Supermemory vs Zep & Graphiti
Supermemory vs Zep & Graphiti: side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Supermemory | Zep & Graphiti | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 72 | 68 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Code Generation Focused | Long Running Session, Memory Augmented Chat, Multi Agent Coordination, Analytical Summarization |
Where they differ (16)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Supermemory | Zep & Graphiti | |
|---|---|---|
| Capability composite | 72 | 68 |
| Use cases | Long Running Session, Memory Augmented Chat, Code Generation Focused | Long Running Session, Memory Augmented Chat, Multi Agent Coordination, Analytical Summarization |
| Type | Memory graph + extraction + RAG | Bi-temporal knowledge graph |
| Created | 2024-02 | 2024-08 |
| Latest release | no releases | v0.29.0 2026-04-27 |
| License | MIT | Apache-2.0 |
| GitHub | 22.4k★ +113/mo TypeScript | 25.7k★ +137/mo Python |
| Pricing | OSS | Free + paid |
| Funding | $6M total Seed · 2025-10 | $3M total Seed (additional) · 2024-04 |
| Backend storage | custom | Postgres + Neo4j (Graphiti) |
| Deployment | Both | Managed-only |
| API surface | REST, SDK: Python, JS/TS | REST, SDK: Python, JS/TS, Go |
| Multi-tenancy | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org | Logical namespace per user/session; AWS VPC self-hosted option for full data residency; HIPAA BAA on Enterprise plan |
| MCP | native (first-party) — claude-supermemory plugin + MCP | native (first-party) — Graphiti MCP server |
| Optimised for | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph | memory operation tracing + drift / poisoning detection |
| Anti-fit | no anti-fit explicitly stated | not for use cases that don't run agent workloads in production |
At a glance
| Supermemory | Zep & Graphiti | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Memory graph + extraction + RAG | Bi-temporal knowledge graph |
| Created | 2024-02 | 2024-08 |
| Latest release | no releases | v0.29.0 2026-04-27 |
| License | MIT | Apache-2.0 |
| GitHub | 22.4k★ +113/mo TypeScript | 25.7k★ +137/mo Python |
| Pricing | OSS | Free + paid |
| Funding | $6M total Seed · 2025-10 | $3M total Seed (additional) · 2024-04 |
| Backend storage | custom | Postgres + Neo4j (Graphiti) |
| Deployment | Both | Managed-only |
| API surface | REST, SDK: Python, JS/TS | REST, SDK: Python, JS/TS, Go |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org | Logical namespace per user/session; AWS VPC self-hosted option for full data residency; HIPAA BAA on Enterprise plan |
| MCP | native (first-party) — claude-supermemory plugin + MCP | native (first-party) — Graphiti MCP server |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | not documented publicly | not documented publicly |
| Optimised for | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph | memory operation tracing + drift / poisoning detection |
| Anti-fit | no anti-fit explicitly stated | not for use cases that don't run agent workloads in production |
Taxonomy
| Axis | Supermemory | Zep & Graphiti |
|---|---|---|
| storage | graph | graph |
| retrieval | similarity | graph-traversal |
| persistence | long-term | long-term |
| update | extraction | append-only |
| unit | document | episode |
| governance | opaque | auditable |
| conflict | llm-arbitrate | bi-temporal |
Pros & cons
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.
Zep & Graphiti
Pros: Bi-temporal graph captures event time + ingestion time, making contradiction tracking and chronological reasoning correct by construction.
Cons: KG storage is heavier than vector for the same data volume; smaller funding base than Mem0 ($2.3M vs $24M).