# RushDB

> Turn operational data into connected, searchable context in minutes—without a separate graph database, vector store, or synchronization layer.

This index is generated at request time from the current website configuration and published CMS records. Use the linked documentation for exact API details.

## Core Pages

- [Homepage](https://rushdb.com/): Open-source context infrastructure for agents, applications, and analytics, with connected records, live schema, semantic retrieval, and operational queries through one API.
- [Context layer](https://rushdb.com/context-layer): What a shared context layer is, when it fits, and how RushDB consolidates connected operational context.
- [Architecture](https://rushdb.com/architecture): How ingestion, live schema, relationships, semantic indexes, APIs, and deployment fit together.
- [Applications solution](https://rushdb.com/solutions/applications): Build operational applications on the same connected context used by agents and analytics.
- [Operational analytics solution](https://rushdb.com/solutions/analytics): Analyze current values, relationships, and evolving schema without a second operational model.
- [Features](https://rushdb.com/features): RushDB capabilities and the infrastructure work they replace.
- [Guides](https://rushdb.com/guides): Evergreen explanations of AI memory, graphs, retrieval, and vector search tradeoffs.
- [Use cases](https://rushdb.com/use-cases): Implementation blueprints for agents, GraphRAG, analytics, and AI apps.
- [Pricing](https://rushdb.com/pricing): Cloud pricing, Knowledge Unit billing, free tier, and enterprise options.
- [Security](https://rushdb.com/security): Security, privacy, compliance, and responsible disclosure.
- [Blog](https://rushdb.com/blog): Current product updates and technical articles.
- [Full website content for LLMs](https://rushdb.com/llms-full.txt): Full Markdown from current published CMS posts and pages.
- [GitHub repository](https://github.com/rush-db/rushdb): The open-source RushDB codebase.

## Documentation

- [Quick tutorial](https://docs.rushdb.com/get-started/quick-tutorial): The fastest path to a working RushDB project.
- [REST API](https://docs.rushdb.com/rest-api/introduction/): REST API documentation.
- [TypeScript SDK](https://docs.rushdb.com/typescript-sdk/introduction/): JavaScript and TypeScript SDK documentation.
- [Python SDK](https://docs.rushdb.com/python-sdk/introduction/): Python SDK documentation.
- [Records](https://docs.rushdb.com/concepts/records/): The primary operational context primitive.
- [Relationships](https://docs.rushdb.com/concepts/relationships/): Graph links between records.
- [Transactions](https://docs.rushdb.com/learn/records-and-queries/transactions): ACID transaction support for safe writes.
- [Search](https://docs.rushdb.com/concepts/search/introduction/): Structured filtering, traversal, selection, grouping, and analytics.
- [Semantic search](https://docs.rushdb.com/concepts/semantic-search/): Meaning-based retrieval over indexed text.
- [Embedding indexes](https://docs.rushdb.com/rest-api/ai/indexing/): Create and monitor searchable embedding indexes.
- [MCP server](https://docs.rushdb.com/mcp-server/introduction/): Connect RushDB to MCP-capable agents and clients.
- [Self-hosting](https://docs.rushdb.com/tutorials/deployment/): Deploy RushDB on your own infrastructure.

