An open-source, in-process vector database that embeds directly into applications for low-latency similarity search without running a server.
oceanbase/seekdb
An AI-native search database that stores vector, full-text, structured and semi-structured data in one MySQL-compatible engine, for agent memory and retrieval.
Open-source alternative to:Pinecone
Elasticsearch
- Stars
- 3,165
- Forks
- 414
- Open issues
- 188
- Last commit
- 6 hours ago
What is it?
seekdb is a database built for AI agent workloads. It combines vector search, full-text search and scalar filters in a single engine and exposes them through SQL, so retrieval does not need separate systems or client-side result merging. It is MySQL-compatible and offers full ACID transactions, and it can run as an embedded library from Python or Node.js, as a single-node server, or as part of an OceanBase distributed cluster.
For agent state management, the project adds FORK DATABASE, which creates a copy-on-write snapshot of a database, and MERGE TABLE, which commits sandbox changes back to the main database. Writes are indexed asynchronously through a change stream into a delta HNSW index, so newly written vectors can be queried shortly after they are inserted. The project also provides Python (pylibseekdb, pyseekdb) and JavaScript (@oceanbase/seekdb-node) packages, and it integrates with LangChain, LlamaIndex and Dify.
Key features
- 01Vector, full-text and scalar filters combined in one SQL query
- 02MySQL protocol compatibility with full ACID transactions
- 03Embedded mode for Python and Node.js, plus single-node server deployment
- 04FORK DATABASE copy-on-write snapshots and MERGE TABLE for agent sandboxes
- 05Asynchronous index pipeline with incremental HNSW for near-real-time vector search
Use cases
- Persistent memory and state storage for AI agents
- Retrieval-augmented generation (RAG) backends that need vector and keyword search together
- Sandboxed experimentation on agent data with rollback or merge
- Adding semantic search to an existing MySQL-based application
Quick start
- 1
Install the Python packages (requires Python 3.11 or newer)
python3 -m pip install -U pylibseekdb pymysql - 2
Save the README example as example.py
- 3
Run the example; it should print [{'value': 1}]
python3 example.py
Things to know
- The embedded Python quick start requires Python 3.11 or newer.
- Supported platforms are limited to those listed on the pylibseekdb package page.
Topics
FAQ
- What is seekdb?
- An AI-native search database that stores vector, full-text, structured and semi-structured data in one MySQL-compatible engine, for agent memory and retrieval. seekdb is a database built for AI agent workloads. It combines vector search, full-text search and scalar filters in a single engine and exposes them through SQL, so retrieval does not need separate systems or client-side result merging. It is MySQL-compatible and offers full ACID transactions, and it can run as an embedded library from Python or Node.js, as a single-node server, or as part of an OceanBase distributed cluster.
- Is seekdb free to use? What license does it have?
- seekdb is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.
- Is seekdb still maintained?
- Actively maintained. The last commit to seekdb was on Oct 21, 2026.
- What can I use seekdb for?
- Persistent memory and state storage for AI agents Retrieval-augmented generation (RAG) backends that need vector and keyword search together Sandboxed experimentation on agent data with rollback or merge Adding semantic search to an existing MySQL-based application
- How do I install seekdb?
- Install the Python packages (requires Python 3.11 or newer): python3 -m pip install -U pylibseekdb pymysql Save the README example as example.py Run the example; it should print [{'value': 1}]: python3 example.py
- Where can I get seekdb?
- The source code and installation instructions are on GitHub: https://github.com/oceanbase/seekdb
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