RushDB 2.0: Memory Infrastructure for the Agentic Era
RushDB 2.0 is a major release built for the agentic era: native semantic search, ontology-aware querying, MCP with OAuth, bring-your-own Neo4j, and prebuilt agent skills. It turns memory infrastructure into one unified layer, so developers can store structured context, traverse relationships, and search by meaning without stitching together multiple systems.
Native semantic search, Ontology API, MCP + OAuth, Bring Your Own Neo4j, Skills, and a pricing model that makes sense.
When we built the first version of RushDB, the pitch was simple: push JSON, get a graph database back. No schema design, no migration files, auto-linked nested records, a clean query API. Batteries included.
That pitch still holds. But the industry moved, and we moved with it.
The biggest shift in software development right now isn't a new framework or language. It's that applications need to remember things and reason across structured data — and the infrastructure to do that reliably, without three separate systems, barely existed.
RushDB 2.0 is the answer we kept building toward. Here's everything that shipped.
The old way to add semantic search to a graph database: run a separate vector store, write an embedding pipeline, keep them synchronized, query both, merge results. Four separate concerns, none of them fun.
RushDB 2.0 has native vector support built directly into the graph. Embedding indexes are a first-class concept — you create one on any string property, and RushDB manages everything from there.
# Python
results = db.ai.search({
"labels": ["DECISION"],
"propertyName": "rationale",
"query": "why did we choose that authentication approach",
"limit": 5
})
# Returns records ranked by __score — no exact keyword match needed
The most consistent problem in AI integrations: agents hallucinate field names, query labels that don't exist, and filter on properties with the wrong type. The root cause is that they have no authoritative schema to work from.
The markdown variant (getOntologyMarkdown) is what MCP agents call at session start. It surfaces everything in one pass: labels, types, value ranges, relationship map, and a Semantic Search column that tells the agent which fields support semanticSearch. No guessing. No schema discovery round-trips.
This one API makes analytical queries more reliable, reduces hallucinations, and gives agents the context they need before they ask their first question.
The MCP server ships with full OAuth 2.0 support — standard RS256, JWKS endpoint, per-user token scoping. Connect it to Claude Desktop, Cursor, VS Code, or ChatGPT and each user authenticates independently. Read-only tools work without auth, write tools require a token.
The getSearchQuerySpec tool delivers the complete query syntax into the agent's context window on demand — so it doesn't have to carry the full spec in its system prompt.
Skills are pre-written, structured knowledge files for AI coding agents. Drop them into your project alongside your agent configuration, reference them by name, and the agent already knows how to do the thing correctly.
Turns RushDB into a persistent memory layer. The skill teaches the agent the store → link → recall pattern, session management, the recommended label taxonomy (SESSION, DECISION, ENTITY, TASK, PREFERENCE), and how to traverse related memories across conversations.
The discovery-first workflow for safe, correct query construction. Mandatory ontology check before querying, intent classification, query spec loading for complex operations. Agents that load this skill don't hallucinate label names.
Build filter UIs that derive from live schema. Discover properties → enumerate values → map to widgets → assemble where clause as the user interacts. No hardcoded field names.
Shell
# Install
npm install @rushdb/skills
# Reference in your agent config (VS Code example)
"skills": ["rushdb-query-builder", "rushdb-agent-memory"]
Free accounts, pro accounts, everyone. Connect your own Neo4j instance and keep full control of your data. The LMPG model sits on top of Neo4j — using your own instance means your data never leaves your infrastructure.
Knowledge Units. One number that reflects how much knowledge your application creates and queries. Not instance hours, not storage tiers, not separate API call pricing.
Free tier got a real upgrade: more KUs per month, zero feature restrictions. Every feature in RushDB 2.0 is available to free users.
# Python
from rushdb import RushDB
import os
db = RushDB(os.environ["RUSHDB_API_KEY"])
# Store a session with auto-linked memories
db.records.import_json(
label="SESSION",
data={
"startedAt": "2026-05-15T10:00:00Z",
"topic": "product architecture review",
"DECISION": [
{"topic": "auth", "decision": "Use Clerk", "rationale": "Better Next.js integration", "status": "confirmed"}
],
"ENTITY": [{"name": "Clerk", "type": "service"}]
}
)
# Recall by meaning — no exact keyword match needed
past_decisions = db.ai.search({
"labels": ["DECISION"],
"propertyName": "rationale",
"query": "how we handled login and authentication",
"limit": 5
})
That's the full loop: store structured + relational data, search by meaning, combine with filters. No vector store, no embedding pipeline, no separate graph database.
# Cloud (free)
# 1. Create a project at app.rushdb.com
# 2. Copy your API key
# Install the SDK
npm install @rushdb/javascript-sdk
# Or connect via MCP
npx @rushdb/mcp-server
We're building toward a world where the data layer and the memory layer are the same thing — where agents, humans, and applications all read and write to the same graph, with the same query language, and understand each other's context.
2.0 is a big step in that direction. We'd love to hear what you build with it.