## Features

- [JSON And CSV Data Ingestion For Connected Context](https://rushdb.com/features/connected-data-ingestion): Import JSON, nested objects, flat arrays, or CSV into RushDB. Get typed records, parent-child links, and reviewable relationship suggestions without schema migrations.
- [Natural-Language Smart Search With RushDB ai.search](https://rushdb.com/features/smart-search): See how RushDB ai.search turns natural language into an inspectable SearchQuery, executes it, and returns records, warnings, and the generated query.
- [Managed Embeddings For Agent Memory Search](https://rushdb.com/features/managed-embeddings): Create embedding index policies for RushDB string properties. Use managed server-side embeddings or bring external vectors while records stay queryable by filters and relationships.
- [Vector And Graph Search For Connected AI Retrieval](https://rushdb.com/features/vector-graph-search): Combine semantic similarity, exact where filters, and graph relationships in RushDB. Retrieve ranked records and connected context without syncing a separate vector database.
- [RushDB MCP Server For AI Agent Memory Tools](https://rushdb.com/features/mcp-server): Connect ChatGPT, Claude, Cursor, VS Code, and other MCP clients to RushDB. Give agents discovery-first tools for records, relationships, queries, exports, and transactions.
- [ACID Transactions For Connected Agent Workflows](https://rushdb.com/features/acid-transactions): Use RushDB transactions to group records and relationships into atomic workflows. Commit complete graph changes or roll back failed multi-step operations.
- [RushDB Cloud, External Database, And Self-Hosted Deployment](https://rushdb.com/features/deployment-options): Compare RushDB managed cloud, an External Neo4j or Aura Database, and self-hosted deployment for data residency, operations, raw Cypher, and model control.
- [Unified SearchQuery API For Records And Graph Metadata](https://rushdb.com/features/unified-query-api): Use RushDB SearchQuery to filter records, traverse relationships, inspect labels and properties, fetch values and ranges, and compute select/groupBy analytics.
- [Schema API For Schema-Aware AI Agents](https://rushdb.com/features/schema-api): Use RushDB Schema API to give AI agents a live map of available data before they query. Return Markdown or JSON for grounded filters, relationships, and semantic search readiness.
- [AI Relationship Suggestions For Graph Data](https://rushdb.com/features/relationship-suggestions): RushDB analyzes labels, properties, and existing edges to suggest relationship patterns. Review source, target, type, confidence, and rationale — approval is what changes the graph.

## Guides

- [AI Agent Memory: Persistent Context For Agents And Apps](https://rushdb.com/guides/ai-agent-memory): A practical guide to AI agent memory: what to store, why app glue becomes fragile, and how RushDB keeps records, relationships, and semantic recall together.
- [Knowledge Graph Memory For AI Agents](https://rushdb.com/guides/knowledge-graph-memory): Knowledge graph memory guide for AI agents: graph memory vs flat vector chunks, connected retrieval, schema discovery, and RushDB implementation paths.
- [Vector Database vs Memory Layer For AI Agents](https://rushdb.com/guides/vector-database-vs-memory-layer): A neutral guide to vector databases vs AI memory layers: semantic search, structured records, graph relationships, schema, and how RushDB combines them.
- [GraphRAG vs RAG: What Changes And When It Matters](https://rushdb.com/guides/graphrag-vs-rag): GraphRAG vs RAG compared for retrieval quality, explainability, and multi-hop reasoning. Learn when graph-aware retrieval outperforms flat vector-only RAG.
- [JSON To Graph Database: Import Nested Data Without Schema](https://rushdb.com/guides/json-to-graph-database): Learn how to turn JSON into a graph database. RushDB infers property types and parent-child relationships from nested payloads on write — no migration required.
- [MCP Memory Backend: Persistent Memory For MCP Agents](https://rushdb.com/guides/mcp-memory-backend): Set up RushDB as an MCP memory backend. Give MCP-compatible clients discovery-first tools for durable, queryable agent memory instead of prompt-only context.
- [Semantic + Relational Retrieval For AI Agents](https://rushdb.com/guides/semantic-relational-retrieval): Semantic and relational retrieval explained: how to combine vector similarity, exact filters, and graph relationships in one query for grounded AI retrieval.
- [Types Of AI Agent Memory: Episodic, Semantic, Procedural](https://rushdb.com/guides/agent-memory-types): Episodic, semantic, and procedural AI agent memory explained with concrete record shapes for each type and how they combine in a durable memory layer.
- [Ontology-Aware Querying For AI Agents](https://rushdb.com/guides/ontology-aware-querying): Ontology-aware querying explained: how live schema discovery lets AI agents build valid queries against real labels, properties, and relationships.
- [Multi-Hop Analytics: Relationship Depth Without SQL Joins](https://rushdb.com/guides/multi-hop-analytics): Multi-hop analytics explained: why relationship depth breaks down in SQL joins, and how graph traversal answers multi-hop questions directly.
- [Graph Analytics: Query Relationships, Not Just Rows](https://rushdb.com/guides/graph-analytics): Graph analytics explained: analyzing data through its relationships instead of isolated rows. When it beats SQL analytics, with applied examples from fraud to supply chains.

