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rushdb MCP Server

rush-db/rushdb

Score: 66.1 Rank #4869 Search & RAG
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RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.

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Overview

rush-db/rushdb is a TypeScript tool. RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j. Topics: app-backend, database, docker, graphs, instant-apps, neo4j, self-hosted, graph-database, cloud, ai, ai-agents, ai-memory, ai-tools, embeddings, llm-memory, llm-tooling, mcp, rag, semantic-search, vector-search.

Ranked #4869 out of 24846 indexed tools.

Ecosystem

TypeScript No license
app-backenddatabasedockergraphsinstant-appsneo4jself-hostedgraph-databasecloudaiai-agentsai-memoryai-toolsembeddingsllm-memoryllm-toolingmcpragsemantic-searchvector-search

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 325

325 stars → growing community

Freshness 25% 2mo ago

Last commit 2mo ago → moderate activity

Issue Health 25% 69%

45/65 issues closed → decent issue management

Contributors 10% 5

5 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 26
Description Detailed
License None

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

License low impact

Add an MIT or Apache-2.0 license to signal trust and enable adoption

Freshness high impact

Last commit was 71 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for rush-db/rushdb
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Matched Queries

topic:mcp

From the README

<div align="center">

# RushDB

### The memory layer for AI agents and apps.

Push any JSON. Your agent gets a live, queryable schema, graph relationships, and semantic search — inferred automatically.
No pipeline. No separate stores. No schema to design.

[🌐 Website](https://rushdb.com) • [📖 Documentation](https://docs.rushdb.com) • [☁️ Cloud](https://app.rushdb.com) • [🔍 Examples](https://github.com/rush-db/examples)

**English** • [中文](README_ZH.md) • [日本語](README_JA.md) • [한국어](README_KO.md) • [Deutsch](README_DE.md) • [Français](README_FR.md) • [Português](README_PT.md) • [Español](README_ES.md) • [हिन्दी](README_HI.md) • [العربية](README_AR.md) • [Bahasa Indonesia](README_ID.md) • [ไทย](README_TH.md)

</div>

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## The problem

Your agent needs memory. The standard answer is three databases: Redis for key-value, a vector store for semantic search, a graph DB for relationships — plus glue code to keep them in sync.

RushDB replaces all three. Push JSON once. Query it with gra
Read full README on GitHub →

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