RushDB vs Mem0: a database vs a memory-management layer.

Side by side

The same task in both: store a memory, recall it by meaning for one user.

Both snippets persist conversational memory and retrieve it semantically, scoped to one user. The difference is what sits underneath the call — and who decides what gets stored.

from mem0 import Memory

# Requires a configured LLM + embedder + vector store
# underneath (defaults: OpenAI + Qdrant)
m = Memory()

# An LLM extraction pass decides what gets stored
m.add(messages, user_id='alice')

results = m.search(
    query='What did we decide about the Pro launch?',
    filters={'user_id': 'alice'},
)

from rushdb import RushDB

db = RushDB('RUSHDB_API_KEY')

db.ai.indexes.create({'label': 'MEMORY', 'propertyName': 'output'})

# Your application decides exactly what is stored
db.records.create(label='MEMORY', data={
    'user_id': 'alice',
    'kind': 'decision',
    'output': 'Ship the Pro launch after the pricing review.',
})

results = db.ai.search({
    'labels': ['MEMORY'],
    'propertyName': 'output',
    'query': 'What did we decide about the Pro launch?',
    'where': {'user_id': 'alice'},
})

Switching

Moving from Mem0: own the memory layer instead of renting the glue.

You keep your agent framework — the migration is re-pointing memory calls, not rebuilding the agent. Mem0 scopes (user_id, agent_id, run_id) map directly to record properties, extracted memories become records you control verbatim, and the graph memory Mem0 simulates inside a vector store becomes real, traversable relationships.

Who this is for

Pick the right tool for the job.

Mem0 is built to bolt onto an existing agent framework with minimal setup, using LLM-based extraction to decide what’s worth remembering. RushDB is built for teams that want to own the data layer directly — write structured records, query them with exact filters and relationships, and choose to also index them for semantic recall, without an LLM call deciding what gets stored.

Comparison table

Storage model, extraction, and deployment

The core difference is architectural: Mem0 decides what to remember via an LLM call and stores it across pluggable external systems. RushDB stores whatever records you write, indexed for retrieval the way you configure it.

Mem0 facts sourced from mem0.ai, docs.mem0.ai, and github.com/mem0ai/mem0 as of the dates shown above; the Mem0 snippet follows its documented quickstart shape. Mem0’s graph-memory architecture changed recently — verify against current docs before relying on this row. RushDB facts are drawn from RushDB’s own docs and feature pages linked throughout this site. Report a stale claim to hi@rushdb.com.

Implementation example

Write and recall a structured memory record

RushDB stores exactly what you write, then lets you recall it by meaning within an exact filter scope.

from rushdb import RushDB

db = RushDB("RUSHDB_API_KEY")

db.ai.indexes.create({
    "label": "MEMORY",
    "propertyName": "output",
})

db.records.create(
    label="MEMORY",
    data={
        "agent_id": "agent-42",
        "kind": "decision",
        "output": "Keep the free plan, but increase Pro onboarding limits after launch feedback.",
        "status": "approved",
    },
)

recall = db.ai.search({
    "labels": ["MEMORY"],
    "propertyName": "output",
    "query": "What did we decide about Pro onboarding?",
    "where": {"agent_id": "agent-42", "status": "approved"},
    "limit": 5,
})

print(recall.data)

Questions developers ask.

Related

Go deeper on the workflow.