Context engineering platform and memory layer for AI agents with user profiles, memory graph, retrieval capabilities, and enterprise APIs.
Supermemory is a context engineering platform and memory infrastructure layer for AI agents that provides user profiles, a memory graph, retrieval, extractors, and connectors through a single unified API, with pricing starting free and scaling to $399/month. It targets AI developers, startups, and enterprise teams building agents that require persistent, cross-session understanding of users and data.
Based on our analysis of 870+ AI tools in the Development category, Supermemory differentiates itself by offering a full five-layer context stack (connectors, extractors, retrieval, graph, and profiles) rather than the single memory layer most competitors provide. The platform processes over 100 billion tokens monthly with a sub-300ms p95 latency, and claims the #1 position on MemoryBench as well as state-of-the-art results on LongMemEval (85.2%), LoCoMo, and ConvoMem benchmarks. Its custom-built Vector Graph Engine maps real relationships between memories using ontology-aware edges rather than relying purely on similarity scores, while the User Understanding Model builds deep behavioral profiles that capture intent and preferences.
For developers, Supermemory offers TypeScript, Python, and REST SDKs with a claimed 5-minute setup time, plus integrations with Claude Code, Cursor, OpenCode, OpenClaw, Vercel AI SDK, LangChain, LangGraph, CrewAI, OpenAI SDK, Mastra, Zapier, n8n, and Pipecat. The Personal Supermemory product serves over 10,000 power users with a Chrome extension and app that lets individuals capture links, chats, PDFs, images, and videos into a single memory shared across every AI tool they use. Enterprise deployments support self-hosting in customer VPCs with SOC 2, HIPAA, and GDPR compliance, and a guarantee that customer data is never used for model training. Compared to direct competitors Mem0 and Zep, Supermemory is the only option in the comparison table to offer all six capabilities simultaneously: memory graph, user profiles, document retrieval, connectors, document extractors, and consumer plugins.
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Supermemory combines connectors, extractors, retrieval, graph, and profiles into one API. Most competitors offer only one or two of these layers, forcing teams to integrate multiple services. This consolidation reduces infrastructure cost and latency while giving agents richer context than a pure vector store can provide.
Rather than relying purely on embedding similarity, the engine builds ontology-aware edges that map real relationships between memories. This lets retrieval surface connected concepts across projects, not just lexically similar chunks. Users on Twitter specifically highlight the graph visualization and cross-repo context linking as standout features.
Supermemory builds deep behavioral profiles from user interactions, capturing intent, preferences, and context over time. This is what allows agents to move from recall ('you said X last Tuesday') to understanding ('you prefer dark mode and TypeScript, so here is a tailored answer'). It differentiates Supermemory from memory tools that only store and retrieve facts.
The platform processes more than 100 billion tokens monthly while maintaining sub-300ms 95th-percentile retrieval latency. This makes it viable for real-time applications like voice agents, where one user reported reducing average response time from 40s to 12s by switching from traditional RAG to Supermemory. It is also one of the few memory providers to publish p95 latency numbers at this scale.
Enterprise customers can deploy Supermemory inside their own VPC and cloud environment, with SOC 2, HIPAA, and GDPR certifications in place. Supermemory commits in writing to never training models on customer data and allows full data export at any time. This combination is rare among memory-layer startups and is why regulated teams adopt it.
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$19/month
$399/month
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As of early 2026, Supermemory publicly claims the #1 position on MemoryBench (its own open eval platform) across latency, quality, and cost. The platform now processes over 100 billion tokens monthly and reports state-of-the-art results on LongMemEval (85.2%), LoCoMo, and ConvoMem benchmarks. Recent integrations highlighted in 2026 testimonials include OpenClaw, Mastra, and Pipecat, and users have shared workflows for migrating full ChatGPT histories into Supermemory containers.
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