Instant graph memory for AI agents.

Push JSON into RushDB. Get semantic recall, graph traversal, analytical queries, and live schema over the same memory.

Typical stack

Memory split across systems.

App records, session state, embeddings, metadata, tool output, and relationships drift across separate stores.

Store context

Application DB

facts, history, metadata

Redis

session state, cache

Vector DB

embeddings, similarity search

Your code owns:

sync · embed · index · join · retry

With RushDB

Push JSON. Get connected memory.

RushDB keeps facts, events, tool output, embeddings, and relationships in one queryable memory layer.

Any JSON
Records
Typed fields
Searchable text
Graph links
Live schema
Aggregations

Agents can recall by meaning, traverse relationships, and aggregate over the same memory.

How it works

Push JSON. RushDB reconstructs the graph.

RushDB keeps typed fields, searchable text, vector embeddings, and relationships together as data arrives. Agents can recall by meaning, traverse relationships, and aggregate over memory without a sync pipeline.

json input
 
push
graph — auto-linked nodes
MEMORYAGENTACTIONSESSIONSTEPTOPICTAGSSCOREINDEX
vector embeddings
Durable memory layer

Memory should survive the agent stack.

Models, frameworks, MCP clients, and workflows will change. RushDB keeps the records, relationships, semantic indexes, and live schema outside any one session.

Change the stack without rebuilding memory.

Memory lives outside any one model, framework, MCP client, or provider session, so the next agent can continue from the same durable context.

Durable memory layer

Model changes

Every provider reads the same records, relationships, and searchable context.

Framework changes

SDK, MCP, and application workflows share one durable memory layer.

Workflow changes

Keep context available without replaying transcripts or migrating session state.

See how RushDB connects your data
Quickstart

Running in minutes.

Install

Add the SDK for TypeScript, Python, or use the REST API directly — no extra dependencies.

Push

Write any JSON object. RushDB parses field types, builds the graph structure, and indexes vectors server-side on the same write.

Recall

Query by meaning, by graph relationship, or both in a single call using the same SearchQuery shape every time.

install
push · recall
Use cases

Memory that adapts to your domain.

RushDB is domain-agnostic: the same graph memory layer adapts to agents, RAG, apps, sales, support, and healthcare without hard-coding a new store for each workflow.

Persistent context

Decisions, tool outputs, and entities survive across runs as reusable records.

Semantic recall

Recall prior work by meaning, scoped to a session, agent, or task.

Shared state

Multiple agents read and write the same durable memory graph.

Field notes

Agent memory built for production teams.

RushDB is built for the memory requirements production AI teams actually face: connected context, live schemas, semantic retrieval, and a storage model agents can safely inspect.

In a few minutes, RushDB can go from Docker Compose to Python SDK to nested JSON turning into a real graph. For developer education, that matters: builders can see the data land in the visualizer and Neo4j Aura, not just read about it.

Jason Koo

Fractional DevRel | Video-First Developer Content & Community Growth | ex-Neo4j

Our agents and our zone grid run on 25,062+ behavioral routing trips, context that has to persist and connect, not reset every session. RushDB gives Billboardbug that memory layer out of the box. The agent remembers, so it moves.

Ben Gauthier

Founder & CEO, Billboardbug

EU AI Act compliance needs more than static documents. RushDB gives Antifragile AI a graph-backed RAG layer for articles, controls, evidence, versions, and retrieval logs, so every answer can trace back to one evidence chain.

Andrei Bâcu

Founder, Antifragile AI

Event-driven autonomous operations need memory that connects every trigger, decision, entity, and outcome. RushDB gives agents a graph-native layer for turning business events into context they can reason over and act on.

Harsha Valluri

Founder @ SymboSystems | Sr. AI Engineer

NODES 2025 · hosted by Neo4j

RushDB in Action: Building AI-Native Apps with Self-Aware Graphs

A Neo4j-hosted conference session by Artemiy Vereshchinskiy, Founder, RushDB, showing RushDB in action for AI-native apps, self-aware graphs, and practical graph-backed agent workflows.

Artemiy Vereshchinskiy · Founder, RushDB

Pricing

Start free. Scale when memory grows.

Free includes 100K KU/month. Pro starts at $30/month. Reads are free; writes are usage-based. Full pricing details →

MonthlyAnnual

Free

FREE

Evaluate RushDB in production conditions

Get started instantly:

  • 100K KU / month

    ~3,000 records with 10 fields each

  • 2 projects

    No time limits, no feature restrictions

  • Full REST API and SDKs
  • Self-hosted & BYOC

    Connect to your own Neo4j or Aura

  • Vector & AI search
  • Community support

Pro

from$24/ mo

Perfect for production applications

Everything in Free, plus:

  • 10M KU / month

    100× Free plan — ~300,000 records with 10 fields each

  • Overage billing

    Continue beyond 10M KU at $3 / M KU

  • Unlimited projects
  • 3 team members

    then $10 / seat

  • Self-hosted & BYOC

Scale

from$73/ mo

For high-volume and data-intensive apps

Everything in Pro, plus:

  • Unlimited KU

    Usage-based — pay only for what you consume

  • Unlimited team members
  • Self-hosted & BYOC
  • SLA guarantee
  • Priority support

Give your agent memory.