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RetainDB vs Supermemory

RetainDB vs Supermemory: 88% recall, published methodology, typed memory

Both companies talk about memory, context orchestration, and benchmarks. The difference is what you can verify — and what the system actually covers. RetainDB handles user memory, context assembly, and knowledge base ingestion (22 built-in connectors) with published LongMemEval scores. Supermemory makes claims without verifiable methodology.

88% Preference recall
79% Overall memory score
0% Hallucination rate
<40ms Retrieval latency
88%
Preference recall
LongMemEval · RetainDB
79%
Overall memory score
LongMemEval · RetainDB
0%
Hallucination rate
In benchmark testing · RetainDB
<40ms
Retrieval latency
Global average · RetainDB
TL;DR

Supermemory makes bold performance claims. RetainDB publishes verifiable scores — 88% preference recall on LongMemEval, full methodology at retaindb.com/benchmark. Compare the specifics before you decide.

At a glance

RetainDB vs Supermemory

Feature
RetainDB
Supermemory
Preference recall (LongMemEval)
88%
Not published on a verifiable page
Overall score (LongMemEval)
79%
Not published
Retrieval latency
<40ms
Claims speed vs Mem0; exact latency not published
Memory taxonomy
13 typed categories
User profile objects
Memory scopes
6 dimensions
User-level with space-based org
Search
Vector + BM25 + reranking
Context orchestration — architecture not fully public
Benchmark verifiability
Published at /benchmark with methodology
Research hub page — claims not independently verifiable
Knowledge base ingestion
22 connectors — Notion, PDF, Confluence, YouTube, arXiv, Playwright, GitHub, GitLab, Discord, Slack, HuggingFace, sitemaps
Not a primary product narrative
Context + knowledge together
Memory and KB assembled per query by type and scope
Context orchestration — KB architecture not fully documented
The specifics

Why the difference matters

01

Published scores vs published claims

Supermemory makes strong performance claims on its research pages. RetainDB publishes LongMemEval scores at retaindb.com/benchmark — 88% preference recall, 79% overall — with methodology you can verify. That's the difference between a marketing page and a proof page.

02

Typed memory vs profile objects

Supermemory builds on user profile objects. RetainDB labels every memory with one of 13 typed categories. When your agent asks 'what constraint has this user stated?', typed retrieval targets exactly that. Profile object retrieval can't.

03

Memory, context, and knowledge — three layers, one system

Supermemory talks about context orchestration but isn't primarily a knowledge base product. RetainDB handles all three: user memory (typed, scoped, persisted across sessions), context assembly (hybrid retrieval decides what to inject per query), and knowledge base (Notion workspaces, Confluence pages, PDFs, YouTube transcripts, arXiv papers, Playwright sessions, sitemaps — 22 built-in connectors). Your agents can know the user and your product documentation in the same retrieval call.

Pick your fit

Who should use what

Choose RetainDB when
You need a benchmark you can verify, not just claims
You need KB ingestion: Notion, Confluence, PDFs, YouTube alongside user memory
Typed memory matters — preference ≠ decision
You want hybrid retrieval with published recall scores
Consider Supermemory when
Speed marketing aligns with your internal narrative
Context-orchestration story fits your product vision
You want to evaluate their architecture before seeing numbers
Common questions

What people ask before deciding

Why does benchmark verifiability matter?

Because you're betting engineering time and user data on it. A claim on a research page and a published LongMemEval score with methodology are different things. Read the methodology at retaindb.com/benchmark and evaluate it yourself.

Does Supermemory's speed advantage matter?

Depends on your bottleneck. If retrieval latency is the problem, evaluate their numbers carefully — they aren't published in a verifiable format in our March 2026 review. RetainDB's <40ms retrieval is measured and published.

Start today — free

Your agents deserve memory
that actually works.

88% preference recall on LongMemEval. Under 40ms retrieval. Most teams are in production in under 30 minutes — no infrastructure to manage.

88% preference recall·0% hallucination rate·<40ms retrieval·No training on your data