RushDB Agent Setup
Give any AI agent persistent, graph-structured memory — sessions, decisions, tasks, entities, and preferences — stored in RushDB and queryable by meaning.
Unlike flat key-value stores, RushDB memory auto-links nested JSON into a relationship graph, survives across conversations, and lets agents recall context by traversal or semantic similarity: "What did we decide about auth? What's related to that service?"
Step 1 — Connect the MCP Server
Web clients (ChatGPT, Claude.ai) — no install required
Use the hosted OAuth endpoint. No API key in config — you authenticate with your RushDB account.
ChatGPT: Settings → Connectors → Add connector → https://mcp.rushdb.com/mcp
Claude.ai: Settings → Integrations → Add integration → https://mcp.rushdb.com/mcp
Local clients (Claude Desktop, Cursor, VS Code)
Requires a RushDB API key → app.rushdb.com
Claude Desktop — edit ~/Library/Application Support/Claude/claude_desktop_config.json:
Cursor — add to .cursor/mcp.json:
VS Code — add to .vscode/mcp.json:
Step 2 — Install Skills (recommended)
Skills give your agent built-in knowledge of RushDB's query syntax, memory patterns, and data modeling conventions — so you don't have to explain them in every session. They work best alongside the MCP server: skills tell the agent how; the MCP server gives it the tools.
Or via npm:
Start a new agent session after installation so the skills are discovered.
| Skill | What it enables |
|---|---|
rushdb-agent-memory | Session patterns, memory model, recall strategies |
rushdb-query-builder | Build findRecords filters, aggregations, and semantic searches |
rushdb-data-modeling | Design labels, properties, relationships, and nested schemas |
rushdb-faceted-search | Build faceted filter UIs from live property metadata |
rushdb-domain-template | Design a schema for any domain through guided conversation |
Bootstrap Prompt
After the MCP server is connected, paste this prompt to your agent to initialize memory.
Memory Model
RushDB memory is a property graph. Each memory is a Record with a Label (type) and flat JSON properties (primitives and lists of primitives). Nested JSON is automatically normalized: each nested object becomes its own record, and parent → child relationships are created for you.
Recommended Labels
| Label | What it stores |
|---|---|
SESSION | A conversation or work session |
DECISION | A decision made, with rationale and timestamp |
ENTITY | A named thing — person, service, project, concept |
TASK | A work item with status and assignee |
PREFERENCE | A persistent user preference or constraint |
OBSERVATION | A raw note or finding |
PLAN | A proposed sequence of steps |
ARTIFACT | A produced output — code, doc, design |
Auto-linking Example
A single importJson call with nested labels creates a full linked subgraph:
RushDB creates 1 SESSION, 1 DECISION, 2 ENTITY, and 1 TASK record — all linked automatically.
Recall Patterns
"What did we decide about X?"
"What sessions have we had?"
"What do I prefer?"
"What happened in the last 7 days?"
Semantic Search (Memory by Meaning)
$contains is exact substring match — fast, no setup. Use it for IDs, slugs, and known keywords.
Semantic search finds memories by meaning even when exact words differ — "auth system" matches a record that says "we chose Clerk for login". It requires a one-time index setup per label + property.
Create an index (one-time, per project)
The agent calls createEmbeddingIndex, polls getEmbeddingIndexStats until indexedRecords === totalRecords, then runs a test semanticSearch.
Query by meaning
Combine with metadata filters
Candidates are narrowed by metadata first, then ranked by similarity.
End-of-Session Pattern
Ask your agent to save the session before closing:
Resources
- MCP Server —
npx -y @rushdb/mcp-server· npm · GitHub - Agent Memory Skill — detailed patterns and query reference:
packages/skills/rushdb-agent-memory/SKILL.md - Query Builder Skill — full SearchQuery syntax:
packages/skills/rushdb-query-builder/SKILL.md - Docs — docs.rushdb.com
- Dashboard — app.rushdb.com