how, under the Data×AI strategy, the three work together to build a complete AI data infrastructure.

✨ For those interested in PowerMem, welcome to try it out at https://github.com/oceanbase/powermem. I believe it can help your AI applications better manage long-term memory!

Hi everyone, I’m an engineer on the OceanBase open-source team. Over the past year I’ve been doing R&D work closely aligned with the company’s DATA X AI strategy, so today I’ll share, from my own perspective, my take on the three products OceanBase recently open-sourced—seekdb, PowerRAG, and PowerMem:

  1. seekdb: an AI-native hybrid-search database, open-sourced under Apache 2.0
  2. PowerRAG: an enterprise-grade RAG solution for building smarter, more accurate multimodal retrieval-augmented generation systems
  3. PowerMem: an AI memory engine that solves the long-term memory problem for AI applications

Many people’s first reaction on seeing these three products is: “Isn’t OceanBase a database company? Why are they getting into AI too? And how are these three products related?”

Today, I’ll talk—from my developer’s perspective—about: why a database company would launch three AI products at once, and what core logic lies behind them.

(Everything below reflects the author Jingshun’s personal views and does not represent the position of this community’s official account.)

Data Challenges of the AI Era: From “Storing Data” to “Understanding Data”

Over the past two years, the rise of AI applications has brought entirely new data challenges:

Challenge 1: The Diversification of Data Forms

Traditional databases mainly focus on storing and querying structured data, but AI applications need to handle:

  • Unstructured data: text, images, audio, video
  • Multimodal data: mixed text + image + audio content within the same scenario
  • Vector data: embedding vectors, semantic representations
  • Graph data: knowledge graphs, relationship networks

Challenge 2: The “Pseudo-Growth” of the Context Window

Large models’ context windows have skyrocketed:

  • GPT-3: 4K tokens
  • Claude-2: 100K
  • Some specialized models: even supporting 1 million+ tokens

It looks like AI can finally “remember an entire book”—so does that mean we can just cram all our chat history, user profiles, and product docs in there?

Unfortunately, the reality is exactly the opposite.

Research has found that as the context grows longer, the model’s ability to retrieve key information actually declines—a phenomenon called “Context Rot.”

Why does this happen?

  1. Attention is a finite resource: the more tokens there are, the less “attention” each piece of information gets.
  2. The Transformer’s O(n²) complexity: increase the context 10x and the compute grows 100x.
  3. Training data skews short: the model never learned to handle “ultra-long logical chains.”
  4. Side effects of position-encoding interpolation: forcibly stretching the context blurs the model’s understanding of “temporal order.”

Even trickier, models have an “edge advantage“—they remember the beginning and end of the context best, while the middle is easiest to ignore.

So it’s not that the model can’t remember; it’s that we fed it the wrong things.

Challenge 3: The Complexity of Data Management

AI applications’ data-management needs far exceed traditional scenarios:

  • ✅ Persistent storage: don’t reassemble the context every time
  • ✅ Cross-session association: what was said yesterday can still be used today
  • ✅ Structured management: who said it? when? does it matter?
  • ✅ Security and compliance: tenant isolation, sharing and isolation across multiple Agents
  • ✅ Real-time analytics: which data is used frequently? which is noise?

These needs can’t be met by plain caches, vector stores, or temporary variables.

OceanBase’s Strategic Thinking: Data×AI

Facing these challenges, OceanBase realized:

The database of the future must not only “store” data but also “understand” it, becoming a solid foundation for AI applications.

OceanBase therefore launched the “Data×AI” strategy, aiming to explore the paradigm shift of databases in the AI era.

We believe: one of an AI application’s core competitive advantages lies in how accurate its data is, how fast its retrieval is, and how intelligent its memory is.

And managing data is exactly what a database company does best.