## Use Cases

- [AI Agent Memory Database — Persistent Context Store](https://rushdb.com/use-cases/agent-memory): Build persistent AI agent memory with RushDB. Store JSON records, index searchable text, recall context by meaning, value, and relationship, and keep memory across sessions.
- [Graph-Aware RAG And Knowledge Base](https://rushdb.com/use-cases/rag-and-knowledge-base): Build GraphRAG and knowledge-base retrieval with RushDB. Combine semantic search, filters, document relationships, provenance, and connected context in one query layer.
- [Context Layer And Backend For Connected Applications](https://rushdb.com/use-cases/ai-powered-apps): Build applications with evolving records, exact filters, relationships, semantic retrieval, live schema, and operational queries through one RushDB context layer.
- [Multi-Agent Incident Response: Shared Memory For AI Agents](https://rushdb.com/use-cases/multi-agent-incident-response): Use RushDB as shared memory for multi-agent incident response. Persist goals, observations, evidence, runbooks, handoffs, and decisions in one auditable graph.
- [Shared LLM Memory Across Providers](https://rushdb.com/use-cases/shared-llm-memory): Keep customer facts, episodes, references, and provider runs in one RushDB memory graph so workflows can switch LLM providers without losing context.
- [Operational Analytics On Connected Context](https://rushdb.com/use-cases/analytical-workloads): Run operational analytics with RushDB. Use select, groupBy, time buckets, and graph relationships to connect KPI trends with accounts, features, and incidents.
- [Transaction Monitoring For Fraud And AML Graphs](https://rushdb.com/use-cases/transaction-monitoring): Use RushDB for fraud and AML transaction monitoring. Traverse accounts, devices, counterparties, merchants, alerts, and KYC records across multiple hops.
- [Agent Harness Portability With Durable Memory](https://rushdb.com/use-cases/agent-harness-portability): Move between MCP clients, SDK agents, orchestration frameworks, and backend workers while facts, tool output, references, and runs stay in RushDB.
- [Authorized Pentest And Red-Team Evidence Graph](https://rushdb.com/use-cases/authorized-pentest-red-team): Store authorized pentest findings, evidence, triage state, controls, and red-team log events in RushDB for defensive review, reporting, and visualization.
- [On-Prem AI For Wealth Management Intelligence](https://rushdb.com/use-cases/onprem-inference-wealth-management): Combine RushDB and local vLLM inference for private wealth intelligence. Ground portfolio, deal, memo, policy, and diligence analysis in cited records.
- [Mining Simulation Analytics For EdTech](https://rushdb.com/use-cases/mining-edtech-simulation-analytics): Ingest 3D drilling-rig simulator assessments into RushDB and build cohort dashboards with step results, telemetry events, scores, groupBy, and drill-downs.
- [Property Intelligence For Real Estate Marketplaces](https://rushdb.com/use-cases/property-intelligence-marketplace): Build property marketplace recommendations with RushDB. Translate investor intent into schema-grounded filters across listings, comps, owners, and market signals.
- [Vibe-Coded Prototyping With Instant Faceted Search](https://rushdb.com/use-cases/vibecoding-rapid-prototyping): Use RushDB for vibe-coded product prototypes. Push UI-shaped JSON, fetch labels, properties, and values, and build ecommerce-style faceted search fast.
- [Agentic Automation With Durable Workflow State](https://rushdb.com/use-cases/agentic-automation): Build resumable agentic automation with RushDB. Persist workflows, goals, steps, approvals, tool output, and SOP retrieval across retries and restarts.
- [GraphRAG For Book Catalogues: Cited Chapter Search](https://rushdb.com/use-cases/technical-books-rag): Build a technical-books GraphRAG catalogue with RushDB. Search chunks by meaning while preserving book, chapter, author, topic, and citation provenance.
- [Legal Contract Review Memory](https://rushdb.com/use-cases/legal-contract-review): Use RushDB for legal contract-review memory. Persist clauses, facts, revisions, references, and cited retrieval so review workflows stay focused and auditable.
- [Medical Research Loops With Graph Context](https://rushdb.com/use-cases/medical-research-loops): Use RushDB to support medical research loops with papers, PDF chunks, trials, cohorts, biomarkers, hypotheses, semantic retrieval, and cited graph context.
- [Sales & CRM Memory For AI Agents](https://rushdb.com/use-cases/sales-crm-memory): Use RushDB for sales and CRM memory. Persist accounts, opportunities, and interactions, then recall objections and next steps by meaning and relationship.
- [Customer Support Memory With Graph Context](https://rushdb.com/use-cases/customer-support-memory): Use RushDB for customer support memory. Persist tickets, messages, and resolutions, then recall similar past issues by meaning across channels.
- [Healthcare Care Context With Graph Memory](https://rushdb.com/use-cases/healthcare-care-context): Use RushDB for healthcare care context. Connect encounters, notes, conditions, and medications, then retrieve relevant patient history for care-team workflows.
- [Legal Research Citation Graph For Case Law](https://rushdb.com/use-cases/legal-research-citation-graph): Connect cases, statutes, and holdings in a citation graph. Retrieve case law by meaning, then traverse what cites, distinguishes, or overturns a holding.
- [Commercial Lease Abstraction And Portfolio Database](https://rushdb.com/use-cases/commercial-lease-portfolio-memory): Turn extracted lease terms into a queryable portfolio graph. Filter expirations, escalations, and renewal options across every property in one query.
- [Vulnerability Management Asset Risk Graph](https://rushdb.com/use-cases/vulnerability-asset-risk-graph): Connect assets, CVEs, and patch status in one graph. Query unpatched critical vulnerabilities on internet-facing assets by team ownership.
- [Customer 360 Graph Analytics Platform](https://rushdb.com/use-cases/customer-360-graph-analytics): Connect users, orders, subscriptions, support tickets, and events into one customer graph for churn, recommendation, and journey analysis.
- [E-Commerce Graph Analytics For Basket Analysis](https://rushdb.com/use-cases/ecommerce-graph-analytics): Connect products, orders, and customers for basket analysis, cross-sell recommendations, and lifetime-value queries in one graph.
- [Supply Chain Graph Analytics For Dependency Mapping](https://rushdb.com/use-cases/supply-chain-graph-analytics): Connect suppliers, shipments, products, and warehouses to trace disruption impact and map dependency risk in one graph query.

