Supermemory and RetainDB both offer cloud-hosted AI agent memory, but they differ in approach and positioning. Supermemory is a well-funded cloud engine with multi-modal support and productivity connectors. RetainDB is a newer entrant focused on chronological retrieval and preference recall, claiming 0% hallucination rate on documentation questions.
This comparison covers their architectures, benchmark performance, pricing, and ideal use cases to help you decide.
Quick Comparison
| Factor | Supermemory | RetainDB |
|---|
| Architecture | Hybrid RAG with fact extraction | PostgreSQL + pgvector with chronological retrieval |
| LongMemEval* | 85.2% | 79% |
| Deployment | Cloud API | Cloud or self-hosted (PostgreSQL required) |
| Pricing | Not publicly disclosed | Free tier (10K ops/mo) / Pro $20/mo |
| GitHub Stars | 21.7K | 8 |
| Funding | $2.6M seed (Google/Cloudflare execs) | Not disclosed |
What is Supermemory?
Supermemory is a cloud-native memory engine that combines hybrid RAG with structured fact extraction. It scores 85.2% on LongMemEval and includes connectors for Google Drive, Gmail, Notion, OneDrive, and GitHub.
Backed by $2.6M from Google and Cloudflare executives, Supermemory has 21.7K GitHub stars and offers multi-modal processing for PDFs, images, videos, and code.
Key strengths:
- Solid benchmark performance (85.2% LongMemEval)
- Multi-modal support (PDFs, images via OCR, videos via transcription, code)
- Built-in connectors for productivity tools
- Fast retrieval (~50ms)
- Large community (21.7K stars)
What is RetainDB?
RetainDB provides persistent memory with turn-by-turn extraction and chronological retrieval. Rather than using semantic search to find relevant memories, RetainDB provides the complete memory chronology to the answering model—no lossy retrieval step.
RetainDB claims SOTA on preference recall (88%) and 0% hallucination rate on documentation questions. Its benchmark runner ships in the repository for reproducibility.
Key strengths:
- SOTA on preference recall (88%)
- 0% hallucination rate claimed on documentation questions
- Reproducible benchmarks (runner ships in repo)
- Transparent, affordable pricing ($20/mo Pro)
- Full chronological retrieval—no lossy search step
Architecture Comparison
Supermemory's Approach
Supermemory combines vector search with LLM-based fact extraction. The pipeline extracts structured facts from conversations, embeds them alongside raw content, and serves hybrid retrieval. Multi-modal processing handles PDFs, images, videos, and code.
Retrieval is query-driven: given a query, the system finds the most relevant memories via semantic similarity.
RetainDB's Approach
RetainDB's pipeline is deliberately different:
- Turn-by-turn extraction: Every conversational turn is processed individually with 3-turn context
- Atomic memory writes: Stored with eventDate, documentDate, and confidence scores
- Chronological retrieval: Full timeline visible to the answering model
- Answer generation: Based on complete memory dumps, not search results
The critical difference: RetainDB doesn't use semantic search for retrieval. Instead, it provides the complete memory chronology to the answering model. This eliminates retrieval failures but increases prompt size.
The Key Difference
Supermemory retrieves selectively—only memories matching the query. RetainDB dumps everything chronologically—the model sees the full history.
Selective retrieval (Supermemory) is efficient but lossy: relevant memories that don't match the query embedding are missed. Chronological dump (RetainDB) is complete but expensive: the model must process the entire memory timeline for every query.
RetainDB's approach explains its strong preference recall (88%)—preferences mentioned once months ago are never missed because they're always in the dump. Supermemory's approach is more scalable but can miss memories that are semantically distant from the current query.
| Benchmark | Supermemory | RetainDB |
|---|
| LongMemEval* | 85.2% | 79% |
| Preference Recall | — | 88% (SOTA) |
| Hallucination Rate | — | 0% claimed |
Supermemory outperforms RetainDB by about 6 points on overall LongMemEval. But RetainDB excels at preference recall (88% SOTA) and claims zero hallucination on documentation questions.
RetainDB's benchmarks are reproducible: the runner ships in the repository, so teams can verify claims independently.
Both score significantly below Hypabase (87.4%), which uses AMR-based extraction for higher retrieval accuracy and achieves 100% on personalization tasks.
Pricing Comparison
Supermemory
Supermemory's pricing is not publicly disclosed. Teams must contact sales.
RetainDB
| Tier | Price | Limits |
|---|
| Free | $0 | 10K ops/month |
| Pro | $20/month | 100K queries |
The Real Cost Difference
RetainDB is one of the most transparent and affordable options in the space. $20/month for 100K queries is a known quantity. Supermemory's undisclosed pricing creates uncertainty.
For budget-conscious teams, RetainDB's clear pricing is a significant advantage. For self-hosting, RetainDB requires PostgreSQL; Supermemory has no self-hosting option.
When to Choose Supermemory
Choose Supermemory if you:
- Need multi-modal support (PDFs, images, video)
- Want built-in connectors to productivity tools
- Need the larger community for troubleshooting (21.7K stars)
Supermemory's cloud-only model and opaque pricing limit flexibility. The retrieval engine hasn't been independently validated, and benchmark scores haven't significantly evolved.