The Positioning of the Three Products: Building a Complete AI Data Infrastructure

Based on the “Data×AI” strategy, OceanBase introduced three products. They aren’t isolated; together they form a complete AI data infrastructure ecosystem:

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────────────────────────────────
          AI Application Layer
  (intelligent customer service, knowledge bases, Agents, etc.)
────────────────────────────────
                    ↓
────────────────────────────────
  PowerMem: AI Memory Engine
  - Long-term memory management
  - Context engineering
  - Smart memory extraction and forgetting
────────────────────────────────
                    ↓
────────────────────────────────
  PowerRAG: Enterprise-Grade RAG Solution
  - Multimodal document parsing
  - Knowledge base construction
  - Retrieval-augmented generation
────────────────────────────────
                    ↓
────────────────────────────────
  seekdb: AI-Native Hybrid-Search Database
  - Unified retrieval over vector + full-text + scalar + spatial
  - Lightweight, out-of-the-box
  - AI-native design
────────────────────────────────

1. seekdb: AI-Native Hybrid-Search Database (Foundation Layer)

Positioning: the data foundation for AI applications

seekdb isn’t a patch on top of OceanBase; starting from the real needs of AI applications, it rethinks what a database should be.

Core features:

  • AI-native design: unified hybrid search over vector, full-text, scalar, and spatial-geographic data
  • Lightweight: runs on just 1C2G of resources, ideal for rapid prototyping
  • Out-of-the-box: a brand-new SDK design lets developers build a basic application in just three lines of code
  • Fast iteration: by trimming complex distributed-transaction management modules, it responds to developer needs more quickly
  • Deep integration: compatible with 30+ mainstream AI frameworks such as Hugging Face and LangChain

Why do we need seekdb?

Traditional databases are designed for OLTP/OLAP scenarios, but what AI applications need is:

  • Vector similarity search
  • Unified retrieval over multimodal data
  • Millisecond-level response
  • Lightweight deployment

seekdb was built precisely for these needs.

2. PowerRAG: Enterprise-Grade RAG Solution (Knowledge Base Layer)

Positioning: building smarter, more accurate knowledge bases and Agent applications

PowerRAG is built on top of RAGFlow, providing an enterprise-grade retrieval-augmented generation (RAG) solution.

Core features:

  • Multimodal retrieval: combined with OceanBase’s multimodal retrieval capabilities, it supports unified retrieval of text, images, and audio
  • Enterprise-grade fit: provides high availability, permission management, and other features
  • Rich component support: DeepSeek OCR, MinerU, and more, meeting enterprise-grade RAG needs
  • Smart document parsing: automatically extracts key information to build high-quality knowledge bases

Why do we need PowerRAG?

RAG is the mainstream architecture for AI applications today, but building a production-grade RAG system requires:

  • Document parsing, chunking, and vectorization
  • Multimodal content processing
  • Retrieval-strategy optimization
  • Enterprise-grade security and permissions

PowerRAG integrates these capabilities, sparing developers the tedious process of combining multiple tools and tuning them repeatedly.

3. PowerMem: AI Memory Engine (Memory Layer)

Positioning: the long-term memory system for AI applications

PowerMem solves the most central problem in AI applications: how to let AI persistently “remember” historical conversations, user preferences, and context?

Core features:

  • Persistence and structuring: writes each memory into an OceanBase table with metadata like user ID, timestamp, and importance score
  • Hybrid retrieval architecture: combines vector retrieval, full-text retrieval, graph databases, and structured filtering
  • Smart memory management: introduces the Ebbinghaus forgetting curve theory to automatically extract, deduplicate, merge, and forget
  • Enterprise-grade features: multi-tenant isolation, multi-Agent support, audit traceability

Why do we need PowerMem?

The context-rot problem tells us: it’s not that the model can’t remember; it’s that we fed it the wrong things.