## Comparisons

- [RushDB vs Neo4j: Graph Database Comparison](https://rushdb.com/compare/rushdb-vs-neo4j): RushDB vs Neo4j compared on query interface, schema model, vector search, agent tooling, and deployment. RushDB runs on Neo4j-compatible storage with a higher-level API.
- [RushDB vs Mem0: Agent Memory Comparison](https://rushdb.com/compare/rushdb-vs-mem0): RushDB vs Mem0 compared on storage model, memory extraction, graph memory, retrieval, and deployment for AI agent memory.
- [RushDB vs SurrealDB: Multi-Model Database Comparison](https://rushdb.com/compare/rushdb-vs-surrealdb): RushDB vs SurrealDB compared on data model, graph traversal syntax, vector search, schema, embedded vs server deployment, and pricing.
- [RushDB vs Weaviate: AI Database Comparison](https://rushdb.com/compare/rushdb-vs-weaviate): RushDB vs Weaviate compared on data model, cross-references vs graph traversal, hybrid search, managed embeddings, agent tooling, and pricing.
- [RushDB vs Pinecone: Vector Database Comparison](https://rushdb.com/compare/rushdb-vs-pinecone): RushDB vs Pinecone compared on data model, relationships between records, hybrid search, managed embeddings, deployment, and pricing.

