seekdb

// What it does

• Hybrid search in a single SQL statement: vector distance, full-text `MATCH … AGAINST` and scalar filters pushed into one execution plan
• Asynchronous Change Stream index pipeline with a two-level HNSW (incremental delta + snapshot), so writes commit without waiting on index construction
• `FORK DATABASE` / `MERGE TABLE` / `DROP DATABASE` copy-on-write sandboxes for agent exploration and rollback
• MySQL-compatible with full ACID — works with SQLAlchemy, any MySQL driver, and with LangChain, LlamaIndex and Dify
• Three deployment shapes from the same engine: embedded library, single-node server, or an OceanBase distributed cluster
• `pyseekdb`, a Python SDK with a collection API (`get_or_create_collection`, `upsert`, `query`) and built-in embedding functions

// Getting started

Embedded mode needs nothing but the Python SDK; the server modes ship as a Docker image, a binary installer and a Homebrew tap.

pip install -U pyseekdb

import pyseekdb

client = pyseekdb.Client(path="./agent_state.db")
memory = client.get_or_create_collection(name="episodic")

for step in agent.run():
    # Persist the observation
    memory.upsert(ids=[step.id], documents=[step.observation])

    # Retrieve relevant context — milliseconds after the write
    relevant = memory.query(query_texts=step.next_query, n_results=5)

    agent.act(relevant)

docker run -d \
  --name seekdb \
  -p 2881:2881 \
  -p 2886:2886 \
  -v ./data:/var/lib/oceanbase \
  oceanbase/seekdb:latest

CREATE TABLE articles (
  id        INT PRIMARY KEY,
  title     TEXT,
  content   TEXT,
  embedding VECTOR(384),
  FULLTEXT INDEX idx_fts (content) WITH PARSER ik,
  VECTOR   INDEX idx_vec (embedding) WITH (DISTANCE=l2, TYPE=hnsw, LIB=vsag)
) ORGANIZATION = HEAP;

SELECT id, title,
       l2_distance(embedding, '[0.12, 0.34, ...]') AS dist
FROM articles
WHERE MATCH(content) AGAINST('quarterly report')
ORDER BY dist APPROXIMATE
LIMIT 10;

FORK DATABASE agent_state TO agent_sandbox_42;

USE agent_sandbox_42;
INSERT INTO memory (session_id, embedding, content) VALUES (...);

MERGE TABLE agent_sandbox_42.memory INTO agent_state.memory STRATEGY THEIRS;
-- ...or throw it away:
DROP DATABASE agent_sandbox_42;

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Reach for it when an agent needs durable memory that is writable and searchable in the same loop, without a separate vector store and relational database
• Reach for it when you want speculative agent work isolated in a branch you can merge or throw away, rather than reconciled by hand
• Reach for it for RAG where the query needs vector similarity, keyword match and access-control filters resolved together in one plan
• Reach for it when a MySQL-compatible, ACID engine is easier to operate and embed than another bespoke database

// How seekdb compares

seekdb alongside other open-source vector databases tools AI/TLDR tracks, ranked by GitHub stars.