Mem0 and Supermemory are both cloud-focused memory solutions for AI agents, but they target different stages of maturity. Mem0 brings the largest community and broadest framework ecosystem. Supermemory, founded by a 19-year-old with backing from G...
Mem0 and Supermemory are both cloud-focused memory solutions for AI agents, but they target different stages of maturity. Mem0 brings the largest community and broadest framework ecosystem. Supermemory, founded by a 19-year-old with backing from Google and Cloudflare executives, offers stronger benchmark performance and built-in connectors for productivity tools.
This comparison covers their architectures, benchmark performance, pricing, and ideal use cases to help you decide.
| Factor | Mem0 | Supermemory |
|---|---|---|
| Architecture | Vector + knowledge graph | Hybrid RAG with fact extraction |
| LongMemEval* | 49% | 85.2% |
| Deployment | Cloud or self-hosted (Apache 2.0) | Cloud API only |
| Pricing | Free / $19 / $249 / Enterprise | Not publicly disclosed |
| GitHub Stars | 52.8K | 21.7K |
| Funding | $24M Series A | $2.6M seed |
Mem0 is a memory layer for AI applications that combines vector embeddings with knowledge graph capabilities. It extracts facts from conversations using LLM-based extraction and stores them for semantic retrieval.
With 52.8K GitHub stars and $24M in funding, Mem0 has the largest community in the agent memory space. The open-source version (Apache 2.0) includes graph memory support via pip install mem0ai[graph].
Key strengths:
Supermemory uses a hybrid RAG approach combining vector search with LLM-based fact extraction. It handles temporal changes and contradictions in its memory engine, and includes connectors for Google Drive, Gmail, Notion, OneDrive, and GitHub.
With $2.6M in seed funding from Google and Cloudflare executives, and 21.7K GitHub stars, Supermemory is one of the newer but rapidly growing players.
Key strengths:
Mem0 uses LLM-based extraction to identify facts from conversations. Facts are embedded in a vector database for semantic retrieval, with optional knowledge graph support for relationship queries.
The graph layer enables queries beyond pure similarity search, connecting entities through their relationships. This is now available in the open-source version, not just paid tiers.
Supermemory combines vector search with structured fact extraction in a hybrid RAG pipeline. The memory engine extracts facts from conversations while handling temporal changes and contradictions.
Beyond conversations, Supermemory ingests content from productivity tools—Google Drive documents, Notion pages, Gmail threads, GitHub repositories—creating a unified memory layer across data sources. It also supports multi-modal content: PDFs, images (via OCR), and videos (via transcription).
Mem0 focuses on conversation memory; Supermemory casts a wider net across data sources.
When you need memory from a conversation, both systems extract and store facts. But when you need memory that spans documents, emails, and code repositories, Supermemory's built-in connectors handle this natively. With Mem0, you'd need to build ingestion pipelines yourself.
The tradeoff is deployment flexibility. Mem0 is fully self-hostable (Apache 2.0). Supermemory is cloud-only—there's no self-hosted option, and pricing isn't publicly disclosed.
| Benchmark | Mem0 | Supermemory |
|---|---|---|
| LongMemEval* | 49% | 85.2% |
Supermemory outperforms Mem0 by over 36 percentage points on LongMemEval. The gap reflects Supermemory's hybrid RAG approach, which combines fact extraction with vector search for better retrieval accuracy.
Both score below Hypabase (87.4%), which uses AMR-based extraction for structured knowledge representation.
| Tier | Price | Limits |
|---|---|---|
| Hobby | Free | 10K add / 1K retrieval per month |
| Starter | $19/month | 50K add / 5K retrieval |
| Pro | $249/month | 500K add / 50K retrieval + graph + analytics |
| Enterprise | Custom | Unlimited + SSO + on-prem |
Supermemory's pricing is not publicly disclosed. Contact their team for details.
Mem0 has a clear advantage in pricing transparency. You know exactly what each tier costs and what limits apply. With Supermemory, you'll need to engage with sales before understanding total cost of ownership.