## Published Blog Posts

- [Vector RAG vs GraphRAG vs Agentic RAG: Why There Is No Winner](https://rushdb.com/blog/vector-rag-graphrag-agentic-rag-comparison-c203b6): Vector RAG, GraphRAG, and Agentic RAG preserve different context. Learn when each works, where each fails, and how to combine them in one app.
- [Persistent Agent Memory for OpenClaw and Hermes with RushDB](https://rushdb.com/blog/persistent-agent-memory-openclaw-hermes-rushdb): RushDB brings scoped, durable, lifecycle-aware memory to OpenClaw and Hermes Agent through native connectors and one shared event contract.
- [Vector Search Doesn't Understand Data Structure](https://rushdb.com/blog/vector-search-doesn-t-understand-data-structure-cda544): Embeddings rank similarity but ignore joins, cardinality, and constraints. Learn how RushDB combines semantic retrieval with explicit graph relationships and live schema discovery.
- [Why Every AI Stack Grows Into Five Data Pipelines](https://rushdb.com/blog/why-every-ai-stack-starts-with-five-data-pipelines-2a1d57): LLM applications naturally fragment into ETL, embedding, graph sync, search indexing, and metadata pipelines. Learn why this happens and how a single ingestion layer can replace.
- [Stop Teaching Agents Your Schema](https://rushdb.com/blog/stop-teaching-agents-your-schema-d57a76): Every new agent needs a prompt explaining your tables and fields. RushDB lets agents fetch a structured snapshot of the live graph at runtime.
- [RushDB 2.0: Memory Infrastructure for the Agentic Era](https://rushdb.com/blog/rushdb-2-0-release): 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.
- [Labeled Meta Property Graphs (LMPG): A Property-Centric Approach to Graph Database Architecture](https://rushdb.com/blog/labeled-meta-property-graphs-rushdb-s-revolutionary-approach-to-graph-database-architecture): Discover how LMPG transforms graph databases by treating properties as first-class citizens rather than simple node attributes. This comprehensive technical guide explores RushDB's groundbreaking architecture that enables automatic schema evolution, property-first queries, and cross-domain analytics impossible in traditional property graphs or RDF systems.
- [Knowledge Graphs: Semantic Reasoning Meets Graph Architecture](https://rushdb.com/blog/knowledge-graphs-semantic-reasoning-meets-graph-architecture): RushDB article.
- [Labeled Property Graphs: A Comprehensive Guide to Enhanced Graph Data Modeling](https://rushdb.com/blog/labeled-property-graphs-a-comprehensive-guide-to-enhanced-graph-data-modeling): Labeled Property Graphs (LPGs) represent an evolution of traditional property graphs, introducing explicit type labels for nodes and relationships. This enhancement not only improves schema clarity but also boosts query performance, making LPGs a preferred choice for complex data modeling scenarios. In this article, we explore the advantages, challenges, and practical applications of LPGs.
- [RDF: A Comprehensive Guide to Semantic Web Data Modeling](https://rushdb.com/blog/rdf-a-comprehensive-guide-to-semantic-web-data-modeling): Resource Description Framework (RDF) represents a fundamental paradigm shift in how we model and represent knowledge on the web. As the cornerstone of the Semantic Web, RDF provides a standardized method for describing resources and their relationships in a way that is both machine-readable and semantically rich. Unlike traditional data models that focus on storage and retrieval efficiency, RDF prioritizes meaning, interoperability, and automated reasoning.
- [Rethinking the Graph: How Labeled Meta-Property Graphs Unlock Structure Without Sacrificing Flexibility](https://rushdb.com/blog/rethinking-the-graph-how-labeled-meta-property-graphs-unlock-structure-without-sacrificing-flexibility): RushDB article.
- [Breaking Down the Hidden Costs of Data Silos in Scientific Labs](https://rushdb.com/blog/breaking-down-the-hidden-costs-of-data-silos-in-scientific-labs): RushDB article.
- [Graph Databases Explained: Property Graphs vs RDF vs Knowledge Graphs - Complete Developer Guide 2025](https://rushdb.com/blog/graph-databases-explained-property-graphs-vs-rdf-vs-knowledge-graphs-complete-developer-guide-2025): Complete guide to graph database models: property graphs, RDF, labeled graphs, and knowledge graphs. Learn which graph model fits your use case and how to choose the right technology.
- [Backendless Fullstack Development: React useForm + RushDB](https://rushdb.com/blog/backendless-fullstack-development-react-useform-rushdb): See how a single line of code can replace an entire backend infrastructure while preserving your data's natural structure. Perfect for rapid prototyping, MVPs, and applications with evolving data models.
- [Self-Hosted RushDB: Quick Setup](https://rushdb.com/blog/rushdb-selfhosted-quick-setup): Learn how to run RushDB in self-hosted mode using Docker with either Neo4j Aura or a local container.
- [Introducing RushDB: Zero-Config Instant Database for Modern Apps & AI Era](https://rushdb.com/blog/rushdb-the-zero-config-database-for-modern-apps-and-ai-solutions): RushDB is a zero-config, graph-powered instant database with bulk semi-structured data ingestion, automatic normalization, and powerful querying

## CMS Pages

- [RushDB Cookie Policy](https://rushdb.com/cookie-policy): RushDB information page.
- [RushDB Privacy Policy](https://rushdb.com/privacy-policy): RushDB information page.
- [RushDB Terms of Service](https://rushdb.com/terms-of-service): RushDB information page.
- [RushDB Agent Setup — Persistent Graph Memory for AI Agents](https://rushdb.com/agent-setup): Give any AI agent persistent, graph-structured memory using RushDB. Store sessions, decisions, tasks, and preferences — queryable by meaning across conversations.