When to Choose RetainDB
Choose RetainDB if you:
- Prioritize preference recall and personalization (88% SOTA)
- Need reproducible benchmarks for evaluation
- Want transparent, affordable pricing ($20/mo)
RetainDB's small community (8 stars) creates adoption risk. It requires PostgreSQL for self-hosting, and overall LongMemEval (79%) is below both Supermemory and Hypabase.
Consider Hypabase
RetainDB's 88% preference recall is strong—but it still misses 12% of preferences. Supermemory's multi-modal cloud pipeline scores 85.2% overall but doesn't even publish personalization numbers. If preference recall and personalization are what matter most to your application, Hypabase achieves 100% on personalization tasks through AMR-based extraction into hyperedges—structured knowledge representation purpose-built for precise recall.
| Factor | Supermemory | RetainDB | Hypabase |
|---|
| Extraction | LLM-based, hybrid RAG | Turn-by-turn, atomic writes | AMR (formal linguistic framework) |
| Representation | Facts + vector embeddings | Chronological memory dump | N-ary hyperedges |
| LongMemEval* | 85.2% | 79% | 87.4% |
| Personalization | — | 88% (preference recall) | 100% |
RetainDB's chronological dump approach gets strong preference recall by showing the model everything—but at the cost of prompt size and with an 8-point gap on overall LongMemEval. Supermemory retrieves selectively but fragments facts into disconnected triples. Hypabase uses Abstract Meaning Representation (AMR) to store preferences as structured hyperedges that are both compact and precisely queryable.
Consider a customer relationship detail that RetainDB would dump and Supermemory would fragment:
"Maria manages customer escalations and prefers phone calls"
Ad-hoc extraction (Supermemory):
(Maria, manages, customer escalations)
(Maria, prefers, phone calls)
Chronological dump (RetainDB):
[2024-03-15T10:23] "Maria manages customer escalations and prefers phone calls"
(included in full memory dump every query)
AMR extraction (Hypabase):
(manage :agent Maria :object escalation :attribute customer)
(prefer :agent Maria :object call :attribute phone)
The difference: RetainDB preserves the information but dumps everything—Maria's preference sits alongside thousands of other chronological entries the model must sift through. Supermemory extracts two separate triples with no link between Maria's role and her communication preference. Hypabase stores two compact hyperedges that are directly queryable: "How does Maria prefer to be contacted?" hits :agent Maria + prefer instantly.
From 88% to 100% Personalization
RetainDB's chronological approach gets close on preferences—88% is genuinely strong. But the remaining 12% gap matters for production systems where missed preferences degrade user experience. Hypabase closes this gap by storing preferences as role-tagged hyperedges rather than timestamped text:
| Benefit | How AMR + Hyperedges Deliver It |
|---|
| 100% preference recall | Preferences stored with explicit :agent and :attribute roles—never buried in a chronological dump |
| No prompt bloat | Structured hyperedges retrieved by role, not full timeline dumps growing with every conversation |
| Affordable self-hosting | Single SQLite file—no PostgreSQL required, simpler than RetainDB's infrastructure |
| Higher overall accuracy | 87.4% LongMemEval vs RetainDB's 79%—better general retrieval alongside perfect personalization |
This is why Hypabase achieves 100% on personalization tasks—customer relationships, communication preferences, and escalation patterns stored as structured hyperedges are precisely retrievable without dumping the entire memory chronology into every prompt.
Learn more about Hypabase →
FAQ
Is Supermemory better than RetainDB?
They're close on overall LongMemEval (85.2% vs 79%). Supermemory has multi-modal support and productivity connectors. RetainDB excels at preference recall (88%) and offers transparent pricing ($20/mo). For higher accuracy with structured extraction, consider Hypabase (87.4%, 100% personalization).
Can I migrate from Supermemory to RetainDB?
There's no direct migration path—they use different storage approaches. Migration requires re-ingesting conversation history through the new system. If you're evaluating both, RetainDB's affordable pricing makes piloting low-risk.
What's the main difference?
Supermemory uses hybrid RAG with selective retrieval. RetainDB uses chronological memory dumps with no lossy search step. Hypabase optimizes for extraction quality using AMR and structured hyperedge representation.
Which is better for self-hosting?
RetainDB is self-hostable but requires PostgreSQL. Supermemory has no self-hosting option. Hypabase runs entirely in a single SQLite file with no external database required—the simplest self-hosting option.
Conclusion
Supermemory scores 85.2% on LongMemEval with strong multi-modal support and productivity tool connectors. Cloud-only with opaque pricing limits flexibility.
RetainDB scores 79% overall but achieves SOTA preference recall (88%) with transparent $20/mo pricing. Small community and PostgreSQL requirement limit adoption.
Hypabase achieves 87.4% through AMR-based extraction into hyperedges—structured knowledge representation that preserves relationships ad-hoc extraction fragments. 100% on personalization tasks.
All three are straightforward to integrate:
Try Hypabase →
LongMemEval scores: Supermemory (85.2%) from published benchmarks. RetainDB (79%) self-reported. Hypabase (87.4%) from published benchmark harness.