PowerMem’s core logic is:

  • Distill: extract high-value facts from massive conversations
  • Compress: remove redundancy and reduce token cost
  • Precisely deliver: place the most critical information where the model is most likely to notice it

This is essentially data engineering:

  • Extract = ETL
  • Compress = data archiving
  • Deliver = indexing strategy

On the LOCOMO benchmark, PowerMem achieved:

  • 48.77% higher accuracy (78.70% vs. 52.9%)
  • 91.83% faster response (1.44s vs. 17.12s)
  • 96.53% lower token usage (0.9k vs. 26k)

How Do the Three Work Together?

The three products form a complete AI data infrastructure stack:

Typical Scenario: Intelligent Customer Service System

  1. seekdb: store and retrieve the knowledge base
    • Store vector representations of FAQs and product docs
    • Support semantic search for “what the user is asking”
  2. PowerRAG: build and maintain the knowledge base
    • Parse enterprise documents (PDF, Word, PPT, etc.)
    • Process multimodal content (documents containing images)
    • Generate high-quality retrieval results
  3. PowerMem: manage user memory and context
    • Remember “what the user asked last time”
    • Remember “the user’s preferences and habits”
    • Precisely deliver the most relevant historical information within a limited token budget

Typical Scenario: Multi-Agent Collaboration System

  1. seekdb: a shared knowledge base across Agents
    • Store shared domain knowledge
    • Support cross-Agent knowledge retrieval
  2. PowerRAG: the Agents’ knowledge-acquisition capability
    • Extract knowledge from external documents
    • Build the Agents’ specialized knowledge bases
  3. PowerMem: each Agent’s independent memory space
    • Each Agent has its own independent memory space
    • Support cross-Agent memory sharing and collaboration
    • Fine-grained permission control

Core Logic: Not Crossing Over, but a Paradigm Shift

OceanBase built these three products not to chase a trend, but because we believe:

One of an AI application’s core competitive advantages lies in how accurate its data is, how fast its retrieval is, and how intelligent its memory is.

And these three things are, at their core, all data-management problems:

  1. Data storage: how to store multimodal, vectorized data? → seekdb
  2. Data retrieval: how to retrieve precisely from massive documents? → PowerRAG
  3. Data memory: how to let AI persistently remember key information? → PowerMem

This isn’t crossing over; it’s a database company’s paradigm shift in the AI era.

From “storing data” to “understanding data,” from “query optimization” to “context engineering,” from “transaction processing” to “memory management”—these seemingly different domains all share the same underlying logic: how to manage data efficiently.

And that is exactly OceanBase’s home turf.

A Personal Hot Take: Data Is Intelligence

As AI applications move from “toys” to “production” today, the quality of the data determines the ceiling of the intelligence.

  • A RAG system that can precisely retrieve a knowledge base is smarter than a bot that merely recites documents
  • A customer-service assistant that remembers user preferences is more trustworthy than a tool that starts from scratch every time
  • An Agent that can connect past decisions is more efficient than a system that relearns everything each time

And the prerequisite for all of this is a reliable, scalable, governable AI data infrastructure.

seekdb + PowerRAG + PowerMem = a complete AI data infrastructure

These aren’t three isolated products, but a complete ecosystem:

  • seekdb provides the foundational capabilities for data storage and retrieval
  • PowerRAG provides knowledge-base construction and document-processing capabilities
  • PowerMem provides memory-management and context-engineering capabilities

Working together, the three build the data foundation for the next generation of intelligent applications.

Summary

From “context rot” to “context engineering,” from “vector stores” to “AI data infrastructure,” the logic behind the birth of OceanBase’s three products is actually simple:

  1. Problem identification: AI applications face entirely new data challenges (multimodal data, context rot, memory management)
  2. Essential insight: these challenges are, at their core, all data-management problems
  3. Capability match: a database company’s data-management strength is exactly the core capability AI applications need
  4. Product delivery: use OceanBase’s technical accumulation to build a complete AI data infrastructure

seekdb + PowerRAG + PowerMem = a complete AI data infrastructure

This is why OceanBase launched three AI products at once.

Not crossing over, but returning to first principles.

seekdb

PowerRAG

PowerMem