For self-hosting: Mem0 is straightforward (Apache 2.0, containerized). Supermemory has no self-hosted option—cloud API is the only deployment model.
Choose Mem0 if you:
Mem0 has the most integrations but hasn't significantly updated its retrieval engine despite lower benchmark scores.
Choose Supermemory if you:
Supermemory's 85.2% LongMemEval score is strong, but the cloud-only model and undisclosed pricing may be deal-breakers for some teams.
Supermemory casts a wide net across data sources but still relies on ad-hoc fact extraction under the hood. Mem0 offers the broadest community but scores lowest on retrieval benchmarks. Hypabase takes a different path: precise role-based retrieval powered by a formal linguistic framework, so queries like "when is Sarah's meeting?" return exactly the right hyperedge—not a list of loosely related facts to sift through.
| Factor | Mem0 | Supermemory | Hypabase |
|---|---|---|---|
| Extraction | LLM-based, ad-hoc | Hybrid RAG + fact extraction | AMR (formal linguistic framework) |
| Representation | Triples | Vector + extracted facts | N-ary hyperedges |
| LongMemEval* | 49% | 85.2% | 87.4% |
| Personalization | — | — | 100% |
Hypabase uses Abstract Meaning Representation (AMR)—a formal framework from computational linguistics—to extract structured facts in PENMAN notation with karaka semantic roles (from Panini's Sanskrit grammar):
"Sarah scheduled a meeting with the team for 3pm Thursday"
Ad-hoc extraction (Mem0, Supermemory):
(Sarah, scheduled, meeting)
(meeting_321, attendees, team)
(meeting_321, time, 3pm_Thursday)
AMR extraction (Hypabase):
(schedule :agent Sarah :object meeting :recipient team :locus 3pm :locus Thursday)
The difference: Hypabase preserves who scheduled what, with whom, and when in a single retrievable unit. Ask "what time is the team meeting?" and the :recipient team + :locus roles return 3pm Thursday directly—no cross-referencing fragmented triples.
| Benefit | How AMR + Hyperedges Deliver It |
|---|---|
| Precise retrieval | Query by role: :agent Sarah + schedule returns her meetings; add :recipient team to filter |
| No fragmentation | The meeting's organizer, attendees, and time are stored as one atomic hyperedge |
| Consistent extraction | 6 karaka roles map all semantic relationships—no invented relation types per fact |
| Parseable output | PENMAN notation has a defined grammar; broken extractions are caught at parse time |
Mem0's vector similarity can't distinguish "Sarah scheduled" from "Sarah attended." Supermemory's hybrid RAG improves retrieval breadth but still fragments multi-party events into separate triples. Hypabase's role-based hyperedges keep the full event structure intact, delivering 100% accuracy on personalization tasks.
Not for retrieval accuracy. Supermemory (85.2%) significantly outperforms Mem0 (49%) on LongMemEval. Mem0 has broader framework coverage and transparent pricing. For the highest accuracy with structured extraction, consider Hypabase (87.4%).
There's no direct migration path—they store different data structures. Migration requires re-ingesting conversation history through the new system. If you're evaluating both, consider running a small pilot before committing.
Mem0 optimizes for framework ecosystem breadth and self-hosting flexibility. Supermemory optimizes for retrieval accuracy and multi-source data ingestion. Hypabase optimizes for extraction quality using AMR and structured hyperedge representation.
Mem0 is the clear winner here—fully self-hostable under Apache 2.0. Supermemory is cloud-only with no self-hosted option. Hypabase runs entirely in a single SQLite file with no external database required—the simplest self-hosting option.
Mem0 has the broadest framework ecosystem but scores 49% on LongMemEval—adequate for simple use cases but limited for complex retrieval.
Supermemory achieves 85.2% with built-in multi-modal support and productivity tool connectors. Cloud-only deployment and undisclosed pricing are the main limitations.
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:
LongMemEval scores: Mem0 (49%) from Vectorize independent evaluation. Supermemory (85.2%) from published benchmarks. Hypabase (87.4%) from published benchmark harness.