Mem0 vs Supermemory
Mem0 vs Supermemory: side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Mem0 | Supermemory | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 70 | 72 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Code Generation Focused |
Where they differ (15)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Mem0 | Supermemory | |
|---|---|---|
| Capability composite | 70 | 72 |
| Use cases | Long Running Session, Memory Augmented Chat, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Code Generation Focused |
| Type | Vector + graph + KV (hybrid) | Memory graph + extraction + RAG |
| Created | 2023-06 | 2024-02 |
| Latest release | openclaw-v1.0.11 2026-04-29 | no releases |
| License | Apache-2.0 | MIT |
| GitHub | 54.9k★ +1.6k/mo Python | 22.4k★ +113/mo TypeScript |
| Pricing | Free + paid | OSS |
| Funding | $24M total $150M val Series A · 2025-10 | $6M total Seed · 2025-10 |
| Backend storage | hybrid (vector + graph + KV) | custom |
| API surface | REST, SDK: Python, Node.js | REST, SDK: Python, JS/TS |
| Multi-tenancy | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org |
| MCP | native (first-party) — official mem0-mcp server | native (first-party) — claude-supermemory plugin + MCP |
| OpenTelemetry | via adapter — AgentOps integration | not documented publicly |
| Optimised for | developer experience + universal memory layer (model-agnostic, multi-store) | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph |
At a glance
| Mem0 | Supermemory | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Vector + graph + KV (hybrid) | Memory graph + extraction + RAG |
| Created | 2023-06 | 2024-02 |
| Latest release | openclaw-v1.0.11 2026-04-29 | no releases |
| License | Apache-2.0 | MIT |
| GitHub | 54.9k★ +1.6k/mo Python | 22.4k★ +113/mo TypeScript |
| Pricing | Free + paid | OSS |
| Funding | $24M total $150M val Series A · 2025-10 | $6M total Seed · 2025-10 |
| Backend storage | hybrid (vector + graph + KV) | custom |
| Deployment | Both | Both |
| API surface | REST, SDK: Python, Node.js | REST, SDK: Python, JS/TS |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org |
| MCP | native (first-party) — official mem0-mcp server | native (first-party) — claude-supermemory plugin + MCP |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | via adapter — AgentOps integration | not documented publicly |
| Optimised for | developer experience + universal memory layer (model-agnostic, multi-store) | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph |
| Anti-fit | no anti-fit explicitly stated | no anti-fit explicitly stated |
Taxonomy
| Axis | Mem0 | Supermemory |
|---|---|---|
| storage | vector | graph |
| retrieval | similarity | similarity |
| persistence | long-term | long-term |
| update | extraction | extraction |
| unit | fact | document |
| governance | opaque | opaque |
| conflict | llm-arbitrate | llm-arbitrate |
Pros & cons
Mem0
Pros: Hybrid (vector + graph + KV) gives the most architectural flexibility of any memory layer; AWS Agent SDK exclusivity and 51k★ make it the field's de-facto reference.
Cons: LOCOMO benchmark numbers were publicly disputed by Zep in counter-analysis; LLM-extraction approach risks dropping facts that don't fit the prompt.
Supermemory
Pros: Simple universal API surface — wraps memory in a single SDK call without forcing extraction/retrieval choices; YC-backed.
Cons: Architectural opacity is the cost of simplicity — limited control over structure or eviction; small team.