Supermemory
by Supermemory · Memory and retrieval layer for AI agents
BenchRank score
Screenshots of Supermemory
Homepage
Overview
Supermemory is a context layer for AI agents that combines memory, retrieval, user profiles, connectors and extractors behind one API. Ingested data is resolved into a knowledge graph holding memory, RAG and profiles together, which agents query at request time via semantic search and graph traversal in a single call. A personal app, Chrome extension and plugins for AI coding tools are also offered.
- Best for
- Developers giving AI agents persistent memory and retrieval through a single hosted API.
- Pricing
- Free tier with about $5/month of usage included, then $19, $100 or $399 per month with $20, $130 and $600 of usage built in respectively, billed against usage rates from $0.001 per 1K SM tokens, with custom Enterprise pricing.
- Runs on
- WebmacOSSelf-hostedCLI
Strengths and trade-offs
Strengths
- One API covers memory, RAG, profiles, connectors and extractors
- TypeScript and Python SDKs; self-hosting available on higher plans
- Repeat content is deduplicated and not billed again
- Free tier includes ~$5/month of usage, no card required
Trade-offs
- Self-hosting only on Scale ($399/mo) and Enterprise
- Free plan pauses when the balance runs out; no pay-as-you-go
- Hard spend caps are limited to Scale and Enterprise
- Subscription credits reset monthly and do not roll over
How Supermemory markets itself
A structured read of the promise, proof and page design on Supermemory’s captured homepage.
Homepage capture
“The context cloud for 🧠 agents.”
- Angle: AI-native
- Hero: Abstract graphic
Pricing
Published plans and prices from Supermemory’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Supermemory pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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