Maximem Synap vs Zep
Zep pioneered temporal knowledge graphs for agent memory with Graphiti. Maximem Synap is the agentic context management system that scored 92% on LongMemEval (and 93.2% on LoCoMo) where Zep's own paper reports 71.2%, and serves in-conversation reads from a pre-fetched in-process cache at an asserted P75 under 15 milliseconds.
| Feature | Maximem Synap | Zep |
|---|---|---|
| LongMemEval, reproduced on our open harness | 92% (Best) | Not verified (Not applicable) |
| LongMemEval (self-reported) | 92% (Best) | 71.2% (Partial) |
| Published retrieval latency | Under 15ms P75 in-conversation, from a pre-fetched in-process cache (Best) | 87ms p50 / 155ms p95 on LoCoMo; 104ms p50 / 162ms p95 on LongMemEval (Best) |
| Open-source eval harness | Full config published (Best) | No (No) |
| Context management paradigm | Active: captures, compacts, recalls per agent (Best) | Semi-active: auto-extracts, explicit retrieval (Partial) |
| Ingestion approach | Extract-first: multi-stage pipeline (Best) | Extract-first: temporal graph built (Best) |
| Pipeline customization per agent | Custom architecture per agent via YAML (Best) | Universal graph pipeline (No) |
| Data connectors | Connectors for structured data sources (Best) | Text ingestion only (No) |
| Memory types | 5 structured: facts, preferences, episodes, emotions, temporal (Best) | Entities + relationships + episodes (Partial) |
| Context compaction | 4 strategies + quality validation score (Best) | Graph-based, no validation score (Partial) |
| Contradiction handling | Explicit detection & resolution (HITL when needed) (Best) | Old facts invalidated, not deleted (audit trail) (Best) |
| Temporal awareness | Full bitemporal awareness (Best) | Full bitemporal model (Best) |
| Entity resolution | Automatic, multi-strategy (Best) | Automatic with temporal tracking (Best) |
| Memory scoping mechanism | Intelligent & automated (Best) | Semi-automated (Partial) |
| Scoping levels | Org hierarchy: User → Customer → Client (Best) | User + group (Partial) |
| Context sharing in agent-swarm systems | Native: shared context + agent-specific memories (Best) | Multiple agents query same graph (Partial) |
| SDK languages | Python + JavaScript (Best) | Python (Graphiti) (Partial) |
| Framework integrations | 23 frameworks (Best) | 4+ frameworks (Partial) |
| Async-first SDK | Non-blocking, returns immediately (Best) | Async episode processing (Partial) |
Last updated: April 2026. Benchmarks sourced from Synap's open-source LongMemEval and LoCoMo harness and vendor documentation. Feature availability may change.
01Why Synap
Why Teams Choose Maximem Synap Over Zep
Different strengths, different design centers.
Zep and Synap are architecturally distinct. Zep is built around Graphiti, a temporal knowledge graph. Synap is built around a multi-stage ingestion pipeline with per-agent customization. Both systems take extraction seriously, but they optimize for different things.
Synap's accuracy advantage holds on standardized evaluation.
On LongMemEval, Synap scored 92% reproduced on our open harness. Zep has not been run on that harness, and its own published LongMemEval figure has moved: 71.2% with gpt-4o in its January 2025 paper, 90.2% on its current research page. Different answer models, judges and dates sit behind each of those, so none of them is a like-for-like comparison with ours. What is comparable is the architecture: Synap's per-agent pipeline customization means retrieval logic adapts to what matters for a given domain, where Zep's universal graph pipeline applies the same traversal strategy regardless of agent type.
Per-agent customization changes production outcomes.
Every agent in Zep uses the same Graphiti pipeline. In Synap, each agent gets a custom context architecture via YAML: storage, extraction, retrieval, and retention settings are tuned per domain. A customer support agent and a voice concierge get different pipelines because they have different context needs.
Both publish latency, and they are measuring different things.
Synap serves in-conversation reads from a cache pre-fetched into your own process, at an asserted P75 under 15 milliseconds, with a cold-retrieval P95 of 450 milliseconds stated as a target. Zep publishes 87 milliseconds at the median and 155 at the ninety-fifth percentile on LoCoMo, and 104 and 162 on LongMemEval, which is more of the read path than any other vendor in this category discloses. The two are measuring different things, so run both on your own traffic before you treat either as a production number.
Where Zep has the edge.
Zep's temporal knowledge graph and bitemporal model are mature. Both Synap and Zep support bitemporal awareness, but Zep's implementation through Graphiti (temporal validity windows, fact invalidation with audit trail) has been in production longer. Zep also offers VPC deployment for teams that need their data in a private cloud environment.
Benchmark note: Synap's 92% was reproduced on our open harness, not verified by any third party. Zep has never been run on that harness, so the table states its absence rather than guessing at a number. Zep's own published LongMemEval figures are 71.2% with gpt-4o in its January 2025 paper and 90.2% on its current research page, measured under conditions that are not ours.
Compare Synap against other alternatives
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Frequently Asked Questions
Yes. Both systems take extraction seriously and both support bitemporal awareness. Synap adds per-agent pipeline customization, an in-conversation P75 under 15 ms served from a pre-fetched in-process cache, and a 92% LongMemEval score reproduced on our open harness. Zep has not been run on that harness, so treat the two scores as separate measurements rather than a head-to-head.
Synap uses a multi-stage extract-first pipeline with entity resolution and temporal awareness, not a single unified knowledge graph. The retrieval logic is customized per agent via YAML configuration rather than applied uniformly across all agents.
Pick Zep if you need a mature temporal knowledge graph with a longer production track record, or if VPC deployment in a specific private cloud environment is a hard requirement today.