Agent Runtime
LangChain·LlamaIndex·CrewAI·Google ADK·custom
Any agent framework — or none. The Context Lake is invoked through a single SDK.
Zep vs. Supermemory
Agent memory for the whole customer, not just the chat
Supermemory is built for conversational memory. Zep fuses every customer touchpoint across the enterprise — chat, CRM, support tickets, billing, documents, app events — into one governed context graph per subject, with bi-temporal facts and sub-200ms retrieval at enterprise scale.
Key takeaways
The conversation, or the whole customer
- Supermemory is built for conversational memory. Zep fuses every customer touchpoint — chat, CRM, support, billing, documents, events — into one governed context graph per subject.
- Every fact carries valid-from and valid-to timestamps with provenance back to the source episode — Zep tracks when each fact was true, rather than simply overwriting old information.
- On LongMemEval_S, Zep reports 90.2% accuracy at 104ms retrieval (p50); Supermemory reports 85.2% accuracy, with latency and context size unreported (results).
- Governance is enforced in the substrate — entity-level ABAC, retention with legal hold, audit — alongside SOC 2 Type II, HIPAA, and BYOC.
The distinction
Supermemory remembers the conversation. Zep remembers the whole customer.
What Supermemory is. Supermemory is built for conversational memory — what the user said, across sessions. For a single app that needs to recall its own conversations, that's the appeal.
What Zep is. Enterprise agents need more. A customer leaves a trail across CRM, support, billing, product events, and documents, and an agent that only remembers chat is working from a fraction of the picture. Zep ingests every source the agent touches and unifies it in one bi-temporal context graph per subject — the Context Lake for AI agents, built on open-source Graphitiand Zep's Context Graph Engine.
Ingestion
chat·JSON·documents·app events
Raw signal arrives from any source the agent touches.
Context Assembly
context blocks·templates·token-efficient
Relevant context is assembled on demand into token-efficient blocks.
Graphiti
Learn moreentity extraction·relationships·ontology·invalidation
Signal becomes a temporal context graph as new facts arrive and stale ones are invalidated.
Retrieval
sub-200ms·auto-optimized·provenance-linked·policy-filtered
Selects what's relevant and what adds the most information within the token budget.
Governance
ABAC·multi-tenant isolation·customer key encryption·retention policies·audit·provenance
Native to the substrate, not a layer bolted on. Every read and write is policy-gated for access and provenance; retention runs across the data lifecycle.
Context Graph Engine
entities·facts & edges·decision traces·episodes
Temporal context graph with provenance — sub-200ms retrieval at scale.
Benchmarks
Zep vs. Supermemory on LongMemEval
Both systems report results on LongMemEval_S (500 questions, LLM-as-judge). Zep reports accuracy, retrieval latency, and context size; Supermemory reports accuracy only.
| Supermemory | Zep | |
|---|---|---|
| Scope | Conversational memory | Every touchpoint — chat, CRM, support, billing, events, documents |
| Data model | General-purpose memory store | Bi-temporal context graph per subject, with provenance |
| Entities & schema | General-purpose store | Custom entities and edges, your schema enforced at ingest |
| Governance | Account-level | Entity-level ABAC, retention with legal hold, audit |
| LongMemEval_S accuracy | 85.2% | 90.2% |
| Retrieval latency, p50 | Unreported | 104 ms |
| Deployment | — | Managed, BYOK, or BYOC; SOC 2 Type II, HIPAA |
| Scale | App-level | Millions of context graphs, sub-200ms |
When to choose
Pick the layer that fits the agent
Stay with Supermemory when
Conversational memory for a single app is all you need.
- Conversational memory for a single app is all you need
- You don't need to fuse business systems — CRM, support, billing — into agent memory
- App-level memory and quick integration matter more than entity-level governance and bi-temporal validity
Choose Zep when you need
Memory that spans every customer touchpoint, governed and served at enterprise scale.
- Memory across chat, CRM, support, billing, product events, and documents — not the conversation alone
- Bi-temporal facts with point-in-time queries built into the data model
- Entity-level governance — ABAC, retention with legal hold, per-request audit
- Custom entities and relationships specific to your domain, with your schema enforced
- Retrieval that holds at sub-200ms across millions of subjects
- Enterprise deployment with SOC 2 Type II, HIPAA, and BYOC
FAQ
Frequently asked questions
What's the difference between Supermemory and Zep?
Supermemory is built for conversational memory — what the user said, across sessions. Zep fuses every customer touchpoint — chat, CRM, support, billing, product events, and documents — into one governed, bi-temporal context graph per subject, served in sub-200ms at enterprise scale.
How do Zep and Supermemory compare on benchmarks?
Both report on LongMemEval_S (500 questions, LLM-as-judge). Zep reports 90.2% accuracy at 104ms retrieval latency (p50); Supermemory reports 85.2% accuracy and does not publish retrieval latency or context size. See the methodology and results.
Can Zep ingest business data beyond chat?
Yes. Zep ingests chat, JSON, app events, documents, and business data (CRM, support, billing) through a single SDK and unifies them in one context graph per subject — user, customer, team, or topic.