Hindsight (Vectorize) vs Supermemory
Hindsight (Vectorize) vs Supermemory: side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Hindsight (Vectorize) | Supermemory | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 68 | 72 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Offline Capable | Long Running Session, Memory Augmented Chat, Code Generation Focused |
Where they differ (13)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Hindsight (Vectorize) | Supermemory | |
|---|---|---|
| Capability composite | 68 | 72 |
| Use cases | Long Running Session, Memory Augmented Chat, Offline Capable | Long Running Session, Memory Augmented Chat, Code Generation Focused |
| Type | Vector + reflection / summarisation | Memory graph + extraction + RAG |
| Created | 2025-10 | 2024-02 |
| Latest release | v0.6.0 2026-05-05 | no releases |
| GitHub | 12.3k★ +121/mo Python | 22.4k★ +113/mo TypeScript |
| Pricing | Free + paid | OSS |
| Funding | $4M total Seed · 2024-10 | $6M total Seed · 2025-10 |
| API surface | REST, SDK: Python, TS | REST, SDK: Python, JS/TS |
| Multi-tenancy | namespace | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org |
| MCP | native (first-party) — Hindsight MCP | native (first-party) — claude-supermemory plugin + MCP |
| Optimised for | retrieval engineering + reflection / summarisation as service | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph |
| Anti-fit | searched not found | no anti-fit explicitly stated |
At a glance
| Hindsight (Vectorize) | Supermemory | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Vector + reflection / summarisation | Memory graph + extraction + RAG |
| Created | 2025-10 | 2024-02 |
| Latest release | v0.6.0 2026-05-05 | no releases |
| License | MIT | MIT |
| GitHub | 12.3k★ +121/mo Python | 22.4k★ +113/mo TypeScript |
| Pricing | Free + paid | OSS |
| Funding | $4M total Seed · 2024-10 | $6M total Seed · 2025-10 |
| Backend storage | custom | custom |
| Deployment | Both | Both |
| API surface | REST, SDK: Python, TS | REST, SDK: Python, JS/TS |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | namespace | Enterprise: deploy in customer VPC for full tenant isolation; standard SaaS uses logical namespace per user/org |
| MCP | native (first-party) — Hindsight MCP | native (first-party) — claude-supermemory plugin + MCP |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | not documented publicly | not documented publicly |
| Optimised for | retrieval engineering + reflection / summarisation as service | multi-channel capture (API, app, browser ext, MCP) + RAG over personal graph |
| Anti-fit | searched not found | no anti-fit explicitly stated |
Taxonomy
| Axis | Hindsight (Vectorize) | Supermemory |
|---|---|---|
| storage | vector | graph |
| retrieval | similarity | similarity |
| persistence | long-term | long-term |
| update | consolidation | extraction |
| unit | episode | document |
| governance | opaque | opaque |
| conflict | none | llm-arbitrate |
Pros & cons
Hindsight (Vectorize)
Pros: Built on Vectorize's RAG pipeline expertise — retrieval tuning is a first-class concern; strong Pydantic-AI integration story.
Cons: Newer entrant with smaller adoption; positioning straddles RAG and memory which can muddy the value prop vs pure-play layers.
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.