Supermemory vs Zep
Detailed side-by-side comparison to help you choose the right tool
Supermemory
Development
Context engineering platform and memory layer for AI agents with user profiles, memory graph, retrieval capabilities, and enterprise APIs.
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CustomZep
🔴DeveloperAI Knowledge Tools
Context engineering platform that builds temporal knowledge graphs from conversations and business data, delivering personalized context to AI agents with <200ms retrieval latency.
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FreeFeature Comparison
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💡 Our Take
Choose Supermemory if you need a complete memory graph (not just partial), consumer plugins, document extractors, and an open eval platform — capabilities Zep does not offer per Supermemory's own comparison. Choose Zep if you are already committed to its temporal knowledge graph approach and your use case is narrowly focused on conversational memory for chatbots.
Supermemory - Pros & Cons
Pros
- ✓Only platform in its comparison set offering all five context layers (connectors, extractors, retrieval, graph, profiles) in a single API
- ✓Verifiable performance leadership: 85.2% on LongMemEval and #1 rankings on LoCoMo, ConvoMem, and MemoryBench benchmarks
- ✓Proven production scale, handling 100B+ tokens monthly with sub-300ms p95 latency
- ✓Broad ecosystem with 14+ named integrations including LangChain, LangGraph, CrewAI, Vercel AI SDK, and Zapier
- ✓Generous free tier with 1M tokens/month and 10K search queries, with Pro tier at just $19/month
- ✓Enterprise-ready with SOC 2, HIPAA, GDPR, self-hosting in customer VPC, and a no-training data policy
Cons
- ✗Scale tier jumps sharply from $19/month Pro to $399/month, leaving a large gap for mid-sized teams
- ✗Gmail, S3, and Web Crawler connectors are gated to the $399 Scale tier and above
- ✗Overage charges ($0.01 per 1,000 tokens, $0.10 per 1,000 queries) can add up for unpredictable workloads
- ✗As a newer memory-layer category, best practices and community tutorials are still maturing compared to established vector DBs
- ✗Enterprise features like SSO, forward-deployed engineers, and custom integrations require a custom-priced contract with no public pricing
Zep - Pros & Cons
Pros
- ✓Temporal knowledge graph captures entity relationships and fact evolution over time that flat memory stores completely miss
- ✓Unified context assembly from chat, business data, and documents in single API call eliminates complex integration work
- ✓Industry-leading <200ms retrieval latency with 80.32% accuracy enables real-time voice and interactive applications
- ✓Framework-agnostic design with three-line integration works with any agent framework or custom implementation
- ✓Enterprise-grade security with SOC2 Type 2, HIPAA compliance, and flexible deployment options including on-premises
Cons
- ✗Credit-based pricing model can become expensive for high-volume production applications requiring frequent context retrieval
- ✗Temporal knowledge graph is more complex to set up and debug compared to simple vector-based memory systems
- ✗Advanced features like custom entity types and enterprise compliance are limited to paid tiers, restricting free tier capabilities
- ✗Graph quality depends on rich conversational data—technical or sparse interactions may not produce meaningful relationship structures
Not sure which to pick?
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