Push any JSON. Get graph relationships and vector search — automatically. No schema. No pipeline. No glue code.
Your agent needs memory. The standard answer is three databases — Redis, a vector store, a graph DB — plus glue code to keep them in sync.
RushDB replaces all three. One API. Push JSON, query by meaning and by relationship, in a single call.
from rushdb import RushDB
db = RushDB('RUSHDB_API_KEY')
# One-time: tell RushDB to auto-embed 'output' on every write
db.ai.indexes.create(label='MEMORY', property_name='output')
# Store an agent action — no embedder, no vectors array
db.records.create(
label='MEMORY',
data={'agent_id': 'agent-42', 'topic': 'auth decision', 'output': summary_text},
)
# Recall semantically — just pass the query string
memories = db.ai.search({
'labels': ['MEMORY'],
'propertyName': 'output',
'query': 'what did we decide about auth?',
'where': {'agent_id': 'agent-42'},
'limit': 10,
})| Repo | What it is |
|---|---|
| rushdb | Platform — core API, dashboard, self-hosting |
| rushdb-python | Python SDK |
| mcp-server | MCP server for Claude, Cursor, Windsurf |
| examples | Code samples across frameworks and use cases |
{
"mcpServers": {
"rushdb": {
"command": "npx",
"args": ["@rushdb/mcp-server"],
"env": { "RUSHDB_API_KEY": "your-api-key-here" }
}
}
}Get an API key at app.rushdb.com · hi@rushdb.com